Advances in Intelligent, Interactive Systems and Applications: Proceedings of the 3rd International Conference on Intelligent, Interactive Systems and Applications (IISA2018) [1st ed.] 978-3-030-02803-9, 978-3-030-02804-6

Table of contents :
Front Matter ....Pages i-xxi
Front Matter ....Pages 1-1
An Android-Based Remote Monitoring System (Lina Jia, Meng Zhang, Wei Zhang, Jiaguo Lv)....Pages 3-13
An Intelligent Registration Management System for Freshmen (Hao Zhu, Mengshu Hou, Yaohua Xie, Kai Yan, Junming Li)....Pages 14-20
Investigation of Wireless Sensor Network of the Internet of Things (Yibin Hou, Jin Wang)....Pages 21-29
A Remote Phase Change System for Low-Voltage Power Distribution Area (Jiafeng Ding, Jing Liu, Xinmei Li, Zhifeng Li, Fei Gong, Xiao Liang et al.)....Pages 30-39
Design and Implement of Speech Interaction with Indoor Substation Inspection Robot (Xiaobin Yu, Shiliang Lv, Kun Mao, Anshan Wang, Shouguo Lv, Lei Han et al.)....Pages 40-46
Speech Recognition Algorithm of Substation Inspection Robot Based on Improved DTW (Lei Han, Changchun Gao, Shujing Zhang, Dongsong Li, Zhizhou Sun, Guoqing Yang et al.)....Pages 47-54
The Design of High Accuracy Pedometer Based on an Adaptive and Dynamic Low-Pass Filtering Algorithm (Deng Xu, Baohua Yang)....Pages 55-62
Research on Multi-sensor and Multi-target Data Association Problem (Fu Shuai)....Pages 63-73
A Detecting System for Wheel Balancer Based on the Effect Coefficient Method (Honghui Zhang, Wanli Zhang)....Pages 74-80
Study of Substation Inspection Robot Voice Recognition Algorithm Based on Wavelet Transform (Chongguang Fu, Zhizhou Sun, Kechao Tian, Maoqi Dong, Guoqing Yang, Jian Li et al.)....Pages 81-87
Research on the Development of Intelligent Industrial Control (Liu Miao, Che Lei, Xuepo Li, Lujun Tan)....Pages 88-94
Toward Human Motion Sensing: Design and Performance Evaluation of a Minimized Wearable Platform Using Inertial and TOA Sensors (Cheng Xu, Jie He, Xiaotong Zhang, Yue Qi, Shihong Duan)....Pages 95-102
Deep Learning of Intelligent Speech Recognition in Power Dispatching (Jianzhong Dou, Qunshan Li, Hongyi Lai, Chao Yang, Shenzeng Luo, Ziyu Lin et al.)....Pages 103-108
An Improved Multi-factor Dimensionality Reduction Approach to Identify Gene-Gene Interactions (Li-Yeh Chuang, Yu-Da Lin, Cheng-Hong Yang)....Pages 109-114
Nonlocal Estimation and BM3D Based Face Illumination Normalization (Yingkun Hou)....Pages 115-122
Design of Rice Traceability System Based on WSN and RFID (Fengjuan Miao, Xiaoxu Lu, Bairui Tao, Kaida Liu, Ding Liu)....Pages 123-130
Design of Stereoscopic Garage System Based on PLC Control (Libo Yang)....Pages 131-136
Intelligent Scenario Computing Workflow Fusion Design Technology for Power Mobile Operations (Haoran Wu, Min Xu, Xiao-Ling Wang, Jun Zhu, Xueling Huang, Lin Peng et al.)....Pages 137-141
New Human-Computer Interaction Solution for Next-Generation Power Grid Dispatching System (Hai Yu, Min Xu, Lin Peng, He Wang, Zhansheng Hou)....Pages 142-147
Towards a Framework for Agent-Based Healthcare Monitoring (Udsanee Pakdeetrakulwong)....Pages 148-158
Design and Implementation of Network Traffic Capture Prober Based on General PC (Zhang Mei, Zeng Bin)....Pages 159-166
PAPR Reduction of FBMC-OQAM Signals Using Particle Swam Optimization Algorithm Based on MBJO-PTS (Yan Yang, Pingping Xu)....Pages 167-176
On Abelian Tensor Decomposition and Gradient-Descent Algorithm (Hailing Dong, Yichao Zhang, Ming Yang, Wen Liu, Rong Fan, Yu Shi)....Pages 177-185
Collision Avoidance Method for UAV Using A* Search Algorithm (Jung Kyu Park, Jaeho Kim)....Pages 186-193
The Bayes Quantile Regression Theory and Application (Xiaoliang Lv, Chunli Wang, Lu Qiu, Haizhen Li, Liang Feng)....Pages 194-201
Active Semi-supervised K-Means Clustering Based on Silhouette Coefficient (Hongchen Guo, Junbang Ma, Zhiqiang Li)....Pages 202-209
Intelligent Creative Design of Textile Patterns Based on Convolutional Neural Network (Wang Ying, Liu Zhengdong)....Pages 210-215
Higher Individuality for Effective Swarm Intelligence (Jia Xiao Cai, Hui Ying Chen)....Pages 216-224
Front Matter ....Pages 225-225
Temperature Anomaly Detection by Integrating Local Contrast and Global Contrast (Liu Peng, Li Qiang, Liu Wen, Duan Min, Dai Yue, Wang Yanrong)....Pages 227-233
Temperature Anomaly Detection Based on Gaussian Distribution Hypothesis (Liu Peng, Li Qiang, Liu Wen, Duan Min, Dai Yue, Wang Yanrong)....Pages 234-240
A Market Interaction Model for the Integration of Energy Efficiency Top-Runner and Energy Conservation Standard (Jianwei Tian, Yujuan Xia, Haihong Chen)....Pages 241-248
Micro Leverage Design of Silicon Resonant Accelerometer (Yan Li, Xinrui Zhang)....Pages 249-255
Sensitive Structure Design of Resonant Accelerometer (Yan Li, Zhuoni Zhang)....Pages 256-263
Research on 3D Lightweight Engine Technology for Power Grid Service Scenarios (Gang Wang, Xiaodong Zhang, Chengzhi Zhu, He Wang, Lin Peng, Min Xu)....Pages 264-269
Design of Ship Monitoring System Based on Unsupervised Learning (Li Guanglei, Zeng Hong, Jiang Dingyu, Wang Hao)....Pages 270-276
Users Research of Ice and Snow Theme Games in the Context of Virtual Tourism (Zhu Ran)....Pages 277-282
The Exploration of Multiplatform 2D Game Development (Yuting Yang, Houliang Kang)....Pages 283-290
Evaluation of Underwater Target Scheme Based on Improved Back Propagation Neural Network (Li-ting Lian, Ming-ming Yang)....Pages 291-297
Cross-Linguistic Speaker Identification by Monophthongal Vowels (Yuting Xu, Hongyan Wang)....Pages 298-305
Study on Establishment and Proof of Inequality Based on Descending Dimension Method (Qingpeng Ran)....Pages 306-311
Multilevel Minimum Cross Entropy Threshold Selection Based on the Improved Bat Optimization (Si Chen, Guo-Hua Peng)....Pages 312-320
Large Scale Text Categorization Based on Density Statistics Merging (Rujuan Wang, Suhua Wang)....Pages 321-327
Study on the Automatic Classification Algorithm of Dongba Hieroglyphs (Yuting Yang, Houliang Kang)....Pages 328-333
A Comparison Study of Different Algorithms for Energy-Aware Placement of Virtual Machines (Alejandro Olvera, Fatos Xhafa)....Pages 334-343
Research on Customer Churn Prediction Using Logistic Regression Model (Hong-Yu Hu)....Pages 344-350
One of the Smote_rf’s Gender Prediction Methods in Recommendation System (Huang Meigen, Cui Wenhao)....Pages 351-355
Traffic Flow Control Model with Two-Way Stop for Left-Turn (Na Wang, Xinshe Qi, Xin Wang, Ruiping Huang)....Pages 356-362
Automotive Brake System Design (Zhiqiang Xu)....Pages 363-368
A Model of the Traffic Circle Flow Control (Xinshe Qi, Guo Li, Jing Li, Xin Wang, Na Wang, Qingzheng Xu)....Pages 369-374
An Extraction Method of STPA Variable Based on Four-Variable Model (Miaofang Chen, Lisong Wang, Jun Hu, Tao Feng)....Pages 375-381
Jaya Algorithm-Optimized PID Controller for AVR System (Chibing Gong)....Pages 382-393
Front Matter ....Pages 395-395
Research on Short Text Classification Method Based on Convolution Neural Network (Lei Wang, Qiaohong Chen, Qi Sun, Yubo Jia)....Pages 397-403
Vanishing Point Conducted Diffusion for Crop Rows Detection (Jian Wu, Mengwei Deng, Lianlian Fu, Jianqun Miao)....Pages 404-416
Research on TCAS Fault Diagnosis Based on Directed Graph Fault Tree (Xiaomin Xie, Fan Zhang, Changkai Li, Yong Zeng)....Pages 417-423
Analysis on Injury Mechanism of Toy Scooter (Liu Xia, Liu Bisong, Ruan Li, Jiang Kan)....Pages 424-431
Application of Text Classification Method Based on Depth Learning in Email Handwriting Analysis (Changqing Pang, Ruibin Sun, Xiaodan Mou, Zhiwei Yan, Shuo Mi, Huimin Liu)....Pages 432-439
Research on Digital Evaluation System for Experimental Score (Baoqin Liu)....Pages 440-445
LSD and Skeleton Extraction Combined with Farmland Ridge Detection (Yibo Li, Han Qu)....Pages 446-453
Native Language Identification from English Noise Bursts by Chinese Listeners (Hongyan Wang, Yuting Xu, Lifen Chen, Vincent J. van Heuven)....Pages 454-461
Two Dimensional Orthogonal Constrained Maximum Variance Mapping for Face Recognition (Yu’e Lin, Chengjin Wang, Xingzhu Liang)....Pages 462-467
The INS and UWB Fusion System Based on Kalman Filter (Guoxiang Xu, Cheng Xu, Cui Yao, Yue Qi, Jie He)....Pages 468-475
Research of Digital Signal Processing Based on System Learning Model (Jun Luo, Ruifang Zhai, Hui Peng)....Pages 476-481
Research of Computer Vision Based on System Learning Ability (Jun Luo, Ruifang Zhai, Hui Peng)....Pages 482-488
Point-to-Point Rotation Orientation Algorithm Based on the Secondary Template Matching (Yanzhong Liu, Shihong Duan, Jie He, Yue Qi)....Pages 489-497
Effects of Lecture Video Types on Student Learning: An Analysis of Eye-Tracking and Electroencephalography Data (Xiaoming Cao, Miaoting Cheng, Xiya Xue, Shan Zhu)....Pages 498-505
Optimize Projection Access Order for Deflection Tomography Reconstruction (Huaxin Li, Jinxiao Pan)....Pages 506-514
Front Matter ....Pages 515-515
Simulation of Evaluate the Effect on Big Data Pricing Scheme Model (Chenghui Yang)....Pages 517-522
Analysis of a Novel 1T Spatial Multi-loop Coupled Mechanism (Shuang Zhang, Jingfang Liu, Jian Wang, Huafeng Ding)....Pages 523-532
A Study on Online Fault Diagnosis Technology for Shield Core Components (Honghui Zhang)....Pages 533-539
Design of Sparse Two-Dimensional FIR Notch Filter Based on BP Neural Network (Wei Xu, Ruihua Zhang, Jiaxiang Zhao)....Pages 540-549
Design and Implementation of Multi-level CIC Filter Based on FPGA (Pu Wang, Yuming Zhang, Jun Yang)....Pages 550-557
A Multi-dimensional Electronic Channel Unified Identity Authentication Strategy Based on Role Control (Baoxian Guo, Ying Xu, Renjie Li, Xingxiong Zhu)....Pages 558-565
Vegetable Technology Information Visual Service System Based on Knowledge Map (Qingfeng Wei, Changshou Luo, Jun Yu, Xuezhong Chen, Sufen Shun)....Pages 566-571
Research on Answerer Recommending Method Based on Online Learning Community (Jun-min Ye, Song Xu, Xiao-min Xu, Da-Xiong Luo, Shu Chen, Zhi-feng Wang)....Pages 572-577
Study on the Relationship Between Eysenck Personality and Sleep Quality Based on Multiple Ordered Logistic Regression (Zhihan Yang, Mengge Sun, Minghui Wang)....Pages 578-585
Study on Catching-up-Element of Risk in Talent Cultivation Project (Xinfa Tang, Zhuangwen Sun)....Pages 586-592
Research on Computer Aided Innovation Software Based on Extenics (Weitao He, Rui Fan, Fuyu Ma, Fuli Chen, Bifeng Guo)....Pages 593-600
Analysis and Improvement of User Behavior of Free-Floating Bike Sharing in China Based on Questionnaire (Meiyu Li, Xifu Wang, Xi Zhang, Yuan Yuan)....Pages 601-610
A Study of Learning Effects in MOOC: An Example of Ideological and Political Education Courses in China (Tingting Duan)....Pages 611-619
Quality Management Research of the Manufacturing Process Based on Q Company Products (Xinmiao Zhou, Shuguang Sun)....Pages 620-626
Virtual Writing Interactive Display Based on Unity (Xuemei Tang, Shuyuan Shang)....Pages 627-632
Extensive Mind Mapping for the Contradiction of the Organic Rice Planting Precautions’ Cost (Penghui Liu, Rui Fan, Bifeng Guo, Fuyu Ma, Yongzhao Feng)....Pages 633-641
The Solution of Environmental Damage in Scenic Spots by Extensible Mind Mapping (Enna Wu, Rui Fan, Bifeng Guo, Fuli Chen, Qiubin Liu)....Pages 642-649
Prediction of Remaining Useful Life for Equipment Based on Improved Metabolic GM(1,1) Model (Liu Yuwen, Cai Hongtu, Li Zhiyong, Fang Shidong, Jiang Min)....Pages 650-660
Supply Chain Managerial Decision-Making Practices Effect Under Various Scenarios (Azamat Rajapov, Ming Jian, Saidjahon Hayrutdinov, Botir Ergashev)....Pages 661-667
The Application of Virtual Reality Technology in Logistics Training (Yipeng Li, Di Wang, Yaqi Liu)....Pages 668-675
Computer Application Technology Development and Practice (Xue Zhao)....Pages 676-681
Analysis of the Information Demand and Supply of New Occupational Farmers—A Survey Based on Beijing (Changshou Luo, Xiaohui Liu, Yaming Zheng, Sufen Sun)....Pages 682-688
Front Matter ....Pages 689-689
Construction and Implementation of Information Class Experiment Course Group Based on Cloud Platform (Ting Huang, Peng He)....Pages 691-698
A Framework for Shop Floor Material Delivery Optimization Based on RFID-Enabled Production Big Data (Xin Zhao, Wei Zhang, Hengling Meng, Fangfang He, Shan Ren)....Pages 699-705
An Association Rule Mining Approach for Shop Floor Material Handling Based on Real-Time Manufacturing Big Data (Xin Zhao, Hengling Meng, Wei Zhang, Xun Li, Shan Ren)....Pages 706-713
Font-End Achievement of Extensive Innovative Services Community Based on Web (Qiubin Liu, Rui Fan, Bifeng Guo, Zihao Li, Enna Wu)....Pages 714-721
Software-Defined Data Flow Detection and Control Approach for Industrial Modbus/TCP Communication (Ming Wan, Yan Song, Yuan Jing, Zhaowei Wang, Jianming Zhao, Zhongshui Zhang)....Pages 722-729
Research on the Application of Block Chain Technology in Internet Finance (Qiusheng Zhang, Xingyun Zhang)....Pages 730-735
Categorical Data Clustering Method Based on Improved Fruit Fly Optimization Algorithm (Dong Li, Huifeng Xue, Wenyu Zhang, Yan Zhang)....Pages 736-744
Discussion on Computer Network Security Solution (Min Xiao, Mei Guo)....Pages 745-751
An Internet of Things Framework Based on Upper Limb Rehabilitation Robots for Rehabilitation (Qiaoling Meng, Hui Zhang, Hongliu Yu)....Pages 752-759
Research on Power Big Data Storage Platform Based on Distributed File System (Liu Fei, Pang Hao-Yuan, Zhang Yi-Ying, Liang Kun, He Ye-Shen, Li Xiang-Zhen et al.)....Pages 760-767
Abnormal Detection Methods of Information Security for Power Big Data (Pang Hao-Yuan, Liu Fei, Zhang Yi-Ying, Cong Wang, He Ye-Shen, Li Xiang-Zhen et al.)....Pages 768-774
Research on Data Center Topological Structure Analysis Technology Based on Graph Database (Liang Zhu, Mingjie Yang, He Wang)....Pages 775-781
A Survey of Code Reuse Attack and Defense (Bingbing Luo, Yimin Yang, Changhe Zhang, Yi Wang, Baoying Zhang)....Pages 782-788
A Survey of Vulnerability Defense Measures (Yang Gao, Jing Lou, Changhe Zhang, Hai Wang, Yijuan Yan)....Pages 789-796
Research on the User Fatigue of Household Appliances Human-Computer Interaction Based on Wearable Devices and Bare-Hand Gesture Recognition (Yixuan Xue, Shuxia Li, Jinsheng Lu, Zhe Tong, Hongbo Shan)....Pages 797-804
Research on Precision Marketing Model of Beijing Agricultural Products Under Big Data Environment (Xiangyu Chen, Jing Gong)....Pages 805-812
Front Matter ....Pages 813-813
A Layered Secure Communication Model for Cooperative Vehicle Infrastructure System (Yao Zhang, Qun Wang)....Pages 815-822
A Relay Protocol in AF Relaying Wireless Energy Harvesting Network (Xian Li, Yulong Han, Qiuling Tang, Jiahao Shi)....Pages 823-829
Information Sharing Technology in Device-to-Device Cellular Networks (Min Wang, Qiaoyun Sun, Shuguang Zhang, Yu Zhang)....Pages 830-835
The Weakness of the Self-encryption Mechanism for Authentication of Roaming Services (Min-Shiang Hwang, Song-Kong Chong, Cheng-Ying Yang)....Pages 836-841
Cryptanalysis of the Serverless RFID Authentication and Search Protocols (Chia-Hui Wei, Cheng-Ying Yang, Min-Shiang Hwang)....Pages 842-846
Near Ground UWB Channel Modeling in Different Terrain Surface (Shihong Duan, Jiacan Si, Cheng Xu, Junluo Yin, Jie He)....Pages 847-855
Research on Visible Light Communication (Hongwei Zhu, Shuguang Zhang, Hui Jia, Bo Zhou)....Pages 856-861
Sampling Redundancy Removal Algorithms for Stepped Frequency Continuous Wave (Yue Pan, Ming Diao, Zengmao Chen)....Pages 862-869
The Performance of Chirp-BOK Modulation in the Time Fading Channel (Zhiguo Sun, Shiming Li, Zengmao Chen, Xiaoyan Ning)....Pages 870-878
An Improved Direct Sequence Spread Spectrum Signal Detection Algorithm (Zengmao Chen, Fangpeng Wan, Shiming Li)....Pages 879-886
Research on Non-contact Palmprint Recognition Positioning Method in Mobile Terminal (Chunyu Zhang, Chenghao Zhang)....Pages 887-894
Android Palmprint Recognition System Design and Implementations (Chunyu Zhang, Chenghao Zhang)....Pages 895-903
A Cache-Aware Multicast Routing for Mobile Social Networks (Xia Deng, Shuxian Bao, Yu Lin, Zhishuang Xu)....Pages 904-913
Front Matter ....Pages 914-914
Research and Design of Expert System Based on Oil-Gas Field Energy Saving Information Platform (Yidong Guo, Yufeng Lu, Xiaomei He, Tongyang Zhang)....Pages 917-927
A New Training Sample Selection Method Avoiding Over-Fitting Based on Nearest Neighbor Rule (Guang Li)....Pages 928-935
A Safety Analysis Method for FGS Based on STPA (Tao Feng, Lisong Wang, Jun Hu, Miaofang Chen)....Pages 936-944
Hybrid Evaluating Method for Battlefield Support Capability of Field Petroleum Pipeline Unit Based on Support Vector Machine (Wen-ming Zhou, Yao-wu Wu, Lin Zheng, Xiang-sen Yu, Peng Jia, Yun-he Wang et al.)....Pages 945-953
The Computer Formula of the Smarandache Dual Function (Liu Miaohua, Song Xiuchao, Jao Hongying)....Pages 954-957
Safety Risk Assessment for Formamide in Yoga Mats (Fang Jia, Xia Liu, Wenjian Xie, Xiaolei Feng)....Pages 958-965
A Model of Probabilistic Event for CPS Based on Hidden Markov Model (Cheng Zhou, Youqian Feng, Zhonghai Yin, Yali Wang, Yunmin Huang)....Pages 966-973
Abnormal Condition Analysis and Preventive Measures of a 220 KV Transformer Neutral Point Bushing (Zhou Yuxiao, Han Honggang, Guo Tie, Liu Lu, Chen Hao, Liu Yang et al.)....Pages 974-984
Limitations of the DuPont Financial Index System and Its Improvement (Zhang Mei, Feng Fei, Zhang Zhilong, Wen Jinghua)....Pages 985-993
Requirements Analysis and Design of Extensive Innovative Services Community Based on Web (Bifeng Guo, Rui Fan, Sheng Gao, Qiubin Liu, Weitao He)....Pages 994-1001
Integrated Object Layout and Supporting Structure Topology Optimization Method Based on MMC (Dongliang Zhang, Xiaoyan Zhang, Jun Mo, Yunrong Luo)....Pages 1002-1009
Second Order Necessary Optimality Conditions for a Class of Optimization Problem in Banach Spaces (Xuanwei Zhou)....Pages 1010-1016
The EGARCH Effect Test of Chinese Stock Market from the Perspective of Behavioral Finance (Wenting Cao, Jiangyue Luo, Xiaojuan Wu)....Pages 1017-1024
Dongba Hieroglyphic Similarity Measure Algorithm Based on Grid Resolution (Yuting Yang, Houliang Kang)....Pages 1025-1030
Research on Course Selection Algorithm in Colleges Based on Collaborative Filtering Recommendation Algorithm (Yong Zhang, Yipeng Li, Renzhong Huang)....Pages 1031-1037
Reliability Data Analysis of Aviation Equipment Components Based on Lognormal Distribution (Yanming Yang, Huayuan Zhu)....Pages 1038-1045
Application Analysis of Engine Emission Technology (Fan Yang)....Pages 1046-1052
Research and Application of Dual Active Disaster Preparedness System Based on Redo Read-Write Separation Cluster (Qianjun Wu, Qingquan Dong, Guangxin Zhu, Jun Yu)....Pages 1053-1063
Analysis on the Governance Path of Financial Fraud of Listed Companies from the Perspective of New Media (Yueming Cheng, Mengge Gu, Yi Cheng, Xiaoping Hu, Kang Cheng)....Pages 1064-1071
Research on the Application of Financial Sharing Service Center Information System (Lei Xia)....Pages 1072-1079
Research on the Teaching Effects of Flipped Class Model Based on SPOC (Binghui Wu, Tingting Duan)....Pages 1080-1087
A Comparative Analysis of the Utilization of FDI in Six Central Provinces (Min Cheng, Lan Liu)....Pages 1088-1095
Solid Edge’s Application in Vertical Mill Design (Kunshan Li, Yang Li)....Pages 1096-1103
Analysis on Development Trends of Research Topics in Agricultural Sciences (Changshou Luo, Liying Zhou, Qingfeng Wei, Sufen Sun)....Pages 1104-1112
The Impact of China Manufacturing on the Environment (Meng Li, Gang Yu, Liang Yang)....Pages 1113-1116
The Influence of Cultural Creativity on Beijing Textile and Clothing Industry Analyse (Sun Jie, Xi Yang)....Pages 1117-1123
Application Traceability in the Food Supply Chain (Gang Liu)....Pages 1124-1129
Research on Irregular Block Spatial Scheduling Algorithm in Shipbuilding (Yongjian Zhang, Huiyue Ci)....Pages 1130-1136
Study on the Cutting Force in Elliptical Ultrasonic Vibration Cutting (Chengmao Zhang)....Pages 1137-1142
The Influence of Climate in Lingnan Area on the Architectural Design of Library and Its Countermeasures (Zhenwei Wang)....Pages 1143-1148
Designed of Ball Rolling Control System Based on STM32 (Si-Lian Xie, Zhou Yue, He-Ping Li)....Pages 1149-1156
Influencing Factors of Government Microblogs’ Communication Effects: A Research Based on User Behavior (Manrui Zhang, Wenbo Liu)....Pages 1157-1162
Back Matter ....Pages 1163-1167

Citation preview

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Advances in Intelligent Systems and Computing 885

Fatos Xhafa Srikanta Patnaik Madjid Tavana Editors

Advances in Intelligent, Interactive Systems and Applications Proceedings of the 3rd International Conference on Intelligent, Interactive Systems and Applications (IISA2018)

Advances in Intelligent Systems and Computing Volume 885

Series editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]

The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results.

Advisory Board Chairman Nikhil R. Pal, Indian Statistical Institute, Kolkata, India e-mail: [email protected] Members Rafael Bello Perez, Faculty of Mathematics, Physics and Computing, Universidad Central de Las Villas, Santa Clara, Cuba e-mail: [email protected] Emilio S. Corchado, University of Salamanca, Salamanca, Spain e-mail: [email protected] Hani Hagras, School of Computer Science & Electronic Engineering, University of Essex, Colchester, UK e-mail: [email protected] László T. Kóczy, Department of Information Technology, Faculty of Engineering Sciences, Győr, Hungary e-mail: [email protected] Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA e-mail: [email protected] Chin-Teng Lin, Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan e-mail: [email protected] Jie Lu, Faculty of Engineering and Information, University of Technology Sydney, Sydney, NSW, Australia e-mail: [email protected] Patricia Melin, Graduate Program of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico e-mail: [email protected] Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro, Rio de Janeiro, Brazil e-mail: [email protected] Ngoc Thanh Nguyen, Wrocław University of Technology, Wrocław, Poland e-mail: [email protected] Jun Wang, Department of Mechanical and Automation, The Chinese University of Hong Kong, Shatin, Hong Kong e-mail: [email protected]

More information about this series at http://www.springer.com/series/11156

Fatos Xhafa Srikanta Patnaik Madjid Tavana •

Editors

Advances in Intelligent, Interactive Systems and Applications Proceedings of the 3rd International Conference on Intelligent, Interactive Systems and Applications (IISA2018)

123

Editors Fatos Xhafa Department de Ciències de la Computació Universitat Politècnica de Catalunya Barcelona, Spain

Madjid Tavana Department of Business Systems and Analytics La Salle University Philadelphia, PA, USA

Srikanta Patnaik Department of Computer Science and Engineering, Faculty of Engineering and Technology SOA University Bhubaneswar, Odisha, India

ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-3-030-02803-9 ISBN 978-3-030-02804-6 (eBook) https://doi.org/10.1007/978-3-030-02804-6 Library of Congress Control Number: 2018958520 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Intelligent and Interactive Systems (IISs) are systems designed to interact between humans and their environment through digital technologies. IISs comprise of artificial intelligence, computer vision, human–robot interaction, and cognitive science. Intelligent and interactive technologies have become increasingly popular. Robots are embedded with intelligence, cooperate with, and enable humans to perform various repetitive and monotonous jobs. Wearable devices such as Fitbit monitor and track the daily habits and activities through cloud-based systems. Smart mobility system is also an application area of IISs, where navigation system tool in automobiles and mobile applications assist smartphone users to trace and navigate to the desired location, as well as assisting users in optimizing the routes. The IISs now use the Internet of things (IoT). In today’s era, IoT plays a major role in many areas, such as day-to-day life activities, education system, health monitoring, intelligent farming system, intelligent waste management system, smart electricity to each household, intelligent election system, smart security systems, various defense applications, traffic management system, and smart emergency systems. The ultimate goal of the IoT is to ease the lifestyle of human beings with minimum effort and betterment of the society by using IISs. Various dimensions of the IISs include intelligent user interfaces, context awareness and adaptability, human–computer interaction, wearable technologies, smart solutions for real-time problems, smart navigation, data-driven social analytics, mobile robotics, virtual communication environments, face and gesture analysis, and crowdsourcing. This volume explores how novel interactive systems can intelligently face various challenges and limitations previously encountered by human beings using different machine learning algorithms along with analysis of recent trends. However, design and development of IISs are quite hard to implement due to variations in abilities, preferences, and limitations of human being. This volume contains 150 contributions from diverse areas of IISs which have been categorized into seven sections, namely (i) Intelligent Systems; (ii) Autonomous Systems; (iii) Pattern Recognition and Vision Systems;

v

vi

Preface

(iv) E-Enabled Systems; (v) Internet and Cloud Computing; (vi) Mobile Computing and Intelligent Networking; and (vii) Various Applications. (i) Intelligent Systems: This is the nervous system of IISs, and many researchers are engaged in this area of research. This section contains twenty-nine (29) contributions. (ii) Autonomous Systems: This is one of the established areas of interactive intelligence system, which consists of learning, reasoning, and decision making which supports the system’s primary function. There are twenty-three contributions consisting of various algorithms, models, and learning techniques. (iii) Pattern Recognition and Vision Systems: This is one of the primary functions of any interactive intelligent systems. There are fifteen contributions comprised in this section covering the developments in this area of deep learning to binocular stereovision to 3D vision. (iv) E-Enabled Systems: This is one of the essential areas of IISs, as many interactive systems are now designed through the Internet. It covers information navigation and retrieval, designing intelligent learning environments, and model-based user interface design. There are twenty-two contributions covered in this section. (v) Internet and Cloud Computing: It is one of the essential areas of IISs, which are used to enhance communication between the system and users, in a way which may not be closely related to the system’s main function. This is commonly found in multimodal interactions, natural language processing, computer graphics, and accessible computing. In this section, there are sixteen contributions consisting of microblogging, user satisfaction modeling to the design, and construction of graphical cloud computing platform. (vi) Mobile and Wireless Communication: This area is one of the leading areas of IISs, which covers ubiquitous or mobile computing and networking. This section covers thirteen contributions. (vii) Applications: Applications of IISs in various domains are covered in the last section, which consists of thirty-two contributions.

Acknowledgements The contributions covered in this proceeding are the outcome of the contributions from more than two hundred researchers. We are thankful to the authors and paper contributors of this volume. We are also grateful to the Editor-in-Chief of the Springer Book series on “Advances in Intelligent Systems and Computing,” Prof. Janusz Kacprzyk, for his support to bring out the third volume of the IISA-2018 conference. It is noteworthy to mention here that constant support from the Editor-in-Chief and the members of the publishing house makes the conference fruitful for the third edition.

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We would like to extend our heartfelt thanks to Dr. Thomas Ditzinger, Executive Editor, and his Springer publishing team for their encouragement and support. We are thankful to Prof. Schahram Dustdar, Computer Science and Head of the Distributed Systems Group at the TU Wien, Austria, for his enlightening keynote address “Cyber-Human Partnerships - Software Engineering Perspectives for IOT and Distributed Systems.” We are also equally thankful to Prof. Andrew W. H. Ip, Hong Kong Polytechnic University, for his talk on “IOT and Space Informatics.” We are also thankful to the experts and reviewers who have worked for this volume despite the veil of their anonymity. We extend our thanks to Prof. Dr. Yingxu Wang, Professor of Department of Electrical and Computer Engineering, University of Calgary, Canada, for his talk on “Cognitive Machine Learning and Reasoning by Cognitive Robots.” We look forward to your valued contribution and support to the next editions of the International Conference on Intelligent and Interactive Systems and Applications. Finally, we are extremely thankful to Interscience Research Network (IRNet) International for the initiation of this event and IRNet China Academic Communication Center for organizing this conference for their constant support and services for organizing this event successfully. We are sure that the readers will get immense benefit and knowledge from the third volume and the papers in Intelligent and Interactive Systems and Applications. Fatos Xhafa Srikanta Patnaik Madjid Tavana

Contents

Intelligent Systems An Android-Based Remote Monitoring System . . . . . . . . . . . . . . . . . . . Lina Jia, Meng Zhang, Wei Zhang, and Jiaguo Lv

3

An Intelligent Registration Management System for Freshmen . . . . . . . Hao Zhu, Mengshu Hou, Yaohua Xie, Kai Yan, and Junming Li

14

Investigation of Wireless Sensor Network of the Internet of Things . . . . Yibin Hou and Jin Wang

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A Remote Phase Change System for Low-Voltage Power Distribution Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiafeng Ding, Jing Liu, Xinmei Li, Zhifeng Li, Fei Gong, Xiao Liang, and Qin Luo Design and Implement of Speech Interaction with Indoor Substation Inspection Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaobin Yu, Shiliang Lv, Kun Mao, Anshan Wang, Shouguo Lv, Lei Han, Changchun Gao, and Guoqing Yang Speech Recognition Algorithm of Substation Inspection Robot Based on Improved DTW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lei Han, Changchun Gao, Shujing Zhang, Dongsong Li, Zhizhou Sun, Guoqing Yang, Jian Li, Chuanyou Zhang, and Guangting Shao

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The Design of High Accuracy Pedometer Based on an Adaptive and Dynamic Low-Pass Filtering Algorithm . . . . . . . . . . . . . . . . . . . . . . Deng Xu and Baohua Yang

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Research on Multi-sensor and Multi-target Data Association Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fu Shuai

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A Detecting System for Wheel Balancer Based on the Effect Coefficient Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Honghui Zhang and Wanli Zhang Study of Substation Inspection Robot Voice Recognition Algorithm Based on Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chongguang Fu, Zhizhou Sun, Kechao Tian, Maoqi Dong, Guoqing Yang, Jian Li, Chuanyou Zhang, and Guangting Shao Research on the Development of Intelligent Industrial Control . . . . . . . Liu Miao, Che Lei, Xuepo Li, and Lujun Tan Toward Human Motion Sensing: Design and Performance Evaluation of a Minimized Wearable Platform Using Inertial and TOA Sensors . . . Cheng Xu, Jie He, Xiaotong Zhang, Yue Qi, and Shihong Duan

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Deep Learning of Intelligent Speech Recognition in Power Dispatching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Jianzhong Dou, Qunshan Li, Hongyi Lai, Chao Yang, Shenzeng Luo, Ziyu Lin, and Xusheng Yang An Improved Multi-factor Dimensionality Reduction Approach to Identify Gene-Gene Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Li-Yeh Chuang, Yu-Da Lin, and Cheng-Hong Yang Nonlocal Estimation and BM3D Based Face Illumination Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Yingkun Hou Design of Rice Traceability System Based on WSN and RFID . . . . . . . . 123 Fengjuan Miao, Xiaoxu Lu, Bairui Tao, Kaida Liu, and Ding Liu Design of Stereoscopic Garage System Based on PLC Control . . . . . . . 131 Libo Yang Intelligent Scenario Computing Workflow Fusion Design Technology for Power Mobile Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Haoran Wu, Min Xu, Xiao-Ling Wang, Jun Zhu, Xueling Huang, Lin Peng, and Xingchuan Bao New Human-Computer Interaction Solution for Next-Generation Power Grid Dispatching System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Hai Yu, Min Xu, Lin Peng, He Wang, and Zhansheng Hou Towards a Framework for Agent-Based Healthcare Monitoring . . . . . . 148 Udsanee Pakdeetrakulwong Design and Implementation of Network Traffic Capture Prober Based on General PC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Zhang Mei and Zeng Bin

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PAPR Reduction of FBMC-OQAM Signals Using Particle Swam Optimization Algorithm Based on MBJO-PTS . . . . . . . . . . . . . . . . . . . . 167 Yan Yang and Pingping Xu On Abelian Tensor Decomposition and Gradient-Descent Algorithm . . . 177 Hailing Dong, Yichao Zhang, Ming Yang, Wen Liu, Rong Fan, and Yu Shi Collision Avoidance Method for UAV Using A* Search Algorithm . . . . 186 Jung Kyu Park and Jaeho Kim The Bayes Quantile Regression Theory and Application . . . . . . . . . . . . 194 Xiaoliang Lv, Chunli Wang, Lu Qiu, Haizhen Li, and Liang Feng Active Semi-supervised K-Means Clustering Based on Silhouette Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 Hongchen Guo, Junbang Ma, and Zhiqiang Li Intelligent Creative Design of Textile Patterns Based on Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 Wang Ying and Liu Zhengdong Higher Individuality for Effective Swarm Intelligence . . . . . . . . . . . . . . 216 Jia Xiao Cai and Hui Ying Chen Autonomous Systems Temperature Anomaly Detection by Integrating Local Contrast and Global Contrast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Liu Peng, Li Qiang, Liu Wen, Duan Min, Dai Yue, and Wang Yanrong Temperature Anomaly Detection Based on Gaussian Distribution Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Liu Peng, Li Qiang, Liu Wen, Duan Min, Dai Yue, and Wang Yanrong A Market Interaction Model for the Integration of Energy Efficiency Top-Runner and Energy Conservation Standard . . . . . . . . . . . . . . . . . . 241 Jianwei Tian, Yujuan Xia, and Haihong Chen Micro Leverage Design of Silicon Resonant Accelerometer . . . . . . . . . . 249 Yan Li and Xinrui Zhang Sensitive Structure Design of Resonant Accelerometer . . . . . . . . . . . . . . 256 Yan Li and Zhuoni Zhang Research on 3D Lightweight Engine Technology for Power Grid Service Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Gang Wang, Xiaodong Zhang, Chengzhi Zhu, He Wang, Lin Peng, and Min Xu

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Design of Ship Monitoring System Based on Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Li Guanglei, Zeng Hong, Jiang Dingyu, and Wang Hao Users Research of Ice and Snow Theme Games in the Context of Virtual Tourism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Zhu Ran The Exploration of Multiplatform 2D Game Development . . . . . . . . . . . 283 Yuting Yang and Houliang Kang Evaluation of Underwater Target Scheme Based on Improved Back Propagation Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Li-ting Lian and Ming-ming Yang Cross-Linguistic Speaker Identification by Monophthongal Vowels . . . . 298 Yuting Xu and Hongyan Wang Study on Establishment and Proof of Inequality Based on Descending Dimension Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Qingpeng Ran Multilevel Minimum Cross Entropy Threshold Selection Based on the Improved Bat Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Si Chen and Guo-Hua Peng Large Scale Text Categorization Based on Density Statistics Merging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Rujuan Wang and Suhua Wang Study on the Automatic Classification Algorithm of Dongba Hieroglyphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 Yuting Yang and Houliang Kang A Comparison Study of Different Algorithms for Energy-Aware Placement of Virtual Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 Alejandro Olvera and Fatos Xhafa Research on Customer Churn Prediction Using Logistic Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 Hong-Yu Hu One of the Smote_rf’s Gender Prediction Methods in Recommendation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Huang Meigen and Cui Wenhao Traffic Flow Control Model with Two-Way Stop for Left-Turn . . . . . . . 356 Na Wang, Xinshe Qi, Xin Wang, and Ruiping Huang

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Automotive Brake System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Zhiqiang Xu A Model of the Traffic Circle Flow Control . . . . . . . . . . . . . . . . . . . . . . 369 Xinshe Qi, Guo Li, Jing Li, Xin Wang, Na Wang, and Qingzheng Xu An Extraction Method of STPA Variable Based on Four-Variable Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Miaofang Chen, Lisong Wang, Jun Hu, and Tao Feng Jaya Algorithm-Optimized PID Controller for AVR System . . . . . . . . . 382 Chibing Gong Pattern Recognition and Vision System Research on Short Text Classification Method Based on Convolution Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Lei Wang, Qiaohong Chen, Qi Sun, and Yubo Jia Vanishing Point Conducted Diffusion for Crop Rows Detection . . . . . . . 404 Jian Wu, Mengwei Deng, Lianlian Fu, and Jianqun Miao Research on TCAS Fault Diagnosis Based on Directed Graph Fault Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Xiaomin Xie, Fan Zhang, Changkai Li, and Yong Zeng Analysis on Injury Mechanism of Toy Scooter . . . . . . . . . . . . . . . . . . . . 424 Liu Xia, Liu Bisong, Ruan Li, and Jiang Kan Application of Text Classification Method Based on Depth Learning in Email Handwriting Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 Changqing Pang, Ruibin Sun, Xiaodan Mou, Zhiwei Yan, Shuo Mi, and Huimin Liu Research on Digital Evaluation System for Experimental Score . . . . . . . 440 Baoqin Liu LSD and Skeleton Extraction Combined with Farmland Ridge Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 Yibo Li and Han Qu Native Language Identification from English Noise Bursts by Chinese Listeners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454 Hongyan Wang, Yuting Xu, Lifen Chen, and Vincent J. van Heuven Two Dimensional Orthogonal Constrained Maximum Variance Mapping for Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Yu’e Lin, Chengjin Wang, and Xingzhu Liang

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The INS and UWB Fusion System Based on Kalman Filter . . . . . . . . . . 468 Guoxiang Xu, Cheng Xu, Cui Yao, Yue Qi, and Jie He Research of Digital Signal Processing Based on System Learning Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476 Jun Luo, Ruifang Zhai, and Hui Peng Research of Computer Vision Based on System Learning Ability . . . . . 482 Jun Luo, Ruifang Zhai, and Hui Peng Point-to-Point Rotation Orientation Algorithm Based on the Secondary Template Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 489 Yanzhong Liu, Shihong Duan, Jie He, and Yue Qi Effects of Lecture Video Types on Student Learning: An Analysis of Eye-Tracking and Electroencephalography Data . . . . . . . . . . . . . . . . 498 Xiaoming Cao, Miaoting Cheng, Xiya Xue, and Shan Zhu Optimize Projection Access Order for Deflection Tomography Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506 Huaxin Li and Jinxiao Pan E-Enabled System Simulation of Evaluate the Effect on Big Data Pricing Scheme Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 Chenghui Yang Analysis of a Novel 1T Spatial Multi-loop Coupled Mechanism . . . . . . . 523 Shuang Zhang, Jingfang Liu, Jian Wang, and Huafeng Ding A Study on Online Fault Diagnosis Technology for Shield Core Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 Honghui Zhang Design of Sparse Two-Dimensional FIR Notch Filter Based on BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 Wei Xu, Ruihua Zhang, and Jiaxiang Zhao Design and Implementation of Multi-level CIC Filter Based on FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550 Pu Wang, Yuming Zhang, and Jun Yang A Multi-dimensional Electronic Channel Unified Identity Authentication Strategy Based on Role Control . . . . . . . . . . . . . . . . . . . 558 Baoxian Guo, Ying Xu, Renjie Li, and Xingxiong Zhu Vegetable Technology Information Visual Service System Based on Knowledge Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566 Qingfeng Wei, Changshou Luo, Jun Yu, Xuezhong Chen, and Sufen Shun

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Research on Answerer Recommending Method Based on Online Learning Community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572 Jun-min Ye, Song Xu, Xiao-min Xu, Da-Xiong Luo, Shu Chen, and Zhi-feng Wang Study on the Relationship Between Eysenck Personality and Sleep Quality Based on Multiple Ordered Logistic Regression . . . . 578 Zhihan Yang, Mengge Sun, and Minghui Wang Study on Catching-up-Element of Risk in Talent Cultivation Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586 Xinfa Tang and Zhuangwen Sun Research on Computer Aided Innovation Software Based on Extenics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 Weitao He, Rui Fan, Fuyu Ma, Fuli Chen, and Bifeng Guo Analysis and Improvement of User Behavior of Free-Floating Bike Sharing in China Based on Questionnaire . . . . . . . . . . . . . . . . . . . 601 Meiyu Li, Xifu Wang, Xi Zhang, and Yuan Yuan A Study of Learning Effects in MOOC: An Example of Ideological and Political Education Courses in China . . . . . . . . . . . . . . . . . . . . . . . 611 Tingting Duan Quality Management Research of the Manufacturing Process Based on Q Company Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620 Xinmiao Zhou and Shuguang Sun Virtual Writing Interactive Display Based on Unity . . . . . . . . . . . . . . . . 627 Xuemei Tang and Shuyuan Shang Extensive Mind Mapping for the Contradiction of the Organic Rice Planting Precautions’ Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633 Penghui Liu, Rui Fan, Bifeng Guo, Fuyu Ma, and Yongzhao Feng The Solution of Environmental Damage in Scenic Spots by Extensible Mind Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642 Enna Wu, Rui Fan, Bifeng Guo, Fuli Chen, and Qiubin Liu Prediction of Remaining Useful Life for Equipment Based on Improved Metabolic GM(1,1) Model . . . . . . . . . . . . . . . . . . . . . . . . . 650 Liu Yuwen, Cai Hongtu, Li Zhiyong, Fang Shidong, and Jiang Min Supply Chain Managerial Decision-Making Practices Effect Under Various Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661 Azamat Rajapov, Ming Jian, Saidjahon Hayrutdinov, and Botir Ergashev The Application of Virtual Reality Technology in Logistics Training . . . 668 Yipeng Li, Di Wang, and Yaqi Liu

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Computer Application Technology Development and Practice . . . . . . . . 676 Xue Zhao Analysis of the Information Demand and Supply of New Occupational Farmers—A Survey Based on Beijing . . . . . . . . . . . . . . . . . . . . . . . . . . 682 Changshou Luo, Xiaohui Liu, Yaming Zheng, and Sufen Sun Internet and Cloud Computing Construction and Implementation of Information Class Experiment Course Group Based on Cloud Platform . . . . . . . . . . . . . . . . . . . . . . . . 691 Ting Huang and Peng He A Framework for Shop Floor Material Delivery Optimization Based on RFID-Enabled Production Big Data . . . . . . . . . . . . . . . . . . . . 699 Xin Zhao, Wei Zhang, Hengling Meng, Fangfang He, and Shan Ren An Association Rule Mining Approach for Shop Floor Material Handling Based on Real-Time Manufacturing Big Data . . . . . . . . . . . . 706 Xin Zhao, Hengling Meng, Wei Zhang, Xun Li, and Shan Ren Font-End Achievement of Extensive Innovative Services Community Based on Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714 Qiubin Liu, Rui Fan, Bifeng Guo, Zihao Li, and Enna Wu Software-Defined Data Flow Detection and Control Approach for Industrial Modbus/TCP Communication . . . . . . . . . . . . . . . . . . . . . 722 Ming Wan, Yan Song, Yuan Jing, Zhaowei Wang, Jianming Zhao, and Zhongshui Zhang Research on the Application of Block Chain Technology in Internet Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 730 Qiusheng Zhang and Xingyun Zhang Categorical Data Clustering Method Based on Improved Fruit Fly Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 736 Dong Li, Huifeng Xue, Wenyu Zhang, and Yan Zhang Discussion on Computer Network Security Solution . . . . . . . . . . . . . . . 745 Min Xiao and Mei Guo An Internet of Things Framework Based on Upper Limb Rehabilitation Robots for Rehabilitation . . . . . . . . . . . . . . . . . . . . . . . . 752 Qiaoling Meng, Hui Zhang, and Hongliu Yu Research on Power Big Data Storage Platform Based on Distributed File System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 760 Liu Fei, Pang Hao-Yuan, Zhang Yi-Ying, Liang Kun, He Ye-Shen, Li Xiang-Zhen, and Liu Zhu

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Abnormal Detection Methods of Information Security for Power Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 768 Pang Hao-Yuan, Liu Fei, Zhang Yi-Ying, Cong Wang, He Ye-Shen, Li Xiang-Zhen, and Liu Zhu Research on Data Center Topological Structure Analysis Technology Based on Graph Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 Liang Zhu, Mingjie Yang, and He Wang A Survey of Code Reuse Attack and Defense . . . . . . . . . . . . . . . . . . . . . 782 Bingbing Luo, Yimin Yang, Changhe Zhang, Yi Wang, and Baoying Zhang A Survey of Vulnerability Defense Measures . . . . . . . . . . . . . . . . . . . . . 789 Yang Gao, Jing Lou, Changhe Zhang, Hai Wang, and Yijuan Yan Research on the User Fatigue of Household Appliances Human-Computer Interaction Based on Wearable Devices and Bare-Hand Gesture Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . 797 Yixuan Xue, Shuxia Li, Jinsheng Lu, Zhe Tong, and Hongbo Shan Research on Precision Marketing Model of Beijing Agricultural Products Under Big Data Environment . . . . . . . . . . . . . . . . . . . . . . . . . 805 Xiangyu Chen and Jing Gong Mobile and Wireless Communication A Layered Secure Communication Model for Cooperative Vehicle Infrastructure System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815 Yao Zhang and Qun Wang A Relay Protocol in AF Relaying Wireless Energy Harvesting Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 Xian Li, Yulong Han, Qiuling Tang, and Jiahao Shi Information Sharing Technology in Device-to-Device Cellular Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 830 Min Wang, Qiaoyun Sun, Shuguang Zhang, and Yu Zhang The Weakness of the Self-encryption Mechanism for Authentication of Roaming Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 836 Min-Shiang Hwang, Song-Kong Chong, and Cheng-Ying Yang Cryptanalysis of the Serverless RFID Authentication and Search Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 842 Chia-Hui Wei, Cheng-Ying Yang, and Min-Shiang Hwang Near Ground UWB Channel Modeling in Different Terrain Surface . . . 847 Shihong Duan, Jiacan Si, Cheng Xu, Junluo Yin, and Jie He

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Research on Visible Light Communication . . . . . . . . . . . . . . . . . . . . . . . 856 Hongwei Zhu, Shuguang Zhang, Hui Jia, and Bo Zhou Sampling Redundancy Removal Algorithms for Stepped Frequency Continuous Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 862 Yue Pan, Ming Diao, and Zengmao Chen The Performance of Chirp-BOK Modulation in the Time Fading Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 870 Zhiguo Sun, Shiming Li, Zengmao Chen, and Xiaoyan Ning An Improved Direct Sequence Spread Spectrum Signal Detection Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 879 Zengmao Chen, Fangpeng Wan, and Shiming Li Research on Non-contact Palmprint Recognition Positioning Method in Mobile Terminal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 887 Chunyu Zhang and Chenghao Zhang Android Palmprint Recognition System Design and Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 895 Chunyu Zhang and Chenghao Zhang A Cache-Aware Multicast Routing for Mobile Social Networks . . . . . . . 904 Xia Deng, Shuxian Bao, Yu Lin, and Zhishuang Xu Applications Research and Design of Expert System Based on Oil-Gas Field Energy Saving Information Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 917 Yidong Guo, Yufeng Lu, Xiaomei He, and Tongyang Zhang A New Training Sample Selection Method Avoiding Over-Fitting Based on Nearest Neighbor Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 928 Guang Li A Safety Analysis Method for FGS Based on STPA . . . . . . . . . . . . . . . 936 Tao Feng, Lisong Wang, Jun Hu, and Miaofang Chen Hybrid Evaluating Method for Battlefield Support Capability of Field Petroleum Pipeline Unit Based on Support Vector Machine . . . 945 Wen-ming Zhou, Yao-wu Wu, Lin Zheng, Xiang-sen Yu, Peng Jia, Yun-he Wang, and Shi-bin Lan The Computer Formula of the Smarandache Dual Function . . . . . . . . . 954 Liu Miaohua, Song Xiuchao, and Jao Hongying Safety Risk Assessment for Formamide in Yoga Mats . . . . . . . . . . . . . . 958 Fang Jia, Xia Liu, Wenjian Xie, and Xiaolei Feng

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A Model of Probabilistic Event for CPS Based on Hidden Markov Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 966 Cheng Zhou, Youqian Feng, Zhonghai Yin, Yali Wang, and Yunmin Huang Abnormal Condition Analysis and Preventive Measures of a 220 KV Transformer Neutral Point Bushing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 974 Zhou Yuxiao, Han Honggang, Guo Tie, Liu Lu, Chen Hao, Liu Yang, and Song Yundong Limitations of the DuPont Financial Index System and Its Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 985 Zhang Mei, Feng Fei, Zhang Zhilong, and Wen Jinghua Requirements Analysis and Design of Extensive Innovative Services Community Based on Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994 Bifeng Guo, Rui Fan, Sheng Gao, Qiubin Liu, and Weitao He Integrated Object Layout and Supporting Structure Topology Optimization Method Based on MMC . . . . . . . . . . . . . . . . . . . . . . . . . . 1002 Dongliang Zhang, Xiaoyan Zhang, Jun Mo, and Yunrong Luo Second Order Necessary Optimality Conditions for a Class of Optimization Problem in Banach Spaces . . . . . . . . . . . . . . . . . . . . . . 1010 Xuanwei Zhou The EGARCH Effect Test of Chinese Stock Market from the Perspective of Behavioral Finance . . . . . . . . . . . . . . . . . . . . . . 1017 Wenting Cao, Jiangyue Luo, and Xiaojuan Wu Dongba Hieroglyphic Similarity Measure Algorithm Based on Grid Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025 Yuting Yang and Houliang Kang Research on Course Selection Algorithm in Colleges Based on Collaborative Filtering Recommendation Algorithm . . . . . . . . 1031 Yong Zhang, Yipeng Li, and Renzhong Huang Reliability Data Analysis of Aviation Equipment Components Based on Lognormal Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1038 Yanming Yang and Huayuan Zhu Application Analysis of Engine Emission Technology . . . . . . . . . . . . . . . 1046 Fan Yang Research and Application of Dual Active Disaster Preparedness System Based on Redo Read-Write Separation Cluster . . . . . . . . . . . . . 1053 Qianjun Wu, Qingquan Dong, Guangxin Zhu, and Jun Yu

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Analysis on the Governance Path of Financial Fraud of Listed Companies from the Perspective of New Media . . . . . . . . . . . . . . . . . . . 1064 Yueming Cheng, Mengge Gu, Yi Cheng, Xiaoping Hu, and Kang Cheng Research on the Application of Financial Sharing Service Center Information System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1072 Lei Xia Research on the Teaching Effects of Flipped Class Model Based on SPOC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1080 Binghui Wu and Tingting Duan A Comparative Analysis of the Utilization of FDI in Six Central Provinces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088 Min Cheng and Lan Liu Solid Edge’s Application in Vertical Mill Design . . . . . . . . . . . . . . . . . . 1096 Kunshan Li and Yang Li Analysis on Development Trends of Research Topics in Agricultural Sciences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1104 Changshou Luo, Liying Zhou, Qingfeng Wei, and Sufen Sun The Impact of China Manufacturing on the Environment . . . . . . . . . . . 1113 Meng Li, Gang Yu, and Liang Yang The Influence of Cultural Creativity on Beijing Textile and Clothing Industry Analyse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1117 Sun Jie and Xi Yang Application Traceability in the Food Supply Chain . . . . . . . . . . . . . . . . 1124 Gang Liu Research on Irregular Block Spatial Scheduling Algorithm in Shipbuilding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1130 Yongjian Zhang and Huiyue Ci Study on the Cutting Force in Elliptical Ultrasonic Vibration Cutting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 Chengmao Zhang The Influence of Climate in Lingnan Area on the Architectural Design of Library and Its Countermeasures . . . . . . . . . . . . . . . . . . . . . . 1143 Zhenwei Wang Designed of Ball Rolling Control System Based on STM32 . . . . . . . . . . 1149 Si-Lian Xie, Zhou Yue, and He-Ping Li

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Influencing Factors of Government Microblogs’ Communication Effects: A Research Based on User Behavior . . . . . . . . . . . . . . . . . . . . . 1157 Manrui Zhang and Wenbo Liu Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1163

Intelligent Systems

An Android-Based Remote Monitoring System Lina Jia, Meng Zhang, Wei Zhang, and Jiaguo Lv(&) School of Information Science and Technology, Zaozhuang University, Zaozhuang 277101, Shandong, China [email protected]

Abstract. Based on Android and FFmpeg API, a remote monitoring system was developed. In this system, libx264.so library wad employed to encode and encode the video, and data was sent to the server with the Real Time Messaging Protocol (RTMP). The monitoring side obtains the live stream by accessing the live server, and decodes the libx264.so library by receiving the stream in the thread to monitor the real-time monitoring screen. Test results show that, the picture of the monitor side o can be clearly transmitted to the viewing side, and the location information of the monitoring side can be displayed in the viewing side clearly. The system can be widely used in It has an important significance for hospital care, unmanned aerial vehicle search and rescue, and other scenarios. Keywords: Android


Watching side
Monitoring side
FFmpeg

1 Introduction With the increasing development of remote monitoring technology, higher demand for the intelligence and real-time of the remote monitoring system has been put forward. It hopes that, with the remote monitoring system, regardless where they are, they can watch the real-time situation of their house. Especially, for the families with poorphysical elder people, there is an increasing demand for the real-time monitoring for the situation of the house at all times and places. In recent years, with the rapid development of the mobile terminal technology, the intelligentially and multi-functionality of the video monitoring system have been promoted. In 2015, a remote video monitoring system was implemented in [1]. In this system, the server side was built with the Linux operating system, and the client side was built with the Android smart mobile phone. In 2016, a real-time monitoring system with historical video playback was developed in [2]. In this monitoring system, web server was used to collect video data, computer as well as mobile phone were employed as the client. In 2017, based on the android platform, a remote monitoring system to watch vehicle status was implemented in [3]. In [4], a mobile monitoring system with invoice communication was designed. In [5], an intelligent monitoring system with motion tracking and intelligent alarm was developed. With these functions, the practicability of the system was improved greatly. In [6], a variety of technologies related to the internet of things were added to the mobile home environment monitoring system.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 3–13, 2019. https://doi.org/10.1007/978-3-030-02804-6_1

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In view of the weakness of the Android-based remote monitoring systems, such as low real-time, single function and so on, a cloud service based remote monitoring system is designed in this work. Some functions, such as voice communication, realtime video, live chat, have been added to the system, which improves the users’ experience, and enhances the interactivity and real time of the system. The system is composed of monitoring video collecting side, cloud server and watching side. The employment of Client/Server structure makes the users’ visibility greatly enhanced, and the responses speeds of the client side and server side have been improved too. Mobile terminal is used as the monitoring video collection side in this system, which improves the users’ comfort and convince greatly. Experiment results show that, the system has good interactivity and robustness, and it can transfer the high-quality video from the monitoring video collecting side to the watching side.

2 Requirement Analysis and Overall Design 2.1

Requirement Analysis

In the era of intelligent information, intelligent equipment is becoming widely used in various occasions. For example, in smart home, all kinds of household electronic applications are connected together with the technology of internet of things, which makes the management and control of these devices more convenient. However, it will also bring some potential security risk. If we have a remote video monitoring system, we can know the status of all devices at home. Whenever some device has problems, the system will give an alarm. In this way, we can solve the problem. Although the monitoring system has been widely used in various palaces, such as home, campus, and other public places, the implementation of the monitoring system still needs some professional construction team. Based on such an actual need, we develop a remote monitoring system. In this system, android-based smart mobile phone is used as the real-time monitoring terminal. With this system, users can view the realtime situation of the monitored place by their mobile phone. In addition, the system is extendible. As long as multiple video collecting devices are added to the system, the user can view the monitoring video of different monitored places. According to the requirements of the applications, the system collects video data with the camera of the monitoring terminal, converts and encodes the video with FFmpeg technology, and then sends data with RTMP protocol. The monitoring side sends the connection request to the server first. When the monitoring side receives the response from the server, it will process the video data, so as to realize the real-time monitoring. At the same time, by this system, users can save the monitoring video data to the mobile terminal in MP4 format. By this way, users can view the monitoring video data at any time. The main components of the system are as follows. (1) Video capturing module The video capturing module is installed in the android-based mobile terminal. With the camera of the mobile terminal, it can capture the real-time video data.

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(2) Video processing module The video data captured by the monitoring side is very big, and it is difficult to transfer through the network. So, the system will first compress it with the video processing module before sending it to the network. (3) Video sending module When the server receives the request of the viewing side, it will send the real-time video data compressed by the video processing module to the viewing side through the network. (4) Video receiving module When the viewing side receives the response of the server, with the video receiving module, users can view the real-time monitoring video captured by the monitoring side. (5) Video saving module According to the requirement, users can save the monitoring video to the local file system or upload to the cloud server. 2.2

Overall Design

In this monitoring system, the cloud server is used as the data processing server, mobile phone and smart camera are employed as the video capturing devices. With the camera in the mobile phone, video data is captured, and then it will be encoded with the FFmpeg technology. With the wireless network, video data is transferred from the monitoring side to the server, and then it will be transferred from the server to the viewing side. The overall design of this system is shown in Fig. 1.

Fig. 1. Overall design of the monitoring system

The monitoring system adopts Client/Server as its system structure, and it is composed of the monitoring side, the server and the view side. With the class Camera, the monitoring side can control the camera and set the interface Preview Callback. With the interface, the system can obtain the video data, and compress it with H.264. Finally, the video data will be transmitted to the server with the network. When the video information is compressed, the server will save them according to the monitoring device which capture the video. Then, the server will transmit them to

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the viewing side with the network. Once the viewing side connects to the network, with the app of the monitoring system, users can not only view the real-time video captured by the monitoring side, get the previous monitoring video from the cloud server, but also have a real-time call with the monitoring side. 2.2.1 Monitoring Side The monitoring side is composed of device information displaying module, monitoring video capturing module, video encoding and transmitting module. (1) Device information displaying module After installing the monitoring APP, when the user opens the APP, a unique account and password will display in the GUI. The account and password are randomly assigned to the device by the server. Calling ZXing library, the account and password will be combined and encrypted, and finally transformed to a two-dimension code. With the two-dimension code, the viewing side can add the device conveniently. (2) Monitoring video capturing module In the monitoring system, the monitoring side controls the monitoring camera and sets the interface Preview Callback with the class Camera. With the interface Preview Callback, the system can obtain the monitoring video, and process it further. For example, the server can compress the video data with H.264 code and transmit them to the server, or record these data into MP4 file and save them locally. (3) Video encoding and transmitting module The system encodes and processes the video data with FFmpeg. With SrsFFMpegMuxer, the video data is encoded into H.264 compressed video stream, and sent to the Server. 2.2.2 Viewing Side The monitoring side consists of my monitoring, micro community, shopping mall and personal center. The graphic interface adopts the flat design style, with light green system as the main color. On the whole, the interface is fresh and bright, flexible and easy to learn. The usage flow chart of the viewing side is shown in Fig. 2.

Fig. 2. The usage flowchart of the viewing side

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The main function of the viewing side is to view the video transmitted from the monitoring side, which is realized by the monitoring module. The monitoring module consists of four parts, adding monitoring devices manually, adding monitoring devices by scanning two-dimension code, navigation positioning and viewing video information. (1) Adding monitoring devices manually Input the account and password of the monitoring device, the viewing side will receive the data with method handle Message(Message msg). And then the viewing side will compare the received data with the data in server, if the account exists in the server and it hasn’t been added, the information of the monitoring device will be added to the monitoring device list of the viewing side. (2) Adding monitoring devices by scanning two-dimension code Call the Zxing library, the viewing side can scan and decode the two-dimension code with the built-in camera of the phone. The viewing side will compare the information decoded from the two-dimension code and the information in server, if the monitoring device exists in server and hasn’t been added, it will be added to the monitoring device list. (3) Navigation positioning By calling the SDK of baidu map, the viewing side also realized the function of navigation positioning. (4) Viewing video information By calling libPlay in FFmpeg, the viewing side can visit the live broadcast server, decode the video in sub thread, and then it can play the real-time monitoring video. 2.2.3

Server

(1) Login The account and password and their corresponding two-dimension code of the monitoring side are stored in the login server, which is used for the monitoring side to compare with the information of the monitoring device to be added. The account and password of the viewing side are also stored in the login server. When a user logs on, the login information will be compared with the information in server. If it is right, the viewing side will query the data in server, find the monitoring device bounded with the account, and view the monitoring video captured by the monitoring device. (2) Live Server The monitoring side send the monitoring video to the sever through the wireless network. Then, the server will decode and compress the video. Then, the server will store the video according to the account of the monitoring device. Finally, the server will send the video to the viewing side through the network.

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The viewing side visit the live server with RTMP protocol which carries the same parameter head with the matching monitoring side. The server will check the parameter head to judge whether there is a matching input video stream. If any, it will forward the matching video to the viewing side.

3 Detailed Design 3.1

System Structure

The monitoring system is developed with android technology. It adopted the android device as the client, used the built-in camera of the android device as the video capturing device. With the data structure, API provided by android system, the system realized the calling of the camera and the capturing of the monitoring video. The system encodes and decodes the video with H.264, sends and receives the video data with the WLAN of the android mobile phone or the mobile 4G wireless network. The system structure is shown in Fig. 3.

Fig. 3. The system structure of the monitoring system

3.2

Process Design of the Monitoring System

H.264 is a digit video compression format proposed by ISO and ITU, which has a high data compression ratio. In addition, it has the following advantages of low bit rate, providing continuous and high-quality images, high fault-tolerant ability and high network adaptability. The raw data format of microphone PCM is very large. So, the Advanced Audio Coding(ACC) is employed in the system. With ACC, for the audio data, the system can obtain a compression ratio of 1/18. In the design of the remote monitoring system, the encoding and decoding of audio and video data is very important. In this system, libx264.so is adopted to encode and decode the video data with H.264, and ACC is adopted to encode and decode the audio data. The encoded video and audio data are assembled into frame data and send to the live serer with RTMP protocol.

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When the user views the monitoring video in the viewing side, the viewing side will visit the live server with RTMP protocol which carries a specific parameter head. The server will check the parameter head to judge whether there is a matching input video stream. If any, it will forward the matching video to the viewing side. Then, the RTMP will pull the video stream into the buffer of the viewing side, and divide the data into audio and video, so as to play the monitoring video and audio real time. The process flow of the monitoring system is shown in Fig. 4.

Fig. 4. The process flow of the monitoring system

3.3

Collection and Display of Image

Open the android camera, the system collects the images with the frequency of 30 Hz. For the collected images in YUV format, the system will cut them to a size of 640*480, and then encode them. When SurfaceView is added to the callback interface of the camera, the video will be displayed in the local device. The running of the monitoring side is shown in Fig. 5.

Fig. 5. The running of the monitoring side

3.4

Login Process of the Client

When a user uses the viewing client, he should first log on the server with his ID and password. When the login is confirmed, the mobile phone will communicate with the server through TCP protocol, and compare the user’s login information with the data in

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database. If the user logs on successfully, the viewing client will query the database and find the devices bounded with the ID, and return the list of the devices to the viewing client. When the monitoring device is added, the information of the device will be send to the server, and the server confirm the device and add the bounded information of the device to the database. The sequence diagram of the viewing side is shown in Fig. 6.

Fig. 6. The login sequence diagram of the viewing side

Similar to the viewing client, when a user uses the monitoring client, he will also log on the server. The monitoring client will send the user’s logon information to the server to verify. If the user login fails, the server will generate a CID and password, add the information to the database and return it to the monitoring side. The login sequence diagram of the monitoring side is shown in Fig. 7.

Fig. 7. The login sequence diagram of the monitoring side

3.5

Database Design

The monitoring system consists of two entities, user and monitoring device. There is many-to-many relationship between the two entities. 3.5.1 User The table user describes the login information of user in viewing side, such as account, password, register time, name, gender, email, telephone number, QQ and bonus point. The structure of the table user is described in Table 1.

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Table 1. Table of the user Field name username password regtime zhenshiname sex email tel qq jifen

Description Account Password Register time Name Gender Email Telephone number QQ Bonus point

Data type Int (4) Varchar (50) Varchar (50) Varchar (50) Varchar (50) Varchar (50) Varchar (50) Varchar (50) Varchar (50)

Comments Primary key, not null Not null Not null Null Null Null Null Null Not null

3.5.2 Monitoring Device The table monitoring device describes the information of the monitoring device, such as CID, password. The structure of the table monitoring device is described in Table 2. Table 2. Table of the monitoring device Field name Description Data type Comments collecter_CID CID Varchar (50) Primary key, not null collecter_password Password Varchar (50) Not null

3.5.3 Matched Information The table matched information describes the matching information between the user of the viewing side and the monitoring device. The structure of the table matched information is described in Table 3. Table 3. Table of the matched information Field name id collecter_CID username

3.6

Description Primary key CID Account

Data type Varchar (50) Varchar (50) Varchar (50)

Comments Primary key, not null Not null Not null

Live Server

In this system, Nginx is installed in the Linux OS, and as the live server. When the live module is added to Nginx, it can be used as a live server. Nginx is an excellent live server in Linux OS. In Linux OS, its video live broadcast is more fluent, which can bring better experience to users. The process of the live server is shown in Fig. 8.

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Fig. 8. The process flow of the live server

4 Experiment and Analysis To evaluate the performance of the monitoring system, experiments are conducted on the Android-based smart phone with 4G memory. And the system is running on 4G or Wi-Fi network environments. At the beginning, the monitoring side, the server and the viewing side of the system will be initialized. Then, the monitoring side will scan the two-dimension code generated by the viewing side, and begin to record video, the map button of the system will be clicked for real-time positioning, and the motion track will be tracked, the button of shopping mall will be clicked to buy the goods necessary, users’ demands and discussion will be published in the comment area. At the same time, software of Ethereal in the viewing side will be used for network traffic detection. In the experiments, Vivo and Huawei Android-based smart phones are used as the monitoring side and viewing side to run the monitoring system. The main page of the monitoring side is shown in Fig. 9. The running results of the viewing side are shown in Fig. 10.

Fig. 9. The main page of the monitoring side

Fig. 10. The main page of the viewing side

The experiments are conducted in 4G and Wi-fi environments. In the experiments, the video transmission rate is more than 1000 KB/s. The system runs stably and obtains a high speed in video connection. Under the condition of high-quality video, the system can reach a video transmission rate of 1 MB/s. In the experiments, the system runs smoothly, has a little delay, and achieve the expected effect.

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5 Conclusion With the FFmpeg video compression push stream and Nginx live broadcast technology, a remote video monitoring system is implemented. The monitoring system has the characteristics of free touch, multi-platform compatibility, timed recording and etc. In view of this, the system can be used in hospital care, home monitoring, kindergarten monitoring, shop monitoring, traffic recorders, unmanned aerial vehicle search and rescue, and other fields. The feasibility and practicability of the system has been verified by the experiments. However, the system had the following drawbacks. (1) The resolution of the monitoring video should be improved further. (2) There is little delay in viewing the real-time monitoring video, which should be improved further. (3) It is convenient for the smart phone be used as the monitoring equipment. However, to improve the performance of the monitoring system, the monitoring device interface should be extended later, smart camera and other equipment should also be used as the monitoring device.

References 1. Wu, J.: The design of remote monitoring system based on Android phone. Master thesis, North china university of technology, Beijing, China (2015) 2. Lin, Z., Chenwei, F., Chao, Z.: Design of remote video surveillance system based on Android system. J. Comput. Appl. 36(S1), 301–304 (2016) 3. Hu, J., Huang, H., Zhou, H.: Design and implementation of remote video monitoring system based on 4G network. Highw. Automot. Appl. 2017(2), 13–16 (2017) 4. Bu, Z., Yang, H., Jia, J.: Mobile video monitoring terminal based on the android system. Comput. Syst. Appl. 26(4), 275–279 (2017) 5. Lin, B., Zheng, Q., Cheng, S.: Intelligent video surveillance system based on Android. Video Eng. 41(4), 78–83 (2017) 6. Jie, Y.: Mobile home environment monitoring system based on android platform. J. Jinzhong Univ. 34(3), 57–63 (2017) 7. Li, A., Song, H., Song, X.: Design and implementation of video monitoring system based on Android. Appl. Electron. Tech. 38(7), 138–143 (2012) 8. Gavalas, D., Economou, D.: Development platforms for mobile applications: status and trends. IEEE Softw. 28(1), 77–86 (2011)

An Intelligent Registration Management System for Freshmen Hao Zhu1(&), Mengshu Hou1, Yaohua Xie2, Kai Yan1, and Junming Li2 1

Information Center, University of Electronic Science and Technology of China, Chengdu 611731, China [email protected] 2 Yale University, New Haven, USA

Abstract. The registration management for freshmen is an important work in a university. The traditional registration mode is based on a paper form. The freshmen need to walk to each department to register and get passing stamp. It’s inefficient. In order to solve this problem, an intelligent registration management system based on the Internet is proposed in this paper. The functional structure, identity authentication and the data integration are also discussed. Through the intelligent registration management system, the freshmen can complete the registration procedures online. They don’t have to walk to all the departments, and there is no restriction on time and site. Finally, the efficiency of the registration management is improved. Keywords: Registration management system
Identity authentication Data integration
Online processing
Real-time statistics

1 Introduction Every freshman is excited when he receives the admission letter from a university. But when he arrives at the university, he will find that it is a bother to complete his registration. In the traditional registration management mode, all registrations are based on a paper form. The freshmen need to walk to all the departments on the form to register before they can officially become registered university students. The efficiency of the traditional mode is very low, and it makes the freshmen feeling very tired. With the development of information technology, it is possible to complete the registration of the freshmen through the application system [1, 2]. In this paper, an intelligent registration management system based on the Internet is proposed. The disadvantages of the traditional mode have been analyzed. The structure, identity authentication, and data integration of the intelligent registration management system are discussed. 1.1

Disadvantages of the Traditional Registration Mode

When the freshmen arrive at the university, they all need to register. They need to complete the academy registration, financial payment, dormitory check-in and © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 14–20, 2019. https://doi.org/10.1007/978-3-030-02804-6_2

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questionnaire. In the traditional registration mode, they will first receive a paper registration form. The registration form is shown in Fig. 1.

Registration form Name:Jonathan ID:201706081688 Academy registration

Dormitory check in

Financial payment

Questionnaire

Fig. 1. Registration form

In this form, each step requires freshmen to walk to the relevant department to get the passing stamp. The registration process for freshmen in the traditional mode is shown in Fig. 2.

Academy

Financial department

Dormitory

Computer lab

Fig. 2. Traditional process of the registration

(1) Firstly, the freshmen need to walk to their academy to register. (2) Although the freshmen can pay for their tuition fees through the bank, the dormitory administrators cannot check the payment online. The dormitory administrators can only check whether the freshmen have paid for their tuition fees through the passing stamp from the financial department in the registration form. Therefore, the dormitory administrators will only allow the freshmen to check in if they have got the passing stamp from the financial department. Regardless of whether the freshmen paid for their tuition fees before arriving at the university or pay for their tuition fees after arriving at the university, they all need to walk to the financial department to obtain the passing stamp. We often see the freshmen lining up at the financial department with their luggage, but they cannot drop their luggage at the dormitory firstly.

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(3) After obtaining the passing stamp from the financial department, the freshmen can go to the dormitory to check-in. (4) Finally, the freshmen need to go to the computer lab to fill out the questionnaire. Because of the limited number of computers in the computer lab, the freshmen often line up for a long time. From the process we can see that the traditional registration procedures are very complicated. The freshmen have to walk to the academy, the financial department, the dormitory and the computer lab to register. Assuming that the amount of workload for walking to a department is 1 unit, the total amount of workload for completing all the registration process is 4 units. We can see that the freshmen all feel tired because of the registration process. 1.2

Purpose of Constructing an Intelligent Registration Management System

The traditional registration management mode is based on a paper form, it is very backward, and the freshmen all feel tired. If the dormitory administrators can check the payment of the freshmen online, the freshmen do not need to walk to the financial department to obtain the passing stamp. If the freshmen can fill out the questionnaire on their cell phones, they will not have to walk to the computer lab anymore. The workload of the freshmen will be greatly reduced. Therefore, we hope to solve these problems through the intelligent registration management system.

2 Construction of the Intelligent Registration Management System 2.1

Functional Structure of the Intelligent Registration Management System

The intelligent registration management system is based on the Internet. The functional structure of the system is shown in Fig. 3. The freshmen, department staffs, and leaders can access the intelligent registration management system through the Internet anytime, anywhere [3]. They can access the system through their personal computers or cell phones. After they are authorized by the unified identity authentication platform, they can perform many registration-related operations in the system. The freshmen can check online whether their payment has been accepted, if the payment has been accepted, they do not need to go to the financial department and they can go directly to the dormitory to check in. The freshmen can fill out the questionnaire through their cell phones, and there is no need to go to the computer lab. The dormitory administrators can check whether the freshmen have paid for their tuition fees through the system and allow the freshmen to check in. The leaders can also check about the registration of the freshmen real-time. They can get real-time statistics in order to allocate people and resources appropriately.

An Intelligent Registration Management System for Freshmen

Freshmen

Department staffs

17

leaders

Internet Unified identity authentication platform Registration management system Dormitory management

Questionnaire

Registration procedure inquiries

Registration procedure statistics

System configuration management

Security system

Registration database

Exchanging database

Academic Database

Freshmen database

Financial database

Statistical analysis tools

Financial Management

Dormitory database

Fig. 3. Functional structure of the system

2.2

Identity Authentication of the Intelligent Registration Management System

In order to achieve the unified identity authentication of the university, the intelligent registration management system is integrated with the unified identity authentication platform [4]. The unified identity authentication platform provides an integrated interface to the other systems. The intelligent registration management system can be integrated with the unified identity authentication platform by the integrated interface. The process of the identity authentication of the intelligent registration management system is shown in Fig. 4. (1) The user inputs the account number and the password in the intelligent registration management system. (2) The intelligent registration management system transfers the account number and the password to the unified identity authentication platform. (3) If the unified identity authentication platform successfully verifies the account and the password, a successful token will be returned to the intelligent registration management system. If the unified identity authentication platform fails to verify the account and the password, it will return the failure information to the intelligent registration management system.

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Fig. 4. Process of the identity authentication

(4) When the intelligent registration management system receives the successful token from the unified identity authentication platform, it will identify the user as a valid user and allow the user to access the system. When the intelligent registration management system receives the failure information from the unified identity authentication platform, it will identify the user as an invalid user and refuse the user to access the system. After the intelligent registration management system is integrated with the unified identity authentication platform, the freshmen can log into the intelligent registration management system with the unified account and password. They can also log into other systems in campus with the unified account and password. It’s very convenient. 2.3

Data Integration of the Intelligent Registration Management System

The intelligent registration management system implements the data integration [5, 6] with other systems by the exchanging database. It’s shown in Fig. 5. The academic database shares the class information of the freshmen to the registration database. The freshmen can check their class information online through the intelligent registration management system. The freshmen database shared the basic information of the freshmen to the registration database. The department staffs and leaders can check the information of the freshmen online through the system. The financial database shares the payment information of the freshmen to the registration database. The dormitory administrators can check the payment information of the freshmen online through the system. The dormitory database shares the dormitory information of the freshmen to the registration database. The freshmen can check their dormitory information online.

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Academic Database

Freshmen database Exchanging database

Registration Database

Financial database

Dormitory database

Fig. 5. Data integration of the system

3 Results After the intelligent registration management system is put into use, the process of the registration for freshmen has been greatly simplified. The new process of the registration is shown in Fig. 6.

Academy

Dormitory

Questionnaire

Fig. 6. New process of the registration

If a freshman has already paid for the tuition fees through the bank before he arrives, he just needs to go to the academy and the dormitory after arriving at the university. He doesn’t need to go to the financial department. He can complete the questionnaire in his cell phone. His registration process which needs he walk is reduced from 4 steps to 2 steps. According to the previous definition of the paper, the workload of a freshman walking to a department is 1 unit. The amount of workload for freshmen to complete all registrations is reduced to 2 units. Therefore, before and after the intelligent registration management system is put into use, the difference of the registration workload for freshmen is shown in Table 1.

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H. Zhu et al. Table 1. Difference of the registration workload for freshmen Period Before After The registration workload 4 2

From Table 1, we can see that the registration workload of freshmen is reduced by 50% after the intelligent registration management system was put into use. Then the freshmen feel simple and easy to register. At the same time, the leaders can query the registration statistics of the freshmen in real time by the system. It is conducive to the rational distribution of staff, and improves the efficiency of registration management [7].

4 Conclusions The intelligent registration management system realizes the digitization of the registration management for freshmen, and it reconstructs and simplifies the process. It takes the advantage of the Internet to enable the users to complete the registration process online. There is no limit of the time and space for the users. It improves the user’s satisfaction. At the same time, it links all aspects of the registration process by the data integration. It greatly improves the efficiency of the registration management for freshmen.

References 1. Mao, L., Gao, J., Li, Q.: Design and application of southeast university’s digital registration system. J. Nanjing Univ. Nat. Sci. 46(9), 131–135 (2010) 2. Fang, W., Chen, W., Zhu, Z., et al.: Design and implementation of registration system for freshmen based on the campus smart card. Acta Scientlarum Naturalium Universitatis Sunyatseni. 48(3), 128–130 (2009) 3. Fu, Z., Mo, L., Xu, F., et al.: Design and implementation of electronic register system for freshmen. J. Hunan Agric. Univ. Nat. Sci. 31(3), 338–341 (2005) 4. Li, D., Fan, Q.: Discussion on the unified authentication of digital campus. J. Jilin Normal Univ. Nat. Sci. Ed. 8(3), 154–156 (2012) 5. Liu, J.: Research of data integration platform based on ODI in the digital campus building. J. Qinghai Normal Univ. Nat. Sci. 2, 16–20 (2016) 6. Liu, Q., Wang, F., Deng, H.: The design technology of freshmen register management information system based on data integration in digital campus. J. Yunnan Univ. Nat. Sci. 29(S2), 203–205 (2007) 7. Chen, Y., Wang, M., Zhang, J., et al.: Innovation and practice of registration service in Fudan University. Acta Scientlarum Naturalium Universitatis Sunyatseni. 48(3), 19–28 (2009)

Investigation of Wireless Sensor Network of the Internet of Things Yibin Hou and Jin Wang(&) School of Software Engineering, Department of Information, Beijing University of Technology, Beijing, China [email protected], [email protected]

Abstract. Big data to use JAVA, group software engineering, networking, cloud computing knowledge and technology. The purpose of this paper is Research on Wireless sensor network of the Internet of things. The Internet of things, mainly includes multicast network, ZIGBEE network, WSN network, Bluetooth network, infrared network and so on. Swarm software engineering is a way to implement cloud computing, Internet of things and big data. Cloud computing comes from big data, the Internet of things can be achieved through cloud computing. This paper mainly studies, computers, software, networks, smart cities, and the use of Excel and Matlab and Microsoft Office Visio 2003 mapping and so forth. And through the practice of research methods, including automotive networking and smart city. What is the Internet of things, objects connected to the Internet is the Internet of things, cup networking, car networking. Things better than other networks, is composed of what objects, what composition, what nature, what innovation and superiority. The four key technologies of the Internet are widely used, and these four technologies are mainly RFID, WSN, M2M, two kinds of integration. RFID can be implemented using MATLAB, NS2, and JAVA, and WSN can be implemented using NS2, and M2M can be developed using JAVA. The research results are that the Internet of things originated and developed in the Internet; on the contrary, the development of the Internet of things further promoted the Internet to a more widespread “Internet” evolution. The Internet of things and the Internet are the relationships between the parent and the child. Wireless networks are just like wireless WSN networks, but wireless nodes are fixed and moved into sensors. The Internet of things includes Internet technology, including wired and wireless networks. The research conclusion is the wireless internet of things are just like the wired internet of things, wired provides the basis for Wireless Research. Baidu Tiangong Internet of things platform, Amazon AWS platform, ERP and so on are also important applications of the Internet of things. The use of mirroring and replication software also requires Internet of things. Video is composed of multi frame images. H.264 and JM encoder are also the future directions. Where to go network Ctrip network is the application of the Internet of things. Recalling what we do today, planning tomorrow, how to spend the university stage and predicting the future of university development can be the application of the Internet of things. I visited the Tangshan Rd. Mart store in Qian’an from January 2017 to March. The feeling is that Beijing is still Carrefour and Jing Kelong and WALMART and Hualian. The electricity supplier which Ma Yun’s Alibaba still feels more powerful. Now Hebei province and all parts of the country have venture capital and subsidy, which will undoubtedly promote the Internet of things. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 21–29, 2019. https://doi.org/10.1007/978-3-030-02804-6_3

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Y. Hou and J. Wang Keywords: Internet of Things MATLAB
NS2


Wireless sensor networks
Visio

1 Introduction Big data to use JAVA, group software engineering, networking, cloud computing knowledge and technology. This paper mainly studies, computers, software, networks, smart cities (smart haircuts, smart, hard disks and U disks, smart games, Intelligent toy), and the use of Excel and Matlab and Microsoft Office Visio 2003 mapping and so forth. Is the networking objects here mainly refers to a variety of terminal contact is how objects connected to the network, mainly through RFID technology, network is the network what is here on a distributed WSN network can include P2P routing and so on. Objects, cups, cars connected to the Internet is the Internet of things, cup networking, car networking. The Internet of things and the Internet are the relationships between the parent and the child. Wireless networks are just like wireless WSN networks, but wireless nodes are fixed and moved into sensors [1].

2 Method The following figure is a nonlinear mapping schematic Fig. 1(a) is a linearly separable sample space, Fig. 1(b) is a new space which is linearly separable after the nonlinear mapping. It can be imagined that a sheet of white paper is two-dimensional, flat laid, rolled up, is three-dimensional, that is, low dimensional, high dimensional. A highdimensional [2] with linearly separable, low dimensional, linearly separable.

Fig. 1. A linearly separable sample space

Different inner product functions will form different algorithms. In the SVM [3–5] algorithm, different kernel functions can be chosen to generate different SVM [3–5], and the commonly used kernel functions have the following four kinds: (1) linear kernel function. (2) polynomial kernel function. (3) Gaussian radial basis function

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(RBF). (4) Sigmoid (S shape) kernel function. WSN network foundation. 1. the six components of modern information science: intelligent and networked. Definition. 2. WSN: large-scale, wireless, self-organizing, multi hop, no partition, no node network, the infrastructure is homogeneous, low cost and small volume, most of the nodes do not move, be spreading in the work area for network system is possible to long working time. 3. WSN and Ad-hoc network distinction: difference: (1) network topology and working mode are different. Ad hoc networks: dynamic changes in network topology. The WSN: network topology is static. (2) the working model is different. WSN: is data centric (Data, Centric), just the opposite of multicast. Ad Hoc network: the same point: basically no human intervention, most of the work is done in a self-organizing way, collectively referred to as Networks (Self-Organization). These two studies are pursued for low power ad hoc network design. (Note: multicast concept: one to many). 4. characteristics of wireless sensor networks: (1) The number of sensor nodes is large, the density is high, and the space location is used. (2) Sensor contact energy, computing power and storage capacity are limited (energy, computing, storage is low, the key is the effective simple routing protocol). (3) The topology of wireless sensor networks is easy to change, and the self-organizing ability is strong. (with traditional features and technical requirements: according to the need to switch the work and sleep, easy to network topology changes, the traditional network focused on QoS broadband, ensure more easily, wireless sensor networks need to save energy and ensure the connection and extension of life). (4) The sensor node has the data fusion capability (Mesh network, a small amount of data, mobile, and overload capacity of wireless Ad-hoc networks, dense, vulnerable, the number of frequent topology broadcast to multiple channels, node energy limited computing capacity.). 5. Network topology: network configuration and mode, centralized, distributed and hybrid, node function and structure, divided into planar network structure as shown in Fig. 2, a hierarchical network structure as shown in Fig. 3, the hybrid network structure as shown in Fig. 4, the Mesh network architecture is shown in Fig. 5. foreman_qcif. Yuv as shown in Fig. 6, mother-daughter_qcif.Yuv as shown in Fig. 7, FOOTBALL. Yuv as shown in Fig. 8, ANSI_SRC_3inrow_qcif as shown in Fig. 9, src13_ref_525.yuv as shown in Fig. 10, src22_ref_525.yuv as shown in Fig. 11.

Fig. 2. Plane network structure

Fig. 3. Hierarchical network structure

Network upper layer

Network lower layer

Sensor nodes Backbone node

General sensor node

Fig. 4. Hybrid network structure

Fig. 5. Mesh schematic)

network

structure

(simple

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Fig. 6. foreman_qcif.yuv

Fig. 7. Mother-daughter_qcif.Yuv

Fig. 8. FOOTBALL.yuv

Fig. 9. ANSI_SRC_3inrow_qcif

Fig. 10. src13_ref_525.yuv

Fig. 11. src22_ref_525.yuv

3 Results and Discussion This is the result of my experiment. Football.yuv video Quantitative and avgPSNR as shown in Fig. 12.

Fig. 12. Football.yuv video Quantitative and avgPSNR

Using MSU to obtain VQM and SSIM [6] and NS2 to obtain PSNR [7] results are shown below, you can use VQM to compute SSIM [8]. VQM stands for video quality metrics, SSIM (structural similarity index), Structural similarity is an index to measure the similarity between two images. The index was first proposed by the Dezhou University of Austen’s image and video engineering laboratory (Laboratory for Image and Video Engineering). One of the two images used by SSIM is an undistorted, undistorted image, and the other is a distorted image. MOS (Mean Opinion Score), it is often used as an important index to measure the speech quality of communication systems, the commonly used MOS scoring methods include subjective MOS evaluation and objective MOS evaluation. Subjective MOS is divided into ITU-T, P.800 and P.830 recommendations, and different subjects are compared with the original corpus and the processed data, and the subjective sense is compared. Then the MOS score is obtained and the average value is obtained. The objective MOS evaluation uses the PESQ (Perceptual Evaluation of Speech Quality) method provided by the ITU-T P.862 recommendation, and is tested by special instruments (such as Agilent’s VQT tester)

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or software. DMOS (Differential mean opinion score), that is, the human eye has no difference in the score of the undistorted image and the distorted image. MOStu express MOS*20 figure, MOSo express MOS original, MOSd express MOS distorted. PSNR expresses Peak Signal to Noise Ratio, that is to say, the peak signal-to-noise ratio is an objective standard for evaluating images. It has limitations. It is generally used as an engineering project between the maximum signal and the background noise. PSNRMOS express PSNR and MOS. MOSd-MOSo express MOSd and MOSo. Method 1: For src13 wireless (src13 wired the same), x input, the use of y = 24.3816 * (0.5 − 1/ (1 + exp (0.56962 * (27.49855 x)))) + 1.9663 * x 1.9663, there is no space between the symbols in the matlab can run successfully. This formula can be used to find out the MOS distortion. After the distortion of MOS values into a 0–100 range is as follows: MOS* 5 (Used to calculate DMOS) distortion can be characterized to the range of 0– 100. Calculation of MOS value is above the MOS value after the distortion, the following using MOS = y/(1 − x) calculate the original MOS, x represents the packet loss rate [9], 1 − x indicates no lost that part, y is the PSNR, video distortion after the objective quality of divided by that part without distortion, MOS value is the original [8]. Calculation of the MOS of the original use of MOS distortion calculation: after the MOS original = MOS distortion (no into the range of 0–100)/(1 − x) = y/(1 − x), calculate the MOS original later, 5 times to expand, as computing DMOS the 0–100 range. Matlab run without “,”, but need to take “[]”. x = [11.777672, 11.927628, 11.927628, 11.938189, 11.938189, 12.133455, 11.777672, 11.777672, 11.927628, 11.938189, 11.938189, 12.133455, 11.777672, 11.777672, 11.777672, 11.927628, 11.927628, 11.938189, 11.938189, 12.133455, 12.176212, 11.777672, 11.927628, 11.927628, 11.938189, 11.938189, 12.133455, 11.927628, 11.777672, 11.927628, 11.927628, 11.938189, 11.938189, 12.133455, 11.777672, 11.927628, 11.927628, 11.938189, 11.938189, 12.133455]. y = −24.3816 * (0.5 − 1/(1 + exp(−0.56962 * (x − 27.49855)))) + 1.9663 * x − 2.37071. MOS distorted = y = [8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 8.6001, 8.6001, 8.8952, 8.9160, 8.9160, 9.3004, 8.6001, 8.6001, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 9.3845, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 8.8952, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004]. Calculation of the original MOS, by computing the distortion after MOS: the original MOS = MOS distortion (not put on the range of 0–100 MOS)/(1 − x) = y/(1 − x). y = [8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 8.6001, 8.6001, 8.8952, 8.9160, 8.9160, 9.3004, 8.6001, 8.6001, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 9.3845, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 8.8952, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004]. x = [0, 0.002273, 0.002237, 0.00404, 0.005979, 0.007963, 0, 0, 0.002237, 0.00404, 0.005979, 0.007963, 0, 0, 0, 0.002273, 0.002237, 0.00404, 0.005979, 0.007963, 0.008438, 0, 0.002273, 0.002237, 0.00404, 0.005979, 0.007963, 0.002273, 0, 0.002273, 0.002237, 0.00404, 0.005979, 0.007963, 0, 0.002273, 0.002237, 0.00404, 0.005979, 0.007963]. MOS original = y/(1 − x), MOS original = 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 8.6001, 8.6001, 8.8952, 8.9160, 8.9160, 9.3004, 8.6001, 8.6001, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 9.3845, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 8.8952, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004, 8.6001, 8.8952, 8.8952, 8.9160, 8.9160, 9.3004. MOS original expand five times

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(Used to calculate DMOS) = MOSo = [43.0005, 44.4760, 44.4760, 44.5800, 44.5800, 46.5020, 43.0005, 43.0005, 44.4760, 44.5800, 44.5800, 46.5020, 43.0005, 43.0005, 43.0005, 44.4760, 44.4760, 44.5800, 44.5800, 46.5020, 46.9225, 43.0005, 44.4760, 44.4760, 44.5800, 44.5800, 46.5020, 44.4760, 43.0005, 44.4760, 44.4760, 44.5800, 44.5800, 46.5020, 43.0005, 44.4760, 44.4760, 44.5800, 44.5800, 46.5020]. MOS distorted * 5 = MOSd = 43.0005 44.4760 44.4760 44.5800 44.5800 46.5020 43.0005 43.0005 44.4760 44.5800 44.5800 46.5020 43.0005 43.0005 43.0005 44.4760 44.4760 44.5800 44.5800 46.5020 46.9225 43.0005 44.4760 44.4760 44.5800 44.5800 46.5020 44.4760 43.0005 44.4760 44.4760 44.5800 44.5800 46.5020 43.0005 44.4760 44.4760 44.5800 44.5800 46.5020. Calculation of DMOS method: DMOS = x − y + 5. DMOS = 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5. Method 2: For src13 wireless (src13 wired the same), MOS of objective value is the quantitative method of QOE MOS. MOS = PSNR/10 (MOS * 20 is assigned to the 0– 100 section), MOS quantitative method of QOE. DMOS: objective quality assessment MOS. Video quality as subjective testing - a brownie points (scheme Option Score, MOS) or difference divide (Differential Mean Opinion Score, DMOS) measurement. In order to eliminate this problem, the author maintains the see the original frame rate, subjective test. DMOS = MOS (distorted) − MOS (original) + 5. SSIM: calculate SSIM to MSU. MSU’s official website to download the latest version of the MSU. MSU_VQMT_9. 0 r11658beta_free_64. Mother-daughter_qcif. yuv’s VQM and FOOTBALL. yuv’s PSNR and VQM and SRC13’s VQM and SRC22’s PSNR 1068 kb and PSNR 1062 kb respectively as shown in Figs. 13, 14, 15, 16, 17, 18. For Fig. 13, the abscissa is the number of data, the ordinate is the avgVQM. The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. In Fig. 14, the abscissa is the number of data, the ordinate is the avgPSNR. The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. In Fig. 15, the abscissa is the number of data, the ordinate is the avgVQM. The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. In Fig. 16, the abscissa is the number of data, the ordinate is the avgVQM. The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. In Fig. 17, the abscissa is the number of data, the ordinate is the avgVQM. The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. In Fig. 18, the abscissa is the number of data, the ordinate is the avgVQM. The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. Src13 wired packet loss rate and MOS as shown in Fig. 19, src13 wired packet loss rate and MOS*20 as shown in Fig. 20, src13 wired MOSd and MOSo and DMOS as shown in Fig. 21, src13 wireless packet loss rate and mos as shown in Fig. 22, src13 wireless PSNR and MOS and DMOS as shown in Fig. 23, src13 wireless packet loss rate and MOS*20 as shown in Fig. 24, src13 wireless MOSd and MOSo and DMOS as shown in Fig. 25. For Fig. 19, the abscissa is the number of data, the ordinate is the packet loss rate (blue) and the MOS (red). The two are presented curves,

Investigation of Wireless Sensor Network of the Internet of Things

Fig. 13. Mother-daughter_qcif.yuv VQM

Fig. 14. FOOTBALL.yuv PSNR

Fig. 15. FOOTBALL.yuv VQM

Fig. 16. SRC13 VQM

Fig. 17. SRC22 PSNR 1068kb

Fig. 18. SRC22 PSNR 1062kb

Fig. 19. src13 wired packet loss rate and MOS

27

Fig. 20. src13 wired packet loss rate and MOS * 20

the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. In Fig. 20, the abscissa is the number of data, the ordinate is the packet loss rate (blue) and the MOS*20 (red). The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. For Fig. 21, the absisa is the number of datas, the ordinate is the MOSd (blue) and MOSo (red). The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. In Fig. 22, the abscissa is the number of data, the ordinate is the packet loss rate (blue) and the MOS (red). The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. In figure Fig. 23, the abscissa is the number of data, the ordinate is the PSNR (blue) and MOS * 20 (red) and DMOS (green). The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. In Fig. 24, the abscissa is the number of data, the ordinate is the packet loss rate (blue) and the MOS * 20 (red). The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually. For Fig. 25, the absisa is the

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number of datas, the ordinate is the MOSd (blue) and MOSo (red). The two are presented curves, the relationship between them is complicated, and the area between the abscissa and the ordinate and the curve increases gradually.

Fig. 21. src13 wired MOSd and MOSo and DMOS

Fig. 22. src13 wireless packet loss rate and MOS

Fig. 23. src13 wireless PSNR and MOS and DMOS

Fig. 24. src13 wireless packet loss rate and MOS * 20

Fig. 25. src13 wireless MOSd and MOSo and DMOS

4 Conclusion Wired and wireless have been studied and concluded that the wireless internet of things is just like the wired internet of things, wired provides the basis for Wireless Research. Do IOT problem definition and research. Research on Internet of things, first research object, Re research alliance, Re study network. Objects are things in the Internet of things, Link is how objects connect to the network, Network is what this network is. The objective function is the key problem. Can start with simple and critical questions. The algorithm is the solution to the problem stops. What is the Internet of things, objects connected to the Internet is the Internet of things, cup networking, car networking. Things better than other networks, is composed of what objects, what composition, what nature, what innovation and superiority. Internet of things four key technologies is widely used, these four technologies are mainly RFID, WSN, M2M, as well as the integration of the two. RFID can be achieved using MATLAB, NS2, Android, WSN can use NS2 [10, 11], OMNET++ implementation, M2M can be developed using JAVA. Therefore, this paper focuses on the advantages of the Internet of things than the internet. The Internet of things has no unified definition. Some people

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believe that the interconnection of RFID is the Internet of things, some think that a sensor network is the Internet of things, some think that M2M (machine to machine) is the Internet of things. Some people think make the Internet stretched and extended to any goods and goods is the Internet of things. The Internet of things not only meets the demands for information about goods’ networking, but also the current technology development’s push. And final the most important thing is the internet of things can boost the economy, so the investigation on the Internet of things is very important. SVM modeling and submission to SCI journals will be the future direction if they are not translated. Lisbon, Portugal Conference and Opening Report and Feasibility Analysis and Internet of Things and Everybody's Network Forum and CSDN and Beijing University of Technology database and student recruitment brochure and Airport and Yansha Autleys and Jingkelong, Xidan and Foreign Trade Garments and Dresses and Hair Design and Activist website and Peking University Conference, BIRTV Conference and DQMIS Conference and Intel are important for this article. Acknowledgments. Thank you for the scholarship grant of Beijing University of Technology and the database of Beijing University of Technology.

References 1. Hou, Y., Wang, J.: QOS-QOE energy saving optimization model for wireless sensor networks. J. Sensor Technol. Appl. 6(2), 50–57 (2018) 2. Liu, S., Yang, L., Wang, C.: Recognition of aircraft cockpit signals based on kernel principal component analysis and support vector machines. J. Southeast Univ. Nat. Sci. Ed. 38(s, II), 123–127 (2008) 3. Wang, J., Hou, Y.B.: No-reference network packet loss video quality assessment model based on LS-SVM. In: International Conference on Intelligent and Interactive Systems and Applications, pp. 403–410. Springer, Cham (2016) 4. Hou, Y.B., Wang, J.: No-reference video quality assessment model considering the network packet loss based on Least squares support vector machine under the Internet of things. In: 14th IEEE International Conference on Advanced and Trusted Computing, IEEE ATC 2017 Conference (2017) 5. Hou, Y.B., Wang, J.: LS-SVM research under the internet of things. Comput. Knowl. Technol. 13(4), 145–146 (2017) 6. Wang, J., Hou, Y.: Packet loss rate mapped to the quality of experience. Multimed. Tools Appl. 77(1), 387–422 (2018) 7. Hou, Y.B., Wang, J.: Investigation at the QOE and packet loss rate of the IOT network. Am. J. Data Min. Knowl. Discov. 2(1), 15–30 (2017) 8. Hou, Y.B., Wang, J.: QOE forecast under the distributed Internet of Things. In: Advances in Cloud Internet Things (2017) 9. Hou, Y.B., Wang, J.: Investigation on mapping model of packet loss rate and the quality of experience. Int. J. Curr. Trends Eng. Technol. IJCTET 2(6), 467–470 (2017) 10. Wang, J.: No-reference video quality assessment model considering the network packet loss. Shijiazhuang Tiedao University (2015) 11. Wang, J.: Mapping model of packet loss rate and the quality of experience on the influence of packet loss on QoE. In: 2014 International Symposium on Information Technology Convergence, ISITC2014 (2014)

A Remote Phase Change System for Low-Voltage Power Distribution Area Jiafeng Ding1, Jing Liu1, Xinmei Li1(&), Zhifeng Li2, Fei Gong1, Xiao Liang1, and Qin Luo1 1

2

School of Physics and Electronics, Central South University, Changsha 410083, China [email protected] State Grid Puyang Electric Power Company, Puyang 457000, China

Abstract. Aiming at solving the problem of three-phase imbalance in threephase four-wire low-voltage distribution network, a remote phase change system based on bus ZigBee network for low-voltage power distribution area has been designed. The system consists of a master control terminal and multiple intelligent phase change switches and the bus ZigBee communication network is constructed with ARM microprocessor and CC2630 as core: The master control terminal implements the download of the phase change command and the upload of the load status by the ZigBee coordinator technology; Intelligent phase change switch can swap phase sequence within 20 ms without power interruption by zero-cross switching technology based on permanent magnet relay. Compared with traditional compensation and other methods, this design provides a fast and safe way to controls three-phase balance from the load management and has better application prospects. Experimental tests showed that the system could run well and meet the power system requirements. Keywords: Low-voltage distribution power area
Three-phase imbalance Zero-cross switching
Bus ZigBee network
Phase change

1 Introduction In three-phase four-wire low-voltage distribution network, the user load is mostly single-phase or three-phase mixed. Three-phase imbalance exists objectively and has no regularity, which cannot be predicted in advance [1–3]. The three-phase imbalance not only causes the increase of the loss of line, but also does harm to the quality of the electric energy, which seriously threatens the safety or life-span of the distribution transformer [4–6]. With the development of science and technology, the large-scale integration of new intermittent energy sources, the various energy storage equipment and electric locomotives have emerged, all of which will make people’s demand for electrical energy becoming larger in the future [7]. Therefore, the management of threephase imbalance is very urgent. Three-phase imbalance control of low-voltage distribution network includes the method of switching load and additional compensation in the past [8]. Load switching includes artificial phase change and automatic phase change. Artificial phase change often need experienced operator, the high cost of © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 30–39, 2019. https://doi.org/10.1007/978-3-030-02804-6_4

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investment made it gradually be eliminated [9]. Additional compensation implements reactive power compensation for important power equipment [10], the installation of active power filter [11], improves the power factor of the system, these approaches can alleviate the unbalanced three-phase phenomenon caused by unbalanced load to some extent [12]. Because of the spatio-temporal asymmetry of user load, these measures can only alleviate the three-phase imbalance phenomenon temporarily in low voltage distribution area, and they can’t adapt to the seasonal change of user’s load. In this paper, a remote bus ZigBee based phase change system has been designed, which uses the zero-cross switching phase technology of permanent magnetic relay and the one host multi-slave bus ZigBee wireless communication technology constructed by the latest Texas Instruments (TI) wireless chip CC2630. The system synchronizes the phase information of the user load side to the management center in real time. The power system manager determines whether the user’s power supply needs to be changed according to the three-phase imbalance information of the power quality supervision and management platform, and then realizes the remote phase change of the load through this system. This system provides a quick and safe three-phase imbalance management method for power system manager.

2 The Structure of System Figure 1 shows the structure of the system which consists of two parts: the master control terminal and intelligent phase change switch. The master control terminal is composed of an ARM processor, a ZigBee short-distance wireless communication module, a GPRS remote wireless communication module, a SD storage module and a line current acquisition module. The intelligent phase change switch is mainly composed of an ARM processor, a ZigBee short-distance wireless communication module, a zero-point detection module, a relay driving and locking module and a storage and power module. The ARM processor adopts STMicroelectronics STM32F107 processor, which integrates a variety of high-performance industrial standard interfaces to expand peripherals according to functional requirements. The bus ZigBee wireless communication network is built with newly TI wireless chip CC2630. The master control terminal is a coordinator, and the switch node is the router node and execute device. The line current acquisition module collects the load data of the distribution network. The Alternating Current (AC) load’s voltage or current has been transformed into 50 Hz square wave by the zero-point detection module and then been imported to the ARM processor. The relay driving and locking module guarantee only one of the three-way relay inputs of A, B and C can be connected at any time. The master control terminal is installed on the low voltage side of the distribution transformer and some loads (1/3 to 1/2 of the loads in the whole area) in the low voltage distribution area are selected to install the intelligent switch. The master control terminal is the hub of the entire control system that not only gathers load data of the distribution network from each intelligent switch and uploads them to the monitor center through GPRS module, but also sends the monitor center’s phase change instruction to specific phase change switch through ZigBee network at the same time. The intelligent switch is the execute device of the whole control system which changes

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Fig. 1. The structure of system

the phase sequence of load according to the phase change command and provides feedbacks of the phase sequence to the master control terminal after switching. The monitor center of distribution network provides a human-computer interface for the power system manager, by which the manager can know load phase information in real time and send phase change commands to specific load. The following will describe the three core functional modules in the system: the intelligent phase-change unit, the communication network unit and the system control unit. The intelligent phase change unit realizes the switching of the user-end phase, the communication unit realizes a bus ZigBee network, and the master control unit realizes the overall system control.

3 Intelligent Phase-Change Unit 3.1

Permanent Magnet Relay

Switching elements that are commonly used for AC include AC contactor, compound switch, relay switch, etc. The AC contactor is prone to surge inrush current at the closing time and is easy to generate an electric arc at the disconnecting time, which inevitably leads to harmonic pollution. The compound switch is a thyristor to cast at the moment of switching. It is analogous to mechanical switch when connected. But when the thyristor is connected, it has high voltage drop and can generate a lot of heat. So, it needs a radiator when it works, and needs to cooperate with a strict timing control circuit as well, which makes it difficult to apply to the phase change field. With the development of electronic devices, the permanent magnet relay gradually becomes the ideal choice for phase change switches for its advantages of high voltage or large current carrying and fast switching action. It also has some advantages such as simple operation, low power consumption and no switching loss, all of which make it more and more widely used. The permanent magnetic relay is similar to mechanical switch that has more than ten thousand switching times.

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AC Zero-Crossing Phase Change Control Strategy

Zero-cross switching technology is widely used in capacitor switching or reactive power compensation. According to the characteristics of the electric load used in the low-voltage power station, the process of phase change needs to meet the requirements of real-time synchronization, no impulse, safety, reliability and easy maintenance. The inputs of the intelligent switch are three phases and the output is single phase. The permanent magnetic relay is connected to A, B and C respectively. The intelligent switch selects the appropriate power supply phase sequence for user with the control signal, which can realize the permanent magnet relay’s turning on or off. Considering the permanent magnet relay action time, according to the current and voltage zero-point detection signal, the corresponding control strategy is designed to make sure each action of the relay is at the zero-point of alternating current. The flowchart of phasechange control strategy is shown in Fig. 2. Start Open current detecting trigger interrupt

Automatic control program

N

Detection of zero point of current Y

Delay and turn off current detection interruption

Voltage removed

Open voltage detecting trigger interrupt N

Detection of zero voltage

Relay locking

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Delay and turn off voltage detection interruption Execution voltage cut command

Executive current excision command

End

Fig. 2. Flowchart of phase change control strategy

The phase change period is from the relay at the zero-point of current to the zeropoint of voltage and the whole phase change time can be controlled within 20 ms. Practice shows that the interruption of voltage in 20 ms will not affect the normal use of computer and other household appliances, the system can meet the requirements of real-time synchronous phase change. This method makes phase selection easier than traditional manual operations. The switching action alters at zero-point time of the load’s current or voltage with no loss at all, which meets the requirement of easy maintenance. The permanent magnet relay and the AC Zero-crossing phase change control strategy make less than 20 ms phase swapping and cutting off at current zeropoint and cutting in at voltage zero-point possible.

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Anti-phase Short Circuit Protection

For the reliability of the phase change, the relay driving and locking circuit has been designed in hardware. The relay driving signals use machine encoding instruction in software, which can ensure only one phase of A, B and C can be connected at the same time. When uncontrollable factors appear during the phase change process, the relay is latched and phase A would be connected, which can meet the reliability requirement.

4 Communication Network Unit At present, communication schemes that are commonly used in low-voltage Distribution are as follows: (1) Low voltage power line carrier communication scheme: Because the low-voltage power network is directly oriented to the user, it is easy to be disturbed by the external circumstances. (2) The general wireless packet service GPRS technology scheme: Although it doesn’t need to be relaid, it has high design cost and is not suitable for large-scale user network. (3) The wire communication technology based on optical fiber or RS-485: It needs wiring, and has difficulties of increasing or decreasing node, which is not suitable for large-scale network too. Therefore, it is difficult to apply them in three-phase imbalance management. According to the distribution characteristics of low-voltage distribution network, a remote bus wireless communication scheme based on ZigBee technology has been designed, which can avoid repeated wiring and be adaptable to meet the requirement of random increasing or decreasing nodes. ZigBee wireless communication network is constructed with CC2630 as its core, the topology structure is Mesh that can supports dynamic routing. The CC2630 device adopts an ultralow power consumption design that contains a 32-bit ARM processor runs at 48 MHz as main processor. It also contains a rich peripheral feature set that includes a unique ultralow power sensor controller. When the rest of the system is in sleep mode, this sensor is an ideal choice for interfacing external sensors and for collecting analog and digital data autonomously. Every intelligent phase change switch nodes have relay forwarding function, which can greatly reduce the router node’s congestion and interference due to excessive concentration control. The master control terminal is set as a coordinator which selects the appropriate channel to create the PANID network, then monitors the wireless network and wait for the request data to be received. When intelligent switch is turned on, it begins to search the PANID network according to the protocol stack, then sends the request to the coordinator for network. Once the ZigBee network is set up, the coordinator assigns each intelligent switch node a unique 16-bit address in the network. When the phase change command of the system manager is sent to the master control terminal through the GPRS channel, the master control terminal analyzes the command and sends the corresponding command to the specific phase change switch. After the phase change switch action is completed, the feedback information will be sent to the master control terminal. Finally, the master control unit feeds back the status of the phase change switch to the system manager. This is a complete phase change cycle.

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5 System Control Unit 5.1

Software Design for the Intelligent Phase Change Switch

Figure 3 is the software flow chart of intelligent phase change switch. After the system’s initialization, the intelligent phase change switch firstly reads the last phase information from the EEPROM, and then enters the automatic control program to wait for the phase change command. The software is divided into three sub-programs: automatic control program, relay lockout processing program and ZigBee communication program. The automatic control program is the main execution program, which completes two functions: the data acquisition and wakes ZigBee node on time. The relay lockout processing program ensures the safety of phase-change and records fault processing information. The ZigBee communication program is the channel of data transmission between the control terminal and the intelligent phase change switch. Commutation strategy program

Main function entrance System initialization

Commutation success

Read the current connection sequence from EEPROM

N

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Y Write EEPROM to save the current phase sequence

Whether commutation command Y

ZigBee communication program

Automatic control program

Fig. 3. Program flowchart of intelligent switch

Considering the security of ZigBee network transmission, CRC32 redundancy check has been added in data transmission, and XOR check has been added to the data containing phase change instructions, the use of the double checks greatly improves the system transmission reliability. For better practical applications, the phase sequence of the intelligent phase change switch also can be changed by the human-computer interaction. 5.2

Software Design for the Mater Control Terminal

Figure 4 is the software flow chart of the master control terminal based on ARM. After system’s initialization, the software enters the monitoring subroutine and the data of the two transmission channels are monitored and processed: (1) All intelligent phase change switch nodes are monitored: When the master control terminal receives the request signal of phase change switches, the coordinator will respond and dynamically assign address according to the existing network addresses. If the master control terminal receives the feedback data from the phase change switch, it will upload the data to the monitor center of the distribution network through GPRS module;

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Monitor Center Phase Change command

Broadcast phase change command

Add switch node to network

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Upload monitor center with the GPRS

Fig. 4. Program flowchart of the master terminal

(2) The distribution network center is monitored: When the master control terminal receives the phase change command from the distribution network center, it will broadcast the command to the intelligent phase change switch nodes through the ZigBee network.

6 Experiments and Results The experimental platform has been built to test the synchronization of the intelligent switch and the robustness of the bus ZigBee communication network. 6.1

Synchronous Phase Change Switching Experiment

In the experiment, combined loads containing a 275 W heating lamp and some capacitive inductors have been used. Digital Oscilloscope Tektronix MDO3104 has been used to record the current and voltage waveforms of the load during each phase change cycle. The computer has been used to send the phase change command. Figure 5 shows the waveform of the load’s voltage and current, the relay driving signal and zero-point detection of load during the phase change process. According to characteristics of the AC frequency, the zero-point detection circuit can detect the load’s current or voltage zero point twice in 20 ms. In Fig. 5(a), we can see that the relay cuts in at the zero-point of load current and off at the zero-point of load voltage and the phase change time ranges from 14 ms to 20 ms. Because the synchronization requirement is strictly met, no surge current is generated during the phase change process. From Fig. 5(b), we can see that the controller will wait for voltage zero-point interruption after the relay is removed. After the voltage zero-point was detected, the intelligent switch sent out the relay control signal after about 1–2 ms delay, which verifies the operability of phase change control strategy.

A Remote Phase Change System for Low-Voltage Power

(a) Time sequence diagram of load signal

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(b) Diagram of phase swap control signal

Fig. 5. Signal waveform diagram of phase change process

6.2

ZigBee Network Reliability Experiment

Success rate

The reliability test of ZigBee network has been divided into transmission distance test and network stability test. The transmission distance test has also divided into single point test and network test. The coordinator and a routing node have been used in the single point data transmission test and the coordinator position was fixed. The test distance between the test node and the coordinator increased from 50 m to 300 m, and simultaneously the data received by the test node have been recorded in the computer. All nodes have been used in the network transmission test, the distance between adjacent router nodes has been set to about 100 m, and then the distance between the test node and the coordinator was gradually increased, the data received from the farthest test node have been recorded in the computer. During the test, the test nodes sent out one frame of data every 10 s, the number of data packets sent by the coordinator has been recorded in an hour (360 frames of data in all), and the results are shown in Fig. 6. 1.00 0.99 0.98 0.97 0.96 0.95 0.94 0.93 0.92 0.91 0.90

Single point data transmission Network data transmission

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As is shown in Fig. 6, the success rate of single point data transmission and network transmission is roughly similar when the distance is less than 150 m. When the distance is 150 m to 250 m, the success rate of network data transmission is higher than that of single point data transmission. It is CC2630’s relay forwarding routing information that make the network coverage area larger than before. When the distance exceeds 250 m, the success rate of single point data transmission and network transmission is similar, because it is approaching the maximum distance of point to point communication. Therefore, the transit node can be set according to the actual interval between one or two poles of rural areas (about 150 m), so as to ensure reliable communication. The stability of ZigBee network has been tested from August 2017 to October 2017. At 8, 16 and 20 o’clock of each day, the phase change command was broadcasted from the master control terminal, and the switch nodes received phase change command and then fed information back to the master control terminal. If the transmission succeeded, the data would be printed on the Computer serial port monitoring software. Otherwise, it was a failed data transmission. The test data had 200 frames in total, and it was sent at every 10 s, and the distance between test nodes was set to 200 m. Table 1 shows the stability test results of 200 frame data network, from which we can find that after three months of operation, the network runs well and the packet loss rate is less than 1%, and it can meet the needs of the system. Table 1. 200 frames of data transmission network stability test results Data 2017.8.11 2017.8.28 2017.9.16 2017.9.30 2017.10.5 2017.11.15

8:00/frame 198 200 200 200 200 198

16:00/frame 199 199 200 199 200 198

20:00/frame 199 200 200 199 200 199

Average packet loss/% 0.67 0.03 0.00 0.34 0.00 0.83

7 Conclusion In this paper, a remote phase change system for the low voltage distribution area has been designed. The system consists of a master control terminal and several intelligent phase change switches. The intelligent phase change switches have been installed on 30%–50% of user loads. The self-organized bus ZigBee network makes the system highly reliable. The phase change time has been controlled within 20 ms based on the synchronous switching technology of permanent magnetic relay. Compared with traditional control methods, this system manages the source of three-phase imbalance through user’s load management. Experiments have proved that the system has good application prospects and practical value.

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Acknowledgments. This study was supported by the National Basic Research Program of China (2015CB554502), the National Natural Science Foundation Project of China (No.61502538, 61501525) and the Fundamental Research Funds for the Central Universities of Central South University (2018zzts347).

References 1. Dahal, X.S., Salehfar, H.: Impact of distributed generators in the power loss and voltage profile of three phase unbalanced distribution network. Electr. Power Energy Syst. 27(4), 256–262 (2016) 2. Kumawat, M., Gupta, N., Jain, N., Bansal, R.C.: Optimally allocation of distributed generators in three-phase unbalanced distribution network. Energy Procedia 142(3), 749– 754 (2017) 3. Chen, R., He, L., Weng, H.J., Cai, Y.C., Wang, W.: A novel three-phase unbalanced voltage flexible restraining method for distribution systems. In: International Conference on Electric Utility Deregulation & Restructuring & Power Technologies, pp. 1220–1223 (2015) 4. Tareen, W.U.K., Mekhielf, S.: Three-phase transformerless shunt active power filter with reduced switch count for harmonic compensation in grid-connected applications. IEEE Trans. Power Electron. 33(6), 4868–4881 (2018) 5. Yan, S., Tan, S.C., Lee, C.K., Hui, S.Y.R.: Reducing three-phase power imbalance with electric springs. In: IEEE International Symposium on Power Electronics for Distributed Generation Systems, pp. 1–7 (2014) 6. Fang, H.F., Sheng, W.X., Wang, J.L., Liang, Y.: Research on the method for real-time online control of three-phase unbalanced load in distribution area. Proc. CSEE 35(9), 2185– 2193 (2015) 7. Ma, K., Li, R., Li, F.: Quantification of additional reinforcement cost from severe threephase imbalance. IEEE Trans. Power Syst. 40(13), 2885–2891 (2016) 8. Li, F., He, F., Ye, Z., Fernando, T., Wang, X.: A simplified PWM strategy for three-level converters on three-phase four-wire active power filter. IEEE Trans. Power Electron. 40(20), 76–82 (2016) 9. Guo, J.L., Wen, F.S.: Impact of electric vehicle charging on power system and relevant countermeasures. Electr. Power Autom. Equip. 35(06), 1–9 (2015) 10. Tian, M.X., Chen, M., Zhao, Y.X., et al.: A novel method of reactive power compensation network design for three phase unbalanced load system. Power Syst. Technol. 40(3), 897– 903 (2016) 11. Xia, Z.L., Shi, L.P., Yang, X.D., Li, Q.: An improved control strategy for cascaded STATCOM under supply voltage imbalance. Power Syst. Technol. 38(5), 1310–1316 (2014) 12. Zeng, X.J., Hu, J.Y., Wang, Y.Y., Xiong, T.: Suppressing method of three-phase unbalanced overvoltage based on distribution networks flexible grounding control. Proc. CSEE 34(4), 678–684 (2014)

Design and Implement of Speech Interaction with Indoor Substation Inspection Robot Xiaobin Yu1, Shiliang Lv1, Kun Mao1, Anshan Wang1, Shouguo Lv1, Lei Han2, Changchun Gao2, and Guoqing Yang2(&) 1

State Grid Shandong Electric Power Maintenance Company, Jinan, China 2 Shandong Luneng Intelligence Technology Co., Ltd., Jinan, China [email protected]

Abstract. Regular inspecting electrical equipments in substation is an important task, where substation inspection robots are developed and used. After analyzing the application requirement of speech interaction with substation inspection robot, a speech interaction system has been developed based on Microsoft’s speech application programming interface. Then the design ideas of the system and some key steps, including speech recognition, text-to-speech, grammar creation and maintenance, are thoroughly stated. How to further improve the recognition rate is discussed in the end. Keywords: Substation inspection robot
Human–machine interaction Speech recognition
Speech application programming interface

1 Introduction The substation inspection robot can carry out unattended inspection of substation equipment in all weather and omnibearing, so as to replace the heavy manual inspection of substation equipment, and improve the automatic and intelligent level of substation inspection [1]. In the operation mode, the inspection robot can independently complete the inspection process according to the preset instruction set, and can also complete the specified operation under the control of the operator, which the operation command is usually delivered by clicking the button or the menu. At present, substation inspection robot has been widely applied in multiple areas of the country [2]. The site application puts forward new requirements for the robot control mode, which hoping to control the robot with the speech command, and realize the interactive dialogue with the robot. Focusing on the functional requirements of the voice interactive control of the inspection robot in substation, the application of speech recognition and speech synthesis as a means are used to achieve a reliable and efficient robot voice control and interactive conversation in the paper.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 40–46, 2019. https://doi.org/10.1007/978-3-030-02804-6_5

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2 Function and Performance Requirements Interactive control of robot is to allow the robot to recognize the received effective dialogue and speech operation instructions, which according to the results of recognition to respond or complete the specified operation. The speech recognition technology is used to correctly identify the speech commands issued by the operator, and then determine the type of speech command according to the recognition results. According to the interactive content, the speech instruction is divided into the control command and the reply instruction. The control instructions include opening the camera, opening the shutter door, turning left, turning right and other instructions. The reply instructions, such as what is your name, what can you do. In order to meet the field application requirements, the speech interactive control system should meet the following performance indicators: (1) Sensitivity: the response to the normal volume of interactive instructions; (2) Anti-interference: not affected by interference sources below 1/4 amplitude of valid information; (3) Accuracy: the correct recognition rate is not less than 95%; (4) Response speed: Respond within 1 s after receiving voice command.

3 Process Design According to the functional requirements of robot speech interaction control, the system can be divided into four modules: speech acquisition, speech recognition, dialogue response and execution. The structure of system as shown in Fig. 1. speech acquisition speech recognition

perform action

dialogue response

Fig. 1. Speech interactive system

(1) Speech acquisition The sound sensor is used to collect the interactive speech signal, which provides the analysis and processing of the follow-up link. (2) Speech recognition Through the analysis and processing of the collected speech signals, the features are extracted and the voice content is identified. Then, it is determined whether it is a legal command or a command type, thereby controlling the robot to respond accordingly.

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(3) Dialogue response After recognizing of the legal response command, the robot answers the content to achieve man-machine conversation. (4) Performing operation After confirming the legal operation instruction by the speech recognition, the instruction is sent to the robot to complete the corresponding operation. The speech interaction flow chart as shown in Fig. 2. begin initialize the recognition engine

monitorspeech input

preprocessing

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recognition result

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Fig. 2. Speech interaction flow chart

4 Design of Speech Interaction System The key to realize the robot speech interaction control is speech recognition and speech synthesis for dialogue response. Combined with the system requirements and the development of speech technology, the whole speech interaction control system is realized based on the existing speech software development kit.

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Development Mode

The speech recognition technology is that allows the machine to change the speech signal into the corresponding text or command through the process of identifying and understanding, which includes three aspects: feature extraction technology, pattern matching rule and model training technology [3]. Objectively speaking, although some companies provide some commercial applications of speech recognition, the real efficient and reliable speech recognition technology is still a technical difficulty. At present, commonly used voice development tools include Microsoft’s SAPI language engine and InterReco voice recognition system provided by iFLYTEK. Both of them have good effect on the command type recognition of robot speech interaction. Speech application programming interface (SAPI) is a set of application programming interfaces for speech processing provided by Microsoft Speech SDK. It contains basic functions for implementing Speech Recognition and Text-to-Speech programs, which greatly simplifies speech programming and reduces the workload of speech programming [4]. Figure 3 shows the development framework of Microsoft’s SAPI. The voice engine communicates with the SAPI runtime through the Device Driver Interface (DDI) layer, and the application interacts with SAPI through the application programming interface (API) layer. Through the use of these API for speech recognition and speech synthesis development.

application program API SAPI runtime DDI speech recognition engine

speech synthesis engine

Fig. 3. Development framework for SAPI

4.2

Detailed Design

There are two recognition modes that can be used to construct speech recognition system with SAPI: speech command control mode and speech dictation mode [5]. A speech recognition system constructed by using speech command control mode is suitable for small vocabulary, isolated words, speaker-independent speech recognition. However, the grammar rules need to be created and the scope of recognition can only be limited to phrases or words designed within the grammar rules, which the adaptability is poor. The speech recognition system constructed by the speech dictation model is suitable for the recognition of large vocabulary and continuous speech, which does not need to construct complex grammar rules. The speech recognition is adaptable, but the recognition rate is relatively lower [6].

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The speech interactive control instructions of robot are limited and relatively fixed, so the command recognition control pattern with higher recognition rate is adopted. According to the development process of SAPI, the speech recognition can be achieved by completing the basic configuration of speech recognition and the initialization of recognition engine. The key problem of speech command recognition mode is speech rule, which is the standardized description of the command library that can be recognized. The syntax rules of SAPI is XML (eXtensible Markup Language, extensible markup language) format [7]. A grammar rule file needs to be written before identification, in which defines the words and phrases that need to be identified. and the SDK speech recognition engine loads the syntax rules to identify the user’s speech. In XML, each entity or elements are composed of a start tag and end tag , the grammar contains is the entity or element in the middle of the statement. The content of grammar can be ordinary characters, or elements of grammatical elements. The formal definition of legal grammatical content in the XML specification is in the form of multiple set expressions, which can be define exactly the rules of the grammar and grammar of the files. In grammar, a group of words or phrases to be recognized is inserted in

and

[8]. Figure 4 shows the partial grammar rules used in robot speech interaction control. Among them, GRAMMAR LANGID = “804” indicates that the object of recognition is Chinese characters, and the Chinese acoustic model is called in the recognition process, and the identification commands are located between

and

.

Fig. 4. XML format syntax rules

The XML grammar file can be manually edited according to the recognition command. When the recognition command changes, the XML instruction needs to be updated synchronously and the XML file is loaded, so that the speech recognition engine can recognize the new instruction. Because the manual editing is not easy to update the grammar, according to the XML rules, the syntax file is programmed in the development process. MSXML2:: IXMLDOMDocumentPtr and MSXML2:: IXMLDOMElementPtr are used for editing the XML file [9]. Speech synthesis, also known as text to speech conversion, which can translate any text message into a standard, fluent speech in real time [10]. By means of computer speech synthesis, any text can be converted into speech with high naturalness at any time, so as to make the machine speak like a person. This is exactly what robot voice

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interactive response requires, and speech synthesis can be enhanced by installing a third-party speech library, such as the Neospeech speech library. According to the above process, the robot speech interactive control system was developed. After field testing, the correct recognition rate reached 90%. However, the false alarm rate is high, that is, some interference sounds may be easily recognized as an instruction, especially some short instructions such as “recording” and “opening”. In order to solve this problem, a more rigorous alignment rule is designed as shown in Fig. 5. The experimental results also verify the grammar, and the use of this comparison grammar can significantly reduce the false recognition rate.

Fig. 5. Strict matching recognition grammar

5 Conclusion In order to enhance the voice interaction function of substation inspection robot, a complete voice interactive development process is designed according to the application requirements. On this basis, a set of human-computer interactive voice control system is completed based on Microsoft speech application interface SAPI. Field test results show that the performance of the system to meet the design requirements. However, the anti-interference ability of speech interaction system needs to be improved, especially in the noisy background. Acknowledgments. This work was supported by application research and development of intelligent new technology for substation inspection robot (item serial number: ZY201815). Thank you for your support of the paper.

References 1. Liu, Y.F., Ye, H.B., Liu, K.: Research on the application of inspection robot for 500 kV selfservice substations. Distrib. Util. 33(9), 69–72 (2016) 2. Yang, D.X., Huang, Y.Z., Li, J.G., Li, L., Li, B.D.: Research status review of robots applied in substations for equipment inspection. Shandong Electr. Power 42(1), 30–31 (2015) 3. Mittal, T., Sharma, R.K.: Integrated search technique for parameter determination of SVM for speech recognition. J. Cent. South Univ. 23(6), 1390–1398 (2016)

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4. Sultana, S., Akhand, M.A.H., Das, P.K., Rahman, M.M.H.: Bangla speech-to-text conversion using SAPI. In: International Conference on Computer and Communication Engineering, pp. 385–390 (2012) 5. Lin, M.X.: Application of speech recognition technology in 3D simulation based on speech SDK. Comput. Technol. Dev. 21(11), 160–166 (2011) 6. Lv, Z., Wu, X.P., Zhang, C.: Review of robust speech recognition. J. Anhui Univ. Nat. Sci. 37(5), 17–24 (2013) 7. Pan, Q., Zhang, L., Hu, S., Li, P., Li, Y.: Implementation of speech recognition and synthesis in ATC simulator. Comput. Technol. Dev. 27(7), 131–139 (2017) 8. Song, X.K., Chen, W.M., Zhu, M., Gui, C.S.: Study on speech recognition control strategy based on regular expression. Comput. Technol. Dev. 20(2), 106–113 (2010) 9. Jing, X.Y., Luo, F., Wang, Y.Q.: Overview of the Chinese voice synthesis technique. Comput. Sci. 39(z3), 186–190 (2012) 10. Rajeswari, K.C., Uma Maheswari, P.: Prosody modeling techniques for Text-to-Speech synthesis systems a survey. Int. J. Comput. Appl. 39(16), 8–11 (2012)

Speech Recognition Algorithm of Substation Inspection Robot Based on Improved DTW Lei Han, Changchun Gao, Shujing Zhang, Dongsong Li, Zhizhou Sun, Guoqing Yang(&), Jian Li, Chuanyou Zhang, and Guangting Shao Shandong Luneng Intelligence Technology Co., Ltd., Jinan, China [email protected]

Abstract. The voice recognition algorithm is proposed in this paper which can control the substation inspection robot by voice, so the voice-controlled robot is realized. Combined the voice controlled command collected by robot’s pickup device and the basic principle of speech recognition, the intelligent mobile robot can make the corresponding action according to the instructions and complete automatic detection and information query. Firstly, the voice instruction was collected by using robot and transmitted system based to construct the sample database. Secondly, the sample database was analyzed and MFCC feature of speech sample was extracted. Finally, the template matching of voice parameters was achieved by using the improved DTW as the matching algorithm, and the recognition result was transmitted the robot system to control the robot’s action. The experimental results show that the algorithm can quickly recognize and extract the voice command, so it can improve the recognition accuracy and realtime control the inspection robot. Keywords: Inspection robot MFCC
DTW


Speech recognition
Endpoint detection

1 Introduction Speech recognition started in the 1950s. With the deepening of research and the development of related disciplines, speech recognition technology gradually from the laboratory to the practical application. Language is the main way of human communication, and making the machine understand the human speech is the hotspot flush and difficult in the field. Speech recognition is a kind of technical which transforms the voice signal into the corresponding text through certain technology [1, 2]. Speech recognition mainly consists of three main parts: feature extraction, algorithm matching and model training. The realization process of speech recognition is shown in Fig. 1. Substation inspection robot has been popularized and applied at home, and has made outstanding contributions to maintain the safe and stable operation of substation. However, the control commands of robot are issued by the background program of the main control room, and the staff can not directly operate the robot in the equipment area or query the status information. The background of the program is more professional, therefore, operators need to be strict training to familiar with the background program, © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 47–54, 2019. https://doi.org/10.1007/978-3-030-02804-6_6

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feature extraction

pattern matching

recognition result

model base

Fig. 1. Speech recognition process

and the operation path is relatively complex. As a unique function of human beings, language is not only the main means of transmitting information, but also one of the most ideal human-computer interaction methods [3]. Using the voice control inspection robot for routine operation, query related information, relative to the background operation more simple. As long as the operator can master the relevant instructions, the routine operation can be carried out to facilitate the daily application and maintenance of the robot. After the speech recognition function is built on the substation inspection robot platform, the speech recognition technology can also help the unattended substation, and realize the substation intelligent and automatic management. This paper is based on improved DTW algorithm for substation robot speech recognition. The main application of speech recognition algorithms for humancomputer interaction, so that the robot can understand the verbal command has been trained, and in accordance with the command to the appropriate action.

2 Feature Extraction 2.1

Mel-Frequency Cepstrum Coefficients

The human ear has different perception ability to different frequencies of speech, and it is a nonlinear relation [4]. Combining with the physiological structure of the human ear, the logarithmic relationship is used to simulate the human ear’s perception of speech at different frequencies. Davies and Mermelstein proposed the concept of Melfrequency cepstrum coefficients (MFCC) in 1980. The specific relationship between the Mel frequency and the actual frequency is shown in Formula (1). The auditory properties of the human ear are consistent with the increase of Mel frequency. With the actual frequency linearly under 1000 Hz distribution, More than 1000 Hz showed logarithmic growth [5]. fmel ðf Þ ¼ 2595
logð1 þ

f Þ 700 Hz

ð1Þ

MFCC is to use the above relationship to calculate the spectral characteristics of Hz. At present, MFCC has become the mainstream algorithm of speech recognition. The flow chart of the MFCC process is shown in Fig. 2, the main MFCC parameters are the following steps: (1) Pre-emphasis: the spectrum of the signal is flatten; (2) Sub-frame: according to the short-time stationary property of speech, the speech is processed in frame;

Speech Recognition Algorithm of Substation Inspection Robot original signal

preprocessing

feature parameters

Fourier transform

discrete cosine transform

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Mel-frequency filter

Log logarithm

Fig. 2. Process diagram of MFCC

(3) Plus windows: the window function can reduce the influence of Gibbs effect; (4) Fast Fourier Transformation (FFT): the time domain signal is converted into frequency spectrum; (5) Triangular window filter: the masking effect of the human ear is simulated by filtering; (6) Logarithmic processing; (7) Discrete Cosine Transformation (DCT): removing the correlation of signals and reducing dimension; (8) Parameter difference: improving system identification performance. 2.2

Endpoint Detection

When the speech signal is collected, it is necessary to do some preprocessing operations in order to get the real speech signal. The analysis of real and effective speech signals can enhance the credibility of the recognition results, otherwise the confidence of the recognition results will be challenged. For a variety of reasons, the collected speech signals to be processed are often mixed with noise, which distorts the original features of the speech. Therefore, the endpoint detection of speech signal is necessary. Nowadays, the relatively common endpoint detection has short-time energy and short-time zero crossing rate. Simple short-time energy or short time zero crossing rate can not accurately detect the speech signal. Therefore, in this paper the method of combining short-time energy and short-time zero-crossing rate is used to detect the end point to extract the effective speech signal, which is called the double-threshold endpoint detection. Suppose that a speech signal in time domain is x, the signal of the nth frame is xn (m) after the frame division, then the short-time energy of xn (m) is represented by En, and the formula is as shown in Formula (2). En ¼

N 1 X m¼0

x2n ðmÞ

ð2Þ

where, N is the frame length. Thus, En can measure the change of signal amplitude value. However, En is more sensitive to high-levels. In order to reduce the sensitivity, the short-term average amplitude function Mn is used as shown in Eq. (3): Mn ¼

N 1 X m¼0

jxn ðmÞj

ð3Þ

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Mn can also represent the size of the speech signal energy, and will not cause a large contrast due to En’s quadratic power [6, 7]. The short-time zero crossing rate is the number of each frame speech signals passing through the horizontal axis (zero level). For continuous signals, “zero crossing” means the signal passes through the time axis; For discrete signals, if the signal crosses the horizontal axis, the sign of adjacent samples changes, that is, “zero crossing”. The short-time zero crossing rate is the number of times of the sample changes sign. The short-time zero crossing rate of the nth speech signal xn (m) (m2 [0, N-1], where N is the frame length) is as shown in Eq. (4). Zn ¼

N 1 1X jsgn½xn ðmÞ  sgn½xn ðm  1Þj 2 m¼0

ð4Þ

Where sgn[] is a symbolic function, as shown in Eq. (5):  sgn½x ¼

1; ðx  0Þ 1; ðx\0Þ

ð5Þ

When the short-time zero crossing rate is used in this paper, the symbols of the two sampling values and the difference between the two sampling values is needed to be judged. If the symbols of two sampling values before and after are different and the difference is greater than the set threshold, it is considered that the short-time zero crossing rate is meaningful. When judging whether a signal is a valid voice or silence, we first analyze the short-time energy and the short-time zero-crossing rate of the data. Firstly, a threshold is set for the short-time energy and the short-time zero-crossing rate respectively. If one of the two parameters of the short-time energy and the short-time zero-crossing rate of the current data exceeds the corresponding threshold, it is considered to enter the voice segment and begin recording from this data. In the detection process of the sampled data, when the values of the two parameters are found to fall below the threshold, then the current speech segment is finished and the recorded data is stopped.

3 Pattern Recognition Dynamic Time Warping (DTW) is one of the classical algorithms in speech recognition [8]. The performance of algorithm is the same as that of HMM algorithm in the small vocabulary isolated word recognition [9]. Because of the complexity of HMM algorithm and the tedious training process, the DTW algorithm is simple and effective, which makes the application of DTW algorithm more specific than the HMM algorithm in particular occasions. Endpoint detection is to determine the starting point and end point of speech, and runs through the DTW algorithm. Each speech signal in the template library is called a reference template, which is represented as R = {R (1), R (2), …, R (m), …, R (M)}, where m is the mth frame of reference template. When m = 1 represents the first frame,

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and m = M represents the last frame, that is, M is the frame number of the template speech, and R (m) is the eigenvector of the mth frame. The speech signal to be recognized is called test template, which is expressed as T = {T(1), T(2), …… T (n), …… T(N)}, where n represents the n frame of the test speech signal. When n = 1 represents the first frame, and n = N represents the last frame, that is, N is the frame number of the test speech, and T(n) is the eigenvector of the nth frame. In order to compare the similarities between them, the Euclidean distance between them (that is D [T, R]) is calculated. A smaller distance means that the similarity between two signals is higher. The traditional DTW algorithm has some limitations on the path, that is, the variation range of the defined bending rate in the matching process is [0.5, 2], so many points cannot be reached. In order to prevent the search range from expanding, the search range can be further limited [10]. In the paper, the improved algorithm search area is shown in Fig. 3. The best path for a given speech to be matched in the sample library should be close to the diagonal BE of the rectangular MBNE in Fig. 3. Therefore, the upper and lower limits can be set around the diagonal, that is, the similarity between the test frame and the reference frame is calculated in the polygon ABCDEF. Among them, the linear AF and linear CD equations such as (6) and (7), C is the degree of linear offset BE, and the experimental sample can be used to determine the value of C. Y1 ¼

M xþC N

ð6Þ

Y2 ¼

M xC N

ð7Þ

Fig. 3. Search region of DTW algorithm

4 Simulation Experiment In order to verify the performance of the algorithm, the improved algorithm is tested under matlab2016a and the simulation experiment is designed. In the experiment, the MFCC cepstrum coefficient is used as the feature of the sample data, and the Euclidean distance is used as the similarity.

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The substation inspection robot is used for recording the basic control command: forward moving, backward moving, turn left, turn right, temperature query, humidity query, weather query, automatic return, open the rolling shutter, open the visible light camera, open the infrared camera, start video. These control commands are used as test samples to test the performance of the algorithm. According to the analysis of the collected samples, the training and recognition process chart is shown in Fig. 4. Among them, the solid line frame is the training part, and the dotted line frame is the recognition part.

speech signal

processing

framing

feature extraction

mathematical model

classifier

speech signal

processing

framing

feature extraction

recognition result

mathematical model

Fig. 4. Speech processing chart

In the experiment process, the algorithm steps are as follows: Step Step Step Step Step Step

1: 2: 3: 4: 5: 6:

sample collection and set up sample database; sample preprocessed; MFCC feature extraction; forming mathematical model; DTW algorithm identification; output the recognition result;

As shown in Fig. 5, the dual-threshold endpoint detection result of a control command is given. Among them, the first line of the picture is the original signal, the second line is short-time energy, and the third line is short-time zero-crossing rate. It can be seen from the figure that the speech is extracted correctly, which provides the guarantee for the subsequent feature extraction. In the experiment, the control command test is carried out for different people, and the test results can be correctly identified. The traditional DTW algorithm and the improvement algorithm were compared in the later experiment. Table 1 is a comparison of algorithm performance. The experimental results show that compared with the traditional DTW algorithm, the improvement algorithm in the paper is better than the traditional algorithms in both time and system recognition rate in the speaker independent speech recognition system. The algorithm in the paper can be applied to the substation robot because it improves the speed and accuracy of the speech recognition system, and can meet the real-time control of the substation inspection robot.

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Fig. 5. The dual-threshold endpoint detection Table 1. Comparison of algorithms performance Traditional DTW algorithm The improvement algorithm Time (s) 1.2 0.4 Accuracy (%) 86.4 94.5

5 Conclusion In this paper, the improved DTW algorithm is used to extract the MFCC features of speech signals and realize the speaker independent speech recognition in substation environment. Experimental results show that the algorithm has the advantages of short recognition time, high correct rate, stable performance and strong robustness. The algorithm is applied to the substation inspection robot, which can control the movement of the inspection robot in real time. By the way of human-computer interaction, the robot issued relevant voice commands that allow the robot to complete the corresponding instructions of the operation, the human-computer interactive way to simplify the work flow, improve work efficiency, which truly plays an important role in the effect of “what to say and what to do”. The realization of the algorithm not only increases the function of the substation robot, but also promotes the pace of substation intelligent management, which fully meets the automatic detection and identification requirements of unattended automatic detection and identification requirements of the intelligent substation. Acknowledgments. This work was supported by application research and development of intelligent new technology for substation inspection robot (item serial number: ZY201815). Thank you for your support of the paper.

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References 1. Gao, S.N., Kong, L.F.: Deep information recognition of speech instruction of family service robot. J. Chin. Comput. Syst. 36(6), 1347–1352 (2015) 2. Hu, Y.Z., Wang, X.M., Cao, J.T.: Study of voice command recognition system for robot based on improved DTW. Comput. Technol. Dev. 23(7), 70–76 (2013) 3. Zuo, X.C., Han, L.L., Zhuang, J., Shi, Q.Q.: Design of human-robot interaction system for space robot using robot operating system. Comput. Eng. Des. 12, 3370–3374 (2015) 4. Li, H., Xu, X.L., Wu, G.X., Ding, C.Y.: Research on speech emotion feature extraction based on MFCC. J. Electron. Meas. Instrum. 31(3), 448–453 (2017) 5. Yin, R.X., Cheng, J.J.: Improved feature extraction algorithm based on DWT-MFCC. Mod. Electron. Tech. 40(9), 18–21 (2017) 6. Zhao, L.: Speech Signal Processing, vol. 2, pp. 37–38. China Machine Press, Beijing (2011) 7. Lv, X.Y., Wang, H.X.: Abnormal audio recognition algorithm based on MFCC and shortterm energy. J. Comput. Appl. 30(3), 798–799 (2010) 8. Wang, H.L., Cui, R.Y.: Discussion of improved DTW algorithm in speech recognition. Korean Lang. Inf. Sci. 11(2), 106–111 (2009) 9. Cui, J.Z., Zhou, Y.B., Chen, L.T.: Implementation and optimization of embedded speech recognition system based on DHMM. J. Univ. Electron. Sci. Technol. China 6, 930–934 (2013) 10. Fan, B.H., Lu, F., Wang, X.: Implementation of speaker-independent speech recognition system on intelligent prosthetics arm. Comput. Eng. Des. 38(6), 1630–1634 (2017)

The Design of High Accuracy Pedometer Based on an Adaptive and Dynamic Low-Pass Filtering Algorithm Deng Xu1,2(&) and Baohua Yang1,2 1

Jiangsu Internet of Things and Manufacturing Information Engineering Research Center, Changzhou 213164, Jiangsu Province, China [email protected] 2 Department of Information Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, Jiangsu Province, China

Abstract. An electrical pedometer system which based on the processor EFM32LG330F128 and 3-axis accelerometer LIS3DH is designed. A model is established to process the data which is detected by a 3-axis accelerometer LIS3DH in Matlab. The causes of errors of slow step-counting is analyzed. An adaptive and dynamic low-pass filtering algorithm is presented, also simulated by Matlab. Finally the field tests are to be carried out, which show that the pedometer has the advantages of good reliability, good adaptability, strong antiinterference ability, and high accuracy. Keywords: Pedometer High accuracy


A 3-axis accelerometer LIS3DH
Low-pass filtering

1 Introduction In recent years, with the development of microelectronics and sensor technology, the application of motion sensing intelligent hardware system is becoming the current research hotspot [1–3]. The wearable electronic pedometer, which is a typical application product of the technology, has important research significance in motion analysis, identification and indoor positioning, etc. [4–6]. The step counting can be realized by measuring the acceleration information of human motion after collecting and processing the motion data [6, 7]. After a large number of experimental tests, the results shows that there are some problems in the slow pace adaptation of the electronic pedometer at present, including the step precision, and the step lose or error. The reason is that the consistency of the body movement law is poor and the signal of the acceleration sensor is relatively weak. Velocity noise can easily annihilate the real step frequency component. Therefore, it is an important topic to establish a reliable walking model and find a high precision and high anti-interference step algorithm in the field of motion sensor step. A low power and high precision electronic pedometer system is designed, by adopting the Energy Micro 32 bit processor EFM32LG330F128 as the control core, ST three axis acceleration LIS3DH as a three axis waveform acquisition sensor. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 55–62, 2019. https://doi.org/10.1007/978-3-030-02804-6_7

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By modeling and processing the slow step acceleration data collected at the wrist by Matlab, and analyzing the reasons of the slow pace matching accuracy, a step algorithm based on adaptive low pass dynamic filtering is proposed, simulated and tested. Finally, the accuracy of the pedometer system is tested. The step function of different step frequency (especially slow walk) of step counting is realized and its accuracy is improved.

2 System Principle It is shown in Fig. 1 that the block diagram of the pedometer hardware system is composed by five modules, including the processor, the display screen, the acceleration sensor, the lithium battery, and the memory module. The Energy Micro’s 32 bit ultralow power processor EFM32LG330F128 based on the ARM Cortex-M3 kernel core is used as the processor of control modules. The processor is very suitable for the high performance and low power consumption mobile device system with battery power supply, with advantages of strong low power technology, rich peripherals and communication interfaces (SPI/I2C/USB2.0). The ST three axis acceleration sensor LIS3DH is used as the core collection module to collect real-time acceleration of human walking acceleration. The control information writing to the LIS3DH or the acceleration information reading operation is carried out by the processor EFM32LG330F128 which is through the SPI or I2C interface. At the same time, the data is modeled and processed to obtain the accurate step value by the processor. The OLED display screen is mainly used for real-time display of data and interface. Lithium battery is used for mobile power supply. The data produced in the process of software control can be stored and backup with Flash memory.

Lithium battery

OLED screen Processor EFM32LG 330F128 Acceleration sensor LIS3DH

Flash memory

Fig. 1. System hardware schematic diagram

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3 Waveform Acquisition and Synthesis The electronic pedometer can be wearied at the wrist, the chest, the ankle and the leg, and the wrist is the first position as people’s habit. However, because of the complexity of its own activities and the random action elements such as non-walking and other factors, the acceleration waveform is often the most complex. In order to improve the adaptability and versatility of the technology, this article chooses wrist as a place to wear. It is a widely used gait analysis method to extract human walking characteristic parameters which is gait acceleration analysis. The walking motion consists of 3 components, namely the forward (X axis), the lateral (Y axis) and the vertical (Z axis). The step counting can be realized with the vertical movement of the center of gravity, because the vertical acceleration of the Z axis is the most obvious. However, considering the arbitrariness of the wrist wearing position, the data of at least one axis has a larger periodic acceleration change, so the acceleration value of the single axis is uses to represent the human motion in some literature [6, 7]. By comparing the size of the three axis acceleration data in real time, the axis is recorded as the effective axis which has the largest acceleration. Then, the effective axis data is adopted to analyze and judge. But for the wrist application, the hand and wrist are turned over in the process of motion continually, and the effective axis of acceleration may be changed constantly. The data is too sensitive and easy to lose counting points, and the stability is relatively poor. In order not to be affected by the pedometer wear direction, the three axis acceleration values X, Y and Z detected by the speed sensor are integrated as a whole. The following two modeling methods are used for the X, Y and Z waveforms: One is to calculate the modulus sum of the three axis waveform, the 1- norm, which is A, that is A ¼ jXj þ jYj þ jZj The other is to calculate the square root of the energy sum of three axis waveform, the 2-norm, which is B, that is B¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X2 þ Y2 þ Z2

Combined with the characteristics of limb acceleration in human action, the sampling frequency of LIS3DH’s three axis accelerometer is selected 25 Hz, and the range is chosen to be ±4 g. LIS3DH’s X, Y, and Z three axis waveforms, as shown in Fig. 2, are collected by microprocessors for slow walk, and the noise is filtered out by average filtering, because of the noise. In order to compare the above two modeling effects, an accelerometer signal of an adult walking slowly was collected, and 11 steps were taken in 12 s, that is, the step frequency was 0.91 Hz.

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Fig. 2. Accelerometer signal at slow walking

4 Waveform Analysis As shown in Fig. 3, although the waveforms of A and B are different in magnitude, their variation trends and the waveform characteristics are basically the same. The spectrum analysis of the A and B waveforms shown in Fig. 1 shows that: the step frequency in the A waveform is near 1.82, and the peak value of the actual step frequency is not obvious at about 0.91. It is not prominent in the background noise and almost completely annihilated by the noise. The main reason is that the consistency of the wrist movement in the slow step is poor, and the acceleration sensor is confused, so the A waveform processing method is the main reason. It cannot reflect the actual walking frequency at slow walking time. In the B waveform, there are 0.91 frequency components and 1.82 frequency components, which obviously contain the true step frequency components, so the synthetic modeling method of B waveform has better anti-interference ability and can objectively reflect the real step frequency of the slow walk.

Fig. 3. Spectrum analysis of A and B waveforms

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The B waveform is a typical slow walking wrist acceleration waveform. After a number of multiple step experiments and analysis of its spectrum, it is found that the B waveform also has a frequency multiplier component outside the direct current component and the true step frequency component. In addition to the direct current component, there are two main frequency components 0.91 Hz and 1.82 Hz: the former is the real frequency caused by human walking. The latter is caused by the value of accelerometer LIS3DH when its measured value returned to 1 g. When people walk slowly, their body will pause slightly, then the accelerometer will return the initial 1 g value [3, 7]. And when people walk faster, their body movements are consistent, there is no obvious pause, and accelerometer has not enough time to return to 1 g value, the walking wave waveform is sine wave. The sine wave’s step analysis is much simpler, and the general step counting algorithm can be handled well. After a large number of tests, it is found that, the remaining high amplitude frequency component is the effective step frequency, in addition to the DC component and the noise in the acceleration waveform, when the step frequency reaches 1.8 Hz. According to the above analysis, as long as the high frequency noise component of the slow walk and the frequency component caused by the accelerometer self-regressive 1 g are filtered out, the waveform similar to the fast walking can be obtained, and the precise walking can be realized.

5 Calculation and Simulation Firstly, the three axis signal of the accelerometer is filtered and combined; secondly, the FFT transformation of the B waveform is carried out; finally, the first high amplitude frequency component of the non-direct current is the true step frequency, which is recorded as F0. The Kaiser window is used to intercept a certain length of the ideal low pass filter HD, which is used to get better sine wave for step, owing to the high frequency component of three axis accelerometer data and some high frequency noise. The h is Matlab simulation of Kaiser Windows low pass filter system response, the code is as follows:

W0=F0/Fs*2*pi; W1=floor(W0*100)/100; W2=floor(W0*100)/100; [M,Wc,beta,ftpye]=kaiserord([W1/pi W2/pi],[1 0],[R1R2]); N=M+1; Wn=kaiser(N,beta); nn=[0:1:N-1]; alfa=(N-1)/2; hd=sin(Wc*pi*(nn-alfa))./(pi*(nn-alfa)); h=hd.*Wn; The passband boundary frequency W1 is set to F0, the stop band boundary frequency W2 is set to 2F0, the fluctuation R1 in the passband is set to 0.05, and the fluctuation R2 in the stopband is set to 0.005. For the B waveform, the true step

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frequency is F0 = 0.91 Hz, and the sampling frequency is Fs = 25 Hz. The amplitude frequency response characteristics of the corresponding H is shown in Fig. 4, it does not only filter the frequency multiplier produced by the acceleration autoregressive 1 g, but also filter out the high frequency components after the frequency doubling, which are suitable for the waves of walking and fast walking.

Fig. 4. The amplitude frequency response characteristics of the low pass filter

Figure 5 shows the contrast of the waveform before and after the low pass filter. Through the low pass filter of Kaiser Window, the frequency multiplier high frequency components in the B waveform are filtered, and only the C waveform of the actual step frequency component is left.

Fig. 5. Waveform comparison before and after low pass filtering

As shown in Fig. 5, the p-p (peak to peak) value of the B waveform is judged firstly. If the value is higher than a certain value, the acceleration value can be used to analyze the step count. Then the C waveform cycle is T1 and the p-p value is R1.

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Add the time window to T1 and judge the peak value window of R1, if T1 and R1 are in the window scope, a step is added. At the same time, it is analyzed whether the frequency error between the average frequency of the current waveform and the former is more than a certain range. If it is over the range, FFT should be done again, the new F0 and the new lowpass filter unit impulse response h should be recalculate. In this way, dynamic adaptive filtering is formed, and the accurate steps can be realized for slow walking and fast walking. Since filtering is associated with many previous sampling points, a cache count Nc is set up to store the preceding sampling data, and the cache length is related to Nc. When walking slowly, the walking frequency is between 0.7 and 1.5. Assuming that F0 is between 0.7 and 1.8, the maximum Nc is 90. For the sampling frequency of 25 Hz in this paper, the number of sampling points is set to 100, which is equivalent to have no counting steps at the beginning 4 s per time, and then a filter and step is done every second, thus the precise step value can be obtained.

6 Test and Result On the basis of the above analysis, 10 men and 10 women are selected as ten groups for the test, the tests content are as follows: 400 steps for walking, 400 steps for normal walking and 400 steps for running. The three axis accelerometer is worn on the wrist. In order to further verify the reliability and accuracy of the system, the wrist is deliberately reversed during the test. The result of the test is Table 1. Table 1. Test of slow walking, normal walking and running for men and women Sex

Type

Male

Slow walking (3 km/h) Normal walking (4 km/h) Running (8 km/h) Slow walking (3 km/h) Normal walking (4 km/h) Running (8 km/h)

Female

Actual step number 400

Mean absolute value error 2.5

Average error rate 0.63%

400

1.7

0.43%

400 400

1.0 2.7

0.25% 0.68%

400

2.0

0.50%

400

0.7

0.18%

From the test data shown in Table 1, it is known that after modeling and data processing, the step system has a higher precision of step test for different sexes and different walking methods. Especially for the slow walking, its accuracy has reached over 99%.

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7 Conclusion Based on the EFM32LG330F128 processor and the acceleration sensor LIS3DH, a low power and high precision electronic pedometer system is designed. By modeling the three axis acceleration data collected by the LIS3DH and adaptive low pass dynamic filtering, the slow step interference is overcome, the high frequency component and the multiplier frequency component of the slow walk is eliminated effectively. The actual test results show that the system has good adaptability, strong anti-interference ability, high precision and wide application prospect for different step frequency, especially slow step. Acknowledgments. This work was supported by the National Natural Science Fund of China under Grant 61604040, the high level backbone professional construction project for the higher vocational education of Jiangsu Province China and key project of Changzhou Higher Vocational Education Research Institute CDGZ2016008.

References 1. Husted, H.M., Llewellyn, T.L.: The accuracy of pedometers in measuring walking steps on a treadmill in college students. Int. J. Exerc. Sci. 10(1), 146–153 (2017) 2. Lu, Y., Velipasalar, S.: Autonomous footstep counting and traveled distance calculation by mobile devices incorporating camera and accelerometer data. IEEE Sens. J. PP(99), 1 (2017) 3. O’Neill, B., Mcdonough, S.M., Wilson, J.J., et al.: Comparing accelerometer, pedometer and a questionnaire for measuring physical activity in bronchiectasis: a validity and feasibility study. Respir. Res. 18(1), 16 (2017) 4. Koring, M., Parschau, L., Lange, D., et al.: Preparing for physical activity: pedometer acquisition as a self-regulatory strategy. Appl. Psychol. Health Well-being 5(1), 136–147 (2013) 5. Leung, W., Ashton, T., Kolt, G.S., et al.: Cost-effectiveness of pedometer-based versus timebased green prescriptions: the healthy steps study. Aust. J. Primary Health 18(3), 204–211 (2012) 6. Zhong, S., Wang, L., Bernardos, A.M., et al.: An accurate and adaptive pedometer integrated in mobile health application. In: IET International Conference on Wireless Sensor Network. IET-WSN. IET, 2011, pp. 78–83 (2010) 7. Bravata, D.M., Smith-Spangler, C., Vandana, S., et al.: Using pedometers to increase physical activity and improve health: a systematic review. Chicago: JAMA 298(19), 2296–2304 (2007)

Research on Multi-sensor and Multi-target Data Association Problem Fu Shuai(&) College of Humanities & Sciences of Northeast Normal University, Changchun 130117, China [email protected]

Abstract. For problems on multi-target data association, the shortest route in the multi-target data association has been redefined on the basis of Ant Colony Algorithm, which is combined with Genetic Algorithm. Genetic Algorithm has been used to direct pheromone variation of ant colony, to have it convergent more quickly. Finally, a large number of experiments have been done to prove effectiveness of algorithms. Keywords: Ant colony algorithm Pheromone variation


Multi-target data association

1 Introduction Development of flight simulators technology has been getting closer and closer to actual combat levels since the new century. Corresponding supporting training mechanism shall also been established for flight simulators with practice of such subjects as early warning airplane and multi-airplane formation combat exercise, where the multi-target tracking technology is a problem that shall be firstly solved during wartime warning and formation flying. In the early air combat, many practical means, such as node radar network, electromagnetic interference and thermal disturbance, have been put into practice on consideration of both tracking and anti-tracking. Clear target position of both sides can be provided for pilot at any time with developing of early warning airplane and ground command system, to precise guide. Therefore, ground console module will be needed in flight simulator for corresponding combat command simulation settings. For ground console module, the main function is to set a dynamic false target for training subjects and it is required to have targets of both sides distinguished and tracked. It is required to identify real observation target and interference noise under the simulation of complex battlefield. This paper has done some research on multi-sensor and multitarget association technology according to demand for flight simulators. In this paper, it will combine Ant Colony Algorithm with Genetic Algorithm on consideration of problems of multi-target data association, and define the shortest route in multi-target data association based on Ant Colony Algorithm. Genetic algorithm has been combined to direct pheromone variation of ant colony, to have it convergent sooner. Finally, a large number of experiments have been done to prove effectiveness of algorithms. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 63–73, 2019. https://doi.org/10.1007/978-3-030-02804-6_8

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2 Ant Colony Algorithm and Its Application Principle 2.1

Ant Colony Algorithm

Ant Colony Algorithm 6,2 (ACA) was firstly proposed by Dorig M and his colleagues in 1991. Extensive research into Ant Colony Algorithm had not been done in academic circles during the initial stage of proposition of algorithms. The mathematical model [3] and basic principle of Ant Colony Algorithm had been systematically expounded on IEEE by Dorig M in 1996, with corresponding algorithms to such classical problems as TSP problem, assignment problem and workshop scheduling problem. It also had Ant Colony Algorithm compared with such classical algorithms as Genetic Algorithm, Simulated Annealing Algorithm and Hill-climbing Method, which had laid a solid foundation for rapid development of Ant Colony Algorithm in the next few years. When an ant searching for food in the natural world may release a kind of special secretion, which is pheromone. When encountering strange paths, the ant may randomly select a route, and release pheromone that are related with route length, with concentration of pheromone inversely proportional to length of route. So when next ants select from routes, they will select in a certain probability according to amount of information. If large concentration of pheromone in a certain route, more ants will be attracted to select this route, which is the positive feedback mechanism of Ant Colony Algorithm. A large number of ants will be attracted to select an optimal path with time, while pheromone will continue to decline for paths that are selected by no ant or small amount of ants. From a large number of experiments, scholars still find that ant colony is of good cooperation capacity. If there are obstacles in the paths that are for individual ant, individual ant within population will have pheromone as a communication medium, and communicate for cooperation according to guidance of pheromone, which reflects self-organization characteristic of population. Exchange of path information can be done among ant individuals, and optimal path can be found through cooperation. Search principle of ant colony is as follows: E

d=1

A

E

d=1

B

15

C

D 30

30 d=0.5

d=0.5

A

E

15

B

10

C

D 30

30 15

15

A

10

B

C

D 30

30 20

20

F

F

F

(a)

(b)

(c)

Fig. 1. Simulation diagram on searching for food of ants

In Fig. 1, A is a starting point for each ant individual, namely ant nest; D is food source of destination, and strip shadow between EF represents obstacles. In the process of searching for food, ants have to select the path from A to destination D via E or F, with distance between points indicated in Fig. 1(a). Providing there are 30 ants leaving A for D in unit time with pheromone that is left by each ant of one and decrease rate of

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65

pheromone with time is also one. In the initial stage, there is not any information for route BF, FC, BE, EC, with equal probability for ants to randomly select two paths, that are to select path BF-FC and BE-EC, as is shown in Fig. 1(b). After a unit of time, for pheromone that is left by ants is inversely proportional to path length, the pheromone for path BFC will be twice as that for BEC. There are 20 ants selecting path BFC over a period of time, as is shown in Fig. 1(c), where the positive feedback mechanisms have been formed and it will be more and more ants selecting path BFC, with an optimal path found by ant colony at last. 2.2

Classic TSP Problems

The first time that Ant Colony Algorithm is successfully applied to solve TSP problems is when shortest distance is to be resolved in the circumstance that it will be no more than once for a traveler to visit all the cities with number of cities of n, and is required to return to original position. Assume that bi ðtÞ represents number of ants on the element i at the time of t, sij ðtÞ represents the amount of information on the route of ði; jÞ for the time of t, n represents scale of TSP (number of cities), m represents ant colony size, that is number of n P bi ðtÞ; C ¼ fsij ðtÞjci ; cj  Cg will be pheromone of path lij individual ant, m ¼ i¼1

between elements of set C (which represents city i and city j) at the time of t. In the initial stage of application of this algorithm, initial value of information on each path will be zero, and assume that sij ð0Þ ¼ const, with the whole optimization process achieved through directed graph of g ¼ ðC; L; CÞ. In the process of movement, the ant kðk ¼ 1; 2; . . .. . .; mÞ selects a direction for further advancement according to concentration of pheromone on each path. For Ant Colony Algorithm, node for individual k will be recorded through tabu table tabuk , with general record of tabuk ðk ¼ 1; 2; . . .; mÞ. The tabu table tabuk may continually change with nodes that are traversed by ants, and ant individual obtains corresponding transition probability according to pheromone and heuristic information, generally probability of transition from node i to j of ant k at the time of t is described as pkij ðtÞ. pkij ðtÞ ¼

8 ½sij ðtÞa ½gik ðtÞb  < P a : x2allowed; 0;

½six ðtÞ ½gix ðtÞb

; if j 2 allowedk othrewise

Where, allowedk ¼ fC  tabuk g represents city set that can be selected for ant individual k, a represents information heuristic factor, which indicates concentration of pheromone accumulated on the path. The greater value of a, the greater the tendency for subsequent ants to select this path, a will play a decisive role on cooperation of whole population; b represents expected heuristic factor, which mainly represents degree of attention to heuristic information on path selection by ant individual in the process of movement. The greater the value of b is, the closer the transition probability

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to greedy rule under this condition will be. In short, a represents influence degree that pheromone has caused to ant individual; b represents adopting degree that ant individual is to pheromone. gij ðtÞ represents heuristic function: gij ðtÞ ¼

1 dij

Where, dij represents linear distance between city i and city j, from heuristic function gij ðtÞ, we can conclude that for ant individual k, values of gij ðtÞ and pkij ðtÞ are inversely proportional to dij , the smaller dij is, the larger gij ðtÞ and pkij ðtÞ will be. Therefore, heuristic function gij ðtÞ represents transition probability and degree of expectation for ant k from city i to city j. In the initial stage of application of this algorithm, there will be accumulation of pheromone on each path. All the paths will be returned to original condition after each iteration. The strategy to update information is a kind of simulation of human brain memory. When there comes constantly new information, the original old information stored in the brain will be gradually decreased or even disappeared. Therefore, definition update to pheromone for Ant Colony Algorithm is as follows with adjustment rule for pheromone on the path of ði; jÞ at the time of t þ n: sij ðt þ nÞ ¼ ð1  qÞ  sij ðtÞ þ Dsij ðtÞ Dsij ðtÞ ¼

m X

Dskij ðtÞ

k¼1

Where, q represents volatility coefficient of pheromone, 1  q represents remaining part of pheromone. Value scope of q is set as q  ½0; 1Þ, Dsij ðtÞ represents pheromone increment on the path of ði; jÞ in an iterative process usually in order to prevent unlimited growth of pheromone. When t is equal to zero, Dsij ð0Þ ¼ 0 where Dskij ðtÞ represents pheromone left by ant k after taking the path of ði; jÞ. Generally speaking, when solving different practical problems, different pheromone may be selected to update strategies, where the typical example is such models as Ant  Cycle, Ant  Quantity and Ant  Density proposed by Dorigo M, namely ant-tocycle model, ant-to-quantity model and ant-to-density model, with main difference of such three models is difference for Dskij ðtÞ. For Ant  Cycle model, Dskij ðtÞ

¼

Q

Lk

0;

; if ant k has taken the path of ði, jÞ in this circle otherwise

Where, Q represents intensity of pheromone, the greater value of Q it is, the more quickly the algorithm convergence will be, with effect to final result, Lk represents total path length in an iterative process for ant k.

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67

For Ant  Quantity model, Dskij ðtÞ

¼

Q

dij

;

0;

if ant k has taken path of ði; jÞ during the period between t and t þ 1 otherwise

For Ant  Density model, Dskij ðtÞ ¼



Q; 0;

if ant k has taken path of ði; jÞ during the period between t and t þ 1 otherwise

The pheromone model for both Ant  Quantity and Ant  Density model belongs to local increment, that is pheromone update will be done after selection in each step for ant individual, while Ant  Cycle belongs to global pheromone increment, that is unified update for pheromone on the path that is taken by ants after all the nodes for a circle have been iterated by ants. This kind of model can be of good performance in solving TSP problem. Therefore, there are numerous studies regarding Ant  Cycle model as a basic ant colony algorithm model.

3 Occurrence to Multi-target and Multi-sensor Data Association Problems Association problems referred in this paper is part of data fusion process. In the process of track association, echo for observation within associated area shall be firstly obtained and clutter filtering will be done through a flurry of electronic information processing (such as filtration), then correspondence between target data and echo for observation has been established. Finally, target track may be updated. The main track association in the early stage is through electronic means, with the process shown shown in (Fig. 2):

Fig. 2. Schematic diagram for multiple maneuvering target

In the whole Multiple Maneuvering Target Tracking System, multi-target measurement shall be firstly obtained through sensors, with clutter removed through threshold filtering, and then the most reasonable track association for measurement will be determined through data association, where the red box part is the core content of this paper.

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Uncertainty of data sources will be a difficult problem in target tracking area; that is data association problem. Generally speaking, there will be some errors for target observation through sensor measurement, with main reason of lack of prior knowledge about target. Secondly, in actual combat, some target jamming methods will be adopted for all kinds of aircraft to prevent from electromagnetic or infrared tracking. Therefore, for effective tracking, the detection threshold of such kind of target sensor [4] may be set as a lower value, which will cause a lot of false targets mixed in the measurement data while improvement of coverage of target. There will be of no one-to-one correspondence between target value and measurement value, also resulting in uncertainty of correspondence between target value and measurement value. The problems to be solved for data association is to determine correspondence between measurement value or track and target value, that is to determine which target or clutter is for a certain measurement and which target is related with it, with a focus on the study in target tracking area. For the above TSP basic model for Ant Colony Algorithm, it can be extended into models for multi-target tracking. The main research contents for multi-target tracking is to establish correspondence between measurement value and target value, to determine original target for each measurement data, and to adopt data fusion method for post processing of data, to obtain high precision fitting of parameters of target orbits, with basic process shown in Fig. 3. Therefore, both measurement value and target value may be used for TSP model:

Fig. 3. Basic process for data association

4 Bionic Algorithm 4.1

Path Size

In TSP issue, path between cities can be indicated as distance dxy . For instance, total distance of the shortest path exists among City A, City B, City C and City D, path ACDBA, can be calculated as: d ¼ dAC þ dCD þ dDB þ dBA

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69

Following assumptions concerns data association are found in Archiv [5]: 1. For observation resource, one observation only generated from target or clutter. 2. Target defines uniqueness of the observation; it means that one target may only generate one observation or no observation at a fixed timing. Therefore, path size may be redefined as following: Definition 1. To an individual ant r, effective approaches refer to all track-observation pairs explored by ant r during a circle. Correspondingly, total path size explored by ant r equals to total distance of all track-observation pairs. 4.2

Individual Ant

Meanwhile, ants in colony possess have the following characteristics: 1. Ant r left pheromone sij on each observation points when selecting a trackobservation pair, value of the pheromone may increase with development of trackobservation pairs and decrease with interaction and time. 2. Probability of track-observation pair establishment is function of visibility gij when ant selecting corresponding observations on tracks. 3. One target can only be associated with only one observation or no observation. 4. In the path creation process, ant r can associate target with observations that have not been chosen before. 5. Threshold setting, tracks shall not be associated to observations with distance larger than the threshold (Initial threshold value is infinite, and will be calibrated consistently with inetration). 4.3

Pheromone Model

As for pheromone model, si ðtÞ indicates pheromone value observed from observation i at t time, and divided into two parts of pheromone remained by ants passing by i and other observations within area i, superposition generated by ants passed by can be expressed as formula below: X si ðtÞ ¼ di ðtÞ þ Dk ðtÞ ð1Þ k2M;k6¼i

The M thereinto refers to observations in i field in neighborhood, ant k (1  K  m) will select association randomly in accordance with pheromone remained on observation when selecting corresponding observation for tracks. Associations from track i to observation j selected by each ant can be worked out by the formula below. ( j¼

arg maxf½siu ðtÞ  ½giu b g; if q\q0 J;

u2Uk

otherwise

ð2Þ

70

F. Shuai

The Uk thereinto refers to optional observations for ant k to choose for the present track point in this association, giu refers to visibility (inspiration information) of observation aiu , commonly giu ¼ 1=diu ; q0 refers to initial set parameter, q is a randomly sampled number and q0 , q2[0, 1]; J is a random variable, probability of ant k associating track i to observation j may be solved out by the formula below. 8 a b > ½sij ðtÞ ½gij 

k : 0; otherwise

ð3Þ

The a and b thereinto refer to effect weight of remained information and inspiration information in observation on ants selecting direction. In simple words, a refers to pheromone’s affection degree on individual ants, b refers to individual ants’ acceptance degree toward pheromone. 4.4

Implementation of Algorithm

1. Overview algorithm flow chart Assume there are m ants and n tracks, Rw and Rs respectively refers to unselected track sets and selected track sets, Rw equals to set of all tracks under initial condition, Rs is empty, algorithm process is as shown in Fig. 4. Before going through all tracks, ants in colony select one track point from the unselected track set, and select corresponding observation in compliance with moving rules, i.e. inspiration function. Then, the selected tracks shall be added into selected track set and update pheromone amount on other observation points in the neighborhood. When all ants have finished one path selection, according to updating threshold with kDmax , take k as constant quantity, the algorithm will abandon track points with small probability of being associated or no corresponding observation found in threshold range as clutter or noise. If the preset updated generation amount of the colony is met, new individual may be generated by overlapping mutation principle, and will replace individuals exist in the colony by roulette selection strategy. 2. Multi- target association simulation In experiment of multi-target association simulation and comparison, this paper prior performs a five-target tracking simulation experiment, assume detection probability in the five-target tracking experiment as PD ¼ 0:9, clutter density complies to Poisson distribution, inspiration function is formula (1), the result is as shown in Figs. 5 and 6. The sign of * thereinto refers to track observation obtained by detector, o refers to target location, Fig. 5 includes more clutter, data association after excluding most clutter with threshold and processing by AC-GADA algorithm is as shown in Fig. 5.

Research on Multi-sensor and Multi-target Data Association Problem

Fig. 4. Basic processes of data association

Fig. 5. Original data with clutter included

71

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F. Shuai

Fig. 6. Result of five-target data association

Toward target’s six degree of freedom dynamics in three-dimensional space, when only transverse and longitudinal movement are taken into consideration, lifting, dropping, yaw movement, roll movement and pitching movement are all ignored, i.e. movements are only consisted of lateral displacement and vertical displacement. In the experiment, time span of observation sample are set as 1, system noise and observed noised shall be taken as Gaussian distribution with average value equals to 0, association result of overlapping situation of two targets is as shown as Fig. 7. Thereinto, o refers to predicted location, i.e. track, * refers to obtained value of detectors, X axis and Y axis respectively refers to vehicle’s lateral and vertical movement displacement, both two targets move at uniform speed and turn angles are both smaller than 2°. By integrating Figs. 6 and 7, it is obvious that AC-GADA algorithm is able to correctly perform association of moving targets.

Fig. 7. Three degree of freedom dynamics on horizontal plane

5 Summary This paper focuses on research toward multi-target sensor multi-target tracking technology, and proposes multi-target data association algorithm AC-GADA based on ant colony- genetic algorithm. By redefining ant colony algorithm in TSP model by integrating actual problems with multi-target data association, compromising characteristics of genetic algorithm on the base of ant colony algorithm, and randomly allocating amount of carried pheromone on each individuals in compliance with colony difference principle, this algorithm applies overlapping mutation process of genetic algorithm on ant colony, and reach the goal of accelerating algorithm convergence by controlling acceptance degree of the colony with minimum born amount.

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References 1. Colorni, A., Dorigo, M., Maniezzo, V., et al.: Distributed optimization by ant colonies. In: Proceedings of the 1st European Conference on Artificial Life, pp. 134–142 (1991) 2. Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Department of Electronis, Politecnico diMilano, Italy (1992) 3. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996) 4. Li, K., Weixin, X., Jingxiong, H.: ACA based data association method for multi-target tracking. Acta Electronica Sinia 36(3), 586–589 (2008) 5. Hongfeng, X., Guanzheng, T.: Hybrid ant colony algorithm based on genetic algorithm. Comput. Eng. Appl. 44(16), 42–45 (2008)

A Detecting System for Wheel Balancer Based on the Effect Coefficient Method Honghui Zhang1(&) and Wanli Zhang2 1

2

School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523106, China [email protected] School of Mechanical Engineering, Tsinghua University, Beijing 100084, China

Abstract. According to the characteristics of the wheel balancer, it analyzed the balance principle of the effect coefficient method, and displayed the overall design scheme and software flow of the system. Through different specifications of the wheel detection, the balance effect is satisfactory. Keywords: Wheels


Dynamic balance
Effect coefficient method

1 Introduction When the car is running, the vibration of the car body caused by the imbalance of the wheels not only aggravates the wear of the tire surface, reducing the service life of the transmission system, but also affects the comfort and stability of the ride and in severe cases, it even causes a tire burst or rollover accident. The wheel balancer is a device that measures the imbalanced amount of car wheels and indicates the position of the imbalanced amount. The operator balances the wheels by compensating the corresponding counterweight at the designated position. It is an essential equipment for auto repair shops, car tire stores and car tire factories. At present, there is no self-developed auto tire balancing machine in China. Existing manufacturers rely on plagiarizing early foreign product technologies. The wheel balancer produced are not only old-fashioned with single-function and lowprecision, but also have no place on the international stage. With the improvement of highway conditions and the increase of vehicle speed, it is increasingly important to develop wheel balancer with independent intellectual property rights.

2 The Balance Principle of Effect Coefficient Method If using m weighted planes to eliminate the vibration of the n detecting points, the rotor *

*

can be started to the selected equilibrium velocity and the original vibrations S10 , S20 ,… *

Sn0 can be measured at each detecting point. Their relationship with the original *

*

*

imbalance U 10 , U 20 ,… U m0 are: © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 74–80, 2019. https://doi.org/10.1007/978-3-030-02804-6_9

A Detecting System for Wheel Balancer

75

3 2* 3 U


~ a1m 6 * 10 7 * 6


a2m 7 7 6 U 20 7 6 . 7 .. 7 7 . 5 4 .. 5 * *


anm U m0

2

3 2* * ~ S10 a11 a12 6 *S 7 6 *a a22 6 20 7 6 21 ~ 6 . 7¼6 . .. 4 .. 5 4 .. . * * * an1 an2 Sn0

ð1Þ

h* i h ih * i * Abbreviated as S0 ¼ a U 0

ð2Þ

h i * In the formula a is called Effect Coefficient Matrix Then add a known trial weight to the equilibrium plane I, and its mass-radius *

product is U , starting the rotor to the selected equilibrium speed, and measure the *

*

*

vibration values S11 , S21 ,… Sn1 of each points, thus: 2

3 2* * ~ S11 a11 a12 * 6 S 7 6 *a a22 6 21 7 6 21 ~ 6 . 7¼6 . .. . 4 .. 5 4 . . * * * a a Sn1 n1 n2

3 3 2* * U 10 þ U


~ a1m 6 * 7 *


a2m 7 U 20 7 7 6 6 7 6 .. 7 7 .. 5 . 5 4 . * *


anm U m0

ð3Þ

Subtract (1) from (1) to get: 3 2 2* * * S11  ~ S10 a a12 7 6 *11 6* * 6 S21  S20 7 6 a21 ~ a22 7¼6 . 6 .. 7 4 . 6 .. 5 4 . . . * * * * a a n1 n2 Sn1  Sn0

3 2*3


~ a1m U * 6 7


a2m 7 7 607 6 . 7 .. 7 . 5 4 .. 5




*

anm

ð4Þ

0

Thus obtained: * *

a11 ¼

*

S11  S10 *

U * *

a21 ¼

*

S21  S20 *

U * *

an1 ¼

*

Sn1  Sn0 *

U *

*

Similarly, in plane II, the trial weight with the mass-radius product being U , a12 , * * a22 ,… an2 can be obtained. All the effect coefficients can be obtained by adding m trial weights to m planes.

76

H. Zhang and W. Zhang

Once the effect coefficients are known, they can be substituted into the following *

*

*

equation to solve the amount of balance that should be added: U 1 , U 2 ,… U m . 2* * a11 a12 6 *a a22 6 21 ~ 6 . .. 4 .. . * * an1 an2

3 2* 3 2 3 ~ S10 U1


~ a1m * 6 7 * * 6S 7


a2m 7 U2 7 7 6 20 7 7þ6 6 .. 7 6 . 7 6 . 7¼0 . 5 4 .. 5 4 .. 5 * * *


anm Sn0 Um

3 The Overall Design Scheme and Software Flow of the System Selecting the ATMe128 single-chip microcontroller of ATMEL Corporation as the core of the overall system design, it has the following features: (1) High speed, low power consumption, A Reduced Instruction Set Computing (RISC) structure, 32 general-purpose working registers, and its instruction cycle is equal to the machine cycle, its working frequency is up to 16 MHz; (2) With 8-channel 10-bit A/D; (3) With 128 KB Flash, 4 KB EEPROM, 4 KB RAM, up to 48 I/O ports, 34 different interrupt sources and ISP download and JTAG simulation function; (4) Highly confidential, Flash program memory has multiple password protection lock function. The overall scheme of the system is shown in Fig. 1. It uses piezoelectric accelerometers at the left and right bearing positions and is processed through the front channel to enter the ATMeg128 A/D conversion port. Three-channel photoelectric speed signal after shaping is used to control the entire cycle of sampling, and can accurately position the wheels. The automatic measuring ruler uses two potentiometers to sense two components of length and angle. After two-channel A/D conversion, the signal is used to measure two parameters of wheel gauge and wheel diameter. The speed of the drive motor is 240 rpm, whose start and stop are controlled by the singlechip microcomputer system. The keyboard display section uses a 7279 keyboard display driver chip to implement human-computer interaction. The JTAG interface is used for program simulation and debugging. The serial communication interface is used for communication and data exchange with PC. The overall software flow chart is shown in Fig. 2, the software uses C language programming. According to the function, the software system can be divided into six parts: dynamic balance, static balance, motorized balance, wheel balance, and optimization and calibration procedure. For each function, it takes the amplitude and phase of the vibration measured at the two left and right bearings as original data for calculation of the imbalanced amount, among which the dynamic balance and calibration

A Detecting System for Wheel Balancer

77

Automatic measuring ruler

Left sensor

Band pass filter

Program control amplification

Voltage boosting

Single-chip

Drive motor

microcomputer ATMeg128 Right sensor

Band pass filter

Program control amplification

Voltage boosting

Keyboard display including 8 channel A/D 128K Flash 4K

3 channel photoelectric speed signal

Shaping

JTAG interface RAM

Serial communication interface

Fig. 1. The overall scheme of wheel balancer

procedures are the most important, and the calibration procedure includes the calibration of the initial imbalance of the spindle, the calibration of the stiffness coefficient and the calibration of the measuring ruler, which directly relates to the correctness of the final imbalance. According to the operation stage, the software system can be divided into four parts: function selection, parameter input, data acquisition and calculation, result display and positioning. This facilitates time division multiplexing of keys, timer counters, and external interrupts during programming.

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Start

Power-on self-test

Wheel parameters input method

Automatic input

Manual input

Function selection Static balance function

Dynamic balance function

Start motor sampling

Start motor sampling

Motor balance function

ALU function

Start motor sampling

OPT function

Calibration function

OPT operation

Position input Calibration selection Start motor sampling

Calculate left and right side

Calculate static

Calculate static

imbalance amount

imbalance amount

imbalance amount

Imbalance size and

Imbalance size and

Imbalance size and

Calculate left and right

phase display

phase display

phase display

side imbalance amount Calibration of weight

Y Imbalance size and OPT? phase display

Enter OPT

Y Hid?

function

Enter Hid function

N Calibration of measuring ruler

Hid operation

Fig. 2. The overall software flow chart

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4 Examples of Wheel Test It uses three sizes of wheels for testing. The test results are shown in Table 1. Table 1. Large wheel test result (Diameter 16 in., Width 7.5 in.) Interior side

Exterior side

Imbalance amount

Angle

Imbalance amount

Angle

Theoretical value

Measured value

Theoretical value

Measured value

Theoretical value

Measured value

Theoretical value

Measured value

99 99 99 99

97 99 98 98

0 90 180 270

1 91 178 272

99 99 99 99

99 98 98 100

0 90 180 270

1 89 182 271

5 Conclusion The development of wheel balancer with independent intellectual property rights is of great significance to the development of domestic auto protection products. Through repeated testing of different specifications of wheels, balancing mass accuracy of the system is within 2 grams, phase accuracy within 2º range, the balance effects of which meets the requirements for using (Tables 2 and 3). Table 2. Medium wheel test result (Diameter 15 in., Width 7.0 in.) Interior side

Exterior side

Imbalance amount

Angle

Imbalance amount

Angle

Theoretical value

Measured value

Theoretical value

Measured value

Theoretical value

Measured value

Theoretical value

Measured value

101 101 101 101

102 100 102 101

0 90 180 270

1 90 181 271

101 101 101 101

101 100 101 102

0 90 180 270

0 89 181 270

Table 3. Small wheel test result (Diameter 12 in., Width 6.0 in.) Interior side

Exterior side

Imbalance amount

Angle

Imbalance amount

Angle

Theoretical value

Measured value

Theoretical value

Measured value

Theoretical value

Measured value

Theoretical value

Measured value

103 103 103 103

101 101 102 102

0 90 180 270

1 88 181 269

103 103 103 103

102 101 101 102

0 90 180 270

1 91 182 272

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Acknowledgements. This work is supported by National Natural Science Foundation of China (51775112), the Research Program of Higher Education of Guangdong (2016KZDXM054), and the DGUT Research Project (GC300501-08, KCYKYQD2017011).

References 1. Zhang, H.H., Bai, Y.: A smart diagnosis system based on automatic recognition of multiple rotor faults. Adv. Mech. Eng. 9(9), 1–12 (2017) 2. Li, C., et al.: Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram. Mech. Syst. Signal Process. 76–77, 157–173 (2016) 3. Zarei, J., Tajeddini, M.A., Karimi, H.R.: Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics 24(2), 151–157 (2014) 4. Zhang, S.H., Li, W.H.: Bearing condition recognition and degradation assessment under varying running conditions using NPE and SOM. Math. Probl. Eng. 1, 1–10 (2014) 5. Zhang, H.H.: Research on knowledge based rotor fault diagnosis theory and method. Doctoral dissertation, Tsinghua University, Beijing: June 1993 6. Tang, X.K.: Mechanical Dynamics. Higher Education Press, Beijing (1986) 7. Li, C., Sanchez, R.V., Zurita, G., et al.: Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 168, 119–127 (2015) 8. Chen, J.L.: Measures to improve the dynamic balancing performance of tires. Automobile Appl. Technol. 10 (2017) 9. Li, C., Liang, M., Wang, T.Y.: Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals. Mech. Syst. Signal Process. 64–65, 132–148 (2015) 10. Jannati, M., Sutikno, T., Idris, N.R.N., et al.: High performance speed control of singlephase induction motors using switching forward and backward EKF strategy. Int. J. Power Electron. Drive Syst 7(1), 17–27 (2016) 11. Li, C., Cerrada, M., Cabrera, D., et al.: A comparison of fuzzy clustering algorithms for bearing fault diagnosis. J. Intell. Fuzzy Syst. 34(6), 3565–3580 (2018) 12. Bai, Y., Sun, Z.Z., Zeng, B., et al.: A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction. J. Intell. Manuf. (2018). https://doi.org/10.1007/s10845-017-1388-1 13. Long, J.Y., Sun, Z.Z., Chen, H.B., et al.: Variable neighborhood search for integrated determination of charge batching and casting start time in steel plants. J. Intell. Fuzzy Syst. 34(6), 3821–3832 (2018)

Study of Substation Inspection Robot Voice Recognition Algorithm Based on Wavelet Transform Chongguang Fu, Zhizhou Sun, Kechao Tian, Maoqi Dong, Guoqing Yang(&), Jian Li, Chuanyou Zhang, and Guangting Shao Shandong Luneng Intelligence Technology Co., Ltd., Jinan, China [email protected]

Abstract. A kind of substation inspection robot device voice recognition algorithm is proposed based on wavelet decompositions, which can recognize the running state of substation device. Firstly, the sample library was collected by robot pickup. Secondly, the sub-band energy of samples was extracted by using wavelet decomposition; the sample code book was billed by using VQ algorithm and LBG algorithm. Finally, using the codebook to identify sound samples, which can judge the running state of the equipment. The experimental results show that, the algorithm can effectively identify the sound equipment and high accuracy. The requirement of the substation inspection robot for detecting device is satisfied. Keywords: Inspection robot
Voice recognition VQ algorithm
LBG algorithm


Wavelet decompositions

1 Introduction At present, manual inspection is mostly used in the inspection of power substation equipment in China [1]. That is, substation workers enter the equipment area for equipment inspection. This inspection method mainly relies on the subjective qualitative judgment and analysis of the workers, and needs the staff to have rich experience and higher business level. At the same time, it is difficult to input data into management information system in real time. Moreover, the substation is a high-risk place, and there is a greater potential safety hazard when the staff inspects the equipment in bad weather [2]. The sound produced by the equipment in the substation has a certain regularity. When the equipment is normal operation status, the sound is stable and consistent. However, when the equipment fails, the sound will change greatly and the frequency spectrum will be unstable. The change rule of the sound is depend on by manual inspection, so the voice analysis can be used to distinguish the operation state of the substation equipment. The sound recognition system is added to the platform of the substation inspection robot and the sound recognition technology is used to analyze the running state of the substation equipment. It can help the substation to be unattended and realize the automation and intelligence of the substation. Therefore, the substation inspection robot adopts the robot technology to carry on the substation inspection, © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 81–87, 2019. https://doi.org/10.1007/978-3-030-02804-6_10

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which not only can complete all the work of the manual inspection, but also can make up the shortage of the manual inspection. It is the important direction of the development of the unattended substation inspection [2, 3]. In this paper, a sound recognition algorithm for substation equipment is proposed. The training sample library is obtained by the pickup sample collection, and the wavelet decomposition is used to obtain the characteristics of the sample’s subband spectrum energy. The training sample codebook is obtained by using the VQ algorithm and the LBG algorithm. When the sound recognition is carried out, the codebook of the sound signal is obtained by the same method. Compared with the training sample code book, the sound signal is identified, and then the operation state of the equipment is judged. If the abnormal, the alarm warning staff.

2 An Overview of the Algorithm 2.1

Wavelet Decomposition

Wavelet analysis belongs to the time-frequency analysis, and it is a new signal transformation analysis method. Because of its good time-frequency analysis characteristics and low computational complexity, it has been widely used in non-stationary signal processing [4]. It shows some characteristics of signals through mathematical transformation, not only for stationary signal analysis, but also for non-stationary signal analysis [5]. The signal in the power system is just one of the non-stationary signals, and it will change in the local time section. Therefore, the choice of wavelet analysis is more appropriate than Fourier transform. Wavelet decomposition has the following characteristics: (1) Wavelet decomposition can cover the entire frequency domain. (2) wavelet decomposition has the ability of de-correlation, which can greatly reduce or eliminate the correlation among features. (3) Multi-Resolution Analysis (MRA), also known as multi-scale analysis, which is a wavelet decomposition that analysis signal from coarse to fine. 2.2

Vector Quantization

Vector Quantization (VQ) is an important signal processing method. Since its development in the late 1970s, it has been widely used in speech coding, speech recognition and synthesis technology [6]. The process diagram of vector quantization in the audio processing system as shown in Fig. 1. ~  RK input vector to another K-dimension VQ maps a K-dimensional X 2 X quantization vector Y 2 Y~N ¼ fY1 ; Y2 ;


; YN jYi 2 RK g. Y ¼ QðXÞ

ð1Þ

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training sound signal

preprocessing

feature extraction

codebook 1 recognition

sound signal

preprocessing

feature extraction

codebook 2

threshold analysis

recognition results

Fig. 1. VQ algorithm process flow chart

~ source Where, X: input vector, Y: quantization vector (codeword or code vector), X: space, Y~N : output space, RK : K dimensional Euclidean space, Qð
Þ: quantization symbols, N: code size (number of code words). Three kinds of distortion measures commonly used in VQ algorithm are as follows: (1) square distortion measure: d ðX; Y Þ ¼ kX  Y k2 ¼

X

ðxi  yi Þ2

ð2Þ

j xi  yi j

ð3Þ

i

(2) absolute error distortion measure: d ðX; Y Þ ¼ jX  Y j ¼

X i

(3) weighted square distortion measure: dðX; YÞ ¼ ðX  YÞT WðX  YÞ

ð4Þ

Among them, W is a positive definite weighted matrix. 2.3

Codebook Acquisition

Splitting technique is used to calculate codebook. The process is as follows: (1) Suppose the codebook size is N = 1, calculate the centroid of all training ð0Þ sequences, and use the centroid as the first codeword Y1 . The calculation formula of centroid is as follows: Yi ¼

1X X Ni X2S i

ð5Þ

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Where, Ni represents the number of elements in the set Si . ð0Þ

is multiplied by the disturbance coefficient 1  e, which can get two initial ð0Þ ð0Þ ð0Þ ð0Þ ðnÞ codewords, that is Y1 ¼ Y1 ð1 þ eÞ and Y2 ¼ Y1 ð1  eÞ. A codebook Y~2 ¼ n o ðnÞ ðnÞ Y1 ; Y2 that contain two codewords is obtained by using the LBG algorithm

(2) Y1

ðnÞ

ðnÞ

ðnÞ

(3) Y1 and Y2 that are the two codewords of the code book Y~2 , which are multiplied by the disturbance coefficient 1  e, and then are split into four codewords. The above process is repeated until the code book size N reaches the required size. n ð0Þ ð0Þ ð0Þ ~ At this time, there are N codewords, which are the initial codebook YN ¼ Y1 ; Y2 ; ð0Þ




; YN g. 2.4

LBG Algorithm

The LBG algorithm was proposed by Linde, Buzo and Gray in 1980. It is actually equivalent to the multi-dimensional promotion of the Lord-Max method, but it does not need to know the probability distribution of the input vector. The LBG algorithm approximates the optimal regeneration codebook through the training vector set and a certain iterative algorithm [7, 8]. The steps of the LBG algorithm are as follows: Step 1: Step 2: Step 3: Step 4:

The randomly select n blocks as the code vector; The sequence of N codes is divided into N sets, so that the distance between the data in each set and the corresponding code vector is the smallest. Getting n new code vectors from the centroids of these n sets If these code vectors do not change much (convergence) from the original code vector, the codebook training will be completed, otherwise Step 2 and Step 3 will be redone

The time domain and frequency domain are the basic properties of signals. In view of this different nature, signals can be analyzed in many ways. And each way provides different angles, which provides an effective way to display the signal characteristics more intuitively. This different angle of the analysis signal is called a domain. The time domain is the real world and is the only real domain. The frequency domain is a mathematical category that follows certain rules. The most important property of the frequency domain is that it is not real but a mathematical construct. Time domain analysis and frequency domain analysis are two observation surfaces for analog signals. Time domain analysis is based on the time axis as a coordinate to represent the relationship between dynamic signals; frequency domain analysis is to turn the signal to the frequency axis as a coordinate representation. Generally speaking, the representation of time domain is more visualized and intuitive, while frequency domain analysis is more succinct, and the analysis is more profound and convenient. Therefore, in this paper, the voice signal is transformed from the time domain to the frequency domain by wavelet transform. In the frequency domain, the sub-band energy obtained

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from wavelet decomposition is used as the recognition feature. The flow chart of voice recognition is shown in Fig. 2. Preprocessing mainly includes pre-emphasis, framing and windowing, and Hamming window function is used in the windowing algorithm.

sound signal pre-processing wavelet decomposition

feature extraction

VQ+LBG algorithm

larger than threshold N normal

Y

abnormal alarm

Fig. 2. Sound recognition flow chart

3 Simulation Experiment In the experiment, speech sounds, insect sounds, bird sounds, and transformer sound samples were analyzed at random. Among them, each type of sample contains 30 samples. The collected signals are decomposed by one dimension wavelet, and using db4 wavelet base to perform three-layer decomposition. The obtained low-frequency coefficients are divided into frames, and then the energy of each frame is obtained as the sub-band energy of the low-frequency coefficients. The high frequency coefficients of the wavelet coefficients are extracted and divided into frames, and the energy of each frame is used as the high frequency sub-band energy. Figures 3, 4, 5, and 6 are sample high-frequency energy diagrams. It can be seen from the diagram that the range of speech energy change is [5,60], the range of insect acoustic energy change is [35,60], the range of the energy change of bird calls is [35,100], and the range of the transformer sound energy change is [40,50]. It can be seen that the energy of the transformer sound is less fluctuating and the energy is more stable. There is a big difference from other types of sounds. In the training process, the sub-band energy obtained by wavelet decomposition is extracted as the feature, and the corresponding codebook is obtained by VQ+LBG algorithm. In the process of recognition, we also extract the sub-band characteristics of the sample and get codebook. Then the Euclidean distance is calculated with the codebook in the training process and compared with the threshold to judge whether the sample sound is normal.

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Fig. 3. Speech sound

Fig. 5. Bird calls

Fig. 4. Insect sound

Fig. 6. Transformer sound

4 Conclusion In this paper, the wavelet decomposition is used to transform the sound signal from the time domain to the frequency domain, and the energy of the sub band of the wavelet decomposition is extracted as the feature, and the sound recognition of the equipment is realized. Experimental results show that the algorithm is simple in modeling, fast in operation and high in accuracy. Through the robot platform, the sound detection of substation equipment is realized, which improves the work efficiency and the safety of workers. The implementation of this algorithm has promoted the unattended operation of the substation and realized the automation and intelligence of the substation. Acknowledgments. This work was supported by application research and development of intelligent new technology for substation inspection robot (item serial number: ZY201815). Thank you for your support of the paper.

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References 1. Li, Y.: Substation inspection robot intelligent system research and application. Shandong University, pp. 5–11 (2016) 2. Peng, X., Jin, L., Wang, K., Qian, J.: Design and application of robot inspection system in substation. Electr. Power 51(2), 82–89 (2018) 3. Cai, H., Shao, G., Hu, J., Wen, Z.: Analysis of the main performance index and application status of inspection robot in substation. Electr. Measur. Instrum. 54(14), 117–123 (2017) 4. Li, H., Huang, M., Gao, H., Ma C.: Mechanical fault diagnosis using wavelet analysis and SVD. Modular Mach. Tool Autom. Manuf. Tech. 6, 81–83–87 (2016) 5. Chen, S.Y.: Micro motor fault diagnosis based on wavelet analysis and bp neural network. Guangdong University of Technology, pp. 1–16 (2013) 6. Zhu, B., Bu, Q.: Algorithm for feature extraction of speech signal. Modern Electron. Tech. 39 (4), 9–11 (2016) 7. Chen, L.: Sound recognition technology for helicopter using vector quantization. Shandong University, pp. 22–28 (2014) 8. Yan, S.: Self-adaption fuzzy clustering LBG vector-quantization algorithm. Comput. Eng. Appl. 23, 203–205 (2014)

Research on the Development of Intelligent Industrial Control Liu Miao1(&), Che Lei2, Xuepo Li2, and Lujun Tan1 1

China Petroleum Longhui Automation Engineering Co., Ltd., China Petroleum Pipeline Engineering Co., Ltd., Tianjin, China [email protected] 2 China Petroleum Pipeline Engineering Co., Ltd., Tianjin, China

Abstract. Intelligent industrial control is a comprehensive application of the latest computer technology, communication technology, artificial intelligence technology, information technology and coordinated call decision technology to study complex and changeable industrial control system. The development of intelligent industrial control is overviewed in the paper. Keywords: Intelligent industrial control


Smart grid
SCADA system

1 Introduction With the rapid development of new technologies such as Internet of things, big data and artificial intelligence, a new round of industrial transformation competition has been launched around the world. Whether it is Germany’s “Industry 4.0”, the “Advanced Manufacturing National Strategic Plan” in the United States or “China Made 2025”, it has shown the deep integration of traditional industrial manufacturing technology and electronic information [1, 2]. And the industrial manufacturing technology has developed rapidly in the direction of automation, information and intelligence. Industrial automation technology is an integrated technology that uses control theory, instrument, computer and other information technology to detect, control, optimize, dispatch, manage and make decision for the industrial production process in order to increase production, improve quality, reduce consumption, ensure safety and so on. It includes industrial automation soft, hardware and system [3–5]. As one of the most important technologies in the twentieth Century modern manufacturing field, industrial automation technology mainly solves the problem of production efficiency and consistency [6]. With the development of control technology, computer, communication, network and other technologies, the information communication field is rapidly covering all levels from factory site equipment layer to control and management. Today, the simplest understanding of automation has also been changed to using generalized machines (including computers) to partially replace or completely replace or transcend human physical strength [7].

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 88–94, 2019. https://doi.org/10.1007/978-3-030-02804-6_11

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Industrial automation system can be divided into automatic equipment, instrument and measurement equipment, automation software, transmission equipment, computer hardware, communication network, etc. The specific composition includes [8–10]: Automation equipment: including programmable controllers (PLC), sensors, encoders, human-computer interfaces, switches, circuit breakers, buttons, contactors, relays and other industrial appliances and equipment; Instruments and measuring equipment including: pressure instruments, temperature instruments, flow meters, material level instruments, valves and other equipment; Automation software: including computer aided design and manufacturing system (CAD/CAM), industrial control software, network application software, database software, data analysis software and so on. Transmission equipment: speed governor, servo system, motion control, power supply system, motor and so on. Computer hardware including: embedded computer, industrial computer, industrial control computer, etc. Communication networks: network switches, video surveillance devices, communication connectors, bridges and so on. Recently, traditional automation technology and IT technology have accelerated the integration process. IT technology has quickly entered the various levels of industrial automation system which has changed the situation that the automation system cannot increase synchronously with the IT technology for a long time. IT technologies based on Internet include Windows, PC, Ethernet, Web Technology, Wireless and Security [11]. Each technology promotes the new development trend of industrial automation system.

2 Industrial SCADA System The SCADA system can collect data of production process with a long distance and scattered production units. The SCADA system is a production process control and dispatching automation system based on computers. It can monitor and control the operating equipment on the spot to realize the functions of data acquisition, equipment control, measurement, parameter adjustment and all kinds of signal alarm. The SCADA system is widely used in data acquisition and monitoring control in electric power, petroleum, chemical industry, water supply, municipal administration and so on. Due to the different production processes in different industries and different modes of operation management, SCADA system with industry characteristics has been formed. At present, there are special SCADA systems in electric power, aerospace, transportation, petrochemical, water treatment and nuclear industries. 2.1

Electric Power SCADA System

As the main technical means of the industrial control system (ICS) of the process enterprise, SCADA system plays an important role in the automation system of water, electric power, railway and petrochemical industry. The development of its technology has gone through several stages. The development of SCADA system in electric power

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industry has typical representative significance. Under the guidance of “import digestion - development - innovation”, the electric power system localization has been fully realized from automation related hardware, SCADA system software and EMS (Energy Management System, energy management system) system at present. In the automation field of power industry, the concepts and ideas including “object oriented modeling, integration of graphics and models, plug and play” have become mature after years of development and practice. The standard series, such as IEC61970 and IEC61850, are the embodiment of the International Electro technical Commission (IEC) standardizing this advanced technology in the electric power industry. The standard series further promote this idea to be widely used in the power dispatching automation system. With the deep research in the automation field of power system, the concept of SCADA system has been upgraded from the view point to the equipment oriented to the network oriented SCADA system. 2.2

Pipeline SCADA System

With the readjustment of Chinese energy structure and rapid economic development, the construction of oil and gas pipelines is booming. The centralized control mode development of pipe network makes the production and operation management of pipelines more and more dependent on SCADA system and human intervention is less and less. The oil and gas pipelines SCADA system must ensure continuous monitoring and managing the pipe network by the control center and the reliable, stable and safe operation of the pipeline network. The SCADA System of Oil and Gas Pipelines Mainly Has Three Characteristics: (1) Multilevel structure The pipeline SCADA system generally adopts the local level - station level center level. For facilitate management, regional level monitoring centers are set up in some areas. The central level control system is generally based on the Unix/Linux operating system. The Unix/Linux operating system is stable in performance, concise in kernel and efficient in operation. It meets the requirements of real-time multitask processing. In addition, as a professional level operating system, Unix/Linux has a unique advantage over the Windows operating system in protecting viruses and Trojans. (2) Distributed system As a typical multi-task processing system, the pipeline SCADA system requires high real-time performance. The distributed function design and implementation can make the system have an extended architecture and reduce the cost of implementation and maintenance. (3) Large scale The characteristics of long pipeline are a lot of station, the long pipeline and many I/O points. These characteristics determine that the SCADA system software of long distance pipeline must have large-scale application ability. For example, Chinese west- east gas pipeline project, the central control system has about one hundred thousand points of data acquisition and control scale.

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3 Smart Grids The smart grid is a dispatching power grid process with making decision and judgment by artificial intelligence mode, implementing by computer. The artificial experience is extracted into knowledge to realize the transmission of knowledge in the organization. The characteristics of intelligence include: continuous learning, accumulation of experience and knowledge. What happened to the power grid? (3) why did this happen? (4) what kind of control measures should be taken? The realization of intelligent power grid needs to transform the control of the power grid from the traditional local information to the global information, and provide the intelligent dynamic monitoring means for the dispatchers, including the technology including artificial intelligence, communication and computer. At present, the function of power dispatching automation system has gradually developed from the initial data acquisition and monitoring to an energy management system. Most of the current systems have integrated network topology, power flow calculation, state estimation, static security analysis and other advanced applications. It has improved and expanded in function, and has been widely used. Due to the constraints of technical theory, the current scheduling system is still not high in the degree of automation and intelligentization, and these applications are not integrated from the perspective of comprehensive decision-making. How to further expand and expand the traditional SCADA/EMS function on the basis of existing technology and theory, and provide more comprehensive and intelligent decision support for power grid scheduling, is a new direction for the development of power grid dispatching system in the future. The intelligent dispatching of power system means that the dispatching automation system can automatically track the changes of the state of the power grid, help the dispatcher understand and master the real-time running state of the power system, provide an analysis and decision scheme for the current state of the power grid, ensure the security and stability of the operation of the power grid, and improve the economy of the operation of the power grid. Specifically, when the power grid is running normally, it can continuously carry out safety and economic evaluation, and give a comprehensive evaluation report. When the system fails, the intelligent dispatching system can diagnose the fault according to the fault alarm information, judge the fault equipment and type of fault, evaluate the switch and protection action involved in the fault, and give the recovery strategy of the fault equipment. In addition, when the power grid has operational tasks, the system can issue operation tickets intelligently according to the operation tasks. Intelligent scheduling system is a larger application software than the existing system. Its decision making is not only limited to steady state analysis, but also includes fault diagnosis and recovery decision after fault, and the power market operation support system to be added in the future. In this environment, the software system must have the ability to operate independently and coordinate computing. In addition, the interaction between different software modules is more frequent.

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This requires that the system structure system is an open software framework, each module is relatively independent and can work together, and has access ability of third party software. Therefore, the current object-oriented technology has been unable to meet the needs of building such a complex system. It is necessary to explore new ways and methods to build such a complex system with the help of the latest research results in the fields of computer technology and artificial intelligence. The technology of distributed artificial intelligence provides an effective method to solve this problem. It can encapsulate the mature modules of the existing system and evolve into Agent, which has more autonomy and portability compared with the traditional system. In recent years, more and more attention has been paid to the development and research of the scheduling system.

4 Intelligent Refining and Chemical Refining With the progress of technology, the refining industry has entered an efficient and optimized stage, but it still faces many challenges. In order to break through the bottleneck of management and promote quality and efficiency, transformation and upgrading and connotation development, the construction of intelligent refinery by using a new generation of information technology has become an important development trend in the refining industry. The so-called intelligent refinery refers to the new generation of information technology and equipment monitoring technology, such as the Internet of things, large data and cloud computing on the basis of the digital refinery, to strengthen the information management and service, to master the production and marketing process comprehensively and accurately, to improve the controllability of the production process, to reduce the manual intervention on the production line, and to be timely and accurate. Collect all kinds of data of the production line, optimize the production management and control online, improve the level of resource optimization and dispatch, realize the automation and mobile coordination operation management in the field operation, improve the production quality and efficiency, realize the visualization of comprehensive information in the decisionmaking command, and greatly improve the dynamic analysis and auxiliary. To help decision-making ability, support the whole process of refining to achieve intrinsic safety and environmental protection. Intelligent refineries have five key technologies, namely automation, digitalization, visualization, modeling and integration. The construction of intelligent refineries involves 6 core business areas: production control, supply chain management, equipment management, energy management, HSE management and assistant decisionmaking. Through the construction of intelligent refinery, we will promote the transformation of production mode and management and control mode, improve the safety and environmental protection, energy saving and emission reduction, reduce the efficiency and increase efficiency, improve labor efficiency and production efficiency, and promote green low carbon development. Through pilot construction, the basic framework of intelligent refinery has been preliminarily formed, and preliminary results have been achieved: (1) the investment rate of advanced control is increased by 10%, up to 90%; (2) the automatic collection rate of production data is increased by 10%, up to

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95%, and the labor productivity is increased by more than 10%; (4) the key environmental emission points are monitored in real time, and the real-time monitoring and monitoring of the key environmental emission points is achieved. Analysis and early warning.

5 Conclusion Intelligent industrial control is a comprehensive application of the latest computer technology, communication technology, artificial intelligence technology, information technology and coordinated call decision technology to study complex and changeable industrial control system. The computer system is used to replace the dispatcher. According to the operating condition of the system, each advanced application software is invoked in real time. The comprehensive decision scheme of the unified operation is the reference for dispatcher to assist carrying on the operation command and processing accident. So the speed and accuracy of the scheduling operation can be greatly improve, and the monitoring level of the system will be improved. The intelligent scheduling system for the future should have a fairly high level of intelligence. It is able to feel the industrial system as sensitive as the scheduling expert and react quickly to the various problems, improve the automation level of the industrial control system to a large extent, reduce the work intensity of the dispatcher and improve the scheduling task. It is scientific and reasonable to ensure the safety, reliability and economic operation of the system.

References 1. Wang, S., Wan, J., Zhang, D., et al.: Towards smart factory for industry 4.0. Int. J. Comput. Telecommun. Netw. 101(9), 158–168 (2016) 2. Xia, S.: Study of China’s manufacturing Industry development under the background of “4.0 industry” and “Made in China 2025”—uses JIUJIANG petrochemical intelligent factories as an example. Ind. Econ. Rev. (2016) 3. Xia, D.W., Xue, X.F.: University S O. research on the application of intelligent manufacturing technology in industrial automation. Mach. Des. Manuf. (2018) 4. Hua, A.Q.: Analysis on the current situation and development trend of industrial control automation technology in China. Times Agric. Mach. (2017) 5. Liu, M., Shimin, J., Mancang, Y.: Design replica consistency maintenance policy for the oil and gas pipeline clouding SCADA multiple data centers storage system. In: Xhafa, F., Patnaik, S., Zomaya, Albert Y. (eds.) IISA 2017. AISC, vol. 686, pp. 715–721. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-69096-4_101 6. Alfnes, E., Thomassen, M.K., Bostad, M.: Comparing techniques for selecting automation technology. In: Nääs, I., Vendrametto, O., Reis, J.M., Gonçalves, R.F., Silva, M.T., von Cieminski, G., Kiritsis, D. (eds.) APMS 2016. IAICT, vol. 488, pp. 371–378. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-51133-7_44 7. Qian, Y.I.: The Application of Automation Technology in Mechanical Manufacturing. Constr. Des. Eng. (2017)

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8. Kaufman, P.J.: Systems and methods for determining energy information using an organizational model of an industrial automation system (2018) 9. Rischar, C.M., Sinner, W., Kalan, M., et al.: Systems and methods for balancing loads in an industrial automation system (2017) 10. Scott, S.J., Nguyen, T.T., Nair, S., et al.: Recognition-based industrial automation control with redundant system input support (2017)

Toward Human Motion Sensing: Design and Performance Evaluation of a Minimized Wearable Platform Using Inertial and TOA Sensors Cheng Xu1,2, Jie He1,2, Xiaotong Zhang1,2, Yue Qi1,2(&), and Shihong Duan1,2 1

2

School of Computer and Communication Engineering, University of Science and Technology, Beijing, China [email protected] Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China

Abstract. Wearable sensors based applications have been widely used in various applications, such as health care, security monitoring and humancomputer interaction. Despite remarkable research efforts and many encouraging advances in the past decade, accurate recognition of the human motions is still a challenging task. The existing studies mostly face the problems of sensor drift errors and the assumption of independent body part movement. In this study, we take the geometrical relation among joints into consideration and a corresponding platform is designed to obtain potential parameters and perform human motion capturing. CRLB is derived to evaluate the performance of proposed platform. The lower bound of motion sensing accuracy shows apparent reduction with applying our method. The sensing accuracy has an obvious promotion in the experiment results. Keywords: Human motion TOA sensors


Wearable platform
CRLB
Inertial sensors

1 Introduction Human motion related applications have been drawing more and more attentions and applied in so many industrial areas, such as intelligent computing, home security and smart healthcare applications [1–6]. Traditional motion sensing systems are mainly based on multiple high-resolution cameras in fixed scenarios, such as Kinect and Vicon. For example, with the use of Kinect depth camera, Wikstrom et al. [2] proposed an actionlet ensemble model to represent each action and to capture the intra-class variance for both the human motion and the human-object interactions. However, these exiting vision methods have high accuracy and no drift but also have some obvious drawbacks. They require devices to be pre-deployed in specific scenarios, and the background is normally to be clean. In addition, the worst problem is reflective nodes to be deployed on human body in advance. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 95–102, 2019. https://doi.org/10.1007/978-3-030-02804-6_12

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Wearable sensors shows many superiorities in ubiquitous computing applications. Therefore, wearable sensor based human motion sensing has drawn great attention in academic areas [3–6]. Human motion analysis has drawn lots of attentions by famous international journals such as IEEE Transactions on Human-Machine Systems, International Journal of Human Computer Studies (IJHCS), IEEE Transactions on Mobile computing (TMC), IEEE Transactions on Pattern Recognition and Machine Intelligence (TPAMI), and ACM Transactions on Computer-Human Interaction (TOCHI), IEEE Sensors Journal, as well as International Conference on Pervasive Computing and Communications (PerCom), and International Conference on Machine Learning (ICML). On the other hand, HMA has also shown a tremendous potential in the areas of industrial applications. For example, Defense Advanced Research Projects Agency (DARPA) funded a multi-institution project on Video Surveillance and Monitoring (VSAM) [7] to provide continuous coverage of people and vehicles in cluttered environments. Researchers in the UK have also done much research on the tracking of vehicles and people and the recognition of their interactions [8–11]. Xsens [12] is a leading innovator in 3Dtions such as 3D character animation [13], motion analysis [], and industrial control & stabilization. InvenSens [14] offers a more complete sensor platform system solution that delivers a use case, solves a system power problem, or improvement of other companion sensor performance.

2 Hardware Design An ideal wearable platform should balance the following requirements: (1) It is capable of capturing the signals, related with both absolute and relative motion descriptors. (2) In order to be more wearable, it needs to be designed as small as possible. 2.1

Platform Overview

As shown in Fig. 1, the hardware is consist of the following modules: • Main-control module: It schedules the system tasks, to complete data collecting and communication; • Sensor module: It is responsible for the data collecting of various sensors;

Fig. 1. Proposed hardware schema. It is composed of main-control module, sensor module, RF module, power management module and data storage module.

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• Radio-frequency module: It is used to achieve the function of distance ranging and inter-node communication; • Power management module: It works for supplying power to each module; • Data storage module: It records collected sensor data. Next, we will demonstrate the detailed design of each module of our platform node. 2.2

Platform Realization

For the convenience of long-term wearable use, the platform node is designed as small as 3.4 cm*3.0 cm and weighs no more than 10 g. The hardware prototype is shown in Fig. 2.

Fig. 2. A hardware prototype. Its size is 3.4 cm*3.4 cm.

(1) Main control module: Wearable nodes are usually mounted on joints with large number of degrees of freedom. Thus, the node should be as small as possible. Selected chips should have a small size, as well as low power consumption for long time monitoring needs. We choose STM32F103C8T6, an ARM 32-bit CortexT M-M3 processor, whose working frequency can be as high as 72 MHz and its current in sleeping mode is as low as serveral µA. (2) Sensor module: It is one of the core modules in this platform, and used for capture the various sensor data, including acceleration, angular velocity, geomagnetic information and barometer information. In the principles of high precision, small size and low power consumption. The combination of inertial sensors MPU6050 and HMC5883L, we can easily obtain the angles a and h needed by our motion model, presented in Sect. 3. The distance parameters d and a is captured with the TWR ranging of DW1000. Thus, precious human motion could be reconstructed. (3) RF module: RF module should be capable of precious distance ranging and effective data communication. We adopt DW1000, a UWB chip sold by DECAWAVE Ltd. Compared with traditional wireless communication methods, like Zigbee, bluetooth and WLAN, etc., UWB has the characteristics of high precision, strong anti-interference performance, wide pulse width, and low power consumption. These advantages make UWB very suitable for human motion capturing applications.

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(4) Power management module: It should meet the needs of long time motion monitoring requirements. Selected chips are of the features of low power consumption. The manostat chip is TPS79333, powered by rechargeable lithium battery (700 mAh). (5) Data storage module: Proposed platform supports up to 32 GB of MicroSD flash, allowing the node to store complete raw signal for off-line usages. Based on above design, our proposed platform can realize data acquisition. As described in Sect. 2, the parameters needed by proposed model, including distances and angles, can be obtained using this platform. With the fusion of these parameters, high precision motion capturing can be achieved. In the following sections, we will perform theoretical and practical evaluation on this platform.

3 Platform Evaluation 3.1

Error Definition

Human motion sensing is a sequential tracking problem of multiple targets. Pk is denoted as the position of joint at time slot k. Then, it comes that Pk þ 1 = Pk + dk Tk + sk

ð1Þ

where
sk is Gaussian noise whose mean is l0 and its standard deviation is r0, namely  sk  N l0 ; r20 . The coefficient vector could be represented as Tk ¼½sinak coshk ; sinak sinhk ; sinuk T . Thus, the azimuth hk1 and the pitch uk1 can be denoted as d^k ¼ dk þ nd

ð2Þ

where dk is the expected step length under random walk model [10]. The noise of distance parameter also fits Gaussian distribution, namely nd * N(ld, r2d). Vector b d¼ ½d^0 ; d^1 ;


; d^k2 T is defined to present d^k . Motion parameter in horizontal direction could be obtained by inertial sensors which could be represented as ^hk ¼ hk þ uk ; uk  Nð0; 2 Þ k

ð3Þ

where hk is the actual horizontal heading. uk is a Gaussian random variable whose mean value is 0 and its variance is 2k , which is independent from z direction and is also ^ Vector ^hk ¼½^h0 ; ^h1 ;


; ^hk2 T is introduced to represent ^ hk . uncorrelated with d.

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Motion parameter in vertical direction could be obtained by inertial sensors which could be represented as ^ak ¼ ak þ vk ; vk  Nð0; n2k Þ

ð4Þ

where ak is vertical angle in real. vk is a Gaussian random variable whose mean value is 0 and its variance is n2k . Vector ^ak ¼½^a0 ; ^a1 ;


; ^ak2 T is introduced to represent ^ ak . 3.2

Performance Evaluation

CRLB is the inverse of the Fisher information matrix (FIM) [13], which could represent ^ a lower bound on the variance of unbiased estimate PðuÞ of P. Furthermore, we can indicate FIM as: ^ ^ Eð½PðuÞ  P½PðuÞ  PT Þ  F 1

ð5Þ

The element of mth row and nth column in FIM is defined as Fm;n ¼ E½

@ 2 lnpðuk jPk Þ  @Pm;k @Pn;k

ð6Þ

where p(uk|Pk) is the joint conditional p.d.f of the observations given Pk. With the logarithm likelihood function ln p(uk jPk Þ put into formula (2), we could obtain Fm;n ¼

jAi j 1 X ðPj;m;k  Pi;m ÞðPj;n;k  Pi;n Þ þ eTm R1 s en 2 rd i¼1;i6¼j jjPj;k  Pi jj22

ð7Þ

where di;j = jjPj;k - Pi;k jj2 denotes as 2-normal form and the total number of available reference nodes is Ai. Moreover, em ¼ 

@dj;k dPj;m;k

ð8Þ

where dj;k ¼ ðPj;k1  Pj;k  ls Þ is defined as the difference between the true position and the predicted one, in which ls is the mean value of step length error under random walk model [10]. Based on the analysis above, the CRLB of 3-dimensional condition in human motion tracking could be achieved as CRLB ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi trðF 1 Þ

ð9Þ

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^ j;k  Pj;k jj2 , then, its root mean square If the estimation error is denoted as ej;k ¼jjP error (RMSE) may fit that RMSE ¼

3.3

qffiffiffiffiffiffi e2j;k  CRLB

ð10Þ

Results Analysis

Movement characteristics of each body part should be taken into consideration. Because of the spatial features of human motion sensing, it could be classified into two categories: 2-D and 3-D condition. 2D motions refer to the body movements that can be captured by a plane, such as raising one’s arms into ‘Y’ pose or ‘T’ pose. 3D motions refer to stereoscopic movements such as walking, running or swing hands. The different relative positions may contribute to various capture accuracy. Therefore, both the CRLB of human body in 2D and 3D scenarios are taken into consideration. Two sets of reference nodes combination were selected, namely Case 1 = {Neck, Chest, LHip, RHip} and Case 2 = {Neck, LShoulder, RShoulder, Chest, LHip, RHip}. Other parts of human body are able to move freely in this space and CRLB of each location is derived as following demonstration. To demonstrate the typical calculation results, CRLB, when the variance of distance measurement r2d is 0.2 and that of IMU r2IMU is 0.1, is selected upon common commercial motion sensing systems, like Xsens. From the experiment results, the following conclusions could be obtained: (1) In the aspect of two-dimensional condition, shown in Fig. 3(a) and (b), lower CRLB is shown when the position is more close to the trunk. It’s also lower in the lower limb than the upper, which is possibly due to the selection of reference nodes. To verify this, comparison experiment is conducted when the reference nodes are chosen differently. Results shown in Fig. 3(a) and (b) indicate that the relative position of the reference nodes cause different CRLB of human motion and the more uniform the nodes distribute, the relative lower CRLB it achieves. The same result could also be seen in Fig. 3(c) and (d) when only distance measurement is applied in the human motion sensing process. (2) Since human motion is kind of three-dimensional process, stereoscopic presentation is shown in Fig. 3(e) and (f). The human is assumed to be placed in the XOZ plane when the y is set as zero. The 3D version of CRLB is likely to be a 2D one that stretches along the Z-axis. Similar results could be seen in 3D condition that the closer to the trunk, the lower the CRLB that could be achieved. (3) For comparison, the CRLB under two measurement method are calculated, respectively independent distance measurement and the fusion of IMU and distance measurement. Comparison results is shown in Fig. 3(a) and (c) (also in pair of (b) and (d)). A significant superior performance is obtained with our proposed geometrical method compared with independent distance measurement method or independent IMU method.

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Fig. 3. CRLB comparison in two-dimensional and three-dimensional condition. (a), (b) and (e) are CRLB in condition that both distance and IMU information are considered using proposed model. (c), (d) and (e) are CRLB considered with distacne information solely. In all above figures, r2d is set as 0.2 and r2IMUis set as 0.1.

4 Conclusion A minimized wearable platform using Inertial and TOA sensors is proposed in this study. It takes use of both inertial and TOA sensors to enhance the capturing accuracy of human motion sensing applications. Theoretical analysis also proves that the combination of these two kinds of sensors can significantly improve the integrated performance. Acknowledgments. This work is supported by The National Key R&D Program of China, No. 2016YFC0901303, National Natural Science Foundation of China (NSFC) project No. 61671056, No. 61302065, No. 61304257, No. 61402033, Beijing Natural Science Foundation project No. 4152036 and Tianjin Special Program for Science and Technology No. 16ZXCXSF00150.

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References 1. Cao, J., et al.: Optimizing multi-sensor deployment via ensemble pruning for wearable activity recognition. Inf. Fusion 41, 68–79 (2018) 2. Wikstrom, J., Holmberg, A.L., Lofberg, A., et al.: Learning actionlet ensemble for 3D human action recognition. IEEE Trans. Softw. Eng. 36(5), 1290–1297 (2013) 3. Bebek, Ö., Suster, M.A., Rajgopal, S., et al.: Personal navigation via high-resolution gaitcorrected inertial measurement units. IEEE Trans. Instrum. Measur. 59(11), 3018–3027 (2010) 4. Bamberg, S., Benbasat, A.Y., Scarborough, D.M., et al.: Gait analysis using a shoeintegrated wireless sensor system. IEEE Trans. Inf. Technol. Biomed. 12(4), 413–423 (2008) 5. Ghasemzadeh, H., Jafari, R.: Physical movement monitoring using body sensor networks: a phonological approach to construct spatial decision trees. Ind. Inform. IEEE Trans. 7(1), 66– 77 (2011) 6. Zihajehzadeh, S., Yoon, P.K., Kang, B.S., et al.: UWB-aided inertial motion capture for lower body 3-D dynamic activity and trajectory tracking. IEEE Trans. Instrum. Meas. 64 (12), 3577–3587 (2015) 7. Luinge, H.J., Veltink, P.H.: Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med. Biol. Eng. Compu. 43(2), 273 (2005) 8. Xu, C., He, J., Zhang, X., et al.: Geometrical kinematic modeling on human motion using method of multi-sensor fusion. Inf. Fusion 41, 243–254 (2017) 9. Xu, C., He, J., Zhang, X., et al.: Toward near-ground localization: modeling and applications for TOA ranging error. IEEE Trans. Antennas Propag. 65(10), 5658–5662 (2017) 10. Tao, Y., Hu, H.: A novel sensing and data fusion system for 3-D arm motion tracking in telerehabilitation. IEEE Trans. Instrum. Meas. 57(5), 1029–1040 (2008) 11. Xu, C., He, J., Zhang, X., et al.: Recurrent transformation of prior knowledge based model for human motion recognition. Comput. Intell. Neurosci. 2018, 1–12 (2018) 12. Xu, C., He, J., Zhang, X., et al.: Detection of freezing of gait using template-matching-based approaches. J. Sens. 2017(2), 1–8 (2017) 13. Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors. Xsens Motion Technologies BV (2009) 14. Foxlin, E., Harrington, M.: WearTrack: a self-referenced head and hand tracker for wearable computers and portable VR. In: IEEE International Symposium on Wearable Computers, p. 155. IEEE Computer Society (2000)

Deep Learning of Intelligent Speech Recognition in Power Dispatching Jianzhong Dou1(&), Qunshan Li1, Hongyi Lai1, Chao Yang1, Shenzeng Luo1, Ziyu Lin2, and Xusheng Yang3 1

Central China Power Dispatching and Communication Center, Wuhan, China [email protected] 2 School of Finance, Hubei University of Economics, Wuhan, China 3 Beijing Yongshang Technology Co., Ltd., Beijing, China

Abstract. The establishment of speech acoustic model system based on Long Short-Term Memory (LSTM) makes further improvements for the speech recognition. However, the connectionist temporal classification (CTC) training method performances more better in directly corresponding to the phoneme sequence or bound sequence of the speech. This paper combines CTC and LSTM to establish a power dispatching speech recognition model and compares the LSTM-CTC methods with traditional GMM-HMM methods, RNN-based speech recognition methods, and unidirectional LSTM networks through experiments. The results show that the speech recognition framework of LSTM-CTC has higher precision than other methods, and also has strong generalization ability. The LSTM-CTC methods can provide higher speech recognition accuracy and are more suitable for speech recognition in power dispatching as well. Keywords: Power dispatching
Speech recognition Long-Short Term Memory (LSTM) Connectionist Temporal Classification (CTC)

1 Introduction Speech recognition technology has gradually grown with the development of computer science, communications and other disciplines. It has been more than 60 years old. From the very beginning, the concepts of pattern recognition [1], dynamic programming algorithms [2], and linear predictive coding [3] were used in speech recognition. In the 1990s, the speech recognition framework based on Gaussian mixture model-Hidden Markov model (GMM-HMM) was widely used and studied. The proposed technology based on the maximum posterior probability estimate [4] and the maximum likelihood linear regression [5] are used to solve the problem of HMM model parameter adaptation. Discriminative training criteria [6] for a range of acoustic models such as maximum mutual information [7] and minimum classification errors are proposed. Since 2009, deep learning was first applied to speech recognition tasks [8]. In particular, Long Short-Term Memory (LSTM), are very favorable for acoustic models. Since then, LSTM based on deep neural networks have gradually replaced GMM-HMM as the mainstream model for speech recognition [9, 10]. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 103–108, 2019. https://doi.org/10.1007/978-3-030-02804-6_13

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A common technique for mapping variable audio input length to variable output length is to use the CTC loss function to match the temporal information of LSTM whiling processing the model. Graves et al. proposed one RNN-type architectures, and they trained the model by connectionist temporal classification (CTC) criterion, so as to address the frame labeling issue [11]. Senior [12] showed that CTC-LSTM acoustic models performs more better than conventional LSTM. In order to making the CTC training better, one-state and context dependent phones (CD-Phones) were used in CTC-LSTM models [13]. In this paper, we will explore the convenience of LSTM-type models trained by CTC in power dispatching speech recognition. We organize the rest paper as follows. In Sect. 2, we proposed the LSTM model and CTC training, as well as the research opportunities for applying CTC training methods in power dispatching speech recognition. We discuss the results of experiments in Sect. 3, and in Sect. 4, we draw an conclude of this paper.

2 Power Dispatching Speech Recognition Based on LSTM-CTC 2.1

The Framework of Speech Recognition Based on LSTM-CTC

The proposed LSTM-CTC framework of power dispatching speech recognition is illustrated in Fig. 1. Firstly, we extract the acoustic features from speech signal. Then, we find peaky voice posteriors by putting feature vectors into a well-trained acoustic LSTM-CTC network, and a voice lattice generated, which will find out the most similar voice sequence to the target keyword voice sequence in the lattice.

Fig. 1. Framework of the proposed LSTM-CTC method

By comparing the scores of resulting voice sequences, we could make a decision. Different from using a single fixed threshold for all keywords, we estimate different thresholds for different keywords. The threshold estimation module which based on training data and lexicon can realize this function.

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LSTM Neural Network Model

The known input is xt, the output of the LSTM memory block is yt, which is calculated iteratively according to the following formula: it ¼ rðWix xt þ Wim yt1 þ Wic ct þ bi Þ
 ft ¼ r Wfx xt þ Wfm yt1 þ Wfc ct þ bf

ð1Þ ð2Þ

ot ¼ rðWox xt þ Wom yt1 þ Woc ct þ bo Þ

ð3Þ

ct ¼ ft ct1 þ it gðWcx xt þ Wcm mt1 þ bc Þ

ð4Þ

y t ¼ ot hð c t Þ

ð5Þ

In the formula, t = 1, 2, …, T; it, ft, ot and ct are respectively the output of the input gate, the gate of forgetting, the output gate, and the memory cell; Wix , Wim, Wic represent respectively the weights of the network input, the output of the upper moment and the memory cell to the input gate; Wfx, Wfm, Wfc are respectively the weights of the network input, the output of the last moment and the memory cell to the forgotten gate; Wox, Wom, Woc are respectively the weights of the other units to the output gates; bi, bc, bf, bo, are respectively the offsets of the input gates, memory cells, oblivious gates and output gates; r(*) is the log sigmoid function; gðÞ is the input activation function of the memory cell, and f ðÞ is the output function. 2.3

Connectionist Temporal Classification (CTC) Training

CTC training is to apply the CTC objective function at the output layer of the RNN network to automatically complete the alignment between the input sequence and the output label. The probability of the entire CTC path can be combined by the probability of each frame’s corresponding label. PðpjX Þ ¼

YT

y pt t¼1 t

ð6Þ

By defining the mapping /: LnT ! LeT , the label sequence z is mapped onto the CTC path p. This is a 1 to n mapping, i.e. an output tag can correspond to multiple CTC paths. Therefore, the probability of outputting the label z can be represented by the probabilities of all CTC paths. X PðzjX Þ ¼ PðpjX Þ ð7Þ p2/ðzÞ

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However, all possible conditions of the CTC path will exponentially increase with the size of the input sequence, resulting in too much computational complexity. First, expand the output tag sequence z, insert the hblanki tag with the index 0 at the beginning and insert the hblanki tag between each output tag zu to get an augmented tag sequence I = (I1, …, I2U+1). Then calculate the likelihood of z: PðzjX Þ ¼

X2U þ 1 u¼1

aut but

ð8Þ

Where t can be any time from 1 to T. According to ln PðzjXÞ, differentiate the network output yt to obtain: @ln PðzjXÞ 1 1X ¼ au bu k u2cðl;k Þ t t @yt PðzjXÞ ykt

ð9Þ

Where c(l, k) = {u|lu = k} represents returning the x-index of the label k in the extended tag sequence l.

3 Experimental Analysis In experiments, we use 5 LSTM layers to train the CTC model in LSTM models. There is no projection layer and we take 640 hidden units in each layer. This is equivalent to the 20 M parameters. We train the LSTM models with CE by using asynchronous stochastic gradient descent (ASGD) optimization, and train the CTC models by the forced-alignment generated model. There are 202 h in the audio corpus, of which there are 161 h of training data and 41 h of test data. The literature [12] indicated that there are 150 ms of latency between input events and output symbol emission in CTC training. For the sake of fairness, we trained our CTC models with outputs delayed for a few frames to roughly match 150 ms. Table 1. Experimental results of comparisons GMM + HMM RNN LSTM LSTM + CTC

Accuracy rate of train set Accuracy rate of test set 75.24% 62.71% 85.24% 80.12% 90.41% 85.32% 95.28% 92.35%

In addition to the LSTM-CTC model, we also trained the traditional GMM-HMM model, RNN model and LSTM model without CTC in the same power speech dataset. The accuracy rate of these models in train set and test set is listed in Table 1. Table 1 shows that LSTM-CTC has higher accuracy than other methods and also has strong generalization capability. It can provide more accurate speech recognition accuracy and is more suitable for voice recognition in power dispatch.

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Figure 2 shows that LSTM-CTC has always higher accuracy than other methods (LSTM and RNN methods) and also has strong generalization capability vividly. When the number of iterations is close to 100, the accuracy of LSTM-CTC gradually approaches 1.0.

Fig. 2. The accuracy rate between LSTM-CTC, LSTM and RNN

4 Conclusions The paper explores the convenience of LSTM-type models trained with CTC methods in power dispatching speech recognition. The essential factors involved in the model are proposed. The results show that the models proposed in this paper can achieve a 3% relative improvement over the traditional LSTM model. The paper also discusses the research opportunities for applying CTC training methods in power dispatching speech recognition. Because the CTC model does not require a pre-existing alignment [14], so CTC model couldn’t produce the accurate alignment of the input and output sequences. We will focus on these problems in the future. Acknowledgement. This paper is part research results of project ‘Natural language processing and machine learning technology in the application research of dispatching operation (SGHZ0000DKJS1700141)’, which is supported by the Foundation of Central Branch of National Power Net.

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References 1. Velichko, V.M., Zagoruyko, N.G.: Automatic recognition of 200 words. Int. J. Man Mach. Stud. 2(3), 223–234 (1970) 2. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978) 3. Harma, A., Laine, U.K.: A comparison of warped and conventional linear predictive coding. IEEE Trans. Speech Audio Process. 9(5), 579–588 (2001) 4. Gauvain, J.L., Lee, C.H.: Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Trans. Speech Audio Process. 2(2), 291–298 (1994) 5. Leggetter, C.J., Woodland, P.C.: Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Comput. Speech Lang. 9(2), 171– 185 (1995) 6. Juang, B.H., Katagiri, S.: Discriminative learning for minimum error classification. IEEE Trans. Signal Process. 40(12), 3043–3054 (1992) 7. Young, S.J., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The HTK Book (for HTK version 3.4.1). Cambridge University (2009). http://htk.eng.cam.ac.uk 8. Mohamed, A., Dahl, G., Hinton, G.: Deep belief networks for phone recognition. In: Workshop on Deep Learning for Speech Recognition and Related Applications. MIT Press, Whistler (2009) 9. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006) 10. Fullwood, M.J., Liu, M.H., Pan, Y.F., Liu, J., Xu, H., Mohamed, Y.B., Chew, E.G.: An oestrogen-receptor-a-bound human chromatin interactome. Nature 462(7269), 58–64 (2009) 11. Graves, A., Ferńandez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: International Conference on Machine Learning, pp. 369–376 (2006) 12. Senior, A., Sak, H., Quitry, F.D.C., Sainath, T., Rao, K.: Acoustic modelling with CD-CTCSMBR LSTM RNNs. In: Automatic Speech Recognition and Understanding, pp. 604–609 (2016) 13. Senior, A., Sak, H., Shafran, I.: Context dependent phone models for LSTM RNN acoustic modelling. In: IEEE International Conference on Acoustics, pp. 4585–4589 (2015) 14. Sak, H., Senior, A., Rao, K., Beaufays, F.: Fast and accurate recurrent neural network acoustic models for speech recognition. In: INTERSPEECH 2015 Proceedings, pp. 1468– 1472 (2015)

An Improved Multi-factor Dimensionality Reduction Approach to Identify Gene-Gene Interactions Li-Yeh Chuang1, Yu-Da Lin2(&), and Cheng-Hong Yang2,3(&) 1

Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan 2 Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan [email protected], [email protected] 3 Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan

Abstract. Genetic component of disease risk can be determined by gene–gene interactions. Multifactor dimensionality reduction (MDR) has widely used to identify gene–gene interactions based on the binary classification into high- or low-risk to evaluate the gene–gene interactions. However, the binary classification could not reflect the uncertainty of high- or low-risk classification. In this study, an improved classification method based on fuzzy sigmoid function was proposed to enhance MDR to identify GGIs. A total of 40 simulation data sets were used to compare the detection success rates of the improved MDR with the original MDR. The results expressed that our improved MDR obtained better detection success rates than MDR. Keywords: Single-nucleotide polymorphisms Multifactor-dimensionality reduction


Gene-gene interaction

1 Introduction Genome-wide association studies (GWAS) exposed that the diseases could be influenced by the association of single-nucleotide polymorphisms (SNPs) in genes (genegene interaction, GGI) [1]. GGI detection has been regarded as one of the important contributors [2] to the variation of complex disease traits in population [3] and applied in numerous disease studies [4, 5]. For detecting GGIs, the model-free approach was one of the important techniques because it does not need to hypothesize genetic models and data [6]. Among the modelfree approach, multifactor dimensionality reduction (MDR) was a popular method in case–control studies [7]. MDR considered all genotype combinations within a m-locus combination and used dimensionality reduction technique to convert the m-locus combination to low-dimensional space [7]. MDR was successfully used in the cancer studies [8, 9].

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Currently, numerous extensions of MDR have been proposed. Most of the studies focused on enhanced the time cost, including GPU-based MDR [10], DE-based MDR [11], and Fast MDR [12]. In addition, the uncertainty of binary high- or low-risk classification was another important study in MDR researches such as OR-MDR [13]. However, those MDR-based methods remain a challenge to improve the GGI detection ability. In this study, we introduced a fuzzy function based on an improved sigmoid function to enhance MDR for GGI detection. Each cell derived from all genotype combinations can be computed to obtain their own membership degrees so that MDR can detect the more biologically plausible GGIs. We used 40 simulation data sets to evaluate the detection abilities of the proposed method and original MDR. The results revealed that our improved MDR (IMDR) demonstrates satisfactory performance in GGI detection.

2 Methods 2.1

Multifactor-Dimensionality Reduction (MDR)

MDR was introduced to take account of distribution amongst cases and controls in 3m genotype combinations within an m-locus combination (m-order GGI) [7]. MDR can transfer the dimensionality of all genotype combinations into high- and low-risk (2  2 confusion matrix). Then, MDR used a k-fold CV to avoid data overfitting. Then, the CV consistency (CVC) determined the best solution amongst the k–fold CV models. MDR operation contained six steps: (1) k subsets were obtained from the raw data set for CV operation; (2) the m-locus combinations were generated; (3) the ratio between cases and controls within a m-locus combination was computed; (4) the genotype combinations of that m-locus combination were transferred into a two-way contingency table; (5) the m-locus combination was evaluated by the statistical measure; and (6) CVC operation. 2.2

Improved Multifactor-Dimensionality Reduction (IMDR)

In this study, we introduced an improved function for MDR classification operation. The fuzzy cluster technique was used to improve the binary classification of MDR so that the high- and low-risk groups can be regarded as fuzzy sets. The IMDR operation included seven steps: (1) the raw data set was assigned into k subsets for CV operation; (2) the m-locus combinations were generated; (3) the membership degrees of multifactor classes were computed; (4) The number of 3 m genotype combinations were transferred into a two-way contingency table; (5) the m-locus combination was evaluated by the statistical measure; and (6) the CVC operation. Step 1. For CV operation, the raw data set was assigned into k subsets. All samples in the data set were randomly divided into k subsets. Step 2. The m-locus combinations were produced.

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Step 3.1. The 3m genotype combinations were constructed. In training data set, the samples were assigned into the genotype combinations of the m-SNP combination, and then count the number of case and control groups. Step 3.2. The membership degrees of genotype combinations were evaluated by an improved fuzzy function based on the sigmoid function. The probability of a case with the ith genotype combination was formulated as liH ¼

1þ

1


;

x1 xþ1

ð1Þ

Subject to x¼

2  nþ þ 1 ni0 þ ni1

liL ¼ 1  liH

ð2Þ

where n++ was the total number of samples within the subset of k-fold CV. ni0 was the number of controls within ith genotype combination. ni1 was the number of cases within ith genotype combination. Step 4. The numbers of 3m genotype combinations were transferred into a two-way contingency table (Table 1). Each sample has the partial membership of H and L groups simultaneously due to the step 3. Four cells in the two-way contingency table were the membership degrees of H and L samples belonging to the corresponding case and control groups. The four cells were formulated as 8 CH1 > > > > < CH0 > CL1 > > > : CL0

¼ ¼ ¼ ¼

P P P P

ni1 liH ni0 liH ni1 liL

ð3Þ

ni0 liL

where ni0 and ni1 were the sets of individual matches to the ith genotype combination within the control and case groups, respectively. Table 1. Contingency table of MDR. Controls H ni0liH L ni0liL Total ni0liH + ni0liL

Cases Total ni1liH ni0liH + ni1liH ni1liL ni0liL + ni1liL ni1liH + ni1liL

In which, ni0liH and ni1liH are satisfied in high-risk (H); ni0liL and ni1liL are satisfied in low-risk (L).

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Step 5. The m-locus combination was evaluated by the statistical measure. CCR is used to evaluate GGI, which is the proportion of correctly classified individuals with the m-locus combination. The balanced CCR function can consider the unbalanced data set to determine the balanced accuracy by the proportions for the case and control groups [14]. The CCR value is between 0 and 1, and the value 1 indicates the optimal solution. The CCR is formulated as follows:  CCRFuzzy ¼ 0:5

CH1 CL0 þ CH1 þ CL1 CH0 þ CL0

 ð4Þ

Finally, the highest CCR with the m-locus combination was regarded as optimal GGI in that CV. Step 6. The CVC operation. The highest number of m-locus combination existed in k-fold CV was the best GGI.

3 Results and Discussions In this study, we used 40 two-locus disease models without marginal effects to compare the detection abilities of MDR and IMDR. The 40 models were obtained from the study of Wan et al. [15]. Under the parameters including heritability (h2) and minor allele frequencies (MAF), the simulation data sets were generated by GAMETES software [16]. h2 ranges from 0.025 to 0.4. MAF ranges from 0.2 to 0.4. 100 data sets were generated consisting of 1000 SNPs and including 200 cases and 200 controls for each disease model. The detection success rates were evaluated based on the proportion of the 100 data sets. The detection success rates of MDR and IMDR were expressed in Fig. 1. IMDR outperformed MDR in the 40 models without marginal effects. For CVC = 5, IMDR outperformed MDR, indicating that our improved function based on fuzzy approach can improve detection stability in the disease models. Wilcoxon signed-rank test was applied to evaluate IMDR and MDR in the 40 disease models. As shown in the results of CVC = 5 and CVC > 1, IMDR provided a better detection success rates compared with MDR (R+, Table 1). A p-value < 0.05 was the significant superiority of IMDR over other methods. Our results suggest that an improved classification function based on fuzzy approach can effectively enhance MDR, because the function considers the uncertainty of H/L classification in disease loci without marginal effects. Both MDR and IMDR, the computational complex is evaluated by that the times of the optimal m-locus combination amongst a total number of SNPs (denoted as n) in kfold, (n choose m)  total number of samples  3n k. For 1000 SNPs with 1000 cases and 1000 controls, both MDR and IMDR took 41 s to complete the all computation on the same computer. Computational techniques including GPU-based MDR [10], DE-based MDR [11], and Fast MDR [12] could be applied to enhance the highdimensional computations using IMDR.

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Fig. 1. Comparison of the detection abilities of MDR and IMDR in the disease models without marginal effects.

The detection success rates of MDR and IMDR were evaluated by the proportion of 100 data sets under each model setting. The data set includes 1,000 SNPs. In Fig. 1, the gray and blue bars represent the detection success rates of MDR and IMDR, respectively. The absence of bars are zero (Table 2). Table 2. Wilcoxon Signed-Rank test comparison of MDR and IMDR on the 40 epistasis models. N

Mean rank 4.17 15.23

Sum of ranks 12.50 365.50

Z-test

P value

3 −4.245 2.19E−05 R− R+ 24 R= 13 Total 40 IMDR (CVC = 5) vs. MDR R− 2 6.50 13.00 −4.867 1.13E−06 (CVC = 5) R+ 32 18.19 582.00 R= 6 Total 40 R−: the degree that IMDR is inferior to the algorithm, R+: the degree that IMDR is better to the algorithm, R=: the degree that IMDR is equal to the algorithm, N: numbers. IMDR vs. MDR

4 Conclusions In this study, we addressed the limitation of MDR due to binary classification. We proposed an improved function to transfer binary classification to fuzzy classification. Thus, each cell derived from multifactor genotypes could be evaluated for its own membership degrees. To assess the improved MDR performance expressed, the detection success rates of MDR and IMDR were compared in the disease models. The results revealed that we successfully used the improved fuzzy function approach to enhance the performance of MDR.

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Findings. This work was partly supported by the Ministry of Science and Technology in Taiwan (under Grant no. 105 – 2221 – E – 151 – 053 – MY2 and 106 – 2811 – E – 151 – 002 –.

References 1. Moore, J.H., Asselbergs, F.W., Williams, S.M.: Bioinformatics challenges for genome-wide association studies. Bioinformatics 26, 445–455 (2010) 2. Mackay, T.F.C., Moore, J.H.: Why epistasis is important for tackling complex human disease genetics. Genome Med. 6, 42 (2014) 3. Steen, K.V.: Travelling the world of gene-gene interactions. Brief. Bioinform. 13, 1–19 (2012) 4. Fu, O.Y., et al.: The combinational polymorphisms of ORAI1 gene are associated with preventive models of breast cancer in the Taiwanese. Biomed Res. Int. Art no. 281263 (2015) 5. Yang, C.H., Lin, Y.D., Chuang, L.Y., Chen, J.B., Chang, H.W.: Joint analysis of SNP-SNPenvironment interactions for chronic dialysis by an improved branch and bound algorithm. J. Comput. Biol. 24, 1212–1225 (2017) 6. Li, J.H., Dan, J., Li, C.L., Wu, R.L.: A model-free approach for detecting interactions in genetic association studies. Brief. Bioinform. 15, 1057–1068 (2014) 7. Ritchie, M.D., et al.: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69, 138– 147 (2001) 8. Yang, C.H., Lin, Y.D., Yen, C.Y., Chuang, L.Y., Chang, H.W.: A systematic gene-gene and gene-environment interaction analysis of DNA repair genes XRCC1, XRCC2, XRCC3, XRCC4, and oral cancer risk. OMICS-J. Integr. Biol. 19, 238–247 (2015) 9. Fu, O.Y.: Breast cancer-associated high-order SNP-SNP interaction of CXCL12/CXCR4related genes by an improved multifactor dimensionality reduction (MDR-ER). Oncol. Rep. 36, 1739–1747 (2016) 10. Greene, C.S., Sinnott-Armstrong, N.A., Himmelstein, D.S., Park, P.J., Moore, J.H., Harris, B.T.: Multifactor dimensionality reduction for graphics processing units enables genomewide testing of epistasis in sporadic ALS. Bioinformatics 26, 694–695 (2010) 11. Yang, C.-H., Chuang, L.-Y., Lin, Y.-D.: CMDR based differential evolution identify the epistatic interaction in genome-wide association studies. Bioinformatics 33, 2354–2362 (2017) 12. Yang, C.H., Lin, Y.D., Yang, C.S., Chuang, L.Y.: An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction. BMC Genomics 16, Art. no. 489 (2015) 13. Chung, Y.J., Lee, S.Y., Elston, R.C., Park, T.: Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions. Bioinformatics 23, 71–76 (2007) 14. Yang, C.H., Lin, Y.D., Chuang, L.Y., Chen, J.B., Chang, H.W.: MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction. PLoS One 8 (2013) 15. Wan, X.: Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics 26, 30–37 (2010) 16. Urbanowicz, R.J., Kiralis, J., Sinnott-Armstrong, N.A., Heberling, T., Fisher, J.M., Moore, J. H.: GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. Biodata Min. 5, Article no. 16 (2012)

Nonlocal Estimation and BM3D Based Face Illumination Normalization Yingkun Hou(&) School of Information Science and Technology, Taishan University, Taian, Shandong, China [email protected]

Abstract. Various lighting conditions for face image seriously affect the accurate rate of face recognition. This paper proposes a kind of nonlocal illumination normalization method, which compares image block mean with the mean of the whole image, gain or punish the upper-left corner pixel values of the image block according to the mean of the image block by using a little gain or punishment factor, use different block size and various gain or punishment factors to remove the illumination by the multi-step iteration; A lot of noise will be generated after the illumination normalization in the original darker area, so using BM3D to denoise images will achieve the ideal final result. The experimental results show that the obtained images are more natural and can better preserve the image details than most existing illumination normalization methods, thus it can achieve higher face recognition rate than the existing methods. Keywords: Nonlocal estimation Face recognition


BM3D
Illumination normalization

1 Introduction Face recognition, which has always been a very active research field, plays a significant role in pattern recognition and machine vision research [1, 2]. Relevant researches have indicated that illumination normalization is the most important preprocessing step of systems like face recognition, face tracking or face detection, etc., and different light conditions will have a strong impact on practical application of this kind of systems [3]. Although all kinds of different ways of general face recognition have already been extracted, recognition rate have been quite ideal, the technology has tended to be mature, but illumination unevenness has always been bottleneck in face recognition research. Most existing face recognition algorithms are sensitive to illumination variation, and in order to solve the problem of the influence of illumination variation on face recognition, a large number of algorithms have been put forward. In general, these algorithms are classified into three types: the first type is to use normalized technique to conduct preprocessing of face image such as logarithm transformation [4] and histogram equalization [5], this kind of global techniques are only effective for images with light degree of illumination variation while having poor effect on images with complicated illumination variation. Afterwards, people propose histogram equalization © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 115–122, 2019. https://doi.org/10.1007/978-3-030-02804-6_15

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based on block histogram equalization [6] and self-adaptation [7], although the effect has been improved to a certain degree, in general, it’s still unsatisfying. The second type is to use Lambertian model to process illumination variation of face image, this type of methods include multi-scale retina (MSR) [8], self-quotient image (SQI) [9], logarithm total variation (LTV) [10] and wavelet-based illumination normalization (WIN) [11]. MSR firstly conducts logarithm transformation, then conducts smooth filtering of the image after logarithm transformation, and calculates the difference between filtered image and original image to obtain illumination invariant, but this kind of method has strong halo ghost signal. SQI model conducts quotient operation between original image and the image of its smoothing results, as it eliminates some sharp marginal signals which are important for recognition, so its effect is not ideal, either. Although compared with SQI, LTV method has been somewhat improved, its operation is complicated. The method based on wavelet obtains illumination invariant by processing wavelet transformation coefficient, but wavelet transformation usually has Gibbs phenomenon. The third type is to use de-nosing model to obtain illumination invariant such as using the method based on non-subsample contour transformation (NSCT) [12] to conduct de-nosing of images, then reconstructing its high-frequency coefficients to obtain illumination invariant; another type is to conduct de-noising of images with non-local de-noising means (NL-means) which has been popular in recent years, then it illumination invariant is obtained by original image deducting de-noised image. Among the three types of methods, the third type is the best one for the moment as it obtains high recognition rate in face recognition under illumination variation, especially method based on NSCR is the best illumination normalization method at present. This paper proposed a method based on block estimation to realize face illumination removal. This method selects one image block in image, calculates mean value of this image block, then compares the mean value with mean value of the whole image, if block mean value is greater than image mean value, it will multiply block by a constant which is slightly smaller than 1, on the contrary, it will be multiplied by a constant which is slightly greater than 1. After multistep different-constant iterations, illumination can be effectively eliminated. This method uses the best image de-noising method BM3D [13–17] so far to conduct de-noising of image. Finally, it used alpharoot method [18] with BM3D model to enhance the image. A series of operations above can not only eliminate illumination effect but also make images obtained under all kinds of illumination conditions for each person become very consistent. Experimental results indicate that the proposed method obtains illumination-variation face recognition rate which is higher than existing algorithms.

2 Non-local Illumination Normalization Algorithm Face images obtained under different illumination conditions usually have local brightness difference, if this kind of brightness difference isn’t eliminated by image processing, the recognition rate will be seriously reduced. Through in-depth research

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on illumination model, this paper proposed a kind of face illumination normalization method of non-local estimation. Illumination model is as shown in the following formula: Fðx; yÞ ¼ Iðx; yÞRðx; yÞ

ð1Þ

It can be seen from above simple illumination model that different brightness transformations in the image is to locally multiply a quantity which is greater than or lower than 1 on an image without illumination, if the multiplied quantity is greater than 1, this part will brighten, on the contrary, it will darken. Illumination normalization is to seek for this illumination invariant Rðx; yÞ before multiplication, it can be seen from above analysis that if m\M by comparing mean value m of one image block and mean value M of the whole image, this image block will be slightly dark, or it will be slightly bright. According to this analysis, a very simple method is to multiply a light block by a number which is lower than 1 in order to obtain an illumination invariant. This paper proposed the following algorithm to realize illumination normalization: (1) Dark pixel brightens It calculates mean value M of the whole image, conducts symmetric prolongation of the image, selects 16  16 image block B according to sliding step length 1 and calculates its mean value m, if m\M, it conducts gaining of the image block according to the following formula: Be ¼ aB

ð2Þ

It operates each block according to above formula, and finally it extracts actual image from prolonged image. It’s noteworthy that a is a number which is slightly greater than 1, so this paper iterates this step for five times. (2) Bright pixel darkens Most operations are the same as the step 1, only that this operational step is to punish too bright pixel, so Formula (2) is modified as following, if m [ M, it punishes image block according to the following formula: Bf ¼

B b1

ð3Þ

Similarly, b1 is a number which is slightly greater than 1, and this step is also iterated for five times. (3) Large region with bright part darkens Through above two steps, most regions have realized illumination normalization, but there will be very bright local parts on eyelid and nasal tip, and this step is specialized for conducting operations in these places with bright parts. Basic steps are

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similar to above two steps, what’s different is that punishment conditions in this step are relaxed, if m [ M  T1 , punishment of image block will be conducted according to the following formula: Bf ¼

B b2

ð4Þ

b2 in this formula is greater than b1 in step two, and this step is iterated for five times. (4) Small regions with bright part darkens As the first three steps adopt 16  16 large image blocks, some regions with small and bright parts are still not be processed such as pupil part. This operational step is specialized for this situation. Different from the first three steps, this step uses small image blocks, so this paper selects 3  3 image blocks. Punishment conditions are between step 2 and step 3, and its punishment intensity is lighter than that of step 3. If m [ M  T2 , image block will be punished according to the following formula: Bf ¼

B b3

ð5Þ

This step is iterated for 10 times. (5) Image brightness normalization Good illumination normalization results have been obtained through the above 4 steps, but there are many parts with dark images and those with bright images in original image, some parts are dark and some are bright of the image after illumination normalization, in order to obtain image of uniform brightness, this paper proposes using the following method to realize brightness normalization. It firstly calculates mean value M of the image, and it conducts brightness normalization according to the following formula:
In ¼

I b;

if M [ 0:5;

Ib; if M\ ¼ 0:5:

ð6Þ

Where b is a number which is slightly greater than 1, I is the image of the result of previous illumination normalization, In is the image after brightness normalization, through about 30 iterations, average brightness values of all images can be normalized to about 0.5, which can improve subsequent face recognition rate.

3 BM3D Image De-noising and Enhancement After face illumination normalization in section two, originally dark part will have strong noise, and this noise will also exert serious effect on face recognition. BM3D is the best de-noising method which has been recognized at present, and this paper uses BM3D to conduct de-noising of the image after illumination normalization.

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Although visual effect of de-noised image is already good, there is certain difference between images, and this kind of difference is not beneficial for face recognition. Hence, this paper further uses BM3D enhancement method to enhance de-noised images, the difference between images will be further narrowed after enhancement, thus greatly improving accuracy of face recognition. 3.1

BM3D Image Denoising [13]

(1) Block matching: we divide input image z into mutually overlapped block Zx2X ; X is set of block coordinates, and then conduct cluster operation of each Zx2X , making image blocks similar to Zx construct a 3D matrix: Zx3D ¼ grouping(Zx Þ

ð7Þ

(2) Synergy filtering: we used 3D matrix obtained in (1) to shrink transformation coefficients of hard threshold values in order to eliminate noise, and then we conduct inverse 3D transformation: ^ 3D ¼ T1 ðshrink(TðZ3D ÞÞÞ Y x x

ð8Þ

T1 is inverse transformation of separable 3D transformation T.

(3) Aggregation: we conduct weighted average aggregation of all image blocks in ^ 3D of each group, and finally obtain de-noising image. Y x2X 3.2

BM3D Image Enhancement [18]

Firstly, we conduct block matching of the image and construct a 3D matrix, and separable 3D transformation is conducted of the matrix. Similar to BM3D de-noising algorithm, here hard thresholding is used to eliminate noise, and we realize enhancement of de-noised coefficients with a-root method. For conversion spectrum t of given image, tð0Þ is DC coefficient, and a-root image enhancement is realized through the following function: ( tsh ðiÞ ¼

1

tðiÞ a jÞ ; sign½tðiÞjtð0Þjðj tð0Þ tðiÞ;

if tð0Þ 6¼ 0 otherwise

ð9Þ

tsh is transformation coefficient after enhancement, gain factor a which is greater than 1 is used to realize coefficient amplification and reach enhancement effect. Literature [18] gives two image enhancement methods whereby BM3D-SH3D is to conduct a-root operation of 3D transformation coefficients after de-nosing;

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while BM3D-SH2D is to firstly conduct the third dimensional wavelet inverse transformation and then conduct a-root operation, its essence is only conducting operation on 2D transformation spectrum of each image block.

4 Experimental Results In order to conduct an intuitive comparison of illumination normalization effects of the method in this paper and existing methods, this paper used multiple existing methods to conduct the experiment and made a comparison with results of the method in this paper, and results were as shown in Fig. 1. It can be seen from comparison results that the method in this paper not only effectively realized illumination normalization, eliminated noise and reserved image details, but all resultant images were conducted with histogram equalization, and resultant image of this paper was the image before enhancement.

Fig. 1. Comparison of results of various illumination normalization methods. From top to down, rows are respectively about illumination normalization results of original image, multi-scale Retinex(MSR), MSSQ, WIN, means(NLM) and this paper.

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In order to verify effectiveness of the proposed illumination normalization method, this paper used expanded Yale face image library to conduct face recognition experiment, and this face image library consisted of face images of 38 people with each person respectively having 9 postures and 64 illumination conditions. As this paper was only experimenting illumination normalization with posture problem not involved, it only used forward postures to recognize 64 illumination conditions, so experimental data was 38 subsets with each subset containing 64 various images of different illumination. This paper used the first 2 images of each subset—totally 76 images as training set and other images as test set to conduct the experiment, and image sizes were uniformly adjusted to 48  42. In recognition experiment, this paper used PCA to extract features, used nearest neighbor classifier to conduct classification, and finally used Markov cosine distance to measure similarity. Experimental results of this paper were compared with those of multi-scale Retinex (MSR), MSSQ, WIN, NL_means (NLM) and so on. Experimental results showed that the method of this paper obtained relatively ideal recognition rate which was improved to a certain degree when compared with recognition rate of existing good face illumination normalization methods, and results were as shown in Table 1. Table 1. Results of face recognition experiment in which the first two images of each person are taken as training set (%) Method Original image MSR MSSQ WIN NLM Method in this paper Recognition rate 42.32 83.19 95.25 53.74 94.91 95.80

5 Conclusion Starting from essence of face image illumination model, this paper proposed a simple and effective face illumination normalization method which only used gain and punishment of pixel of image blocks, while it didn’t any complicated transformation method, but it realized good illumination normalization results. BM3D has excellent image de-noising effect. This paper used BM3D to de-noise the image after illumination normalization, then used alpha-root to enhance de-noised image, by which it not only effectively eliminated image noise but also reserved essential features of face image which were good for recognition. The method in this paper has been applied to face recognition of uneven illumination and has obtained relatively ideal face recognition rate. Acknowledgments. This work was supported by the National Science Foundation of China under grant numbers 61379015; the Natural Science Foundation of Shandong Province under grant number ZR2011FM004; and the Science and Technology Development Project of Taian City under grant number 20113062.

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References 1. Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: a survey. Proc. IEEE 83(5), 705–741 (1995) 2. Bowyer, K.W., Chang, K., Flynn, P.: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Comput. Vision Image underst. 101(1), 1–15 (2006) 3. Xie, X., Lam, K.M.: An efficient illumination normalization method for face recognition. Pattern Recogn. Lett. 27(6), 609–617 (2006) 4. Savvides, M., Kumar, B.V.K.V.: Illumination normalization using logarithm transforms for face authentication. In: Audio-and Video-Based Biometric Person Authentication, pp. 549– 556 (2003) 5. Shan, S., Gao, W., Cao, B., Zhao, D.: Illumination normalization for robust face recognition against varying lighting conditions. In: IEEE Workshop on Analysis and Modeling of Faces and Gestures, pp. 157–164 (2003) 6. Pizer, S.M., Amburn, E.P., Austin, J.D., et al.: Adaptive histogram equalization and its variations. Comput. Vision Graph. Image Process. 39(3), 355–368 (1987) 7. Xie, X., Lam, K.M.: Face recognition under varying illumination based on a 2D face shape model. Pattern Recogn. 38(2), 221–230 (2005) 8. Jobson, D.J., Rahman, Z., Woodell, G.A.: A multi-scale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. Special Issue Color Process. 6(7), 965–976 (1997) 9. Amnon, S., Tammy, R.R.: The quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 129–139 (2001) 10. Chen, T., Yin, W., Zhou, X.S., et al.: Total variation models for variable lighting face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1519–1524 (2006) 11. Du, S., Ward, R.: Wavelet-based illumination normalization for face recognition. In: IEEE International Conference on Image Processing, vol. 2, II-954-7 (2005) 12. Cheng, Y., Hou, Y., Zhao, C., et al.: Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain. Neurocomputing 73(10), 2217–2224 (2010) 13. Dabov, K., Foi, A., Katkovnik, V., et al.: Image denoising by sparse 3D transform domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007) 14. Hou, Y., Zhao, C., Yang, D., et al.: Comments on “Image denoising by sparse 3-D transform-domain collaborative filtering”. IEEE Trans. Image Process. 20(1), 268–270 (2011) 15. Yingkun, H., Mingxia, L., Deyun, Y.: Multi-stage block-matching transform domain filtering for image denoising. J. Comput. Aided Des. Comput. Graph. 26(2), 225–231 (2014) 16. Hou, Y., Park, S.H., Wang, Q., et al.: Enhancement of perivascular spaces in 7T MR image using Haar transform of non-local cubes and block-matching filtering. Sci. Rep. 7(1), 8569 (2017) 17. Hou, y., Shen, D.: Image denoising with morphology-and-size adaptive block-matching transform domain filtering. EURASIP J. Image Video Process. (in press) 18. Dabov, K., Foi, A., Katkovnik, V., et al.: Joint image sharpening and denoising by 3D transform-domain collaborative filtering. In: Proceedings of International Workshop Spectral Methematical Multirate Signal Processing, pp. 201–208 (2007)

Design of Rice Traceability System Based on WSN and RFID Fengjuan Miao(&), Xiaoxu Lu, Bairui Tao, Kaida Liu, and Ding Liu College of Communications and Electronics Engineering, Qiqihar University, Qiqihar, Heilongjiang 161006, China [email protected]

Abstract. The wireless sensor network and RFID technology have been widely used in agricultural production. In this paper, a rice traceability system based on WSN and RFID is designed to trace the agricultural products precise and realtime on planting, storage, processing, transportation and sales. Using CC2530 wireless module to transmit the collected rice growth environment data, and using RFID to trace the information of rice harvest, storage, transportation and engineering, and constructs the traceability database and the docking of the electronic e-commerce platform of agricultural products. The design is simple, fast and low cost, which makes a complete data chain between agricultural producers, agricultural products and consumers, and has an important application value in promoting the development and construction of agricultural information and agricultural products. Keywords: Rice traceability


Wireless sensor network
Radio frequency

1 Introduction In recent years, with the occurrence of a series of food safety incidents such as Sanlu milk powder, hanging white cakes, and so on, the government has gradually intensified efforts to monitor food safety in order to restrict companies from legal production and dispel people’s concerns about food safety. The concept of food traceability is mentioned by the media [1, 2]. The hidden dangers of quality and safety existing in the production, storage and transportation, processing and sales of agricultural products have attracted more and more attention. In particular, agricultural products sold through e-commerce channels, because consumers and producers are achieved through the network, users at both ends of the product can not directly meet, product quality problems easily lead to disputes, thus limiting the rapid development of agricultural e-commerce [3, 4]. This article takes rice production and sales as a whole to trace the origin of the whole industry chain, and proposes a rice-based traceability system design based on WSN and RFID. Starting from the source of production of agricultural products, specific procedures and loops of agricultural products can be queried to eliminate consumer concerns.

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2 Overall Design Framework of System The design of the rice traceability system was applied to the agricultural environment of the northern facilities and the rice fields in the Qiqihar region were taken as examples. Rice production and processing are more complicated than other food crops. The factors affecting the quality of rice are multiple, and it is necessary to strictly observe the prescribed standards to monitor the cultivation, production, processing, transportation and sales of rice to ensure that it is perfectly safe. The core function of the rice traceability system is to make it easier for consumers to obtain information on the agricultural products purchased, and to trace the source of the purchased rice through mobile phones. The overall design scheme of rice traceability system is shown in Fig. 1.

Fig. 1. Overall scheme of rice traceability system

3 Each Link Design of System The system design is introduced according to the links and is divided into planting, storage, processing, transportation and sales. During the planting process, the growing environment of rice fields is monitored and the rice growth data is collected by sensors. In the storage process, the RFID code is used to read the rice data before the storage to ensure that the distribution bag codes and detection indicators are consistent. After the storage, the WSN technology is applied to the environment. Monitor and export, when linking out specific information such as flow direction to RFID tags, establish associations between consumption information and distribution information; processing links, from rice arrival processing plant to detection of product outbound delivery process information data to RFID Technology as a carrier to achieve traceability of rice information [5]. The finished rice has information on all aspects of rice for easy viewing; during transportation, temperature and humidity are controlled to prevent rice from deteriorating during transportation. The rice in the box/bag is affixed with an RFID tag, and the RFID label reader is used at the transportation site to read the label on the outer package. In the sales process, the merchant writes the product sales information into the RFID tag, which can be returned during the return and merchandise recall. According to the contents of RFID tags can be traced back to the entire process.

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4 Design of Data Acquisition Terminal System 4.1

Terminal Node Circuit Design

The role of the terminal node is to sense, collect and process external physical quantities, and its circuit design is crucial. Figure 2 below shows the end node circuit diagram. The sensor should be connected to the terminal node, and the sensor can collect the information through the terminal node.

Fig. 2. Endpoint circuit diagram

4.2

Coordinator Module Design

The coordinator is responsible for establishing and managing the network to ensure that the system is working properly. The collected sensor data is transmitted to the coordinator through the routing node, which is responsible for the data aggregation and is connected with the upper computer through the serial port. The coordinator can guarantee to establish the communication connection with the computer, is made up of peripheral circuit such as CC2530 chip and voltage stabilizing circuit, resetting circuit, power supply status indicating circuit and USB to change the serial port circuit. 4.3

Router Module Design

In the network, the function of the routing node is to forward and expand the communication distance. Unlike the terminal node, the routing node does not have a sensor interface circuit and a buzzer alarm circuit, but has a core board interface, a switch, and a reset button. 4.4

Interface Circuit Design

Air Temperature and Humidity Sensor. The DHT11’s supply voltage is 3-5.5 V. Add a 100 lF capacitor between the power pins for decoupling. The DHT11’s peripheral circuitry consists of the DHT11 and a 10 kX resistor. The DHT11 collects parameters of the current environment and transmits them as digital signals, and can directly connect the CC2530 to achieve temperature and humidity data acquisition and transmission.

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Water temperature sensor. The DS18B20 performs temperature measurement using an upper temperature measurement technique. Pins 1 and 3 are connected to the power supply. Pin 2 is a data signal line that can be directly connected to the ZigBee node to measure whether the paddy field water temperature is within the optimal range. Soil moisture sensor. The soil moisture sensor is used to detect soil moisture when rice is raised and transmits the humidity information to the ZigBee node. When the humidity is too low, the system activates the replenishing irrigation equipment, when the soil moisture reaches a certain value, close the replenishing irrigation equipment. The dual voltage comparator that compares the voltages of pins 2 and 3, and pin 1 outputs 0 or 1 depending on the result. By adjusting the sliding rheostat VR to change the voltage of 2 to achieve the purpose of setting the soil moisture threshold. PH sensor. This part is composed of PH detection sensor module and E-201-C type PH electrode. PH detection sensor module detects a large range of concentrations, analog voltage signal serial output, the response time is short. Before using this module, the correct PH value can only be obtained after passing the communication test, PH test, and PH calibration. 4.5

WSN Networking Design

The method of clustering ad hoc networks is used for networking, and the clustering network structure can be composed of multiple clusters. Each cluster has one aggregation node. The monitored area is divided into areas, each area is considered as a cluster, and each cluster has a cluster head node responsible for uploading information. For a rice-originated network, due to the large number of paddy fields, each plot can be considered as a cluster. Clusters use fork double-stranded communication. The WSN network has a self-organizing and self-healing function to ensure data reliability and stability, and the WSN has low power consumption. The WSN network topology is shown in Fig. 3.

Fig. 3. WSN network topology design

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Protocol Stack Workflow

The protocol stack is the implementation of the protocol and refers to the code and function library for the upper application call. The protocol stack writes the underlying code that conforms to the protocol standard and provides a callable function module. The entire ZStack programming process is divided into the following 6 steps: Shutdown all interrupts; external chip initialization; internal chip initialization; operating system initialization; open all interrupts; execute operating system. 4.7

Sensor Data Acquisition Node Program Design

The sensor module acquisition procedures include air temperature and humidity, water temperature, soil moisture, and PH. The flow of the sensor module acquisition program is as follows: Start the program, CC2530 enters initialization, determine whether there is a signal acquisition, if there is a signal acquisition, then the host sends a start signal, the sensor will switch to high-speed mode response, and then begin to read the data, The collected data is sent to the internal processor 8051 of the ZigBee chip for processing, and the collected data is sent back to the upper computer for display via the coordinator. This process is the sensor module data acquisition program design ideas. 4.8

Coordinator Data Sending and Receiving Program Design

The coordinator is responsible for returning the data collected by the sensor node back to the computer display. Sending. The node sends an instruction, CC2530 initializes, ZigBee module starts networking, determines whether the networking is successful, the networking is successful, the collected data is processed, the data is sent out through the PA, and the sending process ends. If the network fails, the network is returned to the previous step and the network is reconfigured before being processed and sent. Receiving. The coordinator receives the command and initializes it after starting. Check whether the networking is successful. If the networking is successful, continue the process and wait for data. If there is data, the data will be displayed through the serial port and displayed on the computer. 4.9

Radio Frequency Identification Module Programming

To implement RFID read and write commands on the page, it is necessary to make RFID read and write operations into a user control. Call the API method in the RFID reader driver in the user control, and then use this user control. The RFID reading and writing process is declaring the user control object, operating the user object, opening the port before reading and writing RFID, closing the port after reading and writing, reading the user information stored in the RFID, and completing the reading.

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5 Phone-End Traceability Software Design 5.1

Functional Requirements and Framework Technical Analysis

The rice traceability system can use mobile phones to query rice information. After the customer logs in, operations such as operation information, management information, and analysis data can be entered. Considering the application environment, operation methods, performance index, function of each module, and user’s operating habits in rice traceability process, a corresponding interface was designed for rice planting, storage, processing, transportation and sales. The combination of the framework of Retrofit, RxJava, OkHttp and other mobile phones can easily and quickly implement network requests. Coupled with Butter Knife and ZXing, Butter Knife is a View injection framework that focuses on the Android system, eliminating unnecessary steps and making integration easy to use. ZXing is an open source, two-dimensional code scanning tool used to implement traceability of QR codes. 5.2

Main Page Design

When logging in to the system, it is divided into senior administrators and corresponding operators at all stages. The operations that can be performed by the senior administrator on the left include user information, user management, and rice field management. User information consists of modifying personal information and changing passwords; user management consists of viewing users, assigning permissions, and increasing users; Paddy Field Management can complete the operations of increasing paddy fields and viewing rice fields. The operators on the right side correspond to the relevant management rights. The structure diagram of rice traceability user management system is shown in Fig. 4 below.

Fig. 4. Rice traceability user management structure

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Background Design

Server-side logic. The hierarchical structure of the rice traceability system constructed in this paper includes customer layer, control layer, business layer, data persistence layer, and data layer. Business Logic Design. Layered processing is the core idea of the MVC design pattern. In this mode, the request submitted by the user to the backend server through the browser is handled by the Controller layer. From bottom to top, it can be divided into four layers: Model layer, Dao layer, Service layer, and Controller layer. When the upper application module sends a request, the corresponding controller receives the request, then parses, processes, and calls the model layer to perform interaction with the underlying data, and then the package is returned by the view layer to the upper application module. Business Logic Design. The background server completes the data service request submitted by the mobile client. The submitted service is sent to the server through the network in the form of a data packet. The server parses the requested service and extracts the corresponding data from the database to the client over the network. 5.4

Database Design

In accordance with a certain way to store data together to achieve multi-user sharing redundancy, independent of the application, with integrity and sharing, select the relational database MySQL as a database. The database of this design mainly includes user information, rice production information, and rice production operation information. The rice production information includes rice species information, rice field information, and land information. The rice production operation information includes planting environment information, planting operation information, storage information, processing information, transportation information, and sales information.

6 System Testing and Conclusion After the sensor module is connected with the ZigBee module, it is transmitted back through the coordinator and the collected data is displayed on the serial port. Open the serial port debugging assistant and check whether the sensor data of each node is correctly collected. The debugging of RFID module uses the minimum system module of the singlechip microcomputer to control the RFID card reading action and conduct data processing. The minimum system is then connected with the ZigBee module, and then the ZigBee module exchanges data with the computer through the coordinator, and the input information can be displayed on the upper computer. The RFID module program was written using Keil software. After testing, the various functions of each module basically meet the expected requirements.

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In view of the food safety issues that people are increasingly concerned about, this article selects rice as the research object and conducts security analysis on all aspects. By using technologies such as wireless sensor networks and RFID radio frequency identification, the rice traceability system was designed and implemented. As the government attaches greater importance to the quality and safety of agricultural products, the laws and regulations related to traceability will be more and more perfect, and the input of relevant departments in the traceability system of agricultural products will be greatly increased. At the same time, with the increase in the operating time of the trials of various agricultural products traceability systems, agricultural departments in various regions will accumulate more and more experience. It is believed that in the past few years, the traceability system of agricultural products with a wide coverage and a unified platform will surely become more and more perfect. Acknowledgments. This work was jointly supported by the Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province (Grant No. LBH-Q15142, LBH-Q14157), Science and Technology Project of Qiqihar (Grant No. GYGG-201409, GYGG-201619), Higher School Science and Technology Achievements Industrialization Pre-Research and Development Foundation of Heilongjiang Province (Grant No. 1254CGZH04), University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (Grant No. UNPYSCT2016087), Heilongjiang Education Science “Twelfth Five-Year” Plan for the Record (GBGH27), The Research Project on the Reform of Higher Education in Heilongjiang (SJGY20170384), Educational Science Research Project of Qiqihar University (2016090), Scientific Research Foundation for the Returned Overseas Chinese Scholars in Heilongjiang Province, and this work was supported by the Supercomputing Center of Qiqihar University.

References 1. Bibi, F., Guillaume, C., Gontard, N., Sorli, B.: A review: RFID technology having sensing aptitudes for food industry and their contribution to tracking and monitoring of food products. Trends Food Sci. Technol. 62, 91–103 (2017) 2. Alfian, G., et al.: Integration of RFID, wireless sensor networks, and data mining in an epedigree food traceability system. J. Food Eng. 212, 65–75 (2017) 3. Xiao, X., He, Q., Fu, Z., Xu, M., Zhang, X.: Applying CS and WSN methods for improving efficiency of frozen and chilled aquatic products monitoring system in cold chain logistics. Food Control 60, 656–666 (2016) 4. Badia-Melis, R., et al.: Assessing the dynamic behavior of WSN motes and RFID semipassive tags for temperature monitoring. Comput. Electron. Agric. 103, 11–16 (2014) 5. Mainetti, L., Patrono, L., Stefanizzi, M.L., Vergallo, R.: An innovative and low-cost gapless traceability system of fresh vegetable products using RF technologies and EPCglobal standard. Comput. Electron. Agric. 98, 146–157 (2013)

Design of Stereoscopic Garage System Based on PLC Control Libo Yang(&) Guangdong University of Science and Technology, Dongguan 523083, China [email protected]

Abstract. As a sign of a modern metropolis, three-dimensional architecture and three-dimensional traffic have all experienced significant development. Road congestion and overcrowding have become the most discordant voices in today’s fast-paced society. The development of three-dimensional parking has become a consensus. At present, China’s economy is in a period of rapid development. With the continuous improvement of people’s living standards, the pace of cars entering the family is accelerating, and the parking industry market has broad prospects. This project is based on the design of the stereo garage control system and the design of a three-dimensional garage control system based on the Siemens S-200 series PLC. The mechanical parking garage of the lift-and-cross movement type uses the pallet shift to generate vertical channels, enabling high-rise parking spaces to lift access vehicles [1]. The aim is to realize the intelligent operation of the garage and improve the reliability and anti-jamming capability of the garage in this way. Keywords: Three-dimensional parking Three-dimensional garage
Design


PLC control

1 Introduction With the rapid development of China’s urban economy and automobile industry, there are more and more families with private cars. As of the end of August 2011, the number of motor vehicles in the country reached 219 million vehicles. The mechanical garage of this article can guarantee the safety of people and vehicles more effectively than the underground garage. People in the garage or the car can’t stop accurately, and the whole device controlled by the electronic will not run. It should be said that the management of the mechanical garage can achieve a complete diversion of people and vehicles. The use of mechanical storage in underground garages also eliminates heating and ventilation facilities. Therefore, the power consumption during operation is much lower than that of the underground garage managed by the workers.

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2 General Description of a Three-Dimensional Garage At present, the three-dimensional garage mainly has the following forms: lifting and traversing, roadway stacking, vertical lifting, vertical circulation, box-type horizontal circulation, circular horizontal circulation and so on. Based on the analysis of various domestic and foreign similar products, combined with factors such as cost, technical difficulty, and user needs, it can be found that there are many forms of lift-and-cross parking garages, and the scale is very large and small. The adaptability is strong, and garages using this kind of equipment are common [2]. Therefore, the research object was eventually determined to be a lifting and sliding stereo garage.

3 Stereo Garage Hardware Design Considering the practical operability and various reasons, this article finally determined that the research object is a lifting and sliding stereo garage. The overall structure of the lift-and-throw stereo garage is shown in Fig. 1 below.

Fig. 1. Lifting and moving stereo garage model

3.1

PLC Control Module Design

The core part of this topic is to use the CPU224 in the Siemens S-200 series as the control element. The CPU224 integrates 14 inputs/dot outputs, and a total of 24 digital I/Os. It can connect 7 expansion modules and can be expanded to 168 digital I/O points or 35 analog I/O points. CPU224 has 13 K bytes of program and data storage space, 6 independent 30 kHz high-speed calculators, 2 independent 20 kHz high-speed pulse outputs, and PID controller. CPU224 is equipped with a RS-485 communication/programming port, with PPI communication, MPI communication and free mode communication capabilities [3]. It is a small controller with strong control capability. When the stereo garage is assigned I/O, the input port corresponds to the connection output port, and correspondingly matches the corresponding function operation. PLC host output 1L, 2L, input 1M, 2M, 3M, 4M are connected to the power supply L+, output 3L, 4L, 5L, 6L, 1M, 2M is connected to the power supply M.

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Stepping Drive Module Design

Stepper motors are open-loop control elements that convert electrical pulse signals to angular or linear displacements. In the case of non-overload, the speed of the motor and the position of the stop depend only on the frequency and the number of pulses of the pulse signal, and is not affected by the load change. That is, a pulse signal is applied to the motor, and the motor rotates through a step angle. The existence of this linear relationship, coupled with the fact that the stepper motor has only periodic errors and no cumulative errors, is a feature. It is very simple to use a stepper motor to control the speed and position. Although stepper motors have been widely used, stepper motors cannot be used as conventional DC motors and AC motors are conventionally used. It must be composed of a dual ring pulse signal, power drive circuit and other control systems. Therefore, it is not easy to use a stepper motor. It involves many professional knowledge such as machinery, motors, electronics and computers. Figure 2 shows the design of the stepper motor drive circuit.

Fig. 2. Stepper motor drive circuit

3.3

Screw Driver Module Design

A screw and a nut are engaged to take some measure to prevent the screw nut from rotating relative to one another so that the screw moves axially. In general, there are currently two ways to achieve this transformation. The first is to have a rotor with internal threads in the motor, with the internal thread of the rotor meshing with the screw for linear motion, and the second with the screw as The output shaft of the motor is externally engaged with the screw through an external drive nut to achieve linear motion. The result of this is that the design is greatly simplified, making it possible to use a screw stepper motor for precise linear motion in many applications without the installation of external mechanical linkages. When the screw stepping motor is working, the windings of each phase are not constantly energized, but the electricity is circulated according to a certain regularity. The angle that the rotor rotates is called the step angle every time a pulse electric signal is input. The screw stepping motor can perform angle control according to specific instructions, and can also perform speed control. In angle control, each pulse is input,

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and the stator winding is switched once. The output shaft rotates through an angle. The number of steps is the same as the number of pulses. The angular displacement of the output shaft is proportional to the input pulse [4]. Speed control, the stepper motor windings are fed into a continuous pulse, each phase windings continuously circulation of electricity, stepper motor continuous rotation, and its speed is proportional to the pulse frequency. Changing the power-on sequence, that is, changing the direction of rotation of the stator magnetic field, can control the forward or reverse rotation of the motor. Screw stepper motors have self-locking capability. When the control pulse stops inputting, and let the winding of the last pulse control continue to pass the direct current, the motor can be kept in a fixed position, that is, stopped at the end position of the angular motion of the last pulse control, so that the stepping motor can Realize rotor positioning during parking.

4 Dimensional Garage Software Design 4.1

HMI Design of a Stereo Garage

The touch screen used in this article is Delta DOP-B series touch screen. Its development software is Screen Editor. The author conducted touch screen software design in this development environment. When the user performs the operation of accessing the vehicle, the user will see the identity recognition interface, and only enter the system function interface to access the vehicle after passing the authentication of the credit card. The touch arch screen is a human-computer interaction interface, and the user completes accessing the vehicle by touching the operation on the arch screen, and each carrier board selection button on the touch screen corresponds to a vehicle board selection relay and a vehicle board memory relay in the PLC at the same time. The former is used to store the vehicle when the vehicle is selected, and the latter is used to display the current state of the vehicle (the vehicle has a car on the panel). The touch panel reads the information corresponding to the memory of the panel to display the vehicle panel [5]. At the time of depositing the car, the user touches the car selection interface and selects the button to complete the operation of the stored car. The operating principle of the car pick-up and storage is the same. 4.2

PLC Program Design

4.2.1 How to Establish Downstream Channels The process of accessing the car from the three-dimensional garage is accomplished through the movement of the loading plate. In the process of moving the loading plate, the author draws the principle of optimal path: that is, the bottom loading plate can only move left and right, and the intermediate layer can move up and down. Moving left and right, the top layer can only move up and down: a reasonable use of space to move the carriage plate can ensure the shortest moving distance, get the optimal moving path, and achieve rapid access to the vehicle. After the user selects the access board, the system will run the corresponding shift program only after confirming that the board can move without any obstacle. If the selected carrier board is located on the bottom floor, the vehicle access is directly performed; if the selected carrier board is not on the

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bottom floor and the lower floor space is on the left side of the column where the selected carrier board is located, the selected carrier board needs to be located on the bottom of the column. If the lower floor is the same as the selected access vehicle board, the selected carrier board will be moved directly; if the lower floor is on the right side of the column where the selected carrier board is located, then the board is located. Wear the car plate on the lower deck to the right. 4.2.2 Design of Truck Shifting Program The purpose of designing an intelligent stereo garage is to reduce the workload of the operator and enable the stereoscopic garage control system to operate freely. The main function of the PLC as the control core is to perform the calculation based on the input detection signal and run the corresponding program to complete the operation of the access vehicle. At the same time, the prepared PLC program must have a certain safety protection function. In Fig. 3 below, there are 9 spatial positions in the 3-story 7-car garage. The bottom floor is numbered 789 from left to right, the middle floor is numbered 456 from left to right, and the top floor is numbered 123 from left to right. The following Fig. 3 is a ladder diagram for storing the car on the No. 2 carrier board. Assuming the No. 69 space position is vacant, the PLC analyzes the detection signal and executes the corresponding program when the memory button X2 of the No. 2 carrier board is pressed. Car number 5 and car number 7 have moved to the right. When the PLC detects that the right limit switch of the 5th carrier plate and the 7th carrier plate is closed, it indicates that the 5th and 7th carrier plates have been moved into place.

Fig. 3. Ladder for parking

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At this time, the lower layer of the No. 2 carrier board is empty, and the PLC operation program causes the No. 2 carrier board to descend to the bottom position to complete the parking operation. After the car was parked on the planter board, the vehicle panel 2 detection switch was closed. After a delay, the No. 2 carrier board rose. When the PLC detects that the upper limit switch of the No. 2 carrier board is closed, it indicates that the end of the parking is completed. The author also designed a protection program. When an accident occurs, the emergency stop switch X56 is closed and the warning light is on. Cutting operations are prohibited.

5 Conclusion The characteristics of the three-dimensional garage structure are: the bottom layer can only be translated, and the top layer can only be lifted. In addition to the top floor, an empty parking space must be reserved for the entrance and exit of the car. When the ground floor space enters or exits the car, you can enter and exit the car without moving any other trays. When the top floor enters or exits from the car, it is first necessary to determine whether the corresponding lower position is empty. When it is not empty, it is necessary to perform corresponding translation processing, and it is not until the bottom is empty that the downward movement can be performed. After entering and exiting the car, it is then raised back to the original position. The general principle of its movement is to lift the reset, translation does not reset. Acknowledgment. This paper is funded by Project of: The value of craftsman spirit in the innovation and entrepreneurship of education in vocational college. Higher vocational and technical education research association of Guangdong province in 2016 (GDGZ16Y142). Research on the cultivation of “artisan spirit” in the practice teaching of mechanical electronic engineering, higher-education reform of Guangdong universities in 2016 (Higher Education Division of Guangdong Provincial Education Department No. 236 Document).

References 1. Li, H., Tang, H., Mi, B.: Design of a new underground garage hydraulic lifter. J. Liaoning Univ. Sci. Technol. (2010-02) 2. Ma, Y., Zhang, H., Shao, B., Ma, Y.: The research status and trend of electronic intelligent stereo garage. Electr. Autom. (2008-05) 3. Du, G., Peng, B., Shao, B., Liu Z.: Structure and control system of multistory lifting and translating stereo parking garage. J. Gansu Univ. Technol. (2003-01) 4. Li, G., Yao, X., Ma Y.: Computerized monitoring system for automated large threedimensional parking garage. J. Lanzhou Univ. Technol. (2008-06) 5. Yang, Y.: Research and design of intelligent stereo garage. Ind. Constr. (2006-S1)

Intelligent Scenario Computing Workflow Fusion Design Technology for Power Mobile Operations Haoran Wu1, Min Xu2,3(&), Xiao-Ling Wang1, Jun Zhu1, Xueling Huang1, Lin Peng2,3, and Xingchuan Bao2,3 2

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1 State Grid Wuxi Power Supply Company, Sheng, China Global Energy Interconnection Research Institute, Beijing, China [email protected] State Grid Key Laboratory of Information and Network Security, Beijing, China

Abstract. The purpose of electric power enterprise management is to improve the efficiency and competitiveness of enterprises. For this purpose, advanced information management systems are needed to assist enterprise management. The essence of proposing enterprise automation information management is to use workflow technology to coordinate and control each management process. This article starts with the introduction of the concept of workflow, analyzes the nature of workflow, classification and several modes of workflow implementation. Based on this, the application of workflow in electric power enterprise management was introduced in view of the characteristics of electric power companies. Finally, a detailed analysis of the typical workflow in the management of power companies was conducted. It has certain guiding significance for enterprise information management. Zhenjiang company’s power grid production site standardization operation business application process, based on wireless private network research mobile terminal workflow processing technology, including mobile workflow definition and interpretation, mobile workflow driver, mobile workflow scheduling, mobile workflow management technology, design The mobile workflow basic engine can run and manage processes on the mobile terminal, thus providing mobile standardization operation functions, and automating and intelligentizing the work process management and integrating on-site operation processes. Keywords: Management process
Workflow driver Workflow management technology

1 Introduction Since entering the industrial era, the organization, management, and process optimization of the workflow process have been ongoing. It is one of the main research contents of enterprise management. Before the introduction of the computer support system, these tasks were completed manually. The workflow technology developed on the basis of the maturity of computer network technology and database technology and © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 137–141, 2019. https://doi.org/10.1007/978-3-030-02804-6_18

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multi-computer cooperation technology provides advanced means for enterprises to better achieve this goal. So far, workflow technology has been widely applied to libraries, hospitals, insurance companies, banks and other industries, and its research has been increasingly valued by the academic community and the business community. Many universities and research institutions are committed to the further development of workflow technology. With the rapid development and maturation of computer network technology, communication technology, embedded technology and sensor technology, the power mobile operation technology has also achieved great development. The focus of power enterprise management is to coordinate various resources and ensure the goodness of the equipment. The operating status enables the transmission capacity to meet the demand of the power grid from beginning to end and reduce the costs of operation, maintenance and energy consumption as much as possible. Completing these tasks involves cross-disciplinary integration of multiple disciplines. The use of scenario computing techniques to analyze and reasonably organize workflows plays a key role in improving the efficiency of the management process, thereby increasing the competitiveness of the grid in a market economy. Workflow can be thought of as the theoretical basis for automation of management processes [1].

2 Workflow Integration Design and Implementation for Power Mobile Operations In power mobile operations, there is an urgent need for mobile workflows for on-site process management. This paper adopts a low-coupling and high-availability workflow design to design a workflow execution client to collaborate with the power mobile job client state manager to manage workflow tasks through long connection and SMS methods, avoiding that the mobile workflow cannot maintain the poor communication conditions. In terms of operations, the power mobile job client state manager and workflow process executor work together to manage the mobile workflow task and achieve a high-availability and low-coupling workflow on a low-cost basis. In order to meet the needs of existing on-site mobile workflow technologies, mobile workflow platforms need to meet the requirements of heterogeneous network access, mobile client processing workflow, provide mobile client authentication, workflow engine driver, workflow definition to meet the needs of mobile operations, Workflow management for mobile job requirements. Figure 1 Mobile Workflow Foundation Engine design and management logic. Information exchange with the production management business system to implement data access operations so that users can directly query various database information within the business system through the power wireless private network mobile terminal, and solve the problem caused by inconsistent data standards through a unified information service interface. Difficulties in information collection, reduction of extra workload and heavy duplication of effort. The specific workflow design methodology is as follows: Design a low-coupling, high-availability workflow for power mobile jobs. The following technical solutions are adopted: Workflow process definer defines mobile workflow, process executer

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Fig. 1. Mobile workflow foundation engine design and management logic

executes mobile workflow, workflow execution client is mainly on power mobile work terminal, accepts execution workflow, feedback workflow status, power mobile work customer The end state manager manages the workflow execution client state. The power mobile client state manager manages the workflow in two ways. The first one is to manage the workflow through the TCP long connection management method to perform the client token connection. Through the token unique status, the workflow is monitored and the client task status is executed. Synchronize to the workflow process executor. The second method is to analyze the workflow execution client message by SMS, extract the mobile work task, and synchronize it to the workflow process executor. The workflow execution client accepts the execution workflow in two ways. In the first type, a TCP long connection is used to create a task task that uniquely identifies the token and accepts the user to perform the task. The second method is to parse the power mobile client state service server message through the SMS method and accept the user to perform the task. Workflow process definers can define parallel, serial move workflows, and define workflow execution roles. The workflow process executor is responsible for executing the workflow defined by the workflow process definer, and the power mobile job client state manager and the workflow execution client drive the process operation together.

3 Design and Implementation of Workflow Task-Driven Workflow Function At present, most context-aware systems use context-driven applications to build context models, that is, the system acquires environment- and user-related context information in the information space, and then uses situational reasoning to determine the applications that need to be executed. However, in the information space, there are many sources of context information and a variety of scenarios [3]. Directly driving the application of scenario design patterns will inevitably lead to a dramatic decrease in the complexity and stability of context-aware systems as the system scale increases.

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In order to solve this problem, this paper proposes a scenario-driven context-aware computing framework. Firstly, the context information is integrated, the physical space and the information space logic correspond to each other, an information space service scenario is constructed, and the user is provided with the required resources on the basis of the business context. Information and services. In order to verify the effectiveness of this framework, a situational awareness system for power business scenarios was implemented. The system acquired context information to identify power business scenarios and provide users with diverse and personalized services. The application development of context-aware computing is a very complicated and time-consuming task. A situational awareness system involves the acquisition of contextual information, storage interpretation and reasoning, and finally providing the user with timely and appropriate services [4]. Therefore, it is necessary to simplify the use of a suitable computing framework. Application design prototype system implementation and testing work, as shown in Fig. 4 context-aware computing process. Smart mobile Internet access a large number of sensor information, there will be multiple sensors in the same device or environment to monitor video, infrared, sound, temperature, humidity and other information, data fusion is the fusion of multi-sensor information, to achieve data detection, abnormal data detection, feature extraction, integration analysis, real response to the real situation of power grid equipment. The specific steps are as follows: (1) Message push service function: Judge whether the message is related to the job task of the system, and push the message related to the job task of this system. (2) Task push business function (1) Formulate operational task flow steps, including the definition of work tasks, division of labor, tools and equipment, spare parts, and operational notes; (2) In the on-site operation stage, the on-site work shall be completed and repaired in accordance with the operational procedure steps. The designated staff shall confirm the work content of each step, the use of the instruments and spare parts, and fill out the defects found on the site and relevant maintenance records. (3) Acceptance stage After the on-site operation is completed, the acceptance personnel fills in the acceptance record according to the job task procedure and completes the acceptance of the equipment. Situation-aware applications have changed the way of human-computer interaction in the past and are able to spontaneously provide appropriate services based on the user’s current state. The basic situational information is only the numerical representation of the physical quantity. How to make the application system efficiently and reasonably use a variety of basic contextual information and use these contextual information to infer the current state of the user is the main research content of contextual awareness. Only situational information is reasonably modeled and contextaware applications can use contextual data. A user-centered general situational ontology model is established [5] Based on this, a situational ontology reasoning method is established with behavior recognition as the goal. The purpose of behavior identification can be achieved through custom rule reasoning, and the mapping of situation information to semantic behavior can be achieved.

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4 Summary Based on the status quo of computing technology, an engine based on a mobile design workflow is operated and managed in a mobile terminal, thereby providing mobile standardization operation functions, realizing automation of management processes, intelligentization, and integration of process operations. Zhenjiang Power Grid Co., Ltd. produces on-site standardized business application flow, wireless network based on workflow technology of mobile terminal, mainly includes definition and explanation of mobile workflow, mobile workflow-driven mobile workflow scheduling, mobile workflow management technology, and engine-based The mobile workflow design enables the operation and management of the mobile terminal to provide mobile standardization work functions. Giving full play to the advantages of power field work and improving the efficiency of power field operations has great application prospects.

References 1. Qin, W.: Intelligent Space Situational Information Model of Ontology Research Based on [D]. Tsinghua University, Beijing (2005) 2. Liu, Y.: Study on the Structure Of Context Aware System for Intelligent Space. Hunan Normal University, Changsha (2012) 3. Zhou, X.S, Liang, Y.: A scenario Driven Context Aware Computing Framework. Comput. Sci. 39(3), 216–221 (2012) 4. Yu, Y., Yi, J., Zhao, D.: Intelligent Space Research Overview of Computer Science Thirtyfifth Volumes (8 period), 1–6 (2008) 5. Zhang, J., Deng, Z.: The workflow system of electric power dispatching production management information system. Autom. Electr. Power Syst. 27(16) 78–80 (2003)

New Human-Computer Interaction Solution for Next-Generation Power Grid Dispatching System Hai Yu1,2(&), Min Xu2, Lin Peng1,2, He Wang1,2, and Zhansheng Hou1,2 1

2

Global Energy Interconnection Research Institute Co., Ltd., Nanjing, Jiangsu 210003, China [email protected] State Grid Key Laboratory of Information and Network Security, Nanjing 210003, China

Abstract. This paper aims at the human-machine interaction requirements of the new generation of dispatching systems, and based on the new humancomputer interaction technology trends, studies and proposes a new humancomputer interaction solution for the new generation of dispatching systems. Starting from the on-the-spot requirements analysis, the research foundation and basic principles are described. The implementation route is given. Apply mature and applicable new human-computer interaction technology to support the new grid control architecture with “physical distribution and logical unity”. Keywords: Human-machine interaction
New generation grid dispatch system Visualization technology

1 Introduction With the rapid development of the UHV AC/DC hybrid power grid and clean energy, the characteristics of the power system have undergone profound changes. The integrated features of power grid operation are highlighted; the demand for global monitoring, prevention and control of the entire network, and centralized decision making is increasingly prominent; the reform of power market brings tremendous pressure to the dispatching operation of the power grid. In response to these challenges, State Grid Corporation proposed to develop a new generation of dispatch control system to further enhance the power grid dispatch control technology support capabilities. The new generation power grid dispatching control system fully inherits the technical achievements of the D5000 system, upgrades existing monitoring systems, realizes global monitoring and real-time on-site monitoring and control of the power grids under its jurisdiction, and builds two-level model data centers of the Kokubu and the province to realize networkwide models and data. Unified management and on-demand use; New national-provincial and provincial-level analysis and decision centers to achieve global analysis and decisionmaking and risk prevention and control; Construction of human-machine cloud terminals with location-independent, permission-constrained, co-view display, and support for regulators to implement local There is no difference monitoring in different places. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 142–147, 2019. https://doi.org/10.1007/978-3-030-02804-6_19

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2 New Human-Computer Interaction Research Needs Man-machine cloud terminal of the new generation dispatching control system has the features of local/local non-differential browsing and wide-area network-wide information perception. Under the new architecture, in order to effectively achieve global monitoring and improve the efficiency of backup systems, it is necessary to study the cloud terminal technologies for local and remote non-differential browsing, and to achieve local/offsite information awareness and function calls within the scope of authority; dispatch control system information volume Large, currently grasping the key operating characteristics of the grid and the means for obtaining grid-related information are limited. Therefore, it is urgent to study the visualization methods of multi-source information fusion and active guidance to enhance the panoramic display capability; the human-machine interaction of the existing dispatching control system remains Confined to traditional mouse and keyboard operations, it is urgent to study interactive methods such as voice, touch, and face recognition to simplify the interaction process and improve the human-computer interaction experience. Through the human-machine enhanced visual expression technology to achieve grid operation monitoring and analysis and decision-making under different scenarios of the visual view of the automatic generation, the difficulty lies in how to use mind maps, fishbone maps to support state expression and analysis of public decisionmaking components, Realize the visual expression of the relevance of power grid business information, and provide visual support for operational surveillance and analysis and decision making with visual and technical support.

3 Research Foundation and Principle 3.1

Research Foundation

After years of development, China’s smart grid dispatching and control platform has achieved some research results in man-machine graphic display: the realization of a flexible layout of the human-computer interaction platform, based on the geographical background of the dynamic current diagram, voltage contours and other functions, to achieve The transformation of power grid operation information from the static, twodimensional plane, presentation of isolated data to the display of dynamic, threedimensional, and continuous graphics. However, in recent years, the widespread use of internet man-machine terminals has given people a new human-computer interaction experience. Man-machine interaction forms and graphical display methods have made great strides. At present, the widely used scheduling automation system man-machine has been relatively backward in terms of display contents, display methods, and manmachine interaction modes, which are mainly reflected in four aspects: (1) In terms of man-machine client framework design, the current scheduling control system man-machine is still a fat client based on coarse-grained services, unable to meet the requirements of the application interface for fine-grained services under the new architecture; regulatory system currently only supports Extensive screen browsing in different places cannot yet perform indiscriminate browsing and operation of various types of human-machine screens within the scope of authority; currently,

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man-machines only support the customization of traditional vector screens (single-line diagrams, tidal current diagrams, etc.), and do not support each. Apply the required configuration of the composite screen of the GUI and vector graphics. (2) In terms of human-computer interaction, the current scheduling automation system is still limited to the traditional mouse and keyboard operations. The interaction technologies such as voice recognition, touch operation, and face recognition that are maturely used on mobile devices are not yet in the dispatch control system. Manmachine client widely used; (3) In terms of computer graphics, the graphical browsing of the human-machine client in the current power grid dispatching control system is still based on traditional single-line diagrams and tidal current diagrams. It does not support the operational status of large power grids, external environmental conditions, analysis and decision services, and geographical space. Multivariate information fusion; (4) In terms of visualization, the current grid dispatch automation system has been developed for many years. Although it has achieved some visual results, it has realized visualization based on various forms of pie charts, bar graphs, curves, etc., and has also achieved the trend and contour lines. Visual effects, but no research on enhanced visualization with active guidance in analysis and decision-making. The Spectrum Power power system control platform launched by Siemens, a giant in the foreign industry, is based on the data model of an internationally uniform standard and realizes the scheduling and control of large-scale power grids through a modular structure. However, in terms of computer graphics and visualization, the vast majority still use two-dimensional plane information to display grid information, without using multi-graphical information and virtual reality for comprehensive display; in terms of human-computer interaction, the main use of mouse and keyboard interaction. The United States PJM Dispatch and Control Center is responsible for regulating the largest regional transmission organization in the North American region, the PJM power grid. The advanced control center AC2 (Advanced Control Center) developed by the PJM Dispatch and Control Center is a full-featured power grid dispatching control system that adopts open modules. The software architecture, in a similar manner to “building blocks,” enables analysis and control of the power system, providing comprehensive and effective support for dispatch operations. However, the graphical display technology and visualization technology of the system do not have the ability to actively monitor, perceive and analyze decision-making, and the main interactive method of the system is through the mouse and the keyboard. Visualization technology has made great progress in recent years, especially in the field of augmented reality. Augmented reality is the use of computers to create a virtual environment with realistic feelings of vision, hearing, force, touch and movement, and a variety of sensing devices. The user “immersed” into the environment and realized direct natural interaction between the user and the environment. Augmented Reality technology not only displays real-world information, but also displays virtual information at the same time. The two kinds of information complement and superimpose each other. Augmented reality technology includes new technologies and new methods such as multimedia, 3D modeling, real-time video display and control, multi-sensor fusion, real-time tracking and registration, and scene fusion. Augmented reality provides a powerful means for the intelligent expansion of human beings, and it has greatly promoted the visualization technology.

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The human-computer interaction method has made great progress in recent years. The human-machine dialogue system is a man-machine two-way information exchange system that regards the machine as a cognitive subject. The initial human-computer interaction system used the machine as a tool to execute precise commands and produce predetermined input and output. Such as command line interactive terminal, graphical user interface and keyboard and mouse interaction and so on. Such humancomputer interaction systems are mostly designer-centered and require the user to interact and obtain results in the manner that the designer intended. With the development of technology and application, the user-centered human-computer interaction system has received increasing attention from the end of the last century. This kind of interactive system does not require the user to adapt to the machine, but requires the machine to adapt to the person, that is to allow the user to communicate with the machine in a natural way that communicates with people, including speech, gestures, and visual recognition. This has resulted in a change in the concept. The role of the machine has changed from the “executive subject” to the “cognitive subject” and can communicate with people in multiple modes. 3.2

Principles

In order to improve the interactive experience of the new human-computer interaction, it mainly studies cloud terminal browser technology, multi-modal human-computer interaction, new graphic application technologies for the panoramic monitoring of the power grid, and enhanced visual technologies. Firstly, design a technical solution for cloud terminals that considers permission control and local non-differential browsing, supports local consistent graphic display, data browsing, and interactive operations; completes fine-grained service technology architecture and information interaction mechanisms of human-machine clients. Design to achieve reasonable distribution and efficient collaboration of human-machine clients and back-end service computing resources; and to extend the CIM/G language specification based on the concept of configuration, and to support local/offsite contents such as basic graphics, electrical graphics, and GUI components. No difference browsing. Then, study the application technology of the voice interaction mode in the control platform, realize intelligent voice interaction based on speech recognition such as command input, screen review, and interface operations; based on touch display devices, study the application technology of client touch control mode in the control platform. It realizes the interaction between touch zooming and switching of the screen, researches the application technology of face recognition mode on the control platform, and realizes fast and accurate permission authentication for the control business. Then research on multi-information display technologies that integrate grid, geography, weather, and external events to achieve global power grid operating conditions monitoring, wide-area power grid failure and the risk of hidden awareness of synchronization, for the new terminal for cloud terminal graphics technology support. Lastly, it studies the enhanced control and expression technology of power grid regulation, and uses the latest human-computer interaction methods such as virtual reality and augmented reality to improve humancomputer visibility and expression capabilities.

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4 Implementation Plan This paper studies a new human-computer interaction technology solution for cloud terminals, and implements local/off-site non-differential browsing and graphical configuration display within the permission scope, providing a basic framework support for the new human-computer interaction experience for cloud terminals. Study the application of new interactive methods such as voice, touch, and face recognition for intelligent regulation and control services in the regulatory platform, optimize interactive processes, and improve interactive experience; research new graphic application technologies for panoramic monitoring of the power grid, and enhance the graphical display of human-machine interfaces. Ability to study human-machine enhanced visualization and expression technology to support the global situational awareness of large power grids; based on the above research results, develop new human-computer interaction software for cloud terminals, for real-time monitoring, analysis and early warning of new generation power grid dispatching control systems, plan decisionmaking, comprehensive assessment, simulation training, and other businesses provide friendly and practical means of interaction. (1) Research on cloud terminal technology solutions with new user interaction experience A new human-computer interaction technology solution for cloud terminals that takes into account permissions control was proposed to achieve consistent local and remote graphics display, data browsing and interactive operations; proposed humanmachine client fine-grained services, information exchange mechanisms and other technical solutions, to achieve people Rational distribution and efficient collaboration of machine client and background service computing resources; Extension of CIM/G language specification based on configuration concept, support for remote access and non-differential browsing of GUI components, layout files, scripts, and events. (2) Research on New Human-Computer Interaction Technologies for Intelligent Regulatory Services Study the application technology of the voice interaction mode in the control platform, analyze the functions commonly used in the regulation and control business, establish a voice command library, and implement intelligent voice interaction based on voice recognition such as command input, screen review, interface operation, and screen switching; Display devices, study the application technology of touch control technology on the client, implement functions such as touch zooming and switching of the screen, study the application technology of face recognition on the control platform, and realize fast and accurate identity authentication for regulatory services. Based on the above research of intelligent human-computer interaction technology, the experience of human-computer interaction is improved and the friendliness of humanmachine interface is improved. (3) Research on New Graphic Application Technology for Power Grid Panorama Surveillance Research on dynamic view overlay, perspective, cropping, splicing, and other graphic application technologies for large-scale power grid panoramic monitoring to improve the operational convenience of regulators; study the integration of geographic information on grid operation and panoramic monitoring technology to support

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real-time operating conditions across the entire network Simultaneous monitoring; researching multi-information display technologies for lightning, rainstorm, blizzard, high wind and other external power grid environments, and visually and vividly displaying the state of the external environment of power grid operation. Through the above technical research, the entire network operating conditions monitoring, power grid failure and risk hidden dangers are realized. (4) Research on Man-Machine Enhanced Visualization and Expression Technology To study the automatic generation technology of visualized views in different scenarios of grid operation monitoring and analysis and decision making, and to realize dynamic organization and graphical expression based on relevance of business information; to study support expressions and analysis decisions including mind maps and fishbone diagrams. Visualization technology to realize the visual expression of the relevance of power grid business information, providing visual image support for operational monitoring and analysis and decision-making, as well as visual technology support with enlightening and guiding capabilities; researching technologies such as virtual reality and augmented reality for cloud terminals, and realizing the power grid The visual expression of operating status and equipment status enhances humanmachine visibility and expression capabilities. (5) New human-machine software development for cloud terminals R&D to realize new man-machine software for cloud terminals. Development of intelligent human-machine interaction components based on speech recognition, touch control, and face recognition; development of panoramic monitoring components that integrate real-time conditions of large power grids, external environmental information, and geographic information; development of dynamic visualization view generation components; development of power grid monitoring Visualization components of mind maps for analysis and decision making; development of virtual reality components and augmented reality components for large grid monitoring. Enables global monitoring, global analysis, global prevention, and global decision support. Acknowledgments. Thanks for the support of the science and technology project to State Grid Corporation “ Research on Key Technologies of Supporting Platforms under the Framework of ‘Physical Distribution and Logical Unity’”.

References Hartley, R.: In defense of the eight-point algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 19 (6), 580–593 (1997) Stewenius, H., Engels, C., Nist6r, D.: Recent developments on direct relative orientation. ISPRS J. Photogram. Remote Sens. 60(4), 284–294 (2006) Schall, O., Belyaev, A., Seidel, H.-R.: Adaptive feature-preserving non-local denoising of static and time-varying range data. Comput. Aided Des. 40(6), 701–707 (2008) Galvez-Lopez, D., Salas, M., Tardos, J.D., et al.: Real-time monocular object slam. Robot. Auton. Syst. 75, 435–449 (2016)

Towards a Framework for Agent-Based Healthcare Monitoring Udsanee Pakdeetrakulwong(&) Nakhon Pathom Rajabhat University, Nakhon Pathom 73000, Thailand [email protected]

Abstract. Currently, several countries have become aging societies and are moving towards aged societies. This results from low birth rate and the average life expectancy increasing in these countries. The subsequent concerns are about a lack of carers to look after those elderly people. Therefore, the need of technology-driven solutions to support elderly to live independently is increasing. This research intends to develop a conceptual framework supporting personalised healthcare for seniors. The agent-based, Semantic Web, and Internet of Things technology have been integrated to monitor elderly people’s vital signs when they are at home. In case of the presence of the vital sign abnormality, the carers and physicians will be notified in order to investigate the problem and provide help immediately. The result of this research is expected to provide assisted living facility for elderly people to stay independently as long as possible in their homes and to reduce the burden of carers. Keywords: Healthcare monitoring


Multi-agent systems
Internet of Things

1 Introduction During the last three decades, the average life expectancy in the developed countries have increased [1]. Even in some developing countries such as Thailand, they are also currently facing a problem of aging population which results in substantial economic costs to manage health-related issues. It also includes the need of caregivers or family members to provide necessary care of older people when they are at home. As a result, the requirement of technology-driven solutions to facilitate seniors to live independently and comfortably at home with minimal need of caregivers or nursing care is increasing. Several works have made considerable efforts towards potential solutions for applications in the healthcare domain. Internet of Things (IoT) is one solution to deliver health monitoring services to collect biomedical data for other healthcare data processing facilities. Nonetheless, the lack of interoperability of data generated by different sensors or devices has currently become a main challenge. In addition, the healthcare domain is categorised by shared and distributed management of care which needs complex and various communication between different groups of relevant people such as patients, caregivers, healthcare professionals [2]. Thus, innovative platforms that consist of autonomous, proactive, and collaborative entities interacting in a distributed and dynamic environment are more desirable. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 148–158, 2019. https://doi.org/10.1007/978-3-030-02804-6_20

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In this paper, the framework for agent-based healthcare monitoring is proposed. Various technologies, namely, multi-agent systems, Semantic Web, and Internet of Things are integrated to provide a software solution platform for monitoring seniors health condition and notifying their family members, carers, and physicians in case of detecting abnormality. Therefore, they can provide assistance instantly. The proposed framework is aimed at providing assisted living facility for elderly people to decrease the strain on healthcare services and allow them to stay independently longer at home. The remainder of this paper is organized as follows. In Sect. 2, related work regarding multi agent-based approach is discussed. In Sect. 3, the framework for agentbased healthcare monitoring is proposed. In Sect. 4, the prototype is implemented as a proof-of-concept and followed by a use case scenario demonstration. In Sect. 5, the conclusion and future work are summarized.

2 Related Work Several works have proposed software solutions to provide healthcare services focusing on monitoring and tracking system of biometric measurement of patients or elderly people through Internet of Things-based sensor devices. Nevertheless, these approaches do not provide a degree of autonomy, and nor can they adapt dynamically to any change. They are mostly based on user’s effort to manage health monitoring. In this case, software applications based on agent-oriented software engineering can provide better solution in regard to autonomous and dynamic features. For example, Sernani et al. [3] have introduced the virtual carer for ambient-assisted living based on multi-agent paradigm. Several type of agents, namely, virtual carer agent, sensor agents, actuator agents, register agent collaboratively work to monitor an elderly patient health conditions and to control the surrounding environment. Su and Wu [4] have introduced MADIP, a multi agent-based framework to facilitate vital sign monitoring. MADIP system is aimed at relieving health provider staffs from labor-intensive and time-consuming tasks of constant data monitoring and to provide patient personalised healthcare. Hernández et al. [5] have implemented a multi-agent system healthcare application for monitoring and analysis of basic vital signs of a patient at home. The patient or carer is notified with an alert message if the data recorded from the measurement is outside the normal ranges set by the user. Fernandes and Lucena [6] have developed a software application for remote patient monitoring based on multi-agent systems called Agents4Health. Multiple software agents work autonomously to monitor patient health status and to notify a health provider when an abnormality in patient’s condition is detected. According to the above-mentioned works, it is obvious that several multi-agent system applications have been implemented to support health monitoring through various sensor devices. However, in almost of these works, the semantic interoperability issues between different IoT sensors devices and healthcare domain knowledge are not mainly addressed. Therefore, in this study, the Semantic Web-based approach is used to enable the semantic interoperability and semantic relationship by integrating such information into an ontology knowledge base. An ontology is in a machinereadable and machine-understandable format. Thus, it can enable software agents to

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process knowledge captured in it autonomously without human intervention. It also can enhance multi-agent applications to provide the autonomous and flexible features for ambient-assisted living applications.

3 A Framework for Agent-Based Healthcare Monitoring The proposed agent-based healthcare monitoring framework is intended to provide assisted living facility for elderly people to stay independently as long as possible in their homes to reduce the burden of carers. The vital signs are the critical factors to determine the elderly’s health status [7]. Monitoring the vital signs is an important procedure to obtain information about health condition of seniors in any given scenario [8]. In this research, five types of vital sign sensors are used to monitor health status of elderly people as follows. • Temperature sensor. This sensor is utilised to measure the body temperature which can help to instantly determine if the person has any health problems. • Pulse rate sensor. This sensor is utilised to measure heartbeats of a person. • Respiration rate sensor. This sensor is used to measure the number of breaths per minute. Respiration rate provides information according to a person’s respiratory system performance and condition. • Blood pressure sensor. This sensor is used to measure the pressure of the blood in the arteries as it is pumped around the body by the heart [9]. During the measurement, there are two numbers recorded, namely, the systolic pressure (as the heart beats) and the diastolic pressure (as the heart relaxes between beats). • Oxygen in blood. The sensor is used to measure oxygen saturation which is defined as the amount of oxygen dissolved in the blood. The design of a framework for agent-based healthcare monitoring is discussed in the following section. 3.1

Agent Roles

In the proposed framework, several roles are identified as follows. Mediator – This role is responsible for mediating between a user and the system. It mainly focuses on: (1) generating input from a user (i.e., biosensor data) as an ACL message and sending it to a corresponding agent; and (2) translating an ACL message into a meaningful output and delivering it to the user. SemanticAnnotator - This role is to automate the semantic annotation process. Biosensor data collected from healthcare monitoring IoT devices is assigned with the ontological concepts and populated in the ontology as instances. HealthStatusMonitor - Proactively monitoring health status of an elderly person by analysing and reasoning recorded vital sign data to detect health anomalies that may threaten his life. Notifier - This role is to generate notification messages in real-time to enable effective management of the awareness of elderly person’s health condition to relevant users.

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All the above-mentioned roles derive the specific agent types according to the cluster of roles. It comprises four agent types as shown in Table 1. Table 1. A mapping of agents’ roles and their associated agent types Agent roles Mediator

Description Mediating between a user and the system

Cluster of roles Communication management

SemanticAnnotator

Annotating bio-sensor data according to concepts defined in the ontology Monitoring, analysing and reasoning bio-sensor data

Semantic annotation

HealthStatusMonitor

Notifier

3.2

Generating real-time notification messages

Bio-sensor data analysis and reasoning management Notification management

Agent types User agent (i.e. elderly agent, carer agent, physician agent) Annotation agent

DataAnalysis agent

Notification agent

System Architecture Modelling

The system architecture of the proposed framework is presented in Fig. 1. User agent types, namely, Elderly agent, Carer agent, and Physician agent are distributed within the platform while Annotation agent, DataAnalysis agent, and Notification agent reside in a main container. Elderly agent is regarded as a mediator between the system and an elderly person. It is responsible for reading the value of vital sign measurement sensors and then sending it to the Annotation agent. Annotation agent is responsible for capturing the vital sign data receiving from the biometric sensors into the ontology. DataAnalysis agent is the main agent responsible for monitoring, analysing, and reasoning vital sign data captured in the ontology. Notification agent is in charge of notifying a Carer agent and a Physician agent in case that DataAnalysis agent detect the abnormality of the elderly’s health condition. Carer agent is a user agent type that mediates between a carer of an elderly person and the system. When it receives a message from the Notification agent, it interprets the message into a meaningful output and delivers it to the user. Physician agent is a user agent type that interfaces between a Physician and the system. A message from the Notification agent is translated by the Physician agent and is generated into a meaningful output and delivers it to the user.

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Fig. 1. System architecture

3.3

DataAnalysis Agent

There are several methodologies available for the development of MAS applications. In this work, the Agent Unified Modelling Language (AUML) which is an extension of Unified Modelling Language (UML) [10] is selected. The reason is because AUML offers various types of representations to describe agents’ components and interactions in MAS. The AUML class diagram are used to model functions and interactions within the system. Nonetheless, because of space limitation, the complete internal aspects model of every agent cannot be described. Therefore, as an example in this paper, the AUML class diagram and behaviour diagram are used to demonstrate internal aspects of the DataAnalysis agent as shown in Fig. 2. The above class diagram is explained as follows. – Role: DataAnalysis agent has one main role which is HealthStatusMonitor. This role is composed of four behaviours. – Knowledge asset: The DataAnalysis agent requires the HomeHealthcare ontology, a set of rules, elderly’ user profile as knowledge resources to perform its tasks. The HomeHealthcare ontology is a knowledge representation defining common vocabulary for agents to share information within a healthcare domain. It organises knowledge related to vital sign measurements, diseases, and suggestion for basic home care treatment given to the elderly. A set of rules are needed to carry out logical reasoning tasks to infer new knowledge not exist in the knowledge base. An elderly user profile is used for obtaining elderly personal information and chronic health condition, as well as carer’s information.

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DataAnalysis Agent Role - HealthStatusMonitor Knowledge Asset HomeHealthcare ontology, User profile, Set of rules Behaviour

- Reac [newMessage] ReceiveMessage [messageReceived] - Reac [sensorDataAnnotated] AnalyseData [dataAnalysed]

- Int [isCalled] MonitorHealthStatus [healthStatusMonitored] - Int [isCalled] DeliverMessage [messageDelivered] Perception ACL_Messages(dataAnalysisRequest) Protocol - Responds to FIPA-request protocol with the Annotation agent - Initiates FIPA-request protocol with Notification agent Collaborator - Annotation agent - Notification agent Fig. 2. Class diagram of the DataAnalysis agent

– Behaviour: An agent’s behaviour is illustrated as: Behaviour type [pre-condition] Behaviour name [post-condition] This means that when a pre-condition is obtained, the behaviour is triggered. A post-condition illustrates the goal carried out. Behaviour type is generally categorised into three types which are proactive, reactive, and internal. In the AUML class diagram, these behaviour types are represented with Proactive, Reactive, and Internal symbols, respectively. The DataAnalysis agent has four main behaviours, namely, two reactive behaviours AnalyseData and ReceiveMessage, and two internal behaviours MonitorHealthStatus and DeliverMessage. – Perception: When the vital sign data is annotated and populated into the ontology, the DataAnalysis agent perceives dataAnalysisRequest message. – Protocol: The DataAnalysis agent interact with other agents with FIPA-request, protocol. – Collaborator: The DataAnalysis agent collaboratively works with the Annotation agent and the Notification agent. DataAnalysis Agent Behaviours The behaviour diagram shown in Fig. 3 is used to illustrated the DataAnalysis agent’s behaviours associated with its roles. Reac, and Int are keywords used to present behaviour types which are reactive behaviour and internal behaviour, repspectively.

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There are two common behaviours, namely, ReceiveMessage and DeliverMessage which are used to perform functions of sensors and effectors of the agents. ReceiveMessage, a reactive behaviour, is initiated when a new message is received. It derives a content of the message and then makes it accessible to other behaviours. DeliverMessage is an internal behaviour used for sending out the message to other agents.

Fig. 3. Behaviour diagram of the DataAnalysis agent

The main role of the DataAnalysis agent is HealthStatusMonitor. HealthStatusMonitor role is associated with vital sign measurement data analysis and logical reasoning management as well as health condition monitoring. After the Annotation agent completes the semantic annotation process and the vital sign measurement data is captured in the ontology, it sends a message to notify the DataAnalysis agent. The reactive behaviour AnalyseData responds to a message received from the Annotation agent by analysing this data. The semantic rules corresponding with the type of the vital sign measurement are chosen to process logical reasoning. If any abnormality is detected and it is needed to notify the carer of the senior, the DataAnalysis agent will send a message to the Notification agent to manage a notification which includes an alert message and suggestion about primary treatment. In case that the DataAnalysis agent does not detect any abnormality from the vital sign value measured only at that time. However, the value is borderline, the MonitorHealthStatus behaviour is called internally to keep monitoring that vital sign value recorded to determine a chance of abnormality. For example, the one-time reading of blood pressure measurement may not be able to determine whether the elderly has possibility of high blood pressure symptom or not. Therefore, the MonitorHealthStatus behaviour is called in this case for data analytics. In other words, the differences between the AnalyseData and MonitorHealthStatus behaviours are as following.

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– The AnalyseData behaviour uses the one-time vital sign reading value at the moment of the measurement. The value is processed with a logical reasoning task to draw a conclusion whether the notification will be sent to relevant people or not. It is more suitable when the value is significantly out of normal range and may cause sudden danger to life. – The MonitorHealthStatus behaviour examines the data sets of vital sign measurement values to draw a conclusion whether the notification will be sent to relevant people. It is aim at monitoring vital sign measurement values (one or more than one types, e.g., blood pressure and heart rate) for certain period of time as well as integrating this information with the user profile to find a possibility of abnormality.

4 Prototype Implementation and Usage Scenario The prototype was developed as a proof of concept to evaluate the proposed framework. Java Agent DEvelopment Environment (JADE) is an agent development framework for developing MAS and applications. It is used to implement several agent components and behaviours. JADE complies with the FIPA (Foundation for Intelligent Physical Agents) specifications and provides necessary components for agent management which are automatically initiated when the agent platform is launched. For instance, the Directory Facilitator (DF) agent is the agent providing a yellow pages service to allow other agents in the platform search for the services required to obtain their goals. The Agent Management System (AMS) offers the naming service and make sure that each agent in the platform has a unique name. The Sniffer Agent (SA) monitors all message communications between agents. The Remote Monitoring Agent (RMA) is for controlling the life cycle of the agent platform including all registered agents. Agents communicate with each other through an asynchronous communication channel and Agent Communication Language (ACL) was used as the language of communication between agents. All related agents in the proposed framework are showed in Fig. 4. Furthermore, JENA, an opensource Semantic Web framework for Java is used to provide an API for a connection between agents and the ontology and to offer functionalities to manipulate triples in OWL ontology. JENA framework provides inference engine to load the ontology and SWRL rules to perform the reasoning task. Usage Scenario In this section, the usage scenario of using the proposed framework is described. Somsri Lim, 61 years old, has afflicted with asthma for two years. Somsri lives alone in her house during day time of the working days because other family members must go to work. The prototype of the proposed framework including wearable vital sign sensor devices have been set up to facilitate health status monitoring of Somsri Lim. One day, she is suddenly having an asthma attack. The pulse and oxygen in blood sensor is used to measure the oxygen saturation. Once measured, the Annotation agent semantically captured the value from the sensor as oxygen saturation value and stored them in the ontology. The DataAnalysis agent is then triggered to analyse the data by performing logical reasoning to diagnose Somsri’s health status and create notification proposal if it is needed. Examples of the SWRL rules are shown in Fig. 5.

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Fig. 4. Agents in the proposed framework

Rule 1: Eldery (?e) ^ Sensor (oximeter) ^ hasSensor (?e, oximeter) ^ hasMeasurementValue (?o, ?v) ^ swrlb:lessThanOrEqual (?v, 90.0) -> hasCondition (?e, Hypoxemia) Rule 2: Eldery (?e) ^ hasCondition (?e, Hypoxemia) -> hasRisk (?e, Hypoxia) Rule 3: Eldery (?e) ^ hasRisk (?e, Hypoxia) -> hasCarer (?e, ?c) ^ hasNotification (?c, HypoxiaAlert) ^ hasSuggestion (?c, HypoxiaSuggestion)

Fig. 5. Excerpt of a set of rules for analysing the oxygen saturation value

Fig. 6. Alert message to notify a carer about abnormality

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From the value of blood oxygen saturation (SPO2) measured from the sensor and a set of SWRL rules, the DataAnalysis agent is able to determine Somsri Lim’s health status. Because the value of SPO2 is lower than 90%, it infers that Somsri Lim may be having a hypoxemia condition (low oxygen in blood) (Rule 1) which can cause hypoxia (low oxygen in tissues) (Rule 2). This becomes life threatening if it is not medical emergency treated. The DataAnalysis agent then creates an alert message to notify Somsri Lim’s carer as well as a suggestion for him/her to take such an appropriate action (Rule 3). In this case, it is suggested to give additional oxygen to Somsri Lim as quickly as possible (Fig. 6).

5 Conclusion and Future Work In this paper, the framework for agent-based healthcare monitoring is proposed. A multi-agent system, Semantic Web and Internet of Things technology are integrated to monitor health status of elderly people when they live alone at home. Multiple agents collaboratively work to perform autonomous actions to achieve their goals without direct intervention of humans. In case of the presence of unusual vital signs, relevant people are notified to provide help immediately. The outcome of this research is expected to facilitate elderly people to stay independently as long as possible in their homes and to reduce the burden of carers. For future work, more rules and algorithms will be added to the framework in order to enhance the reasoning tasks to support data analytics and to generate more appropriate medical suggestion. Furthermore, the framework will be extended to work with cross domain application such as home automation in order to build an ambientassisted living environment for the elderly people.

References 1. Kontis, V., Bennett, J.E., Mathers, C.D., Li, G., Foreman, K., Ezzati, M.: Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet 389(10076), 1323–1335 (2017) 2. Shakshuki, E., Reid, M.: Multi-agent system applications in healthcare: current technology and future roadmap. Procedia Comput. Sci. 52, 252–261 (2015) 3. Sernani, P., Claudi, A., Palazzo, L., Dolcini, G., Dragoni, A.F.: Home care expert systems for ambient assisted living: a multi-agent approach 4. Su, C.-J., Wu, C.-Y.: JADE implemented mobile multi-agent based, distributed information platform for pervasive health care monitoring. Appl. Soft. Comput. 11(1), 315–325 (2011) 5. de la Iglesia, D.H., González, G.V., Barriuso, A.L., Murciego, Á.L., Herrero, J.R.: Monitoring and analysis of vital signs of a patient through a multi-agent application system. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. 4(3), 19–30 (2015) 6. Fernandes, C.O., De Lucena, C.J.P.: A software framework for remote patient monitoring by using multi-agent systems support. JMIR Med. Informatics, 5(1) (2017)

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7. Chester, J.G., Rudolph, J.L.: Vital signs in older patients: age-related changes. J. Am. Med. Dir. Assoc. 12(5), 337–343 (2011) 8. Toader, C.G.: Multi-agent based e-health system. In: 2017 21st International Conference on Control Systems and Computer Science (CSCS), pp. 696–700 (2017) 9. Kavitha, K., Perumalraja, R.: Smart Wireless Healthcare Monitoring for Drivers Community, pp. 1105–1108 10. Bauer, B., Odell, J.: UML 2.0 and agents: how to build agent-based systems with the new UML standard. Eng. Appl. Artif. Intell. 18(2), 141–157 (2005)

Design and Implementation of Network Traffic Capture Prober Based on General PC Zhang Mei1(&) and Zeng Bin2 1

2

Central South University of Forestry and Technology, Changsha 410080, China [email protected] Hunan YouDao Information Technology Co., Ltd., Changsha 410080, China

Abstract. Network traffic measurement provides scientific basis for designing, building, and managing the next generation Internet, and is especially important for monitoring network behavior. There are many challenges in high-speed network traffic measurement. One of the most important bottlenecks is traffic capturing. Because of hardware capability and operating system overhead limitations, the existing network traffic measurement tools based on software can only perform well at low speed network with the link rate below 100 Mbps. This paper mainly focuses on the key issue to perform traffic capture for high-speed network based on software. To achieve this point, Distributed network traffic measurement system is discussed, and a traffic capture prober was designed under general PC server with Linux operating system. We also analyze crucial problems on prober’s implementation, building an experiment environment and running tests on it. The results show that our system satisfies all requirements imposed by real time measuring network traffic behavior. Furthermore, the performance of traffic capture has been improved significantly and can capture/process nearly all packets at line speed under Gigabit network. Keywords: Network measurement


Traffic capture
General PC

1 Introduction With the rapid development of network technologies, networks are becoming more and more complicated as the scales of networks are expanding. It is necessary to measure traffic in real time and manage networks on-line. Network traffic measurement and monitoring is one of the most important methods to understand network traffic characteristics [1]. Through network measurement, we can analyze vital applications of network, and understand user’s behavior of using network, such as how often, how many and when. And also, we can find out network abnormity, detect network congestion, warn Denial of Service Attacks. At present, there are many methods and achievement about network measurement [2]. The traffic capture is the foundation of analyzing traffic. There are three method of traffic collection: The first one is based on special hardware like HP/Agilent Advisor, InMon sFlow Probe, Endace DAG [3–6]. Because the special hardware is poor at programming and the price is expensive, the Performance/Price Ratio can hardly satisfy © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 159–166, 2019. https://doi.org/10.1007/978-3-030-02804-6_21

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the user’s requirement. The second packet capture method is based on network processor [7]. Network Processor, whose packet processing unit is optimized, is a special programmable processor designed for processing packet. Therefore, the capability of processing packet can be promoted enormously. However, the network processor is expensive, and due to programming on special hardware, the expense of development is enhanced and the portability is bad. The last method is based on PC construct, which captures packet through common NIC such as Ethereal, Wireshark [8]. Common hardware platforms are adopted in this method in order to reduce cost, but great CPU resources are required to capture packets from network link and copy them from system. At present the tools implemented based on this method can’t offer the process ability more than M-bits. In recent years, with the increase of network bandwidth and the emergence of realtime business of high bandwidth requirement, traffic capture is in the face of challenges from high-bandwidth, large-scale, real-time analysis and providing various metrics of diversified granularity [9]. Therefore, it is meaningful to implement platforms of traffic capture for high speed network link and high monitoring performance on common hardware platform. In this paper, a method of packet traffic capture from high speed network link is proposed, and distributed probes of traffic capture, which are based on this method, are built on common hardware platforms and open source architecture in order to reduce increasing cost and fulfill the requirement of monitoring from Gigabit or even higher link.

2 Network Traffic Measurement Scheme Based on the analysis forenamed, IP network traffic measurement system can be divided into three layers: measurement plat, control plat and analysis plat. Its total structure is displayed in the Fig. 1. The measurement platform is a distributed platform, control platform is installed in the central place of the measured network, and the answer for issuing measurement commands to measurement platform, collecting measurement data, and sending data to analysis platform periodically.

Fig. 1. Traffic measurement scheme

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The key of network traffic monitor is the real-time acquirement and analysis of network traffic data. The IP network monitor system which is network traffic characteristic analysis oriented must satisfy this requirement. Hence the prober is the kernel of the design of system to measure network traffic. The design of prober system can be split into three parts. Task receiving thread: the thread group listen the active connection initiated by control platform on 9090, the default TCP port, receive orders that are sent to the traffic collecting probe, including system configuration, monitoring the downloading of managing tasks, and monitoring the management of managing tasks. Task dispatching thread: Task receiving task group send the system configuration and order of managing tasks to the system tasks queue that is ready, dispatch thread executing tasks or create new thread to execute corresponding tasks. Result storage thread: traffic collecting probe system maintains a measuring result and measurement information base (MIB). And the thread in the task dispatching thread group save the measuring results into MIB so that the control platform can save and fetch the measuring results by polling (Fig. 2).

start

System main function

Task add/delete

Task dispatch

transfer

Communicatin g module

trigger Task execute tread

Measure tool module

Task execute tread call

Task execute tread Task execute tread

manage

Task queue manage

... ... ... ... ... ...

Fig. 2. Traffic capture probe system architecture

3 Crucial Technology of Prober Implementation 3.1

Packet Capture

Probe used in the system is achieved based on common hardware platform. Therefore, high efficient packet capture and less system resources for the flow characteristics of acquisition method are the key to guarantee the accuracy of the systems. We implement a new traffic capture method based on shared memory to improve the efficiency. The key of this method is using shared memory which is between kernel space and user space as packet buffer to reduce the number of system calls and implement packet zerocopy. Taking the advantage of share memory, packet receive path from NIC driver buffer to user level application has been shorten. Figure 3 show that, packet capture method based on shared memory for high-speed network consists of three parts: the packet capture driver module, the shared memory management module and the upperclass application API interface.

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Fig. 3. Shared memory method

The process of packet capture are as follows: ① access to the packet capture drive module, through the revision of Driver Program; ② through shared memory management module inspect packet buffer memory space, if still available will write data packets through DMA (Directly Memory Access) channel to the shared memory and return, otherwise discarded packets directly; ③ flow monitoring and analysis application procedures call upper API interface to access package in the shared memory buffer, and then release of packets buffer after completing packet processing and analysis. The establishment of shared memory space is the key to implement packet capture for high-speed network. The packet capture driver module is responsible for writing packets to the shared memory. Flow monitoring and analysis procedure call API interface reading of the packet for statistics and analysis. Through access to the packet capture driver module for writing packet into shared memory directly and circularly, the applications in the user space call API interface to access shared memory, implement packet zero-copy and eliminate the system calls. 3.2

Prober Management

Under the one-probe condition, the data procedure of system, during which the probe has two states that are data collection and waiting for being affirmed, is from probe to control platform and then to analysis platform. In actual application, there may be more than one probe and every probe has to wait for data operation until other probes have already inserted data for the sake of only one platform. Therefore, average waiting time will multiply, and the system will collapse since contemporarily sending data to platform by more than one probe will lead to the content for the database resource, causing the deadlock. In Fig. 4, the signal is duality (the value is 0 or 1), functioning as mutex variable. After the data sent by probe reached, the control platform will add timestamp to the data, which are reached at the same time, and then try to gain signal for these data. If it fails, the data will enter the waiting queue, waiting for the release of the signal; while if it succeeds, the control platform will execute P operation, letting data enter ring buffer,

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and then execute V operation after the data copy is finished. During the procedure, the only thing that the probe has to do is to wait the data entering waiting queue or ring buffer, and the waiting time is the spending of one-time memory copy operation and content switch. Before the arithmetic in the chart is applied, probe has to wait until the data are recorded into the database, referring to the spending of physical movement of disk location, and therefore it will slower several orders of magnitude than memory operation. After the Producer-Consumer arithmetic is applied, the packet-missing rate decreased from 3% to 0.1%, in the condition that the number of probe are three and the average statistic cycle is 30 s. Sniffer Server monitoring/analysis

Sniffer Server monitoring/analysis

Sniffer Server monitoring/analysis

signal

Fig. 4. Application of producer-consumer model

4 Probe Performance Analysis 4.1

Traffic Capture Performance

In order to validate prober capture method (shared memory), the performance of four methods including Libpcap tool, Libpcap_ mmap tool, kernel model tool and prober’s shared memory method, are contrasted. In the experiment, Smartbits2000 is used for sending packets and two PC, AL1 and AL2, are also included. AL1, connected with smartbit2000 directly, is used for controlling the rate of sending packets and the size of packet rate. And AL2 is used as traffic receiver. And the traffic, sent by smartbit, is copied to AL2 via mirroring. Figure 5 shows the detailed configuration. The curve of Fig. 6(a) reflects performance changes of four capture methods with the increase of the rate of sending packets. Among these four methods based on general NIC, the performance of shared memory, which can capture all packets in wire speed, is best. The performance of Kernel model is less than that of share memory; and its highest capture rate is closed to 1,300,000 pps along with 12% loss rate. Figure 6(b) shows that the optimum method is packet capture method based on shared memory, and the next is Kernel model, whose highest capture rate is 1,700,000 pps along with 25% loss rate, and the worst one is Libpcap, whose highest capture rate is 175,000 pps along with about 87.5% loss rate. The curve of Fig. 4 also shows that

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Fig. 5. Testing connection

(a) curve graph reflecting changes of the capability of deep analysis on data. (b) curve graph reflecting changes of cpu usage Fig. 6. The performance of capturing data of four methods

the performance of libcap_mmap, whose highest capture rate is 850,000 pps along with 41.9% loss rate, has been enhanced dramatically compared with libpcap. The curve of CPU usage shows that the CPU usage of share memory is minimum, just 50%, and that of Libpcap is 80%. Although the memory copy of kernel model is the same as that of shared memory, its performance is worse. And the reason is that the former should allocate buffer for each captured data dynamically in the kernel space and release the temporary buffer, resulting in consuming most system calls for managing memory. In addition, since all packets will enter into TCP/IP protocol stack of OS kernel, they need to be queued and maintained at expense of large system cost. 4.2

Traffic Sample Performance

Figure 7 illustrate two original traffic data sets, which are obtained by monitoring network traffic for a long time through network traffic and performance monitoring system. The data sets of Metro are collected from an export link of MAN (metropolitan area network) and data set of Campus is captured from an export link of one campus. The performance of adaptive sampling algorithm is evaluated by two discrete network traffic time sequences, which are formed by 10,000 consecutive network traffic samples selected from two traffic sets and measured at a rate per unit of minutes.

Design and Implementation of Network Traffic

(a)Original network traffic of Metro

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(b)Original network traffic of Campus

Fig. 7. Original network traffic

The red part of Fig. 8 represents the schematic diagram of network traffic, which is collected from above two data sets via using prober’s adaptive sampling algorithm. As the traffic curve graph after sampling is consistent with original traffic, the prober can be adopted for measuring network traffic accurately.

(a)Traffic of Metro after sampling

(b)Traffic of Campus after sampling

Fig. 8. Traffic of prober sampling method

5 Conclusions To reflect Internet traffic behavior roundly, we design a network monitoring system for the analysis on the characteristics. In the course of realization, we present measurement and analysis metrics, which roundly reflect network characteristics; then we discuss the material measurement and analysis method and introduce the crucial technologies which are used in the system; in the end, we validate the system in Campus LAN and gain good effect. In the following work, we need to make further study on the predictability of network performance. Acknowledgments. This research is funded by General Project of Hunan Education Department with grant no. 17C1651(Research on accurate identification, anomaly location and behavior analysis of P2P application traffic), and Youth Fund Project of CSUFT with grant no. QJ2012008B. Thanks to our team in ICT for their efforts to develop the monitoring and measurement system, especially thanks to Guangxing Zhang etc.

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References 1. Fraleigh, C., Moon, S., Lyles, B., et al.: Packet-level traffic measurements from the Sprint IP backbone. IEEE Netw. 17(6), 6–16 (2003) 2. Deri, L.: Passively monitoring networks at gigabit speeds using commodity hardware and open source software. In: Proceedings of PAM 2003, San Diego, California, April (2003) 3. Duffield, N., Lund, C., Thorup, M.: Learn more, sample less: control of volume and variance in network measurement. IEEE Trans. Inf. Theory 51(5), 1756–1775 (2005) 4. Degioanni, L., Varenni, G.: Introducing scalability in network measurement: toward 10 Gbps with commodity hardware. In: IMC2004, Taormina, Silicy, Italy, 25–27 October (2004) 5. Shah, N.: Understanding network processor. Master’s Thesis[R]. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (2001) 6. Michaut, F., Lepage, F.: Application-oriented network metrology: metrics and active measurement tools. IEEE Commun. Surv. Tutor. 7(2), 2–24 (2006) 7. Lee, Y., Lee, Y.: Toward scalable internet traffic measurement and analysis with Hadoop. ACM (2013) 8. Papadogiannakis, A., Polychronakis, M., Markatos, E.P.: Stream-oriented network traffic capture and analysis for high-speed networks. IEEE J. Sel. Areas Commun. 32(10), 1849– 1863 (2014) 9. Liu, Y.I., Center, N.I., University, Y.: Design and implementation of high performance IP network traffic capture system. J. Yanan Univ. (2017)

PAPR Reduction of FBMC-OQAM Signals Using Particle Swam Optimization Algorithm Based on MBJO-PTS Yan Yang1(&) and Pingping Xu2 1

2

School of Electronic and Electrical Engineering, Bengbu University, Bengbu 233030, China [email protected] National Mobile Communications Research Lab., Southeast University, Nanjing 210096, China

Abstract. In recent years one of key technologies of 5G, the system of filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) has been studied widely. The peak-to-average power ratio (PAPR) is reduced for FBMC-OQAM system using particle swam optimization algorithm (PSOA) based multi-block joint optimization with partial transmit sequence (MBJOPTS) technique. Because of overlapping structure of FBMC-OQAM signals the PSOA is not directly used to FBMC-OQAM systems. Multi-blocks joint of transmit sequences for the FBMC system are divided into partial transmit sequence (PTS) for PAPR reduction. PSOA can be guaranteed to the optimal phase factors. It is demonstrated that the PAPR of FBMC-OQAM system with PSOA performers better than the original FBMC-OQAM system via simulations. The simulation results also demonstrate the complementary cumulative distribution function (CCDF) can be obtained under the different numbers of subcarriers and subblocks. Keywords: PAPR


PSOA
MBJO
CCDF

1 Introduction Recently, the FBMC-OQAM system has become the radio waveform in forthcoming 5G radio access technology [1]. Its modulation structure offers numerous advantages such as very low side lobes, excellent frequency localization, frequency offsets and phase noise robustness which makes it more suitable than OFDM for 5G radio access technology [2–4]. But as a multi-carrier technique high PAPR of FBMC-OQAM system is its drawbacks. During the past years, a lot of PAPR reduction techniques have been proposed in multicarrier systems [5–8] partial transmit sequence (PTS) technique has attracted much attention as it is simple and effective no interference exists between neighboring data signals. Compared with OFDM system independent signals adjacent data blocks for FBMC-OQAM system are overlapped with each other. Naturally these overlap signals must interact with each other. So, the researcher proposed multi-block joint optimization (MBJO) to resolve this problem in [6]. In [7] pretreated partial transmit sequence(P-PTS) © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 167–176, 2019. https://doi.org/10.1007/978-3-030-02804-6_23

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method is proposed for reducing the PAPR of FBMC-OQAM signals. Because the inherent PTS structure is very complex it is hard to be reduced and complexity. Particle Swam Optimization (PSO) as an optimizer tool that is applied to solve the phase optimization problem in PTS in [9–11]. For above reasons PSOA is proposed by us for solving the optimization problem to reduce the PAPR in FBMC-OQAM systems based on the MBJO-PTS scheme.

2 FBMC-OQAM System 2.1

OQAM Pre-post Processing Scheme

The original processing blocks of FBMC-OQAM system are shown in Fig. 1. They consist of four parts including pre-processing blocks, synthesis filter banks blocks, analysis filter banks blocks and post-processing blocks. Where, ck;n indicates QAM complex values ðk ¼ 0; 1;


M  1Þ its real and imaginary parts are separated, and time staggered in a half symbol period. Thus, a simple complex-to-real conversion is realized with sample rate by a factor of 2. Those adjacent values will be orthogonal to each other because they are multiplied by hk;n powers. In the adjacent subchannel it is sure that the interference will not happen. In post processing part, hk;n is the multiplication sequence which separate real part. The conversion from real to complex part is operated when the sample rate is decreased by a factor 2.

Fig. 1. The system of FBMC-OQAM

2.2

Synthesis and Analysis Filter Banks

In Fig. 1 symbol Gk ðzÞ is the transform function of every subchannel filter in synthesis filter bank. It is formed by exponential modulation. The prototype filter of FIR G0 ðzÞ is a single real valued linear phase and its impulse response is f ðmÞ. Then k th impulse response gk ðmÞ of synthesis filter is defined as follows

 2pk Lf  1 m gk ðmÞ ¼ f ðmÞ exp j M 2

ð1Þ

PAPR Reduction of FBMC-OQAM Signals

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Where, m ¼ 0; 1;


Lf  1, the filter length is Lf . If perfect reconstruction (PR) condition is satisfied pk ðmÞ, analysis filter impulse response pk ðmÞ can be obtained which is defined by   pk ðmÞ ¼ gk Lf  1  m

ð2Þ

gk ð
Þ is conjugation of gk ð
Þ. 2.3

Prototype Filter Design

If PR or near PR condition is satisfied the prototype filter can be guaranteed to be well designed by frequency sampling method. Inverse fast Fourier transform(IFFT) is used to achieve impulse response coefficients. The desired frequency response is sampled by product of K and M. Where, K is overlap factor (Let K ¼ 4), M is total subcarriers number. Then impulse response of prototype filter f ðmÞ can be written as follows: 1 f ðm Þ ¼ N

k0 þ 2

LX f 1 i¼1


! 2pim ð1Þ ki cos KM i

ð3Þ

Where, m ¼ 0; 1;


Lf  1, overall length of filter is Lf ¼ KM  1. In [3] the P prototype filter has good performance in stopband when k0 ¼ 1; k0 þ 2 K1 l¼1 kl ¼ 0 2 and kl2 þ kKl ¼ 1, where l ¼ 1; 2;


; K=2.

3 PAPR Reduction for FBMC  OQAM Signals 3.1

FBMC  OQAM Signals

cðtÞ indicates FBMC-OQAM signals and must be sampled to compute signals’ PAPR. If sampling period equals T=L, oversampling factor is K, L ¼ KM As known in [6] is only K  4 that sampled signal approximates is very well for the true PAPR. All discrete-time domain symbols in kth data block are divided into vector sequence group as follows:  T cn ½k ¼ c0n ½k; c1n ½k;


; cM1 ½k  n

ð4Þ

Subcarriers of each subblock are selected to weight by phase factors. PTS technology is applied to get minimized PAPR in FBMC  OQAM system. Symbol cn ðkÞ indicates block which can be divided into subblocks S. sth subblock ssn ðkÞ is written as:  T s;1 M1 sn ½k  ¼ ss;0 ½k  n ½k ; sn ½k ;


; sn

ð5Þ

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So, cn ðkÞ ¼

Ps1

s m¼0 sn ½k ,

The subblock signal ~ssn ½k is given as ~ssn ½k ¼

M 1 X

ss;m m ½k 

ð6Þ

m¼0

Thus, the FBMC-OQAM signal in the discrete-time domain is given by phase   factor vector bsn ¼ b1n ; b2n ;


; bsn ; bSn ~cn ½k  ¼

S X

bsn~ssn ½k

ð7Þ

s¼1

n 2pi o Where bsn 2 ej U ; i ¼ 0; 1;


; U  1 and candidate phase number is U. When U [ 2, the phase factors are selected and after being sampled, the PTS signals of FBMC-OQAM can be obtained if all data blocks are added up. ~c½k ¼

N 1 X

~cn ½k

ð8Þ

n¼0

It is obvious that neighbor data blocks lead to signals overlapped with each other at output parts. If the time duration of the output signal is T there are ðM þ AÞ segments for data blocks and M data blocks for an FBMC-OQAM signal. PAPR for the qth segment of FBMC-OQAM signals is defined as PAPRq ¼

Pq

 ; E0  k  ðM þ AÞK1 ½~c½k2 

0qM þA  1

ð9Þ

Where, E ½
 represents the expectation, Pq is peak power of qth segment. Pq can be follow as Pq ¼

max

j~c½kj2 ;

qK  k  ðq þ 1ÞK1

0qM þA  1

ð10Þ

PTS technology is originally used to OFDM system without overlap between adjacent data blocks. So, it is independent for each data block when the original PTS scheme is applied to optimize phase factor vectors. The direct optimization formulation of phase factor vectors of FBMC-OQAM signals can be written as follows:

  min max~c½k2  k bn n 2pi o s jU subject to bn 2 e ; i ¼ 0; 1;


; U  1 s ¼ 1; 2;


; S

ð11Þ

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171

Obviously, the minimum peak power need to be selected from the sth data block. In [6] it is tested that direct PTS scheme for FBMC-OQAM with overlapping data blocks is not effective. 3.2

Model of MBJO-PTS Scheme

MBJO method has been proposed in [6] to deal with overlapping data blocks to get better PAPR reduction. The model of MBJO-PTS scheme is shown as in Fig. 2. Data blocks of FBMC-OQAM signals are optimized with PAPR reduction Thus, independent parameters optimization becomes joint optimization. Of course, the complexity of optimization problem(COP) must be considered during achieving solution to the MBJO-PTS optimization problem. To obtain optimization we can define a penalty function xðPq Þ increasing with Pq . For all M data blocks the summed penalty of signal need to be minimized by joint optimization of the phase factor vectors. The optimization problem can be express as min

b0 ;b1 ;...;bM1

subject to

PM þ A1

xðPq Þ; n 2pi o bsn 2 ej U ; i ¼ 0; . . .; U  1 q¼0

ð12Þ

S ¼ 1; 2. . .; S If A ¼ 0, the optimization problem in (11) is equal to as follows: Pq ; 0  q  M  1 n 2pi o subject to bsn 2 ej U ; i ¼ 0; . . .; U  1 S ¼ 1; 2. . .; S 0  q  M  1 min bq

Fig. 2. The model of MBJO-PTS scheme

ð13Þ

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Proposed PSOA for Optimal Solutions Reducing PAPR of FBMCOQAM System

In this section, PSOA is proposed to solve the optimization phase problem based on MBJO-PTS in (11). As is shown in Fig. 3, the phase factors are optimized by PSOA.

Fig. 3. The model of PSOA based on MBJO-PTS scheme

In the PSOA the phase factors bsn are called particles in possible solution space based on MBJO-PTS scheme. In the PSO algorithm there are M agents, called particles. Each particle depends on its three vectors including current position vector, velocity vector, and best position vector. These N-dimensional vectors contain properties of N decision variables. The particles move around in the search-space as mentioned in the previous section, the optimal solution of the phase vectors will be achieved. The particles are evaluated with the fitness value by PSOA the value is the PAPR in Eq. (9), the objective function. A solution space with a matrix of size M  N is randomly generated, where the number of particles is M and the number of disjoint subblock in the hMBJO-PTS algorithm is N. In fact, the solution space is a matrix with i

rows of: bi;n ¼ b1i;n ; b2i;n ;


; bNi;n , 1  i  M..In solution space the optimum solution h i j 1 2 N ; Vi;n ;


; Vi;n ; Vi;n is searched by PSOA using iteration process. Vi;n ¼ Vi;n

ð1  j  NÞ represents velocity vector denoting increment of current position. Pi;nj ¼ h i j P1i;n ; P2i;n ;


Pi;n ; PNi;n indicates the best position. bi:0 is generated randomly as an  j  j j initial approximation value. Its search domain is Xmin , where Xmin ; Xmax is lower limit j of particle positions, Xmax is upper limit in the jth dimension respectively. Similarly, initial velocity vector Vi:0 is initialized randomly, initial position of best position Pi:n is looked as current position. After n iteration, particles can return the best objective

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173

function value with its best position. This particle is called global particle it is the best one. Let Pg;n indicate the best position where g represents the best index of global particle. For minimization problem of Eq. (13), Pi:n is given by ( Pi;n ¼

    bi;n ; if f bi;n \f Pi;n1     Pi;n ; if f bi;n  f Pi;n1

  and Gn ¼ Pg;n ; where g ¼ arg min f Pi;n 1iM

ð14Þ ð15Þ

After ðn þ 1Þth iteration, If Vi;nj þ 1 represents each particle component it can be written as follows:   j j j j j j Vi;n ¼ xV þ c r P  b r G  b þ c 1 1 2 2 i;n i;n i;n i;n i;n þ1

ð16Þ

j j j bi;n þ 1 ¼ bi;n þ Vi;n þ 1

ð17Þ

Fig. 4. PSOA flowchart based on MBJO-PTS

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Where, c1 and c2 indicate acceleration terms. r1 and r2 are uniform constant distribution of random numbers it is ranged in ½0; 1 x is inertia factor defined as x¼ðxmax  xmin Þ 

Itrmax  Itrcurrent þ xmin Itrmax

ð18Þ

where Itrcurrent and Itrmax are the current iteration and the maximum iteration respectively. Let xmax ¼ 0:9 and xmin ¼ 0:4. In (18), x decreased linearly from 0.9 to 0.4 in a simulation period. The flowchart of PSOA based on MBJO-PTS scheme is shown in Fig. 4.

4 Results and Discussion In this paper, a few simulations are carried out to study the PAPR reduction performance applying PSOA. The experimental results using proposed algorithm are demonstrated by measuring the PAPR via complementary cumulative distribution function (CCDF) and identify some factors that influence the magnitude of the PAPR. CCDF of PAPR for FBMC  OQAM signals is written as: CCDFðPAPR [ PAPR THÞ ¼

dq;r

R M þA X X 1 dq;r ðM þ AÞ  R R¼1 q¼1

8 < 1; PAPR of the qth segment and rth ¼ test [ PAPR TH : 0; else:

ð19Þ

ð20Þ

The form of the penalty function xðPq Þ is written as xðPq Þ ¼ eePq

ð21Þ

Where, e 2 ½0; 1, from Fig. 3 it is easy observed different e value bring about different performance of MBJO-PTS scheme using NIM-PSOA. It is obvious when e ¼ 1:0 is the best one. So, for all simulations. e is set to 1.0

Fig. 5. PAPR CCDF using PSOA based on MBJO-PTS and CCDF of the original FBMCOQAM

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175

In Fig. 5, The parameters are set as 16 subblocks, 256 carriers and 10 particles the CCDF of the PAPR using PSOA for FBMC-OQAM system is compared with the original PAPR of FBMC-OQAM. It is shown that the probability that the PAPR of the PSOA based on MBJO-PTS of FBMC-OQAM system exceeds 4.6 dB was 0.0015, while with the same probability the PAPR of the original FBMC-OQAM system exceeds 6.2 dB.

Fig. 6. PAPR CCDF using PSOA for different number of subblocks

In Fig. 6 the CCDF of the PAPR of the PSOA based on MBJO-PTS of FBMCOQAM system is illustrated when different number of subblocks K ¼ f4; 8g are evaluated. The probability that the PAPR exceeds 5.5 dB is 0.0125 when K ¼ 8 and PAPR exceeds 5.8 dB was 0.015 when K ¼ 4.

Fig. 7. PAPR CCDF of FBMC-OQAM using PSOA for different number of subcarriers

Obviously, the number of subblocks increases the PSOA based on MBJO-PTS performance becomes better and better. Accordingly, the research complexity will increase. So, the number of subblocks an important factor that have to be considered in the MBJO-PTS technique. When the number of subcarrier changes PAPR performance changes as shown in Fig. 7. If the number of sub-carriers increases, the PAPR is increased. The probability that PAPR exceeds 3.2 dB is 0.015 when the number of subcarrier N ¼ 256 while it is 0.0015 when N ¼ 1024.

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5 Conclusions In this article, PSOA is proposed to optimize MBJO-PTS scheme for the PAPR reduction of FBMC-OQAM signals overlapped in data blackspots idea is employed to improve and develop the performance for the searching. Demonstrations has been proved MBJO-PTS scheme employing PSOA is more effective than original FBMCOQAM system without MBJO-PTS structure. Acknowledgments. This research is supported not only by foundation of Nature research for key project of Anhui higher education (No: KJ2016A455) but Engineering Center of Bengbu University (No: BBXYGC2016B04).

References 1. Mogensen, P., et al.: 5G small cell optimized radio design. In: International Workshop on Emerging Technologies for LTE-Advanced and Beyond 4G, in Conjunction with IEEE Globecom (2013) 2. Farhang-Boroujeny, B.: OFDM versus filter bank multi-carrier. IEEE Signal Process. Mag. 8 (3), 92–112 (2006) 3. Farhang-Boroujeny, B., Kempter, R.: Multicarrier communication techniques for spectrum sensing and communication in cognitive radios. IEEE Commun. Mag. 46(4), 80–85 (2008) 4. Mattera, D., Tanda, M., Bellanger, M.: Performance analysis of some timing offset equalizers for FBMC/OQAM systems. Signal Process. 108, 167–182 (2015) 5. Antony, C., Hate, S.G.: PAPR reduction using PTS technique in OFDM-MIMO system. Int. J. Adv. Res. Comput. Commun. Eng. 4(7), 112–115 (2015) 6. Qu, D., Lu, S., Jiang, T.: Multi-block joint optimization for the peak-to-average power ratio reduction of FBMC-OQAM signals. IEEE Trans. Signal Process. 61(7), 1605–1613 (2013) 7. Kaiming, L., Jundan, H., Penga, Z.: PAPR reduction for FBMC-OQAM systems using PPTS scheme. Sci. Direct 22(6), 78–85 (2015) 8. Lu, S., Qu, D., He, Y.: Sliding window tone reservation technique for the peak-to-average power ratio reduction of FBMC-OQAM signals. IEEE Wirel. Commun. Lett. 1(4), 268–271 (2012) 9. Liu, W.-C.: Design of a multiband cpw-fed monopole antenna using a particle swarm optimization approach. IEEE Trans. Antennas Propag. 53(10), 3273–3279 (2005) 10. Najjarzadeh, M., Ayatollahi, A.: FIR digital filters design: Particle swarm optimization utilizing LMS and minimax strategies. In: IEEE International Symposium on Signal processing and information technology, ISSPIT 2008, pp. 129 –132 December 2008 11. Asgari Tabatabaee, S.M.J., Zamiri-Jafarian, H.: Prototype filter design for FBMC systems via evolutionary PSO algorithm in highly doubly dispersive channels. Trans. Emerg. Telecommun. Technol. 28(4), 127–135 (2016)

On Abelian Tensor Decomposition and Gradient-Descent Algorithm Hailing Dong1 , Yichao Zhang1 , Ming Yang2(B) , Wen Liu3 , Rong Fan4 , and Yu Shi5 1

School of Mathematics and Statistics, Shenzhen University, Shenzhen, China Department of Computer Science, Southern Illinois University Carbondale, Carbondale, IL 62901, USA [email protected] 3 Mathematics Department, Lamar University, Beaumont, TX, USA 4 Department of Mathematics and Statistics, South Dakota State University, Brookings, USA 5 Department of Industrial Engineering, University of Houston, Houston, USA 2

Abstract. Volker Strassen introduced a famous commutation equation on tensor rank in his ground-breaking papers [9, 10]. Abelian tensor are rank-I tensors in KI×I×K satisfying Strassen’s equation. In this paper, we introduce Abelian tensor decomposition, which is unique under mild conditions. A gradient based algorithm has been given and the optimal solution is Lyapunov-stable.

Keywords: Abelian Tensor Decomposition

1

· Tensor rank

Introduction

Modern big data analysis involves large amounts of data and a large number of variables, which makes it a high-dimensional problem. Tensor methods are effective at learning such complex high-dimensional problems, and have been applied in numerous domains, from social network analysis, document categorization, genomics, and toward understanding the neuronal behavior in the brain (see [8]). Tensor methods are so effective because they draw on highly optimized linear algebra libraries and can run on modern systems for large-scale computation. A tensor is a multidimensional array of numbers [xi1 i2 ···iN ]. When N = 1, it is a vector; N = 2, it is a matrix, and tensors of order three or higher are called higher-order tensors. More formally, an N -way or N th-order tensor is an element of the tensor product of N vector spaces, each of which has its own coordinate system. A third-order tensor has three indices, as shown in Fig. 1. A tensor decomposition is the expression of a tensor as a linear combination of other tensors (presumably of lower rank). It arises in numerous application areas (see [2,3,7,8]. Throughout this paper, for basic definitions, notation and results, we follow [4]. c Springer Nature Switzerland AG 2019  F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 177–185, 2019. https://doi.org/10.1007/978-3-030-02804-6_24

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Fig. 1. Third tensor

The spectral decomposition of the matrix is the standard singular-value decomposition (SVD), and we already possess efficient algorithms to compute the best such decomposition. Since matrix problems can be solved efficiently despite being non-convex, and given matrices are special cases of tensors, we decided on a new research direction: can we design similar algorithms to solve the decomposition of tensors? It turns out that tensors are much more difficult to analyze and can be NP-hard. Given that, we took a different route and sought to characterize the set of conditions under which such a decomposition can be solved optimally. Volker Strassen introduced a famous commutation equation on tensor rank in [9,10]. Abelian tensor are generic rank-I tensors in KI×I×K . Very recently, Landsberg and Michalek [5] have studied Abelian tensor intensively in a very general Lie algebra point of view. Abelian tensor has a unique tensor decomposition in mild conditions. We introduce Abelian tensor decomposition and give a gradient based algorithm. And then Lyapunov-stability analysis has been provided for Abelian tensor decomposition. The rest of the paper is organized as follows. Section 2 reviews some basic definitions of tensors and tensor decompositions. Section 3 provides Abelian tensor decompositon, which is related to Abelian tensor and has good and novel uniqueness properties. Section 4 study gradient decent algorithm of Abelian tensor decompositon and its Lyapunov stability.

2

Standard Tensor Decompositions

Definition 1 (Rank-One Tensors). An 3-way tensor X ∈ KI×J×K (K stands for R or C) is rank one if it can be written as the outer product of 3 vectors, i.e. X = a ◦ b ◦ c. The symbol “◦” represents the vector outer product. This means that The (i, j, k) element of X is given by xijk = ai bj ck (Fig. 2). Definition 2 (Tensor Rank). A tensor X has rank R, if it is the sum of R rank-one tensors but not fewer. And the border rank is the minimum number of rank-one tensors that are sufficient to approximate the given tensor with arbitrarily small nonzero error.

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Fig. 2. Rank-one third-order tensor

Definition 3 (CP decomposition). The CP decomposition (CPD) factorizes a tensor into a sum of component rank-one tensors. For example, given a thirdorder tensor X ∈ KI×J×K , we wish to write it as X ≈

R 

ar ◦ br ◦ cr = [A, B, C],

(1)

r=1

where R is a positive integer and ar ∈ KI , br ∈ KJ , and cr ∈ KK for r = 1, . . . , R, and A = [a1 , . . . , aR ], B = [b1 , . . . , bR ], C = [c1 , . . . , cR ] are factor matrices. Elementwise, (1) is written as xijk ≈

R 

air bjr ckr

for i = 1, . . . , I, j = 1, . . . , J, k = 1, . . . , K.

r=1

This is illustrated in Fig. 3.

Fig. 3. CP decomposition of a three-way array

The CP decomposition is unique under much weaker conditions. Let X ∈ KI×J×K be a three-way tensor of rank R, i.e., X =

R 

ar ◦ br ◦ cr .

r=1

Uniqueness means that this is the only possible combination of rank-one tensors that sums to X , with the exception of the elementary indeterminacies of scaling and permutation. The following theorem about uniqueness is due to Harshman.

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Definition 4 (Tensor decomposition in orthonormal frame). X KI×J×K can be decomposed as X = X1 ◦ e1 + · · · + XK ◦ eK ,

∈ (2)

where {e1 , . . . , eK } is a given positive oriented orthonormal frame of KK . We write X = [X1 | · · · |XK ] ∈ KI×J×K , Xk ∈ KI×J if {e1 , . . . , eK } is a canonical basis of KK . Consider the tensors X = Xijk , 1 ≤ i ≤ I, 1 ≤ j ≤ J, 1 ≤ k ≤ K, and {ek = [εi1 , . . . , εiK ] , 1 ≤ k ≤ K} is an orthonormal frame. Now the tensor decomposition in orthonormal frame for X ∈ KI×J×K is   ε1k Xijk ]1≤i≤I,1≤j≤J ◦ e1 + · · · + [ εKk Xijk ]1≤i≤I,1≤j≤J ◦ eK . X =[ 1≤k≤K

1≤k≤K

(3) Proposition 1. For a tensor X ∈ KI×J×K , the decomposition X = X1 ◦ e1 + · · · + XK ◦ eK as in definition 4 is essentially unique.  Proof. Assume the contrary that X = X1 ◦ e1 + · · · + XK ◦ eK is different from X = X1 ◦ e1 + · · · + XK ◦ eK . Taking the mode-3 inner product with er , we have Xr = Xr for any r, r = 1, . . . , K, which is a contradiction.

3

Abelian Tensor

Let [, ] de note the Lie bracket, that is [x, y] = xy − yx, for tensor X = [X1 | · · · |XK ] ∈ KI×I×K , Xk ∈ KI×I with X1 nonsingular, Strassen [9,10] introduced the following inequality  ) − I), rank[Xi X1−1 , Xj X1−1 ] ≤ 2(rank(X

(4)

 ) is the border rank of X . where rank(X Definition 5 (Abelian tensor). A tensor X = [X1 | · · · |XK ] ∈ KI×I×K , Xk ∈ KI×I is an Abelian tensor if X1 is nonsingular and {Xr X1−1 ∈ KI×I |1 ≤ r ≤ K} is diagonalizable and commutative. Proposition 2. If a tensor X ∈ KI×I×K has border rank or rank I, then X is an Abelian tensor.

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Proof. Using Strassen inequality (4), we know X is an Abelian tensor if  ) = I. If a tensor X ∈ KI×I×K has rank I, as in definition 4, we have rank(X X = [X1 | · · · |XK ] =

I 

ai ◦ bi ◦ ci

i=1

s.t. Xk =

I 

ek | ci ai ◦ bi ,

i=1

where ek is the canonical basis. So ai , bi are independent and thus are basis for KI . And there exists coordinate transformation matrices P, Q ∈ KI×I such that P Xk Q ∈ diag(KI×I ), where diag(KI×I ) are diagonal I × I matrices. Now it is easy to see P Xk Q (P X1−1 Q )−1 = P Xk X1−1 P −1 ∈ diag(KI×I ). Therefore X is a Abelian tensor. Definition 6. M = [Mij ] ∈ KI×I is a stochastic matrix if I 

Mij =

i=1

I 

Mij ≡ 1.

j=1

Example 1. In [1,6], the authors studied several stochastic model for DNA sequence data. Now we consider a stochastic model based on an Abelian conditional probability tensor. For three observed independent random variables X1 , X2 and X3 and a hidden random variable Xh among A, G, C, T, whose values are from 1 to I, the conditional probability tensor X = [Xijk ] ∈ KI×I×I is an Abelian tensor. And the factor matrices for its CP decompositions are stochastic matrices. Theorem 1 (Existence and uniqueness of decomposition of an Abelian tensor). For an Abelian tensor X = [X1 | · · · |XK ] ∈ KI×I×K , Xk ∈ KI×I , X1 is nonsingular, there exists an invertible matrix P such that ⎞ ⎛ k 1 ⎟ ⎜ P Xk X1−1 P −1 = ⎝ . . . ⎠ . kI And X has a rank-I tensor decomposition, which is essential unique if no pair vectors in di = [1i , . . . , K i ], 1 ≤ i ≤ I are scalar multiples of each other. Proof. If X is an Abelian tensor, {Xr X1−1 ∈ KI×I |1 ≤ r ≤ K}

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is diagonalizable and commutative, then there exists invertible matrices P such that ⎞ ⎛ r 1 ⎟ ⎜ P Xr X1−1 P −1 = ⎝ . . . ⎠ . rI This implies that ⎞⎛ ⎞ ⎛r 1 | | | ⎜ ⎟⎜ Xr = ⎝η1 η2 · · · ηI ⎠ ⎝ . . . ⎠ ⎜ ⎝ | | | rI ⎛

⎞

1 2 ··· I

⎟ ⎟ ⎠

where ηi and i are column vectors of P −1 and X1 P  . Then Xr = r1 η1 ◦ 1 + · · · + rI ηI ◦ I , and we have X = d1 ◦ η1 ◦ 1 + · · · + dI ◦ ηI ◦ I , which has rank I. And according to Harshman’s criterion of uniqueness, it is unique if no pair vectors in di are scalar multiples of each other. Definition 7. For a tensor X = [X1 | · · · |XK ] ∈ KI×I×K , Xk ∈ KI×I , X1 is nonsingular, an Abelian Tensor Decomposition is: X ≈ d1 ◦ η1 ◦ 1 + · · · + dI ◦ ηI ◦ I = [A, P −1 , X1 P  ], with A = [d1 | · · · |dI ], P −1 = [η1 | · · · |ηI ], X1 P  = [1 | · · · |I ] s.t.

4 4.1

P Xk X1−1 P −1

K×I

is a diagonal matrix, 1 ≤ k ≤ K, A ∈ K

(5) I×I

, P ∈K

.

Algorithm for Abelian Tensor Decomposition Gradient-Descent Algorithm

For a tensor X = [X1 | · · · |XK ] ∈ KI×I×K , Xk ∈ KI×I , X1 is nonsingular, this section presents an iterative algorithm to jointly approximately diagonalize a set of matrices {Xk X1−1 ∈ KI×I |1 ≤ k ≤ K}, where each matrix Xk X1−1 has a weight ωk . This algorithm is based on simple gradient descent minimization of the criterion min P

K−1 1  ωk ||[P Xk X1−1 P −1 ]off−diag ||2F , s.t. P ∈ KI×I . 2 k=1

(6)

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We adopt the usual quality criterion for diagonalization, namely the sum of the squares of off-diagonal elements, subject to a constraint to keep the diagonal elements from also going to zero. Using Lagrange multiplier method, we try to solve min P

K−1 1  ωk ||[P Xk X1−1 P −1 ]off−diag ||2F + λ log |P |, 2

(7)

k=1

where |P | is the determinant of P . Note that P = [ξ1 | · · · |ξI ], and let K−1 

Δi =

ωk Xk X1−1 [

k=1







ξj ξj ]X1−1 Xk ,

1≤j=i≤I

and then we could write I

Φ(P ) =

1  ξ Δi ξi + λ log |P |, 2 i=1 i

which is locally positive-definite. However, (7) may not have a global minimum because the domain is non-compact. So we suggest to consider Tikhonov regularized loss funtion I

Ψ (P ) =

1  ρ ξi Δi ξi + λ log |P | + ||P ||2F . 2 i=1 2

Now we could assume ρ2 ||P ||2F ≤ const., which is a compact region. So Ψ (P ) has a global optimal solution. Taking the partial derivative of Ψ (P ) with respect to ξi (note that Δi is independent of ξi ), leads to ∂Ψ (P ) ξ¯i = (Δi + ρI)ξi + λ  ¯ ,  ∂ξi 2ξi ξi

(8)

where ξi ξ¯i is the Laplace cofactor expansion of |P | along the i-th row ξi . With the gradient (8), we can use any suitable gradient optimization method to minimize (t) (7), i.e. steepest descent. If our t-th approximation is ξi , the (t + 1)-th term is (t+1)

ξi

(t)

=ξi − δ (t) (t)

∂Ψ (P (t) ) ∂ξi (t)

=ξi − δ (t) [(Δi + ρI)(t) ξi + λ

(t) ξ¯i ] (t) 2ξ ξ¯  (t)

i

(t)

(P where δ (t) is step length in the direction − ∂Ψ ∂ξ i is easy to see Ψ (P t+1 ) < Ψ (P t ).

)

(9)

i

. Using Taylor expansion, it

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Algorithm 1. Abelian Tensor Decomposition: complexity O(KI 2 ) Require: Task: Compute Abelian tensor decomposition of X = [X1 | · · · |XK ] ∈ KI×I×K , Xk ∈ KI×I , X1 is nonsingular (0) (0) 1): Initialization: initialize nonsingular P (0) = [ξ1 | · · · |ξI ] ∈ RI×I , normalize the (0) columns of P , and initialize constant ρ, ωk 2): Main Iteration: 3): Gradient-Descent Algorithm for solving (7) 4): t = 0 5): repeat    (t) (t)  (t) −1  ]X1−1 Xk 6): Δi = K−1 1≤j=i≤I ξj ξj k=1 ωk Xk X1 [  (t) (t) (t) 7): Compute ξ ξ¯ : the Laplace cofactor expansion of |P (t) | along the i-th row ξ (t+1)

8): ξi

i (t)

i

(t)

= ξi − δ (t) [(Δi + ρI)(t) ξi + λ

(t) ξ¯i  (t) (t) 2ξi ξ¯i

i

]

9): t := t + 1 10): until Convergence 11): return P 12): Compute P −1 = [η1 | · · · |ηI ] and X1 P  = [ 1 | · · · | I ] 1, . . . , K  13): Compute Xr = r1 η1 ◦ 1 + · · · + rI ηI ◦ I , r =  K r K r ◦ η 14): Compute X =

e ◦ + · · · + 1 1 r=1 1 r r=1 I er ◦ ηI ◦ I

4.2

Lyapunov Method in Analysis of Stability

Consider a discrete dynamical system, which satisfies x(t + 1) = f (t, x(t)), x(0) = x0 , x ∈ R, t = 0, 1, 2, . . . The equilibrium point x∗ = 0 is stable (in the sense of Lyapunov) at t = 0, if for any  > 0, there exists δ(0, ) > 0 such that ||x(0)|| < δ(0, ) ⇒ ||x(t)|| < . We consider the discrete dynamical system (9), now according to gradient decent principle, Ψ (P (t) ) is decreasing monotone as t increase. And from the gradient (8), we know Ψ (P (t) ) is continuous, has continuous derivatives, is locally positivedefinite. Therefore, Ψ (P (t) ) is a Lyapunov function and thus the equilibrium is stable. The orbits follow the path of steepest descent of Ψ . If Ψ (P ) represents altitude, then a skier who follows the fall line at all points follows these paths of steepest descent. The method of steepest descent seeks minima of Ψ (P ) by descending toward the bottom of the graph of Ψ on such curves. (t) Therefore, when t goes to infinity, we have ξi → ξi ∈ KI stably and let lim P (t) Xr X1−1 P (t)−1 ∩ diag(KI×I ) = diag(r1 , . . . , rI ).

t→∞

Then we have

⎞⎛ ⎞ ⎛r 1 | | | ⎜ ⎟⎜ Xr ≈ ⎝η1 η2 · · · ηI ⎠ ⎝ . . . ⎠ ⎜ ⎝ | | | rI ⎛

1 2 ··· I

⎞ ⎟ ⎟ ⎠

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where ηi and i are column vectors of P −1 and X1 P  . Then Xr ≈ r1 η1 ◦ 1 + · · · + rI ηI ◦ I , where ei is the canonical basis of KI .

References 1. Allman, E.S., Jarvis, P.D., Rhodes, J.A., Sumner, J.G.: Tensor rank, invariants, inequalities, and applications. SIAM J. Matrix Anal. Appl. 34, 1014–1045 (2013) 2. Anandkumar, A., Ge, R., Hsu, D., Kakade, S.M., Telgarsky, M.: Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15, 2773–2832 (2014) 3. Ke, R., Li, W., Xiao, M.: Characterization of extreme points of multi-stochastic tensors. Comput. Methods Appl. Math. 16, 459–474 (2016) 4. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51, 455–500 (2009) 5. Landsberg, J., Lek, M.M.: Abelian tensors. Journal de Mathe´ematiques Pures et Appliqu´ees 108, 333–371 (2017) 6. Li, W., Ng, M.K.: On the limiting probability distribution of a transition probability tensor. Linear Multilinear Algebr. 62, 362–385 (2014) 7. Moitra, A.: Algorithmic Aspects of Machine Learning. Lecture notes (2014) 8. Papalexakis, E.E., Faloutsos, C., Sidiropoulos, N.D.: Tensors for data mining and data fusion: models, applications, and scalable algorithms. ACM Trans. Intell. Syst. Technol. (TIST) 8, 16 (2016) 9. Strassen, V.: Rank and optimal computation of generic tensors. Linear Algebr. Appl. 52, 645–685 (1983) 10. Strassen, V.: The asymptotic spectrum of tensors. Journal fr die reine und angewandte Mathematik 384, 102–152 (1988)

Collision Avoidance Method for UAV Using A* Search Algorithm Jung Kyu Park1 and Jaeho Kim2(&) 1

Changshin University, Gyeongsangnam-do, 51352 Changwon, Korea [email protected] 2 The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA [email protected]

Abstract. In this paper, we proposed a new collision avoidance algorithm that is utilization of A* algorithm. And, we analyzed the existing research such as Search Tree problem, total field sensing, and A* algorithm. In general, the A* algorithm is often used as a path finding algorithm in a static environment in mobile robots. We introduce several approaches to using the A* algorithm in environments with many moving objects. We will explain experimental results with several set-up and heuristic methods in various environments. We discuss the performance of the proposed method, the appropriate conditions for its operation, and what issues can affect performance. Finally, we will analyze the experimental results and identify the limitations of the proposed algorithm. Keywords: Avoidance


A* algorithm
Collision
UAV

1 Introduction Unmanned aerial vehicle (UAV) is aircraft that have not been piloted by human pilots. UAVs are usually remotely controlled by human pilots or programmed to autonomously drive. A UAV could be a drones, a helicopter, a robotics bee, or a moving object in the air. Early UAVs were originally designed and used for military mission, such as enemy reconnaissance and offensive missions. In recent conflicts, UAVs operated combat missions and numerous spying. Their growth ability and success is increasingly concerned with potential commercial applications in scientific, surveillance and product deliveries [1]. However, there is one problem that prevents UAV from spreading commercial deployments. The Federal Aviation Administration (FAA) is in control of UAV operations in national airspace. The UAV should behave like a human pilot is on board without cooperative communication (such as control commands from a human or information from neighbouring plane) [2]. When a UAV is flying autonomously, it is a major concern that it can not be reliably detected and avoided by other aircraft, and is excluded from commercial use. UAVs should avoid collisions with nearby objects or airplanes. There are many studies to solve UAV collision avoidance problem. Park et al. used the point of closest approach method to solve UAV conflicts detection and collision © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 186–193, 2019. https://doi.org/10.1007/978-3-030-02804-6_25

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resolution [3]. Several studies have used a grid-based methods to perform collision avoidance. Tooren et al. utilized A* algorithm to avoid dynamic obstacles [4]. A* (pronounced as “A star”) is a computer algorithm which is widely used in path planning for mobile robot and graph search, the process of plotting an efficiently directed path between multiple points. In this paper, we will utilize the A* algorithm to plan for the UAV path and use it for other UAV avoidance. This paper is organized as follows. In Sect. 2, related studies are discussed. In Sect. 3, Collision Avoidance Method is presented. Section 4 discusses performance evaluation. Finally, Sect. 5 concludes this paper.

2 Related Works A grid-based algorithm divides a space into squares or cubes of uniform size. A collection of cells is represented by a weighted graph. Vertices are represented by cells and edges are connected by adjacent cells. The vertices in the plane have eight edges at orthogonal (east, west, south, and north) and diagonal (northeast, northwest, southwest, and southeast). Using high weights (or infinite value) at the edges can indicate an obstacle. These graph expressions are used in short-path algorithms such as Dijkstra and Bell-Ford [5–7]. 2.1

Search Tree

Dijkstra discovered the minimum spanning tree algorithm between nodes in a graph. Considering n nodes, there are branches that connect them to a given length. Assuming one node has more than one path to another node, the path with the minimum length between two given nodes S and T can be found by the following algorithm. Dijkstra’s algorithm can solve the problem of being able to reduce to a minimum path finding in a graph with weights. The graph does not naturally have to specify the same weight (branch length) for the branch. Also, the weight may vary depending on the direction you are moving. One limitation of Dijkstra’s algorithm is that the algorithm does not work on negative-weighted graphs. An algorithm similar to the Bell-Ford algorithm considers that there is a negative weight in the graph [6, 7]. Dijkstra’s algorithm has excellent features to solve the least cost problem when there are large number of nodes, but the operation can be extremely long. A few years later, a generalized version of the Dijkstra algorithm was proposed to reduce the number of nodes which need to be searched. 2.2

A* Algorithm

Hart et al. proposed and an A* algorithm (Previous multiple A1, A2, etc.) to optimize Dijkstra’s algorithm [8]. A* is an informed retrieval algorithm. First, search for a route that appears to be connected to the destination. To do this, A* uses a distance and cost heuristic to determine the order in which the potential nodes on the path are visited. A* is well suited to the path planning of a robot that greatly improves the time of the Dijkstra algorithm and navigates around fixed obstacles.

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The environment of UAVs of collision avoidance, the other UAVs are the moving objects. Tooren et al. applied the A* and implemented the motion primitive node, which is a short trajectory segments, for UAV [4]. Each of these motion primitives is selected to produce a flyable and smooth trajectory that is valid for the aircraft’s current state and performance limits. Not all possible segments are shown for simplicity. And the lowest cost path is showed with a solid line. Possible element sets include general flight elements such as linear segments and curved segments, but also include more complex elements such as Dubins (3D) set. The computation time varies greatly with possible changes in the airspace. It is clear that the larger the possible airspace, the greater the computational complexity. On the other hand, if the airspace is divided into fewer larger cells, the details needed to plan the collision path may not be sufficient [9].

3 Collision Avoidance Method 3.1

Collision Detection

Traffic Alert and Collision Avoidance Systems (TCAS) are commonly used in air traffic management management, including the FAA, to advise pilots to detect collisions and avoid potential conflicts [10]. The TCAS has defined a different level of a potential collision area to be protected around the aircraft. Since 1993, the FAA has required all commercial turbine power trains with more than 30 seats (or maximum take-off weight 15,000 kg) to accommodate TCAS II. In our case for ArduPilot, we consulted the definition of TCAS and the distance that the UAV can reach within 35 s of the critical distance to activate the collision avoidance task. In our experiment, we assume that the UAVs fly 5 m/s. So the safety flying distance is 175 m per 35 s. 3.2

Collision Avoidance

If potential conflicts are detected, we use the A * algorithm to calculate a safe path to avoid invaders. Figure 1 shows the process of the collision avoidance model. This figure consists of three parts: ArduPilot, collision detection function, and A* algorithm.

input waypoints

Ardupilot

Collision detection No

Yes

Collision Avoidance

(A* algorithm) transmit information of predicted path

modified waypoints

continue with autopilot

Fig. 1. Process of collision avoidance

ArduPilot is open source software that controls UAV flight [11]. Once ArduPilot has been given a route, UAV uses the route to fly. We have created the software to upload new waypoints to the ArduPilot.

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The collision detection part continuously monitors all UAVs to detect impending collisions. Given the positions and orientation of all UAVs, this part can derive the speed of all UAVs and predict impending collision or conflict. When a conflict or collision is predicted, the collision detection part calls A* algorithm and give it the location of the UAVs that can collide. The positions of future of all UAVs are continuously calculated 35 s ahead. The 35 s equals the response time suggested by TCAS. However, the response time can be modified according to the UAVs speed. When A* algorithm starts, the collision detection part stops for 35 s. After 35 s, the collision detection part is re-enabled to update the positions and bearings to remove the any inconsistency between the A* algorithm and ArduPilot. If the UAV is too close (the UAV can be reached within 35 s), the A* algorithm remains activation status. At the same time, the predicted location of invader (intruder awareness) is provided to the A* algorithm as a dynamic obstacles. Based on the most recent information update, the A* algorithm calculates a new path to avoid collisions. 3.3

Time Grids

To handle the dynamic environment, we extended the A* algorithm with the time grid method, as shown in Fig. 2. time t=3

t=2 t=1

Fig. 2. Time grids

The concept of the time grid is to predict the location of a UAV that already performed in intruder perception and to reconstruct the map with a discrete time system. In Fig. 2, the UAV and the invader are approaching each other at increasing time intervals. In the grid, the color scale indicates that the additional cost of the heuristic function is specified at that time step when the colored scale is expanded by A* algorithm. So A* algorithm can create a map of dynamic obstacles and avoid the higher cost grids.

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4 Experiments This chapter will examine the performance of the conservative and aggressive heuristics of the A* algorithm in the problem of UAV collision avoidance. We also shows the experiment results and uses other heuristics method to discuss the influences. 4.1

Test Environments

We used two criteria to evaluate performance of A* algorithm. (a) Conflict counts This is the values of plane pairs within 6 grids. The method that we calculate the conflicts is to determine how long the UAV is within the collision distance (6 grids). So, if two UAVs continue to fly within the 6 grids, the value of conflict continues to be calculated until the end of the conflict. (b) Crashes counts This is the value of pairs of airplanes within three times the wing width of the plane. In our experiment, we define that there are crashes when the paths of two or more UAVs are on the same grid at the same time step. When a crash occurs, the UAVs associated with the crash are removed and the experiment continues with the rest of the UAVs. We assume that the UAV takes 1 s to move a 5 m  5 m grid. By definition, conflicts occur when two UAVs are within 6 s of each other’s. For this reason, it takes at least 3 s to avoid collisions. In order to evaluate the A* algorithm, we considered several configurations. The settings consist of a fixed UAVs number (2, 4, 8, 16) and a field size (500  500 m, 1000  1000 m). We used 100 randomly generated scenarios from our experiments. Each scenario includes of four randomly selected waypoints per UAV. Each UAV must travel through four waypoints in a pre-determined order. To make a random points, we select any point in the field as the starting point, and then we use the 50 m radius and 7 random directions to make the next waypoint. We have used this method to ensure that each UAV can travel at least 200 m in the test experiment. 4.2

Experiment in 500  500 m Field

As shown in Fig. 3a, the cumulative distribution function (CDF) shows the probability that the number of conflicts is below a predetermined value. For example, if we assume a straight line with 100 conflicts, the probability of 100 collisions is 100% for 4 UAVs, 96% for 8 UAVs, and 1% for 16 UAVs. Comparing the previous figure and Fig. 3b, the CDF is similar to 4 UAVs and 8 UAVs. According to this research, we think that the ability to avoid conflicts if the UAV number is less than 8 is about the same between these two heuristic methods. In the test with 16 UAVs, we observe that when the number of collisions is 200, comparing the CDF, the heuristics with aggressive method more conflicts than the heuristics with conservative method.

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(a) Heuristics with conservative method

(b) Heuristics with aggressive method

Fig. 3. Cumulative density function (CDF) of conflicts in a 500  500 m

4.3

Comparison of Other Algorithms

In this section, we shows the results of experiments using multiple UAVs and fields of various sizes. We compared the proposed method with other algorithms. In James’ study, he compared the performance of three algorithms: Artificial Potential Field (APF), Dynamic Sparse A* Search (DSAS) and Mixed Integer Linear Programming (MILP) [12]. We selected our algorithm at the highest survival rate and compared the rate with the results of James’ experiment. Figures 4 and 5 show the experimental results.

Fig. 4. Average survival rate (500  500 m field)

In Figs. 4 and 5, A* is DAS and Conservative A* is our proposed algorithm that used in this experiments. The survival rate showed that the conservative heuristic method has better results in most cases. For scenarios with 16 UAVs in a 500  500 m field, the conservative heuristic’s survival rate is slightly higher than MILP algorithm. Figure 4 presents the results of the 500  500 m field experiment. For the scenario in the 1000  1000 m field, the survival rate of conservative heuristics is greater than 90% that similar to the performance of A* algorithm in Fig. 5. Moreover, MILP and APF algorithm have certain strength and weakness. For example, the APF has the

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Fig. 5. Average survival rate (1000  1000 m field)

highest survival rate when the UAV is operating in a small environment. However, when the UAV number is greater than 16 in the x 1000 m field, the survival rate drops significantly. Our approach does not have 100% survival rates in most cases. However, our proposed algorithm shows good survival rate under various conditions, so it is competitive when the UAV is smaller than 16.

5 Conclusion According to the results of the paper, we can obtain the best performance by using two alternative heuristic method with the A* algorithm in UAV environment. If the number of UAVs is less than 8, the heuristics with aggressive method will work well and should be used in low density conditions. Then we conclude that A* is flexible by allowing different heuristic method to treat various situations. So we can able to modify the A* algorithm to handle the environment with dynamic obstacles. Therefore, we think that A* algorithm is a good candidate for real-time path planning. Acknowledgments. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2018R1C1B5046282).

References 1. Abeywickrama, H.V., Jayawickrama, B.A., He, Y., Dutkiewicz, E.: Algorithm for energy efficient inter-UAV collision avoidance. In: Proceedings of 17th International Symposium on Communications and Information Technologies (ISCIT), pp. 1–5 (2017) 2. Oleynikova, H., Burri, M., Taylor, Z., Nieto, J., Siegwart, R., Galceran, E.: Continuous-time trajectory optimization for online UAV replanning. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5332–5339 (2017) 3. Park, J.W., Oh, H.D., Tahk, M.J.: UAV collision avoidance based on geometric approach. In: Proceedings of SICE Annual Conference (SICE), pp. 2122–2126 (2008) 4. Tooren, J.V., Heni, M., Knoll, A., Beck, J.: Development of an autonomous avoidance algorithm for UAVs in general airspace. In: Proceedings of First CEAS European Air and Space Conference (DGLR) (2007)

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5. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathmatik. 1, 269–271 (1959) 6. Bellman, R.: On a routing problem. Q. Appl. Math. 16, 87–90 (1958) 7. Ford, L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (1962) 8. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968) 9. Liu, Y., Zhang, X., Guan, X., Delahaye, D.: Potential odor intensity grid based UAV path planning algorithm with particle swarm optimization approach. Math. Probl. Eng. 2016, 1–16 (1968) 10. Introduction to TCAS II Version 7.1. http://www.faa.gov/documentLibrary/media/ Advisory_Circular/TCAS_II_V7.1_Intro_booklet.pdf 11. ArduPilot Open Source. http://ardupilot.org/ 12. Holt, J., Biaz, S., Aji, C.A.: Comparison of unmanned aerial system collision avoidance algorithms in a simulated environment. J. Guidance Control Dyn. 36(3), 881–883 (2013)

The Bayes Quantile Regression Theory and Application Xiaoliang Lv1, Chunli Wang1, Lu Qiu1, Haizhen Li1, and Liang Feng2(&) 1

Institute of Information Technology, Guilin University of Electronic Technology, Guilin 541004, China 2 Shandong Women’s University, Jinan 250000, Shandong, China [email protected]

Abstract. In order to reduce costs and streamline the efficiency of the cohort research, case-based cohort design is a biased sampling scheme widely used in time event data. If the observation or measurement time is random, the observation process can be regarded as a recurrence process. Due to time and cost constraints, individual tracking can not be carried out indefinitely, which makes the recurrence process impossible to be fully observed, we can only observe the part before the deletion time. The application of Bayesian survival analysis theory to the modeling and analysis of biological and medical data will solve several difficult problems in statistical data analysis, such as small sample size, incomplete data and complex operation environment. The application of Bayesian survival analysis theory in biology and medical statistics enriches and perfects the statistical modeling theory of small sample data. An improved minimization algorithm for constrained estimation is proposed. Simulation studies are carried out to verify the performance of the proposed method in finite samples. Keywords: Longitudinal data Standard errors


Parameter estimation
Standard deviation

1 Introduction In the application of Bayesian statistics in biology and medicine, we often encounter a series of complex data, such as longitudinal data, genetic data, survival data, spatiotemporal data, missing data and misclassification data. Longitudinal data refers to the data fused by cross-section and time series from repeated observations of each individual at different times. The biggest advantage of vertical data is that it combines cross-sectional data with time series data, and can better analyze the trend of individual change with time. Survival time studies are often plagued by missing data in covariates, especially when assessing personality-specific lifestyle or behavioral characteristics longitudinally. Generally, when dealing with missing data, you need to consider the processes that lead to incomplete data, such as the taxonomy proposed by Little and Rubin. If the probability of missing data is independent of observed and unobserved data, the data is © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 194–201, 2019. https://doi.org/10.1007/978-3-030-02804-6_26

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considered to be on the conditional mean, which makes it possible to characterize any point of the distribution, thus providing a complete description of the entire response distribution. Also, comparing to the classical mean regression, quantile regression (QR) is more robust to outliers and does not need to specify any error patterns. Therefore, QR has been widely used, see Koneker 2005, Chen et al. 2004, Farcomeni 2012, Reich et al. 2010, among others. QR has been extended to longitudinal data as well. A simple method is to assume the independence of work and ignore the correlation between repeated measures, which may cause some loss of efficiency. Such work be researched in 2006 Wei and He, 2007 Wang and He, 2009 Mu and Wei, 2009 Wang and Fygenson, 2009 Wang, 2009 Wang et al. n the application of Bayesian statistics in biology and medicine, we can clearly see that the role and status of Bayesian statistics are increasing. With the emergence of some new fields in biological and medical research, such as gene chip and spatio-temporal analysis, the problems faced by statistics will become more complex and uncertain. Classical statistics often fail to solve these problems. Bayesian statistics can make up for the shortcomings of classical statistics to some extent. Therefore, the fusion of two statistical methods and the development of Bayesian statistics in medical applications will be very beneficial to biological and medical scientific research.

2 The Development of Proposed Quantile Regression Method for Longitudinal Data In the longitudinal bayesian settings, Suppose that there has a little of number repeated responses, as well as some multivariate covariates, though many non dependent individuals. Let yi1 ; . . .; yij ; . . .; yini For the observed repeated measurements form the ith subject, there i ¼ 1; . . .; m in there m is a non positive number. xij ¼ ðxij1 ; . . .; xijp ÞT can be seen to a p-dimensional covariate vector depending on yij . It is assumed that unknown regression parameters is part of the composite endpoint and also a censoring variable the responses the failure time of our interest can be a terminal event. Under mean regression, this type of data is usually modeled by linear relationships. yi ¼ Xi b þ i ;

ð1Þ

It the number of repeated responses where yi ¼ ðyi1 ; . . .; yij ; . . .; yini ÞT and Xi ¼ ½xi1 ; . . .; xini T is the covariates of the ni  p matrix for the ith individual. The parameter b ¼ ðb1 ; . . .; bp ÞT in Eq. (1) denote the effects of the components of xij on yij , and i ¼ ði1; . . .; ij; . . .; ini ÞT is the ni -dimensional residual vector such that i  ð0; Ri Þ where for all i ¼ 1; . . .; m, i are independently distributed (id) with a 0 mean vector and covariance matrix Ri . Here, the estimation procedure does not depend on consistent and estimation of b. In order to start the quantile regression for longitudinal data, we review what we have discussed before. The model for the conditional quantile functions of the response yij is given by

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Qs ðyij jxij Þ ¼ xTij bs ;

ð2Þ

for a particular s. We are interested in quantile bayesian regression estimating bs consistently and as efficiently as possible. In the longitudinal dataset, We can apply this method. we suppose that the work independence between recurrent argument among available approach individuals. When the working independence is assumed, the repeated measures from the same individual are not correlated any more. That is all the K ¼ n1 þ ; . . .; þ nm responses ^ ; an from all the individuals are treated as independent observations. we can obtain b WIs estimate of bs , with some loss of efficiency by minimizing the following objective function Sðbs Þ ¼

ni m X X

qs ðyij  xTij bs Þ;

ð3Þ

i¼1 j¼1

where qs ðuÞ ¼ uðs  Iðu  0ÞÞ. Through the above formula (3) to solve by setting the estimating equation for differentiation of Sðbs Þ. It is relation to bs to be 0. In other words to say that U0 ðbs Þ ¼

ni m X m X @Sðbs Þ X ¼ xij ws ðyij  xTij bs Þ ¼ XiT ws ðyi  Xi bs Þ ¼ 0; @bs i¼1 j¼1 i¼1

ð4Þ

where Xi ¼ ½xi1 ; . . .; xini T is the ni  p matrix of covariates, yi ¼ ðyi1 ; . . .; yini ÞT is the variable of repeated measures for the ith individual, ws ðuÞ ¼ qs0 ðuÞ ¼ s  Iðu\0Þ extreme-value distribution or the counting process distribution, where, ws ðyi  Xi bs Þ ¼ ðws ðyi1  xTi1 bs Þ; . . .; ws ðyini  xTini bs ÞÞT is a ni  p vector. We can thick of the proportional hazards model ws ðei Þ as well as Vi ¼ covðws ðyi  Xi bs ÞÞ 0 1 s  Iðei1 \0Þ B C .. C; ¼ covB . @ A s  Iðeini \0Þ

ð5Þ

Ci ¼ diag½fi1 ð0Þ; . . .; fini ð0Þ 0 1 fi1 ð0Þ B C .. C; ¼B . @ A fini ð0Þ

ð6Þ

And

be an diagonal matrix of ni  ni with jth diagonal element fij ð0Þ.

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We can get the inverse addition to exponential treatment indicator likelihood lðbs ; yi Þ with respect to bs , it can be used to estimate bs and written as @lðbs ; yi Þ ¼ XiT Ci Vi1 ws ðyi  Xi bs Þ: @bs Let U1 ðbs Þ ¼

m P

ð7Þ

ð@lðbs ; yi Þ=@bs Þ. By solving the equation

i¼1

U1 ðbs Þ ¼

m X

XiT Ci Vi1 ws ðyi  Xi bs Þ ¼ 0

ð8Þ

i¼1

In the estimating equation U1 ðbs Þ ¼ 0, this item Ci represents that the just deviations in eij or sequence of the transformed estimating functions counting process ^ would be obtained. recurrent events the so-called maximum likelihood estimation b s 1 ^ ^ ^fij ð0Þ ¼ 2hn ½xT ðb s þ hn  bshn Þ ; ij

ð9Þ

where hn ! 0 when the n ! 1.

3 The Proposed Quantile Bayesian Regression Model For the sake of obtain efficient estimation from the estimating Eq. (9), a better way to estimate covariance matrix of ws ðei Þ must be applied. Here, by taking the correlations between ws ðei Þ into consideration, the new technique was proposed to solve the following estimation equations. Uðbs Þ ¼

m X

XiT Ci R1 i ðqÞws ðyi  Xi bs Þ ¼ 0;

ð10Þ

i¼1

the Ri ðqÞ is a covariance matrix equations accommodate nonnegative deterministic 1

1

function as Ri ðqÞ ¼ A2i Ci ðqÞA2i , with Ai ¼ diag½ri11 ; . . .; r1ni ni  being an ni  ni diagonal matrix, rijj ¼ varðws ðeij ÞÞ missing data of the Ci ðqÞ is an correlation matrix, q as an index correlation parameter. Suppose that in estimating Eq. (10) the covariance matrix Ri ðqÞ has a general stationary autocorrelation structure as the correlation matrix Ci ðqÞ is given by 0

1 B q1 B Ci ðqÞ ¼ B . @ .. qni 1

q1 1 .. . qni 2

q2 q1 .. . qni 3

In there i = 1,2,….., and q‘ can be estimated by

1


qni 1


qni 2 C C .. C . A


1

ð11Þ

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X. Lv et al. m nP i ‘ P

^‘ ¼ q

~yij~yi;j þ ‘ =mðni  ‘Þ

i¼1 j¼1

ni m P P

i¼1 j¼1

ð12Þ ~y2ij =mni

for ‘ ¼ 1; . . .; ni  1 with ~yij defined as ~yij ¼

ws ðyij  xTij bs Þ : pffiffiffiffiffiffi rijj

ð13Þ

Now, the only thing unknown, except bs , is the variance of ws ðeij Þ. To estimate rijj ¼ varðws ðyij  xTij bs ÞÞ, we apply the fact that ws ðeij Þ ¼ ws ðyij  xTij bs Þ ¼ s  Iðyij \xTij bs Þ. Hence we have rijj ¼ var½ws ðeij Þ ¼ var½s  Iðyij \ xTij bs Þ

ð14Þ

¼ var½Iðyij \ xTij bs Þ ¼ Prðyij \ xTij bs Þð1  Prðyij \ xTij bs ÞÞ;

where Prðyij \xTij bs Þ is the probability of the event fyij \xTij bs g: As we know that xTij bs is exactly the s th quantile of yij , hence Prðyij \xTij bs Þ ¼ s, it can lead to the bs estimator ~ijj ¼ sð1  sÞ: of rijj , r A parametric model recurrent events and the failure event can be estimated by ~ i ¼ diag½~ ~1ni ni  A ri11 ; . . .; r 0 sð1  sÞ B .. ¼B . @ sð1  sÞ

1 C C A

;

ð15Þ

ni ni

and an estimator of the square root of Ai immediately follows as 0 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 sð1  sÞ C .. ~ ¼B A @ i . pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A sð1  sÞ ni ni 1 2

ð16Þ

indicating constant diagonal matrices for a certain s. We can use the same method to estimate Ci as being discussed in QL models. Therefore, proposed estimates can now be obtained by solving the estimating Eqs. (10) ~ijj is applied in estimating Ri . And the proposed parameter within which the estimator r ^ estimator is derived from the Bayesian regression (PQBR) model as b PQRs .

The Bayes Quantile Regression Theory and Application

^ijj ¼ Prðyij \ xTij bs Þð1  Prðyij \ xTij bs ÞÞ r m m 1X 1X Iðyij \ xTij bs Þð1  Iðyij \ xTij bs ÞÞ; ¼ m i¼1 m i¼1

199

ð17Þ

^ijj in estimating Ri , the solution of for all j ¼ 1; . . .; ni and i ¼ 1; . . .; m. By using r estimating Eqs. (10) joint modeling adjusted of estimate bs . The accelerated model depends only on the failure time complete observations. To overcome these difficulties, the proposed smoothing method has been extended to the the same method to estimate the right hand side of the equation. Here, let ~ s Þ ¼ EZ ½Uðbs þ X1=2 ZÞ, with expectation taken over Z, Mixed effect model is Uðb another commonly used model for processing longitudinal data. It is also one of the most commonly used models in biostatistics a smoothed estimating function is obtained as ~ sÞ ¼ Uðb

m X

~ XiT Ci R1 i ðqÞws ðyi  Xi bs Þ

ð18Þ

i¼1

0

1 y xT b s  1 þ Uð i1 ri1i1 s Þ B C C .. ~ ¼B w B C s . @ A T yini xin bs s  1 þ Uð rin i Þ i

And m X ~ sÞ @ Uðb e ¼ XiT Ci R1 i ðqÞ K i Xi ; @bs i¼1

ð19Þ

e i is an ni  ni diagonal matrix with the jth diagonal element where K 1 T rij /ððyij  xij bs Þ=rij Þ:

4 Simulation We generate datasets from the model yij ¼ b0 þ xij1 b1 þ xij2 b2 þ eij The xij1 are sampled from the Bernoulli distribution witch with the probability is 0.500. The xij2 is a standard normal distribution. The true parameters used are b0 ¼ 0:50; b1 ¼ 0:50 and b2 ¼ 1:000. Moreover, the proposed method quantiles of s ¼ 0:050, 0.250, 0.750 and 0.950 are chosen to The biggest advantage of vertical data is that it combines cross-sectional data with time series data, and can better analyze the trend of individual change with time distribution (Table 1).

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Table 1. Biases(Bias) and relative efficiencies(EPF) to the estimators of b0 , b1 and b2 using different methods (AQR,PQR, QLWI and WI).

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The project on improving the basic ability of young teachers in Guang Xi colleges and universities: KY2016YB816.

References 1. Karlsson, A.: Nonlinear quantile regression estimation of longitudinal data. Commun. Stat. Simul. Comput. 37, 114–131 (2008) 2. Brown, B.M., Wang, Y.G.: Induced smoothing for rank regression with censored survival times. Stat. Med. 92, 828–836 (2007) 3. Liu, Y., Bottai, M.: Mixed-effects models for conditional quantiles with longitudinal data. Int. J. Biostat. 5 (2009). Article 28 4. Farcomeni, A.: Quantile regression for longitudinal data based on latent markov subjectspecific parameters. Stat. Comput. 22, 141–152 (2012) 5. Tang, C.Y., Leng, C.: Empirical likelihood and quantile regression in longitudinal data analysis. Biometrika 98, 1001–1006 (2011) 6. Fu, L., Wang, Y.G.: Quantile regression for longitudinal data with a working correlation model. Comput. Stat. Data Anal. 56, 2526–2538 (2012) 7. Leng, C., Zhang, W.: Smoothing combined estimating equations in quantile regression for longitudinal data. Stat. Comput. 1–14 (2012) 8. Brown, B.M., Wang, Y.G.: Standard errors and covariance matrices for smoothed rank estimators. Biometrika 92, 149–158 (2005) 9. Xiaoming, Lu, Fan, Zhaozhi: Weighted quantile regression for longitudinal data. Computat. Stat. 30(2), 569–592 (2015) 10. Jung, S.H.: Quasi-likelihood for median regression models. J. Am. Stat. Assoc. 91, 251–257 (1996) 11. Wang, H., Fygenson, M.: Inference for censored quantile regression models in longitudinal studies. Ann. Stat. 37, 756–781 (2009) 12. Koenker, R.: Quantile regression for longitudinal data. J. Multivar. Anal. 91, 74–89 (2004) 13. Cade, B.S., Guo, Q.: Estimating effects of constraints on plant performance with regression quantiles. Oikos 91, 245–254 (2000)

Active Semi-supervised K-Means Clustering Based on Silhouette Coefficient Hongchen Guo1, Junbang Ma2, and Zhiqiang Li2(&) 1

2

Network Information Technology Centre, Beijing Institute of Technology, Beijing, China School of Computer Science, Beijing Institute of Technology, Beijing, China [email protected]

Abstract. To improve the effectiveness of semi-supervised clustering algorithm that may be influenced by the quality of labeled samples, researchers integrates active learning and semi-supervised clustering to guide the model learning. This paper presents an active semi-supervised k-means clustering model based on silhouette coefficient (SCKmeans). SCKmeans utilizes a pairwise constraint clustering method (PCKmeans) and actively selects valuable samples to establish constraints (query to oracle) based on silhouette coefficient. We iterate the model learning until the number of queries reaches a threshold or the clustering algorithm achieves an acceptable performance. SCKmeans optimizes the semi-supervised k-means by using Local Sample Density (LDS) sampling strategy in order to ensure the stability of the algorithm. In addition, a distance-based sampling method, which can reduce the queries quantity as well as increase the number of constraint samples, is introduced to optimize the process of establishing pairwise constraints. These two methods can promote the effectiveness of clustering algorithm significantly. We conduct considerable amount of experiments over various datasets and baselines, the experimental results indicate that our model has better performance with 5% and 6% boost in MI and ARI respectively. Keywords: K-means clustering


Semi-supervised
Active learning

1 Introduction Clustering is the traditional unsupervised learning algorithm. It divides the unlabeled samples into different clusters according to the cluster internal features. In general, we have a small number of labeled samples in real datasets, which can provide useful information to clustering model and improve the performance of clustering algorithm. In order to make better use of these labeled data, researchers have proposed various semi-supervised clustering algorithm to use a small amount of labeled sample data for guiding the clustering process and promoting the effectiveness. K-means is one of the earliest clustering algorithms applied to the semi-supervised domain. For example, Wagstaff proposed cop-kmeans [1] algorithm that combines the pariwise constraints into the clustering and Basu proposed seed-kmeans [2] algorithm that uses the labeled data to establish initial cluster center to improve the effect of clustering. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 202–209, 2019. https://doi.org/10.1007/978-3-030-02804-6_27

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However, the performance of semi-supervised clustering algorithm depends heavily on the quality and quantity of the labeled sample data. Therefore, we introduce active leaning technology to select valuable samples from unlabeled pool to query oracle and integrate the labeled samples with semi-supervised clustering to improve the effectiveness of clustering algorithm. In this paper, we propose an active learning algorithm based on the silhouette coefficient and apply it to the semi-supervised k-means clustering algorithm. First we use PCKmeans [3] to conduct clustering and then use our active learning algorithm to select most valuable samples to establish pairwise constraints. The constraints are used for next clustering iteration. Besides, we use LDS-based (Local Sample Density) sampling and distance-based sampling to optimize the semi-supervised clustering algorithm.

2 Related Work Our related work concerns the combination of semi-supervised clustering and active learning algorithm, which is mainly divided into two aspects. On the one hand is to take the active learning selection strategy as a sample preprocess to select valuable samples to label them and train the semi-supervised clustering model with labeled data. In this strategy, active learning is just a sample preprocessing process for semisupervised clustering and cannot adjust labeled samples based on clustering results dynamically. On the other hand, active leaning involves in the iteration process of clustering. These methods, with better performance but lower efficiency, cluster the samples in each iteration and use the samples labeled by active leaning strategy as input for the next iteration. For the first strategy, Basu proposed an active leaning algorithm [3] including two parts called explore and consolidate. The explore procedure uses the farthest-first algorithm to select the samples with distant distances as the skeleton nodes of the labeled data set. And the consolidate selects the samples randomly and establishes constraints with these skeleton nodes. Finally, the labeled dataset is used to the semisupervised k-means clustering algorithm with pairwise constraints. On this basis, Pavan proposed the Min-Max algorithm [4] to use the semi-supervised k-means algorithm with pairwise constraints. Min-Max strategy selects valuable samples to improve the clustering performance. Moreover, Qian Jun Xu selected boundary points to improve the spectral clustering [5]. By constructing K-NNG (The k-nearest neighbor graph), Viet proposed the LDS algorithm [6] to select the samples in sparse regions for labeling. For the second strategy, Ruizhang measured the sample by establishing a cost function according to the results of each iteration of clustering [7]. It also improved the final clustering effect by combining clustering and active learning. In addition, Ruizhang proposed a combined framework of active learning and semi-supervised clustering algorithms, and proposed a variety of cost functions for sample selection [8]. In this paper, we use the second strategy to combine the active learning with semisurprised clustering and propose active semi-supervised clustering framework based on silhouette coefficient (SCKmeans). The experimental results show that our model outperforms various baselines over MI and ARI evaluations.

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3 SCKmeans Model Our model SCKmeans (active semi-supervised k-means clustering based on silhouette coefficient) combines the active learning and PCKmeans. First, we use the farthest-first algorithm to select the initial clustering center of the k-means clustering. Then we use the PCKmeans algorithm to clustering. According to the result of clustering, we use the silhouette coefficient to actively select samples with small silhouette coefficient. Finally, we label these samples by creating pairwise constraints and use these labeled sample for next iteration. 3.1

Optimizing the PCKmeans

In our model, we use PCKmeans, a semi-supervised clustering algorithms with pairwise constraints to guide the clustering, as the base clustering algorithm. Its optimization function is Eq. 1. 0 min@12

P xi 2X

jjxi  uli jj2 þ

P ðxi ;xj Þ2M

  wi;j L li 6¼ lj þ

P ðxi ;xj Þ2M

1   A w i;j L li ¼ lj

ð1Þ

wi;j denotes the weights when i and j have Must-Link constraint and w i;j denotes the weights when i and j have Cannot-Link constraint. L (true) = 1 and L (false) = 0. when computing the distance from the target sample to the center point of the cluster, it is affected by the samples in the cluster which has constraints with target sample. If the target sample has more Must-Link constraints with the samples in the cluster, the distance between the sample and the cluster is small. And the sample is easier to be divided into the cluster. If the target sample has more Cannot-Link constraints with the samples in the cluster, the distance between the sample and the cluster is large. And the sample is more difficult to be divided into the cluster. In the process of semi-supervised clustering, the selection of samples to classify them into clusters is often random or in the order of the original data sets. It has an issue if sample x is incorrectly clustered during clustering and then the subsequent sample which has constraints with x will also be clustered incorrectly. For example, a sample y which has a Must-Link constraint with x will be forcibly divided into this class. A sample z that has Cannot-Link constraints with x will never be divided into this class. This will cause a large number of samples to be clustered incorrectly and the final clustering effect will be poor. In order to solve this problem, we modify the original sampling method of PCKmeans algorithm. We add a sorting process before selecting the samples. For the terms of sorting criteria, we select LDS (Local Sample Density) as a ranking indicator. LDS is used to calculate local sample density. Its calculation formula is Eq. 2. P LDSðxi Þ ¼

q2NN ðxi Þ

k

xðxi ;qÞ

ð2Þ

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k is the number of sample neighbors and q represents the neighbor sample of sample xi . xðxi ; qÞ represents the weight of the edge between xi and q. A sample with large LDS value is located in the densely area and is easier to be clustering correctly. A sample with small LDS value is located in the sparse area and is more difficult to be clustering correctly. Therefore, we sort samples by LDS. The algorithm clusters the samples located in densely area first which may easily be clustered correctly. After this, we cluster the difficult samples in sparse area. In this way, we can ensure that the established constraints have a positive impact on the clustering and improve the algorithm stability. In the process of establishing constraints, we should query the constraint between the samples selected by active learning strategy and samples in constraint set. This process is conducted in turn until a Must-Link constraint is established. However, it may query a large number of samples (high cost) in order to establish Must-Link constraints. To solve this problem, we calculate the distance from each sample in the constraint set to the target sample before querying the samples, and sort the constraint set according to the distance. The sample closer to the target sample is more likely to establish a Must-Link constraint due to the fact that samples with a closer distance are more likely to belong to the same class in k-means clustering. With this method, our model can reduce the number of queries as well as ask more samples to improve the performance. 3.2

Active Learning Based on Silhouette Coefficient

In the active learning algorithm, we hope to select the samples which cannot be clustered correctly. To conquer this issue, we propose an active learning algorithm based on silhouette coefficient to measure the value of the sample and help the model determine how samples are difficult to cluster correctly. Silhouette coefficients is an evaluation index proposed by Rousseeuw [9]. It is mainly used to evaluate the quality of clustering algorithms. Its core formula is Eq. 3. bðiÞaðiÞ sðiÞ ¼ max ðaðiÞ;bðiÞÞ

ð3Þ

a(i) represents the average distance of the sample i to other samples in the same cluster. b(i) represents the average distance of sample i to other clusters. The silhouette coefficients of the clustering are to average the silhouette coefficients of each sample. Its value is between 1 and −1. The better clustering has the larger value. In this active learning algorithm, we use the sample’s silhouette coefficient. Its value range from −1 to 1. When the sample has small silhouette coefficient, it is closer to other clusters. Such samples tend to be in the edge region of the cluster. In the k-means clustering, we can find that the samples located near the center of the cluster are often easier to be clustered correctly and the samples located at the cluster boundary are often more difficult to be clustered correctly. Therefore, we can use the silhouette coefficients to select samples located at the boundary of the cluster and establish constraints to make the samples clustered correctly.

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Iterative Algorithm

With above methods, we proposed the iteration of our model SCKmeans as follows. SCKmeans stops the iteration process when the query number reaches a threshold or the clustering algorithm achieves an acceptable performance. Moreover, the complexity of our model is O(Tnkt), where T is the number of the model iterations which maximum value is Q, n is the number of samples, k is number of clusters and t is the number of kmeans iterations.

4 Experiments 4.1

Dataset

To show the performance of our model, we chose three datasets from UCI [10] and one dataset form KEEL-datasets [11]. They are iris, wine, seeds and twonorm. The detail of datasets is shown in Table 1.

Table 1. Datasets statistical. Name Iris Wine Seeds Twonorm

Category number Feature number Sample number 3 4 150 3 13 178 3 7 210 2 20 7400

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Evaluation

In this paper, we user common measurement to evaluate clustering algorithm, such as Adjusted Rand index (ARI) and Mutual Information (MI). Both ARI and MI locates in [−1,1]. The clustering performance is better when the value is large. The equations of ARI and MI are shown in Eqs. 4. and 5. RIE ½RI  ARI ¼ max ðRI ÞE ðRI Þ

MIðU; VÞ ¼

4.3

jU j P jV j P i¼1 j¼1

pði; jÞlog

ð4Þ



Pði;jÞ PðiÞP0 ð jÞ



ð5Þ

Baseline

We choose four algorithms as our baseline, such as k-means, PCKmeans, Min-Max and LDS. For the PCKmeans algorithm, we use its original active learning algorithm, explore and consolidate algorithm. For the LDS algorithm, in [6] it is used to combine AHCC algorithm. In this paper, we combine it with semi-surprised k-means algorithm for comparison. For the parameter adjustment, there are two parameters need to be adjusted. one is the number of neighbors. LDS uses K-NNG to calculate the local sample density. So, before we use that we need to determine the number of neighbors. We take the iris data set as an example. Considering that the samples number of the iris data set is 150, we set the number of queries as 20. On this basis, the number of neighbors is increased from 5 to 50 in order, and the measurements are ARI and MI. The result of experiments is shown in Fig. 1(left). We can see that the measurements no longer change when the number exceeds 25. For iris dataset, therefore, we can set the number of neighbors as 25. The other is number of selected samples for each round. We need to determine the number of samples which are selected by active leaning algorithm in each round. We also take the iris data set as an example. we set the number of queries as 20. On this basis, the number of selected samples is increased from 1 to 10 in order, and the evaluation indicators are ARI and MI. The result of experiments is shown in Fig. 1(right). We can see that the measurements no longer change when the number exceeds 3. For iris dataset, therefore, we can set the number of samples as 3 when the query number is 20.

Fig. 1. Adjusting the number of neighbors

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Results and Discussion

In the experiment, we compare four algorithms on four datasets and the measurement is MI. The result is shown in Fig. 2. From the Experimental results, we can see that our model is more effective than other models in the case of the same queries number. More specifically, for iris、seeds and twonorm datasets, no matter how much the number of query is, we can see that our model is always better than other models. In the best case, our model is 5% better than the suboptimal model. For the twonorm dataset, we can find that the effect of our model promotion is not obvious though our model is still better than other models. The reason is k-means algorithm has good clustering effect on the twonorm dataset which has reached more than 90%. It is difficult to upgrade. As for the wine dataset, we can see at the beginning, our model is not the best model. But when the number of query reaches 30, our model starts to outperform other models. For ARI, we also compare four algorithms on four datasets. We can see from Fig. 3 that our model is also more effective than other models in general.

Fig. 2. MI: experimental result of four datasets.

Fig. 3. ARI: experimental result of four datasets.

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Overall, for the four datasets, our model is more effective than other models in the case of the same number of queries. And when the same clustering effect is achieved, the query number for SCKmeans is also less than other models.

5 Conclusion In this paper, we propose a new combinatorial model SCKmeans, which is based on the PCKmeans. We combine the active learning algorithm based on the silhouette coefficient with the PCKmeans algorithm. Through active learning, the most valuable samples are selected to establish constraints, and then these constraints are used to guide the PCKmeans clustering to improve the final clustering effect. We also optimize the PCKmeans by adding distance-based sampling and LDS-based sampling. In the experiment, we use four datasets to test our model. The experiment shows that, compared with other four models, our model has better performance. In the future, we will optimize our active learning algorithm in order to further improve the performance of our model.

References 1. Wagstaff, K., Cardie, C., Rogers, S., et al.: Constrained k-means clustering with background knowledge. In: ICML, vol. 1, pp. 577–584 (2001) 2. Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Proceedings of 19th International Conference on Machine Learning (ICML-2002) (2002) 3. Basu, S., Banerjee, A., Mooney, R.J.: Active semi-supervision for pairwise constrained clustering. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 333–344. Society for Industrial and Applied Mathematics (2004) 4. Mallapragada, P.K., Jin, R., Jain, A.K.: Active query selection for semi-supervised clustering. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008) 5. Xu, Q., Wagstaff, K.L.: Active constrained clustering by examining spectral eigenvectors. In: International Conference on Discovery Science, pp. 294–307. Springer, Heidelberg (2005) 6. Vu, V.V., Labroche, N., Bouchon-Meunier, B.: An efficient active constraint selection algorithm for clustering. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 2969–2972. IEEE (2010) 7. Huang, R., Lam, W., Zhang, Z: Active learning of constraints for semi-supervised text clustering. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 113–124. Society for Industrial and Applied Mathematics (2007) 8. Huang, R., Lam, W.: An active learning framework for semi-supervised document clustering with language modeling. Data Knowl. Eng. 68(1), 49–67 (2009) 9. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987) 10. http://archive.ics.uci.edu/ml/index.php 11. http://sci2s.ugr.es/keel/datasets.php

Intelligent Creative Design of Textile Patterns Based on Convolutional Neural Network Wang Ying1(&) and Liu Zhengdong2 1

2

Basic Teaching Department, Beijing Institute of Fashion Technology, Beijing, China [email protected] School of Fashion, Beijing Institute of Fashion Technology, Beijing, China

Abstract. Deep learning technology has been developing significantly in the field of pattern recognition in recent years. As an important research achievement by theorized principles of human brain function, multi-layer artificial neural network has achieved impressive results in visual processing. In particular, deep dream (DD) is algorithm, based on deep-learning convolutional neural network (CNN), that blends visual qualities from multiple source images to create a new output image and provides a new opportunity for the design of textile patterns. This paper first introduces the CNN model. A model of textile design aided design based on DD is proposed. And based on the depth learning framework Tensorflow and Torch, using the deep neural network GoogleNet and ResNet, realizes the intelligent aided design system of textile pattern based on convolutional neural network. Keywords: Convolutional neural network (CNN) Textile pattern


Deep dream

Nowadays, the individualization of textiles, small batch, high precision and green environmental protection have become the demand of fashion. It is an important problem for the whole textile art design to get rid of the present situation of the monotonous color, the old style and the lack of cultural connotation as soon as possible. When consumers choose textiles, color and pattern are often the first determinants. Therefore, textile pattern design is particularly important. This article is to introduce the automatic generation technology of textile patterns based on convolutional neural network (CNN). Textile design technology based on CNN, its characteristics are mainly manifested in that it can convert some of the original information that should not be visible to a graphic or image display in front of us, the shape of the graphic image is strange, unique style, inhuman brain can imagine, can become an inexhaustible source of creation.

1 Convolutional Neural Network (CNN) The convolution neural network (CNN) is seen as a series of sieves that gradually form small holes between each other; these sieves gradually “strain” the higher and higherlevel (more and more fine-grained) characteristics of the sensory input (Fig. 1) [1]. For example, in a trained neural network to identify a cat, the lower input layer explains the © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 210–215, 2019. https://doi.org/10.1007/978-3-030-02804-6_28

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basic (or coarse) features, such as edges or corners (such as facial contour), and the middle layer looks for the overall shape. The final layers combine the details to assemble these images into an “answer” so that the image best describes a “cat”. Therefore, after training, each layer extracts the higher and higher-level features of the image step by step, until the output layer basically determines the content of the image display. The following figure is a conceptual representation of a neural network. In essence, images are processed by overlapping parts. The first layer analyzes each part of the image (convolution layer). The subsequent layer (pooling layer) pools together the knowledge obtained from the close parts of each other and creates a more dense representation (the smaller the square in the image is, the greater the density of the representation). This process repeats until we have a dense representation to capture the absolute essence of the image, not the minute details. This final expression is used to determine the actual content contained in the image. In this case, the image contains a realistic representation of “cat”.

Fig. 1. Convolutional neural network (CNN) architecture

2 The Principle of Auxiliary Design of Textile Pattern Based on DD Deap dream (DD) [2] algorithm generates textile patterns from noisy images by convolution neural network. The flow chart of textile pattern generation is shown in Fig. 2 [3]. In the process of generating textile patterns, the source image is first read, and the previously propagating mode is pre-trained by convolution neural network. The source image begins to propagate from the lowest (pixel) layer to a higher set of selected

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layers. The high level encodes the image more broadly based on the abstract feature, which is stored as layer than the guided tensor to find a guided content tensor, which is accompanied by the guide style tensor. If there is no guide content tensor, DD is more divergent. Continuous iterations of patterns and associations make it far away from the source image, and these patterns and associations depend more on the learning deviation of the network and less on the existence of the source images.

Fig. 2. Flowchart of DD generating textile pattern. Dashed lines apply only to mode with guide image

At this point, the algorithm initializes the set of pixels, which will be gradually transformed into the output image (which we call the canvas image, because it has the nature of dynamic update). When there is no guide image, the canvas image is set as a content source image; when the image is guided, the canvas image is initialized into random noise. The canvas image is propagated through the network to generate the canvas style tensor and the canvas content tensor, respectively, in the same layer as the guide style tensor and the guide content tensor. A loss function is defined to measure the similarity between the style tensor of the canvas and the bootstrap tensor (the content tensor and the guide content tensor in the canvas in the guided image). A gradient is found from this loss function, showing how the canvas tensor should be changed step by step to get closer to the guiding tensor. This gradient is propagated backward to the lowest level, and a small step in the desired direction is used to change the pixel value. Repeated cycles (updating pixels, forward propagation, finding gradient, downward propagation) constitute a gradient rising process. The nature of the guide tensors and the loss function determines which visual properties are extracted from the mixture of the source and the guided image. DD maximizes the point product between the canvas activation eigenvector and the best matching location feature vector in the guide activation, so the DD image tends to find some types of shapes and textures similar to their shape and texture in the guided image. DD can also run in a pattern without guide images (we can call it “free hallucination”). In this mode, the number of optimizes is the L2-norm activated by a selected network layer (which means that the strongest activation of nodes will tend to be so).

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As shown in Fig. 2, DD has incorporated a mechanism that plays an important role in creative cognition. It is essential that, in order to create a mixture of two images, the algorithm first generalizes each image by propagating in a deep CNN, and represents each image according to the tensor encoding generated on a network layer. According to the height and type of the network layer, the given encoding is analyzed according to the specific visual feature set. Just as in the parable of the blind men who each describe an elephant in a different way, the different layers “see the image” in different ways. From a network layer encoding point of view, the visual mixture depends on the similarity between the guided and the source images. In turn, the nature of the layer encoding depends on the network architecture and the original training data, which leads to features becoming a form of long-term memory. In order to improve the similarity between the source image and the guided image, the algorithm uses the iterative process of divergence and convergence alternately, and the similarity display pixel level is found on the high/abstract level.

3 Experiment and Analysis In this paper, two convolution neural networks, GoogleNet and ResNet, are adopted. The deep neural network GoogleNet and ResNet are built on the Tensorflow and Torch framework respectively, and the computation efficiency is very high.

b

a

d

e

c

f

Fig. 3. Comparing no-guide textile patterns of DD for low to high network layers (b–f)

Figure 3 presents textile patterns obtained using DD in no-guide-image mode. This illustrates the type of visual features according to which different network layers encode and interpret the image. We note that such visualization does not capture the entire range of visual shapes/textures potentially encoded by a given layer. On the contrary, it tends to display a kind of layer bias—a strong regime of activation (in the L2-norm sense) into which the layer tends to “find its way” using gradient ascent search.

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In Fig. 3, some features of DD output are particularly obvious. First of all, the algorithm is not only in a random way to overlay the specific features of the layer in the image; on the contrary, the feature is often emphasized and developed from the image regions that have included these features. On the other hand, if the algorithm runs multiple iterations, all image regions will eventually focus on the direction of highly activated features, essentially making something from the nihilism. Another notable aspect of DD output is that texture and shape convey a sense of completion and good continuity or mobility. For example, in Fig. 3e, we see a repeating plate or petal pattern, most of which are similar in size and shape, without overlap or interruption. This is due to the optimization type implemented in the search process. When the adjacent features interact without overlapping or destroy each other, the total activation of the layer is more obvious.

4 System Implementation Based on the above features of the DD generation pattern, we suggest that the guided image mode be adopted in the design of the system. In this way, designers need to integrate the man-machine integration to design the creative pattern. This process requires not only dialogue, but also the exchange of information. The process of design is not a simple input program - data processing - output results, but the process of human-computer interaction. The designer puts forward the scheme and the design parameters - the computer carries out the data analysis and completes the display results. The specific plan is as follows: as shown in Fig. 4, the designer determines the creation elements according to the needs of customers and the trend of the year, and loads a picture card on the main page. In the picture card, users can choose source pictures and guide pictures. And you can set output picture size, iterations, initialization pictures, content weight, style weight, TV weight, learning rate. After choosing pictures, you can choose to start, or reset and re select.

Fig. 4. System browser and master interface

Fig. 5. A screenshot of a textile creative pattern

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In Fig. 5, each time a creative pattern task is created, an output picture card is produced. But only 1 task is running, other tasks are in wait state, and the task is set to a cancelling state by clicking the Cancel button. In the output picture card, the source picture and the boot picture are displayed on the top, and the middle result and the final generation diagram are displayed at the bottom. When the Cancel button is clicked, the system will cancel the task of the ID. Many clothing and household textiles are a product of a combination of art and engineering. Decorative pattern is the basic art requirement. The introduction of such a new technology of graphic and image generation is valuable for adapting to the rapidly changing market demand, enriching the variety and promoting the development of textile industry. This digital pattern generation technology combined with the existing textile CAP/CAM system can realize the diversification, fast design, short period and small batch production of the product. In this way, textile designers have more time to do creative work, rather than spend time on duplication of work. Acknowledgements. This work was funded by Beijing Science and Technology Program (Z171100005017004).

References 1. Keshavan, M.S., Sudarshan, M.: Deep dreaming, aberrant salience and psychosis: connecting the dots by artificial neural networks. Schizophr. Res. 188, 178–181 (2017) 2. Mordvintsev, A., Olah, C., Tyka, M.: Inceptionism: going deeper into neural networks. googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-intoneural.html 3. McCaig, G., DiPaola, S., Gabora, L.: Deep convolutional networks as models of generalization and blending within visual creativity. In: Proceedings of the Seventh International Conference on Computational Creativity, pp. 156–163 (2016)

Higher Individuality for Effective Swarm Intelligence Jia Xiao Cai1 and Hui Ying Chen2 ✉ (

)

1

2

ASM Pacific Technology Limited, Kowloon, Hong Kong The Hong Kong Polytechnic University, Kowloon, Hong Kong [email protected]

Abstract. Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots [1]. With the simplicity in individual robot design and flex‐ ibility in the dimension of their collectivity, swarm robot systems have received considerable attention in recent research. However, lots of previous literature has focused on collective behaviors of swarm robot systems to achieve higher capability in both communication and coordination, whereas, individuality of a specific swarm robot has been seldom addressed. Traditionally, because the design of an individual swarm robot is rather simple, using many robots is the only way to tackle complicated tasks. Considering expanding demands of robotic tasks with higher complexity, higher dimensionality, and different information density, other than employing the conventional low cost-effective approach of increasing the number of swarm, this project intends to adopt higher swarm individuality to obtain higher flexibility and re-configurability of the system. The proposed swarm design has been tested with a self-devel‐ oped multi-target following system, the Auto-Cart (a new supermarket service system). In order to optimize the system performance using a simple indi‐ vidual robot design and a minimum number of swarm, a new navigation and communication algorithm was proposed. Simulation and experiment results verified the effectiveness and efficiency of the proposed system and algorithm. Keywords: Swarm intelligence · Individuality · Computer vision · Robotics

1

Introduction

Traditional robots rely mainly on the integration of complex electromechanical hard‐ ware and computational software to perform tasks. Therefore, they are featured with high cost, complicated design and specialized task. Inspired by natural swarm insects, an alternative method of the robotic swarm is a growing and promising field, in which a group of robots work together to perform a task.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 216–224, 2019. https://doi.org/10.1007/978-3-030-02804-6_29

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1.1 Swarm Intelligence The emergence of swarm intelligence is inspired by the biological studies of insects, ants and other fields in nature, where the natural swarm behavior occurs. Robotic swarm system develops group behavior based on simple sensing and communication so that each individual can sense the world and communication with a neighborhood, which is exactly consistent with the natural swarm system. The potential application of robotic swarm system could be wide including collective construction, coverage, self-assembly, collective transport, formation control, and search. One of the most promising uses of swarm robotics is in disaster rescue missions [2]. 1.2 Collective Behavior of Swarm Robot Current studies about swarm robot focus on the collective behavior because collabora‐ tion is the unique feature distinguishing swarm robot from single robot. Communication and coordination between individual robots are the crucial factors for the success of swarm collectivity. There are various collective behaviors and criteria to classify different swarm system up to now. In one of the early reviews about swarms, some reviews [3, 4] summary and category for these behaviors as static behavior and dynamic behavior. There are various collective behaviors and criteria to classify different swarm system up to now. I further modify the classification category and summaries the swarm behavior as shown in the following Fig. 1.

Fig. 1. Collective behavior of swarm robot

1.3 The Proposed Swarm System with Higher Individuality However, the information density or potential density of a space is not distributed evenly [5]. In such case, allocating swarm according to environmental constraints is an optimize solution. The problem of uneven distributed information density or potential density

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within a space is amplified by the lack of significant individual capabilities on the swarm robot. The individuality raised in this paper is associated with how a single robot within a swarm adjusts its behavior and configuration, as well as assembly and disassembly itself according to a specific task and environment, while maximizing the flexibility and functionality of the whole swarm as shown in Fig. 2. Initially, a group of robots will distribute evenly within the space. And then the robots which are free will park in the temporary parking space, while the robots which found the target will broadcast the request to ask other robots for help. After receiving the request, the group of robots will assemble into small groups. It is worth noting the number of robots needed depends on the complexity of the task or information density.

Fig. 2. The proposed swarm system with higher individuality

2

The Proposed Methodology

Rather than choosing a particular swarm task for researching, a multi-target following swarm system is proposed to illustrate the theory of swarm individuality. The reason to choose multi-target following swarm system to study is that it would be useful in several situations. Because the size and number of targets vary from case to case, the swarm should allocate their tasks. 2.1 Swarm Robot Target Following The object used for following could be non-physical signal or physical object. This project will mainly focus on following a physical object by the means of computer vision. Firstly, observation model is essential to figure out the linkage between the swarm robot coordination system and the world coordination system as ⎡ cos 𝛼 cos 𝛽 cos 𝛾 − sin 𝛼 sin 𝛾 −cos 𝛼 cos 𝛽 sin 𝛾 − sin 𝛼 cos 𝛾 cos 𝛼 sin 𝛽 ⎤ R = ⎢ sin 𝛼 cos 𝛽 cos 𝛾 + cos 𝛼 sin 𝛾 −sin 𝛼 cos 𝛽 sin 𝛾 + cos 𝛼 cos 𝛾 sin 𝛼 sin 𝛽 ⎥ ⎥ ⎢ −sin 𝛽 cos𝛾 sin 𝛽 sin𝛾 cos 𝛽 ⎦ ⎣

(1)

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The technique of optical flow [3] is implemented to realize target following. The optical flow methods calculate the motion between image frames which are taken at times t and t + Δt at every voxel position. For a 2-D dimension case, a voxel at location (x, y, t) with intensity I(x, y, t) will have moved by Δx, Δy and Δt between the two image frames and the brightness constancy with Taylor series can be expressed as 𝜕I 𝜕I 𝜕I V + V + = 0, 𝜕x x 𝜕y y 𝜕t

(2)

𝜕I 𝜕I 𝜕I where Vx, Vy are the x and y components of the velocity of I(x, y, t) and , and 𝜕t 𝜕x 𝜕y are the deviates of the image at (x, y, t). Ix, Iy and It can be written as

Ix Vx + Iy Vy = −It

(3)

The above equation in two unknowns and cannot be solved. The SAD (Sum of absolute differences) measure is one of the approaches to solve this problem by calculated by taking the absolute difference between each pixel in the original block and the corre‐ sponding pixel in the block being used for comparison. R(x, y) is the map of comparison result. If search image is W × H and template image is w × h, then result is (W − w + 1) × (H − h + 1). In order to increase the accuracy of the template matching, a template matching of normalized correlation coefficient is used [6]. The method is defined by

∑ R(x, y) = √ ∑

(T(x′ , y′ ) ⋅ I(x + x′ , y + y′ )) ∑ ′ ′ 2 ′ ′ 2 x′ ,y′ I(x + x , y + y ) (x′ ,y′ ) T(x , y ) ⋅ (x′ ,y′ )

(4)

∑ 1 ⋅ (x′′ ,y′′ ) T(x′′ , y′′ ) T ′ (x′ , y′ ) = T(x′ , y′ ) − and ⋅ h) (w) ( ∑ 1 ⋅ (x′′ ,y′′ ) I(x + x′ , y + y′ )2 [7]. With I ′ (x + x′ , y + y′ ) = I(x + x′ , y + y′ ) − (w ⋅ h) Eq. (4), the robot can identify and keep suitable distance with the human it followed.

where

2.2 Communication Protocol Another objective of this paper is to allow information sharing among the whole swarm to ensure a single robot could call its neighbor to help it. Figure 3 demonstrates the proposed communication (protocol. ) The distance between emitter robot and each receiver is defined as dR1 R2 R1 , R2 , R2 ∈ NR, where R1 denotes the emitter, R2 refers to receiver and NR represents total number of receiver within the network. At the start of the deployment of the algorithm, the navigation table of each robot is empty. When a robot T becomes an emitter robot (i.e., it needs another ( swarm ) robot for help), it puts an entry about itself in its table with the distance dR1 R2 R1 , R2 are set to 0 and broadcasts its request with its position information. Once a receiver robot R2 receives the message

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sent by emitter R1, it reads the received information and use the navigation algorithm introduced in next session to calculate the shortest distance from ( ) the emitter R1. The distance value then stored in its navigation table as dR1 R2 R1 , R2 . The receiver robot R2 then will check its status whether it is available to offer help at this moment.

Fig. 3. Communication protocol between emitter and receiver

3

Experiment Result

3.1 Swarm Robot Target Following The implementation of the proposed swarm system with higher individuality was conducted using E-puck mobile robot. This swarm robot includes support for the differ‐ ential wheel motors, the infra-red sensors for proximity and light measurements, the accelerometer, the camera, the surrounding LED and Bluetooth to communication. In particular, a camera with a resolution of 640 × 480 is equipped in front of the robot to ensure the function of computer vision.

Frame 1

Frame 12

Frame 24

Frame 36

Frame 48

Frame 60

Fig. 4. Implementation result of swarm robot target following

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Figure 4 shows the results of object following using E-puck robot. Frame 1 to frame 12 demonstrate the ability to perform U-turn; frame 24 to frame 36 illustrate the capa‐ bility to walk straight; and frame 48 to 60 show the possibility to walk backward. 3.2 Swarm Navigation Algorithm In combination of the communication algorithm and navigation algorithm as discussed will allow an emitter robot finds the nearest receiver robot. Simulation in different envi‐ ronment is examined by executing the algorithms, while the results further prove the effectiveness of this algorithm in solving the tasks. The algorithms have been integrated with two E-puck robots and tested under an environment with five obstacles. Three different environments are used for testing. The video is captures in every 5 frames from a 24 frames/second video.

4

Implementation of Proposed Swarm System

A swarm system with higher individuality could be implemented in various sectors. For instance, it could be used in service sector such as supermarket and airport. The main idea here is treating the trolley in this place as an automated robot so that the trolley could follow a person automatically. A swarm system like this could also be utilized in industrial sector for surveillance and exploration purpose. Because a swarm system with high individuality could assembly and disassembly by themselves, they could finish these tasks with high efficiency and effectiveness. A swarm system like this could also be applied in agriculture sector and military sector. Due to the time constraint of my project, only the possibility in supermarket is examined. However, it is believed this system could be applied in various places and I am open for future opportunities. In this paper, I choose supermarket as a testbed of my proposed system since the current super‐ market shopping system has a room for improvement. In the current supermarket, the supermarket cart is mainly controlled by human power, thereby causing inconvenience, especially for elderly and disable people (Figs. 5, 6, 7 and 8).

Simulation results of one receiver

Simulation results of twelve receivers

Simulation results of two receivers

Simulation results of twenty receivers

Fig. 5. Simulation result of communication-aided navigation algorithm

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Frame 1

Frame 12

Frame 20

Frame 32

Fig. 6. Experiment result of communication-aided navigation algorithm (1st environment)

Frame 1

Frame 12

Frame 20

Frame 32

Fig. 7. Experiment result of communication-aided navigation algorithm (2nd environment)

Frame 1

Frame 12

Frame 20

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Fig. 8. Experiment result of communication-aided navigation algorithm (3rd environment)

The proposed system is the first true fully autonomous and digitalized shopping cart system by assisting each cart as an individual robot and the whole system as a swarm of robots. Consequently, this system demonstrates task accomplishment through swarm intelligence. As show in Figs. 10 and 11, the system is not just a concept – it is a real working model and all the features and algorithms demonstrated already work on both simulation and real robot experiment. The simulation of the proposed Auto-cart system was developed by a computer software, Webots with e-puck mobile robot. The scene includes various rows of shelf, a check point where gathering all shopping carts, a checkout counter and customers. Figure 9 indicates that the process of the system can be divided into 4 cases. Once a customer enters the supermarket, Auto-cart integrates with the customer’s smartphone App or special card to pair up with the customer. After that, the paired autonomous shopping cart can follow the customer via vision tracking, along with recognize and avoid objects as needed throughout the whole shopping process using the multi-target swarm following system as shown in Fig. 10. In addition to fully auton‐ omous following, the existing shopping cart could communicate with other carts when it is full and automatic return to the check-in point when customer has checked out by implementing the navigation algorithm as shown in Fig. 11.

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Fig. 9. Overview of Auto-Cart system

Fig. 10. Implementation of multi-target swarm following in Auto-Cart system

(a) sensors placed in cart

(b) The cart request nearby

(c) Nearest second cart will

sense whether cart is full

empty cart for help

come to help

Fig. 11. Implementation of navigation algorithm in Auto-Cart system

5

Conclusion

This paper aims to explore the implement swarm individuality into task accomplishment. With individuality, a single swarm robot is able to adjust its behavior and configuration, as well as allocate itself according to a specific task and environment, while maximizing the flexibility and functionality of the whole swarm. The design swarm system with higher individuality then was implemented in a swarm robot target following system, because this system could be applied in various circumstances. An object following model using computer vision and optical flow is developed. A self-developed super‐ market system, Auto-Cart, is proposed as a test-bed of the multi-target following swarm system. In the proposed system, each shopping cart is treated as a single swarm robot and is responsible for servicing a collection of tasks including pair-up with customer,

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automatic follow customers, communication with other shopping carts and return to default position. A simulation of proposed shopping market system verifies the effec‐ tiveness of the system.

References 1. Brambilla, M., et al.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013) 2. Campo, A., et al.: Enhancing cooperative transport using negotiation of goal direction. In: Proceedings of the Fifth International Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS 2006). Lecture Notes in Computer Science, vol. 4150, pp. 365–366 (2006) 3. Dudek, G., Jenkin, M., Milios, E.: A Taxonomy of Multirobot Systems, pp. 3–22. Robot Teams, AK Peters, Wellesley (2002) 4. Farinelli, A., Iocchi, L., Nardi, D.: Multirobot systems: a classification focused on coordination. IEEE Trans. Syst. Man Cybern. B Cybern. 34(5), 2015–2028 (2008) 5. Barca, J.C., Sekercioglu, Y.A.: Swarm robotics reviewed. Robotica 31(3), 345–359 (2013) 6. Cortes, J., Martinez, S., Bullo, F.: Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions. IEEE Trans. Autom. Control 51(8), 1289–1298 (2006) 7. Sarvaiya, J.N., et al.: Image registration by template matching using normalized crosscorrelation. In: Advances in Computing, Control, & Telecommunication Technologies (2009)

Autonomous Systems

Temperature Anomaly Detection by Integrating Local Contrast and Global Contrast Liu Peng ✉ , Li Qiang, Liu Wen, Duan Min, Dai Yue, and Wang Yanrong (

)

China National Institute of Standardization, Beijing, China [email protected]

Abstract. In view of the characteristics of real-time temperature management in cold chain logistics, the paper discusses the technology of RFID and outlier mining. First, the paper puts forward the real-time temperature control system of RFID cold chain logistics, and finds that RFID is more suitable for real-time monitoring of temperature. Then the method of data stream mining is discussed, and it is found that the outlier mining method is more suitable for real-time processing of RFID cold chain data. On this basis, a distributed outlier mining algorithm, QOD, is proposed as the core algorithm of real-time temperature control system, combined with the definition of outliers and the temperature control characteristics of cold chain, which shows the effectiveness of the algo‐ rithm. According to its limitations, the pruning strategy is used to demonstrate the neighborhood pruning strategy in detail, and the pruning strategy is used to optimize the QOD algorithm, which improves the algorithm speed. Finally, experiments prove that the performance of the optimized QOD algorithm is improved. Compared with the related algorithm, the analysis shows that the QOD method has some advantages in effectiveness, accuracy and fast response. Finally, the future development direction of RFID cold chain temperature control is prospected. Keywords: Cold chain logistics · Temperature sensing · RFID

1

Introduction

Many studies suggest that improving the level of quality control is the key to solving the problems related to cold chain logistics. The loss in the logistics stage is mainly due to the spoilage of fresh food, which not only restricts agricultural production, but also affects farmers’ income and residents’ food safety. At present, the domestic research on cold chain logistics is mainly focused on the current situation, existing problems and measures of cold chain logistics in China. There are few researches on cold chain quality monitoring system and quick response plan. First of all, the current situation and existing problems of cold chain logistics in China are discussed. Aiming at the core problem of real-time temperature monitoring, RFID temperature control technology is introduced to solve it. Then it focuses on the data explosion problem caused by the application of RFID. By comparing all kinds of feasible RFID data mining methods, and combining the needs of cold chain temperature control, a fast outlier mining algorithm QOD based © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 227–233, 2019. https://doi.org/10.1007/978-3-030-02804-6_30

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on outliers is proposed, and its effectiveness is illustrated through experiments. Finally, the main problems and development direction of the research are summarized and summarized.

2

RFID Cold Chain Data Mining

2.1 The Advantages and Disadvantages of Commonly Used Mining Methods There are usually three kinds of methods for mining RFID data: clustering, classification and frequent pattern mining. Traditional clustering method is no longer suitable for dynamic RFID cold chain temperature data. Algorithm can only be applied to pure numerical attribute data, and can not be applied to multi-attribute data, which is also a problem of many traditional clustering algorithms. At present, the classification process of RFID temperature data is mainly faced with two problems. One is the problem of data representation in memory: the memory size is limited, and the continuous data must be processed in real time. Two, the concept drift problem: that is, the concept of learning from training data is changing over time, and the degree of change of the concept and the specific location of the drift are unknown. The memory representation problem of RFID data can be implemented by using an incremental learning method to train an outline data structure. However, there is still no ideal solution to the problem of concep‐ tual drift. The key step of frequent pattern mining is how to count items (sets) fast and accurately, so as to get frequent items (sets) that satisfy minimum support requirements. 2.2 Outlier Mining Method The characteristics of RFID cold chain data determine that when building a data stream mining model, the data before arrival and departure may deviate from those [5, 6] which is not related to the characteristics. At present, most of the research focuses on the algo‐ rithm [5] that matches the change distribution by discarding old data or giving its smaller weight. Document [7] presents a method to detect when the data flow changes, using a reference window (reference window) and a sliding window (sliding window). Yang Yidong uses dynamic grid to divide the space in dense and sparse regions, a large number of topics in data filtering dense areas in the region, and for the sparse candidate outliers, using an approximate method for calculating the degree of outlier, has a high degree of outlier data as outliers output [8]. Zhou Xiaoyun proposed according to the character‐ istics of the data stream is proposed based on weighted frequent pattern outlier factor (WFPOF) FODFP-Stream [9] high dimensional data stream outliers detection algo‐ rithm. Through the algorithm of dynamic discovery and to calculate the degree of outlier frequent maintenance mode, can effectively deal with high dimensional categorical data streams, and further extended to the numerical attributes and mixed attribute data through the data flow, the attenuation coefficient of the set, can effectively deal with the data in the data stream concept drift problem.

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Outlier Fast Mining Algorithm QOD

Set Object X = {x1, x2, …, xn}, χc the indicates that the specified condition C envi‐ ronment-related relationships under. Definition 4.1. Local Neighbors The local neighbors of the object xi are referring to the object xi in the specified condition C, there are environment-related relationships the object of the χc. Definition 4.2. Local Neighborhood object xi Local neighborhood N(xi) refers to object xi A collection of all local neighbors. Definition 4.3. Neighbor Weights set xi, xj ∈ X, and xj ∈ N(xi), Object xj The neighbor weights for J neighbors are defined as:

Wij =

∑q p=1

𝛼p ⋅

Gpj

∑N (xi ) r=1

Gpr

(1)

where Q the represents the maximum number of ambient attributes that determine the weight value, Gpr representing Objects xi the first R environment attributes for neighbors Gp the value of, αp To determine the environment characteristics GP the factor of importance, and. If the ambient property is ignored in the object xi and its local 1 neighbor xj for the effect of (is q = 0), the specified weight is ( ) , which equals | | |N xi | | | equal consideration of objects xi the impact of local neighbors. Normalized data sets using normalized technology, set Max and min The is the maximum and minimum values of intrinsic properties, f(xi) is an object xi the intrinsic property value of the. Set the ( ) ( ) f xi − min F xi = max − min

(2)

to ensure F(xi) ∈ [0,1]. The definition of weighted distance is given by the definition of neighbor weights and the Euclidean distance formula. Definition 4.4. Weighted Distance set xi, xj ∈ and xj ∈ N(xi), xi and xj is the intrinsic property of the f(xi) and f(xj), the Normalized property is F(xi) and F(xj), and F(xi), F(xj) ∈ [0,1]. Then the distance between xi and xj is: ( )]2 [ ( ) ) √ ∑n ( Wij ⋅ F xi − F xj D xi , xj , Wij = k=1

Definition 4.5. Local out of group object xi’s local outliers are represented as:

(3)

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( ) LO xi =

∑N ( x i ) ( ) ( ) 1 D xi , xj , Wij , xj ∈ N xi ) ( i=1 | | |N xi | | |

(4)

where, N(xi) representing Objects xi The number of local neighbors for the, local neighborhood N(xi) the maximum local neighborhood of the is recorded as MaxLO(N + (xi)), N + (xi) = N(xi) ∪ {xi}where LO(xi)ek represents each subspace space e objects in xi. The local outlier of the. Definition 4.6. Local outlier Factor The local outliers factor for the object xi is defined as:

( ) ( ) LO xi + 𝜀 LOF xi = , ( ) LO xj + 𝜀

( ) xj ∈ N xi

(5)

Set ε is a positive number that is small enough to avoid the denominator in the calcu‐ lation as 0. Finally, the outlier is obtained by arranging the LOF value of all objects from large to small, while the numerator denominator is coupled with a very small positive number ε when, does not change LOF The original order. In the experiment, take the

} ) ( { ( ) ε = min min LO xi ≠ 0, xi ∈ X , MIN All fixed attributes are normalized to in the above calculation [0,l], and Wij ≤ 1, and 𝜀 1+𝜀 Wij ≤ 1, there are , ε Value Decision LOF(xi) the range of values. ≤ LOF ≤ 1+𝜀 𝜀 when the local outlier of an object is 0 indicates that the intrinsic property values of the object and its neighborhood objects are the same, LOF(xi) = 0. When an object’s outlier is the same as the average outlier of a neighborhood object, the intrinsic properties of the object are changed regularly, LOF(xi) = 1. So when LOF(xi) ≤ 1 when the object is normal; LOF(xi) ≥ 1, the object starts off the cluster; LOF(xi) the increase in value increases the degree of outlier. Definition 4.7. Outliers given n Object data set X, the number of outliers to be detected is m, Then the computed LOF(xi) maximum before m objects are outliers (Figs. 1, 2, 3 and 4).

Fig. 1. The original temperature data including Fig. 2. The pre-processed temperature data. The several outliers horizontal axis is the global contrast and the vertical axis is the local contrast.

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Fig. 3. The detection results on feature space.

Fig. 4. Result on original data space. The red points on the right are the detected outliers.

4

Experiment Procedure Training stage input: training dataset including original temperature points. output: a model which can detect outliers

step 1. Calculate the global contrast of each point of original temperature data using Eq. (6) | | 1 ∑m G_Cntrstt = ||Xt − Xi || i=1 m | |

(6)

step 2. Calculate the local contrast of each point of original temperature data using Eq. (7) | | 1 ∑n L_Cntrstt = ||Xt − Xt+i || i=−n 2n | |

(7)

where, 2n is the size of the local window. step 3. Construct the feature vector, Feature_train = [G_Cntrst, L_Cntrst]. step 4. Train the one-class SVM, and output the trained model M_one_class_svm. Test stage input: test dataset including original temperature points. output: predicted result.

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step 1. Calculate the global contrast of each point of original temperature data using Eq. (6) step 2. Calculate the local contrast of each point of original temperature data using Eq. (7) step 3. Construct the feature vector, Feature_test = [G_Cntrst, L_Cntrst]. step 3. Predict using trained model M_one_class_svm, a 1 output indicate a outlier.

5

Summary

1. To interpret detected outliers This paper has done a lot of research work on automatic capture outliers, but the automatic interpretation of outliers is also of great significance, on the basis of data mining, the effective combination of knowledge base and rule Association work will lay the foundation for the further promotion of cold chain logistics management level. 2. Building rule libraries with domain knowledge Cold chain logistics target groups vary widely, each has different characteristics, combining the relevant characteristics of the regular library of personalized construction will be the next phase of the research hotspot, in the future market is becoming saturated, this will be the cold chain logistics enterprises to achieve differentiated competition to provide possible. 3. Drill-down to outlier data using rule libraries With the accumulation of logistics data, outlier data will appear distribution law according to statistic Law, which provides theoretical possibility for the establishment of rule base. The rule base established by the relevant mining tools will be helpful to the discovery of the logistics industry’s own law, so the analysis of outlier data will rise from the product level risk control function to the industry-level cycle prediction func‐ tion, which may have a far-reaching impact on the logistics industry. 4. Visualization of Algorithms The interaction with the user in the detection process and the visualization of the detection result will help the user deepen the understanding of the data and improve the accuracy of the algorithm. The achievement of this research is derived from the Research on the controlling technology of new-type enzymatic reaction and development of smart food package indicator (562015Y-3996), and the Research and Application on the electronic tracea‐ bility of the key technology of the food safety monitor and the early warning (2015BAK36B03).

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References 1. Boshang: Research on the Operation and Management of Cold Chain Logistics. Tongji University, Shanghai (2007) 2. Anthony, T.F., Buffa, F.P.: Strategic purchasing scheduling. J. Purch. Mater. Manag. 13(3), 27–31 (1977) 3. Weidong, Zhou, X., Sun, Y.: Mining based on outliers RFID Cold chain temperature control research. Comput. Syst. Appl. 19, 166–170+175 (2010) 4. Huangcheng, C.: China food cold chain logistics development strategy. Commod. Storage Maint. (4), 37–39 (2007) 5. Zhang, P., Chen, S.: The development and prospect of low temperature storage of fruits and vegetables in China. Refrig. Air Cond. 8(1), 5–10 (2008) 6. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, New York (2001) 7. Ester, M., Kriegel, H.P., Sander, J. et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of KDD 1996, Portland, Oregon, pp. 226–231. AAAI Press (1996) 8. Tao, Y., Xiao, X., Zhou, S.: Mining distance-based outliers from large databases in any metric space. In: Proceedings of KDD 2006, pp. 394–403. ACM Press, New York (2006) 9. Ng, R.T., Han, J.: Efficient and effective clustering method for spatial data mining. In: Proceedings of VLDB 1994, Vancouver, BC, pp. 144–155. University of British Columbia (1994) 10. Johnson, T., Kwok, I., Ng, R.: Fast computation of 2-dimensional depth contours. In: Proceedings of KDD 1998, New York, pp. 224–228 (1998) 11. Preparata, F.P., Shamos, M.I.: Computational Geometry: An Introduction. Springer, New York (1988) 12. Cao, L.J., Lee, H.P., Chong, W.K.: Modified support vector novelty detector using training data with outliers. Pattern Recognit. Lett. 24(14), 2479–2487 (2003) 13. He, Z., Xu, X., Huang, J.Z., et al.: A frequent pattern discovery based method for outlier detection. In: Proceedings of WA IM 2004, pp. 726–732. Springer, Berlin (2004) 14. Breunig, M.M., Kriegel, H.P., Ng, R.T. et al.: LOF: identifying density-based local outliers. In: Proceedings of SIGMOD 2000, pp. 93–104. ACM Press, New York (2000) 15. Tracking weakness links in cold chain. Neil Vass. M.D. (2006)

Temperature Anomaly Detection Based on Gaussian Distribution Hypothesis Liu Peng(&), Li Qiang, Liu Wen, Duan Min, Dai Yue, and Wang Yanrong China National Institute of Standardization, Beijing, China [email protected]

Abstract. Use RFID technology to solve the cold chain logistics management of real-time temperature monitoring problems; Face the ensuing data explosion problem, combined with RFID. The data mining algorithm and the cold chain temperature control actual demand, based on the Gaussian distribution hypothesis temperature anomaly detection, further optimizes, then through the experiment proved the algorithm accuracy; Finally, the future development direction of the RFID cold chain temperature control research is prospected. Keywords: Cold chain logistics Exception detection


Temperature sensing
RFID

1 Introduction In recent years, the demand for fresh products is increasing every year. Adopt the whole process of low-temperature transport to ensure food quality of cold chain logistics rise. But China’s cold chain logistics exists product loss is much higher than the international average. Food safety incidents continue to occur. This shows that the quality control of cold chain logistics is insufficient. Some studies point out that, realtime management of temperature is critical. And the temperature is difficult to monitor in real time, data cannot be analyzed in real time the current situation and bottleneck of cold chain temperature control in China. Once the real-time temperature control problem is resolved, Loss will be significantly reducd. In 2009, the Logistics industry revitalization planning is included in the national Top ten industrial revitalization planning, of which the development of cold chain logistics has attracted much attention. The total production of fruit industry accounts for about the world’s 14%, ranking first in the world. But the food cold chain logistics work is not satisfactory, according to statistics, the current annual production of various perishable food nearly 7 billion tons, fruits and vegetables in the picking, transportation, storage and other logistics links on the loss rate as high as 25%–30%, economic loss of about 750 billion, can meet nearly 2 million people’s basic nutritional needs, the loss of the highest in the world, in the cold chain developed countries, the loss rate of fruits and vegetables is controlled. At the same time, due to improper preservation of food, food poisoning in China has repeatedly occurred, according to the relevant departments, our country each year about million people food poisoning, experts estimate this number is © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 234–240, 2019. https://doi.org/10.1007/978-3-030-02804-6_31

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not yet the actual occurrence of the number of clutch. This shows that the cold chain logistics in China still lags behind in technology, excessive loss and low efficiency, it is necessary to take appropriate measures as soon as possible to solve technical bottlenecks and improve quality assurance. This paper first discusses the current situation and problems of cold chain logistics in China, and introduces the RFID temperature control technology to solve this core problem.

2 RFID Cold Chain Logistics 2.1

Cold Chain Logistics Problems

In recent years, many scholars have carried on the related research to the cold chain logistics. in a series of problems, temperature is the key point of cold chain logistics, monitoring and warning of temperature is the core of controlling the safety and quality of cold chain logistics [2]. At present, the biggest technical bottleneck of cold chain logistics temperature management in China is the lag of the technical means of monitoring, such as manual measurement and paper recording, no unified data system support, poor real-time performance, lack of supervision, difficulty in obtaining evidence, inability to determine responsibility, inability to carry out early warning and high loss rate. To solve this bottleneck, we need to introduce modern temperature monitoring methods first. 2.2

RFID Temperature Monitoring

There are three main types of temperature monitoring devices currently under study, SD Card temperature Recorder, IButton Temperature Loggers and RFID cold chain wireless temperature monitoring. Neither of the first two can be monitored in real time. the RFID Cold-chain wireless temperature monitoring system uses the active RFID technology to obtain temperature data in real time through a temperature sensor, and then by setting the GPRS, CDMA The real-time transmission device, real-time monitoring of target items. Not only can you backtrack on the cause of the damage. Some items can also be rescued in time. The current application of RFID is also concentrated in precious goods or sensitive areas. Although in a short period of time, the RFID is too expensive and the standard is not uniform and other issues make it difficult in the cold chain logistics industry widely used, liquid and metal products to the RFID signal interference, data security and privacy protection, and the recycling and reuse of RFID tags still plague researchers. However, the RFID Large capacity, wide range of transmission, flexible assembly characteristics determines that it is very suitable for cold chain temperature control problems, with the RFID Technology development, The cost and standard issues will certainly be addressed. Therefore, the academic community needs to prepare for a rainy day, the RFID Cold chain temperature control began in-depth research.

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Cold Chain Temperature Data Features

Use RFID technology can greatly improve the efficiency of cold chain logistics, while the RFID the appropriate analysis of the data will further improve the monitoring and early warning capabilities of the cold chain. The RFID Cold chain data has the following characteristics: Large data volume, semi-structured, short response time, realtime data arrival, arrival order is not controlled by mining tools, large amount of data and unpredictable, data will soon be overwritten, difficult to extract after processing. Because of these characteristics, the traditional statistical methods can not provide timely processing, so we need to introduce data mining methods to analyze it.

3 RFID Cold Chain Data Mining 3.1

The Pros and Cons of Common Mining Methods

One is the problem of data representation in memory: the memory size is limited, and the data that is constantly appearing must be processed in real time. The second is the concept drift problem: that is, the concept of learning from training data is changing over time, and the degree of change of this concept and the specific location of drift are unknown. The memory representation of RFID data can be accomplished by training a profile data structure by using incremental learning methods. The problem of conceptual drift still has no ideal solution. A more effective solution today is to load data in the RFID data stream using a fixed-size sliding window, block-build or update the constructed taxonomy model, and respond to the user’s classification requirements with the latest classification model. Frequent pattern mining is an important branch of data mining, and the key step in frequent pattern mining is how to count the frequency of items (set) quickly and accurately. To get the frequent entries that meet the minimum support requirements (set). In summary, existing fit RFID The cold chain temperature data frequent pattern mining algorithm can be divided into the following two categories: Approximate algorithm based on probability error interval and approximate algorithm based on error interval. mining Algorithms for RFID frequent patterns of data flow often face space and time problems. Then, there is a contradiction between the two, that is, when the complexity of an algorithm is low, it usually wastes a lot of time as a cost, while the time complexity is low and usually consumes too much storage space. Outlier Mining methods in cold-chain temperature data mining, the purpose of mining is to find data that is significantly different from most temperature data in a set, that is, rare events are often more appealing than regular events. Therefore, the outlier mining method can be used to monitor the abnormal fluctuation of temperature. The characteristics of the RFID Cold chain data determine that the data before the change arrives may deviate from the model that is associated with the feature that is no longer persisted when building a data flow mining model. At present, most studies focus on the algorithm that matches the distribution by discarding the old data or giving it a smaller weight. The algorithm can compute the outliers by dynamic discovering and maintaining frequent patterns, can effectively handle the high dimension class attribute data stream, and extend to the numerical attribute and mixed attribute data stream,

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through the setting of data attenuation coefficient, the problem of concept drift in the data stream data is effectively handled. the above outlier detection method can dynamically analyze the cold chain temperature data of RFID, but there are also problems such as excessive resource consumption, insufficient response speed, and too much complexity of algorithm.

4 Algorithm Algorithm principle: Suppose the distance between each temperature point and the average of its time window Obey the normal distribution. distt Calculated by:     1 Xn  distt ¼ Xt  Xt þ i  i¼n 2n Temperature anomaly detection algorithm is as follows 1. Calculates the distance between each temperature point and the average of its time window, counted as vectors D 2. Calculates the probability distribution of normal temperature distances. Suppose d  Nðl; r2 Þ; d 2 D Fitting parameters, l; r2 l¼ r2 ¼

1 Xm ðDi Þ i¼1 m

1 Xm ðDi  lÞ2 i¼1 m

3. Given new example x, computed and p(d) 1 ðd  lÞ2 pðdÞ ¼ pffiffiffiffiffiffi exp  2r2 2pr

!

Amomaly if pðdÞ\e:

5 Experimental Procedure 5.1

Data Generation

1. In normal distribution N (20, 2) build m (m = 500), as initial normal data X 2 Rm . 2. By normal distribution N (40, 2) generate p points, this p Point as the exception Point, take p = n * 0.01. 3. Distribute Evenly [0–500] to generate the p Integer, as the index of the exception point. 4. Place P as an index into the original data. 5. Generate label Data, Y, yi ¼ 0 if normal, yi ¼ 1 if anomalous.

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5.2

Training Data

1. take the 60% Normal data in the data as training data. Cross-validation data: costs normal data, P an exception data. 5.3

Test Data

Build n * 0.2 normal data, P an exception data.

6 Experimental Results See Figs. 1, 2 and 3.

Fig. 1. Training data

Fig. 2. Result on validation data

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Fig. 3. Result on test data

7 Summary Cold chain Logistics is an economic growth point in the future Food logistics field, using data mining method to deal with real time RFID the temperature data can effectively monitor the cold-chain temperature automatically. In this paper, the temperature anomaly detection based on Gaussian distribution hypothesis is presented, and the experimental results prove that the algorithm has the advantages of reducing user dependency, reducing algorithm complexity and improving precision when dealing with cold chain temperature data, and effectively solves the use of RFID The problem of mass data processing caused by technology. In the future research, we need to explain the outlier points detected by the algorithm and construct the rule base with domain knowledge. Using rule base to analyze outlier data is also the next research work, in order to make people understand the outlier data detected, the visualization of algorithm is also an important direction, The interaction between users and the visualization of detection results can help users deepen their understanding of the data and improve the accuracy of the algorithm. The achievement of this research is derived from the Research on the controlling technology of new–type enzymatic reaction and development of smart food package indicator (562015Y-3996), and the Research and Application on the electronic traceability of the key technology of the food safety monitor and the early warning (2015BAK36B03).

References 1. Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: Proceeding of VLDB 2004 (2004) 2. Liu, Y., Sprague, A.P.: Outlier detection and evaluation by network flow. Int. J. Comput. Appl. Technol. 33(2–3), 237–246 (2008) 3. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001) 4. Aggarwal, C., Han, C., Wang, J., et al.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases (2003)

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5. Bottani, E., Rizzi, A.: Economical assessment of the impact of RFID technology and EPC system on the fast-moving consumer goods supply chain. Int. J. Prod. Econ. 112(2), 548– 569 (2008) 6. Bendavid, Y., Lefebvre, E., Lefebvre, L.A., Fosso-Wamba, S.: Key performance indicators for the evaluation of RFID-enabled B-to-B E-commerce applications: the case of a five-layer supply chain. Inf. Syst. E-Bus. Manag. 7(1), 1–20 (2009) 7. Amador, C., Emond, J.P., do Nascimento Nunes, M.C.: Application of RFID technologies in the temperature mapping of the pineapple supply chain. Sens. Instrum. Food Qual. Saf. 3(1), 26–33 (2009) 8. Wang, F., Liu, S., Liu, P., Bai, Y.: Temporal management of RFID data. In: Proceedings of the 31st International Conference on Very Large Data Bases (2005) 9. Hoffman, W.: Hot market, cool freight. Can. J. Commer. (2006) 10. Abad, E., Palacio, F., Nuin, M., De Zarate, A.G., Juarros, A., Gómez, J.M., Marco, S.: RFID smart tag for traceability and cold chain monitoring of foods: demonstration in an intercontinental fresh fish logistic chain. J. Food Eng. 93(4), 394–399 (2009) 11. Hu, Y., Sundara, S., Chorma, T., Srinivasan, J.: Supporting RFID-based item tracking applications in Oracle DBMS using a bitmap datatype. In: Proceedings of the 31st International Conference on Very Large Data Bases (2005) 12. Jedermann, R., Ruiz-Garcia, L., Lang, W.: Spatial temperature profiling by semi-passive RFID loggers for perishable food transportation. Comput. Electron. Agric. 65(2), 145–154 (2009) 13. Zhao, W., Zhou, S., Sun, Y.: Application of RFID cold chain temperature control research. Comput. Syst. Based Outlier Min. 19(11), 166–170 (2010)

A Market Interaction Model for the Integration of Energy Efficiency Top-Runner and Energy Conservation Standard Jianwei Tian1,2(&), Yujuan Xia1,2, and Haihong Chen1 1

2

China National Institute of Standardization, Beijing, China [email protected] Energy and Water Efficiency Engineering Research Center, AQSIQ, Beijing, China

Abstract. Energy efficiency top-runner (EET) and energy conservation standard (ECS) are two different mechanisms to energy conservation enhancement. The integration of EET and ECS is crucial for promoting overall social energy efficiency improvement. This study analyzes current situations and characteristics of EET and ECS, then probes into three integration mechanisms. Furthermore, the study develops a market interaction model based on the complex adaptive system (CAS) theory and basic principle of market economy, and proposes an interactive algorithm and fuzzy rule base according to the fuzzy set theory.

1 Introduction With the aggravation of global energy crisis and environmental problem, most countries across the world have increasingly reached a consensus on focusing on energy conservation, promoting energy saving innovation, developing new energy resources and improving energy efficiency. A series of policy measures related to energy conservation and energy efficiency improvement have been launched by the international community successively. In 1975, the USA enacted and implemented the Energy Policy and Conservation Act, aiming to increase energy production and reduce energy demand. In 1995, the UK enacted and implemented the Home Energy Conservation Act, requiring governments to take concrete measures to reducing residential building energy consumption by 30% comparing to 1996 or 1997 in 10 years. In 1996, France formulated the law on Air Quality and the Rational Use of Energy, which set ambient air quality standards, requirements for monitoring pollution, emission standards, save and use energy rationally. In 1998, the Japanese Ministry of Economy, Trade and Industry released the Energy efficiency top-runner (EET) program in the real sense. As of late 2015, the Japanese EET program had covered 28 energy-using products including refrigerators and air conditioners and 3 kinds of building materials such as glass fiber insulation board [1]. Viewed from the implementation effect, the actual energy efficiency improvement effects of most energy-using products covered by the Japanese EET program are better than expected [2]. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 241–248, 2019. https://doi.org/10.1007/978-3-030-02804-6_32

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The second and the third parts of this paper analyze current situations and characteristics of China’s EET program and ECS respectively, the fourth part probes into three integration mechanisms of EET and ECS, the fifth part develops a market interaction model and algorithm based on the CAS theory and basic principle of market economy, and the final part draws conclusions and future work.

2 Current Situation and Characteristics of China’s EET Program 2.1

Current Situation of China’s EET Program

In 2011, the State Council issued the Comprehensive Work Plan on Energy Conservation and Emission Reduction during the 12th Five-Year Plan Period, proposed to establish top-runner program in China. In December 2014, EET program began to be formally carried out in China [3], which includes three areas: energy end-use products, energy-intensive industries and public institutions. In May 2016, the National Development and Reform Commission (NDRC), the Ministry of Industry and Information Technology (MIIT) and the General Administration of Quality Supervision, Inspection and Quarantine (AQSIQ) released the list of EET products among the first batch of three kinds of household electrical appliances: household refrigerators, flat panel TVs and variable speed room air conditioners. Totally 150 specifications of top-runners were included. In July 2016, MIIT, NDRC and AQSIQ released the list of 16 EET enterprises in 5 industries and the list of 20 shortlisted enterprises. 2.2

Characteristics of China’s EET Program

The basic idea of EET program is to form a long-acting mechanism to drive continuous energy efficiency improvement of energy end-use products, energy-intensive industries and public institutions by establishing benchmarks, providing policy incentives, raising standard, and promote energy conservation and emission reduction. In the main process, China government regularly releases the list of energy end-use products with the maximum energy efficiency, the list of enterprises manufacturing energy-intensive products with minimum energy consumption per unit throughput and the list of public institutions, and takes them as energy efficiency benchmarks; leads enterprises and public institutions to strive for EET, and provides policy support to EET; introduces EET indicators into national mandatory energy efficiency and energy consumption quota standards in due time.

3 Current Situation and Characteristics of ECS 3.1

Current Situation of ECS

In order to promote the whole society to save energy, increase energy efficiency, and protect environment, the Energy Conservation Law of P.R.C. (revised in 2007) defines the legal status of mandatory energy efficiency standard, energy consumption quota standard and China energy label.

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As of late 2017, China has released and implemented 73 mandatory energy efficiency standards and 106 energy consumption quota standards. The mandatory energy efficiency standards cover six types of products: household appliances, industrial equipment, commercial equipment, lighting, electronic information products, and passenger vehicle fuel consumption limits. The mandatory energy consumption quota standards involve energy-intensive products such as power generators, crude steel, crude steel, electrolytic aluminum, cement, etc. [4]. 3.2

Characteristics of ECS

Internationally, two types of energy efficiency standards have been formulated for energy-using products or equipments: minimum energy performance standard system and average energy performance standard system. Due to different management references and methods, the two standard systems have different influences on enterprises and energy-saving industries. China adopts the minimum energy performance standard system, which requires energy-using products and equipment that do not meet mandatory energy efficiency standards are not allowed to be produced, imported and sold in China, and the standard on the allowance of energy consumption per unit throughput should be followed for the production of energy-intensive products.

4 Integration Mechanisms of EET and ECS Because of great difference between EET and ECS in aspects of product quantity, coverage and implementation effects, the integration of the two should start from their own current situations and characteristics. Based on different development stages of EET and ECS, the integration modes can fall into three types: EET ! ECS integration mechanism, ECS ! EET integration mechanism and EET $ ECS two-way integration mechanism. 4.1

EET ! ECS Integration Mechanism

EET ! ECS integration mechanism refers to a single-way transmission from EET to ECS through market mechanism. After selecting EET products, government introduces a series of measures to carry forward typical examples, provides honorary awards and policy supports to EET. After market receives the excitation signal, all participants make corresponding responses; producers strive to keep up with the EET benchmark; sellers expand sale channels to promote EET products; consumers begin to accept EET products after a process of focusing on, understanding, experiencing and purchasing; research institutions actively develop products with higher energy efficiency. With further energy efficiency improvement of EET products, relevant administrations will promote timely ECS revision and introduce EET indicators into mandatory energy efficiency standard and energy consumption quota standard. The upgrading demonstrates that an EET ! ECS transmission is completed, and the upgrading from EET to ECS is realized.

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ECS ! EET Integration Mechanism

ECS ! EET integration mechanism refers to a single-way transmission from ECS to EET, which is a reverse transmission of EET ! ECS. As ECS is a mandatory standard, once energy efficiency indices of ECS are revised, voluntary EET must be upgraded based on the revision values of ECS. Market mechanism will drive market participants to adjust strategies according to new ECS standard, producers will carry out energy conservation technical upgrading to meet new ECS standard, sellers will promote new products with higher energy efficiency, consumers will consider purchasing energy efficient products that suit their own interests, research institutions will develop new energy conservation technologies and energy efficient products. New mandatory ECS standard will be taken as a threshold for the EET products selection. In order to ensure their products can be shortlisted, producers will produce products with higher energy efficiency to apply for EET, thus achieving a revolutionary energy efficiency improvement and promoting EET shortlisting threshold to move to a new level. 4.3

EET $ ECS Integration Mechanism

EET $ ECS integration mechanism refers to a two-way transmission between EET and ECS, and it is the main transmission mechanism when both EET and ECS have great influences. On one hand, EET becomes new energy efficiency benchmark as government issues a new EET statement; enterprises actively keep up with benchmark of EET in order to pursue their own development and cater for market demands; relevant administrations will promote ECS revision after overall energy efficiency of products reach revision threshold, and introduce EET indicators into mandatory energy efficiency standard and the energy consumption quota standard. On the other hand, after new mandatory ECS standard implementing, they will be taken as a new threshold for the selection of EET products, which will directly promote ECS ! EET transmission and achieve a revolutionary energy efficiency improvement. All market participants work together to push forward an upward spiral of social energy efficiency through the ECS $ EET integration mechanism.

5 Market Interaction Model and Algorithm John Holland, an American scholar, made a speech titled “Hidden Order” at the 10th anniversary conference of the Santa Fe Institute in 1994, and formally put forward the complex adaptive system (CAS) theory [5], which focuses on individual autonomy and interactivity between individuals and environment, and provides new research thinking for people to simulate complex systems. Based on the CAS theory, this study develops a market interaction model and designs an interactive algorithm.

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Market Interaction Model

Based on distributed artificial intelligence, market participants can feel external environment changes [6]. They have autonomy and judgment abilities and can interact with each other under market mechanism, thus achieving EET and ECS integration, the market interaction model is shown in Fig. 1. Assume that EET and ECS are two relatively independent agents that affect each other through market mechanism, and they carry out self-adaptive adjustment according to market environment and their own decision rules. External environment variables include government and all stakeholders (mainly including producers, sellers, consumers and research institutions). Environment-related parties are rational agents.

Govermant Macro policy

Macro policy

response

response

Macro policy

response

Producer Seller

response

response

Market mechanism

EET

ECS

Consumer

incitation

incitation Research institute

environment

Fig. 1. Market interaction model

5.2

Interactive Algorithm

Considering characteristics of all participants, EET and ECS indicator adjustment values basically fluctuate within a certain range, but they promote each other; environment variables including producers, sellers, consumers and research institutions can affecting adjustment strategies of EET and ECS; government as macro regulator, may adjust policies according to EET and ECS conditions and market responses of all participants. Therefore, based on market interaction mechanism, EET and ECS show obvious features of a fuzzy set. Using fuzzy reasoning algorithm can achieve selfadaptive strategy adjustment. Assume that EET state is Seet , ECS indicator state is Secs , and external environment state is Senv ¼ f ðp; s; c; ri; . . .Þ, wherein p refers to producers, s refers to sellers, c refers to consumers, and ri refers to research institutions. Considering the mutual promotion

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between EET and ECS, we only carry out fuzzy processing in the positive change range only. Seet can be expressed with fuzzy language as below based on the value of EET indicators: ~Seet ¼ fM; PS; PBg

ð1Þ

Wherein, M is medium, PS is positive small, and PB is positive big. Secs can be expressed with fuzzy language as below based on ECS indicators: ~Secs ¼ fM; PS; PBg

ð2Þ

Wherein, M, PS and PB are as same as the one in the above expression. External environment Senv ¼ f ðp; s; c; ri; . . .Þ can be presented with fuzzy language as below according to the response of all participants to energy conservation policies: ~Senv ¼ fS; M; Bg

ð3Þ

Wherein, S is small, M is medium, and B is big. Based on fuzzy processing of market participants and external environment, an interactive algorithm is proposed as below: (1) EET Agent For EET agent, its strategy should be constantly adjusted according to the change in ECS indicators and external environment, but it must meet requirements of mandatory ECS standards. Table 1 shows its fuzzy rule base.

Table 1. Rule base of EET agent Rule no. Rule description Rule 1 If ~Secs is M and ~Senv is S, then ~ Seet is M Rule 2 If ~Secs is M and ~Senv is M, then ~ Seet is M ~ ~ ~ Rule 3 If Secs is M and Senv is B, then Seet is PS Rule 4 If S~ecs is PS and S~env is S, then ~ Seet is PS Rule 5 Rule 6 Rule 7 Rule 8 Rule 9

If ~Secs is PS and ~Senv is M, then ~ Seet is PS If S~ecs is PS and ~Senv is B, then ~ Seet is PB If ~Secs is PB and ~Senv is S, then ~ Seet is PB If S~ecs is PB and ~Senv is M, then ~ Seet is PB ~ ~ ~ If Secs is PB and Senv is B, then Seet is PB

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(2) ECS Agent ECS agent’s interaction strategy should be adjusted according to the change in EET indicators and external environment, and a decision could be made on whether EET energy efficiency level introduced to accessible value, allowable value, advanced value or other energy efficiency indicators. ECS agent’s fuzzy rule base can be found in Table 2. Table 2. Rule base of ECS agent Rule no. Rule description Rule 1 If ~Seet is M and ~Senv is S, then ~ Secs is M Rule 2 If ~Seet is M and ~Senv is M, then ~ Secs is M ~ ~ ~ Rule 3 If Seet is M and Senv is B, then Secs is PS Rule 4 If S~eet is PS and S~env is S, then ~ Secs is M Rule 5 Rule 6 Rule 7 Rule 8 Rule 9

If ~Seet is PS and ~Senv is M, then ~ Secs is PS If S~eet is PS and ~Senv is B, then ~ Secs is PB If ~Seet is PB and ~Senv is S, then ~ Secs is PS If S~eet is PB and ~Senv is M, then ~ Secs is PB ~ ~ ~ If Seet is PB and Senv is B, then Secs is PB

(3) External environment External environment Senv reflects the responses of producers, sellers, consumers, research institutions and other stakeholders. Stakeholders make marketing decisions according to EET and ECS indicator’s change as well as government’s energy conservation policy adjustment. The market adjustment behavior Ssta adj of stakeholders can be expressed as the following mapping relationship: Senv

adj

¼ fSee, Next, Choose, Actiong

ð4Þ

Wherein, Ssta is stakeholder’s state; Ssta obj is objective; Ssta k is knowledge set; Ssta is rule and experience set; and Ssta dec is marketing decision.

w

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(4) Government Agent Considering government with rich knowledge and expert resources, and it can be designed as an open agent in the process of interaction. Energy conservation experts can intervene in market interaction according to Seet , Secs and Senv , then guide market agents to join interaction, so as to improve the binding effect of energy conservation policies or introduce new energy conservation policies for adjustment of market expectations, and gradually achieve the objective of macro regulation.

6 Conclusions and Future Work According to current situations and characteristics of ECS and EET program, this study probes into three integration mechanisms of EET and ECS, and develops a market interaction model based on the CAS theory and basic principle of market economy. Furthermore, an interactive algorithm for all kinds of market agents is proposed. As a phase achievement, this study only presents a market interaction model and corresponding algorithm. Next, we will collect necessary data for case study and propose threshold trigger response to EET and ECS. Acknowledgements. This work is financially supported by the National Key Research and Development Program of China (No. 2016YFF0201504) and the Special Foundation of President of the China National Institute of Standardization (Capacity-building for refrigerating products testing in energy efficiency laboratory, No. 542016Y-4660).

References 1. Top Runner Program: Ministry of Economy Trade and Industry of Japan (2015) 2. Quan, B., Zheng, L.V.: Experience and enlightenment of energy efficiency top-runner program in Japan (Part A). China Stand. 1, 145–151 (2016) 3. National Development and Reform Commission: Notice on Issuing Implementation Plan of Energy Efficiency Top-Runner Program [EB/OL]. http://www.ndrc.gov.cn/zcfb/zcfbtz/ 201501/t20150108_659703.html 4. Chen, H., Lin, L.: Thinking on integration and simplification of mandatory energy efficiency and energy consumption quota standards. Stand. Sci. (2016) 5. Holland, J.: Hidden order: how adaptation builds complexity (transl. by Zhou, X., Han, H.). Shanghai Scientific and Technological Education Publishing House, Shanghai (2000) 6. Wooldridge, M.: An Introduction of Multi-agent System. Wiley, London (2001)

Micro Leverage Design of Silicon Resonant Accelerometer Yan Li(&) and Xinrui Zhang School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China [email protected]

Abstract. This paper designs a leverage structure of the silicon resonant accelerometer. The mathematical model of the micro leverage magnifying structure is established firstly. According to the model, the pivot beam of the micro leverage is designed, then its parameters are optimized to realize the magnifying function of the micro leverage mechanism. Finally, simulation software is used to verify the correctness of the designed micro leverage. The relative error between the simulation result and the theoretical calculation result is 5.4%, which indicates that the designed micro leverage mechanism is feasible and provides a theoretical basis for improving the sensitivity of silicon resonant accelerometer. Keywords: Silicon Resonant Accelerometer Parameter optimization
Simulation analysis


Micro leverage

1 Introduction Silicon resonant accelerometer (SRA) is a typical micro-mechanical device with a good prospect. It is one of the most research hot-spots of Micro-electro-mechanical Systems (MEMS) devices [1]. Its advantages are high anti-interference, high-accuracy, easily realizing integration and miniaturization [2]. The micro leverage is a flexible machine. Through the elastic deformation of beams, the micro leverage mechanism is used to solve the problem that the inertial force of the sensitive mass is very small. Thus, the sensitivity of Silicon resonant accelerometer can be improved. The University of California, Berkeley, pioneered the use of micro leverage structures to magnify inertial forces. Then worldwide research institutes have begun to use micro leverages for magnifying inertial forces. In 2002, the sensitivity of the singlestage micro leverage resonant accelerometer was 45 Hz/g, designed by the University of California, Berkeley [3]. In 2005, Susan X. P. Su used a second-stage micro leverage mechanism increasing the sensitivity of SRA to 160 Hz/g [4]. In 2011, Tsinghua University used the simulation analysis method increasing the theoretical sensitivity from 33.3 Hz/g to 108 Hz/g [5]. In the same year, Nanjing University of Science and Technology made the SRA magnification 25.466 times [6]. This paper designs a micro leverage structure and optimizes its structural parameters, by studying the silicon resonant accelerometer. For the designed structure, the correctness was verified by the finite element simulation. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 249–255, 2019. https://doi.org/10.1007/978-3-030-02804-6_33

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2 Working Principle of SRA The silicon resonant accelerometer measures acceleration according to the resonant measurement principle divided into two stages of sensitive structures in Fig. 1. The first-stage sensitive structure is composed of a mass and elastic beams. When an acceleration inputs in the Y-axis direction, it converts the acceleration into inertial force along the axis. The second-stage sensitive structure is composed of resonators, driverings, and detection units, which converts the inertial force into a natural frequency change of resonators.

elastic beam anchor drivering

resonator detecting

anchor

mass

Y X

Fig. 1. Principle structural diagram of SRA

Due to the limitations of the manufacturing technology, the size of accelerometers are small, so that the volume of the sensitive mass is very small. Thus the inertial force converted by the mass is not ideal, which seriously restricts the sensitivity of the accelerometer. In order to magnify the inertial force and increase the sensitivity, a micro leverage mechanism is used between the first-stage and the second-stage sensitive structures.

3 Structure of the Micro Leverage 3.1

Structure Design of the Micro Leverage

All components of the micro leverage are rigidly connected, and constraints exist at the input and output, which can result in the loss of inertial forces. Therefore, the importance of the design of the micro leverage structure are simplification of the magnifying structure model, optimization of the structural form and parameters for achieving the magnification function, and solving the problem of stiffness matching. Analyzing the structure of each pivot beam, the suitable form of the pivot beam is selected. Then modeling the micro leverage mechanism, the geometric size is optimized. Finally, the correctness of micro leverage is verified by simulation software.

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Generally the micro leverage has four different structures of the pivot beam in Fig. 2. For the convenience of theoretical analysis, the forms are simplified to the mechanical model of Fig. 3. Where kvvp and khmp are tension spring stiffness of a pivot beam and torsion spring stiffness; kvvo and khmo are tension spring stiffness of the output system and torsion spring stiffness; the deformation of the pivot beam can be equivalent to the axial line displacement (Y-axis minus direction is positive) and the leverage rotation angle displacement (counterclockwise is positive); loh − d is axial line displacement of the leverage output system, input Fin, output Fout.

Fig. 2. Four different types of pivot beams.

After a force analysis on the structure in Fig. 3, the magnification of the micro leverage mechanism calculated as A¼


khmo þ khmp þ lin lo 
1 þ kvvp khmo þ khmp þ l2o

1 kvvp 1 kvvo

ð1Þ

According to Eq. (1), with different torsional spring stiffness, the magnification of micro leverage mechanism changes along the variation of tension spring stiffness as shown in Fig. 4. Magnification increases with the spring stiffness of the tension and compression kvvp, with the stiffness of the torsion spring khmp, and they all infinitely approach the ratio of the leverage arm lin/lo. Because the pivot beam should have a large tension spring stiffness and a small torsional spring stiffness. The (1) vertical fulcrum beam in Fig. 3 is selected as the flexible hinge of the present designed micro leverage mechanism. 3.2

Optimization of the Micro Leverage Structural Parameters

Because it is necessary to solve the stiffness matching problem of the input and output of the micro leverage, its design must be considered in combination with other structures connected to the micro leverage. This article uses a double-ended-tuning fork as a resonator, so the entire micro leverage structure is shown in Fig. 5a.

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lin

kvvp

kvvo

kθm p

kθmo

lo

δ

θ

Y

Fin

X

Fig. 3. Mechanical model of micro leverage 1.0x101 8.0x100

A

6.0x100 Kθmp=5e-8Nm

4.0x100

Kθmp=5e-7Nm ?

0

2.0x10

0.0

Kθmp=5e-6Nm 0

500 1000 1500 2000 2500 3000 3500 4000 4500 5000

kvvp /Ν/m

Fig. 4. Magnification variation with tension spring stiffness

kf1 kf 2 kf3

kf 4

kf5 kf6

(a)Micro leverage integral structure

(b)Simplified spring model for the resonator

Fig. 5. Structural diagram of micro leverage structure.

In order to analyze the effect of resonator parameters on the magnification of the micro leverage, the spring stiffness of the tension spring of the resonator can be simplified as a combination of 6 springs of Fig. 5b.

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253

Therefore, the tension spring stiffness of the output system of the micro leverage mechanism can be obtained as 1 1 1 1 1 1 ¼ þ þ þ þ kvvo kf 1 kf 2 kf 3 þ kf 4 kf 5 kf 6

ð2Þ

The subscript f is that the output system is a resonator, beam 3 and beam 4 are the tuning forks of the resonator F, beam 6 is a connecting beam, beam 1 is a connecting beam symmetrical to beam 6, beam 2 and beam 5 are the merge ends of the resonator. Substituting the magnification Eq. (1), the resonator is the output system of the micro leverage mechanism, and its relationship between the magnification and the structural parameter is

A¼



2lc hc



lp hc lc hp

þ

L 2h

 h2c þ h2p þ lin lo  3  h3 l h þ hpp lcc þ lpp þ l2o

lin/lo=12

12.0 11.5 11.0

h=12 h=8 h=4 0

200

10.5

hc=4 hc=10 hc=20

10.0 9.5 400

600

800

9.0

1000

0

40

L /μm

120

160

200

(b) The connecting beam 24

lin/lo=12

lp=10 lp=50 lp=200 lin/lo

22 20 18

hp=4 hp=10 hp=20

A

12.0 11.4 10.8 10.2 9.6 9.0 8.4 7.8 7.2

80

lc /μm

(a) The resonant beam

A

ð3Þ

12.5 lin/lo=12

A

A

12.00 11.98 11.96 11.94 11.92 11.90 11.88 11.86 11.84 11.82 11.80 11.78 11.76

1 12

1 12

16 14 12

0

50

100

150

lp /μm

(c) The pivot beam

200

50

60

70

80

90

100

lo /μm

(d) The output arm length

Fig. 6. The magnification variation of the micro leverage with structural parameters.

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Through Eq. (3), the magnification of the micro leverage mechanism is independent of the width of the resonant beam b, the width of the connecting beam bc and the width of the pivot beam bp. The variation of magnification A is shown in Fig. 6a–d, respectively, along with structural parameters of the resonant beam, structural parameters of the connecting beam, structural parameters of the pivot beam, and the output arm length. After analyzing each variation of magnification, the parameter is chosen as follows: lp = 200 lm, hp = 10 lm, lin = 1200 lm, lo = 95 lm, lc = 100 lm, hc = 10 lm, lin/lo = 12.6316. Substituting data into micro leverage theory model (3), magnification A can be obtained:

A¼

1 12



1 12 2lc hc



lp hc lc hp

þ

L 2h

 h2c þ h2p þ lin lo  3  h3 l h þ hpp lcc þ lpp þ l2o

ð3Þ

The theoretical resolution of magnification of the micro leverage mechanism is 12.4212.

4 Simulation In Fig. 7a, the finite element model of the micro leverage mechanism is established. The concentrated load Fin = 11 lN is applied to the input end, and the stress distribution of the micro leverage system is shown in Fig. 7b.

(a) The model of micro leverage mechanism

(b) Stress distribution diagram

Fig. 7. The simulation diagram of micro leverage mechanism.

The result of the simulation of the magnification of the micro leverage mechanism is 11.9813, and the relative error of the theoretical analysis result is 5.4%, which proves that the micro leverage mechanism designed in this paper is correct.

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5 Conclusions In this paper, a micro leverage magnifying structure of the silicon resonant accelerometer is proposed. The analytical calculation and simulation analysis are used to optimize the structure and its structural parameters of the micro leverage. Through the theoretical analysis of the micro leverage structure, using the resonator as the output system, the mathematical model of the micro leverage is established to determine the structure of the vertical pivot beam and its parameters. Finally, the magnification is verified by simulation software. The theoretical result of the magnification of the micro leverage is 12.4212, and the result of the finite element simulation is 11.9813, the relative error is 5.4%. Within the acceptable range, the feasibility of the designed amplification structure in this paper is proved. Acknowledgments. This study is supported by the National Natural Science Foundation of China under Grant No. 61503018.

References 1. Hopkins, R., Miola, J., Setterlund, R., et al.: The silicon oscillating accelerometer: a high performance MEMS accelerometer for precision navigation and strategic guidance applications. Draper Technol. Digest 10, 4–13 (2006) 2. Seshia, A.A., Roessig, T.A.: A vacuum packaged surface micromachined resonant accelerometer. J. Micro Electromech. Syst. 11(6), 784–793 (2002) 3. Roessig, T.A., Howe, R.T., Pisano, A.P.: Surface micromachined resonant accelerometer. Sens. Actuators 10, 859–862 (1997) 4. Su, S.X.P., Yang, H.S.: A resonant accelerometer with two-stage micro leverage mechanisms fabricated by SOI-MEMS technology. IEEE Sens. J. 5(6), 1214–1223 (2005) 5. Hao, H., Jingxin, D., Yunfeng, L., Cao, Y.: Calculation and improvement of magnifying effect of micro mechanical lever force. J. Chin. Inert. Technol. 19(1), 91–94 (2011) 6. Ran, S., Shaodong, J., Anping, Q., Yan, S.: Application of micro leverage in silicon resonant accelerometer. Opt. Precis. Eng. 19(4), 805–810 (2011)

Sensitive Structure Design of Resonant Accelerometer Yan Li ✉ and Zhuoni Zhang (

)

School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China [email protected]

Abstract. In this paper, the sensitive structure of resonant accelerometer is opti‐ mized. The working principle of the resonant accelerometer, the sensitive mech‐ anism of the sensitive structure and the mechanical model of the resonant tuning fork are analyzed. Based on these theories, it is answered what influence is the structural parameters of the sensitive structure on the natural frequency of the resonator. Theoretical analysis shows that the root stiffness of the resonator has little effect on the inverse phase vibration frequency, but has a great influence on the in-phase vibration frequency. Therefore, the sensitivity of the resonant accel‐ erometer can be improved by controlling the root rigidity of the tuning fork. Finally, finite element simulation of resonant tuning forks with different root thickness and different root stiffness is carried out. The simulation results are coincided with the theoretical analysis. It is proved that the theoretical analysis is reasonable. Keywords: Resonant accelerometer · Sensitive structure · Vibration mode Finite element simulation

1

Introduction

The resonant accelerometer obtains the input acceleration by detecting the change of the natural frequency of the sensor. The main function of the sensitive element is to convert the inertial force caused by acceleration to the natural frequency variation of the sensitive structure. The sensitive structure has a certain influence on the variation of its natural frequency. Therefore, the sensitive structures design plays an important role in optimizing the sensitivity of accelerometer. In 1989, Satchell D.W. and Greenwood J.C. designed the accelerometer sensitive structure of three-beam structure for the first time [1]. National University of Singa‐ pore connects two resonant beams together. It is ensured that the vibration of the two beams is completely inverse phase [2–4]. A high sensitivity single axis direct output frequency accelerometer was designed in 2009 by the Milan Polytechnic University. Its resonator includes two single resonant beams [5]. In the piezoelectric resonant accelerometer, a central symmetric distribution resonator is proposed. The structure maximizes the resonator’s length in a given area and enhances the sensitivity of the resonant accelerometer [6]. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 256–263, 2019. https://doi.org/10.1007/978-3-030-02804-6_34

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The sensitive structure designed in this paper is a double end tuning fork. At first, the mechanical model of resonant tuning fork and its mathematical model are estab‐ lished. Then, the influence of the sensitive structure parameters on its vibration mode is analyzed. Finally, the rationality of the theoretical analysis is verified by simulation.

2

Working Principle of Resonant Accelerometer

The fundamental principle of the resonant accelerometer is to utilize the force and frequency characteristics of the resonant beam. The magnitude of input acceleration is obtained by detecting the variation of resonant frequency. The main structures include mass, elastic beams, resonators (tuning forks) and lever mechanisms. The structure diagram is shown in Fig. 1.

Fig. 1. Structure diagram of resonant accelerometer

When an acceleration occurs in the detection direction, according to Newton’s second law, an inertial force is generated in that direction. The inertia force can be expressed as: F = ma. Where m is the quality of the mass. Thus the acceleration in the detection direction can be transformed into the inertial force in that direction. Then, the inertia force is ampli‐ fied by the lever structure and acts on the resonant tuning fork in the axial direction. There‐ fore, the natural frequency of the tuning fork is changed. There is a linear relationship between the natural frequency variation and the input acceleration. So, the input accelera‐ tion can be measured by measuring the variation of the natural frequency.

3

Mechanical Analysis of Resonator

Tuning fork has the characteristics of high sensitivity, vibration isolation and simple processing technology. Its basic structure is shown in Fig. 2. When the two reso‐ nant beams are vibrated inversely in the plane by external excitation, the stress and torque produced by the two beams at their roots are opposite to each other, and they can cancel each other out. Therefore, it does not require additional vibration isola‐ tion. So, the inverse phase vibration of the resonant tuning fork is regarded as its

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working mode, and the in-phase vibration is its interference mode. The greater the difference between the inverse phase vibration mode and the in-phase vibration mode, the smaller the influence of the interference mode on working mode. Through the mechanical analysis of the resonant tuning fork, the influence of its structural parameters on the working mode can be obtained. Thus, the interference mode can be separated from the working mode by changing its structural parameters. The optimum design of accelerometer can be realized.

Fig. 2. Resonant tuning fork model

When the internal structure of the resonant accelerometer works, the longitudinal vibration and the transverse vibration can be decoupled. Accordingly, the side of the resonator can be taken out alone for analysis. According to the structure principle of the resonator, the mechanical model of the resonator is established, as shown in Fig. 3.

Fig. 3. Mechanical model of resonator

Based on the mechanical model, the following mathematical models can be estab‐ lished:

T=

)2 1 ( )2 1 ( 1 m1 ẋ 12 + m2 ẋ 1 + ẋ 2 + m3 ẋ 1 − ẋ 3 2 2 2 1 1 1 V = 2 ⋅ k1 x12 + k2 x22 + k3 x32 2 2 2

(1) (2)

L=T −V ( ) d 𝜕L 𝜕L =0 − dt 𝜕 ẋ i 𝜕xi

(3)

M ẍ + Kx = 0

(5)

(4)

As a result:

Sensitive Structure Design of Resonant Accelerometer

259

⎛ m1 + m2 + m3 m2 −m3 ⎞ m2 m2 0 ⎟ M=⎜ ⎜ ⎟ −m 0 m3 ⎠ ⎝ 3

Where:

⎛ 2k1 0 0 ⎞ K = ⎜ 0 k2 0 ⎟ ⎜ ⎟ ⎝ 0 0 k3 ⎠

Let the main vibration equation be x = A sin (pt + φ), and inserted into the Eq. (5). The homogeneous equations can be obtained:

( ) K − p2 M A = 0

(6)

If and only if the result of the coefficient determinant is zero, the Eq. (6) have nonzero solutions:

|K − p2 M| = 0 |

(7)

According to symmetry of resonator structure: m2 = m3, m1 = 2m3, k2 = k3. Thus, the natural frequencies of the resonator are: p21 =

1 p22 = 2

p23 =

1 2

k3 m3

m3 k1 + 2m3 k3 +

(8)

√

( ) m23 k12 + 4k32

m23

−m3 k1 − 2m3 k3 +

√ ( ) m23 k12 + 4k32

(9)

(10)

m23

The corresponding mode of p1 is that there is no displacement in the resonator and two beams have reverse displacement; The corresponding modes of p2 and p3 are the positive and reverse displacements of the resonator and the same direction displacement of the two beams. Thus, the corresponding mode of p1 is the working mode required. The following relations can be obtained from the Eqs. (8), (9) and (10): lim p22 = 2p21 k3 →∞ k1

(11)

lim p22 → ∞ k3 →0 k1

(12)

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lim p23 = 0 k3 →∞ k1

(13)

lim p23 = p21 k3 →0 k1

(14)

In a word, when the stiffness k1 of the resonator root is smaller, the mode corre‐ sponding to p3 appears first. When k1 is larger, the frequencies of p1 and p3 should be very close, and p2 will be far away from the other two modes.

4

Simulation

Through the mechanical analysis of the resonator, it can be seen that the root stiffness of the resonator has a great influence on its natural frequency. So, the effects of different stiffness on the natural frequency are verified.

First-order mode

Second-order mode

Fig. 4. Mode diagram of b = 0.8 mm

First-order mode

Second-order mode

Fig. 5. Mode diagram of b = 1 mm

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The tuning fork model shown in Fig. 2. Where b is the resonator root width. Figures 4, 5 and 6 show the mode diagrams of the resonator as b gradually increases. The simulation data is shown in Table 1.

First-order mode

Second-order mode

Fig. 6. Mode diagram of b = 3 mm

Table 1. Simulation dates b = 0.8 mm b = 1 mm b = 3 mm

First-order mode frequency (Hz) 3515 3526 3557

Second-order mode frequency (Hz) 3560 3564 3563

Through the simulation results of different root thickness, it can be seen that with the increase of b, the frequency of the inverse mode is not affected much, but the frequency of the in-phase mode is greatly affected. Therefore, the interference mode can be separated from the working mode by controlling the resonator root stiffness. Figures 7 and 8 are mode diagrams when k1 takes 10 N/mm and 1000 N/mm.

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(a) First-order mode diagram

(c) Fourth-order mode diagram

(b) Third-order mode diagram

(d) Sixth-order mode diagram

Fig. 7. Mode diagram of k1 = 10 N/mm

(a) First-order mode diagram

(b) Third-order mode diagram

(c) Fourth-order mode diagram

(d) Sixth-order mode diagram

Fig. 8. Mode diagram of k1 = 1000 N/mm

Sensitive Structure Design of Resonant Accelerometer

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When k1 = 10 N/mm, that is the root stiffness is smaller, the mode corresponding to p3 will appear very early. As shown in Fig. 7a, the first-order mode is the one corre‐ sponding to p3. When k1 = 1000 N/mm, that is the root stiffness is larger, it can be seen from Fig. 8 that p1 = 3560 Hz and p3 = 3474 Hz. The two are very close. And p2 = 9043 Hz, it is far from the other modes. So, the simulation results are coincided with the theoretical analysis.

5

Conclusions

(1) The fundamental principle of the resonant accelerometer is to utilize the force and frequency characteristics of the resonant beam. The magnitude of input acceleration is obtained by detecting the variation of resonant frequency. The inverse phase vibration mode of resonator is its working mode and other interference modes should be kept away from its working mode. (2) Through the theoretical analysis, it is shown that the in-phase mode will appear first when the root stiffness is smaller, and when the root stiffness is larger, the inphase vibration frequency will approach the inverse phase vibration frequency. (3) The results of simulation show that the root thickness has little influence on the inverse mode frequency, but has a great effect on the in-phase mode frequency. when the root stiffness is smaller, the in-phase mode appears first. when the root stiffness is larger, the in-phase mode frequency is almost equal to the inverse mode frequency. The simulation results are coincided with the theoretical analysis, which proves that the theoretical analysis is reasonable. Acknowledgments. This study is supported by the National Natural Science Foundation of China under Grant No. 61503018.

References 1. Satchell, D.W., Greenwood, J.C.: A thermally-excited silicon accelerometer. Sens. Actuators 17(1), 241–245 (1989) 2. He, L., Xu, Y.-P., Qiu, A.: Folded silicon resonant accelerometer with temperature compensation. In: Proceedings of IEEE Sensors, vol. 1, no. 1, pp. 512–515 (2004) 3. He, L., Xu, Y.-P., Qiu, A.: A COMS readout circuit for silicon resonant accelerometer with 32-ppb bias stability. In: Symposium on VLSI Circuits Digest of Technical Papers, 14–16 June, pp. 146–147 (2007) 4. He, L., Xu, Y.-P., Palaniapan, M.: A COMS readout circuit for SOI resonant accelerometer √

with 4-ug bias stability and 20-ug/ Hz resolution. IEEE J. Solid-State Circuits 43(6), 1480– 1490 (2008) 5. Comi, C., Corigliano, A., Langfelder, G.: A high sensitivity uniaxial resonant accelerometer. In: Proceedings of IEEE MEMS, vol. 33, no. 9, pp. 260–263 (2010) 6. Wang, Y., Ding, H., Le, X.: A MEMS piezoelectric in-plane resonant accelerometer based on aluminum nitride with two-stage microleverage mechanism. Sens. Actuators A Phys. 254, 126–133 (2017)

Research on 3D Lightweight Engine Technology for Power Grid Service Scenarios Gang Wang1,2 ✉ , Xiaodong Zhang3, Chengzhi Zhu4, He Wang1,2, Lin Peng1,2, and Min Xu1,2 (

1

)

Global Energy Interconnection Research Institute Co., Ltd, Nanjing 210003, Jiangsu, China [email protected] 2 State Grid Key Laboratory of Information and Network Security, Nanjing 210003, China 3 State Grid Corporation of China, Beijing 100031, China 4 State Grid Zhejiang Electric Power Co., Ltd, Hangzhou 310007, China

Abstract. The development of information display interaction from planar 2D to stereoscopic 3D has become increasingly mainstream. In the future, there will be more and more 3D content creators and consumers. The core model engine is standardized, lightweight, cross-platform and 3D-based. The interactive content library has become a hot topic in the IT industry. This paper studies the main‐ stream model file conversion compression technology based on the characteristics of the power grid business, and realizes the compression and weight reduction of the three-dimensional model. In this paper, compressive sensing-based 3D model compression method is used to achieve rapid compression of server-side models, which can greatly reduce the model space and increase the loading speed of the model. Compressed sensing compresses data at the beginning of data acquisition. When sampling, it does not follow the frequency twice as high as the maximum cut-off frequency of the initial signal as described by Shannon Nyquist Theorem, which greatly reduces the amount of data sampled, and does not require subse‐ quent data recompression, which can greatly increase the model’s compression efficiency and quality. First, get the original model and split the model data into material data, animation data, grid data, node tree data, and map data. According to the model data classification, compression-sensing algorithm based on the business characteristics of power grid services was used to compress the model for sampling. Get a discrete representation of the model after sampling. Obtain the compressed model, and combine the material data, animation data, grid data, node tree data, and map data according to the characteristics of the power business and associate it with the unique identifier of the business model. The compression of mainstream model files based on the characteristics of power grid services can effectively improve the operational efficiency of 3D models. The research results lay a solid foundation for the engine technology in the future development of interactive business systems for displaying 3D power equipment based on 3D model engines. The development of a prototype of a 3D model engine can provide basic support for the construction of a unified power 3D model library for the future grid and 3D interaction of power services. Keywords: Power grid service scenarios · 3D lightweight engine

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 264–269, 2019. https://doi.org/10.1007/978-3-030-02804-6_35

Research on 3D Lightweight Engine Technology

1

265

Introduction

The development of information display interaction from planar 2D to stereoscopic 3D has become increasingly mainstream [1]. In the future, there will be more and more 3D content creators and consumers. The core model engine is standardized, lightweight, cross-platform and 3D-based [2]. The interactive content library has become a hot topic in the IT industry. In the power industry, the application of 3D models involves the entire process of design, manufacture, construction, operation and maintenance, and has a wide range of applications [3]. Currently, traditional 3D model applications of grids are limited by traditional 3D modeling tools, and are mainly customized and developed for specific applications. Model files are huge, and the running software and hardware envi‐ ronment is complex. It is difficult to reuse models across services, and it is difficult to apply across a computer platform. There are the problems such as low interaction effi‐ ciency, difficulty in loading and running the mobile terminal, single information display, and lack of business logic. At the same time, with the intelligentization of power grids, the complexity of electrical equipment and power engineering is increasing, and higher demands are placed on the finesse, reuse, integration and interaction efficiency of 3D interactive content [4]. Therefore, it is necessary to study the 3D lightweight engine oriented to the grid business scenario, and study the 3D model file compression tech‐ nology, which provides the basic support for the future construction of unified power 3D model library and 3D interaction of power services. The three-dimensional model compression technology and method has become a research focus in the rapid processing of formed data. The most prominent foreign research institutes are: American Bell Labs Center for Mathematical Sciences, Cali‐ fornia Institute of Technology, Georgia Institute of Technology, University of Southern California, Microsoft Research Center, IBM Research Center, Sun Corporation, and Israel Institute of Technology. With the popularity of internet and the development of distributed graphics applications on the internet, people have begun to study 3D geometric compression techniques. Three-dimensional geometric compression techni‐ ques include three-dimensional model compression techniques and simplification tech‐ niques. It simplifies the technology from the geometric point of view, under the premise of ensuring the basic shape of the model remains unchanged, reduce the number of vertices and the number of triangles in the mesh by changing the topology of the model, or minimize the process of simplification by adjusting the positions of the remaining vertices [5]. Compression technology uses coding, prediction, or other means to remove redundant information in the model data (topological structure, vertex position coordi‐ nates, and attribute information) from the perspective of the polygonal surface model representation method [4], without direct detail information about the mesh model. Reduce. This method of encoding compression does not change the shape, structure, and topology of the model (except for remeshing methods), and even the type and number of primitives that make up the model do not change.

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Mainstream Model File Compression Technology Based on Power Network Service Characteristics

Because power grid businesses such as substations, power distribution stations, etc. have the characteristics of wide distribution, large quantities, and complex types of power equipment, the number of mainstream models for power business modeling is huge and varied, and models can’t be locally stored. Therefore, large-scale three-dimensional model library is not stored on the user’s local computer, but on the remote server. The user retrieves the model library through the service interface provided by the 3D model retrieval system and checks the search results. Although network transmission tech‐ nology has made great progress, the rapid increase users has made network data trans‐ mission still very tight. In this case, the 3D model compression process becomes an indispensable part of the 3D model retrieval process. Since the compression of the 3D model is performed on the server side, the compres‐ sion method must be simple and fast, occupying the server resources as little as possible. Traditional 3D model simplification and compression methods require a lot of calcula‐ tions and are not suitable for use in this situation. At present, most of the three-dimensional model compression algorithms are lossless in theory, but the actual operation is lossy compression. Because the vertex coordinates of the mesh model are all floating-point numbers, after coding, the dimension will decrease by half, and after coding, a block structure will be formed in the model, which is very different from the original model. In this paper, improved compressive sensing-based 3D model compression method is used to achieve rapid compression of server-side models, which can greatly reduce the model space and increase the loading speed of the model. Compressive Sensing is a new sampling method jointly proposed by Stanford University statistician and Tao Zhexuan, a mathematician at the University of California, Los Angeles. This sampling method is completely different from the traditional Shannon Nyquist sampling method. It is a kind of sampling method. The block sampling method compresses and samples the data at the beginning of data acquisition. The idea of compressive sensing, simply put, is that the compressed samples are not sampled at twice the frequency of the highest cutoff frequency of the initial signal at the time of sampling, but significantly reduce the amount of data sampled, and therefore also. There is no need for recompression of data later, which can greatly improve the compression efficiency and quality of the model. The improved compression-aware three-dimensional model compression method proposed in this paper is based on the original method, and applies the business char‐ acteristics of power equipment, such as the common three-dimensional model structure and characteristics of power grid equipment, constructing the compressive sensing factor, and improving the compression level by compressing the perceptual factor. The process of composing the compressive sensing factor will consider the common threedimensional model of the grid equipment. For example, the three-dimensional model of the transformer is composed of three-dimensional structural components for common use, three-dimensional components for transformers, three-dimensional components for coils, three-dimensional components for cooling, three-dimensional components for

Research on 3D Lightweight Engine Technology

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control cabinets and related components. The specific model compression process is shown in Fig. 1. Step 1: Get the original model and split the model data into material data, animation data, grid data, node tree data, and map data. Step 2: According to the model data classification, the model is compressed and sampled using the compressive sensing algorithm based on the business characteristics of the power grid service. Step 3: Obtain the discrete representation model after sampling. Step 4: Obtain a compressed model, and combine the material data, the animation data, the grid data, the node tree data, and the map data according to the characteristics of the power business and associate with the unique identifier of the business model.

Discretely expressed model after sampling

Original model

Compressed model

Compression sampling Fig. 1. The compression sensing method compresses 3D models

3

3D Engine Functional Structure Based on 3D Mainstream Model File Compression

The 3D model engine prototype function based on 3D mainstream model file compres‐ sion includes the terminal and server functions, as shown in Fig. 2. Terminal functions are divided into terminal system interfaces, 3D model engines, 3D model interfaces, and 3D application layers. The business system 3D application is based on the 3D model interface for related business function development. The terminal system interface is mainly responsible for the interface of the terminal system’s under‐ lying graphics, human-computer interaction, and sound. The 3D model engine includes functions such as resource loading, high-speed rendering, scene management, grid special effects, message management, and grid animation. The 3D model interface includes functions such as a 3D model control interface and a business logic data inter‐ face. 3D applications include functions such as augmented reality display of transformer equipment. The resource loading function mainly completes the loading of model maps, material data, and animation data, and implements the model and texture as scene back‐ ground. High-speed rendering implements high-speed rendering of models, textures, material data, scenes, and animation data. Grid special effects realize the functions of adjusting and balancing the overall brightness of the screen. The grid animation drives the 3D animation of the grid to support 3D job guidance. Scene management implements

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Fig. 2. The 3D model engine prototype function

terminal scenario control. Message management implements various engine event scheduling of the terminal. The 3D model control interface implements model operations such as model properties, light, music playback, and interaction. Business logic data interfaces enable model-based business data interaction. The server functions are divided into server-side system interfaces, 3D model engines, 3D model interfaces, and 3D application layers. The business system 3D application is based on the 3D model interface for related business function development. The service system interface is mainly responsible for system interface interactions such as the bottom-end graphics and audio processing of the server system. The 3D model engine includes func‐ tions such as 3D model conversion, 3D model compression, model intelligence integration, material management, and scene management. The 3D model interface includes threedimensional model interaction interfaces, business logic data interfaces and other func‐ tions. 3D applications include functions such as transformer equipment data services. Among them, the 3D model compression conversion function realizes compression and conversion of mainstream model files, realizes model resource integration, increases model network access speed and terminal loading speed, and reduces model loading pressure. The intelligent integration function of the model realizes the intelligent correlation between the power equipment and the business knowledge model. Material management realizes mate‐ rial management such as environment and glossiness. Scene management implements the scene scenario effect management and provides it to the terminal scenario library. Model management enables 3D model resource management. The 3D model interaction interface implements model data services. Business logic data interfaces enable model-based busi‐ ness data interaction. The technical of development is as follows. The terminal side uses the eclipse develop‐ ment platform, and uses c, java and other development languages for module function development. Among them, the 3D model engine is developed using the eclipse

Research on 3D Lightweight Engine Technology

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development platform, and the augmented reality display function of the transformation equipment is developed using unity3D. It uses the json structured data format to communi‐ cate with the backend server for web service interfaces. The server side adopts c, java programming language and uses vs, eclipse development platform to carry on the development. The external service interface uses the tomcat to carry on the unified publication of the service. The database uses the open source Mysql database technology. The communication network integration scheme is as follows. The cloud server deploys a 3D model engine-based prototype server function for power devices to display interac‐ tions. The terminal deploys terminal functions and uses Wi-Fi wireless networks to commu‐ nicate with the server.

4

In Conclusion

The development of a 3D model engine prototype can provide a lightweight, cross-platform engine for power 3D applications, laying the engine foundation for a new information display interaction. The research results lay a solid foundation for the engine technology for the future development of interactive business systems for 3D power equipment display based on the 3D model engine. The development of a prototype of a 3D model engine can provide basic support for the construction of a unified power 3D model library for the future grid and 3D interaction of power services. This project explores the feasibility of interactive application of grid 3D engine in grid information display, and its core technology can be subsequently applied to the 3D display interaction of grid business systems. Based on the engine’s research and development of related grid design, manufacturing, procurement, installation, operation and maintenance of three-dimensional applications, the new grid information display interactive mode can be realized, and the level of intelligence in grid training drills, transport inspection operations, engineering design, distribution and picking, and infrastructure construction can be improved. Acknowledgments. This work was financially supported by the science and technology project to State Grid Corporation ‘Research on 3D Lightweight Engine Technology for Power Grid Service Scenarios’.

References 1. Verbree, E., Jansen, F.W.: A multi-view VR interface for 3D GIS. Comput. Graph. 23, 497– 506 (1999) 2. Iourcha, K., Nayak, K., Zhou, H.: System and method for fixed-rate block-based image compression with inferred pixel values, U.S. Patent 5956431, September 1999 3. Chao, S., Shou-Da, J., Jian-Feng, W.: A region-of-interest image coding algorithm based on EBCOT. Acta Autom. Sin. 36(5), 650–654 (2010) 4. Wang, Y., Gong, M., Wang, T., et al.: Projective analysis for 3D shape segmentation. ACM Trans. Graph. (TOG) 32(6), 192 (2013) 5. Zhang, J., Zheng, J., Wu, C., et al.: Variational mesh decomposition. ACM Trans. Graph. (TOG) 31(3), 21 (2012)

Design of Ship Monitoring System Based on Unsupervised Learning Li Guanglei, Zeng Hong ✉ , Jiang Dingyu, and Wang Hao (

)

College of Marine Engineering, Dalian Maritime University, Dalian, Liaoning, China [email protected]

Abstract. This paper aims to present an integrated methodology for designing of the ship monitoring system using machine learning algorithms. We collected ship monitoring data such as engine room monitoring system and AIS, and inte‐ grate and fuse data to form integrated ship information platform, the proposed methodology will train models using complete voyage data and then classify new data points using the improved Gaussian Mixture Model to get the most frequent operating regions of the main engine. Finally, we propose a scheme for perform‐ ance evaluation of equipment using principal component analysis (PCA). This work will provide a flexible but robust framework for the early detection of emerging machinery faults. And provides a new way of thinking for the design of engine room monitoring system. Keywords: Ship big data · Machine learning · Performance monitoring

1

Introduction

Nowadays, the intelligentization of ships has become an inevitable trend. In order to make the ship, management and services more efficient, more energy saving, more security and environmental protection, It is necessary to develop a ship monitoring system with environmental perception [1]. The ship monitoring data has the characteristics of multiple data formats, different sampling frequency and system time. It is difficult for marine engineer to find out the inherent relations and rules in the existing data, however, the judgment of the perform‐ ance of ship equipment mainly by the relevant experience may cause unnecessary or even irreparable damage [2]. How to use data mining technology to extract the perform‐ ance of ship system and equipment from ship big data has become an important issue in the research of intelligent energy efficiency. At present, Sang, L.Z. et al. designed a method to restore the trajectory of an inland waterway ship based on AIS data [3]. Perera et al. used PCA for clustering the working conditions of the marine main engine, and used the relationship between the speed, power, speed and draft data to describe the performance of the diesel engine [4]. C. Gkerekos et al. presented an integrated methodology for the monitoring of marine machinery using vibration data [5]. At present, there is less research on the ship monitoring system by using data mining technology and integrated ship data.

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This work presents an integrated methodology for evaluating the performance of the main engine(ME) based on data of ship monitoring system during a complete voyage. A suitable improved GMM model is used to cluster the engine speed and fuel quantity index, and new methods to evaluate the performance of ME under different working conditions is designed.

2

Data Fusion and Feature Extraction of Ship Machinery

The ship monitoring system has the problem of multi-sensor data fusion, we adopted the time of the engine room monitoring system as standard, and the time axis is unified by the interpolation method [2]. Special analysis and processing are carried out for other missing values, repeated data, and special symbols. The whole technical route is shown in Fig. 1.

Fig. 1. The overall process of ship data machine learning modeling

3

Cluster Analysis of Ship Navigation Data

In order to make the data representative, the AIS data are clustered and data visualized as shown in Fig. 2. The data sets is selected which include three state of ship, such as mooring, maneuver and speed navigation [6]. 256832 groups of data are obtained, and the following analysis and judgment are carried out.

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Fig. 2. Visualization analysis of AIS data

3.1 Gaussians Mixture Model and Em Algorithm This section proposes to use GMMs to identify the most frequent operating regions of ME. This method is to cluster and navigation data [7]. Suppose ship { (1) ship(n)performance } , subject to Mixture Gaussian distribution, monitoring data Y = y , … , y [ ]T is random observation data of D dimension. And the data set obeys y(i) = y1(i) , … , y(n) d a Gauss mixed model of a K component [9]. The model expression is: f (y(i) |k, 𝛩) = p(y(i) |k, 𝛩) =

k ∑

aj 𝜙(y(i) |𝜃j ), 1 ≤ i ≤ n, 1 ≤ j ≤ k

(1)

j=1

aj is a mixture ratio, k is the Gauss fraction of the mixed model, and the probability density of the j component of the Gauss component is 𝜙(y(i) |𝜃j ), so it can be shown as: fj (y(i) |𝜃j ) = 𝜙(y(i) |𝜃j ) =

1 |−1

| (2𝜋)d∕ 2 |𝛴j | | |

) ( 1 exp − (y(i) − 𝜇j )T 𝛴j−1 (y(i) − 𝜇) 2

(2)

𝜃j = (𝜇j , 𝛴j ) is the mean and covariance matrix of the first j Gauss probability func‐ tion. The parameters of model are iteratively solved by using EM algorithm to solve the mixed model likelihood function criterion, and the parameters in the model are esti‐ mated. In general, the E step is to calculate the conditional expectation of the full like‐ lihood function under the condition of the current parameter K. M step: expect to update the parameters by maximizing the E step conditions.

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3.2 Clustering and Recognition of the Operating Area of the Main Engine The selected ME speed and fuel rack scale represent fuel consumption and power. In particular, EM algorithm may converge to a local minimum or a saddle point in some cases. Therefore, the initial mean and variance value of GMM should be selected prop‐ erly [7]. The optimized parameters are obtained through the learning of the model, and the test data are clustered for example as Fig. 3. Analysis of Gauss mixed model and Em algorithm based on the selected data, the training set is trained to get a model that can fit the working state of ME better [4] (Table 1).

(b)

Fig. 3. The result diagram of GMM. (a) The result diagram of training set cluster. (b) the result diagram of test set clustering

Table 1. Clustering center of each main engine condition ME work condition ME speed/(r min−1) ME fuel pump index/%

1 0.01 0

2 3 166.07 121.26 79.86

68.83

As shown in Fig. 3, it is obvious that two features are clustered in three operating regions. The parameters of cluster 1are all close to 0, less than others, we considered it as the mooring condition. The speed of cluster 2 (96% of maximum continuous speed) is greater than that of the 3 cluster (73% of the maximum continuous speed). It is obvious that fuel consumption is negatively correlated with engine speed, because when the diesel engine fuel consumption rate of the lowest rated speed 75%–85%, the ship speed in the deceleration state, while the speed is raised, the oil consumption rate is closer to the lowest point [8]. Obviously, it is very meaningful to identify different stages of ship operation and give the best combination of speed and fuel consumption according to the data analysis. In addition, clustering and developing advanced ship performance and navigation mathematical models is very important for overcoming the challenge of shipping industry facing emission control.

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Design of Intelligent Ship Performance Monitoring and Evaluation System

The classification model of ME can identify the state of engine in a certain precision. At the same time, the data are extracted and analyzed from individual model or two. In this state, 5 characteristics are analyzed, such as ME speed, the fuel rack scale, navigation speed, the tipping value, the average draft and so on. 4.1 The Establishment of PCA Evaluation Method for Intelligent Ship This performance evaluation of intelligent ships is based on the theory of PCA, it can simplify and reconstruct the parameters of large numbers of ships. The method is processed by standardization, reduction and correlation, and the independent multi‐ variate evaluation index system is generated by clustering. The overall technical route is shown as Fig. 4. Start The Process of Intelligent Ship raw data pretreatment. Normalization of normal distribution of data The feasibility test of PCA

Solving the correlation coefficient matrix Recalculation The principal component expression N

Y Change evaluation system N

Decorrelation Y Determine the number of the principal components

Construction of PCA evaluation index

Principal component factor load

Construction of comprehensive evaluation index

Cluster analysis of intelligent ship index

End

Fig. 4. Intelligent ship principal component evaluation system

The advantage of this method is to analyze the load matrix of the PC factor of the ship. The design of the optimal evaluation system is put forward through the analysis of this PCA system, and the comprehensive evaluation function of the PCs is used to evaluate the performance of the ship [8]. What’s more, through multi ship information fusion, it can conduct horizontal comparative analysis among intelligent ships, providing a useful reference for the development of intelligent ship performance monitoring and evaluation.

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The advantage of this method is to analyze the load matrix of the PC factor of the ship. The design of the optimal evaluation system is put forward through the analysis of this PCA system, and the comprehensive evaluation function of the PCs is used to evaluate the performance of the ship [9]. What’s more, through multi ship information fusion, it can conduct horizontal comparative analysis among intelligent ships, providing a useful reference for the development of intelligent ship performance monitoring and evaluation. 4.2 Ship Multi Sensor Fusion and Scene Perception Ships multi-sensor data fusion is of great significance. Model based on the data fusion can perceive the environmental factors of ship operation, and give more practical and accurate the warning and advice. As the data overview of a complete training ship voyage is showed in Fig. 5. It is obvious that there will be a short extremum of speed when sailing in strong winds and waves, while great fluctuations in draught and heeling. We can see the state of the heeling value fluctuates near 6.5311 during the navigation speed is near 0. In fact, the heeling value is 0 at this state. However the change trend of the monitoring value can really reflect the state of the real ship, so the value is directly chosen as the basis for analysis. At a steady speed navigation, the difference rate of the heeling value is small. there will be a transient jump and a sudden decrease in the engine speed and the fuel pump index. This is the “flushing” operation of the ship to maintain the performance of ME in a long and low speed navigation state [10], of course, this is different from the state of mooring and motor navigation. In the state of motor naviga‐ tion, the average draught and heeling value fluctuated for a period of time. fuel oil supply increases, speed increases. At this time, the navigation speed fluctuates for a period of time, and the difference rate of the speed is more than the normal.

Fig. 5. The distribution of characteristic data

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Of course, these indicators only represent a certain performance index, but this is just a simple example. However, this is a simple example, and further research is needed, including the sea condition index, such as wind and wave flow, to make a more accurate assessment of the performance of ship.

5

Conclusions

In the background of marine big data of the ship, there are a lot of complex data in the traditional engine room monitoring and alarming system, it is difficult for marine management to find the hidden relationship behind these data. This study proposed and designed the evaluation method of ship machinery performance through machine learning, which can be learned in different modes according to the different external factors such as the time of use of the machinery and the sea condition. It can evaluate machinery performance more accurately and find out the source of the fault accurately. It can also be used for reference to improve the safety of ship navigation and the effi‐ ciency of ship operation, error of accurate positioning sensor and auxiliary decision. Acknowledgements. The study was supported by “the Fundamental Research Funds for the Central Universities”, (No. 3132016316). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

References 1. Abramowski, T.: Application of artificial intelligence methods to preliminary design of ships and ship performance optimization. Naval Eng. J. 125(3), 101–112 (2013) 2. Huang, D., Zhao, D., Wei, L., et al.: Modeling and analysis in marine big data: advances and challenges. Math. Probl. Eng. (2) (2015) 3. Sang, L.Z., Wall, A., Mao, Z., et al.: A novel method for restoring the trajectory of the inland waterway ship by using AIS data. Ocean Eng. 110, 183–194 (2015) 4. Perera, L.P., Mo, B.: Data analytics for capturing marine engine operating regions for ship performance monitoring. In: The International Conference on Ocean, Offshore and Arctic Engineering (2016) 5. Gkerekos, C., Lazakis, I., Theotokatos, G.: Ship machinery condition monitoring using vibration data through supervised learning. In: The International Conference of Maritime Safety and Operations (2017) 6. Pallotta, G., Vespe, M., Bryan, K.: Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction. Entropy 15(6), 2218–2245 (2013) 7. Wang, W.B., Zhong, R.T.: A clustering algorithm based on Greedy EM algorithm learning GMM. Comput. Simul. 24(2), 65–68 (2007) 8. Perera, L.P., Mo, B.: Marine engine operating regions under principal component analysis to evaluate ship performance and navigation behavior. In: IFAC Conference on Control Applications in Marine Systems (2016) 9. Gao, X., Yan, Z.: Comprehensive assessment of smart grid construction based on principal component analysis and cluster analysis. Power Syst. Technol. 37, 2238–2243 (2013) 10. Sun, F., Huang, L.Z., Liu, Y.F., et al.: A method of evaluating diesel engine performance by using data mining technology. J. Dalian Marit. Univ. 43(3), 83–88 (2017)

Users Research of Ice and Snow Theme Games in the Context of Virtual Tourism Zhu Ran ✉ (

)

Harbin University of Commerce, Harbin, China [email protected]

Abstract. From the perspective of user experience, this paper summarizes the user research ideas for the design of virtual ice and snow tourism commemorative games. In theory, it provides guidance methods for the follow-up of ice and snow virtual souvenir design users. In practice, it establishes local ice and snow tourism souvenirs through the research of typical users. The quality level of interaction design meets the needs of visitors with multiple sensory experiences, thus further enhancing the local tourism image. According to the definition and division of the current virtual tourist souvenirs, firstly, based on the relevant theory of Lene Nielsen user model, the basic steps of the user construction of the virtual ice-snow travel memorial game are designed. Based on the five elements of user evaluation proposed by Schmidt, the survey, questionnaire and other corresponding infor‐ mation are determined. The content of the steps. Through the research of users, a research route for a virtual memorial game of ice and snow tourism is drawn up. The method of researching users in the design of ice and snow tourism virtual memorial game is presented, which provides ideas for the subsequent virtual ice and snow game interface design. Keywords: User experience · Virtual tourism games · Ice and snow tourism

1

Introduction

The product design method based on the interaction concept changes the original oneway communication mode of design information by establishing a typical user model, and forms an effective two-way information communication between the designer and the user, the product and the user, which is in line with the user in a true sense. 1.1 Virtual Travel Game Features Virtual travel games have two characteristics. First is surreal; this is the biggest advantage of virtual travel games. By virtualizing the three-dimensional and multidimensional environment, people can create an “immersive” realism, and users’ various experience needs can be satisfied through virtual scenes. The second is the repeated arousal of experience. One of the shortcomings of traditional tourist souvenirs is that the shape solidification and the function is single. As time goes by, tourists lose their

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sense of freshness to the souvenirs, and they will gradually forget the experience of tourism. The effect of virtual tourist souvenirs is dynamic. 1.2 Virtual Ice and Snow Travel Theme Game Content The virtual travel theme game is an entertainment information product suitable for a handheld terminal developed by a tourist (government, institution or enterprise), and uses virtual reality technology to implement reality or surreal landscape according to game plot arrangement and level setting. A commemorative product of scene reproduc‐ tion, scene restoration, and cultural and cultural communication in tourism. As a virtualized display platform for tourism resource information, virtual tourism has built a virtualized scene that integrates the reality and virtual landscape features with the help of mobile internet and interactive technology. The theme-based virtual travel game formed based on virtual tourism spreads the landscape and human culture of the tourist destination through the game plot. To sum up, the content of the ice and snow tourism theme game can be divided into several parts, natural landscape and humanities characteristics, local specialties, historical and cultural values have produced a “deep immersive” vivid feeling. 1. Ice and snow natural landscape reproduction Visitors enter the virtualized game scene through handheld terminal devices. By simulating the real-life ice and snow landscape and interactive operation mode, visitors can experience the game while experiencing the game, and let the tourists change through different scenes in the game. There is a feeling of “immersive”, which evokes the ice and snow travel experience of tourists from the creation of the atmosphere of the game environment. 2. Ice snowman text reproduction Ice and snow culture is based on the scene of ice and snow, showing the character‐ istics of ice and snow from three aspects: local customs display and ethnic characteristic culture and historical and cultural values. In conjunction with the game’s plot and corre‐ sponding scenes, visitors can participate in local customs and national traditional activ‐ ities. Through the guidance of the plot and interactive operation, visitors will have a more realistic understanding of the relatively unfamiliar customs and habits, empha‐ sizing the participation of tourists.

2

The User Experience in the Virtual Ice and Snow Travel Theme Game

2.1 Virtual Ice and Snow Tourism Souvenir User Experience Level According to Donald Norman’s three levels of user experience, the user’s experience is divided into instinctive levels: people have the instinct of animals, behavioral levels: responsible for the actions and actions of people, reflection level: the part responsible

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for logical thinking. These three levels cover all of the user’s behavior and thinking areas. Through these three levels, the user finally forms a comprehensive experience of the experience. For the virtual ice and snow tourism souvenirs, the user’s final experience is built around these three aspects: First, the virtual tourist souvenirs mainly enable visitors to experience beyond the real experience through the visual and tactile aspects. Through the restoration of real ice and snow scenes and the visual display of ice and snow culture, they participate in the game through the touch screen form, and carry out a more realistic experience. The senses really infect visitors, bringing the most direct feeling. Secondly, on the behavioral level, visitors can realize the ice and snow landscape and the ice and snow culture through the interactive operation of virtual games, through the operation of visual effects and game modes, which are naturally affected from the behavioral level and can bring the scenes appearing in the game. The mission has a clear understanding of the ice and snow culture. Finally, through the participation of tourists in the game, the user’s stickiness grad‐ ually generated. Through the familiarity of the game content, the knowledge of ice and snow and the emotional experience are gradually improved, and the virtual and reality are combined to form a high-level identity. Combine your actual travel experience with the virtual experience to form the highest level of experience. When the tourists continue to advance through the game, they finally reach the emotional recognition of the ice culture and tourism. 2.2 Virtual Ice and Snow Tourism Souvenir User Experience Target The user experience itself refers to the psychological feeling that the user establishes the product during the use of the product or the service. This psychological experience involves all aspects of the interaction between the person and the product, the person and the program, and the person and the product, including the experience. Emotions, preferences, cognitive impressions, physiological and psychological reactions, behav‐ iors and achievements. By definition, different types of tourists obviously have different interest preferences and participation levels for ice and snow tourism. Therefore, the design goal of the user experience of virtual ice and snow tourism souvenirs is to emphasize the layering of experience, for users with different needs, the theme of ice and snow. The content setting of the travel game design can be different. The function of the product should not only stay in the game scene to provide travel consultation, display the tourism landscape, but also emphasize that the user has fun through the participation of the game. It should also emphasize the pleasure of the users through the scene building, the completion of the game task and the interaction of the role of the game. It has a more comprehensive experience on the ice and snow sightseeing landscape and the humanistic characteristics.

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Virtual Ice and Snow Tourism Souvenir Users Research Ideas

This article intends to use the ice and snow virtual mobile game as a research object to design a virtual ice and snow game. To test the experience of visitors on virtual ice and snow tourism souvenirs, the following will elaborate the design research ideas: 3.1 Typical Ice and Snow Tourist Souvenir User Model When building a typical user, first, it is necessary to clarify the number of consumers of virtual snow and ice travel souvenirs. Otherwise the tourists’ experience related to ice and snow tourism generally does not reproduce or be completely copied. Due to the subjective differences of individual situations, the experience changes with time, and the uncontrollable dynamic factors of the environment, it is impossible to cover all the design. Types of tourists, but can be studied from a common point of view, that is, the so-called establishment of a typical user model, providing a reference for user needs for the design of subsequent products. A typical user is a collective representation of a group of visitors. 3.2 Step Design for the Establishment of a Typical User Model In this step, we applied the “10-step character method” established by Lene Nielsen, due to the construction of this method. The user model takes a relatively long time, so we simplify it with a fast, iterative approach, so that we can quickly understand the situation of a typical user by typical user hypothesis - typical user research - typical role construction - related story building to build a typical souvenir uses a user model. 1. Typical user type assumption In the early stage of the survey, we first positioned the user as the winter to the snow city experience through the typical user hypothesis. Ice-themed tourist tourists. In turn, visitors are divided into two basic categories: shallow sightseeing and deep participation experience. Shallow sightseeing tourists are mainly curious about the natural scenery of ice and snow. They hope to experience the novelty of natural landscape through tour explanation and visit. They have a certain experience but not deep in the ice snowman cultural tour, and are restricted by objective reasons such as capital, time and travel route. The deep-seated experience type tourists’ travel needs have not only stayed at the viewing level, they need to complete the travel experience through personal experience. At the level of thinking, their travel experience is somewhat exploratory. 2. Typical user research and construction For these two different types of tourists, tourists are mainly investigated through qualitative interviews and quantitative questionnaires. The focus group interviews to understand the specific behaviors of their travel process and the feelings of ice and snow landscape, ice and snow culture. In the interview

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process need to pay more attention to how users view, use virtual commemorative prod‐ ucts, how to interact with products, from a wider range The angle to understand the tourists’ experience and experience of ice and snow and the corresponding needs. This is a relatively continuous process. According to the user experience evaluation system proposed by Bernd H. Schmitt, namely, sense, emotion, thinking, behavior, and association, these five aspects are regarded as the first-level elements, and then in the respective The five elements are expanded to find specific secondary elements, and the secondary elements are sent out in the form of questionnaires. Through the choice of tourists and the selection of factor questions, they can investigate their views on the virtual game of ice and snow tourism. The five elements and extensions of the virtual ice-themed game user experience are shown in Table 1. Table 1. User experience five elements and expansion content of virtual ice and snow theme games. Primary factor Sense

Emotion Reflection Behavior Relation

Primary factor expansion Ice and snow theme (cold natural scenery or ice and snow cartoon form) Visual shock (the scene is great, attractive) Interface surrealism reduction (introduction to cold landscape and natural attractions) The character of the game (with a strong sense of substitution) Know other tourists who have common interests and form a team Have a certain degree of difficulty and challenge Understanding the ice and snow culture through the task Simple and easy to operate Fast update Consistent with the tourist scene The plot of the game is related to the humanity of ice and snow Have the characteristics of minority nationality

This study intends to conduct a survey of users through these five aspects of design questionnaires. Through the quantitative scoring (Likert scale score) to understand their experience and hopes for virtual ice and snow travel souvenirs, for the subsequent virtual game design ideas, for the higher scores will be the main focus of ice and snow tourism theme game design. 3. Related plot establishment In the previous focus interview phase, each interviewer could tell the special stories and experiences in their ice and snow tourism, and combined with the five aspects of the quantitative questionnaire to construct a typical user model.

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Conclusion

In summary, by applying different kinds of interactive technologies, a virtual souvenir development approach can be explored from the product development level, so that the virtual ice and snow tourism souvenirs can be based on the aesthetics of traditional techni‐ ques and rely on modern technology. Ice and snow tourism should focus on creating a digital brand image and creating a unique brand image through interactive technology. The interactive multi-sensory experience is not only a design method but also a trend of future ice and snow tourism development. The interactive souvenirs and the field tourism land‐ scape complement each other, changing the angle of simple “viewing” in the past, and enriching various sensory and touch display modes. It satisfies people’s needs for ice and snow culture and historical humanities and cultural information. Acknowledgments. This thesis is a stage achievement of the Project (No. 17YSE380) of the Heilongjiang Province Society Scientific Fund. This thesis will never accomplish without all the invaluable contributions selflessly from my group members. It is my greatest pleasure and honor working with my group. I am deeply grateful to them.

References 1. Zhu R., et al.: Design strategies of ice and snow tourism souvenirs under the background of interaction design. Design. 132–133 (2016) 2. Xiong, W., Ye, L.: Research on function evaluation of virtual tourism website in China. Human Geogr. 154–160 (2011) 3. Li, C., Cao, B.: Design ideas and practice of tourist souvenirs in the context of “Internet plus”. Light Textile Ind. Technol. 25–27 (2016) 4. Wang, X.: Research on user experience design based on “Internet +” tourism APP. Sci. Technol. Inf. 1–3 (2016) 5. Yin, Z., Yang, Y.: A quantitative approach to user experience. HHME 2008 (2008)

The Exploration of Multiplatform 2D Game Development Yuting Yang1 ✉ and Houliang Kang2 (

)

1

2

Culture and Tourism College, Yunnan Open University, Kunming, 650000, China [email protected] College of Humanities and Art, Yunnan College of Business Management, Kunming, China

Abstract. With the development of user’s requirements, the users have become increasingly demanding on the games which can support various platforms. These characteristic can meet the demands of users to play games anywhere. Further‐ more, 2D games still occupied the absolute predominance in domestic mobile game market, and have less developing difficulty and shorter developing periods than 3D games. Therefore, we design and develop a 2D shooting game based on Unity 3D which can support multi-platforms. The game includes three parts: game data, game logic and special effects. Through using the particle system, we enhance the game’s sense of reality and interactivity. The whole game supports different operating systems and hardware environments including Windows, Flash, Android and iOS etc. Keywords: 2D game · Shooting game · Unity 3D · Multi-platform

1

Introduction

With the continuous scale expansion of game industry and the progress of game devel‐ opment technologies, by June 2014, the China Game Industry Report displays that the number of China’s game players (including online game market, mobile game market and PC game market) has already reached 400 million. Among them there are about 130 million client game players with 3.7% year-on-year growth and 51.5% market share; about 300 million page game players with 6.5% year-on-year growth and 18.5% market share; and 330 million mobile game players with 89.5% year-on-year growth and 25.2% market share [1]. With the development of user’s requirements, the players are experiencing a process from client online games (at a fixed playing site) to page games (multi-site login and enter the game fast and conveniently) and then portable mobile games (the game is portable and can continue game process at any time). In addition to the mobile games, the client online games and web page games are releasing their mobile versions or using cross-platform methods to solve the game access problems on portable terminal devices [2]. Therefore, the games which can support various platforms should be designed and

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developed to better meet the game players’ demands and adapt to the development of the game industry. Furthermore, 2D games still occupied the absolute predominance in domestic mobile game market, and have less developing difficulty and shorter developing periods than 3D games. Therefore, we design and develop a 2D shooting game based on Unity 3D which can support multi-platforms. The game includes three parts: game data, game logic and special effects. The game data includes multimedia data, background layers data and sprites data; the game logic is mainly responsible for game interface, game interactivity and sprites’ behaviors setting. The special effects of the game mainly obtain better battle effects through the Particle system, and enhance the game’s sense of reality as well. The whole game system has a nice interactivity, and supports different operating systems and hardware environments including Windows, Flash, Android and iOS etc.

2

Unity 3D

Unity is a cross-platform game creation system developed by Unity technologies, including a game engine and integrated development environment (IDE). It is used to develop video games for web sites, desktop platforms, consoles, and mobile devices [3]. Unity first announced only for Mac OS at Apple’s Worldwide Developers Conference in 2005. Now, it has been extended to target more than fifteen platforms, including Black‐ Berry 10, Windows Phone 8, Windows, OS X, Android, iOS, Unity Web Player, Adobe Flash, PlayStation 3, PlayStation 4, PlayStation Vita, Xbox 360, Xbox One, Wii U, and Wii [4]. So that, developers can concentrate on developing games without worry about the problem of which platform they can support [5].

Fig. 1. The framework of unity

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Unity is a real component-based game engine. It includes game logic, game actor, input, AI, steering behaviors, network, 3D rendering, persistent data, path finding, GUI and many other components [2]. Figure 1 illustrates the framework and relationship between different components of Unity.

3

2D Shooting Game Framework

We consider the characteristics of PC games, online game and mobile game during the whole game developing. In order to get the same effect on different platforms, we divide the game framework into three parts: game data, game logic and special effect. Game data includes multimedia data, background layers data and sprites data. Game logic is responsible for the interaction between game and user, including setting sprites behav‐ iors, input hardware and collision detection. Through using particle system, we add some special effect for our game to enhance the sense of reality and immersion. Figure 2 illustrates the framework of the whole game.

Fig. 2. The framework of 2D shooting game

4

The Detail of Implementation

4.1 Game Data The game data includes multimedia data, background layers data and sprites data. • Multimedia data: Multimedia data includes background music and sounds of special effects in game. • Background Layers data: In order to get an illusion of depth and enhance the sense of background hierarchy, we set four different deep layers for background and move

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them at different speeds. The four layers are background with the sky, background with the smaller floating islands, Middle-ground with bigger floating islands and foreground with players and enemies. Figures 3 and 4 illustrates the characteristic of the four deep layers.

Fig. 3. Four deep layers in 3D

Fig. 4. Four deep layers in 2D

• Sprites data: Sprites data includes game actor, enemies, projectile for actor and arrow for enemy. Figure 5 illustrates the four main game sprites.

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Fig. 5. The four main game sprites

4.2 Game Logic The game logic is mainly responsible for creating and destroying sprites, setting move‐ ment mode and collision detection for actor and npc, and scrolling background layers at different speeds. (1) Sprites Creation and damage The steps for creating sprite are the same. The details are as follows: Step 1. Import the texture. Step 2. Create a new sprite in the scene and name it. Step 3. Set the image on the sprite. Step 4. Set it z position with 0 to meet the character of 2D game. Step 5. Add a “Rigidbody 2D” component to tell the physics engine how to handle the game object. Furthermore, it will also allow collision events to be raised in scripts. Step 6. Add a “Box Collider 2D” component to add a 2D box collider and set an appropriate values to size. Step 7. Set the scale so it will look good. In order to use the same sprite without create and set again, we drag it in to Prefabs folder. Then, if we need to use the same sprite next time, we just copy it from Prefabs to scene. Prefabs can be seen as a class in a programming language, which can be instantiated into game objects. (2) Game Scripts We create many scripts to control actor, enemies and background. The details of these scripts are as follows: • PlayerScript: It sets the arrow keys to move the boat. It attaches the “WeaponScript” to let player to shot and call the “HealthScript” to damage the boat when it meets the enemy or arrow fired by enemy.

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• EnemyScript: It uses to trigger the projectile at each frame. The enemies can auto fire. • MoveScript: It is used to set the move speed and direction of game object and can be reused in another context. • HealthScript: It is used to handle total hit points, collisions and damages of game object. If two 2D collider boxes of game object meet to each other, the two game objects should be damaged both. • ShotScript: It sets a trigger collider to the projectile. The trigger collider raises an event when colliding, so that we can call the “HealthScript” to damage the projectile and enemy when they trigger the colliding event. It attaches the “MoveScript” also, so our shots will move. • WeaponScript: Its purpose is to instantiate a projectile in front of the game object it is attached to. • ScrollingScript: In order to make it short, we move the background layers at different speeds (i.e. the farther the layer is, the slower it moves). If done correctly, this gives an illusion of depth. In order to add the parallax scrolling effect to our game, we will have two scrolling: • The player is moving forward along with the camera. • Background elements are moving at different speeds (in addition to the camera movement). We use list to store two backgrounds (background with the smaller floating islands and Middle-ground with bigger floating islands) to process scrolling. If the camera meets the boundary of background, we retrieve the first background stored in the list and change its coordinate to be after the last one in the list. Then, we put it at the last position of the list. • ParticlesScript: Particles are basically simple sprites that will be repeated and displayed for a very short time span. We will make an explosion that is going to be used when an enemy or a player is destroyed. This involves to: • Create a particle system of explosion. • Instantiate and play it when needed. • SoundEffectsScript: We add sounds in our project to improve the special effect. We use the script to trigger the sounds at the right time in the game.

5

Results and Discussion

The whole game we implemented is based on Unity and C#. We use C# to implement the game logic and combine all the game data, game logic and game effects together by using Unity. We tested the game on a Lenovo Y430, equipped with a 2.0 GHz*2 Intel processor, 2 GB of RAM memory and the NVIDIA GeForce 9300 M GS for hardware 3D acceleration. Figure 5 illustrates the start screen of the game. Figure 6 illustrates the start screen of the game. Figures 7 and 8 illustrate the detail and effect of the game.

The Exploration of Multiplatform 2D Game Development

Fig. 6. The start screen of the game

Fig. 7. The running result of the game

Fig. 8. The special effect in game

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Acknowledgments. This research was partially supported by the Scientific Research Fund of Yunnan Education Department (2018JS748).

References 1. China Gaming Industry Report, China Gaming Industry Report from January to June (2014). http://www.cgigc.com.cn/201408/200965967488.html 2. Shang, H., Zheng, Y.G.: Empirical research on the present situation of online game industry. Reform. Strategy 25, 166–169 (2009) 3. Unity 3D, Unity Manual (2014). http://docs.unity3d.com/Manual/ 4. Wiki, Unity (2014). http://en.wikipedia.org/wiki/Unity3D 5. Chen, J.: Research and implementation of a cross-platform mobile online game based on Unity 3D game engine. Master’s thesis. Zhong Shan University (2013)

Evaluation of Underwater Target Scheme Based on Improved Back Propagation Neural Network Li-ting Lian(&) and Ming-ming Yang Unit 91388, Zhanjiang, Guangdong, China [email protected]

Abstract. It is important to evaluate the underwater target scheme scientifically for underwater weapons’ test. Many traditional methods have too many human factors on weight. In this paper, we have built the evaluation index system of underwater target scheme first, and then study the relationship between secondary indexes and evaluation result directly by an improved Back Propagation (BP) neural network. In order to settle the problem that BP neural network is apt to get local optimum, we have optimized the initial weight values and thresholds values by Particle Swarm Optimization (PSO) algorithm. According to compare with the result got by Analytical Hierarchy Process, we have testified that the improved BP neural network can settle the problem better. Keywords: Underwater target PSO


Evaluation
BP neural network

1 Introduction Underwater target is mainly for underwater weapons providing an alternative target that is closest to the physical features of real target such as sound, light and magnetic field. It is essential for us to evaluate the underwater target scheme effectively in order to complete the target support task better. Nowadays, Expert Evaluation Method (EEM) [1, 2] is most commonly used because of its peculiarities which it is simple and easy to implement. But, according to the process of assessment, there are too many human factors at the same time. Since the system and system’s evaluation has improved steadily, more and more new methods for evaluation have been used, for example, the subsection method, the grey evaluation method [3], Analytical Hierarchy Process (AHP) [4], the weight sum method, the neural network method and so on. In this paper, we have built the evaluation index system of underwater target scheme at first. Then we evaluated the underwater target scheme using PSO-BP neural network. The validity of the prediction model has been validated by instance.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 291–297, 2019. https://doi.org/10.1007/978-3-030-02804-6_39

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2 Establishment of Index System of Underwater Target Scheme In order to build the evaluation index system we should define it at first. In this paper, it means that various indexes which can evaluate the underwater target scheme scientifically are an organic whole and independent of each other usually. (a) Identify the target and the purpose of the evaluation The target of evaluation is the object of evaluation, which must be determined at first. The purpose of the assessment is to assess the ultimate goal of the evaluation work, which is the total traction and general direction of the whole evaluation process. It is very easy to cause deviations in the evaluation direction and make the comments unreasonable if we do not explicitly evaluate the purpose or understand its connotation. In this study, the target of evaluation index system of underwater target scheme is the target providing scheme and the purpose is to evaluate the scheme objectively, find the problems and defects in the scheme and optimize the implementation of the scheme. (b) Build the evaluation index system The target providing scheme is a preplan of the equipment departments at all levels to prepare the targets, which is the basic basis for the troops to carry out the equipment providing task. The evaluation index system consists of five primary indexes and sixteen secondary indexes. The evaluation index system’s structure has been shown as Fig. 1.

Evaluation index system of underwater target providing scheme A

Target providing capability A1

Objectivity of the supply target demand A11

Accuracy of supply target demand A12

Availability of supply target demand A13

Complete -ness of supply target demand A14

Reliability A2

Reliability Reliability of of the equipment guarantee performance schme A21 A22

Economy A3

Security of the operation process A23

Environment and support level A4

The degree of risk A5

The The The EnvironmDifficulty Risk situation situation situation ental Equipment of prevention of of troop of adaptabilit level providing measures commitme equipment material y A42 support A51 input nt input A41 A43 A33 A32 A31

Risk Target emergency protection response risk capacity A52 A53

Fig. 1. The chart for evaluation index system of underwater target scheme

(c) Decompose the evaluation index It is essential to introduce the signification of some indexes especially for the primary indexes. Firstly, the index of target providing capability that reflects the future demand for target providing task is very key to the estimation; secondly, the index of reliability is important to evaluate the scheme itself and the feasibility of implementation, which represents the degree of reliability of the whole target providing process in implementation; thirdly, index of economy s important to evaluate the

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feasibility of the scheme that represents the economic input necessary for the target providing to meet the test requirements; lastly, the index of environment and support level and the degree of risk reflects the necessary external support needed to achieve the intended target; and the risk of realizing the test demand respectively.

3 Evaluation of Underwater Target Scheme Based on PSO-BP 3.1

The Basic Principle of BP

Nowadays, the artificial intelligence has developed quickly; neural network have been applicated in many areas such as neural network expert system, pattern recognition, intelligent control, and intelligent prediction model and so on. Thanks to its properties like large scale parallel distributed processing, fault tolerance, self-organized learning and self-adaptive nonlinear approximation and classification etc., there are many successful examples like Hopfield Neural Network, Linear Neural Network [5] and BP Neural Network [6] which is the most effective network because of its simple structure and strong nonlinear learning ability. It is well known that BP neural network is a kind of feed forward network which has three layers. It can settle any nonlinear problem due to the activation function of hidden layer node that is Sigmoid. The evaluation of underwater target scheme can be considered as a nonlinear problem. We can choose BP neural network having only a hidden layers as the calculation model. The classical BP neural network model’s structure has been shown as Fig. 2. Where we choose the score of every evaluation index system of underwater target scheme as the input vectors; the output vector is the evaluation result. There is a pivotal disadvantage that BP algorithm is difficult to escape from a local optimum. The final predict result is sensitive to initial values that are stochastic. The improved BP model has been introduced to reduce the randomicity.

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Fig. 2. The structure chart of BP neural network which has three layers

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The bad robustness of BP neural network comes of that the network starting from different initial weight values and threshold values, and then different initial values may induce different training results. In order to get optimal initial weight values and threshold values, we choose PSO algorithm as its optimal algorithm. 3.2

The Principle of PSO-BP Algorithm

PSO algorithm [7] is an evolutionary technology that originated from a bird predatory behavior. This optimal solution is found through iterative searching. In every iterative step, ever particle updates its extreme value through tracking two extreme values. One is Pbest(k) means the single extreme found by each particle of iteration of k and the other is Gbest (k) that means the global extreme found by the whole particle swarm of iteration of k. The regulating rule of the position and the velocity follow Eqs. (1) and (2): vðk þ 1Þ ¼ wvðkÞ þ C1  R1  ðPbestðkÞ  xðkÞ þ C2  R1  ðGbestðkÞ  xðkÞÞ ð1Þ xðk þ 1Þ ¼ xðkÞ þ vðk þ 1Þ

ð2Þ

Where v(k + 1) and v(k) represent that the velocity is the particle in the iteration k + 1 and iteration k respectively. x(k + 1) and x(k) represent that the position is the particle in the iteration k + 1 and iteration k respectively. w represents the inertia weight, C1 and C2 represent accelerated coefficients that are both positive value with the range of [0, 2], R1 and R2 are two random numbers with the range of [0, 1]. The principle of PSO-BP is that we have PSO algorithm application in BP neural network’s learning mechanism. BP neural network’s optimal initial weight values and bias values have been mapped as the particles and the velocity of PSO algorithm respectively, and then regulate them according to formulas (1) and (2). The detailed procedures have been introduced infra. Step 1: Build the BP neural network ‘s topology and ascertain the key parameters such as the values of R, S1, S2 (see Fig. 2), training methods and so on; Step 2: Initialize the PSO algorithm, generate N groups of particles vector random that is xi ¼ ½xi1 ; xi2 ; . . .; xiR ; xiðR þ 1Þ  ði ¼ 1  NÞ.where the preceding R group s and the last group have been mapped as the weight values and bias values respectively, and then train the net to get root-mean – square error of the net; Step 3: ascertain the basic parameters of PSO algorithm such as inertia weight x, accelerated coefficients C1 and C2, the object accuracy and the fitness function; Step 4: Evaluate and update the particle swarm following the rule of PSO in order to get the optimal weight values and bias values by particle swarm iteration which make the error of net is the least; Step 5: start from the optimal values and train the BP neural network by learning samples. And then valid the networks forecast accuracy by testing samples.

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4 The Validation Test of Underwater Target Scheme’s Evaluation 4.1

Fundamental Parameters of BP Neural Network

We have compared the results calculated by the network with the results calculated by Analytical Hierarchy Process (AHP) in order to valid the forecast accuracy of PSO-BP neural network. In this paper, the BP neural network having three layers has been chose to settle this problem. We should determine the number of network’s three layers. The number of input layer is related to the secondary indexes of evaluation system. So the value of R (see Fig. 2) is equal to sixteen. The number of output layer is related to the result of evaluation. So the value of S2 (see Fig. 2) is equal to one. The number of hidden layer is important to the accuracy and learning efficiency of the network. We determine it by pffiffiffiffiffiffiffiffiffiffiffiffiffiffi the formula S1 ¼ R  S2 þ a in a general way. Where a is an integer between one and ten. The optimum value of S1 is 9 got by test time after time. Levenberg Marquardt (LM) algorithm has been chose to train the neural network to get good result. The selection of learning samples has great influence on the learning effect of neural network. We get fifteen groups evaluation data based on the evaluation index system. In this paper, we choose ten groups as training samples and other five groups as testing samples. The expected output data is evaluated by the method of AHP. The results are usually hundred mark system, which have been normalized for the convenience of calculation. 4.2

The Determination of PSO Algorithm’s Parameters

It is well known that particle swarm size is important to the velocity and time of the algorithm, we choose the value as 100; the dimension of particle is equal to the sum of vectors that need to optimize. The fitness function is root-mean-square error. The values of C1 and C2 are both equal to 2, the value of w is 0.8. 4.3

Study and Train the Network

We should train and optimize BP neural network according to the vectors of test samples, and then the evaluation results predicted by PSO-BP neural network will be compares with those calculated by AHP. For the sake of evaluating the precision of the forecast model, we choose the max relatively root mean square error ERR as the criterion, the equation has been shown as formula (3): ERR ¼ RMSðkÞ=maxðtðkÞÞ; k ¼ 1; 2; . . .; num

ð3Þ

Where RMS(k) represents the root mean square error of evaluation results; t(k) is the evaluation results calculated by AHP; num represents the groups of data and its value is equal to 5 for this example. It is clear that ERR of valuation results are less than 5%,

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which indicated that PSO-BP neural network can predict the evaluation result as well as the method of AHP (see Fig. 3).

Fig. 3. The comparison chart of evaluation result calculated by PSO-BP and AHP

5 Conclusions In this paper, we have used BP neural network to settle the evaluation problem of Underwater Target Scheme area. Comparing with other methods such as EEM and AHP, it presents advantages in simple and efficient calculation and avoids the influence of more human factors. As well as other neural network methods, there is good fault tolerance and strong generation. In order to escape from the local optimums, PSO algorithm has been used to optimize the initial weight values and threshold values. The high accuracy of model and good robustness have testified the validity of the method.

References 1. Zhang, S., Pan, D.: Fuzzy multi-hierarchic and synthetic evaluation of strategic cost management performance. J. Syst. Inf. 2(4), 659–665 (2004) 2. Luo, T., Ren, H.: Performance evaluation on power transmission system by multi-hierarchical fuzzy comprehensive evaluation method. Res. Equip. Technol. Armored Force 27(6), 2–8 (2006). (in Chinese) 3. Tang, Z., Sun, C., Liu, Z., Meng, D.: Research on efficiency evaluation for underwater acoustic countermeasure system based on grey hierarchy analysis. Acta Armamentarii 33(4), 432–436 (2012). (in Chinese) 4. Zhang, Z., Xu, B., He, Y., Liu, L., Wang, G.: Engine performance evaluation based on analytic hierarchy process. Acta Armamentarii 29(5), 625–628 (2008). (in Chinese) 5. Lian, L., Xiao, C., Yang, M.: Magnetic anomaly extrapolation based on linear neural network. In: 2011 3rd International Workshop on Intelligent System and Applications, Wuhan, China, pp. 166–169. IEEE (2011)

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6. Lian, L., Xiao, C., Liu, S., Zhou, G., Yang, M.: Magnetic field extrapolation based on improved back propagation. In: 2010 International Conference on Artificial Intelligence and Computational Intelligence, Sanya, pp. 64–70. Springer (2010) 7. Yang, M., Liu, D., Lian, L.: Particle swarm optimization for ship degaussing coils calibration. In: 2011 3rd International Conference on Computer Design and Applications, Xian, pp. 234–238. IEEE Computer Society (2011)

Cross-Linguistic Speaker Identification by Monophthongal Vowels Yuting Xu and Hongyan Wang(&) Shenzhen University, Shenzhen, People’s Republic of China [email protected]

Abstract. Humans can identify a particular speaker with considerable ease by fairly short speech segments, but tell with great difficulty which voice information cues the speaker identity. This study investigated if reliable identification of a speaker could be achieved solely by an individual vowel. In this research, ten monophthongs were selected as target vowels for an identification test, and these vowel stimuli were recorded in a /hVd/ word frame. The American, Dutch, and Chinese speakers were identified after each stimulus by native Chinesespeaking listeners. The results of the identification tests showed that in a crosslinguistic setting where both native and non-native speakers of English were identified with English, vowels facilitated the identification. The acoustic analysis also indicated that the higher-frequency formants demonstrated more inter-speaker variability than the lower-frequency formants. Further, the height feature of vowels was more important than the backness feature for crosslinguistic speaker identification. Results also showed that in comparison with non-Chinese speakers, Chinese speakers were better identified by Chinese listeners who shared the same native language with the speakers. Keywords: Forensic phonetics
Speaker identification Vowels
Higher-frequency formants


Limited speech data

1 Introduction We can easily recognize famous people (e.g., a singer or a political leader) only by their voices with remarkable accuracy. Experience also shows that we can typically identify a close acquaintance (e.g., a friend or a family member) over the telephone saying an utterance as short as a “hey”. Sometimes only one syllable is enough to trigger our response of recognition. Apparently, voice is one of the individual features by which we can be recognized next to other methods of biometric personal identification, such as dactyloscopy by finger print, iris analysis by the pupil of eye. So far we however, have not found any way to utilize this human-born talent into machine learning for robust speaker identification unless there is amount of data preparation in a special corpus. It has been reported that the overall accuracy of speaker identification decreases when the utterance length becomes shorter. The large amount of speech data for training and testing can therefore yield a good identification performance. However, in some cases where a large amount of speech data is not available due to a poor-quality © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 298–305, 2019. https://doi.org/10.1007/978-3-030-02804-6_40

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recording session or non-cooperative speakers, the identification system cannot obtain sufficient information to discern and identify a speaker. Or sometimes, because of limited time, rapid speaker identification is required. (Nagaraja and Jayanna, 2012). Vowels form the nucleus of syllables in most languages, and therefore, vowels as part of short speech, might contribute significantly to speaker identification. By Antal and Toderean (2006), the rank order of segments with the identification rate from the highest to lowest is as follows: vowels > nasals > fricatives > semi-vowels > stops. But it remains to be discovered which parameters of vowels contribute to a higher identification accuracy. As globalization continues to develop, people around the world have more opportunities to communicate with each other, and more languages are possibly to be spoken by people worldwide. Therefore, the development of cross-linguistic speaker identification is a challenging task. One of the observations in studies of crosslinguistic speaker identification has been that listeners are more accurate at identifying a voice in their native language than an unfamiliar second or foreign language. Nevertheless, no research has demonstrated whether listeners can still identify relatively well speakers who share the same native language when they are both foreign learners of the target language used in the identification experiment. The following research questions are addressed: (i) How well are the American, Dutch and Chinese speakers identified by Chinese listeners solely on the basis of monophthonal vowels of English? (ii) Which vowels are more effective for identifying the American, Dutch and Chinese speakers? Which acoustic parameters are more closely related with these vowels? (iii) Is the speaker identification in English by native Chinese listeners better for Chinese speakers of English than for non-Chinese speakers (i.e., the American and Dutch) of English? 1.1

Methods

12 American, 12 Dutch and 12 Chinese were recorded by a Tascam DR-06 recorder in front of a Sennheiser HD800 microphone with a sampling rate of 44.1 kHz. They were all university students, ±24 years old. Non-native speakers had not specialized in English and had not spent time in an English-speaking environment. To make the whole setup of the experiment much simpler, among 36 speakers 3 representative speakers (1 for each language group) were selected by 30 Chinese listeners (15 males and 15 females, ±22 years old). In each language group, 1 speaker with the identification accuracy best approximating to the average value was chosen as the speaker representative of their language group. Another 30 Chinese listeners (15 males and 15 females, ±22 years old) were asked to take part in identification tests with 3 representative speaker involved. All listeners were selected in the same pool as the Chinese speakers.

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The recording materials include both /hVd/ words and Harvard Sentences. The target vowel stimuli were embedded in a /hVd/ word structure to compare different contributions of vowels to speaker identification. 10 vowel stimuli were /i/ as in heed, /ɪ/ as in hid, /ɛ/ as in head, /æ/ as in had, /ɝ/ as in heard, /ʌ/ as in hud, /ɒ/ as in hod, /ɔ:/ as in hawed, /u/ as in who’d, /ʊ/ as in hood. In each question listeners were supposed to hear 1 Harvard sentence spoken by a target speaker followed by 3 /hVd/ words spoken by the American, Dutch and Chinese speakers, respectively (but the order of 3 alternatives was random for each question). Listeners’ task was to decide which 1 out of the 3 unknown speakers had the same voice as the target speaker. Every recording was played only for once and listeners must make 1 out of 3 alternatives for each question (one-alternative forced-choice test). We also used the linear predictive coding algorithm of Praat to measure the values of the parameters of vowels involved in this study, i.e., the first four formants (F1-F4), fundamental frequency (F0) and duration. We located the onset of a vowel at the start of the first periodic waveform shown in the oscillogram while the offset of the same vowel at the end of acoustic energy shown in the spectrogram (van Heuven and Gooskens 2017).

2 Results and Discussion 2.1

Results of Identification Tests

An inter-rater reliability test was conducted to examine whether 30 Chinese listeners differed significantly with each other on the identification performance. We typed the response variables (two possible variables, 0 or 1, i.e., correct and incorrect identification) of each listener towards each question into SPSS and conducted the reliability test accordingly. The Cronbach’s Alpha was 0.830 as shown in Fig. 1, well above the standard value of 0.7, indicating there was consistency between listeners’ performance in the identification tests.

Fig. 1. Reliability analysis-scale (alpha)

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The identification accuracies of 30 Chinese listeners were different, as shown in Fig. 2. The average means of the correct identification was 66.33% (STDEVA = 0.181596), well above the chance level (25% correct). A higher identification accuracy of 80% to 90% was achieved by some listeners. Although a fairly high identification accuracy with vowels was able to be achieved in this experiment, it was much lower than that reported by Antal and Toderean (2006) with a significantly high rate of 95.39%. It was possible that a higher identification could be achieved with vowels and thus the importance of vowels to speaker identification might be more significant than the present level.

Fig. 2. Identification accuracy broken down by 30 listeners

For Chinese listeners, the Chinese speaker (77.67%) was the most identifiable in comparison to Dutch (64%) and American speakers (57.67%), as represented in Fig. 3. The earlier research reported that due to the language-familiarity effect, listeners can identify better the speakers who were saying the native language the same with the listeners (Thompson 1987). The present study discovered that this language-familiarity effect also extended to the situation where speakers were saying a language learnt as a foreign or second language by both speakers and listeners. It was thus reasonable to explain why a higher correct identification rate was obtained towards the Chinese speaker by Chinese listeners. Some researchers gave their explanations towards this finding, indicating that foreign listeners might be sensitive to cues in their native language because they were well known of the sound systems of their interfering native languages. Conversely, native English-speaking listeners might not be able to pick up those cues (Wang and van Heuven 2006). Moreover, the identification accuracy towards the Dutch speaker was surprisingly higher than that towards the American speaker. This phenomenon might be explained by the assumption that both the Dutch speaker and Chinese listeners shared the same non-native English language background. In other words, both the Dutch speaker and the Chinese listeners were not native-born speakers of English and thus some common features of vowel pronunciation were both known to non-native English speakers. The above findings were

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supported by an analysis of variance (ANOVA) on the speaker nationality and correct identification, with speaker nationality as a fixed factor and correct identification as an independent variable. The analysis indicated a significant effect of speaker nationality for correct identification, F (2, 27) = 7.793 (p < 0.05).

Fig. 3. Identification accuracy broken down by nationality

Generally a rather good identification accuracy was obtained by use of vowels in the present work, well above the chance level (25% correct), but the selection of speech materials for speaker identification was particularly significant for some certain vowels, as shown in Fig. 4. By use of vowels hud and hid around 80% of correct identification and over 65% of identification accuracy for who’d and heard were obtained. Meanwhile, the vowels of heed, hawed, hood and had yielded over 60% of correct identification rate. Only one vowel achieved less than 60% of identification rate.

Fig. 4. Identification accuracy broken down by 10 vowels

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c) who’d Fig. 5. Deviation maps of the American, Dutch and Chinese speakers. The horizontal axis represents the normalized deviation from the average. In each map, the bars represent the deviation of the following features: F1, F2, F3, F4, F0, duration. These three maps represent the deviation values of 3 vowels embedded in the words hud, hid and who’d, respectively.

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Results of Acoustic Analysis

F0, F1, F2, F3 and F4 were quantified by the unit of hertz while the duration was measure by the unit of s, all of which were generated by Praat. The measured hertzvalues were then converted into bark-values by using Bark transformation produced by Traunmuller (1990), where F was the formant frequencies measured in hertz: Bark ¼ ½ð26:81  F Þ=ð1960 þ F Þ  0:53;

ð1Þ

A further examination was made qualitatively on the deviations of different acoustic parameters by use of the prototype model (Lavner et al. 2001). As shown in Fig. 5, the higher-frequency formants had more significant deviations from the average than the lower-frequency formants. In other words, the higher formants demonstrated more inter-speaker variability than the lower formants. This finding was in consistent with the results in the previous research. For example, Rose and Clermont (2001) discovered that F4 outperformed F2 in a speaker discrimination of same-speaker and differentspeaker tests by use of hello word pairs. Although a different test in our study was employed, i.e., an identification test, both of our research and theirs indicated more speaker-specificity shown by the higher-formants. Another observation was that the first formant had more significant deviations than the second formant. It explained that the high/low distinction of vowels was more important than front/back distinction in perceptual speaker identification. Moreover, F1 ranked as the one with the most significant deviations to the average, following closely behind F4, although F1 was constrained by signaling the identity of vowels. It might be due to the cross-linguistic setting involved. In the present experiment, we selected native and non-native speakers of English, who were all identified by English rather than their native languages respectively. For non-native speakers of English, their native languages might interfere with their pronunciation of vowels in English and this difference might provide dominant cues for listeners to identify them. Since the vowel identity was mainly realized by the disposition of the two or three formants, it was understandable why F1 was one of the most important parameters in cross-linguistic speaker identification. With reference to the other parameters of pitch and duration, they showed some difference in signaling the identify of the speakers, but their contributions were significantly less than the formants.

3 Conclusion The results of identification performance across Chinese listeners showed that the American, Dutch and Chinese speakers were identified effectively by Chinese listeners with vowels embedded in a fixed /hVd/ word structure. It was thus concluded that vowels were useful for speaker identification and utterances containing as much vowels as possible might lead to higher identification accuracy. Results of identification accuracy across ten monophthongs led us to conclusions that central vowels gained higher identification accuracy than back vowels; high and mid vowels had better identification rate than low vowels; lax vowels obtained higher correct identification results than tense vowels. If a

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choice had to be made which vowels to be used for speaker identification, it was advised to select the vowels /ʌ/ as in hud, /ɪ/ as in hid, and /u:/ as in who’d. Other vowels except for these three showed a lesser but still rather high identification accuracy at a similar level. Moreover, the higher-frequency formants (F3, F4) were more correlated with higher identification accuracy than lower-frequency formants (F1, F2). However, the first two formants, especially F1, demonstrated high inter-speaker variability in a cross-linguistic speaker identification, where both native and non-native speaker of English were all identified with English. The high/low distinction of vowels was more important than front/back distinction in perceptual speaker identification. It was also observed that the Chinese speaker was better identified by Chinese listeners. Thus, a conclusion was reached that speakers with the same native language background as the listeners were identified more correctly in a cross-linguistic setting, where both native and non-native speakers of English were all identified with English. Moreover, it was discovered that the Dutch speaker was better identified than the American speaker by Chinese listeners. Thus, the familiarity might extend to the situation where listeners shared the same non-native language background with the speakers. However, it was a tentative finding to be tested by future research of identification performances of American-speaking and Dutch-speaking listeners towards speakers in the same pool. Acknowledgments. This work was supported by the Ministry of Education Humanities and Social Sciences Planning Project (14YJA740036) and the National Social Science Fund of China Post-Financed Project (17FYY009).

References Antal, M., Toderean, G.: Speaker recognition and broad phonetic groups. In: Proceedings of the International Conference on Signal Processing, Pattern Recognition, and Applications, pp. 155–159 (2006) Van Heuven, V.J., Gooskens, C.: An acoustic analysis of English vowels produced by speakers of seven different native-language backgrounds. In: From Semantics to Dialectometry: Festschrift in Honor of John Nerbonne, vol. 13, pp. 137–147 (2017) Lavner, Y., Rosenhouse, J., Gath, I.: The prototype model in speaker identification by human listeners. Int. J. Speech Technol. 4, 63–74 (2001) Nagaraja, B.G., Jayanna, H.S.: Mono and cross lingual speaker identification with the constraint of limited data. In: Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, pp. 439–443 (2012) Rose, P., Clermont, F.: A comparison of two acoustic methods for forensic speaker discrimination. Acoust. Aust. 29, 31–36 (2001) Thompson, C.P.: A language effect in voice identification. Appl. Cogn. Psychol. 1, 121–131 (1987) Traunmüller, H.: Analytical expressions for the tonotopic sensory scale. J. Acoust. Soc. Am. 88, 97–100 (1990) Wang, H., van Heuven, V.J.: Acoustical analysis of English vowels produced by Chinese, Dutch and American speakers. Linguist. Netherlands 23, 237–248 (2006) Wang, H., van Heuven, V.J.: The interlanguage speech intelligibility benefit as bias towards native-language phonology. i-Perception 6, 1–13 (2015)

Study on Establishment and Proof of Inequality Based on Descending Dimension Method Qingpeng Ran(&) Basic Teaching Department, Yangtze University College of Technology and Engineering, Jingzhou 434020, China [email protected]

Abstract. In order to establish and prove the inequality easily, the descending dimension method is applied in it. The descending dimension method is applied to establish some inequalities, and the corresponding proof process is obtained. In addition the algorithm programmer is compiled. The descending dimension method can be applied in inequality research widely. Keywords: Descending dimension method


Inequality
Proof

1 Introduction At first, inequality is derived from some practical problems that need to be solved, especially in an age without calculus, and inequality becomes an important tool for solving the maximum and minimum values. With the gradual expansion of mathematical theory and practical problems, many inequalities emerge as the times require. The contents of inequality theory not only involve all fields of mathematical research, but also play an important role in daily life and other disciplines. Inequality has gradually become an important direction in mathematical research, and has made important contributions to the development and progress of mathematics and other disciplines. The research of inequality mainly focuses on proving, popularizing, constructing or establishing inequality. Dimensionality reduction is an important mathematical thinking method. It is ubiquitous in mathematical research. It is also an important basic method in the study of inequality. The parameter range determination in problem of constant inequality involves a wide range of knowledge and strong comprehensiveness. The problem of proving n (n  2) dimensional inequalities decline to k (1  k  n  1) dimensional problem, and then the problem can be handled through routine computation. One hand, the dimensionality reduction method can deal with the high dimensional problem, on the other hand, the optimal problem of inequality can be solved. This kind of method has been applied by many scientists. In 1900 Hilbert put forward the “Mathematical problem”, and put forward twenty three mathematical problems, where seventeenth problem is related to quadratic sum, that is whether the real coefficient semi-definite polynomial can be a form of square sum of several real coefficient rational functions, this theme © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 306–311, 2019. https://doi.org/10.1007/978-3-030-02804-6_41

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was proved by Artin in 1927. The purpose of this project is to extend a classical dimensionality reduction method and apply it to the study of inequality. Dimensionality reduction is a basic and important method for inequality research. It was developed in the late 1990s. It is very helpful for the establishment of analytical inequalities involving multiple variables. In particular, it is more effective when the problem is more difficult and involves the optimal value problem. Chinese mathematicians have also made many achievements in the study of inequality reduction. Prof. Yang Lu has used some dimensionality reduction methods when using computer programming to prove inequalities [1]. However, when the variables involved in the inequality are too many and contain many parameters, it is very difficult to solve these problems by using computers alone. For this reason, Yang Lu led his students Chen Ji, Li Guangxing, Wang Zhen, Wen Jiajin and so on to improve the dimensionality reduction method many times [2, 3]. At the same time, Professor Hooke not only established and improved the basic inequalities by using the dimensionality reduction method, but also applied these functions to the theory of function. Generally speaking, the use of dimensionality reduction method can not only overcome some difficulties, but also make the result better. Because of the many advantages of using the reduced dimension method to study inequalities, a large number of mathematicians at home and abroad have attracted a lot of attention. The theory of dimensionality reduction is constantly developing and growing, and it will play a greater role in the study of mathematics. This research mainly studies the extension of Prof. Li’s method and its application in inequality proving. First, a further study is made on the basis of the Prof. Li’s method. It is hoped that some useful extensions can be obtained on the basis of the dimensionality reduction method to make its application wider. Secondly, the Prof. Li’s method and its extension are used to prove some classical inequalities. Finally, the study of Li Prof. Li’s method and its extension are established and proved in some new ways. The application in inequality.

2 Descending Dimension of Inequality The descending dimension method of inequality can be described as follows: the proof of n ðn  2Þ dimensional inequality can be induced to that of kð1  k  n  1Þ dimensional inequality. And then the conventional means of proof is used to carry out proof, specifically speaking. Specifically speaking the descending dimensional method should be named as successive recursive induction of descending dimension. Set E  Rn ; F : E ! R is a n variate function. For inequality FðxÞ  0; x 2 E, Professor Hu refers that an important method of proving the equation mentioned above is to construct a proper function Fm that is monotonically decreasing, and the above inequality can be transferred to following expression Fm ðyÞ  0; y 2 Em  Rm : Because Fm ðyÞ  Fm1 ðyÞ 


 F1 ðyÞ, the inequality FðxÞ  0; x 2 E can be transferred to F1 ðyÞ  0; y 2 E1  R. It is difficult to construct the function column Fm. Professor Li put forward a method of constructing a kind of function column Fm, set FðxÞ ¼ Fðx1 ; x2 ; . . .; xn Þ is real value function with n variables on ½c; dn ! R.

308

Q. Ran

Set Fk ðxÞ ¼ Fðxk ; xk ; . . .; xk ; xk þ 1 ; . . .; xn Þ; ðk ¼ 1; 2; . . .; nÞ, suppose F(x) has one order continuous partial derivative. Theorem: F(x), Fk(x) are defined based on above method, for random d  x1  x2 


 xn  c, the following inequality is given: @Fk ðxÞ 0 @xk

ð1Þ

F1 ðxÞ  F2 ðxÞ 


 Fn ðxÞ

ð2Þ

and then

if inequality (1) is inverse, the inequality (2) is also inverse. Notes: The above lemma can also be expressed as: for random d  xn 


 x2  x1  c, if the inequality (1) is established, the inequality (2) is inverse, if Eq. (1) is inverse, the inequality (2) is established. The method of Professor Li can be applied to prove many famous inequalities, such as Maclaurin inequality, Jensen inequality, Chebyshev inequality, and Fan type inequality, and it is applied widely. The improved Prof. Li’s method can be applied to the following proof of inequalities.

3 Application of Descending Dimension Example 1: The famous inequality A-G-H is expressed by HðaÞ  GðaÞ  AðaÞ

ð3Þ

If r [ 0, a 2 Rnþ þ , n  2, the minimum value of all real number k that makes inequality ð1  kÞHnr ðaÞ þ kArn ðaÞ  Grn ðaÞ work is listed as follows: kmin ¼ sup f 0\t6¼1

Grn ðamin Þ  Hnr ðamin Þ jamin ¼ ðt; 1;


; 1Þ 2 Rnþ þ ; t 6¼ 1g Arn ðamin Þ  Hnr ðamin Þ

ð4Þ

Proof: If k [ k, the inequality ð1  kÞHnr ðaÞ þ kArn ðaÞ  Grn ðaÞ is proved to be established. Define D ¼ faja 2 Rnþ þ ; a1 þ


þ an ¼ ng, when a 2 D, An ðaÞ ¼ 1, we need to prove that when a 2 D the following inequality is established: FðaÞ ¼ ð1  kÞHnr ðaÞ þ k  Grn ðaÞ  0

ð5Þ

Study on Establishment and Proof of Inequality

309

Because FðaÞ is continuous and differentiable on D, therefore the minimum value of FðaÞ on D is boundary point or standing point. Set an ! 0, we can obtain the following equations: a1 þ a2 þ


þ an1 ¼ n;

0  a1 ; a2 ; . . .; an1  n

1 1 1 Hn ðaÞ ¼ ½n1 ða1 ! 0; 1 þ a2 þ


þ an 

Gn ðaÞ ¼

p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n a1
a2




an ;

FðaÞ ! k  kmin  0

ð6Þ ð7Þ ð8Þ

Therefore, if a is the boundary point of D, the inequality is ð1  kÞHnr ðaÞ þ kArn ðaÞ  Grn ðaÞ established. If a is the standing point of D, suppose that a ¼ ðu; v; . . .; vÞ 2 D, and then u þ ðn  1Þv ¼ n, u; v [ 0, the inequality (10) can be converted to the following form: ð1  kÞHnr ðu; v; . . .; vÞ þ kArn ðu; v; . . .; vÞ  Grn ðu; v; . . .; vÞ  0

ð9Þ

Set t ¼ uv, t [ 0, and the following expressions can be obtained: Hnr ðu; v; . . .; vÞ ¼ v0 Hnr ðamin Þ

ð10Þ

Arn ðu; v; . . .; vÞ ¼ v0 Arn ðamin Þ

ð11Þ

Grn ðu; v; . . .; vÞ ¼ v0 Grn ðamin Þ

ð12Þ

The two sides of inequality (12) are divided by v0 , and the inequality (14) can be converted to the following form: ð1  kÞHnr ðamin Þ þ kArn ðamin Þ  Grn ðamin Þ  0

ð13Þ

When t ¼ 1, the inequality takes “ = ”; when 0\t 6¼ 1,based on A-G-H inequality, the inequality (18) can be converted to the following form: k

Grn ðamin Þ  Hnr ðamin Þ Arn ðamin Þ  Hnr ðamin Þ

ð14Þ

Because k  k, the inequality (19) is established. Next we should prove that k  kmin if ð1  kÞHnr ðaÞ þ kArn ðaÞ  Grn ðaÞ is established. the inequality ð1  kÞHnr ðaÞ þ kArn ðaÞ  Grn ðaÞ can be converted to the inequality (14). Because t is random, k  kmin is established. The proof is end. In inequality ð1  kÞHnr ðaÞ þ kArn ðaÞ  Grn ðaÞ we set a ¼ amin , 0\t 6¼ 1. Example 2: The Wang-Wang’s inequality is expressed by Set 0\xi \1=2, i ¼ 1; 2; . . .; n, and the following inequality is expressed:

310

Q. Ran

HðxÞ GðxÞ  Hð1  xÞ Gð1  xÞ

ð15Þ

Proof: The inequality (15) is converted to the following form: FðxÞ ¼

n X

ln xi 

i¼1

Set

n X

"

n X

lnð1  xi Þ  n ln

i¼1

i¼1

1 1  xi

!  ln

n X 1 i¼1

!# 0

xi

Fk ¼ Fðxk ; . . .; xk ; xk þ 1 ; ; xn Þ " # n n X X ln xi  k lnð1  xk Þ þ lnð1  xi Þ ¼ k ln xk þ "

i¼k þ 1

i¼k þ 1

 n lnðk=ð1  xk Þ þ

n X

1=ð1  xi Þ  ln k=xk þ

i¼k þ 1

n X

!#

ð16Þ

ð17Þ

1=xk

i¼k þ 1

Suppose that 1=2  x1  x2 


 xn  0, and then the following equation can be obtained: @Fk k k ¼ þ  n½ 1  xk @xk xk

k=ð1  xk Þ2 k=x2k  þ n n P P k=ð1  xk Þ þ 1=ð1  xi Þ k=xk þ 1=xk i¼k þ 1

ð18Þ

i¼k þ 1

ð1 - t)1 and t1 are convex function on interval ð0; 1=2. Based on Jensen’s inequality, the following inequality can be got: n 1 X 1  n  k i¼k þ 1 1  xi

n 1 X 1  n  k i¼k þ 1 xi

1 Set x0 ¼ nk

n P i¼k þ 1

1 1  ðn  kÞ1 1

ðn  kÞ

1

n P i¼k þ 1

n P i¼k þ 1

ð19Þ xi

; 1kn  1

ð20Þ

xi

xi , and based on 1=2  x1  x2 


 xn  0, we can know that

1=2  xk  x0 [ 0. Inequality (19) and (20) can be converted to the following form: n X

1 nk  1  x 1  x0 i i¼k þ 1

ð21Þ

Study on Establishment and Proof of Inequality n X

1 nk  ; x x0 i¼k þ 1 i

1kn  1

311

ð22Þ

k x0 Þ Define C ¼ xk ð1xk Þ½kð1x0 ÞkðnkÞðx þ ðnkÞð1xk Þ½kx0 þ ðnkÞxk , and based on the above inequality the following inequality can be obtained finally:

@Fk  C½kð1  xk  x0 Þ þ ðn  kÞð1  2xk Þ @xk

ð23Þ

k It is easy to prove that @F @xk  0, therefore the function Fk ðxÞ is an increasing function, because xk  xk þ 1 , Fk ðxÞ  Fk þ 1 ðxÞ, 1  k  n  1 , therefore the inequality is established.

4 Conclusions The descending dimension method is applied to establishment and proof of inequality, and the theory system of descending dimension can be extended, and the effectiveness of this method is verified by some examples. Acknowledgments. This work is supported by the Fund Project of Yangtze University College of Technology & Engineering (No. 2016KY05). In addition, the author would particularly like to thank the anonymous reviewers for helpful suggestion.

References 1. Lu, Y.: A dimension decreasing algorithm with generic program for automated inequality proving. Chin. High Technol. Lett. 7(7), 20–25 (1998) 2. Wen, J., Wang, W., Lu, Y.: The method of descending dimension for establishing inequalities (I). J. Southwest Univ. Natl. (Nat. Sci.) 29(5), 527–532 (2003) 3. Yang, H., Wen, J., Wang, W.: The method of descending dimension for establishing inequalities (II). J. Sichuan Univ. (Nat. Sci.) 44(4), 753–758 (2007) 4. Bartosz, K., Cheng, X., Kalita, P., Yu, Y., Zheng, C.: Rothe method for parabolic variational– hemivariational inequalities. J. Math. Anal. Appl. 423(2), 841–862 (2015) 5. Ledoux, M.: A (one-dimensional) free Brunn-Minkowski inequality. C. R. Math. 340(4), 301–304 (2005)

Multilevel Minimum Cross Entropy Threshold Selection Based on the Improved Bat Optimization Si Chen(&) and Guo-Hua Peng Department of Applied Mathematics, College of Natural and Applied Sciences, Northwestern Polytechnical University, Xi’an 710072, People’s Republic of China [email protected]

Abstract. Thresholding is a simple and most commonly used method for image segmentation. It’s known that the minimum cross entropy thresholding (MCET) has been widely used in image threshold selection. The bat algorithm (BA) come from the social behavior of the swarm of bats, and it’s one of the popular techniques for optimization. This paper proposed an improved BA (IBA) by using time-varying inertia weights into the update formula, and six benchmark functions were selected for the simulation test. Then, the IBA was used for searching the optimal MCET thresholds. What’s more, three different methods that the improved particle swarm optimization (IPSO), the fuzzyclustering method (FC) and basic BA are carried out for comparison with the proposed algorithm. The results demonstrate that the proposed IBA can obtain more fast and stable results. Keywords: Swarm intelligence algorithm Image segmentation
Cross entropy


Bat algorithm

1 Introduction Multilevel thresholding plays a considerable role in image processing, because explanation of an image requires the image to be appropriate segmented into meaning regions. Comprehensive study of thresholding methods have been proposed for choosing the threshold value (Sankur and Sezgin 2004). The thresholding methods roughly contain two categories. The first category is the methods which use the profile characteristics of the histogram, and to find the threshold value. Rosenfeld and De la Torre (1983) used the convex hull of the concavities in the histogram to determine the threshold selection. Lim and Lee (1990) proposed a new method that use the fuzzy c-means techniques to detect the valleys as thresholds by calculating the derivatives of the smoothed histogram. The second category is the methods which use a certain objective function to get the optimal thresholds. Pun (1981) proposed a method which maximize the entropy of the goal and background parts to find the optimal value. Wu et al. (2013) used the new method of maximum reciprocal entropy thresholding to

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 312–320, 2019. https://doi.org/10.1007/978-3-030-02804-6_42

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obtain the optimal thresholds. Khehra et al. (2015) proposed fuzzy 2-partition entropy modus for threshold selection based on big bang-big crunch. With the development of computational intelligence (Engelbrecht 2007), a new type of nature-inspired algorithms have been proposed, and have been shown to be effective (Yang 2008; Yang 2009; Lukasik and Zak 2009). Yang (2011) used BA algorithm for engineering design of multi-objective optimization. Mishra et al. (2012) used BA algorithm to fuzzy clustering. He and Huang (2017) proposed a modified firefly algorithm and used it into multilevel thresholding. However, we rarely get the studies of BA applied to image segmentation. The paper is thus organized as follows. We will introduce the basics of the BA firstly in Sect. 2, followed by a improved updating model of the BA in Sect. 3. We will then applied the improved algorithm to image segmentation, and the comparative experiments of some selected benchmark figures are presented in Sect. 4. Finally, we give a brief conclusion in Sect. 5.

2 Standard Bat Algorithm The bat algorithm (BA) is inspired by the biological behavior of bats, resulting in a population-based stochastic global optimization. In a d-dimension search space, every of bat has a position vector xti and a flying velocity vti at iteration t according to the basic BA, the key equations are expressed as follows xit þ 1 ¼ xti þ vit þ 1

ð1Þ

vit þ 1 ¼ xvti þ ðp  xti Þfi

ð2Þ

fi ¼ fmin þ ðfmax  fmin Þb

ð3Þ

where x is the inertia weight in the formula of velocity. fi indicates the frequency of the i-th bat in the interval of ½fmin ; fmax . b is a random vector in the range of ½0; 1 and p indicates the current best solution found by entire population. In a local search space, a new solution will be generated randomly around the old solution, expressed as Xnew ¼ Xold þ eAt

ð4Þ

where Xold is chosen from the current best solution set, At is the mean of the bat’s loudness at iteration t, e is a random number in ½1; 1. The loudness Ai and the velocity ri of the bats population can be updated as Ait þ 1 ¼ aAti

ð5Þ

rit þ 1 ¼ rit ½1  expðctÞ

ð6Þ

where a is a constant number in the range of ½0; 1, and 0\c.

314

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3 Improved Bat Algorithm 3.1

Bat Algorithm with Improved Learning Mechanism

Inertia weight x is one of the few parameters in the BA and has a very important role. Generally, the fixed weight and the time-varying weights are two main options for the inertia weight. The fixed weight is fixedly selected as a weight value during the iteration of the algorithm. The time-varying weight is a changing value of the inertia weight with the number of iterations increases. The optimization effect of the algorithm depends largely on the selection of inertia weights, and the time-varying inertia weights are more conducive to increase algorithm diversity and improve search capabilities. This paper proposes a degressive time-varying inertia weight learning mechanism for three different schemes, we have 8 Tmax  t > > > x1 ¼ xmax ðxmax  xmin Þ > Tmax > > >
2 > < t x2 ¼ xmax  ðxmax  xmin Þ ð7Þ Tmax > > > 1 >
 10t > > 1þ > x Tmax > : x3 ¼ xmin max xmin In the above formula, x1 , x1 , x1 respectively represent the time-varying inertia weight expressions of scenario 1, scenario 2, and scenario 3. According to the existing numerical experiments, the algorithm converges better when the values of weight are in the range of ½0:4; 0:9. The curve is as shown in the figure below (Fig. 1).

Fig. 1. value circumstances of time-varying inertia weight

According to the proposed three different degressive inertia weight learning mechanisms, the basic bat algorithm’s speed update formula is improved. The three schemes are as follows:

Multilevel Minimum Cross Entropy Threshold Selection

315

(1) 8 tþ1 t t < vi ¼ x1 vi þ ðp  xi Þfi : x1 ¼ xmax ðxmax  xmin Þ

Tmax  t Tmax

ð8Þ

From Eq. (8), we can see that the time-varying inertia weight x1 takes the current iteration number t as a variable, and it presents a simple linear decay behavior. (2) 8 tþ1 t t > < vi ¼ x2 vi þ ðp  xi Þfi


2 t > : x2 ¼ xmax  ðxmax  xmin Þ Tmax

ð9Þ

From Eq. (9), it can be seen that the decay behavior of the time-varying inertia weight x2 is in the form of a quadratic function with the number of iterations t as a variable. The decayed minimum value is much larger than x1 . (3) 8 tþ1 t t > < vi ¼ x3 vi þ ðp  xi Þfi
 1 xmax 1 þ T10t max > : x3 ¼ xmin xmin

ð10Þ

From Eq. (10), we can obtain that the decay behavior of the inertia weight x3 is in the form of an exponential function, its decay rate is much slower than that of x1 , and its final value is close to x2 . 3.2

Simulation Experiment

So as to certificate the behavior of the three kinds of time-varying inertia weight learning mechanisms proposed in this paper, five standard test functions were selected for simulation test and compared with the basic algorithm. The test function is shown in Table 1. In the simulation experiment, xmax ¼ 0:9, xmin ¼ 0:4, Tmax ¼ 1000. Each of algorithm runs 20 times, the comparison results are display in the Fig. 2. Figure 2 shows the convergence curves of three kinds of improved methods and the basic BA. It is not difficult to find that all the improved algorithms has better performance than the basic algorithm, and it optimized the convergence accuracy of the BA. Overall, the improvement effect with x3 is mostly better than the first two. The inertia weight is always within the range of ½0:4; 0:9 in scenario 3, And it can be basically concluded that scenario 3 works best in these three different improve methods. Next, we will apply the third scenario with the best improvement effect to the multilevel threshold selection.

316

S. Chen and G.-H. Peng Table 1. Simulation benchmarks

Function name Sphere

Dimension Range of x Optima 30 [−100, 100] 0

Formula xÞ ¼ g1 ð~

n P i¼1 n P

x2i N Q

Griewank

g2 ð~ xÞ ¼

Eggcrate

xÞ ¼ x21 þ x22 þ 25ðsin2 x1 þ sin2 x2 Þ 2 g3 ð~ n   P 30 x2i  10 cosð2pxi Þ g4 ¼ 10n þ

Rastrigin Zakharov Schwefel’s problem

g5 ¼

n P i¼1

xÞ ¼ g6 ð~

i¼1

x2i 4000

i¼1

x2i

þ ð12

n P i¼1



i¼1

n P i¼1

jxi j þ

cosðpxiffiiÞ þ 1

ixi Þ2 þ ð12

n Q

i¼1

jxi j

n P i¼1

ixi Þ4

30

[−600, 600] 0 [−2p, 2p]

0 0

30

[−5.12, 5.12] [−10, 10]

30

[−500, 500] 0

Fig. 2. Comparison of convergence with 6 different benchmarks

0

Multilevel Minimum Cross Entropy Threshold Selection

317

4 Applied in Image Segmentation with Multilevel Threshold of Minimum Cross Entropy 4.1

Minimum Cross Entropy Thresholding

Kullback propose the cross entropy (1968). Let F ¼ ff1 ; f2 ; . . .; fN g and G ¼ fg1 ; g2 ; . . .; gN g be two probability distributions on the same set. The cross entropy between F and G is defined by EðF; GÞ ¼

N X

fi log

i¼1

fi gi

ð11Þ

Let I be the original image, L be the number of gray-level, and hðiÞ; i ¼ 1; 2; . . .; L is the histogram. Then the thresholded image, represented by It , using t as the threshold value is composed by  It ðx; yÞ ¼

lð1; tÞ; Iðx; yÞ\t; lðt; L þ 1Þ; Iðx; yÞ  t;

ð12Þ

where lða; bÞ ¼

b1 X

ihðiÞ

, b1 X

i¼a

hðiÞ

ð13Þ

i¼a

The cross entropy is then calculated by EðtÞ ¼

L X

ihðiÞ logðiÞ 

t1 X

i¼1

ihðiÞ logðlð1; tÞÞ 

L X

ihðiÞ logðlðt; L þ 1ÞÞ

ð14Þ

i¼t

i¼1

According to the first part, the objection can be redefined as 1ðtÞ ¼ 

t1 X

ihðiÞ logðlð1; tÞÞ 

i¼1

¼

t1 X

ihðiÞ log

¼ a1 ð1; tÞ log




ihðiÞ logðlðt; L þ 1ÞÞ

i¼t

!

i¼1

L X

t1 X i¼1

,

ihðiÞ

t1 X i¼1

! hðiÞ



L X i¼t

! ihðiÞ log


1  a1 ð1; tÞ a ðt; L þ 1Þ 1  a ðt; L þ 1Þ log a0 ð1; tÞ a0 ðt; L þ 1Þ

L X i¼t

, ihðiÞ

L X

! hðiÞ

i¼t

ð15Þ P Pb1 1 where a0 ða; bÞ ¼ b1 i¼a hðiÞ and a ða; bÞ ¼ i¼a ihðiÞ are the zero-moment and firstmoment on partial range of the image histogram. Let n thresholds ½t1 ; t2 ; . . .; tn  form a threshold vector and divide the onedimensional histogram into n þ 1 regions. Then, the objective function becomes

318

S. Chen and G.-H. Peng

1ðt1 ; t2 ; . . .; tn Þ ¼ 

nX þ1 i¼1


1  a ðti1 ; ti Þ a1 ðti1 ; ti Þ log 0 a ðti1 ; ti Þ

ð16Þ

The optimal threshold t chosen should satisfy: t ¼ arg minf1ðtÞg t

4.2

ð17Þ

Experimental Results and Comparative Performance

There are many effective applications of evolutionary algorithms in image segmentation. However, there are few related applications for the bat algorithms. For comparison, we use four optimization algorithms (IBA), Fuzzy Clustering segmentation algorithm (FC) (Tang et al. 2014), Improved Particle Swarm Optimization (IPSO) algorithm (Wang and Zhao 2012), and basic BA for thresholds selection. Four images named “flowers” “foxes” “horses” and “plane” are applied for conducting our experiments. The histogram of the images are shown in Fig. 3. The algorithm parameters of this article are set as follows: the maximum number of iterations max gen ¼ 200 and population size n ¼ 20. The results of the comparison are shown in Table 2.

Fig. 3. Histogram of original images

It can be indicate from Table 2 that the method can reasonably determine the threshold number according to the image information. According to the comparative experiment, it shows that the computation time of the IBA is fast than three others algorithm. It indicates that the IBA-based method is efficient and effective.

Multilevel Minimum Cross Entropy Threshold Selection

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Table 2. The results of segmentation Image Algorithm Threshold number Threshold Flowers IBA 3 67,95,114 BA 3 80,120,137 IPSO 4 68,100,120,143 FC 3 65,94,103 Foxes IBA 5 52,74,94,124,165 BA 5 36,61,105,164,188 IPSO 4 51,87,121,164 FC 4 61,91,135,167 Horses IBA 5 34,57,96,122,175 BA 4 36,81,123,167 IPSO 5 49,92,141,166,202 FC 4 81,124,168,186 Plane IBA 4 59,81,95,105 BA 4 55,87,106,121 IPSO 4 45,87,99,110 FC 3 38,69,110

Time/s 0.3913 0.9035 0.9278 0.8905 0.4823 0.9980 0.8560 0.8139 0.4051 0.9170 0.9745 0.8743 0.4112 0.8668 0.9872 0.6534

5 Conclusions The bat algorithm has been certify to be efficacious in various applications while it’s still lack of solving the problem of thresholding selection. This paper proposed an improved bat algorithm based on time-varying weights. Numerical experiments are carried out to verify the utility of the improved method. What’s more, we apply the improved algorithm to Berkeley image segmentation dataset and perform multithreshold segmentation with multilevel minimum cross entropy, which results in a good segmentation effect. It shows that this is a very effective and practical fast segmentation algorithm. Acknowledgment. The authors would like to thank the Natural Science Basic Research Plan in Shaanxi province of China No. 2015JM6296 for support of this work.

References Sankur, B., Sezgin, M.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13, 146–165 (2004) Rosenfeld, A., De la Torre, P.: Histogram concavity analysis as an aid in threshold selection. IEEE Trans. Syst. Man Cybern. SMC 13, 231–235 (1983) Lim, Y.K., Lee, S.U.: On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recogn. 23, 935–952 (1990) Pun, T.: Entropy thresholding: a new approach. Comput. Vis. Graph. Image Process. 16, 210– 239 (1981)

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Wu, Y.-Q., Yin, J., Bi, S.-B., Wu, Y.-Q.: Multi-threshold selection using maximum reciprocal entropy/reciprocal gray entropy. J. Signal Process. 29(2), 143–151 (2013) Khehra, B.S., Pharwaha, A.P.S., Kaushal, M.: Fuzzy 2-partition entropy threshold selection based on big bang-big crunch optimization algorithm. Egypt. Inform. J. 16(1), 133–150 (2015) Engelbrecht, A.P.: Computational Intelligence: An Introduction, pp. 5–24. Wiley, Hoboken (2007) Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Beckington (2008) Yang, X.S.: Firefly algorithms for multimodal optimization. In: Stochastic Algorithms: Foundation and Applications, SAGA. Lecture Notes in Computer Sciences, vol. 5792, pp. 169–178 (2009) Lukasik, S., Zak, S.: Firefly algorithm for continuous constrained optimization tasks. In: 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, Wrocław, 5–7 October 2009 Yang, X.S.: Bat algorithm for multi-objective optimization. Int. J. Bio-Inspired Comput. 3(5), 267–274 (2011) Mishra, S., Shaw, K., Mishra, D.: A new metaheuristic classification approach for microarray data. Procedia Technol. 4(1), 802–806 (2012) He, L.F., Huang, S.W.: Modified firefly algorithm based on multilevel thresholding for color image segmentation. Neurocomputing 240, 152–174 (2017) Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization, pp. 65–74 (2010) Kullback, S.: Information Theory and Statistics. Dover, New York (1968) Tang, L.M., Wang, H.K., Chen, Z.H., Huang, D.R.: Image fuzzy clustering segmentation based on variational level set. J. Softw. 25(7), 1570–1582 (2014) Wang, S.L., Zhao, H.J.: Multilevel thresholding gray-scale image segmentation based on improved particle swarm optimization. J. Comput. Appl. 32(S2), 147–150 (2012)

Large Scale Text Categorization Based on Density Statistics Merging Rujuan Wang(&) and Suhua Wang College of Humanities and Sciences of Northeast Normal University, Changchun 130012, China [email protected]

Abstract. In view of the problems faced by the effective classification and management of massive text, a new classification method for mass Web text information is proposed. The core idea is based on the characteristics of the low quantity, high value rate of the long text and the high quantity and low price rate of the short text in the current network environment. The feature selection method based on complex network is proposed. The number of features obtained by this method is more stable, and the accuracy of the selection of features in large text centralization is improved. Secondly, a text classification method based on density statistical merging is proposed, and the classification method is studied from the point of view of data sampling. The method is classified. In the process, we not only use the density information of the text feature set, but also use the difference information of each feature of the text obtained by the statistical merging criteria. Therefore, the algorithm has better robustness to noise and has a better classification effect to the large text set. Keywords: Text categorization


Statistical leaders

1 Introduction With the rapid development and rapid popularization of computer equipment and network technology, the production and transmission efficiency of information has been accelerated, and then the phenomenon of information explosion has been generated, and human beings have entered the era of big data. In the era of big data, the traditional way of information retrieval cannot effectively help users to analyze and understand a large amount of text data. Therefore, in order to meet the needs of the user to obtain the information quickly and accurately, it is necessary to effectively classify and manage the massive text data. The traditional text classification and clustering techniques have many problems such as reducing the scalability, lack of corpus and the accuracy of classification [1]. The main reason is that with the rapid development of Web2.0, the user generated content (User Generate Content, UGC) pages become main stream [2]. In this paper, a new classification method for mass Web text information is proposed. The core idea is based on the low quantity, high value rate of the long text in the current network environment and the high quantity and low price value of short text. A feature selection method based on complex network is proposed. The preprocessed Web text is converted into a complex network. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 321–327, 2019. https://doi.org/10.1007/978-3-030-02804-6_43

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2 Long Text Feature Selection Based on Complex Network In this section, a feature selection method based on complex networks is proposed, which mainly chooses feature words for Web long text. A complex network is a network structure consisting of nodes and the edges of any two nodes. The different nodes represent different individuals and the different edges represent the connections between different nodes. For any node, if there are other nodes connected to it, it is called a neighbor node of the former node, based on such a node. In this paper, the complex network is represented as G = (V, E), where V ¼ fVi ji ¼ 1; 2


N g represents the set of nodes in the network, N is the number of nodes 
in the complex network,  corresponding to the entries in the long text of Web, E ¼ vi ; vj jvi ; vj 2 V represents the set of edges in the network, corresponding to the associated word pairs in the Web long text. In a complex network, the degree D and aggregation coefficient S are usually used to measure the importance of a node. The calculation formula of the degree is as follows: Di ¼

X

ai
j

ð1Þ

vi ;vj 2E

Among them, the ai
j represents the connection between node vi and vj ði 6¼ jÞ, if connect, then ai
j ¼ 1, else ai
j ¼ 0. The value of Di indicates the connection degree between the node vi with other nodes, which reflects the influence of the node to the other nodes in the network, but cannot represent the neighbor nodes of this node, so the calculation formula of the degree D is modified as follows: D0i ¼ Di þ

X

Dj

ð2Þ

vj 2E 0

The D0i represents the sum of node vi and the neighbor nodes, which define as the degree of extension, where Di is the degree of node vi, Dj is the sum of the neighbor nodes, and E0 represents the set of neighbor nodes vi. The calculation formula of aggregation coefficient is as follows: Si ¼ 

Ki 2Ki ¼ Di ðDi  1Þ Di 2

ð3Þ

Among them, Di represents the degree of node vi, and Ki represents the degree of aggregation of node V, representing the number of triangular nodes composed of node vi and any other two neighbor nodes, and the formula of Ki is as follows: X Ki ¼ bi
j
k ð4Þ vi
vj
vk 2E

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Among them, bi
j
k represents the connection between node vi and node vj ði 6¼ j 6¼ kÞ and node vk. If the three nodes connect to each other, the joint is bi
j
k ¼ 1, and conversely, bi
j
k ¼ 0, the value Si indicates the local connection density of the nodes, to a certain extent, reflects the central role of the node in the local network, but cannot be reflected the neighbor node size of the node, therefore defines an expanded aggregation coefficient S+, the formula is as follows: Siþ ¼

Si Diþ

ð5Þ

In order to better reflect the combined effect of node degree and node aggregation coefficient, the formula (3) and formula (5) are treated with the same chemotaxis, and the final word weight evaluation function is obtained. Diþ Siþ ffi q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Wi ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þ PN PN þ 2ffi þ2 D i¼1 i i¼1 Si

ð6Þ

The following is the process of specific feature selection methods: Step1. Segmenting the text and constructing a user dictionary to eliminate invalid words such as function words and pronoun, and get the iList of each Web long text. Step2. Using the word bar in iList as the node V, the connection edge E adopts the method proposed by Cancho [3], that is, the words that span less than two words in each sentence constitute the edge between the corresponding nodes, and merge the same nodes and edges when traversing the whole word set, and remove the isolated points in the network and form a complex network. Step3. Calculate the degree D+ and aggregation coefficient S+ of node v according to the formula (2) and formula (5), and get the comprehensive evaluation value of each entry by means of formula (6). Step4. To sort the entries in the iList according to the value of the evaluation, select the feature words according to the predetermined threshold, and form the feature word set fwList of the Web long text. In addition, we need to assign weight values to the feature words obtained through feature selection. In this paper, we use the weight evaluation function of TF-IDF [4] to calculate the weight values of the feature words. The VSM of Web long text is constructed according to the feature word and its weight value, and the relationship between long text is transformed into the calculation between vectors in vector space through VSM, thus simplifying the complexity of the problem.

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3 Text Classification Method Based on Density Statistical Merging 3.1

Statistical Leaders Algorithm

The Statistical Leaders algorithm [5] is based on the improved Leaders algorithm [6–8]. The Leaders algorithm cannot correctly reflect the density information of leader in the original data set, and because the Leaders algorithm uses a fixed distance threshold in the clustering process, the selected leader is distributed evenly in the original data set. Therefore, the Leaders algorithm is used as the sampling algorithm cannot get satisfactory sampling results. In order to make the sample set more representative and reflect the density information of each leader in the original data set more accurately, Liu et al. on the basis of the Leaders algorithm proposed a Statistical Leaders algorithm combined with statistical ideas. And use the independent finite difference inequality to obtain a specific statistical merging criterion [9]. 3.2

Text Classification Method Based on Density Statistical Merging (TCDSM)

The classification framework for TCDSM is shown in the Fig. 1. Stage1: Stage2: Stage3: Stage4:

Preprocessing text and feature selection. Sampling The clustering of representative point sets Clustering the whole dataset based on mapping relations.

Web Text

Feature SelecƟon

FWList Data Set

Sampling Leaders

Clustering Result

Mapping Points to Corresponding Leaders

Classifying the Leader Set

Fig. 1. Framework of TCDSM

Large Scale Text Categorization Based on Density Statistics Merging

The algorithm 2 as follows:

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4 Experiment and Result Analysis The experimental data sets are for sports, finance, fashion, games, entertainment, military, automobile and education. It involves 40 small classes, 6400 documents (all are long text documents of news report and phenomenon exposition, have certain value). The above 8 plate names are called keyword search, and the other is Web The text has a certain timeliness, which will affect the relationship between the long text and the short text to some extent, so the text information will be extracted with the time axis, and the text content block in the text area of the Web page will be obtained through the Web information extraction system. The Web text classification performance evaluation standard adopts the macro average accuracy rate, the macro average recall rate and the macro average F1 value, MacroP is the average accuracy of macros, and MacroR is the macro average recall rate. The experiment is compared with the method GADT-SVM [10] and cluster-kNN [11]. When the feature dimension is less than 4000, the accuracy of the text classification based on the feature words based on the complex network increases rapidly, and the growth trend slows down after the 4000 dimension. Therefore, in the experiment, the classification precision and the classification speed are considered. The experimental results are shown in Fig. 2. From Fig. 2, we can see that the SVM classifier based on kNN has a certain improvement in the overall classification performance compared with other classifiers. The number of support vectors obtained by the training of these samples by SVM is less, and the information lost in a classification process is relatively small.

TCDSM

Cluster-KNN

GADT-SVM

MacroF1

0.95 0.9 0.85 0.8 0.75 1

2

3

4

5

Category Fig. 2. Experimental results

6

7

8

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5 Concluding Remarks In this paper, the definition, task and function of Web text classification are systematically studied. According to the content and structure characteristics of Web text in the current network environment, a classification method for large-scale Web text is proposed. This method is divided into two parts. First, a text feature selection party based on complex network is proposed. Secondly, secondly, a text classification method based on density statistical merging is proposed. The method uses not only the density information of text features in the classification process, but also the difference information of each characteristic of the text obtained by the statistical merging criteria. The relevant experimental results show that the method can be efficient and accurate to different classes. Other large-scale Web text information is classified, and it has better robustness to noise. In addition, the membership relationship between the Web long text and the short text has certain timeliness. Acknowledgments. This work was partially supported by The Education Department of Jilin province science and technology research project “13th Five-Year” Kyrgyzstan UNESCO Zi [2016] No. 159th.

References 1. Wang, Y.Z., Jin, X.L., Cheng, X.Q.: Network big data: present and future. Chin. J. Comput. 36(6), 1125–1138 (2013) 2. Zhao, Y., Fan, Z.A., Zhu, Q.: Conceptualization and research progress on user-generated content. J. Libr. Sci. China 5, 008 (2012) 3. Cancho, R.F.I., Solé, R.V.: The small world of human language. Proc. Biol. Sci. 268(1482), 2261–2265 (2001) 4. Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. J. Doc. 28(1), 493–502 (2004) 5. Liu, B.B., Ru-Ning, M.A., Ding, J.D.: Density-based statistical merging algorithm for large data sets. J. Softw. 26, 2820–2835 (2015) 6. Vijaya, P.A., Murty, M.N., Subramanian, D.K.: Leaders-Subleaders: An Efficient Hierarchical Clustering Algorithm for Large Data Sets. Elsevier, Amsterdam (2004) 7. Romero, E.: Using the leader algorithm with support vector machines for large data sets. In: Artificial Neural Networks and Machine Learning—ICANN, vol. 6791, pp. 225–232 (2011) 8. Viswanath, P., Babu, V.S.: Rough-DBSCAN: a fast hybrid density based clustering method for large data sets. Pattern Recogn. Lett. 30(16), 1477–1488 (2009) 9. Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26 (11), 1452 (2004) 10. Xu, L., Fu, Y., Li, S.: Web text classifier based on an improved SVM decision tree. J. Soochow Univ. 5, 003 (2011) 11. Zhang, X.F., Huang, H.Y.: An improved KNN text categorization algorithm by adopting cluster technology. Pattern Recogn. Artif. Intell. 22(6), 936–940 (2009)

Study on the Automatic Classification Algorithm of Dongba Hieroglyphs Yuting Yang1(&) and Houliang Kang2 1

2

Culture and Tourism College, Yunnan Open University, Kunming 650000, China [email protected] College of Humanities and Art, Yunnan College of Business Management, Kunming 650000, China

Abstract. By analyzing the structural features of characters, Dongba hieroglyphs can be divided into two types: single graphemes and compound graphemes. The single graphemes can be further divided into contour type and structure type. Using the classification algorithm to automatically separate different types of Dongba hieroglyphs is helpful to the independent study of single and compound graphemes, and extracting the commonalities between the same type and the differences between the different. Therefore, by studying the structure features of Dongba hieroglyphs, we give a preprocessing and classification algorithm. The algorithm can achieve complete separation of single graphemes and compound graphemes, and even includes the separation of contour type and structure type in single graphemes. Finally, we verified the accuracy of the algorithm through experiments. Keywords: Dongba hieroglyphs Structural features of character


Automatic classification algorithm

1 Introduction Dongba hieroglyph is a kind of very primitive hieroglyphs. Naxi call it “Seng Jiu Lu Jiu”, which means “imprinting left on the wood and stone” [1]. As one of the earliest human text form which transition from hieroglyphic to phonetic transcription, Naxi Dong glyphs not only use pictures to express meaning like pictographs, but also have some features of pictographic, ideographic, self-explanatory and echoism like hieroglyphs [2]. In 2003, Dongba scriptures written by Dongba hieroglyphics are listed into Memory of the World Heritage List by UNESCO [3, 4]. From the structure, Dongba hieroglyph can be divided into single graphemes and compound graphemes. Single graphemes can directly display pronunciation and meaning, and compound grapheme is composed of two or more graphemes and express sound and meaning through multiple graphemes [5, 6]. In addition, single graphemes can be further divided into contour-based single graphemes (CSG) and structure-based single graphemes (SSG), as shown in Table 1.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 328–333, 2019. https://doi.org/10.1007/978-3-030-02804-6_44

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Table 1. The classification of Dongba graphemes

Therefore, we use the classification algorithm to automatically separate different types of Dongba hieroglyphs. It is helpful to the independent study of single (including contour type and structure type) and compound graphemes, and extracting the commonalities between the same type and the differences between the different. And it lays the foundation for the search and recognition research of Dongba hieroglyphs.

2 The Classification of Dongba Hieroglyphs 2.1

The Preprocessing of Dongba Hieroglyph

The normal processing of Dongba Hieroglyph includes normalization, graying and binarization of the character image. In order to express more complicated meanings, Naxi ancestors tend to add some affixation elements (including points, lines or blocks) to the single graphemes. Affixation elements, especially the affixation points will cause great interference to the classification algorithm. In order to reduce the impact, we should also remove the affixation points in hieroglyphs while completing the normal processing. The removal result is shown in Fig. 1.

Fig. 1. Removing affixation points

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The Classification of Single Graphemes and Compound Graphemes

The composition method of complex words mainly includes three types. One is to construct a compound grapheme by adding elements on the single grapheme. The other is to use two or more graphemes to form a compound grapheme. The third one is to use one or more single graphemes and add different affixation elements to compose a compound grapheme. The composition methods are shown in Table 2. Table 2. Three composition methods of constructing compound graphemes

Combining with the composition features of compound graphemes, we use the connected domain labeling algorithm to separate single graphemes and compound graphemes. Connected domain labeling algorithm can use decision tree to analyze 8connective segment or 4-connective segment [7]. Therefore, it can be used for edge detection, image segmentation and region filling [8]. The main idea is that starting from a max value point, the algorithm will search for the 8 or 4 neighborhood of the point, and follow some rules to find the next point, and then repeat these operations on the next point [9]. The result of partitioning in the compound word is shown in Fig. 2.

Fig. 2. The original and local skeleton images

In Fig. 2, the compound grapheme is divided into 3 components including 2 single graphemes and 1 affixation block. Since a single grapheme is characters that cannot be further segmented, even after segmentation, the single grapheme still contain only one component. Therefore, by counting the total number of discrete components in Dongba hieroglyph, single words and compound words can be separated quickly.

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2.3

331

The Classification of Contour Type and Structure Type of Single Graphemes

In single graphemes, the contour-based single grapheme (CSG) expresses meanings through the contour of object, and uses contour features to reflect the essence. This type of grapheme generally contains a complete contour. The structural-based single grapheme (SSG) uses simple strokes to express meaning by depicting the structure of objects. The structure or skeleton features of such characters are significant, but there are no obvious contour features. Therefore, we can use contour to express the features of CSG, using structure or skeleton to describe the features of SSG. To achieve the separation of CSG and SSG, we should make full use of the different features contains in the two types of graphemes. The processing steps are: first of all, we remove the noise in the graphemes by extracting the refined lines. Secondly, we fill in the closed curves of graphemes and increase the difference between CSG and SSG. Finally, we use an area ratio algorithm to classify single graphemes. 2.3.1 Refinement of Single Graphemes Dongba master write Dongba Hieroglyphs based on the stroke drawing by using bamboo pen. Because bamboo pen is a hard pen, so the stroke width is basically the same. Therefore, we use refinement techniques to refine the strokes, remove potentially interfering components in the hieroglyphs and make character features more pronounced. In addition, while refining the strokes, we also removed the discrete affixation elements to reduce the influence with extracting the real features in hieroglyphs. The processing results are shown in Fig. 3.

(a) Structural-based Single Graphemes

(b) Contour-based Single Graphemes

Fig. 3. The original, refinement, contour and filled image

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2.3.2 Filling and Comparing of Single Graphemes Filling can further enhance the difference between CSG and SSG, as shown in Fig. 3. Some graphemes are not close enough to be closed, and even if they are CSG, the complete contour cannot be directly extracted. In order to extract effective contours, we use morphological image processing technique. The processing steps are as follows: Step1. We read characters, remove small interference points, and perform grayscale processing; Step2. We use Sobel operator to achieve outline processing, and use the expansion operation to fill the contour gap; Step3. We fill the holes, smooth the edges of graphemes, and extract the contours. The processing result is shown in Fig. 4.

Fig. 4. We use Sobel operator to exact the contour of unclosed CSG

2.3.3 Classification Based on Area Ratio We can see from Fig. 3 that SSG can get skeletons after refinement, and CSG can get contours. And after filling the contour, these two kinds will show obvious differences. Therefore, we can separate the two types by comparing the proportions of the pixels representing the strokes in the character binding box. The processing steps are as follows: Step1. We extract the binding box of single graphemes. Step2. We calculate the total number of pixels representing the strokes in the filled graphemes. Step3. We calculate the proportion of stroke pixels to the total pixels in the bounding box. Step4. We determine the type of grapheme. If the ratio is greater than or equal to 60%, the grapheme is contour type; otherwise, it is structural type.

3 Experiments The classification algorithm was used to separate the 1588 Dongba hieroglyphs in the fonts. In the end, we get 966 single graphemes and 622 compound graphemes. In single graphemes, there are 518 SSG, 438 CSG and 10 controversial single graphemes. These 10 single graphemes have been misclassified because of their own characteristics,

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as shown in Table 3. Therefore, the classification accuracy of single graphemes and compound graphemes is 100%, while the classification accuracy of CSG and SSG is 98.996%. Table 3. 10 Misclassified single graphemes

4 Conclusions The preliminary separation of different types of characters in the Dongba hieroglyphs is conducive to the independent analysis of the essential features of different types. It lays the foundation for the search and recognition research of Dongba hieroglyphs. And it also provides an important technique for studying the composition, creation and evolution of Dongba hieroglyphs. Acknowledgments. This research was partially supported by the Scientific Research Fund of Yunnan Education Department (2018JS748).

References 1. Liming, H.: The research of Dongba culture heritage. Soc. Sci. Yunnan 1, 83–87 (2004) 2. Jinguang, H.: The development trend of Dongba culture studies of the Naxi nationality. J. Yunnan Natl. Univ. 24(1), 81–84 (2007) 3. Ge, A.: Dongba culture review. J. Natl. Artist. Res. 12(2), 71–80 (1999) 4. Guo, H., Zhao, J., Da, M., et al.: NaXi pictographs edge detection using lifting wavelet transform. J. Converg. Inf. Technol. 5(5), 203–210 (2010) 5. Feizhou, Z.: Word Research of Naxi Dongba Hieroglyphic, pp. 1–230. Nationalities Publishing House, Beijing (2005) 6. He, L.: Hieroglyphic Structure of Naxi Dongba and its International Standard. Shanghai Normal University, Shanghai (2016) 7. Suzuki, K., Horiba, I., Sugie, N.: Linear-time connected component labeling based on sequential local operations. Comput. Vis. Image Underst. 1(89), 1–23 (2003) 8. Shijin, C., Yangping, T.: An application of contour tracing technique in the binary image based on the chain code. J. Huazhong Univ. Sci. Technol. 26(12), 26–28 (1998) 9. Chun-li, D., Yu-ning, D., Li, W.: Survey of object tracking algorithms based on active contour models. Comput. Eng. Appl. 44(34), 208–212 (2008)

A Comparison Study of Different Algorithms for Energy-Aware Placement of Virtual Machines Alejandro Olvera(B) and Fatos Xhafa Barcelona School of Informatics (FIB), Technical University of Catalonia, Barcelona, Spain {aolvera,fatos}@cs.upc.edu

Abstract. Cloud Computing services are essential to modern society. The increasing number of people and organisations using these types of services results in a higher demand in datacenters, which in turn, is raising energy consumption and carbon footprint. Reducing energy consumption has become a subject of interest to many researchers, who approach the problem with different optimisation processes and scheduling algorithms. This article shows an extensive vision of the steps followed by a datacenter, upon the arrival of a task or application by showing how it traverses along the processing time-line, and focusing on the energy-aware point of view of the datacenter. A crucial role is played by placement process of Virtual Machines (VM). Simulations using the CloudSim simulator were performed and results are reported to show a performance comparison of several selected algorithms, which focus in the VM placement problem, and considering two scenarios: empty and loaded datacenter. The results are evaluated in terms of energy consumption, quality of service and resource memory efficiency, among others. Keywords: Cloud · Datacenter · Virtual machine · Energy Modeling · Scheduling · Heuristics algorithms · CloudSim simulator

1

Introduction and Motivation

Cloud computing has become one of the major current trends in the modern society for the facilities of usage and interaction with the digital data. The amount of data and services have exponentially increased over the years and their management has turned into a complex scenario. Data centers that manage cloud user requests use critical computing infrastructures, and the proliferation of these datacenters with inefficient scheduling of their resources results in high energy consumption, carbon footprint and monetary cost [1,2]. To deal with the energy problem, some algorithms and heuristics discussed in the literature are used to reduce energy consumption while maintaining a good service to the client. Energy consumption is oriented principally to datacenters which use virtualisation, as a good solution to abstract the physical c Springer Nature Switzerland AG 2019  F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 334–343, 2019. https://doi.org/10.1007/978-3-030-02804-6_45

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infrastructure by logically partitioning a server resources into several virtual machines (VMs), where each VM runs an independent operating system and applications [3,5]. This allows the distribution and exchange of virtual machines between different types of servers without limitation in the infrastructure, giving the portability, manageability and the ease of scalability in the datacenter. Besides of using virtualisation for the servers, task or application assignment to VMs is considered in the study as a key research problem, for selecting incoming applications in terms of different optimisation criteria. The resource requirements of the applications have kept changing during the last years, considering more variability in their characteristics which make the energy optimisation problem more complex. The rest of the paper is organised as follows. We briefly present the problem definition in Sect. 2. Related work is discussed in Sect. 3. Then, in Sect. 4, we present the experimental study using the CloudSim v4.0 [16]. We end the paper in Sect. 6 with some conclusions and outlook for future work.

2

Problem Definition

According to [4], software energy management in data centers could be implemented at three layers: application, virtual machine and physical machines or servers. The two principal optimisation processed are shown in Fig. 1: Application or Task Placement and VM Placement. The responsibilities of the physical machine (PM) layer are the server resource usage, ON/OFF operations, sleep cycles, cooling and dynamic voltage frequency scaling. VM layer is responsible for VM management, including VM placement, sizing and migration. While, the application layer assigns incoming applications requested by cloud users to VMs for execution. Both application and VM placement could be considered as a bin-packing problem. As Bin packing is an NP-hard problem, it is unlikely to find an optimal result in a reasonable time, therefore different heuristics approaches have been considered to deal with the problem.

Fig. 1. Three-layer data center architecture.

The optimisation process presented in this paper, which was partially based on the CloudSim power package, is shown in Fig. 2. Static and dynamic processes

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are identified, where static means a block is executed once, and dynamic means it is executed several times. In the former case, the static steps are executed at the beginning of the simulation. In the later, the dynamic steps are executed iteratively following a predefined scheduling interval. Similarly, there is another scheduling interval for the processing of tasks assigned to a VM, when these are updated. This last scheduling interval checks, for each interval, if the tasks have already finished.

Fig. 2. Optimisation process in CloudSim.

3

Related Work

In relation to application assignment, Meera Vasudevan [4] formulates different approaches for static and dynamic assignments. Repairing Genetic Algorithm is one approach for the static application assignments. It is a classical genetic algorithm, but it has some advantages, such as the improvement of the convergence of the algorithm against the greedy algorithms, and the reduction of the energy consumption while maximizing the resource utilisation. Beloglazov et al. [8,9] discussed different heuristics and algorithms that could be reused in any study based on Fig. 2. Regarding Detect host overload block, the methods discussed were: – Median Absolute Deviation, which is a measure of statistical dispersion that considers the absolute deviations from the history CPU utilisations and the current utilisation;

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– Inter-quartile Range, which is a measure of statistical dispersion which uses the difference of the third and first quartile of the previous CPU utilisations; – Local Regression and Local Robust Regression, which are methods that estimate the next CPU utilisation based on the previous CPU utilisations using the Loess method [6]. Regarding VM Selection block, the approaches discussed were: – Minimum Migration Time, which selects the VM that requires the minimum time to complete the migration; – Random Choice, which selects any VM; – Maximum Correlation, which selects the VMs that have the highest correlation of the CPU utilisation based on the applications running on them; and – Minimum Utilisation, which selects the VM that uses less CPU. Finally, regarding Host Underloaded block, an iterative algorithm which tries to place all the VMs from one host to other hosts keeping them not overloaded and accomplishing resource constraints is discussed. This process is iteratively repeated for all hosts that have not been considered as being overloaded and starting by the host with minimum utilisation. Moving a VM has a negative impact on the performance in the applications running inside the migrated VM. Beloglazov et al. showed that this performance degradation can be estimated as approximately 10% of the CPU utilisation for the migrated VM. CloudSim assumes that images and data of VMs are on a Network Attached Storage (NAS) [8], and I/O costs are not considered for the simulations. QoS is formalized in the form of Service Level Agreement (SLA). Two workload independent metrics were defined to evaluate the SLA of any VM under IaaS. Both have the same importance and weight. The first is the percentage of time that active hosts have experienced CPU utilisation of 100% being a potential host to be overloaded. The second consists in the degradation of performance based on VM migrations. In relation to the VM placement, as discussed in [7,17], there are several interesting approaches: First Fit (FF), First Fit Decreasing (FFD), Best-Fit Decreasing (BFD), Worst Fit Decreasing (WFD), and Second Worst Fit Decreasing (SWFD), which takes the bin with second maximum empty space.

4

Experimental Design

The experimental design comprised: CloudSim configuration and policies; parameters settings for applications, VMs and hosts; tests sets determining the size of the experiments; the optimisation algorithms and heuristics; and the resulting number of instances that each scenario uses.

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CloudSim Configuration and Policies. The task policy selected was the Dynamic Workload, because it is the policy specially dedicated for power purposes in CloudSim. The VM policy selected was the Time Shared Over Subscription. Due to the fact of unpredictable changes of the CPU of the tasks, it is safer and recommended to use this VM policy that protects the simulation when the CPU usage surpasses the 100%. When this happens, it redistributes the usage of the MIPS among all the VMs of the host, and the execution will not fail. Parameters Settings for Applications, VMs and Hosts. Applications have relevant parameters to be considered: CPU usage, Millions of Instructions (MI) and maximum memory capacity (MaxMemTask). The CPU percentage usage was filled by a real data center workload. Some traces from PlanetLab were selected [15], and they were configured that every five minutes of the execution the CPU usage is updated according to their files traces. It is assumed that the applications only use one CPU. To generate different experiments isolating MI and MaxMemTask, the mean and the standard deviation of these variables values were considered. These values follow a Gaussian distribution, making the experiment stable and repeatable. Millions of Instructions (MI) refers to the size of the application to be processed. The minimum mean value for this variable was 400.000, and it was increased by 400.000 until the maximum value of 8.000.000. For the standard deviation, the minimum value considered was 30.000, and it was increased by 30.000 until the maximum value of 90.000. All these combinations (20 different mean lengths and 3 standard deviations) were considered to analyze variations in the application’s length size. Maximum Memory Capacity (MaxMemTask) is the maximum memory required for the application (i.e. it is not the memory used in the execution time) and, consequently, once calculated, it will not change during the execution time. Three levels were considered: high, medium and small (Table 1 –units in MB). Table 1. Application parameters

Table 2. VMs parameters

Application memory levels

VM Type MIPS Ram (MB) Bandwidth PEs Size (MB)

Level

Mean Sd

Large

870

100 Mbit/s 1

High

750

100

Medium 2000 1740

100 Mbit/s 1

2500

Medium 500

100

Small

100 Mbit/s 1

2500

Small

100

Micro

100 Mbit/s 1

2500

250

2500

1000 1740 500

613

2500

Four types of VM were considered in our experiments. VM characteristics were based on Amazon EC2 instance types [13], with the only exception that the VMs were single-core. It is shown in Table 2. It is assumed that the CPU required for a VM must be single-core. Following the same reasoning as [8], if the CPU capacity required for a VM is higher than

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the capacity of a single core, then a VM must be executed on more than one core in parallel (it is not assumed that VMs can be arbitrarily parallelized). The bandwidth of all VMs types are 100 Mbit/s, where each task uses the 10% of its VM bandwidth. The proportion of VM types is the same to ensure that the study is balanced in the allocation of applications. Three host types were considered in our experiments, reusing partially the configuration defined in [8] (see Table 3). Table 3. Parameter settings for Hosts Power Model

MIPS Ram Bandwidth

Storage PEs

HP ProLiant ML110G3 1200

4096 1000000 GB/s 1 TB

2

HP ProLiant ML110G4 1860

4096 1000000 GB/s 1 TB

2

HP ProLiant ML110G5 2760

4096 1000000 GB/s 1 TB

2

Servers power consumption characteristics are shown in Table 4 (consumption in Watts per CPU percentage used in the server). The proportion of hosts types is the same to ensure that the study is balanced in the VMs allocation. Table 4. Servers characteristics of the experiments 0%

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

105 112 86

118

125

89.4 92.6 96

93.7 97

101

105

131

137

147

153

157

164

169

99.5 102

106

108

112

114

117

110

121

125

129

133

135

116

Test Sets. 10 test sets were considered for various experiment sizes (Table 5). The number of selected VMs is multiple of four to have the same proportion of VM types. The number of hosts for each test set was assigned to 2100. This environment simulates many servers of low processing capacity compared with the incoming applications size, but there are enough machines to process the incoming applications. It could be considered as a batch oriented approach. The longer a task is running in the data center, the more intensive the scenario will be, giving the chance for the datacenter to explore the selected algorithms. The number of hosts is multiple of three to have the same proportion of type hosts. Only one datacenter was considered to study the static and dynamic assignments without considering network complexities. Selected Algorithms. Several algorithms were selected for each step of the process of Fig. 2. For the task placement, Repairing Genetic Algorithm was selected [4]. This implementation acts as a non-preemptive scheduling, which means that the tasks are not interrupted until they have finished, and these tasks will not change from the VM that initially execute them.

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1

2

3

4

5

6

7

8

9

10

VMs

12 24 52 100 152 300 500 752 1000 1200

Applications 15 30 60 125 180 350 600 900 1200 1400

VM placement algorithms used for the static and dynamic blocks are the same. The most relevant VM placement algorithms were selected from [7]. The detection of the host overloaded algorithm was Local Regression with a safety parameter of 1.2, and the VM selection was the Minimum Migration Time (MMT). Both heuristics were discussed in [8], and these two algorithms obtained the best results in the reduction of energy consumption. It does not mean that, in other scenarios, other type of algorithms [8,9] could be better. The VM placement algorithms used in the experiments were: Power Aware Best Fit Decreasing (PABFD) [8,9], Guazzone et al. [11], Modified Worst Fit Decreasing VM Placement [10] and Shi et al. [12] approaches. Details of these algorithms are omitted here. Experimental Scenarios. For each scenario, algorithms were executed with the number of test sets, the applications and VM parameters settings. Table 6 shows the number of combinations. Table 6. Experiments for each scenario

5

Test Sets N◦ Apps N◦ Sd’s Apps Length Length

Mem. Levels

Algorithms Combinations

10

3

5

20

3

9000 instances

Experiment Results

Two scenarios were considered: an empty datacenter, which means that no application is running at the beginning of the simulation; a loaded datacenter, which means that some applications are already running in the datacenter when incoming applications arrive. Results are summarised in the next subsections. The characteristics of the hosts and the applications running in the loaded scenario are shown below: – One third of the hosts are loaded creating equally different types of VMs. These VMs are distributed also equally to the different host types. – All the generated applications have a constant CPU usage of 20%. – The mean application length used for the incoming applications is the same for all the applications that are being executed in the datacenter. – The maximum memory application used for the incoming applications is the same for all the applications that are being executed in the datacenter. – All these applications use the 10% of the VM bandwidth.

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341

Empty Datacenter

Figure 3 (left) shows the energy consumption. Guazzone’s algorithm achieves the best results closely followed by PABFD and MWFDVP. ShiAC and ShiPU obtain a high energy consumption when the test size increases. Figure 3 (right) shows the simulation time. Guazzone’s algorithm improves the performance when compared to other algorithms. PABFD and MWFDVP obtain intermediate results, while Shi approaches do not perform well.

Fig. 3. Energy consumption and simulation time in an empty datacenter scenario.

Figure 4 (left) shows the quality of service. PABFD, Guazzone and MWFDVP algorithms guarantee good SLA, while Shi approaches cause lots of SLA violations. Figure 4 (right) shows the number of migrations. Shi approaches obtain a high number of migrations compared to the others, affecting SLA, energy, etc.

Fig. 4. QoS and n◦ of migrations per algorithm in an empty datacenter scenario.

5.2

Loaded Datacenter

Figure 5 (left) shows the energy consumption generated. Guazzone and PABFD obtain the best results, while MWFDVP consumes a bit more energy. Shi approaches generate a high energy compared to the rest. Figure 5 (right) shows the simulation times of each algorithm. PABFD and Guazzone obtain the best results among the other candidates. Shi approaches need almost twice as much time compared to the previous ones.

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Fig. 5. Energy consumption and simulation time in a loaded datacenter scenario.

Figure 6 (left) shows the comparison of quality of service. PABFD, MWFDVP and Guazzone produce nearly zero violations, while Shi’s approaches cause a high number of violations. Figure 6 (right) shows the number of migrations per algorithm. Shi’s approaches spend the vast majority of migrations. PABFD, MWFVP and Guazzone consider that moving VMs towards other hosts will not improve the performance, even though the servers could be overloaded.

Fig. 6. QoS and n◦ of migrations per algorithm in a loaded datacenter scenario.

6

Conclusions

In this paper we have presented a comparison study on various optimisation algorithms for VM placement in data centers aiming to minimise energy consumption. From the results, we conclude that Guazzone algorithm [11] is the most interesting approach in terms of energy consumption, solution time, quality of service and resource memory efficiency considering the predefined tested scenarios. However, PABFD approach from Beloglazov et al. [8] and Chowdhury [10] methods almost obtained similar results. Shi’s approaches presented in [12] and discussed in [7] as one of the best candidates for energy-aware consumption did not obtain good results in our experiments. Shi’s approaches obtained a large number of migrations producing overheads that aggravate metrics such as energy consumption and the quality of service. As future work, other algorithms could be considered to extend this study. Also, other scenarios could be useful to analyze a higher rate of incoming applications or changing the VMs types. Finally, the Container as a Service module could be used for a service model vs. the Infrastructure as a Service model.

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Acknowledgment. This article is based on [17], where a complete study of the optimisation process and loading prediction is discussed. The authors would like to thank to the RDLab cluster [14] for their support.

References 1. AlIsmail, S.M., Kurdi, H.A.: Review of energy reduction techniques for green cloud computing. Int. J. Adv. Comput. Sci. Appl. 7, 189–195 (2016) 2. Agarwal, S., Datta, A., Nath, A.: Impact of green computing in it industry to make eco friendly environment. J. Global Res. Comput. Sci. 5, 5–10 (2014) 3. Ghani, I., Niknejad, N., Seung, R.: Energy saving in green cloud computing data centers: a review. J. Theor. Appl. Inf. Technol. 1074, 16–30 (2015) 4. Vasudevan, M.: Profile-based application management for green data centres. Ph.D. thesis, Queensland University of Technology (2016) 5. Caliskan, M., Ozsiginan, M., Kugu, E.: Benefits of the virtualisation technologies with intrusion detection and prevention systems. In: 7th International Conference on Application of Information and Communication Technologies, pp. 1–5 (2013) 6. Cleveland, W.S.: Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836 (1979) ´ am Mann, Z., Szab´ 7. Ad´ o, M.: Which is the best algorithm for virtual machine placement optimisation? Concurrency Comput. Pract. Experience 29(10), e4083 (2017) 8. Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency Comput. Pract. Experience 24(13), 1397–1420 (2011) 9. Beloglazov, A.: Energy-efficient management of virtual machines in data centers for cloud computing. Ph.D. thesis, University of Melbourne (2013) 10. Chowdhury, M.R., Mahmud, M.R., Rahman, R.M.: Study and performance analysis of various VM placement strategies. In: 16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Japan, pp. 411–416 (2015) 11. Guazzone, M., Anglano, C., Canonico, M.: Exploiting VM migration for the automated power and performance management of green cloud computing systems. In: Energy Efficient Data Centers - First International Workshop, pp. 81–92 (2012) 12. Shi, L., Furlong, J., Wang, R.: Empirical evaluation of vector bin packing algorithms for energy efficient data centers. In: IEEE Symposium on Computers and Communications, Croatia, 9–15 (2013) 13. Amazon EC2 Instances Types. https://aws.amazon.com/es/ec2/instance-types 14. Rdlab. https://rdlab.cs.upc.edu/index.php/en 15. Planetlab. https://www.planet-lab.org/ 16. CloudSim. http://www.cloudbus.org/ 17. Olvera, A.: Implementation and Evaluation of Profile-based Prediction for Energy Consumption in a Cloud Platform. Master’s thesis. Technical University of Catalonia (2017)

Research on Customer Churn Prediction Using Logistic Regression Model Hong-Yu Hu(&) Yongzhou Vocational Technology College, Hunan, China [email protected]

Abstract. How to keep customers’ loyalty and prevent customer churn is an important problem for airlines. Logistic regression model is a tool for prediction customer churn. This paper is to segment airline customers into four groups, set different churn rules to evaluate churn rate and analyze customer churn propensity based on logistic model. With the help of these strategies, the airlines can take positive and effective measures to reduce the company’s operating costs and enhance the company’s core competencies. Keywords: Customer churn prediction Airline company


Logistic regression model

1 Introduction Competition for customers is mainly problem for aviation industry. The view that Customer is God leads many airlines to compete for as many customers at all costs. In the process of fighting for customers, however, airlines tend to ignore the loss of existing customers. The result will lead to such a predicament: the number of new customers is increasing, while the existing customers are declining gradually. So how to maintain customer relationships, to increase customer loyalty and prevent customer churn, is of great significance to reduce operating costs, to enhance competitiveness of enterprises, and obtain the maximum benefit [1]. Classical 2–8 rule tells us that if the loss of 20% the core customers will have a disastrous impact. The aim to predict customer churn tendency for the aviation industry is to prevent the loss of high-value customers, to increase customer value in the low-viscosity, to retain customers effectively and create more profit for the company through accurate prediction of the customer churn [2].

2 Definition of Business Requirements In the fierce market competition, airlines are facing the frequent loss of flyers. Establishing Logistic regression model to gain the churn rate, the quantitative degree of customer churn and churn propensity detected early, then taking positive and effective retention measures are necessary and effective. This requires: (1) clear target customer groups; (2) determine the time window required for the model and definition of customer churn; (3) determine indicator variables required for the churn model, establish © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 344–350, 2019. https://doi.org/10.1007/978-3-030-02804-6_46

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churn score models to rate churn, to achieve accurate early warning; (4) understand customer loyalty. 2.1

Target Customer Groups

According to business understanding, customers who will participate in modeling should meet the following two requirements: (1) Membership Age  one year (namely, the end date of the observation window-the date of admission  one year); (2) the times of the flight  twice. 2.2

Classification Standards of Target Customers

When determining the customer churn standard, the churn definition of different groups is different. According to the business needs, we should divide target customers into four groups based on flight number, upgrade mileage and average discount. Each group is defined as follows: (1) Super high-end group: (the customers whose flight number is in top 1% or upgrade mileage is higher than 80000) and (the customers whose average discount rate is higher than 0.8); (2) High-end group: (the customers whose flight number is between top 1% and 10% or 40000  upgrade mileage  80000) and (the customers whose average discount rate is higher than 0.8); (3) Middle-end group: (the customers whose flight number is between top 10% and 30% or 10000  upgrade mileage  40000; (4) Low-end group: other members. 2.3

Definition of Time Window

Based on the actual business situation, the time window for logistic regression model is set as follows: (1) observation window: 24 months, collecting behavioral data of members; (2) empty window (lag window): 0 months; (3) performance window: 12 months, collecting behavioral data of members. 2.4

Indicator Variables

Logistic churn model involves three types of indicator variables: (1) contents index value of observation window: the total number of flight, the total accumulated points of upgrade mileage, the total accumulated points, the sum of non-flight points, the average discount rate, the interval from the last time by air to the time of observe window, the maximum opportunity interval of the observation window, Observation window for the first time took the opportunity to join the interval of time (beginning of observation window, initiation time), Average flight time interval, the times of change of non-flight points, the length of membership (the age of the card). (2) The window index value last year: flight number, mileage points, the total accumulated points of observation window, the sum of non-flight points. (3) The ratio values of the construction of indicators: the ratio took the opportunity last year, accounting for nearly two points in the

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proportion of mileage. Variables which enter into model need to be selected twice, firstly, for a high degree of similarity variables, doing the variable correlation analysis, leaving only one, removing the other. Then when the variables are be automatically selected in the model building, finally the variables that we need are gained. 2.5

Definition of Churn

Generally, the meaning of customer churn is a phenomenon that customers choose to terminate cooperation caused by various reasons. According to specific data, defining the concept of churn is when compared to behavioral data of the observation window, the behavioral data of the performance window bellows a certain criterion, taking into account the actual business of aviation industry, the churn of each group can be defined as follows. (1) Super high-end group: the customers whose Quarterly average number of flight in performance period is less than 20% of that in observation window and mileage in performance period is less than 20% of that in observation window, are defined as churn. (2) High-end group: the customers whose Quarterly average number of flight in performance period is less than 20% of that in observation window and mileage in performance period is less than 10% of that in observation window, are defined as churn. (3) Middle-end group: the customers whose Quarterly average number of flight is zero and mileage in performance period is zero, are defined as churn. (4) Low-end group: the customers whose Quarterly average number of flight is zero and mileage in performance period is zero, are defined as churn. Because of end-groups and low-end groups have the same definition, we combine them together for processing when predicting churn rate.

3 Modeling Methods and Procedures 3.1

Modeling Idea

Airlines use Logistic regression model for customers churn prediction. Different from classical linear regression model, logistic regression model is a special kind of regression model, and its response variable is a categorical variable rather than continuous variable and is a binary variable which indicates an event occurs or not in 1\0. Logistic model has been applied to get a probability p which means that the probability of an event occurring after a certain time in the future. Logistic regression model variables involved in some calculated variables, specifically, the meaning of the variables as follows: (1) Odds: number of target events/number of non-target events; (2) Odds Ratio: the probability of target event/the probability of non-target event = p/ (1 − p), Thereinto, p = prob(target event), prob means the probability of target event; (3) Logit: log of odds ratio = log(p/(1 − p)), By Logit transformation, the dependent variable can be transformed into a linear function between 0 and 1.

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So we can Construct a linear equation with independent variable, Namely: Logit ¼ a0 þ a1  X1 þ a2  X2 þ


þ an  Xn: The regression coefficients (a0, a1, …, an) is estimated by maximum like hood, finally, the probability of occurrence p is gained by the logit inverse, namely: p = exp (Logit)/(1 + exp(Logit)), the curve of probability P as Fig. 1.

Fig. 1. Logstic regression curve

3.2

Data Preparation

Extracting train data and test data from the data warehouse: the train behavioral data from July 1, 2014 to June 30, 2016 is used for the performance data, while data from July 1, 2016 to June 30, 2017 is used for the observation data. The test behavioral data from October 1, 2014 to September 30, 2016 is used for the performance data, while data from October 1, 2016 to September 30, 2017 is used for the observation data. Test data is used to validate the model correctness. 3.3

SAS Logistic Regression Process

Calculation process of churn prediction involves two main processes: standard and Logistic. The Logistic regression in SAS system [3] is the most important churn prediction algorithm, and its processing techniques are in the SAS in Table 1.

4 Model Results Taking super high-end customer groups as a example, this paper gives decile distribution chart, lift value chart, cumulative churn rate chart of training samples and test samples, and gives some explains of the results. The processing steps of the model score are as follows: (1) Descending the calculated churn rates of each customer according to the model, dividing customers descended into ten groups equally, each group includes two kinds of people, one is churn group, the other is maintain group, the customers who are in more front are more likely to lost.

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H.-Y. Hu Table 1. SAS logistic regression process

Step1: According to business needs, extracting the key variables from dataware; Step2: Data preprocessing:null values for key variables is set to 0, while calculating the corresponding value of derived variables by using value of key variables and the same algorithms; Step3: Doing standardization on data from step2 to eliminate impacts from different dimensions; Step4: Doing correlation analysis on data from step3, testing whether data is suitable for Logistic regression; Step5: Testing whether data is suitable for Logistic regression or not by using Pearson correlation coefficient rules; Step6: Doing Logistic regression on data processed standardization, marking 1_rank_1=1 means that the customer is losing, while 0 indicates that the customer is retaining. According to the formula: P=exp ( logit ) / [ 1 + exp ( logit ) ] to calculate the probability P based on various parameters from Logistic models. It is not easy to understand in the application since P is a decimal between 0 and 1. So we need convert P into a form easy to understand, making P*1000 in the practical application is used; Step7: Drawing cumulative churn rate curve, loss distribution, Lift Value to assess the quality of training model after model fitting, generally, Lift Value of a good predicted model is between 2.5 to 5.5.

(2) The churn rate distribution reflects the relativities between the number of the churn customers and the number of the retained customers each group, the more front the group, the more customers lost. Through the distribution, we can calculate churn rate for each group. (3) Calculating lift value, namely, the each group churn ratio divided by the whole churn ratio. This value reflects that gaining the churn customer through the logistic model is better than through random method. (4) Cumulative churn rate, the cumulative distribution can be obtained by cumulative statistics for ten group churns. For example, the number of the first group and second group churn customers divided by the total number of churn customers is the first group and the second group cumulative churn rate. Based on the cumulative churn rate and according to business needs, Management position many clear the customer to retain and improve the accuracy of marketing and save cost. 4.1

Training Samples

There are 7493 customers in super high-end group and the number of churn customers is 445 for the training samples. The churn rate distribution, lift value chart, cumulative churn rate of training samples are shown in Figs. 2, 3 and 4. 4.2

Test Samples

There are 7493 customers in super high-end group and the number of churn customers is 460 for the test samples. The churn rate distribution, lift value chart, cumulative churn ration of test samples are shown in Figs. 5, 6 and 7. Above data show that: based on actual business, each parameter symbols and size of training samples are line with business relations. From churn rate distribution, Lift curve, cumulative churn rate curve, we can know that the results are similar between training samples and test samples, it indicates that model is stable and can be referenced.

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Fig. 2. The churn rate distribution of training samples

Fig. 3. Lift value chart of training samples

Fig. 4. Cumulative churn rate of training samples

Fig. 5. The churn rate distribution of test samples

Fig. 6. Lift value chart of test samples

Fig. 7. Cumulative churn rate of test samples

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5 Application of Marketing Strategy Businesses need to target the customer to promote based on business policy, the churn rate distribution, the cumulative distribution, to address “on who does what” problems and improve the accuracy of marketing, reduce marketing costs. Do the following: (1) According to the churn distribution, know the number of loss and existed; (2) According to the lift value distribution chart, select the groups whose lift value is greater than or equal to 1 as a candidate for promotion to retain; (3) According to the cumulative churn distribution and marketing costs that the company invested this time to calculate target customer groups into marketing, if the cumulative churn ratio to 70% of the group as the marketing people. So formulating a precise marketing strategy to minimize the cost to retain customers and maximize profits. (4) By churn analysis, we can help airlines to understand customers’ loyalty. For example, we can know which customers are stable groups, which customer groups need further promotion, which customers are irreversible and so that we can implement targeted marketing strategies. Acknowledgments. The work is supported by Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (No. CAAC-ITRB-201206).

References 1. Hui Cong net.Churn due to the eight reasons. http://info.biz.hc360.com/2009/08/ 17082989221.shtml. Accessed 22 Jan 2018 2. Guohe, F.: Analysis of aviation CRM system based on SAS data mining technology. J Inf 25 (5), 56–59 (2006) 3. SAS. http://www.sas.com/. Accessed 20 March 2018

One of the Smote_rf’s Gender Prediction Methods in Recommendation System Huang Meigen(&) and Cui Wenhao School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400000, China [email protected]

Abstract. With the rapid development of the Internet, leading to the problem of information overload, how to find the satisfied demand from the overloaded information becomes an urgent problem to be solved, which leads to the emergence of a recommendation system. In order to solve the problem of the lack of sex of the user and the imbalance of existing samples in the recommendation system, this paper proposes to combine the smote with the random forest to forecast the gender. Compared with other models, the experimental results show the effectiveness of the proposed method by conducting experiments on real e-commerce platform data. Keywords: Recommended system Random forest
K-neighbors


Unbalanced datasets
Classification

1 Introduction With the rapid development of the Internet and the ever-growing e-commerce platform, the passion for online shopping has also risen. However, in the face of an array of objects, how people choose to suit their interests and hobbies has become an important issue. In this way, the recommender system is generated, and it plays a more important role in our daily life. Recommended system is mainly in the user does not have a clear purpose of the demand, recommended items of interest to users based on the user’s historical behavior, this will not only improve the user’s shopping experience, but also to promote business sales, to achieve win–win results [1]. Due to the fact that gender is a very important factor during the recommendation process, but gender is the privacy data of users. Many users keep their own gender in the process of registration, thus resulting in unbalanced samples and the serious lack of gender [2]. This paper presents a fusion algorithm of smote_rf to solve such problems, improve the accuracy of user gender prediction, and then improve the accuracy of recommendation system and improve the user experience.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 351–355, 2019. https://doi.org/10.1007/978-3-030-02804-6_47

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2 Related Technologies For the problem of category imbalance, the existing algorithms are under-sampled and oversampled. Under-sampling is to randomly remove a part of samples from a data set with a large number of categories. The main idea is to randomly sample some samples in the data set with a large number of categories, The sample is proportionally balanced and then studied [3]. The disadvantage of this method is that it makes data loss seriously. Oversampling is to expand the category of small data sets, the main idea is to randomly copy the sample in the category of less data, so that the final positive and negative proportional sample balance, and then learn [4]. The disadvantage of this method is easy to cause over-fitting. For the problem of gender prediction, it is a dichotomous problem in supervised learning. The existing machine learning algorithms include classifier KNN, logistic regression, Decision Tree and Random Forest, among which KNN [5], Decision Tree [6] are classified into a single model, random forest is an integrated model.

3 A Smote_rf Fusion Gender Prediction Method Due to the lack of serious data and the unbalanced categories in the recommendation system, gender is a very important factor during the recommendation process. For the problem of category imbalance, the conventional method is that the data lack of data is serious or easy to be fitted after sampling. Therefore, in this paper, the smote algorithm is used to first sample the samples so that the sample types are balanced. The smote algorithm is an improvement on the random oversampling algorithm. The main idea is to classify the samples Few datasets add new samples by means of interpolation instead of simple duplication. Finally, the categories of samples are balanced [7]. By smiting algorithm to solve the random under-sampling and random oversampling problems, but also to further solve the problem of sample imbalance. After the sample balanced problem is solved, then we need to solve the problem of gender prediction. This paper mainly makes use of the random forest in machine learning for gender prediction. This algorithm uses a random sampling method to train m decision trees. When a new sample is obtained, each decision tree that has undergone the above training is firstly decided by a vote, and finally a vote is used to determine which category the sample belongs to [8]. To aim at the problem of sample imbalance and the lack of categories seriously in the recommended system, this paper puts forward a smote_rf smote algorithm and random forest algorithm integration of a gender prediction method.

4 Experimental Simulation and Analysis of Results The data used in this experiment is the desensitization of 1038 users basic information data and user behavior data from the e-commerce platform. The basic information of the user includes the user’s age stage, the user’s level, the user’s registration time, the user’s gender, and the user’s behavior data including the user’s click behavior on a brand product, including browsing, purchasing, adding to a shopping cart, deleting shopping Car, click and other activities.

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353

Data Preprocessing

First of all, remove the sample data missing data and outliers, the discretization of some of the attribute data, and then the user basic information table with the user’s behavior table to connect to get the experimental data table, and then the data Normalized processing, model processing after the completion of training. 4.2

Using Knn Training Different Sampling Data

According to the data obtained, firstly, 75% of the samples were randomly selected as the training samples and 25% of the samples were used as the test samples by using the smote algorithm sampling data, the smote algorithm sampling data and the random oversampled data, using knn Model to train for further gender predictions. Experiments with confusion matrix [9] as the evaluation criteria for the comparison of experimental results. The experimental results are shown in Table 1. Table 1. Unsampled results, smote sampling results, random oversampling results P R F1 S 0 0.87 0.89 0.88 894 1 0.18 0.16 0.17 144 m 0.77 0.79 0.78 1038

P 0.81 0.78 0.79

R 0.78 0.81 0.79

F1 S 0.79 925 0.79 876 0.79 1801

P 0.82 0.66 0.74

R 0.57 0.87 0.72

F1 S 0.68 925 0.75 876 0.71 1801

The comparison of Table 1 first F1, second F1 shows that the unbalanced data of unsampled samples are poor for prediction of category 1 and tend to be predictor of category 0. After smote sampling, the experimentally balanced prediction of categories. Analysis of second F1, third F1 were compared and found that after smote sampling results than after random oversampling F1 test results increased by 8%, so the smote algorithm selection oversampling better. 4.3

Using Different Models for Experiments

Using smote algorithm for oversampling data using knn, decision tree, random forest three models for comparative experiments, also randomly selected 75% of the sample as a training sample, 25% of the sample as a test sample, the use of different models to Train for further gender predictions. Experiments with confusion matrix and F1 value as the evaluation criteria for the comparison of experimental results. The experimental results are shown in Table 2. Through the comparison of the results of Table 2 first F1, second F1, third F1, it is found that when the data of smote sampling are trained by using random forests, the value of F1 is increased by 9% compared with the gender prediction by knn, the value of F1 is increased by 5% compared with the gender prediction by decision tree, further illustrating the effectiveness of the smote_rf method proposed in this paper.

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4.4

P 0.82 0.83 0.83

R 0.85 0.80 0.83

F1 S 0.83 925 0.82 876 0.83 1801

P 0.89 0.88 0.88

R 0.88 0.86 0.87

F1 S 0.88 925 0.87 876 0.88 1801

After Smote Sampling Data and Non-sampled Data Training Random Forest Model

Smote algorithm using the sampled and unsampled data were used to carry out random forest training for gender prediction, the experimental results shown in Table 3. Table 3. Unsampled, sampled data training random forest results P 0 0.86 1 0.15 m 0.76

R 0.97 0.03 0.84

F1 S 0.91 894 0.09 144 0.80 1038

P 0.89 0.88 0.88

R 0.88 0.86 0.87

F1 S 0.88 925 0.87 876 0.88 1801

Through the analysis of Table 3 first F1, second F1, it is found that when the model is trained after the unsampled data, the experimental results tend to be more categories and show that the forecasting equilibrium of the class is better than that of the smote_rf method Experimental results.

5 Conclusion Aiming at the importance of gender factors in personalized recommendation and user experience in recommendation system, this sample has the problem of sample imbalance and the lack of gender seriously. In view of the above problems, this paper proposes to combine smote algorithm with random forest algorithm smote_rf method to predict the gender of the proposed system. The experimental results show that the proposed method is more effective and accurate than the other existing models to predict the F1 value, which will provide better support for the next step of precision work. Acknowledgments. At the completion of this essay, I would like to thank all those who provided guidance on this essay. At the same time thanks experts and staff for reviewing my dissertation.

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References 1. Liu, H., Guo, M.-M., Pan, W.-Q.: A review of personalized recommender system. J. Changzhou Univ. (Nat. Sci.) 29(3), 50–59 (2017) 2. Zhang, F.Y.: E-Commerce Recommendation System of User Experience Factors. Guangxi University for Nationalities, Nanning (2017) 3. Cheng, X., Li, J., Li, X.: An under-sampled unbalanced data classification algorithm. Comput. Eng. 37(13), 147–149 (2011) 4. Gu, P., Yang, Z.: Oversampling algorithm for minority classifications in unbalanced data sets. Comput. Eng. 43(2), 241–247 (2017) 5. Wu, X., Wang, S., Zhang, Y.: A review of theory and application of K nearest neighbor algorithm. Comput. Eng. Appl. 53(21), 1–7 (2017) 6. Wu, X., Liu, Q., Wang, F.: Decision tree algorithm in practice. Ind. Control Comput. 30(12), 120–121 (2017) 7. Wan, B.-h., Ji, T., Chen, M.-r.: Classification of non-equilibrium attractions based on SMOTE and random forests. In: China Metallurgy Automation Information Network 2016, Maanshan, pp. 135–138 (2016) 8. Shen, J., Yu, H., Fan, G., Guo, J.: Design and implementation of recommendation system based on stochastic forest algorithm. Comput. Sci. 44(11), 164–167 (2017) 9. Xu, J., Miao, D., Zhang, Y.: Multi-objective optimization three-decision model based on confusion matrix. J. Pattern Recognit. Artif. Intell. 30(9), 859–864 (2017)

Traffic Flow Control Model with Two-Way Stop for Left-Turn Na Wang(&), Xinshe Qi, Xin Wang, and Ruiping Huang College of Information and Communication, National University of Defense Technology, Xi’an 710106, China [email protected]

Abstract. In this paper, for large and medium-sized traffic circles with heavy traffic volumes, an ordinary signalized model and a two-way stop-control model are established. It can realize the optimized volume control of a traffic circle to improve its capacity and safety, and to reduce its control delay. Finally, an optimized design is made for the time of green traffic-light. Keywords: Traffic circle


Capacity
Gap-acceptance theory
Signalization

1 Introduction 1.1

Main Idea

In metropolis, generally speaking, the traffic circle faces a heavy volume on both leftturn and straight traffic. Furthermore, the circle is not able to provide enough space for the queuing left-turn traffic stream due to a comparatively small diameter. Thus it is necessary to allocate reasonable space and time for stream of all directions to ensure the operation. Therefore, considering from traffic space, it is to isolate the streams of different directions by different lanes; considering from traffic time, it is to give the right-of-way to streams of different directions during different periods, thus to realize the time isolation of different directions. 1.2

Traffic Space Design

(1) Design of the channelization of traffic circles. The key to this channelization design is how to adjust the traveling route of the streams. The flare design of the incoming lanes is to better separate the left-turn and straight traffic stream in traffic space to make the route of the left-turn and straight traffic in the intersection as short as possible. (2) Lane allocation. According to the incoming traffic demand (volume and direction) and traffic space conditions, the left-turn lanes and straight lanes can be separated easily.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 356–362, 2019. https://doi.org/10.1007/978-3-030-02804-6_48

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(3) Traveling route of the left-turn traffic. Since the traveling space of the intersection reduces after channelization, to improve the traveling speed of left-turn vehicles, avoiding circular driving due to drivers’ uncertainty of this control method, we can deploy the traveling route line for left-turn traffic in the roadway surface of the busy intersection. Furthermore, according to the requirements of the guide lanes, the turning radius of left-turn vehicles should be larger than its theoretical lower bound. 1.3

Signalization

When the traffic circle faces a heavy left-turn and straight flow-volume, the gap between the left-turn vehicles and the straight vehicles is hardly to pass through due to a very small time headway, so it is suitable for multi-phase control [1]. The control method of cross-shape intersections can be generally adopted for signal timing.

2 Traffic Circle with Two-Way Stop-Control for Left-Turn Traffic Flow 2.1

Main Idea

“Two-way stop-control” for left-turn traffic flow is a signalization method for large circular intersections. In principle, this method adopts a two-phase traffic signalization. The incoming lane, yield line and traffic lights are shown in Fig. 1.

Fig. 1. Space structure of the traffic circle.

As illustrated in Fig. 1, the FIRST YIELD LINE and special left-turn lane are built in the incoming lane with a special left-turn traffic light straight ahead. And the SECOND YIELD LINE is also built in front of the conflict point of the left-turn vehicles from each incoming and its opposite lanes in the circulatory roadway, equipped with special traffic lights. Then the left-turn vehicles would pass the circle

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more easily and wait for up to two going signals (the incoming lane and the circulatory roadway separately). 2.2

Technical Process of the Improved Design for Traffic Circles

For the best performance of traffic control and management, it is necessary to improve the design of traffic circles reasonably [2]. Traffic Space Design: According to the characteristics of incoming traffic demand (flow rate and direction included) and travel space needs, the incoming lanes can be categorized into the three groups: left-turn lane, straight lane and right-turn lane, respectively. Correspondingly, the circulatory roadway need to be categorized into two groups: left-turn lane and straight lane. Yield Line Design: To achieve “two-way stop-control” of the left-turn traffic flow, yield lines must be installed in incoming lanes and circulatory roadway separately to ensure the orderly operation of the traffic. Traffic Lights Configuration: For best control of the left-turn and straight vehicles from the incoming lanes and the left-turn vehicles from the circulatory roadway [3], the signal devices and relative traffic lights with gang control function need to be equipped reasonably. Four groups of arrow traffic lights should be installed at a position visible to the drivers ahead of the FIRST YIELD LINE to control the left-turn and straight traffic from the incoming lane. And four other groups of ordinary traffic lights should also be installed at a position visible to the drivers ahead of the SECOND YIELD LINE to control the left-turn vehicles in the circulatory roadway. 2.3

Main Parameters in Signalization

(1) Cycle time Since stop for the left-turn vehicles is permitted in the circulatory roadway, the cycle time should be no less than the sum of the circulation time left-turn vehicles in the circulatory roadway. We assume that tci stands for the circulation time of the left-turn vehicles waiting at the ith YIELD LINE, then the average value tci ¼

Cil  6 þ 19:5 sc

ð1Þ

where sc is the saturation flow rate of the circulatory roadway, i = 1, 2, …, r, and tj is assumed to be the maximum value of the circulation time of each group of left-turn vehicles being waiting during the same process. So the shortest signal cycle time cc needed by the vehicles in the circulatory roadway can be calculated as following cc ¼

k  X  tj þ lj

ð2Þ

j¼1

where k is the phase value of a single cycle time, and l is the lost time of each phase.

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In the incoming lane, the signal cycle time of the straight vehicles could be calculated through the following Webster formula: ce ¼

1:5L þ 5 1Y

where L is the total lost time of each cycle time, and Y ¼

ð3Þ k P

yj , where yj is the maximal  q0  q ratio of the straight traffic flow in phase j, that is to say, yj ¼ max scj ; scj ;


. So the j¼1

cycle time of the traffic lights should be c ¼ maxðce ; cc Þ

ð4Þ

(2) Queue analysis of the left-turn Based on the traffic-signal control proposed in this paper, the queue of the left-turn vehicles in front of the SECOND YIELD LINE can be provided and then analysed on the circulatory roadway. The signal phase sequences are illustrated by Fig. 2. In Fig. 2, “Direction I” represents the vehicle traveling results of approaches 2 and 4, while “Direction II” represents the results of approaches 1 and 3.

Fig. 2. Sequence process of the traffic-light control.

For Direction I in Fig. 2, the traffic-light sequences are explained as follows. (a) At approaches 2 and 4, the straight vehicles in front of the first stop lines can meet the green traffic-light opportunely and then start to move ahead. (b) While the straight vehicles keep moving, those left-turn vehicles can also meet green time, move ahead, and then wait a moment in front of the SECOND YIELD LINE. For example, at approaches 2 and 4, left-turn vehicles will queue in front of the line DD0 . As a result, it will continue to avoid confrontation with those vehicles from approach 4.

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(c) The green traffic-light for left-turn vehicles on approaches 2 and 4 will expire. Obviously, the straight vehicles will keep moving ahead until its corresponding green traffic-light expires. (d) Left-turn vehicles which are queuing in front of the SECOND YIELD LINE can meet the green traffic-light and then start to move ahead. For example, at approach 4, the green traffic-light will expire for straight vehicles. Then those vehicles which are queuing in front of the line DD0 will move ahead until its corresponding green traffic-light expires. As shown in Fig. 2, the traffic-light sequences of Direction II are very similar with Direction I. Note that, if the rate of traffic flow are significant different between different approaches, the straight vehicles flows even from the same direction should not undergo the same sequence process. In this case, the green traffic-light should be adjusted based on the equations mentioned above. (3) Initial time of green traffic-light for the straight vehicles The total effective time (Ge) of green traffic-light for each cycle can be computed as Ge = c − l. In general, it is assumed that the straight vehicles on approaches i and i + 2 (such as approaches 1 and 3 in Fig. 1) undergo the same sequence process. Consequently, the effective time of green traffic-light can be calculated as following   max VSii þ VSii þþ 22 G0i ¼ G0i þ 2 ¼ Ge  ð5Þ k P Yj j¼1

where G0i , Vi and Si are the effective time of green traffic-light, traffic-flow rate and saturation flow rate of straight vehicles on approach i, respectively; G0i þ 2 , Vi+2 and Si+2 are those for straight vehicles on approach i + 2, respectively. (4) Time modification of green traffic-light for straight vehicles Of course, the traffic-flow rates on approaches i and i + 2 (such as approaches 1 and 3 in Fig. 1) are different for the general case. Therefore, the effective time of green traffic-light on those two approaches may much different and then need to be adjusted to get a high similarity of their saturation. "  # min Yis ; Yisþ 2 a 0   ti;i þ 2 ¼ Gi  1  ð6Þ max Yis ; Yisþ 2 where Yis and Yisþ 2 are the ratios of traffic-flow rates to saturation flow rates on approaches i and i + 2, respectively. After the modification, the effective time (Gsi ) of green traffic-light for straight vehicles can be obtained as following

Traffic Flow Control Model with Two-Way Stop for Left-Turn a a Gsi ¼ G0i  b  ti;t þ 2 þ d  ti þ 1;i þ 3

( where b ¼

0; 1;

Yis  Yisþ 2 Yis \Yisþ 2

( , and d ¼

Yisþ 1  Yisþ 3

0; 1;

Yisþ 1 \Yisþ 3

361

ð7Þ

.

(5) Time of green traffic-light for the left-turn vehicles on approach Obviously, in order to determine the effective time Gli of traffic-light for the left-turn vehicles on an approach i, the traffic capacity (Ccl ) of the left-turn vehicles on the circulatory roadway have to be considered in this paper. The calculation equation is described as follows: 8   l s Cc > > < min Gi ; nli Sli Gli ¼ Gsi ; > > : Gs  Yisl ; i

Yi

qli Gc  Ccl qli Gc \Ccl and Yil  Yis qli Gc \Ccl

and

ð8Þ

Yil \Yis

where qli is the left-turn traffic-flow rate on approach i; Yis is the ratio of left-turn trafficflow rate to saturation flow rate on approach i; nli and Sli are the number of left-turn lanes and the left-turn saturation flow rate on approach i, respectively. (6) Time of green traffic-light for left-turn vehicles on the circulatory roadway As illustrated in Fig. 1, the left-turn vehicles in front of the SECOND YIELD LINE BB0 on approach 1 may conflict with the vehicles on approach 3. Thus, we can have Gs3  Gl3 based on the Eq. (8). That is to say, the time of green traffic-light for straight vehicles is larger than that for left-turn vehicles on approach 3. As a result, the effective time (Gdi ) of green traffic-light for the left-turn vehicles in front of the line BB0 on approach i can be obtained as follows Gdi ¼ c  2Iid  Gsi þ 2

ð9Þ

where Gsi þ 2 is the effective time of green traffic-light for straight vehicles on approach i + 2, and Iid is the interval time between Gdi and Gsi þ 2 .

3 Conclusion As mentioned above, we analysis the traffic circles in some different cases such as cycle time, queue of the left-turn, initial time of green traffic-light for the straight vehicles, time modification of green traffic-light for straight vehicles, time of green traffic-light for left-turn vehicles on approach, and time of green traffic-light for left-turn vehicles on circulatory roadway, respectively. For large and medium-sized traffic circles with heavy traffic flow volumes, an ordinary signalized model and a “two-way stop-control” model are established. It can realize the optimized volume control of a traffic circle to

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improve its capacity and safety, and to reduce its control delay. Finally, an optimized design is made for the time of green traffic-light in this paper.

References 1. Hughes, B.P.: So you think you understand gap acceptance. Aust. Road Res. 19(3), 195–204 (1989) 2. Yang, J.D., Yang, X.G., Peng, G.X.: Mode of traffic control in ring intersection. J. Highw. Transp. Res. Dev. 17(3), 47–52 (2000). (in Chinese) 3. Xue, K., Bai, Y., Yang, X.G.: Optimization of control method for roundabout at fixed cycle. J. Highw. Transp. Res. Dev. 21(5), 54–57 (2004). (in Chinese)

Automotive Brake System Design Zhiqiang Xu(&) Guangdong University of Science & Technology, Dongguan 523083, China [email protected]

Abstract. From the moment the car was born, vehicle braking systems play a crucial role in vehicle safety. In recent years, with the advancement of vehicle technology and the increase in the speed of automobiles, this importance has become increasingly apparent. There are many types of automotive brake systems and they come in various forms. The traditional structural types of brake systems include mechanical, pneumatic, hydraulic, and gas-liquid hybrid types. Their working principles are basically the same. They use braking devices to gradually consume the kinetic energy of the vehicle with the frictional heat generated during the operation, so as to achieve the purpose of braking the vehicle to slow down or stop until parking. With the research and development of energy-saving and clean energy vehicles, the automotive power system has undergone great changes, and many new structural types and functional forms have emerged. The emergence of new power systems also requires a corresponding change in the type and function of the braking system. Keywords: Automobile


Braking system
Structural type
Functional form

1 Introduction The automobile brake system refers to a special brake mechanism for installing a brake device on a vehicle in order to make the car’s brake system more secure. The brake system of a car is normally used normally without safety problems. However, there are always special circumstances in life that require a higher performance of the brake system. Therefore, various types of auxiliary brake devices are often added to these vehicles so that when downhill Stable speed. In the following, the composition and working principle of the braking system are introduced first, and then the automobile braking system is designed.

2 The Composition of the Braking System The ABS anti-lock brake system is an automotive electronic system that includes an electronic control unit, several wheel speed sensors, and a valve that contains a solenoid valve. Figure 1 below shows the structure of the braking system.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 363–368, 2019. https://doi.org/10.1007/978-3-030-02804-6_49

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Fig. 1. Braking system

2.1

Electronic Control Unit

Electronic control unit grasps running status of the wheel through the pulse change transmitted from the wheel speed sensor. When a certain wheel is likely to be locked, the electronic control unit will issue a command to the electric booster to achieve the purpose of controlling the brake and adjusting the speed through a quick switch. In general, this fast switching valve can operate up to 12 times per second. Some advanced models of ABS can have four sets of loop systems, namely “four-circuit ABS”. This high-tech product is mainly electronic and mechanical. The electronic safety factor is higher and the price is higher. It was originally deployed only on large luxury cars. Nowadays, small and medium-sized ordinary cars have begun to be deployed. Only the mechanical safety factor is low and the price is low. Generally, it is only used on commercial vehicles. In China, Dongfeng Motor Corporation began to organize the development of ABS in 1992 and passed technical appraisal in December 1995. The “Che” brand ABS developed by Sichuan Che’an Technology Development Co., Ltd. has been used in some mini-vehicles. ABS has become the mainstream of automobile science and technology in the 20th century. The era in which all automobiles are equipped with ABS is not far off. The famous Marxist educator Macalenko once talked about human self-control, for example: “Without the brakes, there can be no cars.” Obviously, the brake system of a car is the same as human self-control, and it is something that must be kept. However, although the traditional braking system can play an emergency braking role, it easily leads to the locking of the automobile tires. When the tire is locked, the car will continue to travel a distance on the road due to inertia control. At the same time, since the brake system of the general automobile is immediately locked after an accident, the tire at this time is completely translated on the ground, and it is easy to wear, and the possibility of an accident is also greatly increased. The installation of ABS is to solve the problem of wheel lock when braking. The working principle is also like a stepping on the above, but the working frequency of ABS is much faster than the manipulation of human [/size]. A car equipped with ABS can effectively control the wheel to remain in a rotating state without being locked, thereby greatly improving the stability of the car during braking and the braking performance of the car under poor road conditions. The principle of this system is to maintain the rotation of the tires and prevent the tires from locking when the

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emergency brake is applied, so as to maintain the maximum braking force. At the same time, the wheel is quickly, accurately, effectively, and safely controlled so that the longitudinal adhesion coefficient of the wheel reaches a peak value. The lateral adhesion coefficient also maintains a high state, so as to achieve the effect of increasing the braking speed, shortening the braking distance, stabilizing the braking direction, and preventing the wheels from locking. 2.2

Wheel Speed Sensor

The main function of the wheel speed sensor is to measure the speed of the car tire. Nowadays, society is an information society. We must know the information such as the speed of tire rotation in real time, because the rotation of tires is the most fundamental source of information for controlling the speed of cars. Under normal circumstances, all speed sensors can be used as wheel speed sensors, but consider practical factors such as the working environment of the wheel and the size of the space. The commonly used wheel speed sensors mainly include the magnetoelectric wheel speed sensor shown in Fig. 2 and the Hall wheel speed sensor shown in Fig. 3.

Fig. 2. Magneto-electric wheel speed sensor

2.3

Fig. 3. Hall-type wheel speed sensor

Valves with Solenoid Valves

The solenoid valve is an industrial device that uses electromagnetically controlled cutting and is an automated base magnetic component used to control fluids. The solenoid valve is essentially an actuator and is not affected by liquid helium and aerodynamics. Solenoid valve valves are widely used in our daily lives. To understand the valve of the solenoid valve, we must first have a preliminary understanding of electricity. The valve of the solenoid valve is composed of an electromagnetic coil and a magnetic core, and the body contains one or several holes. When the coil is energized or de-energized, the operation of the core will cause the fluid to pass through the valve

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body or be cut by the valve body to achieve the purpose of changing the direction of the fluid. By means of the appeal, the solenoid valve can control the current through and off of the electromagnet, thus controlling the mechanical movement. The following Fig. 4 is a schematic diagram of a 4 V series two-position five solenoid valve.

Fig. 4. 4 V series two position five solenoid valve

3 The General Working Principle of the Braking System When we step on the brake pedal, the car will slow down until it stops. But how is this work accomplished? How does the power of your legs pass to the wheels? How is this power expanded so that a heavy car can be stopped? First we divide the braking system into 6 parts. Pedal-to-wheel explanation of the working principle of each part in turn, before we understand the principle of car braking, we first understand some basic theory, the additional part includes the basic operating mode of the brake system.

4 Design of Automobile Brake System 4.1

Determination of Automotive Parameters

In the overall design of the car, there are mainly the following aspects: 1 axle, total length = front suspension + wheelbase + rear suspension, in the design of the main consideration after the shaft distribution and passability; 2 front and rear suspension ratio; 3 wheel axis qualitative view, Large track, good car stability, giving a feeling of stability, such as the golf’s wheelbase < polo, is like a hatchback small car, polo’s price is higher, and the difference between the two wheelbase It is not unrelated; In addition, pay attention to the calculation method of wheelbase in the case of the rear wheel is double. The mandatory requirements for China’s road vehicles, especially large trucks when driving. The uniaxial load should be less than the relevant piping grade limit. 5 axle load distribution, a should be considered whether the tire wear is even, b stability and maneuverability and c distribution; 6 automotive power factor a head gear power factor, direct Determine the maximum speed of the car, affecting the car’s starting, shifting capacity and the maximum climbing ability b directly as the power factor; 7 the maximum speed of the car The maximum speed limit of our highway is 120 km/h, and the theoretical maximum speed of different vehicles in

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design is not Bus: 130–150 km/h Truck: 110 km/h Car: 180–200 km/h; 8 Specific power of the car: Engine power per unit weight of the car. This is a very important indicator to measure the performance of a car. It is a passenger car. Rating standards, the specific power of different models are as follows, passenger cars: 10–20 kw/t trucks: 10 kw/t cars: 30–90 kw/t, similar to the engine power ratio in recent years refers to the power of the engine’s displacement has become very popular in recent years Especially in Shanghai and other places to measure the overall performance of an important indicator of the engine; 9 pass parameters include the angle of asymptote, the minimum departure angle of ground clearance and so on [2]. 4.2

Structure Selection of Brakes

Vehicle brakes are classified into wheel brakes and central brakes according to their position on the vehicle. The former is mounted at the wheel and the latter is mounted on a certain axis of the drive train, such as the rear end of the second shaft of the transmission or the front end of the transmission shaft. Friction brakes can be divided into two major categories, drum and disc, depending on the shape of their rotating elements. The brake is simply the brake system of the car, which affects the driving safety of the car. The brake system is about life safety, and the brake effect is decisive, so the brake pad is the protector of people and cars. However, as a consumable part, it needs to be replaced immediately when the wear is serious. How to choose a more valuable brake pad has become a confusion. The brake pads of the 4 s shop are not all the original car accessories, because the 4 s shop is sold as after-sales. In theory, the products should be produced by the original car manufacturers, but in reality many 4S stores are not from the original car manufacturers. The reality of the auto-sales market, the original assembly is defined as a synonym for high imitation because it carries a variety of car factory logos. The same car logo is not necessarily the original, the car logo is owned by the car factory. At present, the car factory only authorized its 4S shop or direct interest channel to use this trademark [4]. Therefore, 99% of the aftermarket market with automobile trademarks is high imitation. Of course, it is also possible that you buy that 1%. The state has always strictly controlled products such as brake pads that are of vital interest to citizens. The basic requirements of the automobile manufacturer for the brake pads include 9 indexes with stable friction coefficient, long service life, low noise probability, low wear on the brake disc, stable high-temperature braking ability, low thermal expansion, qualified compression ratio, low ash, and environmentally friendly materials.

5 Conclusion In order to ensure the safety of the car and the ability to exercise at high speeds, the brake system must regulate each process and component according to the design requirements. The safe driving of the car, the average driving speed of the car can be improved, and the transportation and productivity can meet people’s production requirements are the ultimate requirements of people for the car braking system. Therefore, strictly follow the braking system design process, and grasp every aspect of the automotive brake system design.

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References 1. Yun, Z.: Structure and design of electric vehicles. In: The Fourth Academic Annual Meeting of Fujian Association of Science and Technology to Promote the Competitiveness of Manufacturing in Fujian Province (2004) 2. Lansheng, S.: Successful development of automotive emergency braking device. China Automotive News (2002) 3. Kaihui, Z.: Research on Thermal Decay Performance and Related Braking Safety Detection of Automobile Brake. Chang’an University, Xi’an (2010) 4. Qingsong, X.: Design and Simulation of Electric Vehicle Power System. Wuhan University of Technology, Wuhan (2007)

A Model of the Traffic Circle Flow Control Xinshe Qi(&), Guo Li, Jing Li, Xin Wang, Na Wang, and Qingzheng Xu College of Information and Communication, National University of Defense Technology, Xi’an 710106, China [email protected]

Abstract. In order to improve its capacity and safety, and reduce its control delay, a mathematical model is proposed to optimize the volume control of a traffic circle in this paper. Firstly, for the minimum traffic circles with low traffic volumes, an unsignalized gap-acceptance model is established based on the gapacceptance theory. And then three corrective measures are obtained to improve the traffic capacity of the traffic circle, which includes: rebuilding the traffic circle, increasing the proportion of queuing vehicles, and decreasing the proportion of right-turn traffic. Keywords: Gap-acceptance theory Signalization


Traffic circle
Traffic capacity

1 Introduction The concept of traffic circles was first proposed during 1870s in England as an available way to keep horses and buggies moving through popular and heavily congested intersections. Nowadays, the traffic circle is designed as an intersection with a circular shape and, usually, a central island. In some traffic circles, two-way traffics are allowed within this circle. It is much more common that, however, the traffic is allowed to go in one direction only around a central island. Generally speaking, circulating traffic already in the traffic circle has the priority to the incoming traffic. In most European countries, such traffic flow control facility is called as roundabout rather than traffic circle. However, there are some technical distinctions between traffic circle, roundabout and even rotary. Actually, the modern roundabout is an upgrade version of the traffic circle, which has a higher risk of road collisions in high speed traffic case. It can provide a safer although slower traffic, which is promoted in traffic calming measures. In this paper, we will ignore the distinctions between roundabout and traffic circle. In some countries where people must drive on the right side, the traffic flow around the central island of a traffic circle is counterclockwise. On the contrary, the traffic flow is clockwise when they must drive on the left side. The clockwise traffic flow in the traffic circle is considered in this paper. The drivers entering a traffic circle must learn and obey the rules as follows: (1) As a driver approaches a traffic circle, there will be a YIELD sign. The driver should slow down, watch for pedestrians and bicyclists, and prepare to stop if necessary. (2) YIELD © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 369–374, 2019. https://doi.org/10.1007/978-3-030-02804-6_50

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signs are posted to either side of lanes entering the traffic circle. (3) Drivers in approach lanes must yield to vehicles in the circle. (4) Drivers must yield to vehicles that are already in the circle. (5) Drivers are supposed to enter the traffic circle or roundabout and immediately head towards the innermost lane. When the traffic is cleared, the driver will quickly merge into the outermost lane and go to the appropriate street. In practice, it is often proved to be a catalyst for numerous collisions. During the heaviest traffic period, some drivers may find themselves hopelessly trapped in the innermost traffic circle, and unable to merge into the traffic flow.

2 Unsignalized Traffic Circle Unsignalized traffic circle is the most common type of traffic circle. Although their traffic capacities may be lower than other types, they can play an important role in controlling the traffic of a street intersection. 2.1

Gap-Acceptance Theory Model

Unsignalized traffic circle gives neither positive indication nor control to the drivers. The driver alone must decide when it is safe to enter the next circle. Typically, he can find a safe opportunity or “gap” in the conflicting traffic. This model of driver behavior is called as “gap acceptance”. At an unsignalized traffic circle, the driver must also respect the priority of other drivers. A traffic circle can obtain some obvious characteristics of the main and branch streams. When the traffic from the incoming lanes emerges with the traffic streams within the circle, those circulating vehicles may have the right-of-way. Only when the right gap emerges in the circulating traffic, vehicles from the incoming lanes can enter the intersection. Currently, the gap acceptance theory is widely applied in the analysis of traffic capacity entering the circle under all kinds of traffic conditions. Theoretical Model of the Capacity with a Single-Lane Traffic Circle. The gap acceptance theory of traffic circle can be derived from the queuing model of the interaction of two traffic streams. Since overtaking is not allowed in the circulatory roadway, it is assumed that the time headway of the circulating vehicles follows the shifted negative exponential distribution. Partial circulating vehicles will travel under the minimum time interval tm (s) under a heavy traffic volume in the circulatory roadway. Suppose @ stands for the ratio of free flow if the time headway is greater than tm, the flow rate of circulating vehicles is q (veh/s), and the probabilities of the time headway being greater than and equal to tm are @ and 1-@, separately. Thus, the time headway of the circulating vehicles is complied with the probability density distribution as following [1] f ðtÞ ¼ akekðttm Þ ðt  tm Þ

ð1Þ

where k ¼ aq=ð1  tm qÞ. Suppose tf (s) is the follow up time of the vehicles entering the circle. If the time headway of the circulating vehicles is long enough for the

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entrance of more than two vehicles, it can stand for the time headway of two neighboring vehicles being waiting in the incoming lane for an entrance to the circle. If tc < h < tc + tf, then only one vehicle is allowed to enter the circle. And if tc + (k − 1) tf < h < tc + ktf, then k vehicles are allowed to enter the circle. It is assumed that the probability of the circulating stream meeting tc + (k − 1)tf < h < tc + ktf is pk, then we get pk ¼ p½h  tc þ ðk  1Þtf   pðh  tc þ tf Þ

ð2Þ

Suppose the number of vehicles accessible to the circulatory roadway per hour is cn. Therefore the capacity is obtained as follows cn ¼ 3600

1 X

pk kq ¼ 3600

k¼1

  aqekðtc tm Þ 2q1 q2 1  q1 þ q 1  ektf

ð3Þ

Theoretical Model of the Capacity with a Two-Lane Traffic Circle. Most of the traffic circles have two incoming lanes and two circular lanes. When two traffic streams from an incoming lane are ready to enter the circular intersection, the stream on the left side will interweave with the two circulating streams, while the stream on the right side only need to interweave with the outer circulating stream. Suppose cn1 and cn2 stand for the number of vehicles accessible to the intersection from two neighboring incoming lanes, and cn ¼ cn1 þ cn2

ð4Þ

The calculation process of cn1 and cn2 can be taken the Eq. (1) as a reference. Note that the parameters within the equation are related to the outer circulating stream. When the left stream is entering the intersection, the circulating stream is assumed to be an equivalent stream, which has the following relationship with the original stream: (1) If the time headway of the equivalent stream is greater than tm, it follows the shifted negative exponential distribution. Otherwise, it follows the uniform distribution. (2) The equivalent traffic volume is equal to the sum of the volumes of two incoming lanes, that is, q = q1 + q2, where q1 and q2 are traffic volumes of inner and outer circulating streams, separately. (3) Suppose the time headway of the equivalent stream is less than tm, then pðt  tm Þ ¼ tm =h, where h is the  mean value of the time headway of two circular 1 1 1 þ . streams, namely h ¼ 2

q1

q2

Based on the above assumptions, we can get the time headway of the equivalent stream, which has the following distribution

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f ðtÞ ¼

8
= 1). It can be Represented by and/or Tables in the RSML Language. System Development Engineers Have Found that Using Tables to Represent Requirements Not Only Helps Developers Understand and Develop Systems, But also Accurately Represents a Large Amount of Demand Information

For example, whether the alignment of the train and the platform needs to be judged by the two input variables of the left platform sensor and the right platform sensor, and the monitoring variables are aligned only when the two input variables of the left platform sensor and the right platform sensor are aligned. That is, there is a certain functional relationship between the monitoring variable and the input variable. The And/or table in the RSML table, the * indicates whether it is true or false (Table 4). Table 4. The relationship between high and low levels of process model variable HEv1_DoorObstructed=

T

F

LEv1_LightCurtain =

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*

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LEv2_DoorForceSensor =

*

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*

*

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*

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F

An Extraction Method of STPA Variable Based on Four-Variable Model

3.6

381

Consider the Device for Obtaining a Controlled Variable, Which is Usually an Actuator, that is, the Output Device, and the Output Device Parameter is the Output Variable

For example, in the train door controller, it is used to open or close the door force actuator, so the door force driver is the output device.

4 Conclusion Based on the four-variable model, this paper extracts the process model variables of the automatic train door system. The results show that: 1) The use of software requirements based on the four-variable model for variable extraction has many potential benefits, including reducing the number of errors and making requirements more complete and accurate. It can ensure that the extraction of process model variables is reliable and has no omissions. 2) The relationship table between the high and low level process model variables can help detect the consistency and completeness of the high and low level process model variables in the subsequently generated context table.

References 1. Leveson, N.G.: An STPA Primer. Engineering Systems. MIT Press, Cambridge (2013) 2. Leveson, N.G.: Engineering a Safer World: Systems Thinking Applied to Safety (Engineering Systems). MIT Press, Cambridge (2011) 3. Thomas, J.: Extending and Automating a Systems-Theoretic Hazard Analysis for Requirements Generation and Analysis. MIT, Boston (2013) 4. Leveson, N.G.: Model-Based Analysis of Socio-Technical Risk. Massachusetts Institute of Technology, Cambridge (2004) 5. Leveson, N.G.: Role of software in spacecraft accidents. J. Spacecr. Rockets 41(4), 564–575 (2004) 6. Patcas, L.M., Lawford, M., Maibaum, T.: From system requirements to software requirements in the four-variable model. In: Automated Verification of Critical Systems (2014) 7. Antoine, B.: Systems Theoretic Hazard Analysis (STPA) Applied to the Risk Review of Complex Systems: An Example from the Medical Device Industry. Engineering Systems Division, Cambridge (2012) 8. Conant, R.C.: Every good regulator of a system must be a model of that system. Int. J. Syst. Sci. 1, 89–97 (1970) 9. Fleming, C.H., Spencer, M., Thomas, J., et al.: Safety assurance in NextGen and complex transportation systems. Saf. Sci. 55, 173–187 (2013) 10. Ishimatsu, T.: Multiple Controller Contributions to Hazards. Massachusetts Institute of Technology, Cambridge (2011)

Jaya Algorithm-Optimized PID Controller for AVR System Chibing Gong(&) Department of Electric Power Engineering, Guangdong Technical College of Water Resource and Electrical Engineering, Guangzhou 510635, China [email protected]

Abstract. This paper presents a new approach to obtaining the optimal tuning parameters of the proportional-integral-derivative (PID) controller in the automatic voltage regulator (AVR) system using the Jaya algorithm. The purpose of this paper is to enhance step response characteristics of the AVR, including a modified performance criterion. Simulation results indicate that the proposed method performs very well. Robustness test results also verified the performance of the Jaya algorithm-optimized PID controller. The proposed method was also compared against optimized PID controllers including the teaching learning based optimization (TLBO), bat algorithm (BA) and firefly algorithm (FA); the Jaya algorithm-optimized PID controller obtains the best performance. Keywords: Jaya algorithm Heuristic algorithms


PID controller
AVR system

1 Introduction The automatic voltage regulator (AVR) is a common component of electrical power systems that can automatically control, adjust, or retain the terminal voltage output of a generator at the desired level. The safety of a typical power system, to this effect, is significantly influenced by AVR system stability. There are persistent problems with the AVR system, such as inefficient oscillated transient response, a maximum overshoot, and steady-state errors. The proportional-integral-derivative (PID) controller is commonly employed in AVR system for performance enhancement. The PID controller has grown into one of the most popular controllers in industrial control systems by virtual of its simplicity, ease of implementation and robustness. It is very difficult for researchers and plant operators to define PID parameters appropriately in the AVR system. A variety of heuristic algorithms have been proposed to tune the parameters of PID controller, e.g., include particle swarm optimization (PSO) [1–3], differential evolution algorithm (DE) [4], chaotic optimization (CO) [5], bat algorithm (BA) [6], firefly algorithm (FA) [7], teaching-learning based optimization (TLBO) [8–10] and hybrid of PSO with the gravitational search algorithm (PSOGSA) [11]. Jaya, which was introduced as a heuristic algorithm in 2015 [12], is a simple, powerful optimization algorithm designed to approximate optimal solutions. The Jaya algorithm demands only general control parameters such as the number of generations and population size, which makes it readily applicable to real-world optimization problems such as © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 382–393, 2019. https://doi.org/10.1007/978-3-030-02804-6_52

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for constrained mechanical design problems [13], automatic clustering problems [14], optimization design for a micro-channel heat sink [15], environment dispatch optimization in micro grids [16], PI controller optimization, photovoltaic distributed static compensator filter parameterization [17], tea category identification [18], surface grinding process optimization [19] and optimum power flow [20]. The remainder of this paper is organized as follows: Sect. 2 describes the process of modeling of an AVR system without a PID controller, the AVR system with a PID controller, and the performance criteria of PID controller. The Jaya algorithm and Jaya algorithm-optimized PID controller are described in Sect. 3. Section 4 presents the simulation results, and Sect. 5 provides a brief summary and conclusion.

2 AVR System with PID Controller 2.1

AVR System Modeling

The primary function of an AVR system is to maintain the terminal voltage magnitude of a generator at the desired level. The AVR system is comprised of four main components: The amplifier, exciter, generator and sensor. Figure 1 shows a block diagram of the simple AVR system.

Fig. 1. AVR system block diagram

The transfer functions, parameter range, and selected values of the four components described above are shown in Table 1 [10]. The transfer function of the AVR system is defined as follows [10, 11]: G ð SÞ ¼

0:0004S4

0:1S þ 10 þ 0:0454S3 þ 0:555S2 þ 1:51S þ 11

ð1Þ

The AVR system’s terminal voltage is depicted in Fig. 2. The system has a high oscillatory response with a maximum overshoot Mp of 50.66%, a rise time Tr of 0.2607 s, a settling time Ts of 6.9856 s, and a steady-state error Ess at 0.091 p.u.; these response characteristics are completely unacceptable. The PID controller is commonly installed in the AVR system by virtue of these favorable characteristics.

384

C. Gong Table 1. Transfer functions and parameter values Name Transfer function Parameter range Amplifier 1 þKsA S 10  KA  40 A

Selected values KA ¼ 10

0:02  sA  0:1

sA ¼ 0:1

Exciter

KE 1 þ sE S

1  KE  10 0:4  sE  1:0

KE ¼ 1 sE ¼ 0:4

Generator

KG 1 þ sG S

0:7  KG  1:0 1:0  sG  2:0

KG ¼ 1 sG ¼ 1

Sensor

KS 1 þ sS S

0:9  KS  1:1 0:001  sS  0:06

KS ¼ 1 sS ¼ 0:01

1.6

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1

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1

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Fig. 2. AVR system response

2.2

AVR System with PID Controller

The purpose of the PID controller is to decrease or remove steady-state errors and to enhance the system’s dynamic response performance. The transfer function of the PID controller can defined as follows: Gc ðSÞ ¼ Kp þ Ki =S þ Kd S

ð2Þ

where Kp , Ki , and Kd are coefficients to be tuned for the proportional, integral and derivative terms, respectively. These three coefficients affect the performance of the system in different ways. Kp cuts down on rise time Tr but does not remove steady-state error Ess. Ki reduces steady-state error. Kd is effectively decreases the overshoot, dampening the dynamic response and enhancing stability of the system.

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A PID controller was installed for a typical AVR system based on the models described above. A block diagram of the PID controller-based AVR system is shown in Fig. 3.

Fig. 3. AVR system with PID controller

2.3

AVR System with PID Controller

There are four performance criteria for the PID controller, Integral square error (ISE), integral absolute error (IAE), integral time square error (ITSE), and integral time absolute error (ITAE). The performance criterion for IAE, ISE, ITAE, and ITSE can be formulated as follows [21]: Z

  Vt  Vref dt

ð3Þ

t

  Vt  Vref 2 dt

ð4Þ

t

  t
Vt  Vref dt

ð5Þ

 2 t
Vt  Vref  dt

ð6Þ

t

IAE ¼ 0

Z ISE ¼ 0

Z ITAE ¼ 0

Z ITSE ¼

t

0

There are advantages and disadvantages to the above four formulas in the frequency domain. The disadvantage of ISE and IAE criteria is that the response may represent a slight overshoot rather than a short settling time, as they are independent of time. The notable disadvantages of IAE and ISE criteria are eliminated by ITAE and ITSE performance criterion, but the latter are complex and time-consuming in practice. Typical performance indices in the time domain include Mp, Tr, Ts, and Ess values. An appropriate step response minimizes these indicators; the performance criterion F(K) is written accordingly as follows [4, 6, 7, 10].

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F ðK Þ ¼ ð1  eq ÞðMP þ ESS Þ þ eq ðtS  tr Þ

ð7Þ

where K = [Kp , Ki , Kd ] are PID controller parameters and q is a weighting coefficient which controls the significance of related parameters. A q value greater than 0.7 indicates the tendency to reduce Mp and Ess, while a q value smaller than 0.7 indicates decrease in Tr and Ts. Here, the above F ðK Þ variable is modified as follows: F ðK Þ ¼ aðMP þ ESS Þ þ bðtS  tr Þ

ð8Þ

where a and b are weighting factors. A larger a value causes Mp and Ess values to decrease, while larger b value causes decrease in Tr and Ts values. In this study, a and b values were set as 10.0 and 1.0, respectively. The purpose of this study was to obtain optimal Kp , Ki and Kd values while minimizing the objective function F(K).

3 PID Control Design Based on Jaya Algorithm 3.1

Jaya Algorithm

Assume the objective function f(x) is minimized, and suppose that the number of design variables is m (h = 1, 2,…, m), the total number of iteration is g (j = 1, 2,…, g), and the population size is n (i = 1, 2,…, n). Of all possible candidate solutions, suppose the best one is the best value of f(x) and the worst is the worst value of f(x). If Xh;i;j denotes the value of the hth variable during the jth iterations for the ith candidate, this value is updated as follows [13]:       0 Xh;i;j ¼ Xh;i;j þ r1;h;j Xh;best;j  Xh;i;j   r2;h;j Xh;worst;j  Xh;i;j 

ð9Þ

where Xh;best;j is the value of variable h for the best value of f(x) and Xh;worst;j is the value 0 of variable h for the worst value of f(x). Xh;i;j is a new value of Xh;i;j . r1;h;j and r2;h;j are two random numbers in thescope [0, 1] of the hth variable during the jth iteration. The term ‘‘r1;h;j Xh;best;j  Xh;i;j  ” shows the result as it approaches the best solution; the    term ‘‘r2;h;j Xh;worst;j  Xh;i;j  ” indicates the result that prevents the worst solution. 0 Xh;i;j is accepted if the better value of f(x) is given. All accepted values of variables are retained at the end of each iteration and are considered in terms of the input for the next iteration.  r1 and r2 affect the exploration capacity in the global search space, while the term (Xh;i;j ) in Eq. (9) affects the ability to exploit the local search space. Only general control parameters such as the population size n and the number of generations g are required in the Jaya algorithm, thus it is fairly convenient for use in practical applications.

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387

Algorithm 1 describes the full version of the Jaya algorithm.

where x 2 ½LB; UB. U(a, b) denotes an uniform distribution between a and b. 3.2

Jaya Algorithm-Optimized PID Controller

In the Jaya algorithm-optimized PID controller, F ðK Þ (Eq. (8)) is the objective function and the design variables are Kp , Ki , and Kd . The first step is acquiring initialization values n, m, and g. In the next step, variables Kp , Ki , and Kd are selected randomly within a certain range, and employed in the AVR system shown in Fig. 3. The response of the AVR system was simulated by

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Matlab; Mp, Tr, Ts, and Ess values were obtained based on the Matlab results, then F ðK Þ was calculated according to Eq. (8). The process was completed according to the steps of the Jaya algorithm in Algorithm 1. The Jaya algorithm parameters are enumerated in Table 2. Table 2. Jaya algorithm parameters

We ran the Jaya algorithm and other algorithms in 100 independent times and select the best one. Several statistical measures were used in order to fully assess the performance of Jaya as compared to the other algorithms, including the best, the worst, mean and median objective values, and standard deviations. We ran the simulations in Matlab R2012b software on a laptop running 4 GB memory, 2.6 GHZ CPU, and 64-bit Windows 7.

4 Simulations and Discussions 4.1

Jaya Algorithm-Optimized PID Controller

The best result was obtained within 100 independent runs. The optimal Kp , Ki , and Kd values as well as the performance of the AVR system are depicted in Table 3. Table 3. Jaya algorithm-optimized PID controller performance in the AVR system Name Kp Jaya

Ki

Kd

Mp (%) Ess Ts (s)

0.60051 0.41386 0.20138 0.0016

0

Tr (s)

F(k)

0.50130 0.32259 0.17886

Table 3 shows where the Jaya algorithm-optimized PID controller obtained a minimum value of F(k) with significantly lower Mp and Ess, but reasonable Ts and Tr values compared to that in Fig. 2. Figure 4 depicts the response of the AVR system using the Jaya-based PID controller.

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1.2

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Fig. 4. AVR system response using the Jaya-based PID controller.

Between Figs. 4 and 2, it is clear that the response of the AVR system using the Jaya-based PID controller is superior to that of the AVR system without the PID controller. The Mp value of 50.66% decreases to 0.0016%; the Ess value of 0.091 decreases to 0 and the Ts value of 6.9856 decreases to 0.5013. To this effect, adding a Jaya-based PID controller greatly improves the performance of the AVR system. 4.2

Robustness Test

The AVR system changes with changes in load due to inconsistency in sA , sE , sG , and sS values, thus, robustness testing is essential for AVR systems. To simulate these changes, we modified the values of sA , sE , sG , and sS by ±50%. The responses of the system to variations (alongside normal conditions) are shown in Fig. 5.

(a)

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Fig. 5. AVR system robustness under varying (a) sA , (b) sE , (c) sG and (d) sS

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The variation of time constant of −50% and +50% and the resulting Mp, Ts and Tr values are shown in Table 4. Table 4. AVR system robustness test results Model parameters Rate of change Mp (%) −50% 0.0025 sA +50% 6.0700 sE −50% 0 +50% 5.5202 sG −50% 4.2732 +50% 3.2189 sS −50% 0 +50% 0.7175

Ts (s) 1.09984 0.88930 1.55484 1.47092 2.10229 2.46466 0.52886 1.04777

Tr (s) 0.38181 0.32917 0.22428 0.40334 0.18179 0.46123 0.33212 0.31377

Table 4 indicates that the simulations with ±50% change in time constant values resulted in higher values of typical performance indices, such as Mp, Ts, and Tr. As sA changed, Mp increased to 6.07% and Ts value increased to 1.09984. As sE changed, Mp value was 5.52% and the Ts value was 1.55484. The changes in sG result in an Mp value of 4.27% and a Ts value of 2.46466. These results are reasonable responses with respect to time constant variations and acceptable responses to abnormal conditions. In short, the AVR system with a Jaya-based PID controller is robust to uncertainties. 4.3

Comparison with Other Algorithms

Alternative algorithms for the PID controller such as TLBO, BA, and FA were compared against the Jaya-based PID controller in the AVR system; the parameters for these algorithms are shown in Table 5. Table 5. Alternative algorithm parameters Algorithms TLBO BA FA

Parameters n = 20, g = 100 n = 20, g = 100, fmin = 0, f = 1.0, A = 0.15, r = 0.7 n = 20, g = 100, a = 0.15, b = 0.10, c = 1.0

The tuning of BA [9] and FA [10] algorithm-specific parameters seriously affect the performance of the algorithms overall. Table 6 presents a comparison among optimized PID controllers for different algorithms.

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391

Table 6. Optimized PID controllers of various algorithms Name Kp

Ki

Kd

Mp (%) Ess

Ts (s)

Tr (s)

F(k)

Jaya TLBO Bat FA

0.41386 0.41145 0.41228 0.41348

0.20138 0.19984 0.20062 0.20105

0.0016 0.0459 0 0

0.50130 0.50307 0.50421 0.50195

0.32259 0.32406 0.32393 0.32299

0.17886 0.18360 0.18040 0.17896

0.60051 0.59953 0.59838 0.60004

0 0 1E−5 0

Table 6 indicates that the Jaya algorithm-optimized PID controller in the AVR system has the lowest F(k) value, 0.17886 among the four algorithms. When compared with the other three optimized PID controller algorithms, the Jaya algorithm-optimized PID controller had the shortest Ts and Tr, zero Ess error, and a reasonable Mp value. To this effect, the Jaya algorithm-optimized PID controller outperformed the other controllers. Table 7 presents statistical results for PID controllers based on various algorithms. Table 7. Statistical results for PID controllers based on various algorithms Name Jaya TLBO Bat FA

Best 0.17886 0.18360 0.18040 0.17896

Mean 0.18243 0.22029 0.23435 0.43422

Median 0.18162 0.20273 0.21302 0.25661

Worst 0.20362 0.49326 0.77588 1.16010

Std. dev. 0.00331 0.05508 0.08236 0.32131

Again, the Jaya algorithm-optimized PID controller not only showed minimum optimal values, but also minimum values in regards to other statistical measures, such as the mean, median, worst and standard deviations. The Jaya algorithm is thus the best possible algorithm for the PID controller.

5 Conclusions The Jaya algorithm was utilized in this study to obtain optimal PID controller tuning parameters. A new performance criterion was also utilized to assess the performance of the PID controller in a typical AVR system. Simulation results clearly indicate that the proposed Jaya algorithm-optimized PID controller in the AVR system performs extremely well; The PID controller is stable and robust to uncertainties. The Jaya algorithm-optimized PID controller was compared against TLBO-, BA-, and FAoptimized controller; the results demonstrate that the proposed controller outperforms the others in regards to several performance criteria.

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References 1. Shabib, G., Moslem, A.G., Rashwan, A.M.: Optimal tuning of PID controller for AVR system using modified particle swarm optimization. In: Proceedings of the 11th WSEAS International Conference on Evolutionary Computing, Iasi, Romania, pp. 104–110 (2010) 2. Rahimian, M.S., Raahemifar, K.: Optimal PID controller design for AVR system using particle swarm optimization algorithm. In: Proceedings of 24th IEEE Canadian Conference Electrical and Computer Engineering (CCECE), Ontario, Canada, pp. 337–340 (2011) 3. Gozde, H., Taplamacioğlu, M.C., Ari, M.: Automatic voltage regulator (AVR) design with chaotic particle swarm optimization. In: Proceedings of 6th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2014 Bucharest, Romania, pp. 23–26 (2014) 4. Vivekanandan, S., Saravanan, G., Kamalakannan, P., Krishnaprabhu, S.: Chaotic differential evolution algorithm based PID controller for automatic voltage regulator system. Int. J. Sci. Res. Publ. 5, 1–6 (2015) 5. Coelho, L.D.S.: Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach. Chaos Solitons Fractals 39, 1504–1514 (2009) 6. Bendjeghaba, O., Ishak, B.S.: Bat algorithm for optimal tuning of PID controller in an AVR system. In: Proceedings of International Conference on Control, Engineering and Information Technology (CEIT’14), Sousse, Tunisia, pp. 158–170 (2014) 7. Bendjeghaba, O.: Continuous firefly algorithm for optimal tuning of PID controller in AVR system. J. Electr. Eng. 65, 44–49 (2014) 8. Priyambada, S., Mohanty, P.K., Sahu, B.K.: Automatic voltage regulator using TLBO algorithm optimized PID controller. In: Proceedings of 9th International Conference on Industrial and Information Systems (ICIIS), Gwalior, India, pp. 1–6 (2014) 9. Rajinikantha, V., Satapathyb, S.C.: Design of controller for automatic voltage regulator using teaching learning based optimization. Procedia Technol. 21, 295–302 (2015) 10. Chatterjee, S., Mukherjee, V.: PID controller for automatic voltage regulator using teaching– learning based optimization technique. Electr. Power Energy Syst. 77, 418–429 (2016) 11. Kansit, S., Assawinchaichote, W.: Optimization of PID controller based on PSOGSA for an automatic voltage regulator system. Procedia Comput. Sci. 86, 87–90 (2016) 12. Rao, R.V.: Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7, 19–34 (2016) 13. Rao, R.V., Waghmare, G.G.: A new optimization algorithm for solving complex constrained design optimization problems. Eng. Optim. 48, 1–24 (2016) 14. Kurada, R.R., Kanadam, K.P.: Automatic unsupervised data classification using Jaya evolutionary algorithm. Adv. Comput. Intell. Int. J. 3, 35–42 (2016) 15. Rao, R.V., More, K.C., Taler, J., Ocłon, P.: Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl. Therm. Eng. 103, 572–582 (2016) 16. Trivedi, I.N., Purohit, S.N., Jangir, P., Bhoye, M.T.: Environment dispatch of distributed energy resources in a micro grid using Jaya algorithm. In: Proceedings of International Conference on Advances in Electrical, Electronics, Information, Communication and BioInformatics (AEEICB16), Chennai, India, pp. 224–228 (2016) 17. Mishra, S., Ray, P.K.: Power quality improvement using photovoltaic fed DSTATCOM based on Jaya optimization. IEEE Trans. Sustain. Energy 99, 1–9 (2016)

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18. Zhang, Y.D., Yang, X.J., Cattani, C., Rao, R.V., Wang, S.H., Phillips, P.: Tea category identification using a novel fractional Fourier entropy and Jaya algorithm. Entropy 18, 77 (2016) 19. Rao, R.V., Rai, D.P., Balic, J.: Surface grinding process optimization using Jaya algorithm. In: Behera, H.S., Mohapatra, D.P. (eds.) Computational Intelligence in Data Mining— Volume 2. AISC, vol. 411, pp. 487–495. Springer, New Delhi (2016). https://doi.org/10. 1007/978-81-322-2731-1_46 20. Warid, W., Hizam, H., Mariun, N., Abdul-Wahab, N.I.: Optimal power flow using the Jaya algorithm. Energies 9, 678 (2016) 21. Mouayad, A.S., Bestoun, S.A.: A new multiobjective performance criterion used in PID tuning optimization algorithms. J. Adv. Res. 7, 125–134 (2016)

Pattern Recognition and Vision System

Research on Short Text Classification Method Based on Convolution Neural Network Lei Wang(&), Qiaohong Chen, Qi Sun, and Yubo Jia School of Information, Zhejiang Sci-Tech University, Hangzhou, China [email protected]

Abstract. Short text classification is one of the hotspots of research in Natural Language Processing. a new model of text representation is proposed in this paper (N-of-DOC), and in order to solve the problem of sparse representation in Chinese, the word2vec distributed representation is used, finally, it is applied to the improved convolution neural network model (CNN) to extract the high level features from the filter layer, the classification model is obtained by connecting the softmax classifier after the pooling layer. In the experiment, the traditional text representation model and the improved text representation model are used as the input of the original data, respectively. It acts on the model of traditional machine learning (KNN, SVM, logistic regression, naive Bayes) and the improved convolution neural network model. The results show that the proposed method can not only solve the dimension disaster and sparse problem of Chinese text vectors, but also improve the classification accuracy by 10.23% compared with traditional methods. Keywords: CNN
Short text classification Machine learning
Deep learning


Text representation

1 Introduction With the widespread popularity of Internet and the rapid increase in the number of Internet users, the number of short texts produced every day on the network has also increased exponentially. Short text of the Internet refers to short text forms, generally not more than 500 words, such as user’s commodity reviews, short blogs [1]. And this kind of semi-structured or unstructured Internet text information is sparse, real-time, normative and popular emerging features, Internet short text classification is one of the key technologies of information processing, it has been widely used in information retrieval, knowledge mining and information supervision [2]. In order to realize the automatic fast classification of short text in the Internet with large data, a large number of researchers have done a lot of research on this problem, including: SVM [3], NB [4], KNN [5], LOGISTIC [6], etc.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 397–403, 2019. https://doi.org/10.1007/978-3-030-02804-6_53

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2 Related Research In order to improve the accuracy of short text classification, a large number of scholars both at home and abroad have done a lot of research. In foreign countries, because researchers start studying machine learning and deep learning earlier than domestic and hardware conditions are relatively advanced in China. Yoon Kim [7] uses convolutional neural network to classify English movie reviews. They only use a convolution, a maximum pool, and finally connect to softmax to get the classification model, which achieves relatively good results. In China, a considerable number of scholars have done a lot of research on the text classification. Huang Wen Ming [8] put forward the application of KNN to text weighting, sampling the initial text through a certain weight, and finally using KNN classifier to get the classification model, which improved the accuracy of KNN algorithm for text classification.

3 Architecture and Experimental Design of Short Text Classification Model Based on Convolution Neural Network The design of short text classification model based on convolutional neural network is mainly composed of 6 parts, including data collection, data preprocessing, new feature representation of each document, convolution neural network filter layer, K-max pool layer and softmax classification layer. 3.1

Data Collection Layer

The data used in this experiment are mainly the text data provided by the Sougou corpus (including a total of 9 categories), and two short reviews of text reviews which are crawled by the crawler library on the movie website. 3.2

Text Preprocessing Layer

The data obtained through the data collection layer can not be directly input to the model of the input layer of the convolution neural network (CNN). It needs preprocessing operation. The pretreatment process mainly includes the following 2 parts. (1): the network text data cannot be directly used as the input of the model calculation, needs to word segmentation, As the special Chinese word segmentation operation is not the same as the English word segmentation directly to the word space, so the Chinese word segmentation, this article uses python word segmentation library (jieba) to segmentation. (2): the removal of disable words is an essential thing for Chinese, which can reduce text redundancy to make text classification more accurate. The disable word used in this article is the stopwords.txt under the Data directory of ICTCLAS, and it has been expanded with some common disused words published online. Common stop words have “no single, full, up and down, again and again”.

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399

New Text Feature Presentation Layer

In order to reduce the dimension of text representation and reduce the complexity of computing, the new text feature representation proposed in this paper is mainly taken into consideration in the following two aspects. (1): after text preprocessing segmentation, every text has some words, but usually a few key words can represent the information of the whole document. So in this paper, we extract the whole training set’s high quality features through information preprocessing, through information gain, Gini purity [9] and chi square test [10], finally, on each of the specific documents, The extracted features must be linearly represented by the high quality features extracted from all training sets. The mathematical model is abstracted as follows. TðXj Þ ¼ Wi

n X

ð1Þ

Xj

j¼1

In the above formula, Xj represents every specific document. Wi is used to linearly n P express the weight coefficient of the document, and Xj represents the high quality j¼1

feature set extracted from the whole training set. (2): The features extracted by the above method for each document, this article uses the word2vec in the gensim library to train each word into a 300 dimensional vector, for example, one of the short texts, after the above processing, extracts the 8 words that can represent the document, this document is a matrix of shape 8 * 300, of course, then as the input layer of convolution, will be extended to a 4 dimensional tensor with convolution operation format. 3.4

Convolution Neural Network Filter Layer

The new text feature representation layer, each document can be represented by a corresponding high quality feature, after word2vec training as a vector for each document, this is similar to a matrix, such as K * 300, K represents the high quality features extracted from this document, 300 is a vector dimension set in this article. It is inputted to the convolution filter layer. During the experiment, the convolution window sets the 3 * 300,4 * 300,5 * 300,6 * 300 four filter cores, and the convolution step is set to 1. Then the document matrix after the filter layer is expressed as (K-3 + 1) * 1, (K-4 + 1) * 1, (K-5 + 1) * 1, (K-6 + 1) * 1. The formulae of the convolution process are as follows: XX Bði; jÞ ¼ Kðm; nÞ  Aði  m; j  nÞ ð2Þ m¼0 n¼0

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K-max Pool Layer

In the experiment process of this article, the pool layer, as a dimensionality reduction operation, further reduces the vector dimension of the text, It can be considered that the pool layer is also a layer of feature extraction. This paper tries to try the size of the Kmax pooling window through experiments. Experimental discovery, The window size of K-max is the same as the size of the matrix vector after convolution. It can bring the best experimental results. The experiment also compares the average pooling operation. The K-max pooling operation reduces the impact of the text matrix null operation, the results of the sample output can be more representative of the text, and the experimental result is better. 3.6

Softmax Classifier

The last layer of the convolution neural network is generally used as a full connection, After the upper layer of K-max pooling layer, Text feature vectors need to be concatenate and reshape(Because there are 4 kinds of filter cores, So before making full connection, the vector of the convolution pool of these filter kernel needs to be stitching and reconstructing the dimension.), Finally connecting the softmax classifier, predictive class probability. The specific process is as follows: After the K-max pool layer through concatenate and reshape, the data of the M training set are obtained, as follows. fðX ð1Þ ; Y ð1Þ ÞðX ð2Þ ; Y ð2Þ Þ. . .ðX ðmÞ ; Y ðmÞ Þg, X ðiÞ represents the features of the input, and Y ðiÞ represents the text category. In this paper, experiments are carried out not only on the two classification data set, but also on the multi classification data set, so the classifier formulas after the full connection layer were as follows: (1): the two classification experiment takes the sigmoid function, the threshold is 0.5. hðhÞ ¼

1 T 1 þ expðh XÞ

ð3Þ

In the above formula, h represents the model parameters, and the minimum cost function JðhÞ can be found by training h. The formula is as follows: JðhÞ ¼ 

i    1 hXm ðiÞ ðiÞ ðiÞ ðiÞ y log h ðx Þ þ 1 þ y ðx Þ log 1  h h h i¼1 m

ð4Þ

(2): softmax function is adopted in the multi classification experiment. ez j rðzÞj ¼ PK k¼1

ez k

ð5Þ

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401

Experimental Comparison and Evaluation Criteria

The classification accuracy of (KNN SVM, logistic, NB) is compared with the classification accuracy of this paper, and the precision rate, recall rate and F1 value are compared. KNN text classification, in addition to the use of the new text representation model (N-of-DOC) to represent a document, plus a threshold, that is, each document must have more than 5 training features of the same word. For SVM and logistic classification, based on this new text representation mode (N-of-DOC), we use IF-IDF [11] value as text feature vector, as the initial input of the model, and get the final classification result. The evaluation standard of text classification is based on the commonly used accuracy rate, precision rate, recall rate and F1 value of text classification.

4 Analysis of Experimental Results Figure 1 shows the classification effect of different methods of classification results for the Internet short text is very obvious, with the accuracy rate of convolutional neural network classification method based on the improved can reach more than 92%, the classification effect is obviously better than the traditional classifiers, the precision, recall and F1 value are also more effective than traditional classification methods. The KNN classifier has the worst classification effect in the short text corpus used in this paper, with accuracy rate of only 60%. The classification accuracy of SVM, LOGISTIC, NB is only reached between 74%-85%. so the classification method of the improved convolution neural network classification method based on this paper is very obvious, it achieves the expected effect (Fig. 2).

Fig. 1. Film review two classification performance display

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Fig. 2. Multi classification performance of Sougou corpus

It is very easy to get through figure two. In the multi classification task, we also use the improved convolutional neural network classification method proposed in this paper, which has the highest accuracy rate and the classification accuracy rate is over 90%, precision, recall, and F1 value are better than traditional machine learning. The average classification accuracy of KNN is about 70%, precision, recall and F1 are also the lowest. The average classification accuracy of SVM and LOGISTIC is about 80%, The NB classifier is slightly improved by a little bit, the average classification accuracy is about 84%. Summing up by table two and table three, whether in the two or the multiple categories, The best accuracy of classification is an improved convolution neural network classification method proposed in this paper, And the accuracy rate is improved obviously, it achieve the expected effect. The reasons for the above results can be summarized as follows: (1) This article uses the word2vec tool provided by the gensim Toolkit, the word vector generated by it is more representative of the features of the word group than the simple TF-IDF generated vector. (2) The new text representation model (N-of-DOC) proposed in this article is more suitable for deep learning, It is more conducive to the extraction of higher level features of the convolution neural network. Acknowledgements. First of all, I would like to thank my tutor, Professor Chen Qiaohong, for his great care and help in my life and my studies, Chen virtuous, friendly, knowledgeable, rigorous scholarship, During my master’s study, She not only taught me the skills of learning, she also taught me the rules of being a man, which will certainly benefit me for life. Finally, I would like to thank my parents for their greatest support, and I love you.

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References 1. Jiang, B.: Micro-blog Automatic Classification Method Research and Application. Harbin Institute of Technology, Harbin (2012) 2. Zhang, Z., Miao, D., Chan, H.: Short text classification method LDA topic model. Based Comput. Appl. 33(6), 1587–1590 (2013) 3. Zhang, A., Liu, G., Liu, C.: Research on multi class text classification based on SVM. Inf. Mag. 23(9), 6–7 (2004) 4. Guo, S.: Research on Short Text Classification Algorithm Based on Bayesian Network. Chongqing University of Posts and Telecommunications, Chongqing (2010) 5. Zhong, W., Liu, R.: An improved KNN text classification. Comput. Eng. Appl. 48(2), 142– 144 (2012) 6. Miaomiao, T.: A study of text classification based on decision tree. J. Jilin Norm. Univ. (Nat. Sci. Edit.) 29(1), 54–56 (2008) 7. Kim, Y.: Convolutional neural networks for sentence classification. Eprint Arxiv (2014) 8. Huang, W., Moyang, : Chinese spam filtering. Comput. Eng. Based Text Weight. KNN Algorithm 43(3), 193–199 (2017) 9. Chen, Y., Wu, J., Xu, K.: Development, Gini index for attribute selection of microcomputer based on decision tree. Microcomput. Dev. 14(5), 66–68 (2004) 10. Hu, W., He, T., Zhang, Y.: Extraction of Chinese terminology based on Chi square test. Comput. Appl. 27(12), 3019–3020 (2007) 11. Tan, S., Li, : Menstrual in text classification TF IDF. Improv. Method Mod. Libr. Inf. Technol. 29(10), 27–30 (2013)

Vanishing Point Conducted Diffusion for Crop Rows Detection Jian Wu(&), Mengwei Deng, Lianlian Fu, and Jianqun Miao College of Science, Jiangxi Agricultural University, Nanchang 330045, China [email protected]

Abstract. A partial differential equation based diffusion is presented for crop rows detection. In the diffusion, the evolving direction is estimated through the vanishing point, which is one of global feature of row-crop images. According to the vanishing point, we generate the orientations of row crop textures, and then integrate the induced field of directions into an oriented diffusion. After processing the row-crop image with the new diffusion, we extract the crop rows from its black-white version using a morphological operation. Experiments on the real row-crop image data show the proposed diffusion can suppress the undesired interference more efficiently than the other diffusion when extracting crop rows. Keywords: Partial differential equation
Vanishing point
Oriented diffusion Crop row detection

1 Introduction Crop row detection is an essential task in some agricultural applications. Extracting crop rows is mainly finished by algorithms, such as [1–3], based on the Hough transform as its high robustness [4]. There are many methods to identify the crop rows. Romeo et al. [5] has summed up the detection algorithms in eight classes. Crop row detection is still one of research focuses in robotic agriculture. For examples, in [6], Reiser et al. considered crop row detection in maize under different growth stages; Guerrero et al. detect crop row in maize, and form an automatic expert system based on a vision system in [7]; Vidović et al. [8] developed a method that is capable of accurately detecting both straight and curved crop rows through minimizing a global energy. Thus it can be seen that extracting crop lines from real field images is now remains active in modern agricultural applications. Here we want to present a partial differential equation (PDE) based diffusion method for crop rows detection. For the past decades, PDE-based diffusions have raised a great interest in the image processing and computer vision community. A lot of evolving models, such as [9–17], have been proposed for image preprocessing. The basic idea of PDE-based diffusion is to evolve an image with a PDE, and deform it according to its structures. By the PDE methods, one can combine several evolving procedures, and couple their advantages into a model. PDE-based diffusions are used to simplify an image in a way that only interesting features are preserved while unimportant data are removed. Their abilities to deal with complicated signals without loss © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 404–416, 2019. https://doi.org/10.1007/978-3-030-02804-6_54

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of main meaning allow they are applied directly for image denoising and feature extraction. In fact, PDE-based diffusions currently play an important role in preprocessing stage in the field of medical image analysis, remote sensing, and object recognition. Usually, to keep the image away from blurring the textures and details, the PDEbased diffusions are propelled by the local information. The gradient, the divergence, the Laplacian, the structure tensor and the local orientation are the typical such information. The Perona and Malik (PM) diffusion [9] is a typical model used the gradient to prevent the important details from obscuring. As it is difficult to balance the capabilities between the noise smoothing and the detail preserving, some people employ other ways to keep them. In You et al.’s model [10], they introduced the Laplacian operator to control the diffusion. However, such models do not fully use the local information, and still blur details to some extent. The selective degenerate diffusion (SDD) model [11] controls the diffusion direction and intensity, and it guarantees the diffusion occurs only along the tangent direction of edges. The coherenceenhancing diffusion (CED) model [12] uses the structure tensor to rule the evolution not across the edges. Tschumperlé and Deriche [13] further developed the idea, and designed an oriented Laplacian based diffusion, which made full use of the underlying geometry. Guidotti et al. [18, 19] introduced other types of diffusion. In their models, the diffusions identify edges through the fractional derivatives, and take place only along the edges. Coupled diffusions are another ways to avoid of blurring image details. Such as [20–23], the class models can combine multiple diffusion properties into one. However, their parameters are always hard to tune. Recently, more ways are proposed to preserve image details for anisotropic diffusions, such as [24–27]. In [28], they employed a training process to select the optimal diffusion threshold, and proposed a dictionary-based anisotropic diffusion. The results show that the diffusion combined multi-scale region analysis is better than the benchmark methods by significantly enhancing the peak signal-to-noise ratio and structural similarity indexes. In [29], Tsiotsios and Petrou studied a stopping criterion depending on the quality of the preserved edges for anisotropic diffusion. Some researchers even present a class of time-dependent diffusion to avoid of blurring, such as [30]. The directions of diffusion effects one PDE model’s properties deeply. In some degraded images, such as the fringe images in optics field, seeing [31, 32], the directions are very vital to process such images. Tang et al. developed an oriented second order PDE-based model [33]
 @t u ¼ gðjrujÞ uxx cos2 h þ 2uxy sin h cos h þ uyy sin2 h

ð1Þ

to deal with the speckles. In the method, the orientation at the coordinate p is calculated accurately within a large local window by the equation 1 hð pÞ ¼ arctan Aw ð pÞ 2

ð2Þ

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P Aw ð pÞ ¼

ði;jÞ2Bð pÞ

P ði;jÞ2Bð pÞ

h

@uði;jÞ @x

ði;jÞ
@u@y

i2 h i2  @uði;jÞ  @y

@uði;jÞ @x

where Bð pÞ is the local window centered at the pixel of p, and the value of w decides the local window size. The expression is originally used to estimate the orientation of fingerprint in [34]. The diffusions which take full advantage of local information may cause negative effect. On one hand, we need a good local information to avoid blurring interesting objects. Not enough orientation information may cause the diffusion to cross the image edges and leave blurs. On the other hand, the precise local direction may ignore the global orientation and bring misguidance. In the row-crop images, the crops form neat textures. When the PDE-based diffusion technique is applied to the kind of image, the local directions are not so important any more. What we need is the rough orientation for the texture. We hope the diffusion is carried out more intelligently, not affected by the local interferences. To tackle this problem, we need the evolution can understand the image contents to a certain extent. So we need to replace the local information by a global one to control the diffusion. Vanishing point (VP) is such a good choice for the global information. In agriculture, crops are planted extensively in the form of rows by seeding machines so that the procedure of spraying, weeding and reaping can be finished conveniently by farm robots. Row identification is important to help field machines staying off crops. Especially in precision agriculture, accurate detection of crops is conducive to find the weeds and crops. For instance, once the crop rows are located, the farm robots can reduce the volume of the herbicides and pesticides to prevent a large area of field pollution [35]. VP is produced when straight lines paralleled in the 3D scene are projected onto a 2D image. As it includes some valuable information about the image depth and the 3D structure, the problem of finding parallel lines in 3D scene is focused on finding the vanishing point in the 2D image plane. Just because the VP is an important ways to find the crops, and provide the right directions to the textures, we need the global property to help the PDE-based diffusion work smarter. Combining the global feature into the PDE-based diffusion, undoubtedly, we will get more intelligent evolution, and see better results. Many existing methods for VP detection are complicated, or not easy to implement, or not developed for row-crop images. Most of them concentrate on the camera calibration [36], road detection [37], 3D scene reconstruction [38]. An obvious approach to finding VP is to analyze the intersections of all line pairs and involve voting method. In these methods, detecting straight lines is an indispensable prerequisite. As the intersections may scatter anywhere in the image plane, and may be outside of the image. To avoid considering the problem in an open space, Bernard [39] used Gaussian sphere to find the set of line endpoints, and then reduced to search for local maxima. McLean and Kotturi [40] detected VP by use of line clustering instead of the Gaussian sphere. Förstner [41] introduced a detection algorithm using random sample consensus. Usually, as pointed out in [42], the VP detection methods, which are forced to rely heavily

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on additional properties, e.g. extracting the straight-line features. Antolovic et al. [43] used the direction that gives the strongest response by convolution with Gabor filters to find the road vanishing point. However, for the row-crop images, we can cancel the preprocessing. As there is a clear orientation on these images, if scattered randomly some particles on the crop image, under the latent field of directions, they will be attracted and concentrated on a common point. Thus, by tracking the moving particles and analyzing the trajectories, we can find the intersection which is the VP located. In this paper, we present a VP-conducted diffusion for crop rows extracting. At first, we propose a new but simple approach to vanishing point detection on such farmland images. Then, we introduce the global feature, VP, into an oriented PDE model. At last, we verify the proposed method, including the VP detection and the VPconducted diffusion, with the real image data.

2 Vanishing Point Detection In the row-crop images, there is strong sense of orientation. Intuitively, if we scatter some particles randomly on the images and track them along the texture orientation, we will find the vanishing point. To reduce the burden of calculation, we do not fully fill the image with particles but drop them randomly on the image with a certain density. At the pixel ði; jÞ, the orientation is calculated in a ð2w þ 1Þ  ð2w þ 1Þ local window by Eq. (2). According to the particle directions, we can vote on the candidates of vanishing point. To increase the chances of true vanishing point being selected, we move every particle to another position along its direction of motion with a stable and small step Dd.

Fig. 1. The process of VP detection. (a) Original image and the final voted VP; (b) Initial particles and their 1st round of voting; (c) The particles and their VP candidates after 1 iteration; (d) The particles and their VP candidates after 3 iterations; (e) The particles and their VP candidates after 5 iterations

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Suppose the input image u has a size of W  H. The main steps of the detection algorithm are as follows: (a) Preprocessing. If the input image is RGB color picture, then let uði; jÞ ¼ 2Gði; jÞ  Rði; jÞ  Bði; jÞ

ð3Þ

where R, G and B are the three components of color. We redefine the input image to make green crops prominent so that the directions are calculated later without too many disturbances. If the input is not the RGB image, then let uði; jÞ denote its gray-scale version. (b) Initializing two matrices M and P with zeros. The matrix M, on which we vote on the vanishing point, has the size ðW þ 2DÞ  ðH þ 2DÞ, extending D pixels around the image u. The value of D should be large enough to include the vanishing point as it may run out of the image domain. The matrix P, which is used to record the positions of the particles, has the same size as the image u. We let Pði; jÞ ¼ 1 when ði; jÞ is occupied by a particle, otherwise Pði; jÞ ¼ 0. The particles are scattered randomly with density of L%. (c) Calculating the local orientation at each particle in the image u with Eq. (2). (d) Voting the VP candidate. Each particle will choose a series of VP candidates through its local orientation. Suppose a particle located on p ¼ ði; jÞ, its local direction is hðopÞ, i.e., the angle of x-axis and the line through o ¼ ð0; 0Þ and p, and let M ð pÞ denote the number of votes on p. For every particle, we examine each coordinate q of the image plane, and sum the votes as follows:  M ð qÞ ¼

M ðqÞ þ 1; M ðqÞ;

hðpqÞ ¼ hðopÞ otherwise:

ð4Þ

After checking all the particles on the image plane, we finish this round of voting. (e) Moving the particles. Each particle will be moved along its orientation with step Dd. Correspondingly, we update the particles which are saved in the matrix P. If a particle is pushed out of the image domain, then the particle will be deleted. This step is ready for next round of voting. (f) Repeat step (c)–(f) till the maximum iteration Nmax is reached. (g) Searching the vanishing point. We search the largest number of votes in the matrix M, the position which has the maximum value has the highest possibility of being vanishing point. Here we want to give some remarks on the algorithm. The input picture may be in gray-scale or in color. If a gray-scale image is fed to the detection method, we calculate the direction only by the luminance values. Otherwise we can extract more information by Eq. (3) to help calculating the direction more accurately. After preprocessing, some particles are scattered randomly with a certain density (for example L = 10) on the crop image, seeing the left side of Fig. 1(b)–(e). The movement of each movable particle is taken place along the direction of texture it located. In each round, the directions are calculated by Eq. (2) and the votes are accumulated by Eq. (4). After each round of movement, we hold a round of voting. The voting results are shown in right

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of Fig. 1(b)–(e). We do it in order to ensure the accurate VP is selected. The maximum iteration Nmax is determined by experiments. We have found that the iteration range [1, 5] is always suitable for most of row-crop images. The searching step (g) is just finding the maximum value in the vote map.

3 Vanishing Point Conducted Diffusion According to the VP, we can generate the orientation for the row-crop image. Suppose the VP is located at (vpx, vpy), we can get the direction of the pixel (x, y) as follows   y  vpy a ¼ arctan : x  vpx

ð5Þ

In Fig. 2, we show such an example. Obviously, when the field of direction is applied to the oriented diffusion, it will be more intelligent as it ignores the local interferences. In the work [33], the second order oriented PDE diffusion can be described as

Fig. 2. An image sized of 375  500 with a VP at (33, 245). (a) The original image and the VPinduced orientations; (b) The orientation for the a; (c) The VP-induced texture directions


 @t u ¼ gðjrujÞ uxx cos2 h þ 2uxy sin h cos h þ uyy sin2 h ; where the h is angle between the edge and x axis. Replacing the angle h in the Tang model with a in Eq. (5) yields the VP-conducted diffusion (VPCD), i.e.,
 @t u ¼ gðjrujÞ uxx cos2 a þ 2uxy sin a cos a þ uyy sin2 a :

ð6Þ

Although sharing the same form, there is an essential difference between the VPCD and Tang model. The latter calculates the direction of diffusion through local properties, whereas the VPCD model is by the whole perspective.

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4 Results and Discussion In this section, we test the presented method, including the VP detection and the VPbased diffusion. Experiments are carried out on the platform of MATLAB 2011b. 4.1

Results of VP Detection

To implement our vanishing point detection method, we fix L = 10, Nmax = 5 and Dd ¼ 5 as constants to cut down the burden of calculation. For the crop images, we set D ¼ 100 to include the VPs that are out of image region, it is always acceptable in our tests. Actually, the parameter we need to tune is the local window size w. The value of w is based on, with respect to the image size, the crop size and the sparse level of planting. The greater the crop image, or the sparser the planting, the larger the value of w. We have found through experimentation that the good value of w is ranged from 8 to 18 in most occasions. In Fig. 3, we show some comparisons with the other two vanishing point methods. One is presented at [43], named Method 1 here, and the other is reported at [39], named Method 2. From the results, we can see that the proposed method is comparative to the Method 2, and more suitable to the crop images than the Method 1.

Fig. 3. Comparative results. The black marker is the estimated VP. (Left) Method 1; (Center) Method 2; (Right) Proposed Method

4.2

Results of VP-Conducted Diffusion

In this subsection, we show some experimental results of the VPCD. To solve the model (6), here we use the scheme of time matching, and all the derivatives are discretized by the central differences. The RGB images are split into R, G, B parts, and processed separately. We compare the new PDE model to the other PDE models: the

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PM [9], the SDD [11] and the Tang models (1) [33]. In all implementation, we choose the controlling function gð
Þ as gðjrujÞ ¼

1þ

1 

2 ;

jruj k

and set the same parameters, i.e., k = 50, Dt ¼ 0:2, and Iteration = 100 for the four models.

Fig. 4. The processed results by the four PDE diffusions. (Column 1) Original; (Column 2) PM; (Column 3) SDD; (Column 4) Tang; (Column 5) VPCD

In Fig. 4, we show the visual results for the four PDE-based diffusion. From the figure, we can see that the PM model blurs details, and the SDD and Tang diffusions were interfered by the local information, while the VPCD preserves the rows well. In Figs. 5, 6 and 7, we demonstrate the identified crop rows for the comparisons between the conventional methods (no treatment, and skeletonizing the original image directly), the PM model, the SDD model, the Tang model and the proposed VPCD method. The skeletonization is finished at the same condition through the bwmorph function, a morphological operation of MATLAB. From the results list in Fig. 5, we can observe clearly that the skeletons of the VPCD outperforms are better than those of the other three methods. Although the trees along the farmland are distorted, we think it is worth as we focus on extracting the crop rows, not the tree lines. In Fig. 6, the SDD and Tang diffusions incorrectly process the rows. This point can be seen from the skeletons, in which they leave exceptional bifurcations and breaks. This point can be see also in Fig. 7. We infer that is due to the accurate local information, which brings misguidance and leads to ignore the global orientations. Whereas the proposed diffusion excludes the local directions and handles the row well, it makes the skeleton more clean and continuous. Our method takes use of the global information, and exclude the local interferences intelligently.

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Fig. 5. The processed results and their skeletons. (a) The conventional method; (b) the PM method; (c) the SDD method; (d) the Tang method; (e) the VPCD method

Fig. 6. Another processed results and their skeletons. (a) Conventional method; (b) the PM method; (c) the SDD method; (d) the Tang method; (e) the VPCD method

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Fig. 7. The comparison between the five methods. (a) Conventional method; (b) the PM method; (c) the SDD method; (d) the Tang method; (e) the VPCD method

The presented method for crop rows extraction has some drawbacks. One is that it is not a real time extraction. Conventionally, our method needs 10–20 s in the stage of finding the vanishing point from the input image. During the stage of diffusion, VPCD usually consumes 20–40 s in image evolving. In Table 1, we list the CPU times for the five methods when extracting crop rows under the same platform. From the table, we can observe that the proposed VPCD does not outperform the other four methods. The other drawback is that our method applies only to straight rows. For the images contained curved crop lines, or densely crop rows, as there is no or hardly detect vanishing point in such images, the proposed detection method will fail to extract the crop rows. Table 1. The CPU times for the five detection methods (in seconds) Conventional method PM method SDD method Tang method VPCD method

Figure 5 Figure 6 Figure 7 1.7 1.0 0.9 10.9 5.7 6.2 11.8 6.3 6.8 26.8 13.7 14.1 61.8 30.9 32.6

5 Conclusion In this paper, a new PDE-based diffusion is introduced for extracting crop rows. The method employs a global information to control the direction of diffusion. This feature allows the proposed method to avoid of the local interferences and to process the

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row-crop images intelligently. Experiments on the real image data demonstrate the vanishing point conducted diffusion is a better preprocessing method when detecting the crop rows. Acknowledgements. This work is supported by the National Natural Science Foundation of China (NNSFC) (Grant 61561025 and 71561014).

References 1. Leemans, V., Destain, M.-F.: Line cluster detection using a variant of the Hough transform for culture row localisation. Image Vis. Comput. 24(5), 541–550 (2006) 2. Roviramas, F., Zhang, Q., Reid, J.F., Will, J.D.: Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 219(8), 999–1010 (2005) 3. Perezortiz, M., Pena, J.M., Gutierrez, P.A., Torressanchez, J., Hervasmartinez, C., Lopezgranados, F.: A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Appl. Soft Comput. 37, 533–544 (2015) 4. Ji, R., Qi, L.: Crop-row detection algorithm based on random Hough transformation. Math. Comput. Model. 54(3), 1016–1020 (2011) 5. Romeo, J., Pajares, G., Montalvo, M., Guerrero, J.M., Guijarro, M., Ribeiro, A.: Crop row detection in maize fields inspired on the human visual perception. Sci. World J. 2012, Article ID 484390 (2012) 6. Reiser, D.: Crop row detection in maize for developing navigation algorithms under changing plant growth stages. In: Robot 2015: Second Iberian Robotics Conference, pp. 371–382 (2016) 7. Guerrero, J.M., Guijarro, M., Montalvo, M., Romeo, J., Emmi, L., Ribeiro, A., Pajares, G.: Automatic expert system based on images for accuracy crop row detection in maize fields. Expert Syst. Appl. 40(2), 656–664 (2013) 8. Vidović, I., Cupec, R., Hocenski, Z.: Crop row detection by global energy minimization. Pattern Recognit. 55, 68–86 (2016) 9. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990) 10. You, Y.-L., Kaveh, M.: Fourth-order partial differential equations for noise removal. IEEE Trans. Image Process. 9(10), 1723–1730 (2000) 11. Alvarez, L., Lions, P.-L., Morel, J.-M.: Image selective smoothing and edge detection by nonlinear diffusion. II. SIAM J. Numer. Anal. 29(3), 845–866 (1992) 12. Weickert, J.: Coherence-enhancing diffusion filtering. Int. J. Comput. Vis. 31(2–3), 111–127 (1999) 13. Tschumperle, D., Deriche, R.: Vector-valued image regularization with PDEs: a common framework for different applications. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 506–517 (2005) 14. Weickert, J.: Anisotropic Diffusion in Image Processing, vol. 16, p. 272. Teubner, Stuttgart (1996) 15. Sapiro, G.: Geometric Partial Differential Equations and Image Analysis. Cambridge University Press, Cambridge (2006) 16. Witkin, A.: Scale-space filtering: a new approach to multi-scale description. In: Acoustics, Speech, and Signal Processing, IEEE ICASSP 1984, vol. 9, pp. 150–153 (1984)

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17. Chan, T.F., Shen, J.J.: Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods, pp. 110–115. Society for Industrial and Applied Mathematics, Philadelphia (2005) 18. Guidotti, P., Lambers, J.V.: Two new nonlinear nonlocal diffusions for noise reduction. J. Math. Imag. Vis. 33(1), 25–37 (2009) 19. Guidotti, P., Longo, K.: Two enhanced fourth order diffusion models for image denoising. J. Math. Imag. Vis. 40(2), 188–198 (2011) 20. Chen, Y., Barcelos, C.A.Z., Mair, B.A.: Smoothing and edge detection by time-varying coupled nonlinear diffusion equations. Comput. Vis. Image Underst. 82(2), 85–100 (2001) 21. Luo, H., Zhu, L., Ding, H.: Coupled anisotropic diffusion for image selective smoothing. Signal Process. 86(7), 1728–1736 (2006) 22. Tang, C., Han, L., Ren, H., Gao, T., Wang, Z., Tang, K.: The oriented-couple partial differential equations for filtering in wrapped phase patterns. Opt. Express 17(7), 5606–5617 (2009) 23. Heydari, M., Karami, M., Babakhani, A.: A new adaptive coupled diffusion PDE for MRI Rician noise. SIViP 10(7), 1211–1218 (2016) 24. Xu, J., Jia, Y., Shi, Z., Pang, K.: An improved anisotropic diffusion filter with semi-adaptive threshold for edge preservation. Signal Process. 119(C), 80–91 (2016) 25. Ramosllordn, G., Vegassnchezferrero, G., Martinfernandez, M., Alberolalpez, C., Ajafernndez, S.: Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images. IEEE Trans. Image Process. 24(1), 345–358 (2015) 26. Jain, S.K., Ray, R.K.: An alternative framework of anisotropic diffusion for image denoising. In: International Conference on Information and Communication Technology for Competitive Strategies, pp. 1–6 (2016) 27. Yuan, J.: Improved anisotropic diffusion equation based on new non-local information scheme for image denoising. IET Comput. Vis. 9(6), 864–870 (2015) 28. Cho, S.I., Kang, S.-J., Kim, H.-S., Kim, Y.H.: Dictionary-based anisotropic diffusion for noise reduction. Pattern Recognit. Lett. 46, 36–45 (2014) 29. Tsiotsios, C., Petrou, M.: On the choice of the parameters for anisotropic diffusion in image processing. Pattern Recognit. 46(5), 1369–1381 (2013) 30. Yu, X., Wu, C., Jia, T., Chen, S.: A time-dependent anisotropic diffusion image smoothing method. In: International Conference on Intelligent Control and Information Processing, pp. 859–862 (2011) 31. Tang, C., Wang, Z., Wang, L., Wu, J., Gao, T., Yan, S.: Estimation of fringe orientation for optical fringe patterns with poor quality based on Fourier transform. Appl. Opt. 49(4), 554– 561 (2010) 32. Wang, H., Qian, K., Gao, W., Lin, F., Seah, H.S.: Fringe pattern denoising using coherenceenhancing diffusion. Opt. Lett. 34(8), 1141–1143 (2009) 33. Tang, C., Han, L., Ren, H., Zhou, D., Chang, Y., Wang, X., Cui, X.: Second-order oriented partial-differential equations for denoising in electronic-speckle-pattern interferometry fringes. Opt. Lett. 33(19), 2179–2181 (2008) 34. Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998) 35. Tellaeche, A., BurgosArtizzu, X.P., Pajares, G., Ribeiro, A., Fernandez-Quintanilla, C.: A new vision-based approach to differential spraying in precision agriculture. Comput. Electron. Agric. 60(2), 144–155 (2008) 36. Hughes, C., McFeely, R., Denny, P., Glavin, M., Jones, E.: Equidistant (fh) fish-eye perspective with application in distortion centre estimation. Image Vis. Comput. 28(3), 538–551 (2010)

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Research on TCAS Fault Diagnosis Based on Directed Graph Fault Tree Xiaomin Xie1, Fan Zhang2(&), Changkai Li2, and Yong Zeng2 1

Mechanical and Electrical Engineering, Anhui Vocational and Technical College, Hefei 230011, China [email protected] 2 Institute of Electronic Communication Engineering, Anhui Xinhua University, Hefei 230088, China [email protected]

Abstract. In order to solve the problem that TCAS fault diagnosis of airborne electronic equipment cannot reach the component level and other bottleneck problems, a diagnosis method based on directed graph and fault tree is proposed, and a diagnosis model based on directed graph and fault tree is designed, so as to accurately locate the fault diagnosis to the component level and achieve the purpose of deep diagnosis. From the mathematical point of view, the propagation matrix characteristics of the fault diagnosis model are analyzed and studied. By using the forward reasoning process based on the occurrence of events and nonoccurrence of events, the problem of large fault tree caused by the complexity of the system in practical application is effectively solved, and the diagnosis efficiency is improved. Through the diagnosis example of TCAS data processor, the effect of locating fault diagnosis to component level is achieved. Keywords: TCAS fault diagnosis Propagation matrix


Directed graph
Fault tree

1 Introduction TCAS (traffic warning collision avoidance system) provides an air separation guarantee independent of air traffic control, which plays an important role in safe flight and is an indispensable system for modern aircraft. The data processor (DP) is the core of TCAS system, it performs the calculation function of all TCAS systems, processing monitoring algorithm and anti-collision logic, complete continuous cycle built-in test, monitor test monitoring, provide the results to record the output data and calculate the output display data, so the performance of the data processor is of great significance to the whole system. In this paper, TCAS system is studied, the already very mature artificial intelligence diagnosis method theory is applied to the system depth fault diagnosis to locate the fault in the chip [2–4], innovative proposed based on directed graph and fault tree diagnosis model [1, 5, 6], make directed graph and fault tree two detection methods can greatly play its own advantages, make the detection process more concise, more accurate results, to achieve TCAS system depth diagnosis and maintenance. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 417–423, 2019. https://doi.org/10.1007/978-3-030-02804-6_55

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2 Knowledge of Directed Graph Fault Tree 2.1

Directed Graph Fault Propagation Matrix

The fault propagation directed graph model is a graph theory model [9–11], which describes a mathematical method of the actual system through the graph composed of points and lines, and analyzes the system according to the properties of the graph. This topic is to study the fault propagation characteristics and fault propagation direction, fault diagnosis is the reverse process of fault propagation, so the direction of the arrow is just the opposite, as shown in Fig. 1. a b d f

c e

h

i

g

Fig. 1. Fault propagation directed graph

Let V ¼ fa; b; c; d; e; f ; g; h; ig be a node set of directed graphs, E ¼ fhb; ai; hc; ai; hd; bi; he; bi; hh; ci; hi; ci; hf ; d i; hg; d ig is the edge set of a directed graph. Where: E  V  V; hb; ai. . .: represents an ordered even. Then G ¼ ðV; E Þ is a directed graph, G satisfies the following relationship: (1) If there is a passageway at any two points, the passageway is unique. (2) Weakly connected graph (if there is a path between any two points a and b in the graph, there is no path between b and a). Generally,
 B ¼ bij nn

n is the number of directed graph nodes: ( bij ¼

)   1




ai ; aj 2 E   0




ai ; aj 62 E

ai ; aj represent nodes on a directed graph.   A fault propagation matrix [8] called B; G. If bij ¼ 1, That is ai ; aj 2 E, ai and aj have a direct path and a length of 1, That is to say, if bij ¼ 1, aj must occur when ai occurs, ai is the cause and aj is the result.

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The fault propagation matrix of Fig. 1 is: 2

0 61 6 61 6 60 6 B¼6 60 60 6 60 6 40 0

2.2

0 0 0 1 1 0 0 0 0

0 0 0 0 0 0 0 1 1

0 0 0 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

3 0 07 7 07 7 07 7 07 7 07 7 07 7 05 0 99

0 0 0 0 0 0 0 0 0

Directed Graph Representation of TCAS System Fault Diagnosis

TCAS system consists of transceiver group, TCAS antenna, S-mode transponder and L-band antenna, the core of which is TPA–81A processor. The TPA–81A processor is composed of four parts: radio equipment (RF), video processor (VP), data processor (DP), input/output processor (IOP). These systems cooperate to complete data acquisition, tracking, analysis and consultation and air-to-air maneuver coordination functions. These systems can be further divided, DP module by the central processing unit, electronic data co-processor, multi-function peripheral equipment (MFP), DP control bus signals and other modules. The characteristics of TCAS system determine the characteristics of fault occurrence and transmission. Therefore, the fault directed graph model of TCAS system as shown in Fig. 2 is established in this paper. TCAS system failure

S mode transponder failure

TCAS Processor failure

TCAS System control unit failure

Radio failure

Video processor failure

Transceiver failure

L - band antenna failure

Data processor failure

Input / output processor failure

Fig. 2. TCAS directed graph fault model

In this paper, the nodes in the directed graph of fault propagation are represented by frames. Each slot represent a failure, such as a TCAS system failure with that frame name in the first layer. Slot names are: s mode transponder failure, TCAS processor failure, transceiver group failure, etc., each side represents the fault characteristic

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parameter. For example, in TCAS system fault frame, slot 1 is the standard value of TCAS processor during normal operation, its side 1 is the voltage of the detection point, and side 2 is the current of the detection point. When the test values do not match these values, a symptom of the failure occurs.

3 Diagnosis Reasoning Process Based on Directed Graph Fault Tree Referring to Fig. 3, the first occurrence event in the occurrence event library is taken to determine whether it is a conclusion of a rule, If so, judging whether the premise of the rule is in the event library; (1) if not, adding the rule into the event library, and taking the next event; (2) if in the event library, the next event to judge. Start

i=1 Take that I - th occurrence

Is it the conclusion of a rule?

i>k?

Is it part of a rule compound conclusion? End i=i+1

Split the composite conclusion

The premise is in the library?

Add premises to the occurrence library

All sub-events occur? More than one sub-event did not occur?

If the library does not occur?

This rule presupposes the occurrence of a library? Add premises to the occurrence library

Add premise to that library that did not occur

Fig. 3. Forward inference process based on occurrence event

If it is not the conclusion of a rule, judging whether it is one of the conclusion of a composite rule (and gate event); (1) if so, judging whether all the conclusion events of the rule occur, (1) and if so, judging whether the rule premise is in the event library or not, and if not, adding the event library, taking down the next occurrence event; If in,

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the next occurrence; (2) if all that conclusion event do not occur, judging whet more than one conclusion event occurs, if so, judging whether the premise event is in an event library which does not occur, if not, adding the premise event into the event library which does not occur, and judging the next occurrence event; If not, that next occurrence event is taken to judge; (2) if not, that next occurrence event is taken for judgment.

4 TCAS Data Processor Diagnostic Application 4.1

Establishment of Directed Graph Fault Tree Diagnosis Model for TCAS Data Processor

In order to realize the fault diagnosis and location of complex equipment, we can first “divide” the system into smaller structural units according to the specific structural form of the system, and express the logical relationship between them through the fault tree. Then, we can decompose the function according to the functions realized by these smaller structural units. If the decomposed functional units are small, such as a general unit circuit, then we can use step-by-step troubleshooting to analyze the componentlevel fault until the fault is located to a specific module or component. The minimum cut set from which the above number of faults can be obtained is {U15} {U23} {U33} {U30} {U20} {U35} {U16} {U17} {Y1} {P18} {XDS1}.The fault tree is simplified to the logic or relationship between the top event and the minimum cut set, and the fault tree can be directly simplified to the form of Fig. 4.

T

1

P18

XDS1

U17

U23

U15

U33

U30

Y1

U20

U16

U35

Fig. 4. Simplified fault tree

Initialize that processing board through an initialization program, and star a processing board fault diagnosis module according to the data obtained by testing the process board. According to that test data, the node event of the fault tree are analyzed, and the state of the processed node events can be determine as occurrence events and non-occurrence events. Fault diagnosis based on fault tree model is to use the existing test data and fault propagation relationship between fault tree nodes for reasoning [7], in order to determine the failure of the chip. The diagnosis process starts from the node of the event occurrence and determines the node state of the fault tree by using the logical

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relationship between the nodes. If the test point setting is sufficient and reasonable, the state of all the bottom events can be determined, but in fact, due to the condition limit cannot obtain enough node information or the test point setting is not reasonable, cannot completely determine the state of all nodes. 4.2

Failure Result Analysis

When fault diagnosis based on fault tree is started, the data processor to be tested is initialized firstly. Where the digital circuit part “1” represents a high level, theoretically the voltage is 2–5 V, in fact, more about 3 V. “0” represents low, theoretical voltage: 0–2 V, actual 0–0.5 V. Other pins are suspended. (1) Slow address data failure, port A5, B5, C5, D5 generate fault signal, start the fault diagnosis program, Event library: U15, U23, U33 No event library: U16, U17, U20, U30, U35, P18, XDS1, Y1 The fault source exists in the event library and the unknown event library. If there is no event in the path with the top event in the unknown event library, it cannot be the fault source and can be eliminated. There are no unknown events in this example, so the fault sources are U15, U23, U33, where to accurately locate a chip, further measures need to be taken to verify, for example, by writing a test program to specifically test a single chip, after testing to determine the fault source is U15. (2) Control signal B-LW/R*, B-ALE* jump, i.e., port A23, A24 generates fault signal, starts diagnostic program, Event library: clock unit, including U20, Y1, U18 No event library: U9, U28, U29, U31, U32 Unknown event library: GAL unit Therefore, it can be diagnosed that the fault source is a clock unit and the possible fault source is a gal unit.

5 Concluding Remarks This paper focuses on the fault diagnosis of TCAS data processor of airborne electronic equipment, and designs the fault diagnosis based on component-level detection. The diagnosis technology of directed graph and fault tree is studied, and the diagnosis scheme based on TCAS data processor is designed, and the detection and diagnosis are carried out in combination with the actual situation. An innovative diagnosis model based on the combination of directed graph and fault tree is proposed, and a fault propagation and fault occurrence segmentation model is designed, so that the directed graph and fault tree two detection methods can greatly exert their own advantages, so that the detection process is more concise and the results are more accurate.

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Fund Project. Special Funding Project of China Postdoctoral Science Foundation (2014T70967); Natural Science Research Key Project of Anhui Province Higher School (KJ2017A630); Quality Engineering Project of Anhui Provincial (2016jxtd055); Key Construction Discipline Project at College Level of Anhui Xinhua University (zdxk201702); Institute Project at College Level of Anhui Xinhua University (yjs201706); Anhui Xinhua university natural key scientific research project (2015zr010).

References 1. Ruiz, I., Paniagua, E., Alberto, J., Sanabria, J.: State analysis: an alternative approach to FMEA, FTA and Markov analysis. In: IEEE Proceedings of Annual Reliability and Maintainability Symposium (2000) 2. Liu, X., Liu, Z.: A hybrid approach of fault inference and fault identification for aircraft fault diagnosis. Inf. Technol. Computer. Intell. 211, 151–158 (2012) 3. Sharma, M.K., Vinesh, K.: Vague reliability analysis for fault diagnosis of cannula fault in power transformer. Appl. Math. Sci. 8(18), 851–863 (2014) 4. Ramesh, V., Saravannan, R.: Reliability assessment of cogeneration power plant in textile mill using fault tree analysis. J. Fail. Anal. Prevent. 11, 56–70 (2011) 5. Andrews, J.D., Brennan, G.: Application of the digraph method of fault tree construction to a complex control configuration. Reliab. Eng. Syst. Saf. 28(3), 357–384 (1990) 6. Bartlett, L.M., Hurdle, E.E., Kelly, E.M.: Integrated system fault diagnostics utilizing digraph and fault tree-based approaches. Reliab. Eng. Syst. Saf. 94(6), 1107–1115 (2009) 7. Bogicevic, J., Aksic, M., Biorac, S.: Fault tree analysis of clutch on a vehicle VAZ 2121. In: 8th International Quality Conference. Center for Quality, Faculty of Engineering, University of Kragujevac, Serbia, pp. 661–668, 23 May 2014 8. Sacks, I.J.: Digraph matrix analysis. IEEE Trans. Reliab. 34(5), 437–445 (1985) 9. Sharma, B., Gandhi, O.P.: Digraph-based reliability assessment of a tribo-pair. Ind. Lubr. Tribol. 60(3), 153–163 (2008) 10. Deo, N.: Graph Theory with Applications to Engineering and Computer Science. PrenticeHall, New Delhi (2007) 11. Balakrishnan, V.K.: Graph Theory. McGraw-Hill, New Delhi (2005)

Analysis on Injury Mechanism of Toy Scooter Liu Xia1, Liu Bisong1, Ruan Li2(&), and Jiang Kan3 1 China National Institute of Standardization, Beijing, China Taizhou Institute of Quality and Technical Supervision and Inspection, Taizhou, China [email protected] Zhejiang Institute of Product Quality and Safety Testing, Hangzhou, China

2

3

Abstract. This paper analyzes the injuring mechanism of the hazardous factors of toy scooter by probing into various hazardous factors of the scooter, and comparing the domestic and foreign standards, laws and regulations, with the product characteristics and industry development condition of toy scooters. In addition, it offers suggestions on countermeasures against the problems with respect to the laws and regulations, enterprise production and consumers’ concept of toy scooter. Those countermeasures include enhancing the safety awareness of consumers, improving laws and regulations, strengthening selfregulation of the industry and reinforcing the government regulation. Keywords: Toy scooter


Safety
Injury mechanism
Supervision

1 Introduction Toy scooter is also called scooter for kids. It is one kind of baby carriages that bear the weight of children. It can be used by children with weight of no more than 50 kg and is driven by the child via muscular movement. As it is for children and it moves very fast, and children have very little self-defensive ability, with the addition of inherit design defect of the scooter, children may easily get pinched, bruised or suffocated when riding on the scooter. There are many kinds of toy scooters, including those propelled by thrusting against the ground or by swinging two legs based on the driving method; those with three wheels, two wheels or four wheels depending on the number of wheels; and those that are foldable or non-foldable. The structure is mainly composed of the pedals which the child may stand on, the vertical pipe with adjustable height, the horizontal pipe and brake device.

2 Product Safety Hazard and Injuring Mechanism In recent years, toy scooters are causing an increasing number of injury accidents, the major hazardous factor of which is mechanical hazard. The injury mechanism is as follows.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 424–431, 2019. https://doi.org/10.1007/978-3-030-02804-6_56

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Accessible Metallic Sharp Edges

The main body structure of a toy scooter is mostly made up of metallic piping assembled by cutting, bending and welding. If the burrs and galling on the edges are not removed during processing, the product may bear dangerous metallic sharp edges, which are accessible by the children when using the product, and may scratch or cut the tender skin of children (see Fig. 1).

Fig. 1. Accessible metallic sharp edges

2.2

Excessively Thin Plastic Packaging Bag

The plastic bags used as package of various components of the toy scooter are respectively put in cartons. As toy scooters are used by children, the children may cover their heads or mouth or nose with the plastic bag and get suffocated if the plastic bags are not thick enough. In serious cases, this may result in asphyxia. 2.3

Thin Circular Holes on Rigid Material

For toy scooters with adjustable height of the vertical pipe, the height of the vertical pipe is generally fixed using a metal pin. The wall thickness of vertical pipe is generally no more than 1.58 mm and if the diameter of the height adjusting circular hole above the vertical pipe is between 6 mm and 12 mm, children may be able to put their fingers into the hole and get stuck. If the finger is stuck for too long, this may cause tissue necrosis due to poor circulation of blood (see Fig. 2).

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Fig. 2. Poorly designed holes on rigid materials

2.4

Improper Clearance Between Moving Parts

Toy scooters are used by children. There are many moving parts on the toy scooter, such as the vertical pipe and wheels. If the clearance between the moving parts on the toy scooter that are accessible by the children, the children may put their fingers or other body parts into the clearance, and may get pinched when the parts start to move (see Fig. 3).

Fig. 3. Poorly designed clearance between moving parts

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Handlebar Grip End Is Too Small and Easily Comes Off

The vertical pipe is a necessary component of most kinds of toy scooters and is generally made of metal. When the scooter falls over, the end of the vertical pipe will function as an extruding part. If the child pulls out the cover when falling over, the end of the vertical pipe will scratch the skin of the child. At the same time, if the child pulls out the cover during normal use, or the diameter of the vertical pipe end is less than 40 mm, the hands of the child may come off the vertical pipe accidently and lose control of the scooter, which may result in turnover of the scooter and hurt the child. (See Fig. 4).

The cover is taken off with pull force of less than 70N.

The diameter of the handle end is less than 40mm.

Fig. 4. Easily taken off cover and small-diameter end

2.6

Adjustable and Foldable Vertical Pipe and Horizontal Pipe

To obtain better portability, the vertical pipe and horizontal pipe of the toy scooter are generally foldable. In the folding mechanism of a toy scooter, there may be clearances between 5 mm and 12 mm, which may pinch children’s fingers, and accessible holes, which may cut fingers and accommodate round bar with diameter of 5 mm. The clearance between moving parts and accessible holes may pinch or cut fingers of the children and cause injury (see Fig. 5).

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Fig. 5. Poorly designed adjustable and foldable vertical pipe and horizontal pipe

2.7

Absence of Rear Wheel Brake

The running speed of toy scooter is high and considerable kinetic energy is generated when a child is riding on a toy scooter. A toy scooter with bearing capacity of 20 kg to 50 kg should be provided with at least one rear-wheel brake to ensure it can slow down to avoid barrier. With out a brake, the toy scooter may not be able to slow down and avoid barrier in time, which may result in collision and turnover and the child may fall over and get hurt (see Fig. 6).

A front-wheel brake

No brake Fig. 6. Poorly designed brake

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Excessively Small Wheels

The diameter of front wheels of a toy scooter should be greater than 90 mm. If the size of front wheels is not enough, the toy scooter will be unable to cross barriers. In such a case, even a very small barrier may cause the toy scooter to tip over and hurt the child (see Fig. 7).

Fig. 7. Improperly sized wheels

3 Current Situation of State Supervision In China, no nationwide quality supervision and random inspection have been carried out on toy scooters. However, a national standard for toy scooter products has been released, i.e., GB 6675.12-2014 Toy Safety: Part 12: Toy Scooter. The applicable standard of toy scooter products is the GB6675.1-2014 Toy Safety Part 1: Basic Regulations, GB6675.2-2014 Toy Safety Part 2: Mechanical and Physical Performance, GB6675.3-2014 Toy Safety Part 3: Inflammability, GB6675.4-2014 Toy Safety Part 4: Migration of Certain Elements and GB 6675.12-2014 Toy Safety: Part 12: Toy Scooter in the GB 6675 series. All standards are effective from on January 1, 2016. These are the first special standards applicable to toy scooters in China. There are specific requirements for toy scooter products in Article 4.15.5 of an European standard for toys, namely, EN71-1:2014 Toy Safety Part 1: Mechanical and Physical Performance, such as “warning and instructions for use”, “strength”, “adjustable and foldable vertical pipe”, “brake”, “wheel size”, and “extruding parts”. The requirements for toy scooter products specified in national standard ISO 8124-1: 2012 Toy Safety Part 1: Mechanical and Physical Performance are the same with that

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specified in EN71-1. The national standard GB 6675.12-2014 Toy Safety: Part 12: Toy Scooter released in 2014 by state government adopts the technical content regarding toy scooters in ISO8124-1. Therefore, the technical level of the national standard GB6675.12-2014 for toy scooters is in line with the international standard. Please see Table 1 for comparison between national and international standards. Table 1. Comparison between National and International Standards Hazardous factors Mechanical and physical injury

GB 6675.2-2014 GB 6675.12-2014 Requirements for “strength”, “adjustable and foldable vertical pipe”, “brake”, “wheel size”, and “extruding parts” applicable to toy scooters are specified in GB 6675.12-2014; general “requirements applicable to all toy products are specified in GB 6675.2-2014

EN71-1:2014

ISO 8124-1:2012

Technical requirements and testing methods are generally the same as those specified in GB and ISO standards

Technical requirements and testing methods are generally the same as those specified in GB and EN standards

As one kind of baby carriages, the toy scooter is used by children. However, it is not included in the CCC certification list. Given the complete industrial chain in the industrial cluster district, any manufacturer is able to manufacture toy scooters only if they can buy parts and components required. As such, there are a number of family workshops engaged in manufacturing of the toy scooters. Those enterprises are unfamiliar with the product standards and the products thus manufactured will inevitably mean safety hazards for children.

4 Measures and Suggestions Strengthening Product Supervision. Related authorities should strengthen supervision and random inspection of the toy scooter manufacturers and carry out publicizing activities to promote the applicable standards, to as to help the manufacturers improve product quality level, modify technological process and quality control and keep defective products out of the market. Setting Requirements for Market Access. As one kind of baby carriages, the toy scooter is used by children, who do not have sufficient self-defensive ability. The toy scooter should be listed in the CCC certification list, like other baby carriages, with compulsory certification required. This way, the enterprises will understand the standards and improve product quality, and guarantee personal safety of children.

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Reinforcing Self-regulation of the Manufacturers. The related industrial associations should be encouraged to organize for enterprises to strengthen their self-control of quality and awareness of quality and safety. Manufacturers should attach equal importance to both benefit and quality, so as to guarantee safety of consumers. Increasing Publicity Efforts. Media and related organizations should make more efforts to publicize quality and safety of toy scooters and guide the consumers to buy reliable and safe products. Collecting Injury Cases and Paying Close Attention to Change of International Standards. The national standards for toy scooters should be modified according to the modification of international standards related and by collecting injury cases involving toy scooters and analyzing the causes. Acknowledgments. This paper has been funded by the national key research and development project “Research on key technical standards for quality and safety control of consumer goods” (2016YFF02022600), and “Research on common technology for integrative services by internet plus” (2017YFF0209604), and Central basic scientific research project “Research on Consumer goods Safety Hazard Identification and risk Assessment based on scenario Simulation” (552018Y-5928).

References 1. Qian, L.-h.: Scooters become the biggest killers in the US toy market. Toy World. 11 (2017) 2. Zhang, Y.-t.: Defects in mechanical and physical properties or potential safety risks for children’s scooters. Brand Stand. 6 (2015) 3. Han, P.: Research on the design of emotional skateboard for children. Mech. Des. 6 (2015) 4. Jiang, Y., Zhang R.-h., Sheng G.-y.: Study on standards of risk assessment and risk assessment on children’s products. Shanghai Stand. 6 (2016) 5. Wang, L.-j.: A Study on the Design of Children’s Toys Combined With Animation—Based on Aofei Products. Master thesis of Guangdong University of Technology (2014) 6. Hu, Y.-x., Jia, Z.-l.: Design strategy of educational toy based on children’s physical and mental development. J. Hubei Univ. Technol. 6 (2009) 7. Haijing, Z., Jin, Y.: The failure mode, effect and criticality analysis and fault tree analysis. Aviation Industry Press, Beijing (2003) 8. Guo, Y.-y., Yang, M.-g., Shi C.-j.: Improved Design of children’s multipurpose vehicle based on user research. Mach. Des. 2 (2015) 9. Wang, Y.-y.: Research on children’s toy design based on chinese market. Art Des. Theory 9 (2010) 10. Xu, B.-b., Lu, W.-j., Li, H., Bi, K.-j.: Risk identification and grade determination in the risk assessment of import and export commodities. Insp. Quar. Sci. 2 (2008) 11. CPSC: Handbook for Manufacturing Safer Consumer Products, US Consumer Product Safety Commission (2006)

Application of Text Classification Method Based on Depth Learning in Email Handwriting Analysis Changqing Pang1(&), Ruibin Sun2, Xiaodan Mou2, Zhiwei Yan2, Shuo Mi2, and Huimin Liu3 1

2

Common Courses Department, Shandong University of Science and Technology, Jinan, Shandong, China [email protected] Electronic Information Department, Shandong University of Science and Technology, Jinan, China 3 Foreign Languages Department, Shandong University of Science and Technology, Qingdao, China

Abstract. Natural Language Processing is an important direction in the fields of computer science and artificial intelligence. It has a wide range of applications, such as text classification, machine translation, emotional analysis, etc. where text classification is typically characterized of capturing the language features of e-mails to identify the authors. In this paper, mathematic models are set up and compared based on the traditional text classification method and the Deep Learning text classification method respectively. Finally, the following results are obtained:

• The accuracy of the model using the improved network architecture based on TextCNN reaches 87% on the training set and 88.97% on the test set. • The accuracy of using the improved network architecture based on CLSTM reaches 90% on the training set and 88.34% on the test set. • The accuracy of the new network architecture based on recurrent neural network (RNN) and convolutional neural network (CNN) has reached 92.32% on the training set and 92.80% on the test set. • Using integrated learning to integrate the model, the accuracy rate is 94% on the training set and 92.36% on the test. • The model based on SVM are tested with the test set to get the accuracy rate 60.44%. Keywords: TextCNN


SVM
CLSTM Deep Learning
Email

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1 Introduction Handwriting analysis is a very special form of investigation used to connect people with written evidence. Handwriting investigators are usually asked to be in court or criminal investigation to determine whether a written sample is from a particular person. Now, many language evidence is in e-mail. In a broad sense, handwriting analysis also includes how to identify the author’s problem through the language features of e-mail. The identity of a writer is the process of identifying the author (s) of a disputed text by using a recognizable language style feature, from word frequency to preferred syntactic structure. The content of e-mail is often short, and the author’s language style is obvious.

2 Model Establishment 2.1

Models Based on Depth Learning and Text Classification Method

2.1.1 Text Classification Process Based on SVM [1] The SVM text classification algorithm is mainly divided into four steps: 1. 2. 3. 4.

Text preprocessing Text feature extraction and representation Normalization processing Text classification

We preprocess the data in the mail through NLTK. The process of preprocessing is as follows (Fig. 1):

Fig. 1. Preprocessing flow chart

2.1.2 Improved Classification Model Learner Based on TextCNN Architecture [2] First, we use the TextCNN, based neural network architecture to build the first classification model learner in integrated learning. The following diagram is the architecture of TextCNN. For this network architecture, we assume that there is a mail to be

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classified, and every word in the body of the mail is represented by the 300 dimensional word vector before. The total number of e-mail content is N, then the input matrix size: N * 300. Convolution neural network requires convolution operation on input samples. For text data, convolution kernel (filter) is no longer moving horizontally, just moving downwards, which is aimed at achieving N-gram like extraction of local correlation between words and words. In the above picture, there are three sliding step strategies, 2, 3 and 4, respectively. Each step has two convolution kernels (filter), and different convolution kernels (filter) are applied on different word windows. After that, we get six convolution vectors. Next we use Max pooling operation to splice each pool value, and get the feature expression of this article. Then we get the final vector to Softmax classifier, and we achieve the goal of mail classification. On the basis of this architecture, this paper improves it as follows: • For input matrix, the number of input channels can be changed into two channels. One channel is the word vector matrix constructed by word2vec model, and the other is the word vector matrix constructed by glove model. • In this paper, the number of convolution nuclei for each step is increased to 20, in order to obtain more different features. The improved network architecture is as follows (Fig. 2):

Fig. 2. Improved network structure diagram

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2.1.3 Improved Classification Model Learner Based on CLSTM [3] Architecture This article uses the model architecture of CLSTM to build second classification model learners in integrated learning. The following diagram is the architecture of CLSTM [4]. This network structure will be the combination of recurrent neural network and convolutional neural network classification using the mail, first by training the convolutional neural network to get the new features of recurrent neural network as input. For this network architecture, first through the convolution kernel (filter) by feature maps for the convolution operation. Then the convolution neural network no longer maximum pool (Max-pooling) operation, but the feature set is rearranged, the same word in the window after comprehensive characteristics of different convolution operation after vector, the same color that is placed in a sequence, and sequentially arranged down, it is Window feature sequence. Each sequence in the Window feature in the sequence layer, in fact it and the original sequence in the sentence is corresponding, they maintain the relative order of the original, but the convolution operation in the middle, and then the new feature as the input variables of LSTM, after LSTM and then Softmax classification of mail. This paper has also been improved on the basis of the CLSTM architecture as follows: • For input matrix, the number of input channels can be changed into two channels. One channel is the vector matrix constructed by word2vec model, and the other is the word vector matrix constructed by glove model. • In the recurrent neural network structure, we transforms the original LSTM based recurrent neural network into a multi-layer LSTM superposition and two-way transfer of recurrent neural network. The following figure is an improved network architecture (Fig. 3):

Fig. 3. Improved network structure diagram

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2.1.4 Classification Model Learner Based on the New Architecture of Recurrent Neural Network and Convolution Neural Network [4] In this paper, a new network architecture based on recurrent neural network (RNN) and convolutional neural network (CNN) is proposed, which is the last classifier model learner in ensemble learning. Its frame composition is as follows (Fig. 4).

Fig. 4. Architecture diagram

According to the above model diagram, in the convolution neural network (part), this paper makes an improved innovation based on TextCNN. The difference lies in the following: • Two layers of coiling layer are used • The number of more convolution cores is used • Using Batch Normalization Feature maps in the second layer convolution operation, no Max-pooling operation, but the use of the same operation and CLSTM architecture, re arrangement of the characteristics of Window feature sequence, and recurrent neural network (RNN) as the input layer, after two two-way transfer LSTM, finally, two layer fully connected layer classification. After building the above three classifiers, we integrate the models and get the final classification results in ensemble learning. Here we use the fusion of probability and other weights. There are one hundred and thirty-three people who sent the mail, so each classifier will output a one hundred and thirty-three dimensional vector, which represents the probability that the mail belongs to the one hundred and thirty-three people. The way of fusion is to add directly the output vectors of each classification model, and then further classify them according to the final vector, that is, one of the most selected probabilities is the result of classification 2.2

Model Test

For each single model, the word vector matrix of the original body text data is replaced by word vector. The final output of the model is one hundred and thirty-three dimensional vector, representing the probability that the mail belongs to the one hundred and thirty-three people.

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2.2.1 The Test of Traditional Text Classification Model [5] As shown in Fig. 5, the final accuracy of the training set is 60.44% (Fig. 6).

Fig. 5. Training set error change graph

Fig. 6. Accuracy change map of training set

2.2.2 Accuracy and Error of the Model Based on Deep Learning Through the training and testing of a single model, the accuracy change map of the corresponding training set and the test set as well as the error change map of the training set are obtained. Then we use the model fusion to get the final test results. The final training set and the test set accuracy change map as well as the training set error change graph after the model fusion: As shown in Figs. 7 and 8, the final accuracy of the training set is 94%, and the final accuracy of the test machine is 93.36% (Fig. 9).

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Fig. 7. Accuracy change map of training set

Fig. 8. Accuracy change map on test set

Fig. 9. Error change graph on training set

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3 Summary Due to the large number of mail, shallow models represented by SVM and CRF are not easy to model the nonlinear relationship in massive data because of their shallow models, so they can not bring about performance improvement. On the contrary, the deep models represented by CNN and RNN can make more precise modeling of data with the increase of the complexity of models, and achieve better results. Therefore, we recommend using a text classification method based on depth learning. In summary, the accuracy rate of the model fusion is 94%, which is better than that of the single classification model in this paper. Therefore, the integrated learning method can solve the problem of email recognition better.

References 1. Wu, Y.: Research on Text Classification Application Based on SVM. Chengdu (2014) 2. Text-CNN text classification. http://blog.csdn.net/chuchus/article/details/77847476,2017.12. 02 3. Deep learning in space – the principle of ConvLSTM and its TensorFlow implementation. http://blog.csdn.net/sinat_26917383/article/details/71817742. Accessed 02 Dec 2017 4. Goodfellow, L.: Deep Learning. People’s Post and Telecommunications Press, Beijing (2017) 5. Huang, W., Tang, Y.: TensorFlow actual combat, pp. 159–172. Electronic Industry Press, Beijing (2017)

Research on Digital Evaluation System for Experimental Score Baoqin Liu(&) Software and Information Engineering Institute, Beijing Information Technology College, Beijing, China [email protected]

Abstract. To increase student’s learning interest and improve teaching efficiency in vocational education, the digital evaluation system of experimental Score is studied. It offers a new approach to record multiple experimental Score quickly using digital method rather than old paper way. The research focus on the system design including server and client, and the key technologies in system implementation. Keywords: Digitizing


Experiment
Score
Evaluation

1 Introduction Students lacking of learning interest is often a universal problem facing by teachers in vocational education. Which results in unsatisfactory teaching effect. Teaching and practicing is a proven effective way to change this situation. Assigning an experiment after teaching a module can help student further understanding the content. There is a prerequisite that dividing students into team before class, usually four teams in each class, and about ten students in each team. The teacher evaluates experiment by the correct rate and completion speed. A number of students who finish faster will get additional Score. For example,the top 20 students (the student number is depending on the class situation) can get Score from 20 to 1 in order of priority. Thus In this way, students who study better will work hard to complete the experiment task to make themselves top. For the rest of the weaker students, the following rules can help and promote them learning: Add and subtract points according to the overall speed of each team, the fastest completion of the team will be given 4 additional points for each student, And so on, add 1 point for each person of the slowest completion team,If students can not finish the experiment, they will get score according to the actual completion. But everyone in the team will lose one point。This rule can encourage students who have completed the task to help students who cannot accomplish the task; it can also promote common learning and improve overall learning efficiency. Students will further consolidate their study content in helping others. This method can greatly improve students’ interest and the overall teaching effect. However, there is a problem during applying the method: teachers need to record the

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scores on the paper first, and then input it into the computer after the lesson. Teachers do the duplicated works, and the efficiency is low. Mobile phones are widely used and very convenient. If we can quickly record and inquire Score through the mobile phone application and export Score to a server, the teaching efficiency can be greatly improve, and duplication of work can be avoid. Therefore, in order to improve teaching efficiency, this paper discusses the design and implementation of the digital evaluation system for experiment Score in higher vocational education based on mobile clients.

2 System Architecture The architecture of the system is as shown in Fig. 1 below, using the C/S architecture, namely the client/server architecture. The client provides teachers and students with user-friendly interactive interface, so that teachers can evaluate the student’s mark efficiently, and send export requirements to server. Students can enquiry their own scores. Data exchange between the server and the client achieves through the Http protocol based on the internet. The server provides downloading of the score. The server can receive requests from client such as login, query, and export, and make corresponding processing. Administrators can manage information including teachers, classes, courses, and students.

Fig. 1. System architecture

3 Server Design 3.1

Developing Environment and Tools

The developing language is Java, Html and Java Script at server, the developing tool is JDK, Apache Tomcat and SQL Server Enterprise software. The operating system is Windows7 or later edition. Using Html and Java Script to create static pages, Using Java to create dynamic pages. Using Apache Tomcat as web server and SQL Server as database server. Deploy the project to an accessible web server to enable network connectivity and data communication with clients.

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Monitor Client

The server monitor the request from client in real time by using multithreading technology. If it capture the information, the server will deal with the request quickly, and send response data to client. 3.3

Login

Administrator and teacher must enter correct user name and password to enter the system. After successful verification, they can enter the home page and start to work. 3.4

Teacher Management

Administrator can manage teacher information such as querying, adding, modifying and deleting. Create a teacher information table in the SQL Server database to store teacher details; Create JavaBean encapsulation database operations like database connection, database close, query and update operations. After verifying the client login information, send the class and course information of the teacher to the client. 3.5

Class Management

Administrator can manage class information such as updating and querying class details, which include class id, specialty and student amount. 3.6

Student Management

Administrator can operate student information including querying, modifying, editing and adding. Students can query scores only after they login successfully at client. Therefore, the server needs to register and manage student information, and authenticate when receiving a student login request. 3.7

Class Management

Administrator can manage course information including adding, modifying, deleting and querying. Course details include course id, course name and course credit. 3.8

Score Management

Teacher can manage scores including checking, modifying, deleting and exporting. Score details include date, experiment id, ranking, score, and total score in one class.

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4 Client Design 4.1

Developing Environment and Tools

The client runs on both iOS and Android platforms. This study uses the iOS platform as an example. Use the operating system of Apple Mac OS X 10.6 (i.e. Snow Leopard) or later edition to develop the client. The latest version of Mac OS is Mac OS High Sierra 10.14 Mojave, which is released on June 5, 2018. The development language is Objective-C, development tools is Xcode software and iOS SDK. Xcode is an integrated development environment (IDE) that provides all the tools to create and manage iOS projects and source code, build code into executable files, and run or debug code on iPhone or iPad simulators or real devices. The iOS SDK contains code, information, and tools for developing, testing, running, debugging, and optimizing performance of iOS applications. 4.2

Teacher Login

The teacher inputs ID and password, and sends the login information to the server through the network. After the server verifies the result, it returns the class and course information to the client. The client receives the class and course data and displays it. Then the teacher selects the class and the course, and clicks the “new score” button, after entering the number, the teacher can start scoring. 4.3

Evaluation

The teacher scores the students, ranks and records according to the student’s completion order. Firstly, selects the student’s name, and the default experimental score is 5 points. The ranking is incremented by default starting from one. The total score consists of the sum of the experimental results and the ranking plus points. Ranking plus points is automatically added according to the rankings, if taking the top 20 as example, 20 points for the first place, 19 points for the second place, and 1 point for the 20th place. Other students do not add points. After the assessment is completed, the teacher can click the “View” button to view the grades and make changes. 4.4

Save and Export

After the teacher evaluates the experiment, the score can be stored on the client machine or exported to the server. After clicking on the “export” button, the client send score information to the server and the success or failure message returned. If want to save score to the mobile device, the teacher stores the can click “save” button to achieve it. When there is no internet, the teacher can view locally stored information.

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The student enters ID and password, and sends login information to the server through the network. After the server verifies the result, it returns the course information to the client. The client receives the course data and displays. The student can select the course and view the score information.

5 Technical Difficulty and Core Algorithm The main technical difficulties of this system lie in data interaction between server and client, and client-side data storage. The client needs to send the login information (name and password) to the server. The server receives the login request information and parses it, and then accesses the database for authentication. If the verification is successful, all class, course, or grade information is sent to the client. The client needs to receive, parse, and display received information so that the teacher can assess the grade, query results, and student can query results. 5.1

Client Send Data to Server

Generally, the HTTP network request API in the iOS SDK can be used, including NSMutableURLRequest, NSString, NSData, or NSMutableURLRequest. However, the above APIs are relatively complicated. To improve the development efficiency, the ASIHTTPRequest tool can be used to interact with the server. ASIHTTPRequest is a relatively simple but powerful open source project of HTTP accessing, written in Objective-C, packaged CFNetwork API, can be used in this research to exchange data with the server. The core code for sending a login request using ASIHTTPRequest is as follows: NSURL *surl = [NSURL URLWithString:serverUrl]; ASIFormDataRequest *req = [ASIFormDataRequest requestWithURL: surl]; Two delegate methods of requestFinished and requestFailed need to be implemented. 5.2

Server Responds to Client

The server first parse the login data from the client, and then accesses the database for verification. If the verification is successful, all classes, courses, or score information is sent to the client according to the requester’s identity. The server can uses JSON technology to send score information to the client. 5.3

Client Parse Data from Server

The client use ASIHTTPRequest to access and parse the data from server, it obtains all student score information passed by the server through the responseString method, converts the data into JSON type of data by calling the JSONValue method, and then uses the objectForKey method to obtain the score details.

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Data Storage at Client

The score obtained from the server can be stored in two ways. One way is temporarily storing it in memory. After the program exits, data also do not exist. You can use an array of objects to achieve this; the other way is persistent data. Generally, four different storage mechanisms are used in iOS platform: plist, object archiving, SQLite, Core Data. We choose SQLite3 to store information locally. The FMDB framework encapsulates SQLite3 and is easy to use. It is used in this study.

6 Conclusions This research mainly discusses the design and implementation of the evaluation system for higher vocational classroom experiments based on mobile client. The system helps to increase teaching efficiency and improve student study motivation. Client is mainly on iOS and Android platform. This study takes the iOS platform as an example, and it deeply discuss the design and implementation of the evaluation system. It focuses on the system design, the technical difficulties during the implementation, and the solution and core code. This research result has been applied in teaching practice, greatly improving teaching efficiency and student learning initiative. The problems analyzed and solved in this study are also the technical difficulties faced by many other similar systems. Therefore, it can be applied to the study of other similar projects. Follow up research mainly lies in further intelligence such as using face recognition technology to log in and score, and combining sound effects when scoring to encourage student and increasing interest.

References 1. Zhang, B., Yue, K., Zhang, J.: Design of operating system experiments and evaluation criteria. Exp. Sci. Technol. 15(3), 127–130 (2017) 2. Long, J., Tang, Z.: Design and implementation of mobile terminal operating system based on university resource management. Popular Sci. Technol. 5, P11–P13 (2017) 3. Fu, Y.: The design and analysis of the hand-held library system based on APP technology. Libr. Work Study 2, P54–P57 (2016) 4. Li, S., Feng, J.: Research and application of dynamic evaluation model of college students usual grades. Comput. Knowl. Technol. 23, 209–211 (2016) 5. Liu, B.: Web information transfer between android client and server. In: International Conference on Intelligent and Interactive Systems and Applications, pp. 435–441. Springer, Cham (2017) 6. Chang, Y., Deng, F., Xiao, Y., Li, A., Jiang, D.: The designation and realization of compus social terminal service based on android/iOS. Comput. Knowl. Technol. 24, 52–54 (2016)

LSD and Skeleton Extraction Combined with Farmland Ridge Detection Yibo Li and Han Qu(&) Department of Automation, Shenyang Aerospace University, Shenyang 110136, Liaoning, China [email protected]

Abstract. In the traditional agricultural machine-vision navigation, Hough transform was widely used to carry on the straight-line detection of the ridge line. As for the high complexity of Hough transform, amounts of pretreatment on the images need be done to reach the effective results, however, the detection results are unsatisfied. Plentiful false positive and false negative exist during the detection, so, it is difficult to attain the working accuracy of unmanned agricultural machine and hard to be used in the actual conditions. LSD (a Line segment detector) algorithm is a partial optimal detection algorithm, which bases the pixels gradient and obtains straight line through the regional growth, it owns some advantages such as rapid calculating, better testing result, rare false positive and false negative, etc. This paper attempted to apply the LSD algorithm to the agricultural machine-vision navigation, and advance the detection accuracy and results by extracting the skeleton of crops images. By experimenting on four different crops, the result demonstrates that the LSD algorithm is better than the Hough transform on the accuracy and effect, meanwhile, it has the better engineering-applied value. Keywords: Machine-vision Skeleton extracting


Ridge-line testing
LSD algorithm

1 Introduction Machine-vision measurement technology was widely applied in various fields for its advantages such as non-contact, high precision, quick response and low cost [1]. In agricultural automation, identification of farmland ridge line is the most important problem of agricultural robot vision navigation. So, it is the key of the agricultural subsequent working whether the farmland ridge could be identified efficiently, quickly and accurately. In the traditional agricultural machine-vision navigation, the improved algorithm based on Hough transform is usually adopted to detect ridge lines. Hough transform is a shape matching technology proposed by Paul Hough in 1962 [2], which realize the straight-line detection through several steps, first, the algorithm maps the pixel points in the image to the parameter space, and then accumulates in the Hough parameter space to find the method of the accumulator’s peak value. In the practical application, Hough transform usually has plentiful false positive and false negative, at the same time, it is © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 446–453, 2019. https://doi.org/10.1007/978-3-030-02804-6_59

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an exhaustive algorithm, which occupies a large space and calculates in low speed. Burns et al. [3] proposed straight-line detection algorithm based on pixel gradient direction, which detect linear edge by calculating the gradient value and direction of pixels, meanwhile, it improves the detection efficiency, but the false positive and false negative are still existed. Von Gioi et al. [4] have improved on the basis of Burns and Desolneux [5, 6], and introduced the concept of line-support regions as well as proposed the LSD (a line segment detector) algorithm. LSD algorithm has the good robustness and high detection efficiency. However, consideration from the point of practical application, due to the complexity of farmland environment and many interference factors, that the LSD algorithm was directly used to perform the straight-line detection will be affected by a lot of interference factors, and led to false positive and false negative. In response to the existing problems in the application, this paper proposes a method that combining skeleton extraction with LSD detection to detect the ridge of farmland crops. By extracting skeleton, affirming the location of ridge and decreasing data amount of images, the calculating speed and extracting accuracy could be effectively advanced before LSD algorithm was used to detect. Experiment shows that the method combined LSD algorithm with skeleton extraction, compared with Hough transform, own the more accurate results.

2 The Pretreatment About Image As for the enormous information amount, it is difficult to obtain accurate ridge situation information by exactly extracting skeleton. Therefore, through the simple pretreatment, the interest area could be preliminarily extracted and the interference factors could be removed as well. 2.1

Grayscale and Binarization

In the working environment of unmanned agricultural machine, the green features of crops are obvious, so the G value could be increased to change the gray scale, and the crops and the land could be separated. In this paper, the supergreen features 2G-R-R [7] are adopted to deal with the grayscale. After grayscale, the effective features in the image are extracted, in order to simplify the operation of subsequent algorithm, the binary processing was performed in the next step. The adaptive threshold value of OTSU [8] was used for the binarization processing. Histogram of gray image was segmented into foreground and background by optimal threshold value to maximize the inter-class variance. 2.2

Morphological Filter

For the different growth of crops, and the existent discontinuous ridges in the planting process, the images that have been binarized usually contain spacing, holes, noise, and needed morphological filtering. Median filtering is able to remove noise and interference as well as keep the details of objects (edges) in the images. Therefore, median

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filter is adopted to remove isolated noise points and smooth the image. 6  6 rectangular structural elements were used to expand, eliminate spacing, and fill holes. 4  4 rectangular structural elements were used for corrosion, separating adhesive part, and removing the small protrusion. The original image farmland ridges of soybeans, peanuts, potatoes and corn were shown in Fig. 1, the corresponding morphological images were shown in Fig. 2.

a.soybeans

b. peanut

c. potato

d. corn

Fig. 1. Original images of crop ridge images

a.result of soybean

b.result of peanut

c.result of potato

d.result of corn

Fig. 2. Results of morphological treatment of crop ridge images

3 Skeleton Extraction The skeleton, which also be known as the mid-axis, is a good character trait [9]. The skeleton is a kind of connectivity and topological structure, which is composed by a single pixel and has ability to effectively reflect the shape of the original object. Generally speaking, the process of obtaining the skeleton of an image is also the process to “refine” the image [10]. Skeleton extraction based on refinement processing is usually described by using the burning-grass model [11], its key step is to continuously delete the edge points until the image no longer changed. The skeleton extraction adopted in this paper is based on this model. The ridges of crops are more obvious after extracting the skeleton. After morphological filtering, the basic features of farmland images have been revealed, but the repeated detection will occur if the LSD algorithm was directly used. But through the skeleton extraction of the image, the central line of the crops could be founded, and the amount of data in the image without changing the main characteristics was also induced, at the same time, advancing the accuracy of detection. The corresponding skeleton extraction images of the four crops were shown in Fig. 3.

LSD and Skeleton Extraction

a.result of soybean

b. result of peanut

c.result of potato

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d. result of corn

Fig. 3. Results of skeleton extraction of crop ridge images

4 LSD Straight-Line Detection Arithmetic LSD is a greedy algorithm to obtain the local optimal solution, which is faster than Hough transform and more robustness and accurate. In this paper, LSD algorithm be used for line detection, and obtain the continuous line. 4.1

Image Scaling

Digital image is composed of discrete gray pixels, which has the sawtooth effect, and will disassembles segments of lines that are essentially belong to the same line by directed detection. Aforesaid conditions could be improved through the image zooming. Gaussian kernel function was used to filter, eliminate the sawtooth effect, and then reduce the sampling. The standard deviation of gaussian kernel is 0.6. 4.2

Gradient Computation

The 2  2 template was adopted to compute the gradient value and gradient direction of each pixel on the image. Setting the point I(x, y) as the grayscale value of pixel point (x, y), so: (

gx ðx; yÞ ¼ iðx þ 1;yÞ þ iðx þ 1;y þ2 1Þiðx;yÞiðx;y þ 1Þ gy ðx; yÞ ¼ iðx;y þ 1Þ þ iðx þ 1;y þ2 1Þiðx;yÞiðx þ 1;yÞ

The gradient value is: Gðx; yÞ ¼

ð1Þ

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi g2x ðx; yÞ þ g2y ðx; yÞ

gx ðx; yÞ The gradient direction is: h ¼ arctan gy ðx; yÞ

ð2Þ

! ð3Þ

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Gradient Pseudo-ordering

Line-support regions are determined by adopting the gradient domain growth method. The method and order of selecting pixels will directly influence the result of region growing. For accord with the core idea of fast and efficient with LSD algorithm. So, the pseudo-ordering algorithm with less computation was selected. According to the minimum and maximum value of all pixel gradients in the image, the gradient value in the image was divided into 1024 intervals, and the corresponding interval is placed according to the gradient value of each pixel. It will increase the calculation and the bigger error caused by small gradient pixels, if all pixel points are both in region growing. Therefore, the gradient threshold is set up to restrict the participation of small gradient pixel points in subsequent calculation. Setting the gradient threshold as q ¼ q= sin s, which q represents the possible error range, the empirical value of it is 2; s is the angle tolerance in the region growing, the value of it is 22.5°. 4.4

Region Growing

After gradient pseudo-ordering, the set of predetermined pixels for region growing was obtained, meanwhile, selecting one of the biggest gradient values of pixels as the seed point and setting this pixel’s gradient direction as the level direction, searching in its neighborhood pixels and calculating the difference between their gradient directions and level direction, then, compared with s: if someone bigger than s, deleting it from the alternative field; if someone less than s, putting this pixel into line-support regions and updating the level direction. The updating method was shown in (4). Then, searching based on the new level direction and repeating operation until no other pixel join in this line-support region, finally, stopping regional growth. Getting rid of the pixels which join the line-support regions from the pixel set, and reselecting the seed pixels in order to start the next regional growth. P b ¼ arctan P

j

sin h

j

cos h

! ð4Þ

h is the angle between the gradient direction and level direction of pixel j in this line-support region. 4.5

Line Detection

4.5.1 Determination of Rectangle Parameters In LSD algorithm, each line-support region corresponds to a rectangular region, and each rectangular area corresponds to a line. So, in order to obtain a line, the parameters from the corresponding rectangle region need to be extracted. cx ¼

n X j¼1

xj =n;

cy ¼

n X j¼1

yj =n

ð5Þ

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In the formula (5), (cx, cy) means the center of the rectangle, (xj, yj) represents the coordinates of the j pixel in this line-support region, and n represents the total number of pixels in this line-support region. The long axis direction of the rectangle is obtained by Eq. (6)  M¼

mxx mxy

mxy myy

 ð6Þ

By calculating the matrix M, the eigenvalues a and b (a > b) can be obtained, the corresponding eigenvector is (a1, a2)T, (b1, b2)T. The slope of the long axis and the short axis is ka ¼ tan ha ¼ a2 =a1 , kb ¼ tan hb ¼ b2 =b1 respectively. 4.5.2 Line Determination The concept of NFA (Number of False Alarms) is introduced in LSD algorithm, which was shown in the formula (7) NFA ¼ ðNMÞ

5=2

n   X n p j ð1  pÞnj
j j¼k

ð7Þ

N and M represent the number of columns and rows of the reduced image; p is usually set as s=p. When the NFA  1, the rectangle is judged to be a straight line.

5 Experiments The common economic crops such as soybean, peanut, potato and corn were selected as experimental object, by comparing the test results (Fig. 4) and accuracy (Table 1) between Hough transform and LSD, obtain the conclusions.

a. result of soybean

c. result of potato

b. result of peanut

d. result of corn

Fig. 4. Comparison between Hough transform and LSD crop ridge images

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Table 1. Comparison between Hough transform and LSD algorithm to effective points Method

Effective crop points Soybean Peanut Potato Corn Hough transform 220 685 1008 631 LSD 755 1819 1112 1865

It can be seen from Fig. 4 that the Hough transform basically detected the location of ridge row, but the false negative was serious as well, especially in the detection of corn, and the detection results were fragmented, which was difficult to meet the requirements of subsequent operations. The straight-line information detected by LSD algorithm is accurate in location, no false positive and false negative, the continuous results, which satisfy the navigation requirements. As shown in Table 1, the number of effective points detected by Hough transform is far less than that of LSD algorithm, which has a great impact on navigation accuracy, and is possible to interrupt the operation in the actual operation process.

6 Conclusion LSD algorithm is an emerging straight-line detection algorithm in recent years, which owns the characteristics of fast, efficient and robust. This paper tries to use it on the detection of crops ridge line, and combine it with the actual application situation. On the basis of the original LSD algorithm, the algorithm adopted in this paper increases the skeleton extraction, and exact the location of ridge line, reduces the computational complexity and improve the detection efficiency. It is proved by experiments that the algorithm realize the detection of ridges in crops, and has the advantages of high precision and fast detection speed, etc., which is of great significance to the realization of autonomous navigation of farm robots in the future.

References 1. Zhang, L., Xu, J., Xia, Q.: Pose estimation algorithm and verification based on binocular stereo vision for unmanned aerial vehicle. J. Harbin Inst. Technol. 46(5), 66–72 (2014) 2. Chen, J., Jiang, G., Du, S.: Crop rows detection based on parallel characteristic of crop rows using visual navigation. Trans. CSAE 12(12), 107–113 (2009) 3. Burns, J.B., Hanson, A.R., Riseman, E.M.: Extracting straight lines. IEEE Trans. Pattern Anal. Mach. Intell. 8(4), 425–455 (1986) 4. Von Gioi, R.G., Jakubowicz, J., Morel, J.M.: LSD: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722–732 (2010) 5. Desolneux, A., Mosian, L., Morel, J.M.: Meaningful alignments. Int J. Comput. Vis. 40(1), 7–23 (2000) 6. Desolneux, A., Moisan, L., Morel, J.M.: From Gestalt Theory to Image Analysis: A Probabilistic Approach. Springer, Cham (2008)

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7. Woebbecke, D.M., Meyer, G.E., Von Bargen, K., et al.: Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 38(1), 259–269 (1995) 8. Zhang, W., Du, S.: Machine vision recognizing position baseline in cropland. J. China Agric. Univ. 11(4), 75–77 (2006) 9. Liao, Z.: A survey of 2-D skeletonization algorithm. J. Sichuan Normal Univ. Nat. Sci. 32(5), 676–688 (2009) 10. Diao, Z., Wu, B., Wu, Y.: Application research of skeleton extraction algorithm based on image processing. Comput. Sci. 43(6A), 232–235 (2016) 11. Liu, L., Chambers, E.W., Letscher, D., et al.: Extended grassfire transform on medial axes of 2D shapes. Comput. Aided Des. 43(11), 1496 (2011)

Native Language Identification from English Noise Bursts by Chinese Listeners Hongyan Wang1(&), Yuting Xu1, Lifen Chen1, and Vincent J. van Heuven2 1

Shenzhen University, Shenzhen, People’s Republic of China [email protected] 2 University of Pannonia, Veszprém, Hungary

Abstract. Identifying speakers by their spoken output is a specialist task for forensic investigators. In the present study we focused on cross-linguistic speaker (Chinese, English, Dutch) identification based on (components of) English stops and fricatives, /p, b, t, d, k, g/ and the fricatives /f, v, h, ð, s, z, ʃ, ʒ/. The contribution of English noise bursts to native language identification will be presented and the special tokens which contribute the most will be analyzed. Keywords: Forensic phonetics
Native language identification Stops
Fricatives
Noise bursts

1 Introduction Numerous studies have been done on speaker identification, whether text-dependent or text-independent, by linguists and engineers. Speaker identification will be more difficult as we have to work with shorter sound fragments. In the present study, we ask how well very short sound fragments can be attributed to a single speaker if the competitors speak the same language, i.e. English, but hail from different native language backgrounds, i.e. Netherlandic Dutch, Mandarin Chinese and American English. Dutch and English are genealogically related, since they belong to the West-Germanic group of languages, whereas Chinese has no relationship with either of these two and is typologically radically different in virtually all relevant linguistic aspects. The listeners in the experiment have Mandarin Chinese as their native language. Assuming that sharing the native language between speaker and listener yields an advantage, we predict that the speaker identification task is best performed when the Chinese listeners respond to a Chinese speaker, even if the speech is (an approximation to) another language, e.g. English. Identifying speakers by their spoken output is not only an important social skill but also a specialist task for forensic investigators. It is often said that at least one minute of speech is needed in order to derive a reliable reference model of a speaker. More often than not, however, in a forensic context only short segments of speech are available so that other methods are needed to determine the odds that a fragment spoken by an unknown (“questioned”) speaker is indeed produced by the reference speaker (“known”) speaker rather than by some other speaker. In the present study we focused on cross-linguistic speaker identification based on (components of) English stops and fricatives. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 454–461, 2019. https://doi.org/10.1007/978-3-030-02804-6_60

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Indeed, a substantial amount of work has been done on the contribution of various types of sounds to speaker identification if only short fragments are available for comparison. Pollack et al. (1954), and later Bricker and Pruzansky (1966), found that speaker identification improved with the number of phonemes in the speech sample. Nasal consonants and vowels were found to contribute more to perceptual speaker identification than other phonemes (Fernández Gallardo 2016). In the present experiment, however, the listeners will not be native listeners of the language that is being spoken, i.e. English. Rather, the listeners’ native language is Mandarin Chinese. Since an English syllable may contain only one vowel (with a choice from 20) but many more consonants, the number of consonant tokens in English is larger than the number of vowel tokens. It makes sense, therefore, to concentrate our research effort on consonants, especially the stops and fricatives.1 These tokens will be found more often in a short fragment than vowel tokens. There is a lot of evidence to suggest that it is advantageous for a listener to respond to a speaker with whom he shares the native language. The greatest advantage will be found when speaker and listener communicate with each other in their native language. But there has been a growing body of evidence that the same advantage of sharing the native language is found when speaker and listener communicate in a language that is foreign to them. When native Chinese listeners respond to native Chinese speakers who produce English speech, they identify the sounds and other, higher-order, linguistic units relatively better than when the talker is a native speaker of English or has some other native language than Chinese (Wang 2007; Wang and Van Heuven 2015, and references therein). This phenomenon is commonly referred to as the shared interlanguage speech intelligibility benefit (Bent and Bradlow 2003). The reason for the shared interlanguage benefit (henceforth SIB) is that the non-native speech will reflect the speech habits of the talker’s native language (called foreign or second language interference, e.g. Lado 1957). We predict therefore that Chinese listeners will find it relatively easy to identify the speaker of an English fragment as a fellow Chinese, and that it will be rather more difficult for them to distinguish between English speakers with non-Chinese language backgrounds such as Dutch or American native speakers. Moreover, we will test the hypothesis that this SIB effect will persist even when the speech fragments are very short and contain information on one single stop or fricative only, i.e. the subsets /p, b, t, d, k, g/ and the fricatives /f, v, h, ð, s, z, ʃ, ʒ/.

1

The vowels /ə/ and /ɪ/ are the most frequent phonemes in English, with relative frequencies of 9.96% and 9.75%, respectively. This is due to the circumstance that these are reduction vowels, which are the only choice of vowel available in unstressed syllables, as in e.g. the, a(n), and many. Because of their susceptibility to reduction (but temporal and spectral) they are dispreferred target sounds for forensic speaker identification purposes. Most normal (full, unreduced) American English vowels have consistently lower token frequencies than consonants (e.g. Hayden 1950). The most frequent normal vowel is /æ/ (3.09%). Seven consonants, including four obstruents, have (substantially) higher relative frequencies than this vowel, i.e. /n, t, r, s, l, ð, d).

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We end this introduction by a summary of the research questions we aim to answer: (1) How well are the cross-linguistic speakers identified according to English noise bursts of the stops and fricatives? (2) Which type of noise burst will provide the most effective cue for the crosslinguistic speaker identification? Theoretically it should be fricatives because they have longer duration. But because of the cross-linguistic contrast, Chinese plosives are different from the Dutch plosives, viz. aspirated vs. unaspirated in Chinese (which is similar to the way the voicing contrast is signaled in English, cf. Lisker and Abramson 1971), and voiced (i.e. with voicing during part of the silent interval preceding the noise burst) vs. unvoiced (no glottal pulses prior to the noise burst) in Dutch.

2 Methods 2.1

Speaker Selection

In order to keep the experiment manageable we decided to select a single representative speaker for each of the three native language backgrounds. For this speaker to be optimally representative of his peer group we can a preliminary experiment with nine male speakers of Mandarin Chinese, Netherlandic Dutch, and American English. The 27 speakers produced the same two Harvard sentences each (IEEE Subcommittee on Subjective Measurements (1969). Trials were then presented to a large group of Mandarin-Chinese listeners. In each trial the participants listened to the same sentence produced by the one Chinese, one Dutch and one American speaker. Then a different test sentence followed, spoken by one of the same three speakers. The listeners’ task was to identify the speaker of the test sentence as either Speaker 1, Speaker 2 or Speaker 3. A random selection of nine speaker triplets was adopted as the basic material. No speaker participated in more than one triplet. Each of the three speakers in a triplet provided two reference stimuli, Harvard sentence H1 and Harvard sentence H2. When H1 was used as the reference, the test sentences were H2 and vice versa. The order of the test sentences was varied at random over the three speakers in the triplet. This yielded a stimulus set of 9 (triplets)  3 (reference speakers)  2 (reference sentences) = 54 trials. Eight classes of male or female first year university students, all native speakers of Mandarin (N  300), served as listeners. Stimuli were presented in classrooms over good quality loudspeakers at a comfortable listening level. The listener’s task was to decide for each triplet of speakers which speaker also produced the reference stimulus. Identification scores were computed for each of the 54 speakers. For each language background the speaker with the median score was then selected as the speaker in the actual experiment. Using this procedure we aimed to simplify the experiment such that each language background could be represented by a single, optimally representative speaker.

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Materials

The designated Chinese, Dutch and American speaker then recorded the 14 English stops and fricatives in a fixed carrier phrase Say xx again. In this frame xx was a (nonsense) syllable, containing the stop or fricative as the onset consonant, followed by the vowel /ɑ:/ (as in English father), i.e. /pɑ:, bɑ:, tɑ:, dɑ:, kɑ:, gɑ:, fɑ:, vɑ:, hɑ:, ðɑ:, sɑ:, zɑ:, ʃɑ:, ʒɑ:/. In this set only /ʃɑ:/ may be taken as a meaningful word in American English, as in Shah of Persia (although most speakers will not have been familiar with it).2 The stimuli were shown on the screen of a silent laptop in quasi phonetic spelling (see Maniwa et al. 2009 for details) and exemplified by everyday keywords so that it would be perfectly clear for the speakers which obstruent was being targeted. The recordings were made individually in a sound-proofed room on a Tascam DR-06 solid-state recorder (44.1 kHz, 16 bit) and a Sennheiser HD800 close-talking headset microphone. The 14 target consonants were excised from their spoken context using the wave editing facility of the Praat speech processing software (Boersma and Van Heuven 2001). Target consonants were gated between the last glottal pulse in the preconsonantal vowel in Say and the first glottal period of the post-consonantal vowel /ɑ:/. Cuts were made without fade-in and fade-out but always at zero crossings in order to avoid clicks at the beginning and/or end of the gate. This excised interval included a potentially voiced “silent” interval in the case of stop consonants and the stop or fricative noise burst. Aspiration following the noise burst, i.e. the voiceless onset of the next vowel, was included in the consonant interval – as is usually done in this type of speech segmentation. Aspiration (also called long positive Voice Onset Time) is normally claimed to be a characteristic of the voiceless (or: fortis) stop consonants /p, t, k/ in English (Lisker and Abramson 1971; Flege 1987) and in Mandarin (Zhao 1995) – but not in Dutch (Collins and Mees 1981; Gussenhoven and Broeders 1981). A stimulus list of 42 trials (i.e. 14 consonants  3 reference speakers) was then prepared. Each trial began with one token of an utterance of the sentence Now say … again, which was then followed with the three excised consonant intervals as spoken by the same speaker and the two counterpart tokens realized by the other two speakers. The order of the three speakers producing the consonant intervals was randomly varied over the trials, such that each possible order occurred equally often over the course of the experiment. The 42 trials were presented twice, in different random orders, to small groups of Mandarin Chinese listeners, a subset of 120 individuals from the larger group who participated in the speaker selection test. Stimuli were presented in a small conference room with little reverberation over good-quality loudspeakers. The 2  42 stimuli were presented without a break, and were preceded and followed by three random tokens, yielding a total stimulus presentation of 90 trials. The added stimuli at the beginning and the end of the order were not included in the statistical analysis. The listeners’ task was to indicate on their (printed) response sheets whether fragment A, B or C was the consonant that was contained in the reference utterance Say /Cɑ:/ again. Participants were explicitly instructed to make one choice per trial, no more, no less (i.e. three-alternative forced choice). 2

For British listeners (or American listeners in the New England area) many more forms may constitute meaningful words, i.e. par, bar, tar, car, far and /zɑ:/ (czar, i.e. the Russian monarch until 1917).

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3 Results Table 1 lists the results of the speaker identification experiment. The native language of the reference speaker (American English, Mandarin Chinese, Netherlandic Dutch) is listed in the rows while the listeners’ responses are listed in the columns in terms of a percentage. The table lists this information for each of the 14 obstruents separately, yielding 14 small 3  3 confusion matrices. The correct decisions are found along the main diagonal of each confusion matrix, and are printed in bold face in a shaded cell. 3.1

Main Effect of Speaker

The overall mean correct speaker identification rates are 45.5% for the American speaker, 50.2% for the Chinese speaker and 45.2 for the Dutch speaker. Although the better score for the Chinese speaker was predicted by the shared interlanguage hypothesis, the difference in scores between the three speakers is not systematic enough to be statistically significant. This was shown by a repeated measures one-way Analysis of Variance with speaker as a within-item variable, F(2, 26) = 1.4 (p = .256, ins.). Table 1. Perceptual identification of speakers (in the columns) against actual speakers (in the rows) for 6 English stops and 8 fricatives. Correct speaker identification is indicated in bold face in grey cells. Mean correct speaker identification for each type of consonant in parentheses Ref. Speaker American Dutch Chinese American Dutch Chinese American Dutch Chinese American Dutch Chinese American Dutch Chinese American Dutch Chinese American Dutch Chinese American Dutch Chinese

Stop p (52.2) b (65.5) t (28.9) d (42.2) k (26.7) g (38.9)

Am. 56.7 13.3 40.0 60.0 33.3 3.3 16.7 53.3 26.7 43.3 26.7 23.3 20.0 43.3 30.0 33.3 40.0 24.7

Response Du. Ch. 16.7 26.7 60.0 26.7 20.0 40.0 23.3 16.7 3.3 63.3 23.4 73.3 30.0 53.3 30.0 16.7 33.3 40.0 33.3 23.4 40.0 33.3 33.4 43.3 43.3 36.7 23.3 33.4 33.3 36.7 36.7 30.0 40.0 20.0 30.0 43.3

Fric. f (36.7 v (65.5) θ (66.7 ð (64.5) s (54.4) z (48.9)

(28.9)

Mean

44.4

45.3

46.1

(37.8) Mean

Am. 36.7 30.0 26.7 83.3 13.3 3.3 63.3 13.3 20.0 66.7 16.7 13.3 53.3 33.3 20.0 50.0 30.0 20.0 30.0 40.0 36.7 23.3 53.3 20.0 34.4

Response Du. Ch. 26.7 36.7 33.3 36.7 33.3 40.0 6.7 10.0 50.0 36.7 33.4 63.3 16.7 20.0 76.7 10.0 20.0 60.0 26.7 6.6 60.0 23.3 20.0 66.7 20.0 26.7 50.0 16.7 20.0 60.0 30.0 20.0 46.7 23.3 30.0 50.0 36.7 33.3 26.7 33.3 33.3 30.0 46.7 30.0 33.3 13.4 20.0 56.7 47.1 53.3

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3.2

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Main Effect of Consonant

Figure 1 presents the percentage of correct speaker identification scores for each of the 14 target consonants, separately for the three speakers. The responses to stop consonants are on the left-hand part of the graph, whereas the responses obtained for the fricatives are towards the right of the figure. Within each manner class the scores are in ascending order of magnitude based on the responses for the Chinese speaker. Since the fricative consonants last considerably longer, these may be expected to contain more spectral information on the speaker’s identity. Therefore, we hypothesized that the scores for the fricatives would be better than those for the stops. Visual inspection of Fig. 1 shows that this indeed the case, but it is also clear that there is considerable between-consonant variability in the results. The mean score for the stop is 42.4% against a score of 50.4% for the fricatives. However, the effect, although in the predicted direction, fails to reach significance, F(1, 12) = 1.0 (p = .333).

Fig. 1. Percent correct speaker identification as a function of the native language of the speaker. Scores are listed in ascending order for the Chinese speaker. Scores are shown separately for the six stop consonants (left) and the eight fricatives (right).

Further inspection of Fig. 1 reveals that there is a tendency for voiced stops and fricatives (mean = 51.9% correct) to yield more successful speaker identification scores than their voiceless counterparts (mean = 42.1%). The effect is significant by a paired ttest on the voiced and voiceless members of the seven pairs matched for manner and place of articulation, t(6) = 2.3 (p = .031, one-tailed). The reason why the voiced counterparts yield better speaker identification might be an artifact of the stimulus construction.

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The three speakers may have differed in the pitch (i.e. fundamental frequency) of their voiced sounds. Given the comparison task that was used in the present experiment, matching or mismatching pitch between the reference and the comparison stimuli may have been used as an effective cue to assign the matching comparison stimulus to the reference speaker. This alternative explanation may be checked by replicating the study after artificially changing the vocal pitch of speakers so that no pitch differences remain. Figure 1 also reveals that some of the 42 stimulus types yield particularly high speaker identification scores. For instance, the best score of all is observed for /v/ as produced by the American native speaker (83.3%). Native English /v/ should be clearly voiced throughout. Chinese has no /v/ at all and Dutch speakers, especially those from the west of the Netherlands, devoice their /v/ (Western Dutch dialects have no voiced fricatives). Both the Chinese and the Dutch speaker use a fully or partially voiceless substitute for English /v/, which would then explain the high score for this consonant in terms of speaker identification. The second-highest score is obtained for the voiceless dental fricative /h/ (76.6% correct), which might also explained by the fact that this sound is not part of the consonant inventories of Dutch and Chinese, so that these nonnative speakers may use an imperfect approximation of /h/ or substitute the nearest sound that is available in their native language – either /f/ or /s/. This would be a cue on which speaker identification could proceed successfully. 3.3

Conclusion and Discussion

The first question we asked was: how well can native American, and non-native Dutch and Chinese speakers of English be identified solely on the basis of a single token of either a stop or a fricative consonant? The results of this study indicate that such speaker identification is better than can be expected on the basis of chance alone (in a three-alternative forced choice task the score should at least be 33%). However, the scores are not more than between 12 and 27% points better than change, although scores for some specific trials were better than 70% correct. There was a clear tendency for the Chinese listeners to be more successful when the speaker was Chinese than when the speaker was either the Dutchman or the American. This effect was predicted by the shared interlanguage benefit hypothesis but did not reach statistical significance. The second question was if the type of consonant, i.e. stop or fricative, would influence the success rate of speaker identification. Indeed, and as predicted, the fricatives contained more speaker-specific information, but again, the difference found (8% points better for the fricatives) failed to reach significance. The only significant effect we found was due to the difference between the voiced and voiceless counterparts of consonant pairs that were matched for manner and place of articulation. Speaker identification was more successful from voiced than from voiceless consonants, with a difference of 11 points. However, we cannot rule out an alternative explanation for this effect, which would be based on purely indexical (rather than linguistically relevant) difference in vocal pitch between the three speakers used in the experiment. The present study should be seen as a mere pilot experiment on the use of consonants for purposes of speaker identification in a cross-linguistic situation. In future research the number of native and non-native speakers will be increased, so as to

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guarantee better generalizability of the results. Be this as it may, some of the 14 target English consonants provide information that is good enough to successfully identify speakers and/or their native language background. Specifically, the consonants /v/, /b/, /s/ and /p/ allow better than 50% speaker identification (with a highest score 65.5% for /v/). This finding suggests that speaker identification may be attempted in forensic applications when only very short sound fragments are available for a given speaker. Combining the results for the more successful consonants (by multiplication of odds) may allow a forensic expert to come up with an odds ratio that is acceptable in a forensic context. Acknowledgments. This work was supported by the Ministry of Education Humanities and Social Sciences Planning Project (14YJA740036) and the National Social Science Fund of China Post-Financed Project (17FYY009).

References Bent, T., Bradlow, A.R.: The interlanguage speech intelligibility benefit. J. Acoust. Soc. Am. 114(3), 1600–1610 (2003) Boersma, P., van Heuven, V.J.: Speak and unSpeak with PRAAT. Glot Int. 5(9/10), 341–347 (2001) Bricker, P.D., Pruzansky, S.: Effects of stimulus content and duration on talker identification. J. Acoust. Soc. Am. 40(6), 1441 (1966) Collins, B., Mees, I.: The Sounds of English and Dutch. Leiden University Press, The Hague (1981) Fernández Gallardo, L.: Human and Automatic Speaker Recognition Over Telecommunication Channels. Springer, Singapore (2016) Flege, J.E.: The production of ‘new’ and ‘similar’ phones in a foreign language: evidence for the effect of equivalence classification. J. Phonetics 15(1), 47–65 (1987) Gussenhoven, C., Broeders, T.: English Pronunciation for Student Teachers. Wolters-NoordhoffLongman, Groningen (1981) Hayden, R.E.: The relative frequency of phonemes in General-American English. Word 6(3), 217–223 (1950). https://doi.org/10.1080/00437956.1950.11659381 Lado, R.: Linguistics Across Cultures. University of Michigan Press, Ann Arbor (1957) Lisker, L., Abramson, A.S.: A cross-language study of voicing in initial stops: acoustical measurements. Word 20(3), 384–422 (1971) Maniwa, K., Jongman, A., Wade, T.: Acoustic characteristics of clearly spoken English fricatives. J. Acoust. Soc. Am. 125(6), 3962–3973 (2009) Pollack, I., Pickett, J.M., Sumby, W.H.: Voice identification of speakers. J. Acoust. Soc. Am. 26 (3), 501 (1954) Wang, H.: English as a lingua franca. Mutual Intelligibility of American, Chinese and Dutch Speakers of English. LOT, Utrecht (2007) Wang, H., van Heuven, V.J.: The interlanguage speech intelligibility benefit as bias towards native-language phonology. i-Perception 6(6), 1–13 (2015) Zhao, D.: English Phonetics and Phonology: As Compared with Chinese Features. Qingdao hai yang da xue chu ban she, Qingdao Shi (1995)

Two Dimensional Orthogonal Constrained Maximum Variance Mapping for Face Recognition Yu’e Lin(&), Chengjin Wang, and Xingzhu Liang School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China [email protected]

Abstract. Constrained maximum variance mapping (CMVM) is one promising feature extraction technique for face recognition. However, CMVM suffers from the well-known small sample size (SSS) problem, where the number of samples is less than the dimension of samples. In this paper, a novel supervised feature extraction method, called two-dimensional orthogonal constrained maximum variance mapping (2DOCMVM), is proposed. The proposed 2DOCMVM can address the SSS problem since it preserves the original image matrix and then avoids the high-dimensional image vector. In order to further improve the performance of 2DOCMVM, the optimal orthogonal projection matrix is computed using Gram–Schmidt orthogonalization. Experiments on YALE database show that 2DOCMVM outperforms than CMVM and other orthogonal methods. Keywords: Constrained Maximum Variance Mapping
Face recognition Small sample size problem
Two-dimensional
Orthogonal projection matrix

1 Introduction One of the important problems of face recognition is to extract the discriminant features from face images, which is called feature extraction. Feature extraction is an important role in face recognition task. Feature extraction aims to project the high-dimensional samples onto lower dimensional subspace in which some desired information can be preserved as much as possible. Over past few decades, face recognition have received much attention and many powerful feature extraction methods for face recognition have been well developed. Principal component analysis (PCA) [1], Fisher linear discriminant analysis (FLDA) [1] and locality preserving projection (LPP) [2, 3] are three popular linear feature extraction methods for face recognition. The PCA is an unsupervised method that aims to minimize the variance over all data samples, which is optimal for data reconstruction. FLDA is a supervised method that aims to find projection matrix that can maximize the between-class scatter matrix and minimize the within-class scatter matrix simultaneously. Thus the FLDA can obtain more optimal projection matrix than PCA. LPP aims to keep the locality characterization of data samples. LPP can find a manifold structure projection matrix that preserves local © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 462–467, 2019. https://doi.org/10.1007/978-3-030-02804-6_61

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information, which is more favorable for pattern recognition. Based on LPP, many local improved approaches [4–6] have been proposed for pattern recognition. Constrained maximum variance mapping (CMVM) is the more efficient one among these local improved approaches. CMVM globally maximizes the distances between different manifolds under the constraint of locality preserving and achieves the better recognition rates than LPP and others for face recognition. Although CMVM shows their superiority in the real-world applications, using CMVM for face recognition usually involves feature input of one-dimensional vectors, which may damage the local structure information among pixels of face image and make CMVU suffer from the small sample size (SSS) problem. Furthermore, CMVM ignores the class information of the data and the optimal matrix obtained is nonorthogonal, which are very important to the face recognition. In order to overcome the above-mentioned problems of CMVU, we propose a supervised feature extraction method based on CMVU, called two-dimensional orthogonal constrained maximum variance mapping (2DOCMVM), which uses the input of two-dimensional image matrices to avoid the SSS problem and improve the efficiency of method. Furthermore, using Gram–Schmidt orthogonalization, the orthogonal projection matrix is obtained to further improve the performance of 2DOCMVM. The remainder of this paper is organized as follows. We briefly review the classical CMVU in Sect. 2. In Sect. 3, we propose 2DOCMVU and the efficient procedure for solving 2DOCMVU. Section 4 describes some experimental results and the effectiveness of 2DOCMVU. Conclusions are summarized in Sect. 5.

2 Overview of the CMVU Suppose that there are C classes and N elements. Given a n-dimensional face sample matrix X ¼ ½x1 . . .xi . . .xN  and xi the ith image. CMVM aims to find an optimal projection matrix of maximizing the dissimilarities between manifolds and preserving the local structure of the samples. The dissimilarities between manifolds is represented as the sum of distances between samples from different class, and the local structure is represented as the sum of distances between two samples from neighbors. In CMVM, the local structure matrix is defined as: SW ¼

N X N X

ðxi  xj Þðxi  xj ÞT Sij

i¼1 j¼1

¼ XðDS  SÞX T ¼ XLS X T

ð1Þ

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The DS in Eq. 1 is a diagonal matrix and the diagonal elements DSii ¼ S is defined as:  Sij ¼

1; 0;

P j

 2 if xi  xj  \d otherwise

Sij . The

ð2Þ

The matrix of dissimilarities between manifolds is defined as: SB ¼

N X N X

ðxi  xj Þðxi  xj ÞT Bi;j

i¼1 j¼1

ð3Þ

¼ XðDB  BÞX T ¼ XLB X T The DB in Eq. 3 is a diagonal matrix and the diagonal elements DBii ¼ B is defined as:  Bij ¼

0; if xi and xj have the same class label 1; otherwise

P j

Bij . The

ð4Þ

The objection function of CMVM is represented as follows: J(WÞ ¼ max tr(W T SB WÞ     s:t: tr W T XSW X T W ¼ tr XSW X T

ð5Þ

The above optimization problem can be solved by the maximization the problem as follows: SB wi ¼ ki SW wi i ¼ 1; 2; . . .; l

ð6Þ

The details of CMVM can be found in [6].

3 2DOCMVM 2DOCMVM is an enhance algorithm based on CMVM, which runs directly on the twodimensional images. In order to avoid setting the parameters of the CMVM, we firstly redefine the Sij of the local structure matrix as follows:  Sij ¼

1; 0;

if xi and xj belong to same class otherwise:

ð7Þ

According to the definition of Eq. 7, we find that the Sij needn’t set the size of neighbors and uses the label information, which are favorable for recognition tasks.

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The goal of 2DOCMVM is to maximize the two-dimensional image dissimilarities between manifolds and preserve the local structure between two samples belong to same class. Defining the matrix of the two-dimensional image dissimilarities between manifolds as following: S2DW ¼ XLS X T ¼ XðLS  IN ÞX T

ð8Þ

where the operator ⊗ is the Kronecher product of the matrices. X ¼ ½X1 ; X2 ; . . .XN  is the two-dimensional image sample set and Xi is a m  n dimensional face sample matrix. xi is a mn  1 dimensional vector. IN is a N  N dimensional identity matrix. Similarly, the two-dimensional image local structure matrix of 2DOCMVM is defined as: S2DB ¼ XLB X T ¼ XðLB  IN ÞX T

ð9Þ

Then, 2DOCMVM defines the objection function as following: J(WÞ ¼ max tr(W T S2DB WÞ     s:t: tr W T XS2DW X T W ¼ tr XS2DW X T

ð10Þ

Equation 10 can be obtained by the maximization to the generalized problem as follows S2DB w ¼ kS2DW w

ð11Þ

The projection matrix of 2DOCMVM is the generalized eigenvectors w1, w2, …, wr of Eq. 10, which correspond to d largest eigenvalues of S2DB w ¼ kS2DW w. However, the basis vectors w1 ; w2 ; . . .; wr are not orthogonal since the matrix S1 2DW S2DB is asymmetric. The optimal orthogonal matrix of 2DOCMVM can be computed using the Gram–Schmidt orthogonalization. The optimal orthogonal matrix computed by 2DOCMVM can be summarized as follows: 1. 2. 3. 4. 5.

Compute B and S according Eqs. 4 and 7; Compute LS = DS − S andLB = DB − B; Compute S2DW and S2DB according Eqs. 8 and 9; Solve Eq. 11 and then obtain the matrix W ¼ ½w1 ; w2 ; . . .; wr . Decompose the W using the Gram–Schmidt orthogonalization and then we have W = QP. We know that the QTQ = I and the Q is the optimal orthogonal matrix.

4 Experiments In this section, four feature extraction methods including OLPP, ODLPP, CMVM and 2DOCMVM are applied to the YALE face database. The nearest neighbor classifier is used for classification. The YALE face database has 15 human subjects and 11 images for each human. The images have different lighting variations and different facial

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expression. The size of the images is 64  64 pixel. In the experiment, we randomly selected 3, 4, 5, 6 and 7 images of 15 human subjects for training set, while the remaining images were used for testing set. 2DOCMVM is compared with OLPP, ODLPP and CMVM. The experiments were repeated 10 times for each algorithm. The average recognition accuracy of 10 times for each algorithm was taken as their final recognition accuracy. Table 1 shows the average recognition accuracy of four algorithms. Table 1. Recognition accuracy (%) of four approaches on YALE face database Methods OLPP ODLPP CMVM 2DOCMVM

3 78.13 80.25 82.03 83.75

4 80.42 81.55 83.35 84.17

5 82.14 84.22 86.25 87.22

6 84.63 87.33 88.12 89.66

7 85.17 88.56 90.24 91.65

From the Table 1, we can see that all algorithms perform better with the increased training number. We also can see that 2DOCMVM is the most effective algorithm, and is superior to OLPP, ODLPP and CMVM. This is probably because that 2DOCMVM directly computes the optimal projection matrix from two-dimensional image samples, which better maintain the structure information among pixels, and while others extract the optimal projection matrix from one-dimensional vector samples. The other reason is that the 2DOCMVM is parameterless while OLPP, ODLPP and CMVM suffer the parameter selection issue. In addition, 2DOCMVM can get orthogonal optimal projection matrix and avoid the small sample size problem, which are favorable for face recognition.

5 Conclusions In this paper, we design a new method called 2DOCMVM to overcome the SSS problem encountered by CMVM. The 2DOCMVM can directly compute the optimal projection matrix from two-dimensional images and then avoid the SSS problem. Furthermore, 2DOCMVM is parameterless method and the optimal projection matrix obtained is orthogonal, which are favorable for face recognition. The experiments carried out on the Yale face databases show that 2DOCMVM are more effective than the other methods for feature extraction. Acknowledgements. This work is supported by the Key Project of Higher Education Natural Science Foundation of Anhui Province (No. KJ2016A203) and the Master and Doctor Foundation of Anhui University Of Science and Technology(No. 2010yb026).

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References 1. Belhumeur, P., Hespanha, J., et al.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997) 2. He, X.F., Yan, S.C., Hu, Y., et al.: Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27, 328–340 (2005) 3. Yang, J., David, Z., Yang, J.Y., et al.: Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 29, 650–664 (2007) 4. Cai, D., He, X.F., Han, J.W.: Orthogonal laplacianfaces for face recognition. IEEE Trans. Image Process. 15, 3608–3614 (2006) 5. Zhu, L., Zhu, S.N.: Face recognition based on orthogonal discriminant locality preserving projections. Neurocomputing 70, 1543–1546 (2007) 6. Li, B., Huang, D.S., Wang, C., et al.: Feature extraction using constrained maximum variance mapping. Pattern Recognit. 41, 3287–3294 (2008)

The INS and UWB Fusion System Based on Kalman Filter Guoxiang Xu1,2, Cheng Xu1,2, Cui Yao1,2, Yue Qi1,2(&), and Jie He1,2 1

2

University of Science and Technology Beijing, Beijing 100083, China [email protected] Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China

Abstract. Location information is very important for warehouse management, robot or pedestrian positioning. Because of the poor indoor environment, global position system (GPS) cannot reflect the advantages. Therefore, in this paper, a position system based on Kalman Filter (KF) algorithm is proposed, which integrates the information of inertial navigation system (INS) and ultrawideband system (UWB) to improve the position accuracy. The most critical part of the Kalman Filter is prediction and measuring feedback. At present, inertial navigation technology and ultra-wideband technology have a major part in indoor positioning technology. However, due to their respective disadvantages, it cannot have high precision when using one of them separately. The error of inertial measurement unit (IMU) will increase with time and the ultrawideband will be affected by multipath effect. The system designed in this paper uses InvenSense’s MPU9150 module and DW1000 module, the UWB measurement information is used to correct the error from IMU. The experimental result show that the positioning accuracy of the fusion system proposed in this paper is obviously higher than that of a single system. Keywords: INS


UWB
Kalman filter
Fusion system

1 Introduction The overall popularization of mobile devices and the rapid development of the Internet of things lead to the increasing demand for location awareness, so it is very important to obtain location information. Reliable location is needed in applications such as personnel location, material location management, large business super intelligent purchasing guidance, etc. Outdoor positioning based on global positioning system (GPS) and map location services is already well established. In spite of this, they show obvious disadvantages in indoor positioning. Because of the blocking of the building, especially the multiple walls, it is difficult to receive enough satellite signals for positioning. Even if a satellite signal can be received, it will be difficult to meet the demand due to lack of precision. Indoor location techniques are mentioned in references [1, 2]. They are Wi-Fi location, inertial navigation technology, RFID technology, ultrasound, ultra-wideband. Using them alone has obvious disadvantages, and by combining them, performance can be improved. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 468–475, 2019. https://doi.org/10.1007/978-3-030-02804-6_62

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Inertial navigation technology is independent navigation using IMU. It mainly uses accelerometers and gyroscopes for navigation and positioning. The inertial navigation system can calculate the velocity and position by measuring the IMU. However, due to the long-term drift of the gyroscope and accelerometer scale factor errors, the calculated navigation state (position, velocity, and attitude) errors will also increase over time. Ultra Wide Band (UWB) is a radio technology that transmits signals of several hundreds of MHz bandwidth simultaneously in multiple frequency bands. UWB location affected by indoor multipath and non-line-of-sight. By accurately measuring the time of flight of the target and anchor, the distance between the known target node and the anchor node can be calculated. Using the calculated distance, various positioning schemes can be used to determine the location of the target node [3, 4]. Therefore, the above two technologies can be combined, and the two technologies can complement each other [5–9]. In [10], an indoor positioning system based on area fingerprint recognition and inertial sensors is proposed. In [11], a fusion of a radio location system based on received signal strength indicator (RSSI) and INS is proposed.

2 Navigation System The navigation system consists of INS and UWB systems. Among them, the UWB radio ranging system includes a UWB anchor node at a known location and measures the distance. At the same time, INS uses IMU to provide linear acceleration and angular rate derivation of mechanical equations to obtain navigation state estimates [12–15]. 2.1

Inertial Navigation System

The inertial navigation system includes an IMU sensor section and a calculation section. The IMU includes a triaxial accelerometer and a gyroscope. The state vector p consisting of position r, velocity v, and attitude h. pnav ¼ ½r v hT

ð1Þ

Linear acceleration @ and angular rate x, two vectors combined into an input vector. u ¼ ½@ xT

ð2Þ

The measurement item can be modeled as ~uk ¼ uk  bk þ n1;k

ð3Þ

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Where b is composed of slowly varying accelerometer bias b@ and gyro bias bx . b ¼ ½b@ bx T

ð4Þ

The INS can give an estimated measure of the system state for all k index according to the following equation. ^pk þ 1 ¼ fins ð^pk ; ^uk Þ ð5Þ Coordinate conversion of accelerometer data to obtain gravity compensation. As mentioned above, the INS can give an estimate (relative to the initial value) of a complete set of navigation states (position, velocity, and attitude) for an indefinite period of time. Inertial measurements can be performed at high speed (1000 Hz in current systems) (Fig. 1).

Fig. 1. Inertial navigation system structure

The hardware platform and its components [16] are shown in Fig. 2 below. The key components of the PCB are the AT32UC3C2512 microcontroller with hardware floating-point functionality from Atmel, and the four MPU9150 combination IMU and magnetometer chips from InvenSense. SPBT2632C2A Bluetooth module from STMicroelectronics. The 4-layer PCB has a size of 22.5  22 [mm].

Fig. 2. PCB and module assemblies

2.2

Ultra-Wideband Radio Ranging System

Ultra-Wideband (UWB) is a wireless carrier communication technology, which uses narrow nanosecond non-sinusoidal pulses instead of sinusoidal carriers to transmit data, so it occupies a wide spectrum range. UWB is a technology of transmitting wireless signal using nanosecond narrow pulse, which is suitable for high speed and short

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distance wireless personal communication. According to FCC regulations, the bandwidth frequency of 7.5 GHz from 3.1 GHz to 10.6 GHz is within the frequency range used by UWB. TOA is a method of estimating the length of a distance by measuring the transmission time of a wireless signal. In the principle of the asynchronous TOA algorithm in Fig. 3, Device A and Device B are two nodes that perform TOA ranging. T1 is the time when Device A sends the Ranging Data to Device B; T2 is the time when Device B receives Ranging Data; T3 is the time when Device B sends an ACK to Device A; and T4 is the time when Device A receives the ACK. Device A and Device B respectively measure the time length of the troundA and the treplyB through the local clock and calculate the transmission time of the signal between the two devices. The distance between devices is calculated as follows: troundA  treplyB ðT4  T1 Þ  ðT3  T2 Þ d^AB ¼ tp  C ¼ C C ¼ 2 2

ð6Þ

C is equal to 3  108 m/s which is the speed of light.

Fig. 3. Asynchronous TOA algorithm and UWB module

Due to the difficulty of achieving high-precision synchronization techniques, asynchronous TOA ranging methods are commonly used by related international standards and actual TOA chips. The node master chip used in this paper’s experiment is STM32, and the UWB module is DW1000. The figure shows the UWB node. It uses 4 anchor nodes and 1 master node and its size is 34.5  35 [mm].

3 Design of Fusion Algorithm Based on Kalman Filter The Kalman filter is A linear system state equation is used to estimate the optimal state of the system through the input and output observation data of the system. Since the observed data include the effects of noise and interference in the system, the optimal estimation can also be regarded as a filtering process.

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The Kalman filter attempts to estimate the state x of a discrete-time process controlled by a linear stochastic difference equation. xk ¼ Axk1 þ Buk1 þ wk1

ð7Þ

Estimation requires measurement z: zk ¼ Hxk þ vk

ð8Þ

Assume that two variables are independent of each other: pðwÞ  N ð0; QÞ

ð9Þ

pðvÞ  N ð0; RÞ

ð10Þ

The state propagation equation can be written as: xk ¼ xk1 þ dk1

ð11Þ

dk can be obtained by distance interception. By rotating the IMU’s displacement of two moments to the direction of UWB, the IMU’s direction can be used to correct the disadvantage of the IMU’s offset over time. P represents the position vector of the current time and the previous time of the IMU and UWB, respectively. Next, there are two definitions to introduce. First, the a priori estimate of step k is represented ^x xk represents the posterior estimate of step k given the meak . Second, ^ sured value zk . The prior and posterior estimation errors are respectively: x e k  xk  ^ k

ð12Þ

ek  xk  ^xk

ð13Þ

With these errors, the prior estimate error covariance and the posterior estimate error covariance can be calculated as:   T  P k ¼ E ek e k

ð14Þ

  Pk ¼ E ek eTk

ð15Þ

First, we should find an equation that the posterior estimate ^xk equals to the prior estimate ^x k and the weighted difference between the actual measurement zk and the measured prediction H^x k . ^xk ¼ ^x x k þ K zk  H^ k




ð16Þ

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The matrix K is the Kalman gain that minimizes the posterior error covariance Pk . The general expression of K in step k is:
1 T  T Kk ¼ P k H HPk H þ R

ð17Þ

4 Experimental Tests and Results 4.1

Experimental Layout

In this section, we fuse the information output by IMU and UWB with the proposed information fusion algorithm based on Kalman filter to locate. Obtain experimental results and analyze experimental errors. This experiment designed a 7.2 m 4.8 m 2D rectangular sensor network environment. The four corners of the rectangle are respectively deployed with four anchor nodes and the coordinates are (0, 0), (0, 7.2), (4.8, 7.2), and (4.8, 0). The experimenter attached the IMU to the left foot, UWB was placed on the left shoulder, IMU communicated with the PC via Bluetooth, and UWB communicated with the PC through the USB serial port. In this sensor network environment, the experimenter walks a rectangular path of 3.6 m  6.0 m at a speed of 1 m/s. In order to make the cumulative error more obvious, the same track was repeated 10 times. The experimental scenario is shown below (Fig. 4):

Fig. 4. Experimental scene

The four anchor nodes are supported by brackets to ensure that the communication between the master node and the anchor nodes is normal, and the experimenter wear the IMU device and the UWB device to walk in the rectangles within the four anchor nodes with a PC. 4.2

Experimental Analysis and Results

Because the sampling frequency of IMU and UWB is not the same, IMU sampling frequency is higher than UWB sampling frequency, and more data is collected. Therefore, when acquiring UWB data, an index of the current corresponding IMU data is obtained, so that data of two different frequencies are alignment.

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From the results of the positioning trajectory, it can be seen that using the IMU navigation method alone, the trajectory will deviate severely with time, which is an inherent disadvantage of the IMU. UWB positioning method alone, although the UWB positioning accuracy is better, but there are still multipath effects affect the positioning results. The fusion location algorithm based on Kalman filter proposed in this paper is more accurate than a single location system, and it can achieve better positioning effect in indoor complex environment. Next, error analysis and comparison of the three positioning methods are performed. As shown in Fig. 5, Cumulative Distribution Function (CDF) of the positioning error is shown. The abscissa indicates the positioning error, and the ordinate indicates the cumulative distribution function Fx ð xÞ ¼ PðX  xÞ. Because the IMU’s error will accumulate over time, causing the IMU’s error to be 1.2 m, but the UWB’s error is more consistent, so the UWB curve also reached 1.1 m at the end, but it can be clearly seen from the figure, the IMU’s instantaneous accuracy is higher than UWB. The green line in the figure indicates the results obtained by the fusion system proposed in this paper. It can be clearly seen that the errors of the fusion system are relatively concentrated, approximately 80 cm. The single system of IMU and UWB has its own shortcomings, so to play the advantages of the two systems, combining the information of the two systems can clearly improve the positioning accuracy.

Fig. 5. Positioning results

5 Summary and Outlook In a complex indoor environment where GPS cannot be used normally, the fusion system designed in this paper is used to achieve the tracking of the target node’s position in indoor complex environments. The fusion effect of IMU and UWB systems based on Kalman filter is obviously better than that of IMU or UWB. The proposed fusion algorithm well solves the disadvantages of the IMU system with increasing offset over time, and the effects of multipath effects of the UWB system. In the future work, resources and power consumption may also need to be considered, because the use of the IMU does not require the installation of an anchor node, which saves a lot of resources, but the integration of the IMU and UWB requires a good allocation of resources. There is also a need to improve the robustness of the

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system. When a single system has high errors, the fusion system can also discard unnecessary information and perform only useful measurement information estimation.

References 1. Liu, H., Darabi, H., Banerjee, P., et al.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(6), 1067–1080 (2007) 2. Xu, C., He, J., Zhang, X., et al.: Geometrical kinematic modeling on human motion using method of multi-sensor fusion. Inf. Fusion 41, 243 (2017) 3. Dong, F., Shen, C., Zhang, J., et al.: A TOF and Kalman filtering joint algorithm for IEEE802. 15.4 a UWB locating. In: Information Technology, Networking, Electronic and Automation Control Conference, pp. 948–951. IEEE (2016) 4. Cheung, K.W., So, H.C., Ma, W.K., et al.: Least squares algorithms for time-of-arrival-based mobile location. IEEE Trans. Signal Process. 52(4), 1121–1130 (2004) 5. Xu, C., He, J., Zhang, X., et al.: Toward near-ground localization: modeling and applications for TOA ranging error. IEEE Trans. Antennas Propag. 65(10), 5658–5662 (2017) 6. Yao, L., Wu, Y.W.A., Yao, L., et al.: An integrated IMU and UWB sensor based indoor positioning system. In: International Conference on Indoor Positioning and Indoor Navigation, pp. 1–8. IEEE (2017) 7. Liu Tao, X., Aigong, S.X.: Application of UWB/INS combination in indoor navigation and positioning. Sci. Surv. Map. 41(12), 162–166 (2016) 8. Xu, C., He, J., Zhang, X., et al.: Detection of freezing of gait using template-matching-based approaches. J. Sens. 2017(2), 1–8 (2017) 9. De Angelis, A., Nilsson, J., Skog, I., et al.: Indoor positioning by ultrawide band radio aided inertial navigation. Metrol. Meas. Syst. 17(3), 447–460 (2010) 10. Chang, Q., Velde, S.V.D., Wang, W., et al.: Wi-Fi fingerprint positioning updated by pedestrian dead reckoning for mobile phone indoor localization. In: China Satellite Navigation Conference (CSNC) 2015 Proceedings, vol. III, pp. 729–739. Springer, Berlin (2015) 11. Malyavej, V., Kumkeaw, W., Aorpimai, M.: Indoor robot localization by RSSI/IMU sensor fusion. In: International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 1–6. IEEE (2013) 12. Baird, W.H.: An introduction to inertial navigation. Am. J. Phys. 77(9), 844–847 (2009) 13. Jimenez, A.R., Seco, F., Prieto, C., et al.: A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU. In: IEEE International Symposium on Intelligent Signal Processing, pp. 37–42. IEEE (2009) 14. Höflinger, F., Müller, J., Zhang, R., et al.: A wireless micro inertial measurement unit (IMU). IEEE Trans. Instrum. Meas. 62(9), 2583–2595 (2013) 15. Xu, C., He, J., Zhang, X., et al.: Recurrent transformation of prior knowledge based model for human motion recognition. Comput. Intell. Neurosci. 1–12, 2018 (2018) 16. Nilsson, J.O., Gupta, A.K., Handel, P.: Foot-mounted inertial navigation made easy. In: International Conference on Indoor Positioning and Indoor Navigation, pp. 24–29. IEEE (2014)

Research of Digital Signal Processing Based on System Learning Model Jun Luo(&), Ruifang Zhai, and Hui Peng College of Informatics, Huazhong Agricultural University, Wuhan 430070, China [email protected]

Abstract. Undergraduates majoring in information science usually offer compulsory or elective courses in signal processing, while digital signal processing courses are both theoretically and practically strong. According to the learning objectives of undergraduates in universities, the author introduces the digital signal processing in the background of the development of modern information field, this paper puts forward a practice learning method oriented to system of thinking and engineering mode of ability cultivation, which can improve traditional learning of theory and practice and has reference significance for agricultural colleges to enhance the professional foundation of information students. Keywords: Digital signal processing Practice learning


Learning model
System concept

1 Introduction With the development of electronic technology, “digital signal processing” is becoming a compulsory part of the students and technicians in the IT field [1–3]. This type of curriculum more theoretical, abstract, systematic, practical than the strong [4–7]. Its technology has been widely used in modern computer, communications, voice, image and other fields, it is necessary to strengthen engineering practice training to help students really understand and grasp the basic principles [8–10]. Therefore, we need to deal with practical problems in the learning process, enhance the ability to use knowledge to solve practical problems, so as to achieve the goal of capacity-building. Digital signal processing courses mainly involve two basic skills: First, mathematical understanding, and the other is the ability to write programs. The two aspects of the ability are more important, if the mathematical understanding is not enough, then many of the basic principles of signal processing will not be able to properly grasp; other digital signal processing mainly for engineering students, we must emphasize the practical application, that is, to pure mathematics The combination of the principle and the actual examples is not easy to do. It requires teachers and students to cooperate with each other in order to implement it well. Only teacher unilateral instillation is not enough, students’ interest must be stimulated. Therefore, traditional learning methods must be improved. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 476–481, 2019. https://doi.org/10.1007/978-3-030-02804-6_63

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Undergraduates majoring in information science usually offer compulsory or elective courses in signal processing, while digital signal processing courses are both theoretically and practically strong. According to the training objectives of undergraduates in agricultural universities, the author introduces the digital signal processing In the background of the development of modern information field, this paper puts forward a practice learning method oriented to system of thinking and engineering mode of ability cultivation, which can improve traditional learning of theory and practice and has reference significance for agricultural colleges to enhance the professional foundation of information students.

2 Limitations of Traditional Learning Methods The theoretical formula of digital signal processing is abstract and the math calculation is complicated. It has the characteristics of many contents, conceptual abstraction and complicated design. Students often feel dull and hard to understand and master in this course. At the same time, the algorithm of digital signal processing Is based on the computer numerical calculation, just by doing homework still cannot deeply feel the actual value, and curriculum experiments are often limited time, thus affecting the learning effectiveness. Before the multimedia teaching, the main learning method of digital signal processing was “teacher-blackboard-student”, which mainly explained the derivation of basic theory and algorithm. Students used formula and off-the-shelf algorithm to deduce the problem. Content rarely has the opportunity to use computer design, debugging and analysis, reducing teaching effectiveness. How to help students improve their learning interest and enthusiasm, enhance their understanding and mastery of knowledge, and cultivate students’ comprehensive ability to apply their knowledge to solve practical problems are the key tasks of this course. Therefore, more and more colleges and universities nowadays introduce the Matlab experiment into the learning process, and have achieved good results. However, it should be noticed that it is difficult to realize the engineering way of thinking simply by using Matlab.

3 System Learning Model Signal processing course generally were taught in the third year in the college. In general, all kinds of learning materials about engineering examples are less in teaching process must introduce programming examples, thereby enhancing the quality of learning. In the implementation process should pay attention to the following aspects: 3.1

Establish System Concepts

In the first lecture, do not prematurely introduce the formula, but should first consider the development trend of digital signal processing and technical characteristics, to allow students to think and learn from the system point of view as soon as possible, that is, the establishment of system-level concepts: “Pre-sampling ! A/D conversion

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! digital signal processing technology ! D/A conversion ! post-filter.” This systematic thinking model is very important; it reflects the basic ideas to solve practical problems, which is determined by the engineering characteristics of digital signal processing. In accordance with this idea, we used a systematic model from the first lesson during the learning time, as shown in Fig. 1.

Signal Input Signal Analysis D/FFT Filter Fig. 1. Systematic learning of digital signal processing

3.2

Learning Ideas

Because of the large number of mathematical formulas involved in digital signal processing courses, sometimes thinking needs to be taught rather than mathematical derivation. Mathematical formulas for technical services, mathematics is the basic level, and technology is the application level, so to the right combination. For example, encountered similar “frequency domain sampling” and other large sections of the content, it should not be large-scale deduction, should be thought-based. In fact, digital signal processing is often filtered, so in the classroom should be more involved, and then teach students to grasp this idea in a timely manner. 3.3

Example Learning

The example-based learning mentioned here, instead of citing math application problems, is then deduced together with the students. On the basis of thinking teaching, the example learning is appropriately analyzed and explained according to the technical examples in the field of engineering. Prior to the teaching of examples, we should first understand that digital signal processing is a one-dimensional discrete time signal. Naturally, many people immediately think of common time signals such as sine and cosine waves or square waves. At the same time, it should be noted that the objects we teach are Electronic information or computer-related majors, these students in the undergraduate phase of the program more preferred learning, because it can easily get the data output. Then, we put forward new requirements for the teaching of teachers at this stage, whether we should continue walking on the old road or teaching innovation, and introduce more and richer modern achievements. In the real learning process, taking into account the processing of digital signal processing and voice signals are

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very similar, so the voice signal processing can be slightly adjusted to the classroom, although the content of the two studies are different, but at the same time look To the same two sides. For example: a section of analog sound sampling, spectrum analysis, filtering and other operations. Allow students to understand as much as possible the practical value of mathematical formulas. In the process of learning, we use CEG chord as an example to conduct spectrum analysis and explanation. As shown in Fig. 2, the theory connects reality with the students’ interest in learning.

Fig. 2. Chord CEG time-domain waveform and spectrum analysis

3.4

Speak Analysis

Teaching is not an introduction to an encyclopedia, but an efficient and energetic discussion and study. It is impossible for anyone course to be exhaustive and requires some trade-offs. The best part and the common part should be placed in the classroom while others should be taken Overview of the strategy, not what they learn, learn everything is equal to learn nothing, because the same did not learn. In reality, the class hours of digital image processing are not sufficient. For the efficiency of classroom teaching, it is necessary to set up condensed learning content, to make the content transparent in the classroom and to let students master the basic principle and systematic thinking in class. 3.5

Practical Learning

Many teachers before teaching digital signal processing in fact, both learned at the university stage, as a student and as a teacher is not the same, students learn a course, many are kept doing problems, there are few opportunities Deep into the process of practice, and therefore cannot well appreciate the basic principles of digital signal processing and the value of the basic formula, always say what the teacher, students accept what. As a result, the theory is out of practice, is not conducive to students to fully grasp the basic knowledge. Therefore, we have taken open issues such as extracting a certain frequency segment of sound and restoring the actual signal that is

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disturbed so that students can use their basic principles to think independently and solve problems. In short, the system learning mode of image processing should run through the teaching, so as to lay the foundation for further study and practical application.

4 Engineering Practice Learning Theory teaching is to pass on to the students on the digital signal processing system framework, which will contain a lot of basic principles containing formulas, more abstract, in order to enhance students ability to apply the knowledge and engineering quality, the theory teaching and experimental teaching must be organic Combination, the purpose is to train and develop students’ innovative ability. Traditional digital signal processing experiments are often verified mainly lack of comprehensive, design, not very good for engineering practice, it is not conducive to engineering students in-depth study. However, taking into account that students must review the rationale further during the experiment, the confirmatory experiment must be set up, but the quantity should be reduced. Digital signal processing course focuses on the principle of DFT, FFT and filter, so the experiment should also reflect this feature. In our course setup, the experiment is divided into three sections: ① discrete time signal analysis. This part allows students to record their own voice signals, combined with MATLAB understand the meaning of the sampling theorem, solving the difference equation, the basis for the follow-up of the filter theory. For example, we combine the echo with Eq. (1) for analysis: HðzÞ ¼ 1 þ a
zR

ð1Þ

② DFT and FFT application analysis. Focus on familiar with the basic signal and mixed-signal spectrum, and then spectrum analysis, a full understanding of the advantages and disadvantages of frequency domain analysis. ③ IIR and FIR filter design. This part of more content, and the actual close, the concept of system concepts in the filter here has been concentrated expression, and a large number of actual signals for filtering, you can learn the contents of the previous comprehensive utilization. Therefore, the experimental part of the filter should be carefully set, and give full consideration to the characteristics of engineering practice, focusing on the use of the basic principles of designing classic filters. For example, put forward the actual voice problem, requires the design of a filter to select one of the band frequency signal, and the filter passband frequency, stopband frequency completely designed by the students themselves, and to improve, and then promote students to design a variety of Contrast, experience differences. Only in this way can the whole theory of digital signal processing be activated. The above experiment is mainly to test the quality of student learning. For this course, in order to cultivate students’ computer application ability and sense of technological innovation, you can continue to improve, in addition to the use of MATLAB simulation experiments, but also should set up an open project with engineering

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background—DSP-based filter design Wait. As a result, the entire experiment was promoted to a higher level, laid a good professional application capability. Therefore, the practical teaching improves the quality of teaching, enhances students’ interest in learning and improves the comprehensive ability of students, which lays the foundation for the follow-up related courses and graduation design.

5 Conclusions Digital signal processing course should be lectures, in a limited class time, clear basic principles, so that students establish a system of thinking mode, combined with MATLAB for the actual sound, image signal simulation, and experience formula value. This enrichment of teaching, improve learning effectiveness, but also enable students to intuitively understand the abstract content of textbooks, enhance learning interest, combined with experiments to further enhance students’ ability to solve practical problems, so as to fully and fully grasp the basic ability of digital signal processing. All of these provide examples for the reform of learning methods and learning methods in computer-assisted instruction. Acknowledgements. This research was supported by the Fundamental Research Funds for the Central Universities (Grant Nos. 2662017PY059 and 2662015PY066), and the National Natural Science Foundation of China (Grant Nos. 61176052 and 61432007).

References 1. Liu, Y., Zhao, Q., Zhang, H., Li, L.: Teaching method research of digital signal processing with simulation technology. J. Zhejiang Ocean Univ. (Nat. Sci.) 27, 301–305 (2008) 2. Gao, Y.: Practice of the teaching reform in digital signal processing course based on MATLAB. Higher Educ. Forum 4, 141–143 (2007) 3. Nie, X.: Application of Matlab in the teaching of digital signal processing. J. Yuxi Normal Univ. 4, 65–67 (2011) 4. Cao, X.: Course innovation of digital signal processing based on Matlab. J. Changchun Univ. 17, 95–97 (2007) 5. Zhu, Y., Ni, F.: Teaching reform and practice of the digital signal processing course. J. Jiangsu Teach. Univ. Technol. 18, 104–107 (2012) 6. Luo, Z.: Discuss of digital signal processing teaching about local college. J. Shaoguan Univ. 30, 141–146 (2009) 7. Li, N., Xue, Y.: The investigation of teaching innovation for the curricula digital signal processing. Electron. World 18, 140–141 (2012) 8. Yu, Y.: Research on the teaching practice of digital signal processing. J. Anhui Univ. Technol. (Soc. Sci.) 29, 99–100 (2012) 9. Gao, J., Wang, Xia, Li, Qi, Yan, Lin: The investigation and experience of teaching innovation for the curricula digital signal processing. J. Electr. Electron. Educ. 29, 19–21 (2007) 10. Gu, J.: Bilingual teaching practice and exploration for digital signal processing using MATLAB course. J. Hefei Univ. Technol. (Soc. Sci.) 24, 154–157 (2010)

Research of Computer Vision Based on System Learning Ability Jun Luo(&), Ruifang Zhai, and Hui Peng College of Informatics, Huazhong Agricultural University, Wuhan 430070, China [email protected]

Abstract. Although the computer vision course has a strong theoretical and practical characteristics, many colleges or universities are willing to offer this course as compulsory or elective course for students of information specialty. To aim at the training objectives of colleges’ information undergraduates, we introduce the basic requirements of digital image processing in modern information technology field, point out the limitations of traditional textbooks and learning method on training students ability, and then propose system ability oriented mode for learning practices, it is essential to improve the effects of theoretical and practical learning, and meanwhile, it is significant to enhance the professional basis for undergraduates of information related specialty. Keywords: Computer vision


Course group
System learning ability

1 Introduction A number of universities have already set up a wide range of closely related courses in computer vision field, such as digital image processing, computer graphics, computer vision, and machine learning [1–6]. It should be noted that the theoretical analysis and engineering application abilities of computer vision course group are important. In order to enhance the abilities demonstrated above, some virtual projects have been used in many digital image processing classes; include practical engineering technologies in image analysis and computer vision courses, which can improve the ability of research and software development in the course of computer vision. Besides, some learning aid systems integrated with real-time image and visual processing experiments turn teacher roles into coaches in computer vision course group [7–10]. The above studies show that the current teaching reform mainly for digital image processing or computer vision courses, do not consider the system characteristics of the whole curriculum group. So far, it is expected to combine the theoretical knowledge and practical application problems in the future teaching reform of computer vision course group. It must be pointed out that there is a strong demand for computer vision technology in the fields of agricultural sciences, such as the requirements of cell imaging and super-resolution processing in the field of life science, the phenotypic monitoring requirements in plant growth, the regulatory requirements of animal physiological characteristics, etc., which are being more and more concerned by experts, students, and businesses. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 482–488, 2019. https://doi.org/10.1007/978-3-030-02804-6_64

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Therefore, the current situation of computer vision course in theory and practice learning can be presented as follows: (1) the system ability is obviously insufficient; (2) the learning content of computer vision course is seriously lagging behind; (3) computer vision technologies in agricultural information discipline are demand strongly. Therefore, the course of computer vision needs to be constantly updated in learning content and learning methods. In view of the characteristics of strong applicability and internationalization of computer vision course group, this work adopts course group learning mode, combined with the related project of “internet + computer vision”, focuses on training students’ system ability, creative thinking and software applications.

2 Limitations of Traditional Learning Methods Digital image processing theory formula abstraction, mathematical calculation is more complicated, with more content, conceptual abstraction, complex design and other characteristics of students in the course of this course often feel boring, difficult to understand and grasp; the same time, digital image processing algorithms Is based on the computer numerical calculation, just by doing homework still cannot deeply feel the actual value, and curriculum experiments are often limited time, thus affecting the learning effectiveness. Before the multimedia teaching, the teaching method of digital image processing was mainly “teacher-blackboard-student”, which mainly explained the derivation of basic theories and algorithms. Students used formulas and off-theshelf algorithms to deduce problems; Content rarely has the opportunity to use computer design, debugging and analysis, reducing teaching effectiveness. How to help students improve their learning interest and enthusiasm, enhance their understanding and mastery of knowledge, and cultivate students’ comprehensive ability to apply their knowledge to solve practical problems are the key tasks of this course. Therefore, more and more colleges and universities nowadays combine C language and computer vision examples with good results, but at the same time, it should be noted that it is difficult to understand the systematic practice by simply using procedural analysis.

3 Learning Model Based on System Ability Computer vision course group generally were taught in the third year in the college. In general, all kinds of learning materials engineering examples less teaching process must introduce programming examples, thereby enhancing the quality of teaching. In the implementation process should pay attention to the following aspects: 3.1

Establish System Concepts

In the first lecture, do not prematurely introduce the formula, but should first consider the development trend of digital image processing and technical characteristics, to allow students to think and learn from the system point of view as early as possible,

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that is, the establishment of system-level concepts: “Imaging Selection ! Image Processing ! Data Transfer.” This system model is very important; it reflects the basic idea of solving practical problems, which is determined by the engineering characteristics of digital image processing. In accordance with this idea, we used a systematic model from the first lesson during the learning time, as shown in Fig. 1.

Imaging selection Image processing System analysis Data transfer Fig. 1. The learning model of image-typed courses

3.2

The Importance of Imaging System

At this stage a variety of image processing materials rarely involved in the introduction of imaging systems, even if the students learn a complete course, do not know how to structure a practical image processing system, which is due to lack of knowledge of the teaching material or teaching process, however This section, however, is a basic prerequisite for implementing an actual image processing system, so we introduced this knowledge in the first step of the teaching process, as shown in Fig. 2.

Visible light CCD characteristics Camera parameters Classical examples Fig. 2. The learning model of image-typed courses

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3.3

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Theoretical Algorithms Emphasize Ideas Teaching

Due to the large number of mathematical formulas involved in digital image processing courses, there is sometimes a need for mentoring rather than mathematical derivation. Mathematical formulas for technical services, mathematics is the basic level, and technology is the application level, so to the right combination. For example, encountered a similar “image segmentation” and other large sections of the content should be thought-based, to explain to students different goals which ideas should be considered in order to achieve more effective segmentation. In fact, an actual image processing is the integration of various algorithms, so in the classroom should be more in accordance with the characteristics of the various algorithms to be combined, so that students see the differences in algorithms to further understand the advantages and limitations of different algorithms, Once students have mastered this law, when faced with practical problems, they can quickly put forward solutions to the problem according to this idea. 3.4

Speak Analysis

Learning is not an introduction to an encyclopedia, but an efficient and energetic discussion and study. It is impossible for anyone course to be exhaustive and requires some trade-offs. The best part and the common part should be placed in the classroom while others should be taken Overview of the strategy, not what they learn, learn everything is equal to learn nothing, because the same did not learn. In reality, the class hours of digital image processing are not sufficient. For the efficiency of classroom teaching, it is necessary to set up condensed learning content, to make the content transparent in the classroom and to let students master the basic principle and systematic thinking in class. 3.5

Example Learning

The example learning mentioned here, instead of citing math application problems, is then deduced together with the students. On the basis of thinking teaching, the example learning is appropriately analyzed and explained according to the technical examples in the field of engineering. Before the teaching of examples, we should first understand that the object of digital image processing is the two-dimensional image data acquired by the camera and how the image data is organized. The learning of the knowledge must be well understood through programming. Then, we put forward new requirements for the teaching of teachers at this stage, whether we should continue walking the old ways or teaching innovations and introduce more and richer modern achievements. In the real teaching process, we combine the image content and C language closely from the beginning, each chapter should introduce the example of C language image processing, at the same time combine the imaging and algorithm to construct a complete image processing system, further emphasize the system The ability to apply. For example, in class, we present examples of actual fruit classification, as shown in Fig. 3, and give the length and breadth parameters of the fruit target. Students are required to choose their own shooting distance, CCD size and focal length of the lens to achieve a

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complete image System, on the basis of which a step by step guide students to design their own algorithms to achieve the corresponding fruit classification, the theory with practice at the same time enhance the students’ interest in learning.

Fig. 3. The example learning of grape classification

3.6

Practice Learning

Many teachers in the digital image processing before teaching this course, have learned at the university stage, as a student and as a teacher is not the same, students learn a course, many are constantly questioning, there are few opportunities Deep into the process of practice, and therefore not well understand the basic principles of digital image processing and the value of the basic formula, always say what the teacher, students accept what. As a result, the theory is out of practice, is not conducive to students to fully grasp the basic knowledge. Therefore, we rely on the imaging equipment provided by the Graphics and Image Laboratory of Huazhong Agricultural University to provide students practical and open-ended topics such as Handwritten Character Recognition, Near-Infrared Road Recognition and Citrus Fruit Positioning “And other topics, allowing students to team up freely, using extra-curricular time hands-on experiment, in the process of giving top priority to practice, and constantly carry out systematic thinking and practical engineering training. In short, the system learning mode of image processing should run through the teaching, so as to lay the foundation for further study and practical application.

4 Engineering Practice Learning Theory learning contain a lot of basic principles formulas, more abstract, in order to enhance students ability to apply the knowledge and engineering quality, the theory learning and experimental learning must be organic combination, the purpose is to train and develop students’ innovative ability. For example, we can see a HSV transformation of RGB grape image in the following experiment. The component of hue is abbreviated as H, which can be defined by Eq. (1).

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8 1 GB ; > > < 6
MAXMIN  BR ; H ¼ 16 2 þ MAXMIN > >  :1
RG 6 4 þ MAXMIN ;

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R ¼ MAX G ¼ MAX

ð1Þ

B ¼ MAX

The color component of saturation is abbreviated as S, which can be defined by Eq. (2). S¼

MAX  MIN MAX

ð2Þ

The color component of value is abbreviated as V, which can be defined by Eq. (3). V¼

MAX 255

ð3Þ

As demonstration in the above, it should be noted that the symbols MAX and MIN are defined by Eq. (4) and (5), respectively. MAX ¼ maxðR; G; BÞ

ð4Þ

MIN ¼ minðR; G; BÞ

ð5Þ

Therefore, the practical teaching improves the quality of learning, enhances students’ interest in learning and improves the comprehensive ability of students, which lays the foundation for the follow-up related courses and graduation design.

5 Conclusions Digital image processing course should be concise, in a limited class time, clear basic principles, so that students establish a system of thinking, at the same time the theory with practice, based on the framework of the imaging system, combined with C language image target The processing, to achieve specific applications. This enrichment of learning content, improve learning effectiveness, but also enable students to intuitively understand the abstract content of textbooks and enhance interest in learning to further improve students’ ability to solve practical problems, and then fully grasp the basic content of digital image processing. All of these provide reference for the learning reform of engineering students. Acknowledgements. This research was supported by the Fundamental Research Funds for the Central Universities (Grant Nos. 2662017PY059 and 2662015PY066), and the National Natural Science Foundation of China (Grant Nos. 61176052 and 61432007).

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References 1. Li, G., Wan, Y., Liu, J.: Innovatory experiment teaching demonstration of digital image processing practice. Bull. Surv. Mapp. 10, 101–103 (2012) 2. Liu, W., He, P., Yuan, Q., Fang, H.: Advantage of applying network multimedia in digital image processing teaching. China Mod. Educ. Equip. 23, 57–58 (2010) 3. Wu, D.: Reform on teaching methods and concrete practice based on the research-oriented teaching. J. Wuhan Univ. (Nat. Sci. Ed.) 58, 160–162 (2012) 4. Chen, J., Li, W.: Application of project-driven mode teaching in digital image processing. China Educ. Technol. Equip. 3, 38–39 (2011) 5. Shen, L., Li, H., Sun, K.: Research on experimental teaching of digital image processing and development of the experimental teaching software. J. Electr. Electron. Eng. Educ. 27, 75–77 (2005) 6. Zhang, K., Ji, Z.: Exploration of visualization teaching system for digital image processing. J. Electr. Electron. Educ. 29, 113–115 (2007) 7. Yang, S., Zhang, H.: The development and design of teaching software on “Digital Image Processing”. J. Tianjin Norm. Univ. (Nat. Sci. Ed.) 29, 76–77 (2009) 8. Zhang, Y., Huang, Y., Wang, H.: Exploration on task-modularization teaching pattern in digital image processing. Lab. Sci. 15, 52–54 (2012) 9. Wei, G., Wang, Y., Ding, X., He, A.: Explore on teaching reform of digital image processing course. J. Electr. Electron. Educ. 31, 24–25 (2009) 10. Xue, Y., Han, G., Liang, G., et al.: Discussing and practice on digital signal processing course. J. Electr. Electron. Educ. 31, 22–23 (2009)

Point-to-Point Rotation Orientation Algorithm Based on the Secondary Template Matching Yanzhong Liu, Shihong Duan, Jie He, and Yue Qi(&) School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China [email protected]

Abstract. In order to achieve effective and accurate relative positioning in the environment without infrastructure deployment, this paper proposes a rotating directional algorithm based on Time of Arrival (TOA) ranging. In order to confirm the relative orientation between base anchor and target, wireless anchor is rotated around signal-blocking obstacle (the user’s body), which can effectively emulate the sensitivity and functionality of a directional antenna. During rotation, blocking obstacles will theoretically cause the largest ranging error, we can use this finding to produce a directional analysis method that accurately predicts the direction of the target, along with an associated confidence value. In the Non-Line of Sight (NLOS) environment, multipath effect, human body and other external factors will cause large TOA ranging errors and the error distribution is non-Gaussian, the accuracy of orientation simply according to largest TOA ranging error is low, with 40-degree deviation. More sophisticated techniques to determine the AP direction should be used, this paper mainly implements the secondary matching algorithm based on three typical template matching algorithms and analyzes the key parameters in the secondary matching for optimal value analysis and realizes high-precision horizontal direction detection and orientation. Deviation of about 4° is achieved, and accuracy is improved by 9% relative to the single match method. Keywords: Relative positioning
Direction recognition
Rotation positioning Template matching

1 Introduction Due to high ranging accuracy, time of arrival (TOA) based on ultra-wideband(UWB) technology has been widely used in indoor localization in GPS denied environment [1, 2]. There is no more economical, convenient and efficient positioning technology solution [3]. Traditionally, to find the target, we need to obtain the distances from the target, also at least three anchors placed at known location [4]. However, in emergency applications, such as finding trapped firefighters in a burning building, it is not possible to deploy anchors as localization infrastructure. Inspired by radar, we proposed a rotating anchor-based orientation method using TOA measurement to collect the relative positional relationship during rotation.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 489–497, 2019. https://doi.org/10.1007/978-3-030-02804-6_65

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Figure 1 shows a typical rescue process of target people in a disaster environment. The victim target T wearing an ultra-wideband (UWB) communication device is lying in a room at a high level, as shown as the red figure; Rescuer R takes UWB equipment with compass enters from the entrance of the building. The relative positions of T and R at this time can be represented by a triplet TR = {h: relative height; h: horizontal relative angle; d: horizontal relative distance}.

Fig. 1. Emergency application positioning scene

When the rescuer and the trapped rescued are on different floors, the atmospheric pressure measured by the barometers can direct the rescuers to the right floor. Also, inertial measurement unit (IMU) can be employed to acquire the height value. So, exchange of respective height can get the height difference h. The rescued with UWB chip send out help signal continuously, equipment integrated with UWB TOA ranging communication chip held by the rescuer will collect TOA ranging value as relative distance d. The difficulty is how to compute the relative direction. [8] propose Borealis, a new AP localization system for smartphones that leverages signal strength artifacts to compute the direction of an AP. Unlike conventional solutions that either require sophisticated radio hardware (i.e. directional antennas), or extensive war-driving measurements. [8] uses off-the-shelf smartphones and produces real-time results with a small number of measurements. The underlying principle behind Borealis, using signal dips from blocking obstacles to locate wireless transmitters, is general and could be applied to locate other types of transmitters. Our solution is derived by the said key insight:” by rotating a standard wireless receiver around a blocking object, we can effectively emulate the sensitivity and functionality of a directional antenna”. We exploit the property that the signal strength observed by a wireless receiver drops most significantly, and ranging value increased significantly when there is a large obstacle directly between it and transmitter. The rescuer hold the anchor node and rotated around his own body. During the 360-degree rotation, the anchor collects a series of ranging data and corresponding direction to the target. Theoretically, the direction can be defined as the direction with the minimum distance. However, complicated indoor environment factors [7], such as multipath [5, 6], wall blockage and human body blockage, frequently cause huge distance measurement errors in TPA ranging. These factors make it impossible to find the exact signal

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direction based solely on the minimum ranging value. In Non-Line of Sight (NLOS) environment, TOA ranging error is large, which cannot be omitted and the ranging error and the error distribution is non-Gaussian [9]. Based on the measurement dataset (distance vs. angle) direction detection with a rotating anchor will be a matching problem. The sequence data are as the signal, and the template of signal can be get by calculating the real distance. This paper firstly corrects the measurement data with RLOWESS smoothing algorithm as the analysis basis. Then, this paper analyzes three matching algorithms, Euclidean distance, correlation coefficient and the directional efficiency of the matched filter and proposes the second-matching algorithm and the optimal choice of parameters. Second-matching algorithm is applied to the above three matching algorithms, the results are all made The accuracy of the angle has increased by about 9%.

2 Rotary Orientation System The UWB signal contains multiple frequency bands and has high transmission stability. The TOA (time-of-arrival) based UWB signal can achieve centimeter-level ranging [10]. The UWB transmission equipment worn by search and rescue personnel integrates an electronic compass called a rotating base station Rx, and the UWB transmission equipment carried by the trapped person is called a target point Tx. During the search and rescue process, Tx continuously sends UWB signals. The search and rescue personnel extend Rx in parallel and rotate itself for one cycle. The human body is always facing the rotating base station. The above rotation positioning scenario is shown in Fig. 2. Binary data (hi, li) are recorded every 2o. Li is the distance between the rotating base station and the target point obtained by the TOA ranging, and hi is the angle obtained by the electronic compass measurement, which is the included angle of the rotating base station with respect to the magnetic north direction. The rotating base station rotates one revolution to obtain a set of angle and distance information sequences {(h1, ^l1 ), (h2, ^l2 ), …, (hn, ^ln )}, Where n is the number of measurements per rotation, the test in this paper is n = 180.

Fig. 2. Rotating anchor-based relative positioning

Then one cycle of test data is matched with two cycles of template data, and the angle value in the test data corresponding to the minimum distance value in the

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theoretical template is the direction of matching, which is the direction of the rescuer facing the rescuer. The content of this section is the establishment of a template. 2.1

Rotary Positioning Geometry Model

This article describes the rotational positioning scenario described above as the geometric relationship shown in Fig. 3. Where r is the radius of rotation of the human body holding Rx rotation, d is the distance between the target point Tx and the rotating base station Rx, and h is the angle between the direction the human body faces and Tx, and the direct distance between Rx and Tx is l.

Fig. 3. Rotation positioning geometry

According to the above set relations, based on r, d, the formula for the change of l with h can be obtained as follows. lðhÞ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðd  r cos hÞ2 þ ðr sin hÞ2 ;

ð1Þ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi r 2 þ d 2  2dr cos h;

ð2Þ

lðhÞ ¼

2.2

Feature Model of TOA Ranging Error

The composition formula of the TOA ranging error is defined in [9], as shown below. Where l(hi) is the true distance between the rotating base station and the target point at a specific direction angle hi, and ^l(hi) is the TOA ranging value at hi, and e(hi) is the ranging error value. eðhi Þ ¼ ^lðhi Þ  lðhi Þ ¼ ed þ em þ eNHB  dNHB ðhi Þ þ eHB  dHB ðhi Þ

ð3Þ

ed is the measurement error caused by a physical device and its value is negligible compared to other errors. em is a multipath-caused ranging error and is usually Gaussian. eNHB is a range error caused by occlusion of a wall or other object in a nonline-of-sight environment, and eHB is an error caused by human body obstruction. dðhi Þ is the impulse response function. When hi is within the range of angles where the signal is blocked, dðhi Þ ¼ 1; otherwise dðhi Þ ¼ 0.

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In this paper, a large number of tests were performed under the line-of-sight (LOS) and non-line-of-sight (NLOS) environments. The rotation measurement in each scene was 100 revolutions. Figure 4 analyzes the typical measurements obtained under the two scenarios. The vertical green line describes the actual angle +180°. Each scene rotates 100 revolutions. The 500 groups of {(hi, li)} measured data correspond to the zigzag curve in the figure. It can be seen that the TOA ranging error features as described in [9]: (1) due to the large number of environmental changes and influencing factors, the ranging error coincides with heavy-tailed distribution during rotation, with obvious pulse characteristics; (2) under NLOS environment the variation of the ranging error is larger. In comparison, the variation of the ranging error in the LOS environment is closer to the Gaussian distribution. In order to eliminate the pulse characteristics in the measurement, this paper first uses the Rlowess algorithm [11] to eliminate the abnormal points and noise of the data. In the test of this paper, the Rlowess smoothed signal-to-noise ratio is improved from the original data of 11.76 to 22.88.

(a) TOA Ranging Error Analysis under LOS scenario 1 and scenario 2

(b)Analysis of TOA Ranging Error under NLOS scenario 1 and scenario 2 Fig. 4. Analysis of rotation positioning TOA error in LOS and NLOS environment

2.3

The Influence of Human Body on TOA Ranging Error Model

In [12], the segmentation effect model of TOA ranging error from human body is defined. The final template used in this paper is based on the standard template to increase the TOA ranging error caused by human body shielding, as shown in Formula (4).  lðhi Þ ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1350 ; 2250  d 2 þ r 2  2  d  r  cos hi þ eHB ; hi 2 ½S pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 0 0 d 2 þ r 2  2  d  r  cos hi ; hi 2 ½0 ; 135 Þ ð2250 ; 3600 

ð4Þ

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3 Rotational Positioning Algorithm The rotation positioning designed in this paper is a rotation positioning algorithm that matches the secondary template. Firstly, three large-scale template single-matching strategies were tested. Finally, the weighted Euclidean matching algorithm was selected. The TOA ranging value received was matched with the standard template established by Formula (4). The matching starting point was The pre-positioning direction hfirst; the data before and after hfirst are taken as spans to create a new to-bematched dataset{(hfirst−span,^lfirstspan Þ, …, (hfirst+span, ^lfirst þ span )}, denoted as ^ Squad , and establish a sub-template with a range of T. The sub-template and the retrieved data are used for secondary matching, and then the orientation direction hT is obtained based on the secondary matching. 3.1

Step 1, to Achieve a Wide Range of Matching

The data set S = {(h1, ^l1 ), (h2, ^l2 ), …, (hn, ^ln )} obtained by one rotation is matched with the standard template data defined in Formula 4, found and rotated The data matches the template with the largest degree of similarity function. The offset is denoted by k. The starting point angle and distance corresponding to k are the results of the largescale matching: {hfirst, lfirst }, thus completing the initial orientation. 3.2

Step 2: Secondary Sub-template Matching

After a large range of rough matching, the test data can already be associated with the standard template. However, since the human body occlusion will result in a larger range error during the one-rotation process, the preliminary results obtained under wide-range matching are not accurate enough high, in order to further improve the orientation accuracy, this paper proposes a method of using the sub-template to achieve the secondary matching. In the standard template, 0° is positive for the direction of the helper, so the subtemplate starts from 0° and the interval Δh takes t template data, so the sub-template ST = {(h1, l1),…, (ht, lt)} The measurement data used for matching is a subset of S. It is centered on hfirst in the initial match and takes ns data to obtain the measurement subset Sspan, Sspan ¼ fðu1 ; ^l1 Þ; ðu2 ; ^l2 Þ; . . .; ðuns ; ^lns Þg ¼ fð^hfirstns=2 ; . . .^lfirstns=2 Þ; . . .; ð^hfirst þ ns=2 ; ^lfirst þ ns=2 Þg: The matching algorithm used for the second match is the Euclidean distance matching method. The data is matched with the t data in Sspan in turn. The starting P offset of the data in Sspan is k(0  k  (ns−r)), Ek ¼ ni¼1 ð^li þ k  li Þ2 , the Euclidean distance and the minimum are the highest matching, the matching degree function is defined as J(k)=1/Ek . Select the largest J(k) piece of measurement data. Select the element in the Sspan corresponding to h1 (uk, ^lk ), which is the rotation obtained after

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the second matching. The direct and direct distances between the base station and the target point are denoted as (hT, lT ).

4 Experimental Verification 4.1

Measurement Platform

In order to verify the performance of the rotational orientation algorithm, we conducted field tests at the Electromechanical Information Building of USTB. The measurement system used is shown in Fig. 5, which includes a rotating base station and a target tag. The rotating base station is mainly composed of a radio frequency module, a microcontroller (MCU), an electronic compass, an air pressure sensor, a display screen, and the like. The target is mainly composed of wireless RF modules, MCUs, and pressure sensors. The specific test scenarios are divided into NLOS environments and LOS environments. As shown in Figs. 6 and 7 respectively. In the classroom shown in Fig. 6, there is a wall barrier, there are a variety of tables and chairs, is a more complex non-line-of-sight environment; in the test scenario shown in Fig. 7 there is no wall, relatively empty, there is a direct path between the rotating base station and the target point. In both scenarios, this article has done 500 sets of tests.

Fig. 5. Device photo

Fig. 6. Non-line-of-sight test scenario

Fig. 7. line-of-sight test scenario

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Experimental Analysis and Results

This paper chooses span = 40° and T = 4. The initial matching method chooses the weighted Euclidean distance, matching coefficient and matching filter algorithm respectively. The effect of weighted Euclidean distance orientation is best in the initial matching, but the improvement of the accuracy of each algorithm after the secondary matching is very effective. The comparison of the orientation errors of the different initial matching algorithms and the secondary matching sub-template matching algorithm are shown in Fig. 8, respectively. It can be seen that there is still a large error in the angular error after the secondary matching match, but overall, the accuracy of the orientation angle is still significantly improved.

Fig. 8. The order is (matching filter, Euclidean distance, correlation coefficient) and secondary matching comparison

5 Conclusion This paper proposes a peer-to-peer relative orientation algorithm based on rotating base stations in an indoor environment, which solves the key issues such as searching for trapped firefighters or guiding lost firefighters to exits in non-satellite environments. This paper proposes a secondary sub-template matching algorithm, which obviously improves the orientation accuracy of the initial matching algorithm. Finally, the experimental results show that the proposed algorithm is practical and effective. Due to the complex and varied indoor environment and the tight rescue time, further optimization of direction recognition accuracy in different environments and improvement of the real-time performance of the algorithm will become the focus of research. The work after this article will focus on the establishment of the template in a complex environment, reduce the time complexity of the algorithm, etc., and then improve the search and rescue efficiency.

References 1. Lin, X.Y., Ho, T.W., Fang, C.C., et al.: A mobile indoor positioning system based on iBeacon technology. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4970–4973. IEEE (2015) 2. Xu, C., He, J., Zhang, X., et al.: Detection of freezing of gait using template-matching-based approaches. J. Sens. 2017(2), 1–8 (2017)

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3. Moayeri, N., Mapar, J., Tompkins, S., Pahlavan, K.: Emerging opportunities for localization and tracking. IEEE Wirel. Commun. 18(2), 8–9 (2011) 4. Xia, Y., Wan, J., Liu, S., Liu, Z.: Design and implementation of indoor positioning system based on Wi-Fi and base station signal strength. Digit. Commun. 39(06), 21–25 (2012) 5. Wu, Z.H., Han, Y., Chen, Y., et al.: A time-reversal paradigm for indoor positioning system. IEEE Trans. Veh. Technol. 64(4), 1331–1339 (2015) 6. He, J., Geng, Y., Liu, F., et al.: CC-KF: enhanced TOA performance in multipath and NLOS indoor extreme environment. IEEE Sens. J. 14(11), 3766–3774 (2014) 7. Moeglein, M., Riley, W.: Method and Apparatus for Creating and Using a Base Station Almanac for Position Determination. U.S. Patent 8,532,567. 2013-9-10 8. Xu, C., He, J., Zhang, X., et al.: Geometrical kinematic modeling on human motion using method of multi-sensor fusion. Inf. Fusion 41, 243–254 (2017) 9. Wang, P., He, J., Xu, L., et al.: Characteristic modeling of TOA ranging error in rotating anchor-based relative positioning. IEEE Sens. J. 17(23), 7945–7953 (2017) 10. Gezici, S., Tian, Z., Giannakis, G.B., et al.: Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. IEEE Signal Process. Mag. 22(4), 70–84 (2005) 11. Feng, Y., Xue, Y., Song, J.: An iterative-weighted-average algorithm for background modeling in surveillance video scenarios. In: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 1–4. IEEE (2017) 12. He, J., Wu, Y., Duan, S., et al.: Human body influence on UWB ranging error model. Corresp. J. 38(a01), 58–66 (2017)

Effects of Lecture Video Types on Student Learning: An Analysis of Eye-Tracking and Electroencephalography Data Xiaoming Cao1(&), Miaoting Cheng2, Xiya Xue1, and Shan Zhu1 1

2

Normal College, Shenzhen University, Shenzhen, China [email protected] Division of Information and Technology Studies, Faculty of Education, The University of Hong Kong, Hong Kong, China

Abstract. Previous research studies have demonstrated the influence of different user interface designs on student learning. This paper aims to examine the effect of different lecture video types on student learning using the eye-tracking and electroencephalography (EEG) techniques jointly. A two-factor experimental study was conducted on 62 undergraduate students who were randomly assigned to two groups, one of which used lecture video without teacher presence (Group 1, N = 31) and the other used lecture video with teacher presence (Group 2, N = 31). A survey was also conducted on both groups of students to collect data on their cognitive load and perceived satisfaction towards the lecture videos. The results of eye-tracking and EEG data indicate that teacher presence influences learners’ concentration and attention towards lecture video learning. Moreover, the learners’ perceived satisfaction are also related to students’ learning. Results of this study could provide future directions for studies on massively open online courses (MOOCs) and insights for MOOCs designers and educators to improve student learning with online courses. Keywords: MOOC
Lecture video design Student learning
Higher education


Eye tracking
EEG

1 Introduction In recent years, Massively Open Online Courses (MOOCs) have been rapidly expanded world widely. For a MOOC course, the design of lecture video has a significant impact on student’s learning effectiveness such as learning performance. Previous studies have demonstrated that the quality of MOOC video design is associated with teaching quality, for example, teachers appear to have low teaching quality with rough designed MOOC (Zhu and Xu 2015). A review of literature suggests that though the quantitative analysis of data with quantitative tools is also a method to analyze students’ learning processes, and it can be applied in pre-implementation stage of a MOOC. Using the eye-tracking and brain-computer interaction technologies, students’ learning behaviors can be accurately measured by recording their eye movements and brain activities. The experiment of Saß et al. (2017) on 60 primary students showed that students’ eye movement data was associated with their academic achievement. Using eye trackers, © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 498–505, 2019. https://doi.org/10.1007/978-3-030-02804-6_66

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Wang and Wang (2007) also conducted an experimental study to compare students’ learning effectiveness in different multimedia representations and learning pace. Combing the use of eye-tracking and EEG techniques, Szajerman and Napieralski (2017) conducted an experiment on undergraduate students to capture their cognitive processes in watching videos, and articulated that the joint analysis of eye-tracing and EEG data was a promising direction for future research in analyzing user behaviors in interaction with videos. Similarly, Shi et al. (2017) also used eye-tracking and EEG data simultaneously to analyze students’ attention. Experimental results demonstrated the joint use of both techniques can help generate effective solutions to improve subject’s performance. Accordingly, this study attempted to jointly use eye-tracking and EEG data to analyze students’ learning processes in engaging in lecture videos, controlling the factor as teacher presence in different lecture videos types. The purpose of the present study is to examine the influence of teacher presence in lecture video designs on students learning in terms of their learning processes, academic performance, cognitive load and perceived satisfaction. We explore this issue by focusing specifically in preimplementation stage of an online course in the higher education context. The results of this study could provide a future direction for researchers to evaluate online courses and examine students’ learning with online courses, provide empirical evidences for online courses designers in lecture videos design, and provide valuable insights for practitioners to better select and promote online courses for student learning in higher education context.

2 Method 2.1

Participants and Procedures

Altogether 62 undergraduate students (25 males and 37 females) who had never use Flash courses and with normal eyesight gave consent to participate in this study. The students were randomly assigned into Group 1 (N = 31) and Group 2 (N = 31). Students in Group 1 were arranged to watch lecture videos without teacher presence, that is, teacher’s image presents in text-based and flash-based lecture videos. Students in Group 2 were arranged to watch lecture videos with teacher presence, that is, teacher’s image presents in text-based and Flash-based lecture videos. All the participants took part in the experiment went through the same procedures in the same computer and the same set of eye tracker (GazeTech mini) and brain-computer interfaces device (EEG headset). 2.2

Measurement

The data collected in this study covered three constructs: (1) students’ learning performance, including their pre-test and post test scores, (2) students’ cognitive load in watching the lecture videos, (3) students’ perceived satisfaction towards watch the lecture videos. Students’ learning performance was measured with pre-test question (basic level test) and posttest questions. Though both the total score for pre-test and

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protest are 20 points, the scores in posttest questions included 10 points of recognize test and 10 points of transfer test. The cognitive load construct was measured on a fivepoint Likert scale. Students’ perceived satisfaction scale measured on a four-point Likert scale, ranging from ‘very satisfied’ (1), ‘satisfied’ (2), ‘neutral’ (3), and ‘very dissatisfied’ (4).

3 Results In order to better perform analysis, we cut and coded the lecture videos in the two groups in terms of course content, which includes concept explanation (i.e., A1 for Group 1 and B1 for Group 2), example explanation (i.e., A2 for Group 1 and B2 for Group 2), and operation explanation (i.e., A3 for Group 1 and B3 for Group 2), as shown in Table 1. The data analysis of the collected data was then analyzed using the software SPSS (Statistical Package for Social Science) 21.0. Table 1. Coding of different course contents in time periods Group 1 (Lecture videos with teacher presence) Group 2 (Lecture videos without teacher presence)

3.1

Concept explanation A1 Concept B1

Example explanation A2 Examples B2

Operation explanation A3 Practice B3

Eye Movement Data

Using the eye-tracker devices, we recorded the eye-tracking data per second, including total fixation duration, total tracking time lost, number of fixations. The results of descriptive analysis on the collected eye movement data were presented, as shown in Table 2. Table 2. Total fixation duration, total tracking time lost, and number of fixations Groups Code Total fixation duration 1 A1 170.604 ± 42.426 A2 A3 2 B1 141.590 ± 58.004 B2 B3

Total tracking time lost 55.924 ± 46.501

74.417 ± 43.747

No. of fixation

Fixation (times/second) 419.35 ± 96.091 1.314 1.223 1.277 331.23 ± 91.974 1.174 0.977 0.914

An independent sample t-test was performed to compare the difference between groups in total fixation duration, total tracking time lost, and number of fixations.

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The results show that the two groups are significantly different in total fixation time (p = 0.028 < 0.05) and numbers of fixations (p = 0.000 < 0.001), the two groups are not statistically different in total tracking time lost (p = 0.1120 > 0.05). From the coding of video clips, the fixation time of A1 is less than B1, while that of A2 and A3 is more than B2. The results indicate that participants who are viewing A1, B2, and B3 video clips are more likely to locate knowledge points more accurately and tend to acquire and process information easier. For the results that the total tracking time loss of A1, A2, and A3 are less than B1, B2, and B3, they show that the video design of B1, B2, and B3 is not conducive to students’ information acquisition, knowledge processing, and understanding. It may also indicate that teacher presence in videos is likely to negatively influence student learning, which leads to a lower level concentration and more total tracking loss time. Such findings are in line with Djamasbi et al.’s (2012) study. More precisely, though by including human image in a video could help attract user attention, it would also negatively influence users through distract them course content. Accordingly, they suggested that human image located in less obvious positions in a video might possibly improve user effectiveness in viewing a video. 3.2

EEG Data

The EEG data was also collected about students’ level of concentration and relaxation. The results of descriptive analysis on the EEG data are also shown in Table 3. From Table 3, three findings can be seen: first, for the concept explanation part of the lecture video, students appear to have larger mean value of concentration level and smaller mean vale of relaxation level, in watching the video clip A1; second, for the example explanation part, students appear to have larger mean value of both concentration and relaxation level, in watching the video clip B2; third, for the operation explanation part, students also have both higher means value of both aspects in watching the video clip B3. Table 3. Results of EEG analysis for two groups (per second) Group

1

Mean of concentration value 51.53

2

50.67

Code

Mean of concentration value per person

Mean of relaxation value per person

A1 A2 A3 B1 B2 B3

53.13 50.41 47.39 47.45 53.41 49.41

47.75 50.43 43.71 53.73 52.43 52.43

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Test Score

The academic performance of the 62 students in pretest and posttest scores are also collected. The results of independent sample t-test on the scores are shown in Table 4. The results show that there is no significant difference between Group 1 and Group 2 in their pretest scores (p = 0.352 > 0.05), but significant differences are found between the two groups in their posttest scores in both the recognize test (p = 0.034 < 0.05) and transfer test (p = 0.000 < 0.001). Overall, the results showed that Group 1 and Group 2 are statistically different in their the recognize test sores and transfer test scores, with students in Group 1 have higher recognize test score and students in Group 2 have higher transfer test score. Table 4. Independent sample t-test of test scores Academic performance Pretest score Posttest score: recognize test Posttest score: transfer test

3.4

Group 1 (Mean ± SD) 7.42 ± 1.876 7.71 ± 1.243

Group 2 (Mean ± SD) 7.84 ± 1.635 6.97 ± 1.449

F

t

p

0.561 0.578

−0.938 2.164

0.352 0.034*

5.58 ± 2.592

7.81 ± 2.088

0.284

−3.723

0.000***

Cognitive Load

Students’ cognitive changes, including both students’ cognitive load before, ongoing, and after watching the lecture videos are measured. From the results of independent sample t-test, we can see that there exists no significant difference in students’ pretest scores (p = 0.890 > 0.05) and ongoing test scores (p = 0.437 > 0.05) of cognitive load between groups. However, when it comes to the posttest scores, the results demonstrate significant differences in both the recognize test (p = 0.036 < 0.05) and transfer test scores (p = 0.000 < 0.001). More specifically, the recognize test score in cognitive load of Group 1 (Mean = 2.68) is significantly lower than that of Group 2 (Mean = 3.10), while the transfer test score in cognitive load in Group 1 (Mean = 3.81) is significantly higher that of Group 2 (Mean = 2.55). The results are summarized in Table 5. 3.5

Satisfaction Towards Lecture Videos Learning

Table 7 shows that descriptive results of data collected on the satisfaction construct. The results showed three patterns in terms of students’ satisfaction towards lecture videos learning: (1), for the content presents as the concept explanation part of the MOOC video, a majority of students rated their satisfaction with A1 video segments as “satisfied” while a majority students rate their satisfaction with B1 video segments as “neutral”, which indicates that students were more inclined to learning content presented in the mode as A1 video segment; (2), for the content presents as the example explanation part of the MOOC video, a majority of students rated their satisfaction with

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Table 5. Independent sample t-test of cognitive load between groups Cognitive load

Group 1 (N = 31) Group 2 (N = 31) F t p (Mean ± SD) (Mean ± SD) Pre-test 4.13 ± 0.991 4.10 ± 0.831 1.474 0.139 0.890 In process 3.00 ± 0.856 2.81 ± 1.077 2.295 0.783 0.437 Posttest: recognize test 2.68 ± 0.748 3.10 ± 0.790 0.144 −2.147 0.036* Posttest: transfer test 3.81 ± 1.195 2.55 ± 0.961 1.006 4.569 0.000***

the A2 video segment as “neutral”, while most students were satisfied with the B2 video segments, which indicates that students appear to prefer online learning content in the mode of the B2 video segment; (3), for the content presents in the operation explanation part of the MOOC video, most students tended to be neutral about the A3 video segments while most students perceived they were very satisfied with the B3 video segments, which indicated that students were more inclined to online learning content in the mode of the B3 video segment. Such findings suggest that the interface design of MOOC lecture videos should also be student-centered and focus more students’ learning experience during their learning process. The results also revealed that students’ learning experience is one of the key factors in influencing their learning effectiveness. Table 7. Descriptive results of students’ perceived satisfaction Group Code Very satisfied Satisfied 1 A1 3 11 A2 6 4 A3 5 5 2 B1 4 5 B2 6 13 B3 15 8

Neutral 9 18 13 20 8 5

Very dissatisfied 8 3 8 2 4 3

4 Discussion and Conclusion Nowadays, many researchers have set out to use eye-tracking technologies to explore the relationships between video play speeds with learning effectiveness. Using eyetracking data, Duan et al. (2013) showed that the presentation speed of animation video material would influence students understanding of learning content and cognitive processing of related knowledge. For example, they suggested that for video learning content in a progressive and relatively discrete manner, slowing down the speed of video play could facilitate students’ memorization and understanding of knowledge. The experiment of Meyer et al. (2010) found that though the speed in playing videos did not affect the number of fixations and learners’ attention toward video learning, it did affect students’ learning effectiveness. Moreover, they elaborated the conditions

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that if lecture videos could play faster, learners were more likely to have a better holistic understanding of the course content. In contrast, if the videos played slower, learners could obtain a better understanding of the details. In addition, previous studies also supported the influence of MOOC video design on learners’ attention toward video learning. Zain et al. (2011) conducted an eye movement experiment to explore the relationships between learners’ eye movements in different user interface designs. By analyzing the sight hotspot and gaze trajectory diagrams, the results showed that the design of user interface was related to users’ emotional experience. Combing the use of eye-tracking and head-tracking data, Bărbuceanu et al. (2011) analyzed users’ average selection speed in a user interface. The results found that users tended to focus on the object of interest before they noticed interaction devices such as mouse, pointing stick, and infrared marker. Accordingly, important titles, pictures, or content should be located in appropriate positions based on the characteristics of learners’ eye movement. By taking into account of this issue in MOOC video design, learners could possibly focus on key content sooner and better. Moreover, since interaction devices such as mouse also played a role engaging student learning with MOOC lecture videos, they suggested to use these device to mark key content in MOOC video, which might also be helpful to attract learners’ attention in online learning. To summarize, we reach the following findings: (1) whether teacher presence or not in lecture video affects students’ duration of fixation, lost time, and number of fixations in the learning process, which influences their learning effectiveness; (2) whether teacher presence or not in lecture video affects students’ level of concentration and relaxation in their online learning; (3) whether teacher presence or not in lecture video affects students’ learning performance. In a more detailed way, lecture videos design with teacher presence seems to be more suitable for concept explanation, while for those lecture videos with no teacher image presence are more suitable for example and operation explanation; (4) whether teacher presence or not in lecture video does not directly affect learners’ cognitive load in their learning process. Rather, it affects students’ learning performance, which in turn influences students’ cognitive loading; (6) whether teacher presence or not in lecture video affects students’ perceived satisfaction towards lecture videos learning, which in turn influences students learning experience and learning performance. For learning content as concept explanation, students tend to prefer MOOC lecture video design with no teacher image presence. For learning context as operation explanation, student may prefer MOOC video design with teacher image presence. In conclusion, this study has proposed a joint analysis of both eye-tracking and EEG data to examine students’ learning with lecture videos over multiple multimedia formats. The findings established the salience of this method in evaluating the quality of MOOC lecture videos before it is implemented in a large scale online. Acknowledgments. The work described in this paper was fully supported by a grant from the Humanities and Social Sciences Foundation of the Ministry of Education in China, China (Project No.: 13YJC880001) and “13th Five-Year” planning of Education Science in Guangdong (Project No.: 2017JKDY43).

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References Bărbuceanu, F., Duguleană, M., Vlad, S., Nedelcu, A.: Evaluation of the average selection speed ratio between an eye tracking and a head tracking interaction interface. In: Doctoral Conference on Computing, Electrical and Industrial Systems, pp. 181–186. Springer, Heidelberg, February 2011 Djamasbi, S., Siegel, M., Tullis, T.S.: Faces and viewing behavior: an exploratory investigation. AIS Trans. Hum. Comput. Interact. 4(3), 190–211 (2012) Duan, Z., Yan, Z., Wang, F., Zhou, Z.: The effect of animation’s presentation speed on multimedia learning: an eye movement study. Psychol. Dev. Educ. 29(01), 46–53 (2013) Hoogerheide, V., Loyens, S.M., van Gog, T.: Learning from video modeling examples: does gender matter? Instr. Sci. 44(1), 69–86 (2016) Meyer, K., Rasch, T., Schnotz, W.: Effects of animation’s speed of presentation on perceptual processing and learning. Learn. Instr. 20(2), 136–145 (2010) Saß, S., Schütte, K., Lindner, M.A.: Test-takers’ eye movements: effects of integration aids and types of graphical representations. Comput. Educ. 109, 85–97 (2017) Shi, Z.F., Zhou, C., Zheng, W.L., Lu, B.L.: Attention evaluation with eye tracking glasses for EEG-based emotion recognition. In: 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 86–89. IEEE, May 2017 Szajerman, D., Napieralski, P.: Joint analysis of simultaneous EEG and eye tracking data for video picture. In: 2017 18th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering (ISEF) Book of Abstracts, pp. 1–2. IEEE, September 2017 Wang, Y.Q., Wang, X.W.: Eye movement study on the influence of media composition and learning pace on multimedia learning. Audio Vis. Educ. Res. 11, 61–66 (2007) Zain, N.H.M., Razak, F.H.A., Jaafar, A., Zulkipli, M.F.: Eye tracking in educational games environment: evaluating user interface design through eye tracking patterns. In: International Visual Informatics Conference, pp. 64–73. Springer, Heidelberg, November 2011 Zhu, J., Xu, R.H.: The current situation, problems and suggestions of mob construction in China. J. Hebei United Univ. 1, 77–79 (2015)

Optimize Projection Access Order for Deflection Tomography Reconstruction Huaxin Li(&) and Jinxiao Pan School of Information and Communication Engineering, North University of China, Taiyuan, China [email protected]

Abstract. In deflection tomography, Algebraic reconstruction technique (ART) is a common reconstruction technique. In this paper, we study that projection access order has an important effect on the reconstruction of the measured field in ART. We show that a multilevel scheme (MLS) projection access ordering in ART for deflection tomography. The algorithm is carried out by a three-peak Gaussian simulative temperature field. The simulation results are showed in this paper, and compared with the traditional sequential access projection method. Comparisons show that this algorithm improved reconstruction quality and the speed of convergence, especially in cases with noisy data added. Keywords: Deflection tomography Algebraic reconstruction technique (ART)


Projection access ordering

1 Introduction Optical computed tomography (OCT) is a technique developed by computed tomography, and to measure various physical parameters in the flow field by optical method. Phase measurement tomography [1] and beam deflection tomography [2] are two main aspects of optical calculation tomography. In practical optical computation tomography problems, often accompanied by strong vibration environment and large dynamic range of flow field. However, the optical path of deflection tomography is simple, and mechanical stability is required to be lower than that of interference tomography. Usually, a projection is an integral transformation. The projection can be obtained by the light beam passing through the measured field. For optical tomography, the optical path difference and deflection angle of the beam passing through the measured field can be measured as projection data. For optical interference tomography [3–5], the projection is shown as interference fringe, which reflects the sum of phase difference along the ray path. On the other hand, on the basis of mathematics, the projection data is only the sum of phase difference, it has exactly the same mathematical form as the classical Radon transformation, that is, the projection is the integral of field function along the ray path. Moiré tomography [6–8] takes the angle of deflection as the projection data, the projection of the deflected tomography is the sum of the refractive index gradient along the incident ray path. The deflection tomography reconstruction techniques can be divided into transform algorithm and series expansion algorithm. transform algorithm, such as inverse Abel © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 506–514, 2019. https://doi.org/10.1007/978-3-030-02804-6_67

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transform [9, 10], It has been used in the measurement of complex density field of wind tunnel for a long time, but it can only measure axial symmetric flow field; such as filtered back projection (FBP) [8], it can get better results when using a large number of equal interval sampling data in the field of view, but these two methods are difficult to deal with incomplete data. In contrast, series expansion algorithm [11], especially algebraic reconstruction technique (ART) [12], is more practical because it can deal with incomplete sampling flexibly. In recent years, a large number of studies have shown that in the reconstruction of deflection tomography, the information of deflected angle can be converted into optical path difference to reconstruct the three dimensional refractive index field. In ART reconstruction process there are many factors will affect the reconstruction results, such as the calculation of projection coefficient, projection access order, relaxation factor and the influence of prior knowledge and so on. In this paper, a multilevel scheme (MLS) of ART for deflected tomography is present. In this paper, the new algorithm based on the basic principle of moiré tomography. The moiré tomography deflection angle is the sum of small deflection angles along the ray path, and the obtained deflection angle must be transformed to the optical path difference. Compared with an ART algorithm, this new algorithm can improve the reconstruction precision and convergence speed. In Sect. 2, we discuss the conversion of deflected projection information and the derivation of MLS-ART algorithm. In Sect. 3, the three-peak Gaussian simulation field was reconstructed using MLS-ART and traditional ART. Section 4 analyzes the experimental results.

2 Algorithm Analyses 2.1

Convert Deflected Projection Information _

As shown in Fig. 1, the refractive index nðx; yÞ of a 2-D phase object is: _

nðx; yÞ ¼ n0 þ nðx; yÞ

ð1Þ

Where, n0 is the environmental refractive index, and nðx; yÞ is the relative refractive index. The rotation by an angle h in the x0  y0 coordinate system is x  y. When the incident ray parallel to the x0 axis passes through the flow field, the deflected angle of light along the direction of y0 is uðy0 ; hÞ [2], uðy0 ; hÞ ¼

1 n0

Z

1

@nðx; yÞ 0 dx @y0 1

And this incident ray of the optical path difference /ðy0 ; hÞ is: Z 0 nðx; yÞdl /ðy ; hÞ ¼ l

ð2Þ

ð3Þ

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Fig. 1. Schematic diagram of the beam deflection and the coordinate system; h is the viewing angle; u is the deflection angle

and l is the optical path through the measured field. When the deflection angle is small, the light can be approximately straight in the measured field. @/ðy0 ; hÞ @ ¼ 0 @y0 @y

Z l

Z

1

@nðx; yÞ 0 dx ¼ n0 uðy0 ; hÞ 0 @y 1

nðx; yÞdl ¼

ð4Þ

So, by integration operation, we can convert the deflected projection into the optical path difference projection data [13, 14], /ðy0 ; hÞ ¼

2.2

Z

1

1

n0 uðy0 ; hÞdy0

ð5Þ

Deflection Tomography by ART

The reconstruction area is divide into K ¼ N  N grids, assuming that there are q projection directions, the sampling number in each direction is r, then the total projection number is m ¼ q  r. Equation (5) can transform the partial differential equation about deflected projection into a linear system of optical path difference projection An ¼ P

ð6Þ

Where n 2 RK1 , n ¼ ½n1 ; n2 . . .; nK T represent discrete refractive index, RK1 is the real domain of K  1 dimension, P 2 Rm , P ¼ ½p1 ; p2 . . .; pm T represent the optical path difference. A 2 RmK represent coefficient matrix, where Aij is the length of the ith ray intersecting with the jth grid. ART algorithm can be used to solve linear equations. Each equation represents the relationship between the reconstructed relative refractive index and its projected data. K X

Aij nj ¼ pi

i ¼ 1; 2; . . .; m

ð7Þ

j¼1

For linear equations such as Eq. (7), the classical algebraic reconstruction method is adopted for reconstruction. The basic iterative steps are as follows:

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(1) Given an appropriate initial value of n0j , n0j is generally equal to n0 . (2) For the ith ray, the iterative formula of ART is as follows: ðl þ 1Þ nj

¼

ðlÞ nj

þk

pi 

Pk

Pr¼1 k

r¼1

ðlÞ

Air
nr A2ir

Aij

j ¼ 1; 2; . . .; k

ð8Þ

Where l is the number of iterations, k is the relaxation parameter. The ART algorithm only uses one projection for each iteration, and the measurement noise is easy to be introduced, and requires a large number of iterations, which is not efficient. 2.3

Deflection Tomography by Multilevel Scheme Access (MLS) ART

In algebraic iteration algorithm, two main factors influencing the reconstruction speed of the algorithm are projection access order and the relaxation parameter. For ART algorithm, the access order of each ray in the same angle has no effect on the convergence of the algorithm, so we mainly focus on the order between different angle projections. Then we focus on the multilevel scheme projection access ordering. First assume that the projection number P is an power of 2, that is, P ¼ 2L , the order is labeled 0; 1; . . .; P  1, the interval between the two adjacent projections is 180 =P. Then the first level of access to the two angles are view 0ð0 Þ and view P=2ð90 Þ respectively, because they have a maximum orthogonality. The second level is view P=4ð45 Þ and view 3P=4ð135 Þ projection, they just halving the upper projection angles. If two projections have the same level of access, we agree that small angle  priority access, in the third level, we will access the view P=8ð22:5 Þ and then    5P=8ð112:5 Þ, 3P=8ð67:5 Þ and 7P=8ð157:5 Þ in turn. The rest of the projection access order, according to this rule, and so on. The projection access order for a multilevel scheme access mode can be represented in Fig. 2, Fig. 2 clearly illustrates the process of multilevel access, where the numbers represent the order of angular access.

5 3

7

1

6 2 4 0

Fig. 2. The projection access order determined by MLS (P ¼ 8)

The algorithm is simple to implement, If the projection number P is the power of 2, that is P ¼ 2L , then the projection access order are determined by MLS in the same order of calculating the one-dimensional FFT with 2L data.

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3 Experiment and Result Analysis 3.1

Simulated Temperature Field

To test this theory, using three-peak gaussian function to simulate the temperature field, Fig. 3 is the simulated field. Set the number of grids to 30  30, the actual length of the region was 30 cm  30 cm, the temperature distribution function is "

# " # ðx  10Þ2 þ ðy  20Þ2 ðx  20Þ2 þ ðy  12Þ2 tðx; yÞ ¼150 exp  þ 200 exp  10 10 " # 2 2 ðx  8Þ þ ðy  8Þ þ 200 exp  15

for

ð9aÞ

0\x\30 0\y\30

tðx; yÞ ¼ 0

ð9bÞ

outside the region:

200 150 100 50 0 -50 30 30

20

20

10

10 0

0

Fig. 3. Simulated temperature field

Environmental temperature 0 °C, and the temperature t and the relationship between the refractive index n is obtained by G-D (Gladstone - Dale) formula: n1¼

0:292015  103 1 þ 0:368184  102 t

ð10Þ

In order to study the anti-noise capability of the algorithm, in the simulation experiment, we superimposed gaussian noise on the projection of deflection angle, we set white-noise with a mean of 0 and a variance of 0.0006. We use the following two error indicators to evaluate the reconstruction results: the root mean square error (RMSE), and peak value error (PVE).

Optimize Projection Access Order for Deflection Tomography

N1 P RMSE ¼ PVE ¼

i¼0

511

1=2 ð^ti  ti Þ2 N
tmax

j^tmax  tmax j tmax

ð11Þ ð12Þ

Where ti and ^ti are respectively the values of the original field and the reconstructed field in the ith grid, tmax and ^tmax is the maximum value of the corresponding field, N is the total number of grids in the reconstructed region. In the calculation, thirty projector views with 6° equal intervals were used in the range of 0°–180°. The sampling number of each projection direction was 40, the relaxation parameter in the ART and MLS_ART algorithms are 0.5. When P ¼ 30, for the same number of projections, we give the sequential order and the MLS order. The sequential order: 0; 1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29:

MLS order: 0; 15; 8; 23; 4; 19; 11; 26; 2; 17; 9; 24; 6; 21; 13; 28; 1; 16; 7; 22; 5; 20; 12; 27; 3; 18; 10; 25; 14; 29:

3.2

Reconstruction Results and Analysis

The environmental refractive index n0 is 1.00029. In the iteration process, the refractive index distribution was reconstructed by only incorporation ^ti  0 constraint conditions, and then the temperature distribution was deduced by G-D formula. In the ideal noiseless condition, the best reconstruction results from the sequential accessing scheme (SAS) ART and MLS_ART algorithms are shown in Fig. 4, the reconstruction results of the two algorithms with white-noise are shown in Fig. 6. RMSE curves and PVE curves of the two algorithms without noise are shown in Fig. 5. RMSE curves and PVE curves of the two algorithms with noise are shown in Fig. 7. After 10 iterations, the temperature filed of three peaks was reconstructed by both algorithms. Without noise, it can be seen from Fig. 4 that the result of MLS-ART algorithm is similar to that of SAS-ART algorithm, and the difference is not obvious, but The peak shape of MLS-ART algorithm is also significantly better than that of MLSART algorithm. The two groups of errors in Fig. 5 have similar trend of curve variation, and both methods effectively reduce the reconstruction error during the iterative process. As for cases with white-noise, Fig. 6(a) shows sharp burrs on the entire grid surface. Compared with Fig. 6(a), the surface of the reconstruction result in Fig. 6(b) is smooth. In Fig. 7, the reconstruction of MLS_ART algorithm converged faster than SAS_ART algorithm. The MLS_ART algorithm is less affected by noise. Thus, whether it’s noisy or no, the iteration speed of SAS-ART algorithm is slower than that of MLS-ART algorithm, and its precision is not as good as that of MLS-ART algorithm.

H. Li and J. Pan

300

300

200

200 Temperature

Temperature

512

100 0

100 0 -100 30

-100 30 30

20

Y/cm

30

20

20

10 0

0

20

10

10

10 0

Y/cm

X/cm

(a) ART

0

X/cm

(b)MLS_ART

Fig. 4. Comparative reconstruction experiment between SAS_ART and MLS_ART (without noise) -4

3

x 10

1.4

mlsart art

mslart art

1.2

2.8

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2.6 PVE

RMSE

0.8

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7

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(a) root mean square error

3

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(b) peak value error

Fig. 5. Comparison error curves of SAS_ART and MLS_ART (without noise)

300 300 200

Temperature

Temperature

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100 0 -100 30 30

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(a)ART

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Y/cm

10 0

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(b)MLS_ART

Fig. 6. Comparative reconstruction experiment between SAS_ART and MLS_ART (whitenoise)

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x 10

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2.6

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(a) root mean square error

8

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(b) peak value error

Fig. 7. Comparison error curves of SAS_ART and MLS_ART (white-noise)

4 Conclusions When using algebraic reconstruction technique to reconstruct the measured field, the projection access order has an important influence on the reconstruction results. In this paper, multilevel scheme access method is adopted to make the projection sequence uniformly distributed and unclustered within the perspective range as far as possible. Simulation experiments show that the MLS-ART algorithm can converge the reconstruction distribution to a high precision. The experiments results show that this algorithm can improve the precision of reconstruction and accelerate convergence. Whether it’s noisy or not, the multilevel access order can produce the better reconstruction results than the sequential order.

References 1. Sweeny, D.W., Vest, C.M.: Reconstruction of three-dimensional refractive index fields from multidirection interferometric data. Appl. Opt. 12(11), 2649–2664 (1973) 2. Kafri, O.: Moiré deflectometry: a ray deflection approach to optical testing. Opt. Eng. 24(6), 944–960 (1985) 3. Cha, S., Vest, C.M.: Tomographic reconstruction of strongly refracting fields and its application to interferometric measurement of boundary layers. Appl. Opt. 20, 2787–2794 (1981) 4. Vukievic, D., Jager, H., Neger, T., Philipp, H., Woisetschlager, J.: Tomographic reconstruction of the temperature distribution in a convective heat flow using multidirectional hologrpahic interferometry. Appl. Opt. 28, 1508–1516 (1989) 5. Faris, G.W., Hertz, H.M.: Tunable differential interferometer for optical tomography. Appl. Opt. 28, 4662–4667 (1989) 6. Bar-Ziv, E., Sgulim, S., Kafri, O., Keren, E.: Temperature mapping in flames by moiré deflectometry. Appl. Opt. 22, 698–705 (1983) 7. Faris, G.W., Byer, R.L.: Three-dimensional beam deflection optical tomography of a supersonic jet. Appl. Opt. 27, 5202–5212 (1988)

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8. Lewitt, R.M.: Reconstruction algorithms: transform methods. Proc. IEEE 71, 390–408 (1983) 9. Stricker, J., Keren, E., Kafri, O.: Axisymmetric density field measurements by Moire deflectometry. AIAA 21(12), 1767–1769 (1983) 10. Stricker, J., Kafri, O.: A new method for density gradient measurements in compressible flows. AIAA 20(6), 820–823 (1982) 11. Censor, Y.: Finite series-expansion reconstruction methods. Proc. IEEE 71, 409–419 (1983) 12. Gordon, R., Bender, R., Herman, G.T.: Algebraic reconstruction techniques (ART) for three dimensional electron microscopy and X-ray photography. Theor. Biol. 29, 471–481 (1970) 13. Yan, D.P., He, A.Z., Ni, X.W.: New method of asymmetric flow field measurement in hypersonic shock tunnel. Appl. Opt. 30(7), 770–774 (1991) 14. He, Z., Yan, D.P., Wang, H.L.: Three-dimensional measurement of asymmetric flow field in hypersonic shock tunnel with moiré interferometry. Proc. SPIE 1553, 676–681 (1991)

E-Enabled System

Simulation of Evaluate the Effect on Big Data Pricing Scheme Model Chenghui Yang1,2,3,4(&) 1

2

College of Electrical Engineering, Northwest Minzu University, No. 1, Northwest Xincun, Lanzhou 730030, Gansu, China [email protected] China National Institute of Information Research, No. 1, Northwest Xincun, Lanzhou 730030, Gansu, China 3 School of Automation and Electrical Engineering, Lanzhou Jiaotong University, No. 188, AnNing West Rode, Lanzhou 730070, Gansu, China 4 Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing, No. 188, AnNing West Rode, Lanzhou 730070, Gansu, China

Abstract. Given the new project according to Annex III task pricing scheme for you, and evaluate the implementation effect of the scheme. The use of MATLAB for the new project data processing and analysis, by region, combined with the local consumption of bus, subway and other modes of transport, the price of linear fitting, Crowd sourcing scheme, discount system, deduct the basic traffic can the price game costs, the new pricing policy. Keywords: Component


Formatting
Style
Styling
Insert

1 Introduction Take money is a kind of self-help service. The user can download the registration become a member of APP, and then receive the task need to take pictures from the APP, complete the task can earn APP calibration to complete the task of remuneration. This self-service service based on mobile Internet Crowd sourcing platform, provide a variety of commercial inspection and information gathering for the enterprise, compared to the traditional market survey method can greatly reduce the cost of the investigation on the one hand, many APP members can easily make the calibration of the remuneration and access to a large number of members, on the other hand, effectively ensure data authenticity and reliability, but also shorten the research cycle. Therefore, APP has become the core of the platform operation, and the task pricing in APP is its core element. If the price is unreasonable, some tasks will be nobody interested, and lead to the failure of commodity inspection. We need to discuss the pricing rule from China, and propose solutions.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 517–522, 2019. https://doi.org/10.1007/978-3-030-02804-6_68

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2 Design Ideas and Methods The first use of MATLAB for the new project data processing and analysis, by region, combined with the local consumption of bus, subway and other modes of transport, the price of linear fitting, Crowd sourcing scheme, discount system, deduct the basic transportation expenses, can the price game policies for new pricing. Using the assumption of this data is true and in accordance with the actual local situation. Ignoring the local weather, impact on the special data caused by psychological aspects. Malicious tasks and in violation of the provisions of the act. Assuming no platform.

3 Model Implementation 3.1

Solution of Problem Four

Under the model of the third problem, a large number of new project pricing scheme is set up. (I) Analysis of tasks, locations and data of new projects Through the data processing of the new project task by MATLAB software, under the establishment of the model I, the analog model I makes the same division area for each point, then the latitude and longitude of each task number is imported into the high moral map, as shown in Fig. 1. Through Fig. 1 and comparison, we can find that the points in Annex 3 are mainly distributed in Guangzhou, Nanshan District, Shenzhen, Longgang and Shenzhen District of Tianhe District. Therefore, we intend to adopt different pricing models according to different regions. (a) Tianhe District Analysis of the region by a problem, we can find that the regional economy developed, the transportation is convenient, but the population is less, more tasks, far less than the number of members meet the quantity required to complete the task, so we put forward the task Crowdsourcing to membership in the price of 65 yuan; and because Tianhe District and other places smooth connection, convenient transportation, so in assuming that we know the position of members, non members of Tianhe District, increase their remuneration, let it go to Tianhe District to complete the task of taking pictures. First of all, Guangzhou as a first tier city, its resident time cost must be higher, so the longest distance from other areas to Tianhe District should not be more than 10 km; after consulting the data, the city of Guangzhou within 10 km of bus, subway consumption fitting as shown in Fig. 2. At the same time, because of the rapid development of shared bicycles, the time cost of the members moving from the site to the mission point can be greatly reduced. In combination with the above factors, the membership fee of non Tianhe District going to Tianhe District to take photos is increased by 3 yuan, and the price is set at 68 yuan.

Simulation of Evaluate the Effect on Big Data

Fig. 1. Location partition

Fig. 2. Bus and subway consumption fitting map

519

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C. Yang

Tianhe District directly take crowd sourcing. Non Tianhe area to take non crowd sourcing, and then add 3 yuan on the fee. (b) Shenzhen Nanshan District First, we should ignore the enterprise confidentiality problem, and then say that the region tourism developed, and then directly take crowdsourcing pricing. At the same time in crowd sourcing members must adopt the system model, increase the pricing competition between crowd sourcing members, reduce the cost of task providers, crowdsourcing price is set at 64 yuan. (c) Longgang district After investigation, learned that Longgang district is the largest area of Shenzhen City area, complete the reform in recent years, although in recent years, the development speed is very fast, but the main residents, the average educational level is still relatively low, so in the area of potential members and a small number of members, but the second industry of Longgang District in the year of considerable development. Reached 200 billion 86 million yuan, can be seen to be taking pictures of the number of tasks is relatively more, so the pricing scheme can refer to the city of Guangzhou in Tianhe District on its pricing. Firstly, the paper analyzes the toll situation of public transport in Shenzhen: Bus 2.5 yuan per person, 5 km per kilometer after 0.5 yuan, the start of the subway is the first 4 km 2 yuan; mileage price is 4 km to 12 km, 4 yuan per 1 km; 12 km to 24 km, 6 yuan per 1 km can be used; more than 24 km, 1 yuan per 8 km;

Fig. 3. Price fitting chart

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521

Since Shenzhen is a first tier city, the urban life is faster, so it is acceptable to analyze the time cost of arriving at the mission site within 10 km through public transportation. So, the cost of transportation is very large, and it costs much more than other areas. In the same way, the members will not go to the task provided by the task provider, and get the following figure by price fitting, as shown in Fig. 3. Non Longgang area first crowdsourcing, and then each fee plus 3 yuan, set to 70 yuan. In Longgang, the members who have to finish the task directly have price game with the task provider, and the task maker should check the reputation value of the show, so as to ensure that the completion of the task and the market price are set at 65 yuan.

4 Conclusion 4.1

Implementation Evaluation of Program Effect

If there is no error in the implementation of this program, other factors are not considered, the economic level, development status, personnel flow, transportation mode, price, social support, the number of members and the amount of tasks in different regions are also discussed. (I) In areas far away from the developed economy, if the amount of tasks is small, the members can complete the task individually, and they will play a game with the task providers on the pricing, and the transportation costs should be priced. (II) In the economically developed areas, if the task is to take a large amount of Crowd sourcing mode, it is necessary to carry out the system model of input crowd sourcing collective member selection, price slightly lower. (III) If the individual member to complete the task, to carry out all aspects of credit value considerations, inspection compliance with their own results after the payment of remuneration. In the economically developed areas, it is best to use crowd sourcing model to provide crowd sourcing members, while the members complete the task at the same time, the task price should increase the remuneration, reduce the expensive traffic costs should go out. Acknowledgement. 1. 2017 “13th five-year plan” education scientific planning key project of Gansu province “advanced education targeted poverty alleviation research” (Item no.: GS[2017] GHBZ034); 2. Northwest Minzu University 2017 central university project fund (Item no.: 31920170079); 3. Research and practice on the course process reform of digital electronic technology in northwest nationalities university in 2017 (Item no.: 2017XJJG-22); 4. In 2017, the special fund for basic scientific research operation of the central university of northwest nationalities university (Item no.: 31920170141); 5. In 2016, the youth team of the central university project fund of northwest nationalities university (Item no.: 31920160003); 6. Supported by Program for Changjiang Scholars and Innovative Research Team in University (IRT_16R36);

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7. Supported by Program Northwest Minzu University: SYSKF-2018002, SYSKF-2018007, SYSKF-2018025, SYSKF-2018030; 8. Supported by Program of 2018 Scientific research project of colleges and universities of Gansu province department of education, “advanced education targeted poverty alleviation research”: (Item no.: 2018B-026).

References 1. Yang, J., Yang, S., Li, M., Fu, Q.: Autonomous pricing strategy toward market economy in computational grids. In: Proceedings of the International Conference on Information Technology: Coding and Computing (2005) 2. Roy, N., Das, S.K., Basu, K.: A pricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework. J. Parallel Distrib. Comput. (2005) 3. Schwind, M., Gujo, O., Stockheim, T.: Dynamic resource prices in a combinatorial grid system. In: Proceedings of the 8th IEEE International Conference on E-Commerce Technology and 3rd IEEE International Conference on ENTERPRISE Computing, E-Commerce, and E-Services (2006) 4. Li, H., Zhong,Y., Lu, J.: A banking based grid recourse allocation scheduling. In: Proceedings of the 3rd International Conference on Grid and Pervasive Computing, Kunming, pp. 239–244. IEEE (2008) 5. Chen, H., Wang, R.: Cost time optimized grid directed acyclic graph scheduling algorithm. J. Electron. 33(8), 1375–1380 (2005) 6. Kesselman, C., Foster, I., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. (2001) 7. Buyya, R., Abramson, D., Giddy, J., Stockinger, H.: Economic models for resource management and scheduling in grid computing. J. Concurr. Pract. Exp. (2002) 8. Kwok, Y.K., Amad, I.: Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors. IEEE Trans. Parallel Distrib. Syst. (1996) 9. Gerasoulis, A.,Yang, T.: A comparison of clustering heuristics for scheduling directed acyclic graphs of multiprocessors. J. Parallel Distrib. Comput. (1992) 10. Wu, J., Deng, L., Hu, Z.: Grid computing economic model of price negotiation and control. J. Northwest. Polytech. Univ. 26(4), 497–502 (2008) 11. Menasce, D.A., Casalicchio, E.: A framework for resource allocation in grid computing. In: Proceedings of 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, pp. 259–267. IEEE (2004) 12. Cao, H., Xiao, N., Lu, X., Liu, Y.: A resource allocation method for computing grids based on market mechanism. Comput. Res. Dev. (2002) 13. Czajkowski, K., Foster, I., et al.: A resource management architecture for metacomputing systems. In: Proceedings of IPPS/SPDP 1998 Workshop on Job Scheduling Strategies for Parallel Processing (1998) 14. Subramoniam, K., Maheswaran, M., Toulouse, M.: Towards a micro-economic model for resource allocation in grid computing system. In: Proceedings of the 2002 IEEE Canadian Conference on Electrical and Computer Engineering (2002) 15. Li, L., Liu, Y., Ma, X.: Grid resource allocation based on combinatorial double auction. J. Electron. 37(1), 165–169 (2009)

Analysis of a Novel 1T Spatial Multi-loop Coupled Mechanism Shuang Zhang, Jingfang Liu(&), Jian Wang, and Huafeng Ding College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China [email protected]

Abstract. A novel spatial multi-loop coupled mechanism (SMCM) with a single translational mobility (1T) is proposed in the paper. The mechanism is different from traditional parallel mechanisms due to three coupled chains connected with branches. Three parts are obtained through separating the mechanism, and mobility analysis is given from the motion and constraint analysis of each part. Then, position solution is given out to prove the correctness of mobility analysis. Finally, trajectory simulation is obtained by MATLAB, which shows the mechanism can achieve the general plane curve by one actuator. Keywords: Spatial mechanism Trajectory simulation


Mobility analysis
Position solution

1 Introduction For a traditional parallel mechanism, the moving platform is connected to the base by at least two independent serial kinematic chains, so the parallel mechanism has the characteristics of strong carrying capacity and large rigidity. In the past decades, most of parallel manipulators have been proposed and studied. Some mature theories of type synthesis are proposed, such as the screw theory [1], Lie group theory [2], Gf set [3] and so on. The researches of parallel mechanisms, such as analysis of singularity, mobility, stiffness and so on, have achieved fruitful results [4–8]. With the development of society, better performances and functionality are required in industry. The spatial multi-loop coupled mechanism is a mechanism more complicated than the parallel mechanism. Compatible with the advantages of parallel mechanism and series mechanism, the SMCM also has a good application prospect. So lots of researchers have studied the novel mechanism with different topology in recent years. Shen et al. [9] proposed lots of 6-DoF multi-loop coupled mechanisms by splitting and combination of parallel mechanisms’ branches. Campos et al. [10] proposed type synthesis method for SMCMs based on segmentation and combination of Assur rod set. Zeng [11] proposed type synthesis method for SMCMs based on mathematical logic and topological layout. Tian C [12, 13] studied type synthesis method for parallel mechanisms with coupled sub-chains. From discussing the constraint of traditional parallel mechanisms, he proposed lots of SMCMs by adding coupled chains to the parallel mechanism’s branches. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 523–532, 2019. https://doi.org/10.1007/978-3-030-02804-6_69

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The mechanisms with a translational degree of freedom are widely used in punching machine. However, most of them are planar which lateral stiffness is not enough, and 1T spatial mechanisms are rarely proposed. This paper presents a novel 1T SMCM which has high stiffness. Accordingly, the remainder of this paper is organized as follows: In Sect. 1, structure of the new mechanism is introduced in detail, and mobility analysis is presented. Forward position solution and trajectory simulation are given in Sect. 2. Finally, conclusions are presented.

2 Structure of the Novel 1T Smcm The novel 1T SMCM is symmetric about a plane, as shown in Fig. 1. The mechanism consists of nine kinematic chains, where S1-S3 are three coupled chains connecting with three coupled nodes A, B and C. (Except moving and fixed platforms, the bar that connects with more than two kinematic pairs is called coupled node).

Fig. 1. The novel 1T SMCM

The kinematic pairs of each kinematic chain are parallel to each other. And kinematic pairs of S1 are perpendicular to fixed platform in the initial posture. Each of the coupled nodes has a symmetric plane. The coupled nodes A and B are symmetric about the mechanism’s symmetric plane in movement process. The angle between the axis of A3 and nodes A’s symmetric plane is 60°. The angle between the axis of A2 and vertical axis is 60°. The angle between axis of C3 and nodes C’s symmetric plane is 60°. The angle between the axis of C2 and vertical axis is 45°. The fixed platform is symmetric, and its radius is r1. The moving platform is symmetric about the mechanism’s symmetric plane. An equilateral triangle is formed by centers of three rotary pairs of the moving platform. The distances between center of the triangle and centers of moving platform’s rotary pairs are r2. Taking fixed

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platform’s center as the origin of coordinate system O-XYZ, X axis along the intersection of symmetric plane and fixed platform, Z axis is perpendicular to the fixed platform, and the coordinate system is established according to the right hand rule, as shown in Fig. 1. This mechanism can be divided into three parts from the coupling nodes, as shown in Fig. 2. The first part is a symmetrical seven-bar closed loop chain, as shown in Fig. 2 (a), and its motion screw system is:  pffiffi 8 > 6 Sa1 ¼ 23  12 > >  pffiffi > > > > > 6 Sa2 ¼ 23  12 > >  pffiffi > >

> 6 Sa4 ¼ 23 12 0 > > > > > 6 Sa5 ¼ð 0 0 1 > > > > > : 6 Sa6 ¼ð 0 0 1 6 Sa7 ¼ð 0 0 1

 r1 pffiffi  pffiffi 0 12 c1 23 c1  a21  32b1  0 0 r1 pffiffi  pffiffi  12 c1 23 c1 a21 þ 32b1 b2 a2 0 Þ b3 a3 0 Þ b4 a4 0 Þ 0

0 0

F ¼ lþc  6

ð1Þ

ð2Þ

where l is the number of the single loop chain’s bars; c is the order of the single loop chain’s constraint screw system; F is mobility of the single loop chain.

Fig. 2. Three parts of the mechanism

From Eq. (1), we can find that in the above 7-bar loop, l is equal to 7, c is equal to 0. According to mobility criterion of single loop chain, we can obtain the mobility of the first part is one. Regarding the 7-bar loop as a parallel mechanism, and coupled node A as its moving platform. Based on Eq. (1), the motion screw of coupled nodes A can be obtained. Similarly, regarding coupled nodes B as the moving platform, Based on

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Eq. (1), the motion screw of coupled nodes B can also be obtained. The motion screws of coupled nodes A and B are as follow 8  pffiffi  pffiffi > a1 3b1 3 < 6 Sa ¼ 0 0 0 1 c 1 c   2 1 2 2 2  ð3Þ pffiffi  pffiffi > a1 3b1 3 : 6 Sb ¼ 0 0 0  1 c 1 2 c1 2 2 þ 2 The motions of coupled nodes A and B are translational, and motion directions are perpendicular to the bars connected to the nodes, respectively The second part shown in Fig. 2(b) can be regarded as a parallel mechanism 2-(P) RR-RRR. The P pairs in bracket represent the translational motion of coupled nodes A and B. The motion screw systems of branches are respectively expressed as follows: 8

> >0 < 1 >2

> > :

1 2

8 > 0 > > 2

> > :

1 2

0 

0 pffiffi

3 2 pffiffi  23

1 0 1 0 1 0

0 0 r1 Þ c5 0 a5 Þ c6 0 a6 Þ

ð4Þ

pffiffi  pffiffi a1 3b1 3 1 c c   1 1 2 2 2 2 pffiffi pffiffi 0 23 c7 12 c7  23 a7  pffiffi pffiffi 0 23 c8 12 c8  23 a8 

0 0  12 c1 pffiffi pffiffi 3 0  23 c7 2 pffiffi pffiffi 3 0  23 c8 2

pffiffi 3 2 c1 1 2 c7 1 2 c8

1 2 b7 1 2 b8

pffiffi  a1 3b1 2 þ 2 pffiffi 3 1 2 a7 þ 2 b7 pffiffi 3 1 2 a8 þ 2 b8

 

 

ð5Þ

ð6Þ

where ð ai 0 ci Þ (i = 5, 6) denote the coordinates of centers of rotating pairs in limb S4; ð ai bi ci Þ (i = 7, 8) denote the coordinates of centers of rotating pairs in coupled chain S2. Equations (4), (5) and (6) represent the motion screw systems of S4, S2 and S3, respectively. The constraint screw system of 2-(P)RR-RRR can be obtained by symmetry and reciprocal product: 8 6S > > > c1 > < 6 Sc2 6 Sc3 > > 6S > > : c4 6 Sc5

¼ ð0 0 0 1 0 ¼ ð0 0 0 0 1 ¼ ð0 0 0 0 0 ¼ ð ðE1  F1 Þ  D1 ¼ ð ðE2  F2 Þ  D2

0Þ 0Þ 1Þ 0 0 0 0

ð7Þ 0Þ 0Þ

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where D1 ¼ E1 ¼ F1 ¼

F2 ¼

1 2 c1

 pffiffi

3 2 c7

 pffiffi

3 2 c8

D2 ¼ E2 ¼



 



pffiffi 3 2 c1

 a21 

pffiffi 3b1 2

 ;

1 2 a7



pffiffi 3 2 a7

  12 b7 ;

1 2 a8



pffiffi 3 2 a8

  12 b8 ;

pffiffi 3 2 c1

 12 c1

a1 2

þ

pffiffi 3b1 2



;



pffiffi 3 2 c7

1 2 a7

pffiffi 3 2 a7

 þ 12 b7 ;



pffiffi 3 2 c8

1 2 a8

pffiffi 3 2 a8

 þ 12 b8 :

The motion screw of the moving platform of 2-(P)RR-RRR can be obtained from reciprocal product of the constrained screw system 6 Sm ¼ ð 0

0

0 H

0

FÞ

ð8Þ

where H¼

pffiffiffi pffiffiffi pffiffiffi i c1 hpffiffiffi 3ða1 þ 3b1 Þðc7  c8 Þ  c1 ðb7  b8 þ 3a7  3a8 Þ ½ða7  a8 Þ  3ðc7  c8 Þ 8

F¼

i pffiffiffi pffiffiffi pffiffiffi 1hpffiffiffi 3c1 ðb7  b8 þ 3a7  3a8 Þ  ða1  3b1 Þða7  a8 Þ 8h pffiffiffi pffiffiffi pffiffiffi pffiffiffi i 3ða1 þ 3b1 Þðc7  c8 Þ  c1 ðb7  b8 þ 3a7  3a8 Þ

We can know that node C has a translational degree of freedom, and its motion screw is located in the symmetric plane Due to the translational motion of three coupling nodes, the third part shown in Fig. 2(c) can be regarded as a parallel mechanism 3-(P)RRR which P pairs in bracket are replaced by the coupled nodes A, B and C, respectively. From literature [1], we can know that when P pair is not vertical to R pairs of each branches, 3-PRRR has 3 translational degrees of freedom. From above analysis, we can find that A, B and C are moving at the same time, and the directions are not perpendicular to R pairs connected with them. When P pairs are replaced by coupled nodes A, B and C, respectively, the 3-(P)RRR has a translational degree of freedom, but its motion direction is uncertain. And we can also know that the new mechanism has a translational degree of freedom, and motion direction is uncertain.

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3 Kinematics Analysis of the Novel 1T SMCM 3.1

Forward Position Kinematics Analysis

The coordinates of A1 and B1 can be obtained through the symmetrical structure   8 pffiffi pffiffi 3 3 < A1 ¼ 1 r1  1 l1 cos h1 ; r  l cos h ; l sin h 1 1 1 1 1 2 2 2 2  pffiffi pffiffi : B1 ¼ 1 r  1 l cos h ;  3 r þ 3 l cos h ; l sin h 1 1 1 1 1 1 1 1 2 2 2 2

ð9Þ

where r1 is radius of the fixed platform, l1 is the length of chain L1. Because the motions of A and B are translational, the coordinates of point A2, A3 and B2 can be obtained: ! ! ! OA2 ¼ OA1 þ A1 A2

ð10Þ

! ! ! OA3 ¼ OA1 þ A1 A3

ð11Þ

! ! ! OB2 ¼ OB1 þ B1 B2

ð12Þ

! ! ! where A1 A2 ,A1 A3 and B1 B2 are determined by structural parameters of coupled nodes A and B. Because of special structure, we can get an equilateral triangle as shown in Fig. 3 when project points A1, B1 and C1 into XY plane. So the X-coordinate of point C1 is C1x ¼ 

pffiffiffi  3A1y  A1x

And the Y-coordinate of point C1 is always equal to zero.

Fig. 3. Projection diagram

ð13Þ

Analysis of a Novel 1T Spatial Multi-loop Coupled Mechanism

Assuming coordinate of C1 is ð C1x ; 0; the coordinate of C3 can be depicted as:

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C1z Þ, C1z is unknown. In the same way,

! !  ! OC3 ¼ OC1 þ C1 C3

ð14Þ

 ! of coupled node C. So only Z where C1 C3 is determined by the structural parameters    ! coordinate of C3 is unknown. Also because A3 C3  ¼ l2 , we can obtain: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2 ðA3x  C3x Þ2 þ A3y  C3y þ ðA3z  C3z Þ2 ¼ l2

ð15Þ

where l1 is the length of chain L1.

Fig. 4. Two kinds of position solutions of coupled node C

So coordinate of C3 is obtained, there are two solutions, as shown in Fig. 4. The coordinates of the points of coupled nodes A, B and C are all obtained. In the following analysis, the bigger one of Z coordinate of C3 is preserved. In parallel 3-(P)RRR, assume that coordinate of the center of moving platform is P ð Px Py Pz Þ. The centers of rotary pairs connecting with moving platform are A4, B4 and C4. These three points are coplanar with and symmetric about P. And their coordinates can be depicted as: 8   pffiffi 3 1 > > < A4 ¼ Px þ 2 r2 ; Py þ 2 r2 ; Pz  pffiffi B4 ¼ Px þ 12 r2 ; Py  23 r2 ; Pz > > : C4 ¼ð Px  r2 ; Py ; Pz Þ

ð16Þ

where r2 is radius of moving platform. The expressions of the planes in where branches of 3-(P)RRR locate are pffiffiffi  1 3 3 ðx  A4x Þ þ y  A4y þ ðz  A4z Þ ¼ 0 4 2 4

ð17Þ

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pffiffiffi  1 3 3 ðx  B4x Þ  y  B4y þ ðz  B4z Þ ¼ 0 4 4 2

ð18Þ

x  C4x ¼ z  C4z

ð19Þ

Substitute coordinates of A2, B2 and C2 into Eq. (17)–Eq. (19) pffiffiffi pffiffiffi



3 3 1 3 1 Px þ r2  A2x þ r2  A2y þ ðPz  A2z Þ ¼ 0 Py þ 4 2 2 4 2

ð20Þ

pffiffiffi pffiffiffi



3 3 1 3 1 Px þ r2  B2x  r2  B2y þ ðPz  B2z Þ ¼ 0 Py  4 2 2 4 2

ð21Þ

Px  r2  C2x ¼ Pz  C2z

ð22Þ

Here are three equations and three unknowns, so the solution of above equations can be obtained

Px ¼

pffiffi 3 3 4 Eþ 4F

 pffiffi þ 12 G þ H þ 1  3 4 3 r2 pffiffi 1 þ 23 Py ¼ 0

Pz ¼

pffiffi 3 3 4 Eþ 4F

pffiffi pffiffi þ 12 G  23 H  5 4 3 r2 pffiffi 1 þ 23

ð23Þ ð24Þ ð25Þ

where E ¼ A2x þ B2x F ¼ A2y  B2y G ¼ A2z þ B2z H ¼ C2x  C2z

3.2

Position Simulation

In this section, the necessary parameters are given in Table 1, and forward position solution is obtained by MATLAB soft. The simulation result is shown as Fig. 5. The trajectory of moving platform is shown in Fig. 5(a). Different from 1T parallel mechanisms, the novel mechanism’ trajectory of moving platform is a general curve located in symmetric plane. This feature indicates that the novel 1T mechanism has a good application prospect. For example, one can use this mechanism as forging press to machine a certain surface.

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Table 1. Main parameter Parameter Value (mm) Parameter ! 300 r1 A1A3 ! ! 100 r2 A1A2; B1B2 ! 180 l1 C1C3 ! 220 l2 C1C2

Value (mm) (49.3, 49.3, 43) (14, 14, 134.5) (41.6, 69.8, 43) (39.6, 0, 125.6)

Fig. 5. The trajectories of moving platform

4 Conclusion A novel spatial multi-loop coupled mechanism is proposed, which is symmetrical about a plane. Mobility analysis based on screw theory is carried out to convince that the moving platform has a single translational mobility, and motions of three coupled nodes are also translational. Then, forward position analysis is given out according to the special structure. Based on the position analysis, the trajectory of moving platform is obtained, which shows that the trajectory is a general curve in the symmetrical plane. Due to the spatial structure, the new mechanism has better stiffness than planar mechanisms and only needs one actuator. Acknowledgments. The work reported here is supported by NSFC under Grant No. 51475015.

References 1. Huang, Z., Li, Q.C.: General methodology for type synthesis of symmetrical lower-mobility parallel manipulators and several novel manipulators. Int. J. Robot. Res. 21, 131–145 (2002) 2. Hervé, J.M.: The Lie group of rigid body displacements, a fundamental tool for mechanism design. Mech. Mach. Theory 34, 719–730 (1999) 3. Yang, J., Gao, F., Ge, Q.J., et al.: Type synthesis of parallel mechanisms having the first class Gf sets and one-dimensional rotation. Robotica 29, 895–902 (2011)

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4. Huang, Z., Kong, L.F., Fang, Y.F.: Mechanism Theory of Parallel Robotic Manipulator and Control. China Machine Press, Beijing (1997) 5. Huang, Z., Fang, Y.F.: Kinematic characteristics analysis of 3-dof in-parallel actuated pyramid mechanisms. Mech. Mach. Theory 31, 1009–1018 (1996) 6. Pashkevich, A., Chablat, D., Wenger, P.: Stiffness analysis of overconstrained parallel manipulators. Mech. Mach. Theory 44, 966–982 (2009) 7. Huang, T., Liu, H.T., Chetwynd, D.G.: Generalized Jacobian analysis of lower mobility manipulators. Mech. Mach. Theory 46, 831–844 (2011) 8. Huang, Z., Zhao, Y.S., Zhao, T.S.: Advanced Spatial Mechanisms. Higher Education Press, Beijing (2006) 9. Shen, H.P., Yang, T.L., Ma, L.Z.: Synthesis and structure analysis of kinematic structures of 6-DOF parallel robotic mechanisms. Mech. Mach. Theory 40, 1164–1180 (2005) 10. Campos, A., Budde, C., Hesselbach, J.: A type synthesis method for hybrid robot structures. Mech. Mach. Theory 43, 984–995 (2008) 11. Zeng, Q., Fang, Y.F.: Structural synthesis of serial parallel hybrid mechanisms based on representation and operation of logical matrix. ASME J. Mech. Robot. 1, 041003 (2009) 12. Tian, C., Fang, Y.F., Ge, Q.J.: Structural synthesis of parallel manipulators with coupling sub-chains. Mech. Mach. Theory 118, 84–99 (2017) 13. Tian, C., Fang, Y.F., Guo, S.: Structural synthesis of a class of 2R2T hybrid mechanisms. Chin. J. Mech. Eng. 29, 703–709 (2016)

A Study on Online Fault Diagnosis Technology for Shield Core Components Honghui Zhang(&) School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523106, China [email protected]

Abstract. The development of micro-electronic techniques, large-scale application of integrated circuit technology, progress in computer technology and sensor technology, and increasing signal analysis means have provided favorable opportunities for the advance of shield fault diagnosis technology. This paper, on the basis of analyzing Chinese and foreign studies on fault diagnosis technology and its application status quo, explores the necessity of introducing online fault diagnosis technology in the construction process of shield equipment, analyzes the difficulty in online diagnosis technology for shield fault and proposes suggestions and countermeasures. Keywords: Shield


Components
Online fault diagnosis

1 Introduction Shield is a kind of special engineering machinery used in tunneling. It has been about 180 years since it’s launched on the market. It’s started in Britain and developed in Japan and Germany [1]. In recent 30 years, shield has witnessed rapid development by exploring and studying key technology in earth pressure balance and slurry type shields such as effective sealing of shield, how to ensure a stable excavation surface, avoid upheaval of earth surface and control collapse in a certain scope, service life of cutters and change of cutters under sealed conditions as well as treatment of high hydraulic pressure in some unfavorable geological circumstances, etc. Modern shield tunneling machines integrate light, mechanical, electrical, liquid, sensing and information technology and possess many functions such as excavating and cutting earth, conveying earth residue, piecing together tunnel lining and correcting measurement orientation. It involves multiple disciplines including geology, construction, machinery, mechanics, hydraulic pressure, electric, control and measurement, etc. Moreover, design and manufacture shall be tailored to different geological conditions and it’s considerably demanding for reliability [2, 3]. Compared to traditional tunneling with mining approach, it’s characterized by high degree of automation, safety and quickness. Shield has been extensively applied to varied tunnel projects such as subway, railway, highway, municipal administration and water and electricity.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 533–539, 2019. https://doi.org/10.1007/978-3-030-02804-6_70

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However, a number of problems in practice need to be urgently solved. With increasingly intense market competition, it’s imperative to study how to prolong continuous working hours of shield, reduce non-fault shutdowns, weaken the impact of noise and vibration of shield on surrounding environment, develop new cutters which are resistant and wear-proof, analyze the dynamics features of whole shields and key components, work out vibration attenuation approaches of shield, and enhance the level of online monitoring and fault diagnosis of key components’ working status.

2 Overview of Chinese and Foreign Status Quos 2.1

Status Quo of Foreign Studies

As constant progress has been made in science and technology especially computer technology, fault diagnosis technology for shield equipment has gradually grown into an emerging marginal comprehensive discipline. The discipline which regards equipment management, status monitoring and fault diagnosis as its contents and establishment of a new maintenance system as its goal has become a popular subject in the world. Equipment fault diagnosis technology is the product of modernized production. The appearance and application of varied sensors and recorders in the early 1940s and late 1950s provided powerful means for diagnosis of modern equipment fault. Later on, plenty of detection methods and signal processing means were invented. In the late 1960s, NASA set up a Mechanical Fault Prevention Group (MFPG) and Britain established a Mechanical Health Monitoring Center (MHMC). From 1970s to present, equipment fault diagnosis technology has gradually transformed from conventional diagnosis to modern fault diagnosis that centers on artificial intelligence. So far, foreign shield equipment fault diagnosis technology has bordered on consummation. 2.2

Status Quo of Chinese Studies

Studies on diagnosis of mechanical equipment fault were started in China in 1983, which was late but rapid development has been achieved. Equipment diagnosis technology was applied in many industries such as chemical engineering, metallurgy, electricity and railway with satisfying effect being generated. With development of diagnosis technology, some relevant factories were set up. Some sensors and data collectors reached international standards. In the meantime, some diagnosis instrument and equipment were researched and developed. In terms of diagnosis of shield equipment fault, what’s often applied includes fault tree analysis technology regarding vibration, heat and leakage fault, ultrasonic technology, remote fault diagnosis technology, fault diagnosis technology based on neural network, oil-water detection technology, online fault information collection technology and online monitoring technology, etc. The shield status monitoring and fault diagnosis during construction has tended to be automatic, digitalized, intelligent and comprehensive; software application has become standardized while hardware is professional and standard; diagnosis meters and devices are turning more systematic as a network. Some Chinese universities,

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scientific research institutions and shield construction enterprises have made greater efforts to research and develop shield equipment fault diagnosis technology [4, 5]. Yet, due to complicated shield equipment system and complex faults, a complete failure diagnosis technology for shield equipment falls short. Hence, it’s necessary to formulate some standards for shield equipment fault diagnosis technology and thus provide a basis for equipment maintenance and management.

3 Analyses of Problems Existing in Shield Equipment Although foreign and domestic studies on and production of shield are relatively mature, shield still has some shortcomings such as strong vibration, susceptibility of key components to fatigue and breakdown and frequent change of cutters, which can no longer keep up with rapid science & technology development and human demands for production and living. As a result, it’s extremely difficult to guarantee the construction period. Besides, studies on dynamics features of shield system and key components and vibration attenuation as well as online fault diagnosis system are still incomplete [6]. At present, shield status monitoring systems can mostly track bearing, gear case, oil liquid, oil level and temperature only. Moreover, most of them are based on PLC monitoring system which emphasizes monitoring and ignores diagnosis. As shield is a kind of complicated and precise modern equipment which integrates mechanical, electrical and liquid fields, process monitoring based on PLC alone cannot achieve the purpose of guaranteeing safe equipment. Therefore, much as the majority of shield are equipped with a PLC monitoring system, unexpected failure to report fault and report by mistake still happen time and again [7]. It’s reflected in the following aspects in detail: (1) Overly Strong Vibration of Shield Equipment When shield comes across hard rocks or cobblestones, the constant impulsive load will lead to strong vibration of shield, which feels like an earthquake when vibration is transmitted to the ground. Accordingly, when tunneling takes place in the city or residential area, it will severely affect their production and life. (2) Susceptibility of Key Components to Damage Key components such as main bearing and gear case are easy to fall off and damage. Besides, these key components are mainly imported, which means they are expensive and the order cycle is long. Once damaged, it will take a heavy toll on the tunnel construction period. (3) Rapid Wear and Tear of Cutters When it comes to hard rocks or cobblestones, cutters will be severely worn down. In addition to abrasion, tipping may be caused by constant impact, which would make tunneling inefficient. When hard rocks exist, cutters have to be replaced about every 100 m in order to reduce the negative impact of cutter wear on tunneling efficiency. (4) Frequent Unnecessary Halt Unnecessary halt lowers tunneling efficiency as well.

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(5) Incomplete Detection System Existing online monitoring systems are incomplete and weak in fault diagnosis. Shield is costly and likely to malfunction. Therefore, it’s generally accompanied by an online monitoring system to monitor bearing, gear case, oil liquid, oil level and temperature, etc. As shield is a kind of complicated and precise modern equipment which integrates mechanical, electrical and liquid fields, process monitoring based on PLC alone cannot achieve the purpose of guaranteeing safe equipment. Therefore, much as the majority of shield are equipped with a PLC monitoring system, unexpected failure to report fault and report by mistake still happen time and again.

4 Analyses of Online Diagnosis Technical Difficulty (1) Complex Noise Treatment Concerning loud background noise and complicated frequency structure [8], noise elimination must be fully considered to gain signals with a high signal-noise ratio. Except conventional techniques, modern signal filtering and noise elimination technology should be adopted for secondary processing in order to obtain reliable signals. However, modern signal processing technology and noise elimination technology are still being developed and immature. Some pioneering researches need to be carried out. (2) Great Difficulty in Simulation Analysis The actual construction circumstances are complicated and shield is a sophisticated mechanical system on which multiple loads function such as impact load, random load and periodical load. Key components such as cutter, main bearing and gear case are likely to get fatigue and broke. Moreover, there are many components in the entire system with a complex structure. System modeling is difficult and a huge amount of data needs to be calculated [9, 10]. Therefore, theoretical analysis and simulated calculation are quite difficult. (3) Difficulty in Establishment of a Fault Feature Library It’s hard to set up status monitoring standards and a fault feature library. Shield is usually designed for a special project or a kind of project so it’s basically produced by single piece or on a small scale. Hence, dynamic features vary greatly between different shields, which make it difficult to establish universal status monitoring standards and a fault feature library [11]. (4) Major Technical Difficulty in Design of a Vibration Attenuation System Concerning complicated construction circumstances and wide vibration band of shield, it’s extremely difficult to design a vibration attenuation system with desirable effect. The system is greatly related with vibration frequency. Generally speaking, it’s often designed for a certain frequency to produce better effect while not working and even backfiring for other frequencies [12]. Therefore, it’s demanding to come up with a vibration attenuation system that matches features of shield vibration.

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5 Analyses of Research Thoughts Based on shield equipment and its construction features, thoughts on the research of online fault diagnosis technology for shield core components are as follows: (1) An overall finite element model of shield and finite element model of key components are established according to the structure of shield and some key components and on the prerequisite that mechanical calculation is met. Besides, dynamics features of the whole structure are calculated to conclude key and intrinsic characteristics of the whole shield and key components. (2) Pursuant to working principles of shield and structure features, boundary conditions are set to conduct a harmonic response analysis on whole shield and key components. Next, the actual load of shield is simplified into impact load and random load. Afterwards, the transient response of the while shield and key components will be calculated under respective function of impact load and random load, thus working out dynamics features of shield under different loads. (3) An on-site test plan will be designed to utilize different sensors to conduct load and vibration tests on shield in actual work. A precision analysis will be carried out on test signals and transmission features of the system will be obtained according to principles of system identification and input and output calculation. Besides, it will be compared with the outcome of simulated calculation to test accuracy and validity of the simulation model. (4) The whole shield vibration attenuation plan will be studied according to the simulation and on-site test analysis. In order to examine the effect of attenuation, the transient response of the system after design of attenuation under different load conditions is researched via the simulation approach. (5) A data collection system is designed. According to the demand for shield signal collection and with vibration signal collection as the core as well as concerning other process signals (such as voltage, current and temperature), filtering technology is considered to weaken the interference of noise. (6) An online monitoring and fault diagnosis system is developed. On the basis of existing systems, a unique fault diagnosis expert system will be added. In addition to conventional fault diagnosis logic, the expert system should take into account shield features and should be constantly improved through on-site test and application. (7) Standards for shield status monitoring and fault diagnosis technology are established. Eventually a set of status monitoring, alarm and fault diagnosis standards will be formulated based on existing experience and on-site practice to guide the shield monitoring and diagnosis design.

6 Suggestions and Countermeasures (1) Theoretical deduction, simulated calculation and experiment, etc. will be employed to analyze dynamics features of shield and its transient response under random load and impact load, and furthermore gain accurate dynamics features and provide data support for shield vibration attenuation studies.

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(2) Effective shield vibration attenuation approaches and means will be located by exploring mechanical vibration theories and studying dynamics features of shield as well as conducting on-site experiment. By doing so, the adverse influences of shield vibration over external environment will be reduced. (3) Online fault diagnosis technology will be studied mainly based on key components of shield (such as main bearing and reduction gearbox). These key components will be monitored online real time and fault will be diagnosed to track the occurrence and development process of fault, predict the service life of components and prevent occurrence of major accidents and sudden accidents. (4) Accurate acquisition of dynamic signal is key to keeping abreast of equipment running status and predicting its service life, and the foundation for studying dynamics features of equipment. The project adopts filtering and highly sensitive sensing technology, etc. to collect and process data concerning features of shield such as strong background noise, low frequency, non-stability and time varying. (5) Advanced signal collection, signal analysis, network transmission, alarm and early warning technologies are applied to develop an online monitoring and fault diagnosis system which matches features of shield and incorporates multiple functions such as remote access, browsing, online monitoring, alarm, early warning and fault diagnosis, thus fully guaranteeing safety of key components and maximizing continuous working hours of shield. Acknowledgements. This work is supported by National Natural Science Foundation of China (51775112), the Research Program of Higher Education of Guangdong (2016KZDXM054), and the DGUT Research Project (GC300501-08, KCYKYQD2017011).

References 1. Wang, L., Zhang, G., Wang, L.: Development and outlook of shield construction technology. Res. Appl. Build. Mater. 1, 8–9 (2011) 2. Hu, M.: Development and outlook of shield technology in China. Munic. Eng. Technol. S1, 275–278 (2010) 3. Feng, K.: Shield tunneling construction risk knowledge management and its informationization. Shanghai Constr. Sci. Technol. 5, 68–71 (2012) 4. Li, C., Cerrada, M., Cabrera, D., et al.: A comparison of fuzzy clustering algorithms for bearing fault diagnosis. J. Intell. Fuzzy Syst. 34(6), 3565–3580 (2018) 5. Bai, Y., Sun, Z.Z., Zeng, B., et al.: A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction. J. Intell. Manuf. (2018). https://doi.org/10.1007/s10845-017-1388-1 6. Zhao, J., Wang, W., Zheng, S., et al.: A study on online monitoring and remote diagnosis technology for key components of shield. Manuf. Autom. 14, 1–5 (2010) 7. Liu, S.C., Zhang, D., Huang, J., et al.: A study on design of large-scale shield tunnel structure health monitoring system. Chin. J. Undergr. Space Eng. 4, 741–748 (2011) 8. Jiang, Q.: A study on status monitoring and fault diagnosis of railway shield equipment, Master’s thesis (2010)

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9. Long, J.Y., Sun, Z.Z., Chen, H.B., et al.: Variable neighborhood search for integrated determination of charge batching and casting start time in steel plants. J. Intell. Fuzzy Syst. 34(6), 3821–3832 (2018) 10. Zhang, S.H., Li, W.H.: Bearing condition recognition and degradation assessment under varying running conditions using NPE and SOM. Math. Probl. Eng. 1, 1–10 (2014) 11. Zhang, H.H., Zheng, X., Ma, T.Y., et al.: Development and experiment on an iron content monitor for rapid detection of ferromagnetic wear particle in lubricating oil. Adv. Mech. Eng. 9(6), 1–11 (2017) 12. Li, C., Cabrera, D., Oliveira, J.V.D., et al.: Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram. Mech. Syst. Signal Process. 76–77, 157–173 (2016)

Design of Sparse Two-Dimensional FIR Notch Filter Based on BP Neural Network Wei Xu1,2(&), Ruihua Zhang1, and Jiaxiang Zhao3 1

2

3

School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China [email protected] Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin, China College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China

Abstract. In this paper, a method for designing a sparse 2-D (two-dimensional) FIR notch filter based on BP neural network and iterative reweighted orthogonal matching pursuit algorithm is proposed. At each iteration, the iteratively reweighted orthogonal matching pursuit algorithm is employed to generate a sparse filter whose response is the best approximation to the desired filter. The sparse filter obtained by the iteration is then modified through the BP neural network. The iteration stops when the modified filter meets the design specifications. A sparse 2-D FIR notch filter contains a large number of zero coefficients, so it can save the multiplier and adder corresponding to the zero coefficients during the circuit implementation, which will reduce the cost and power of the hardware implementation. Moreover, the BP neural networks are simple in structure and avoid complicated computation of matrix inversion. Simulation results demonstrate that the proposed scheme can obtain sparse solutions and is effective in image processing. Keywords: Sparse coefficients


2-D FIR notch filter
BP neural network

1 Introduction The 2-D notch filters can suppress interference signals with notch frequency and provide efficient transmission of signals other than the notch frequency. Since the 2-D FIR notch filter is widely used in image processing and signal reconstruction, there are many design algorithms for the 2-D FIR notch filters in the previous literatures. In [1], a method for designing the 2-D filters using singular value decomposition is proposed. In [2], an equiripple 2-D FIR notch filter design method based on multiple switching algorithms is proposed. In [3], a design algorithm for a 2-D FIR notch filter based on the Zolotarev polynomial is proposed. The design method for the sparse 2-D filters is proposed in [4]. In [5], a design method of sparse 2-D FIR filters based on weighted iteration l1 norm is proposed.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 540–549, 2019. https://doi.org/10.1007/978-3-030-02804-6_71

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Artificial Neural Networks (ANNs) is an algorithm mathematical model that can perform distributed parallel information processing. This kind of network has the ability of self-learning and self-adaption. Information can be processed by adjusting the interconnected relationships among the internal nodes. BP neural network is the most widely used neural network in artificial neural networks. It was proposed as a multilayer feedforward network trained by the error back propagation algorithm. The characteristics of the BP network are: all neuronal layers are only completely connected with adjacent neuronal layers, there is no connection between neurons in the same layer, and there is no feedback connection between the neurons in each layer. In [6], a method for the designing of digital filters based on single layer feed-forward neural networks is proposed. The proposed filter is obtained under the minimum mean square error criterion. In [7], in order to show the possibility of correcting digital images, the authors used a linear filter with 3  3 mask, which was generated by the neural network. The received digital filter meet its requirements. There are many literatures for designing FIR filters based on the Hopfield neural network [8–10]. This algorithm converts the optimization problem into a Lyapunvo energy function. The minimum of the Lyapunvo energy function is the optimal solution. In this paper, a method for designing a sparse 2-D (two-dimensional) FIR notch filter based on BP neural network and iterative reweighted orthogonal matching pursuit algorithm is proposed. At each iteration, the iteratively reweighted orthogonal matching pursuit algorithm is employed to generate a sparse filter whose response is the best approximation to the desired filter. The sparse filter obtained by the iteration is then modified through the BP neural network. The iteration stops when the modified filter meets the design specifications. A sparse 2-D FIR notch filter contains a large number of zero coefficients, so it can save the multiplier and adder corresponding to the zero coefficients during the circuit implementation, which will reduce the cost and power of the hardware implementation. Moreover, the BP neural networks are simple in structure and avoid complicated computation of matrix inversion. Simulation results demonstrate that the proposed scheme can obtain sparse solutions and is effective in image processing. The rest of the paper is organized as follows: In Sect. 2, the basic theory and related formula of 2-D filters are introduced. In Sect. 3, the algorithm of the design method is proposed. The simulation results of the proposed method are shown in Sect. 4. The conclusion is shown in Sect. 5.

2 The Problem Formulation 2.1

The 2-D Filters

The frequency response of a 2-D FIR filter can be expressed as Hðx1 ; x2 Þ ¼

N1 X

N2 X

n1 ¼N1 n2 ¼N2

hðn1 ; n2 Þejðn1 x1 þ n2 x2 Þ ;

ð1Þ

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where hðn1 ; n2 Þ is the impulse response of the 2-D FIR filter, both N1 and N2 are even number. The expression of the frequency response implies that Hðx1 ; x2 Þ is quadrantally symmetric, that is, the following equation exists hðn1 ; n2 Þ ¼ hðn1 ; n2 Þ ¼ hðn1 ; n2 Þ;

ð2Þ

where n1 ¼ 0; 1;


; N1 , n2 ¼ 0; 1;


; N2 . With the condition of quadrantally symmetric, the frequency response of the filter can be expanded to Hðx1 ; x2 Þ ¼ hð0; 0Þ þ

N1 X

2hðn1 ; 0Þ cosðn1 x1 Þ þ

n1 ¼1

þ

N1 X N2 X

N2 X

2hð0; n2 Þ cosðn2 x2 Þ

n2 ¼1

ð3Þ

4hðn1 ; n2 Þ cosðn1 x1 Þ cosðn2 x2 Þ:

n1 ¼1 n2 ¼1

In order to facilitate calculation, the formula (3) can be rewritten as an inner product of the frequency sampling matrix and the impulse response is as follows Hðx1 ; x2 Þ ¼ cðx1 ; x2 Þh

ð4Þ

where h is the vector form of the impulse response of the 2-D FIR filter, cðx1 ; x2 Þ is a vector by stacking the rows of the matrix c2 ðx2 ÞcT1 ðx1 Þ from up to down, c1 ðx1 Þ and c2 ðx2 Þ are the sampling matrix of 2-D FIR filters in direction x1 and x2 respectively, the expressions are as follows

2.2

 c1 ðx1 Þ ¼ 1

cos x1




cos N21 x1

 c2 ðx2 Þ ¼ 1

cos x2




cos N22 x2

T T

; :

The BP Neural Network

A simple BP neural network with simple structure is used in the optimization of the nonzero coefficients of the filter. Figure 1 shows the structure of the BP neural network. In this kind of neural networks, the number of neurons in the input layer, the hidden layer and the output layer is M, I and J respectively. The m-th neuron in the input layer is denoted as xm , the i-th neuron in the hidden layer is denoted as ki , and the j-th neuron in the output layer is denoted as yj . The connection weight from xm to ki is wmi , and the threshold of the i-th neuron in the hidden layer is bi . The input state of the i-th neuron M P xm wmi þ bi . The activation function of in the hidden layer can be expressed as ki ¼ m¼1

Design of Sparse Two-Dimensional FIR Notch Filter wmi

k1

x1

Σ

f (•)

y0

x2

b1 k 2 Σ

f (•)

y1

f (•)

yJ

543

b2 kI xM

Σ

bI

Fig. 1. The structure of the used neural network

the hidden layer of the neural network is denoted by f ½
, then the output state of the j-th neuron in the hidden layer can be expressed as " yj ¼ f

M X

# xm Wmj þ bj :

ð5Þ

m¼1

In the training process, if the signal of the output layer fails to reach the ideal value, the weight and bias should be adjusted until the output of the output layer meets the requirement.

3 The Proposed Method In this paper, a method for designing a sparse 2-D (two-dimensional) FIR notch filter based on BP neural network and iterative reweighted orthogonal matching pursuit algorithm is proposed. At each iteration, the iteratively reweighted orthogonal matching pursuit algorithm is employed to generate a sparse filter whose response is the best approximation to the desired filter. The sparse filter obtained by the iteration is then modified through the BP neural network. The iteration stops when the modified filter meets the design specifications. The frequency response of an ideal quadrantally symmetric 2-D FIR notch filter is as follows: 8 < Hd ðx1 ; x2 Þ ¼ 0; 0\Hd ðx1 ; x2 Þ\1; : Hd ðx1 ; x2 Þ ¼ 1;

ðx1 ; x2 Þ ¼ Xnotch ðx1 ; x2 Þ 2 X0  Xnotch ðx1 ; x2 Þ 2 X1

ð6Þ

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where Xnotch , X0 and X1 are defined as Xnotch ¼ ðxnotch ; xnotch Þ; 1 2

 X0 ¼

    BW1    x2  xnotch   BW2 ; ; ðx1 ; x2 Þx1  xnotch 1 2 2 2 X1 ¼ ½p; p  ½p; p  X0 ;

ðxnotch ; xnotch Þ is the given notch frequency, BW1 and BW2 represents the width of 1 2 the stopband of the 2-D notch filter in frequency x1 and x2 respectively. The purpose of the proposed design method is to design a sparse 2-D FIR notch filter whose frequency response Hðx1 ; x2 Þ approximate to the ideal frequency response Hd ðx1 ; x2 Þ as well as possible. Then the design problem of a sparse 2-D FIR notch filter is converted to the following optimization problem: min

khk0

s:t:

jBh  f j  d
1L1 ; cðxnotch ; xnotch Þh ¼ 0 1 2

h

ð7Þ

where B and f are defined as 3 cðx11 ; x21 Þ 6 cðx12 ; x21 Þ 7 7 6 7 6 .. 7 6 . 7 6 6 B ¼ 6 cðx1L ; x21 Þ 7 7; 6 cðx11 ; x22 Þ 7 7 6 7 6 .. 5 4 . 2

cðx1L ; x2L Þ

3 Hðx11 ; x21 Þ 6 Hðx12 ; x21 Þ 7 7 6 7 6 .. 7 6 . 7 6 6 f ¼ 6 Hðx1L ; x21 Þ 7 7; 6 Hðx21 ; x22 Þ 7 7 6 7 6 .. 5 4 . 2

ðx1l ; x2l Þ 2 X1 ; l ¼ 1; 2;


; L;

Hðx1L ; x2L Þ

L represents the number of sampling points in different frequency directions, d is the ; xnotch Þ is the vector form of the sampling matrix at the given passband ripple, cðxnotch 1 2 notch frequency, and h is the vector form of the impulse response of the sparse 2-D FIR notch filter to be designed. In order to solve the above optimization problem, in the k-th ðk  1Þ iteration process, the specific design steps are as follows: Step 1: Using the iterative reweighted orthogonal matching pursuit algorithm to calculate the following optimization problems: min

kBh  f k22  e

s:t:

khk0 ¼ k

h; e

:

ð8Þ

The set of positions of nonzero coefficients obtained at the k-th iteration is denoted as SðkÞ .

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Step 2: The BP neural network is then applied to optimize these nonzero coefficients in this step. First check whether the element f1g is included in the set SðkÞ . If so, KðkÞ ¼ SðkÞ  f1g. Otherwise, KðkÞ ¼ SðkÞ . According to the set KðkÞ and the ideal frequency response f, the training samples of BP neural network PðkÞ and the error between ideal output value and training value of neural network g are determined. The expressions of training samples PðkÞ and ideal output values of neural network f d are expressed as follows ðkÞ

P



 BKðkÞ ¼ ; cðxnotch ; xnotch ÞKðkÞ 1 2

fd ¼

" # f 0

;

where BKðkÞ denotes the matrix obtained by extracting from B the columns corresponding to KðkÞ , cðxnotch ; xnotch ÞKðkÞ denotes the vector obtained by extracting from 1 2 notch notch cðx1 ; x2 Þ the elements corresponding to KðkÞ . Since the number of elements in the set KðkÞ is m, the number of neurons in the input layer is set to be m, the number of neurons in the hidden layer is set to be 1, and the number of neurons in the output layer is set to be 1, the learning rate of the neural network is set to be g, the maximum number of iterations is set to be K. According to Eqs. (3) and (5), when the activation function of the output layer of the neural network is a linear activation function, i.e. f ðxÞ ¼ x, it exists hð1Þ ¼ b : hðKðkÞ ðiÞÞ ¼ wi1 ; 2  i  m

ð9Þ

The Bayesian regularization is chosen as the training algorithm for neural network, and then the MATLAB toolbox is used to construct the neural network, as shown in Fig. 2, to solve the following optimization problem with constraint min l;h

s:t:

l   :  ðkÞ  P hðKðkÞ Þ þ hð1Þ  f d   l  1m1

Fig. 2. The BP Neural Network

ð10Þ

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By solving the above optimization problem, the obtained vector h is vector form of the impulse response of the optimized sparse 2-D FIR notch filter. Step 3: According to the impulse response of the sparse 2-D FIR notch filter, the frequency response of the filter Hðx1 ; x2 Þ can be obtained by Eq. (4). Then the passband ripple can be calculated as:     ^d ¼ 20
lg minðHðx1 ; x2 ÞÞ ; ðx1 ; x2 Þ 2 X1 : maxðHðx ; x ÞÞ 1 2

ð11Þ

Check whether the passband ripple satisfies the given parameter conditions, and if so, stop. Otherwise go to step 4. Step 4: In this step, we will update the neural network weights. First, calculate the residual of the neural network as follows: rðkÞ ¼ f d  Bh:

ð12Þ

Then according to reference [6] the new weight can be expressed as 0

ðk þ 1Þ

wi1 ðk þ 1Þ

12 114 B A C ¼ @1 þ @ A ; 1  i  m; j hðkÞ j maxf 100 g 0

   ðkÞ  ri 

ð13Þ

ðkÞ

where wi and ri represents the i-th element of the new weight matrix and the residual matrix respectively. Replace wðkÞ with a new weight matrix wðk þ 1Þ and then repeat the steps above.

4 Simulation In this section, the simulation results demonstrate that the proposed scheme can obtain sparse solutions and is effective in image processing. The design parameters of the sparse 2-D FIR notch filter are as follows: the notch frequency is ð0:2p; 0:8pÞ, the stopband bandwidth BW1 ¼ BW2 ¼ 0:12p, the passband ripple d ¼ 1 dB, the number of the sample points L ¼ 51, the size of the sparse 2-D FIR notch filter is 45  45, so the number of coefficients is 2025. The iterative reweighted orthogonal matching pursuit algorithm is applied to yield the sparse 2-D FIR notch filter with notch frequency at ð0:2p; 0:8pÞ, the BP neural network is then applied to optimize the nonzero coefficients of the filter. The number of zero coefficients of the obtained filter is 868, the passband ripple ^ d ¼ 0:9828 dB, the width of the stopband BW1 ¼ BW2 ¼ 0:12p. The frequency of the obtained sparse 2-D FIR notch filter is shown in Fig. 3. Through the frequency response, the attenuation at the notch frequency point is 65:7573 dB. The contour lines of the amplitude frequency response is shown in Fig. 4.

Design of Sparse Two-Dimensional FIR Notch Filter

Fig. 3. The frequency response of the obtained sparse 2-D FIR notch filter

Fig. 4. The contour map of the amplitude frequency response

Fig. 5. The original image

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Fig. 6. (a) The image with sinusoidal interference. (b) The output image

In order to demonstrate the effectiveness of the sparse 2-D FIR notch filter obtained by the proposed algorithm, it is applied to process the image with the sinusoidal interference. The original image is shown in Fig. 5. Then add the sinusoidal interference with notch frequency to the original image. The image with sinusoidal interference is shown in Fig. 6(a). Applying the designed filter to process the image with noise interference, the output image is shown in Fig. 6(b).

5 Conclusion In this paper, a novel design method of sparse 2-D FIR notch filter based on BP neural network and iterative reweighted orthogonal matching pursuit algorithm is proposed. The sparse 2-D FIR notch filter contains a large number of zero coefficients, so it can save the multiplier and adder corresponding to the zero coefficients during the circuit implementation, which will reduce the cost and power of the hardware implementation. Moreover, the BP neural networks are simple in structure and avoid complicated computation of matrix inversion. Simulation results demonstrate that the proposed scheme can obtain sparse solutions and is effective in image processing. Acknowledgements. The research was completed under the National Natural Science Foundation of China (61501324). The author is grateful to the reviewers, teachers and friends who have provided valuable suggestions for the revision of this article. The first author would like to thank the National Natural Science Foundation of China (61501324) for its support.

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References 1. Pei, S.-C., Lu, W.-S., Tseng, C.-C.: 2-D FIR notch filter design using singular value decomposition. IEEE Trans. Circ. Syst. I 45, 290–294 (1998) 2. Pei, S.-C., Wang, P.-H.: Design of equiripple FIR filters with constraint using a multiple exchange algorithm. IEEE Trans. Circ. Syst. I 49(1), 113–116 (2002) 3. Zahradnik, P., Vlcek, M., Simak, B., Kopp, M.: 2-D notch FIR filters. In: The 35th International Conference on Telecommunications and Signal Processing (TSP), pp. 621–624 (2012) 4. Lu, W.-S., Hinamoto, T.: 2-D digital filters with sparse coefficients. Multidimens. Syst. Signal Process. 22, 173–189 (2011) 5. Rusu, C., Dumitrescu, B.: Iterative reweighted l1 design of sparse FIR filters. Signal Process. 92, 905–911 (2011) 6. Pachori, K., Mishra, A.: Design of FIR digital filters using ADALINE neural network. In: 2012 Fourth International Conference on Computational Intelligence and Communication Networks, pp. 800–803 (2012) 7. Pęksiński, J., Mikołajczak, G.: Generation of FIR filters by using neural networks to improve digital images. In: 2011 34th International Conference on Telecommunications and Signal Processing (TSP), pp. 527–529 (2011) 8. Bhattacharya, D., Antoniou, A.: Real-time design of FIR filters by feedback neural networks. IEEE Signal Process. Lett. 3(5), 158–161 (1996) 9. Jou, Y.-D.: Design of real FIR filters with arbitrary magnitude and phase specifications using a neural-based approach. IEEE Trans. Circ. Syst. II 53(10), 1068–1072 (2006) 10. Jou, Y.-D., Chen, F.-K.: Least-squares design of FIR filters based on a compacted feedback neural network. IEEE Trans. Circ. Syst. II 54(5), 427–431 (2007)

Design and Implementation of Multi-level CIC Filter Based on FPGA Pu Wang, Yuming Zhang, and Jun Yang(&) School of Information Science and Engineering, Yunnan University, Kunming 650091, China [email protected] Abstract. The integral comb (CIC) filter is an efficient filter which is widely used in the digital down-conversion and up-conversion of wireless communication technology. However, the level width of each register of the traditional structure is fixed, and the low frequency signal can cause high frequency operation bandwidth and waste the shortage of computer hardware resources. This paper, by using Hogenauer “cut off” theory on each level of output bits wide truncated, improve the performance of CIC filter, through a cascade of multiple single stage CIC filter to optimize its structure, build the multi-stage CIC filter; At the same time using FPGA technology is strong, good extensibility and occupies less hardware resources, the characteristics of low cost, high reliability, using Verilog HDL language design the various modules, the final model of multistage CIC filter based on FPGA design, not only save the hardware resources, also make the CIC filter each register bits wide variable. The model was simulated by Modelsim and downloaded to the EP2C35F672C6 of Altera DE2 as the target chip verification, which met the design requirements. Keywords: CIC filter
Digital frequency conversion Digital down-conversion
Hogenauer “cut off” theory


FPGA

1 Introduction At present, digital down-conversion and up-conversion technology are the core technologies of wireless communication [1], the integral comb (CIC) filter with extraction, sampling rate and low pass filtering, is often used to filter out noise and separation of various signals, thus in the field of modern communications has good theoretical and practical application value. FPGA has a well-organized internal logic array and rich connection resources, combined with its own technology with reconfigurable sex strong, less hardware resources possession, low cost, high reliability and scalability good characteristics, particularly suitable for digital signal processing tasks, can design and efficient CIC filter [2]. So the paper uses the FPGA technology to build a multi-stage CIC filter model, using Verilog HDL language to design the various modules, each register bits wide variable make CIC filter, and by using Hogenauer “cut off” theory improved CIC filter, not only save the computer hardware resources, and effectively improve the performance of CIC filter [3]. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 550–557, 2019. https://doi.org/10.1007/978-3-030-02804-6_72

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2 The Principle is Introduced 2.1

The Principle of Multi-stage CIC Filter

The transfer function FðzÞ [4] of the single-stage CIC filter is: FðzÞ ¼

XL1

hð xÞ
zx ¼ x¼0

1  zL 1  Z 1

ð1Þ

L is the order number of CIC filter, and L = DM, the formula becomes FðzÞ ¼

1  zDM 1  Z 1

ð2Þ

Where D is the frequency conversion coefficient and M is the difference delay. It is not hard to see by (2) that the CIC filter achieves its own function through cumulative data, which not only greatly improves the operational efficiency, but also reduces the waste of hardware resources. For a multi-stage CIC filter, the transfer function of a single stage CIC filter is cascaded to the N stage to obtain a transfer function GðzÞ of a multi-stage CIC filter:  GðzÞ ¼

1  zDM 1  Z 1

N ð3Þ

Figure 1 shows the structure of the initial multi-level CIC filter.

Fig. 1. Initial structure of the multi-level CIC filter

CIC filter is mainly divided into two parts: integral and comb. The integrator and comb are realized by the multiplexing adder, selector and shift register. The selector implements a filter for a particular frequency signal, and the shift register is used to store the signal data, and the adder completes the accumulation and output of the signal.

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Hogenauer’s “cut off” Theory

In Hogenauer [5], “cut off” theory assumes that the r2T;2N þ 1 is cut off by introducing quantitative noise in the output, the Hogenauer proposed set it equal to the sum of all previous part introduced the noise of the r2k , for containing N integrator and N comb part of CIC filter, there are: X2N k¼1

r2T;k ¼

X2x K¼1

r2T;k ¼ p2k ¼

X x

r2k p2k  r2T;2N þ 1

1 2 r 2N T;2N þ 1

ð hk ½ x  Þ 2

k ¼ 1; 2; . . .; 2N

ð4Þ ð5Þ ð6Þ

Where p2k is the power gain from the k-level to the output power output, the part that should be cut off when calculating the next Bk is:   6 2 2 Bk ¼ b0:5 log2 pk   rT;2N þ 1 S r2T;k jj¼2N þ 1 ¼

1 2Bk 1 2 ¼ 22ðBint Bout þ Bgrowth Þ 12 12

ð7Þ ð8Þ

In the formula, Bint is the input bit width, and Bout is the output bit width. The power gain p2k , k ¼ 1; 2; . . .; 2N of the comb part can be calculated using the following binomial coefficient: P K ðzÞ ¼

X2N þ 1k x¼0

 ð1Þx

 2N þ 1  k kRM z x

ð9Þ

If the first step MC signal is entered, the aliasing of the low-frequency response baseband is generated. The amplitude response of the frequency domain is calculated by taking the GðzÞ in accordance with arc z ¼ ej2pfT in the frequency domain:  jGð f Þj ¼

 sinð2pfTDM=2Þ N sinð2pfT=2Þ

ð10Þ

For the type of aliasing components, using Hogenauer “cut off” theory on each level of output bits wide truncated, effectively improve the performance of CIC filter, and can be the initial structure of the multi-stage CIC filter is optimized, with the structure change of Hogenauer CIC filter always integral part of the high data rate, comb some low data rate, and take part in them. This structure is the easiest to implement and occupies the least resources. Figure 2 is the Hogenauer structure diagram of the multistage CIC filter.

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Fig. 2. Hogenauer structure diagram of multi-level CIC filter

3 Design and Implementation of Multi-level CIC Filter Based on FPGA The CIC filter consists of two modules, integrator and comb, which are composed of the multiplexing selector, adder and shift register. The selector realizes the selection of the specific frequency signal, the shift register is used to store the signal data, and the adder completes the accumulation and output of the signal, and the structure is simple and easy to implement the hardware. Single-stage CIC filter by extractor series an integrator and a comb, and multistage CIC filter just cascade N integrator and N comb device can realize, this design cascade three 3 integrator and comb, three levels of CIC filter is constructed. Using Altera DSP Builder for system design, the integral optimization design with Verilog HDL language module and comb module, and through the cascade integrator and comb the multistage CIC filter model is build, by the FPGA implementation of three-level CIC filter the top chart shown in Fig. 3. cic_gear:cic_gear_inst cic_integration:cic_integration_inst I_clk I_data_eof I_data_sof I_data_v I_data[15..0]

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Fig. 3. The top-level structure diagram of the three-level CIC filter

CLK is the clock signal, high potential is effective; I_data_in is the input data, and the bit width is set to 18; RST is the reset signal; I_data_out is the output data. The following is the FPGA implementation of specific modules: (1) integrator module: complete the integration of input signals and output, that is, the integrator is a sum of many input values in the front. In this process, some of the larger jitter of different input values in the integrator is passivated, which is equivalent to the suppression of the high frequency part, which realizes the low-pass filtering. In the integrator, I_clk is the main clock, I_data is the signal input, and o_data is the output of the integrator, and the port is defined and programmed by FPGA. The integrator module is shown in Fig. 4.

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Fig. 4. Integrator module diagram

(2) comb module: receive the output signal of the integrator and complete the comb processing of the signal. Because the integrator produces low frequency response baseband aliasing when the low-pass filtering is completed, it is necessary to filter the required signal components. The I_clk in the comb is the main clock, and I_data is the signal input, and o_data is the output of the comb device. Similarly, the port is defined and programmed by FPGA. The comb module diagram is shown in Fig. 5.

Fig. 5. Comb block diagram

This level 3 CIC filter can perform digital conversion and digital down-conversion processing. In the upper frequency conversion process, the data is passed through the 3class coupler, 8 times interpolator, and 3 class integrator, and output at 8 times data rate. In the lower frequency conversion process, the data is processed in sequence through three class integrators, eight times extractors, and three class couplers, and output at 1/8 times the data rate. Under frequency conversion process, all levels of the register bits wide is: 3 log2 8 þ Win , Win as the input data bit width; Up-conversion process, various register bits wide is: 3 log2 8  log2 8 þ Win , bit width can be optimized.

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At the same time, according to Hogenauer’s “pruning” theory, the last number of bits can be cut off at each level of output, so as to reduce the use of the filter for hardware resources. By formula (7) and the calculation of formula (8), cut off the digits can be set, the integral part of the design can cut off six per level, comb part cut off 1 per level, the input data bits wide Win to 18. In the process of frequency conversion, the integral part takes 33, 39 and 45 bits at each level, 25, 26 and 27 respectively at each level of the comb section, and the output bit width of the 3-level CIC filter is 24. In the lower frequency conversion process, the integral part takes 36, 42 and 48 bits at each level, and 28, 29 and 30 respectively at each level of the comb section, and the output bit width of the 3-level CIC filter is 27.

4 System Testing and Performance Comparison 4.1

The Simulation Test

Using the Modelsim software, two simulation tests were conducted for the three-level CIC filter, which was 8 times lower frequency conversion simulation and 8 times upconversion simulation, and the simulation diagram was shown in Figs. 6 and 7.

Fig. 6. Lower frequency conversion simulation diagram

In the lower frequency conversion process, the data can be seen by the port I_data_sof and port O_data_sof, and the data is output at 1/8 times the data rate. C_DIN_WIDTH by port and port C_DOUT_WIDTH can be concluded that the input bits wide to 18, the output bits wide at 27, using the formula 3 log2 8 þ Win , calculate the output bits wide at 27, the simulation results are consistent with actual results. In the up-conversion process, the data is output at 8 times data rate through port I_data_sof and port O_data_sof. C_DIN_WIDTH by port and port C_DOUT_WIDTH can be concluded that the input bits wide to 18, the output bits wide is 24, using the formula 3 log2 8  log2 8 þ Win , calculate the output bits wide is 24, the simulation results are consistent with actual results.

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Fig. 7. Up-conversion simulation schematic diagram

4.2

Performance Comparison

The correctness of the design of the multi-level CIC filter is shown by the simulation waveform diagram, and the design requirements are proved. At the same time with the traditional cascade CIC filter [6], this paper design the level 3 CIC filter using Hogenauer “cut off” theory after only 240 logical unit, and the structure of the traditional level 3 CIC filter is 372 logical unit, we can see the improved CIC filter hardware resources to save up to 30% (Table 1). Table 1. Multi-level CIC filter performance comparison table Parameter

Input bit width

Required logical unit

Hardware resource saving

18

Register width Up Down convesion conversion 24 27

This design The same design

240

30%

13

13

372

5 Summary Based on the FPGA technology, the design and implementation of the multi-level CIC filter model was completed by using Verilog HDL language. By using Hogenaur “cut off” theory, the output bit width of each level was truncated, and the hardware resources were saved. The multi-level CIC filter model implemented by FPGA technology not only makes the position width of CIC filter variable in each register, but also has the advantages of good real-time performance and portability.

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Acknowledgements. The author, Pu Wang, thanks the Application of basic research project in yunnan province, “key technology research of automatic real-time detection of solar radio explosion based on video treatment” (No. 2015FB115).

References 1. Li, K.-Y.: The improvement of CIC filter and its implementation on FPGA. J. Qinghai Normal Univ. (2017) 2. Chen, L.J., Zhao, J.: Analysis and design of the integrated comb filter (CIC). Inf. Commun. 2015(1), 80–82 (2015) 3. Yang, F., Fu, W., Qin, T., et al.: Design of high frequency digital extraction filter. Electron. Technol. Appl. 43(12), 25–28 (2017) 4. Qin, T., Gao, Q., Wang, Z.: Research on high-performance digital extraction filter. J. Nankai Univ. (Nat. Sci. Ed.) 2017(3), 37–39 (2017) 5. Zhang, L., Shao, Z., Deng, J.: Design of frequency conversion based on FPGA. Electron. World 2017(13), 161 (2017) 6. Shi, W., Huang, P.: FPGA implementation of CIC filter. Inf. Secur. Commun. Confid. 2005 (6), 28–30 (2005)

A Multi-dimensional Electronic Channel Unified Identity Authentication Strategy Based on Role Control Baoxian Guo1,3(&), Ying Xu2, Renjie Li1, and Xingxiong Zhu3 1

3

State Grid Electronic Commerce Co., Ltd., Beijing, China [email protected] 2 State Grid Zhejiang Electric Power Company, Hangzhou, China State Grid Huitongjincai (Beijing) Information Technology Co., Ltd., Beijing, China

Abstract. Because the previous marketing system lacked a platform for unified management and monitoring of various electronic channels, management was decentralized and the means of support were lacking. Therefore, in this paper, we conducted in-depth research on the user’s unified identity authentication and service supervision in order to optimize the user experience of various electronic channels. We present a role-control based mechanism for multi-dimensional electronic channel unified identity authentication model, and designed the strategy by biometric authentication technologies. The practical application proves that we can effectively manage multi-electronic channel access issues and achieve unified role management for multi-service users. Keywords: Multi-dimensional electronic channel Unified identity authentication
Role control
Smart grid

1 Introduction In order to adapt to the new form of “Internet +” marketing services, the State Grid Corporation of China relied on the smart grid and the Internet to further deepen the application of marketing business systems, realize high-end applications for marketing automation based on big data and cloud computing, and promote the intelligent interaction level of power supply services; Begins with a wealth of e-service channels, including 95598 website, 95598 pay, Handheld Power, State Grid Mall, E-recharge, WeChat; marketing and intelligent interactive service access management to meet interactive websites, Weibo, WeChat, video, mobile terminals, Unified access and services such as SMS and other service channels [1, 2]. As described above, more and more electronic channels frequently use information and communication services, which makes information and communication service models increasingly complex and diverse. There is a great deal of security in protecting the smooth evolution of existing systems, quickly adjusting internal operational

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 558–565, 2019. https://doi.org/10.1007/978-3-030-02804-6_73

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processes, and properly coordinating the market mechanisms of old and new services problem. Service diversity often increases the complexity of user operations and may add a lot of extra work in terminal configuration operations, personal data management and sharing, and customer service. Meanwhile, problems such as user registration, query service sharing, and poor experience have arisen. There is a lack of unified management of multiple electronic service channels and various electronic channels. There is a lack of service supervision. Therefore, it is imperative to start from the source and carry out specific research on issues such as unified identity authentication and electronic channel service supervision among multiple electronic channels. Therefore, it is inevitable to design a unified user authorization management system for multi-channel business services. The user authorization management system is a unified user management, rights management, and audit management platform for each information system. It provides user management, identity authentication, resource management, rights management, permission judgment, and log auditing services for information systems. In this paper, we establish a multi-dimensional electronic channel security access control module to achieve centralized and centralized management of multidimensional electronic channel users and rights resources, so as to achieve multidimensional electronic channel system user single-point authentication; at the same time, to realize the content and network behavior of various electronic channel network communications, also real-time monitoring to improve information system security management capabilities.

2 System Model Because the unified service platform integrates multiple network channels, it is required to ensure excellent compatibility, scalability, and security to support the stable operation of the platform. When planning and constructing, the business functions must meet the differentiated requirements of different channels, and can be based on actual services. Demand expansion; Unified control of account information at the source to facilitate unified channel accounts and unified client authority; Strong guarantee for data transmission and account information security. Meanwhile, consider the customer level of different channels to improve system load capacity to fully meet service requirements; avoiding too long response time affects customer experience and service acquisition. Through the establishment of a unified service platform, promote the optimization of the unified construction of network channels, open the information path of service channels and marketing systems [3], and achieve vertical and horizontal integration, horizontal multi-platform and multi-system interconnection and intercommunication, as shown in Fig. 1, to build a unified service based on multiple channels. The platform realizes the unified reception, dispatch and command of the full amount of customer service work, and realizes intelligent and interactive power services.

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Fig. 1. System model

3 Distributed Security Framework and Strategy Based on a distributed security strategy, a multi-point word-driven construction is based on the “Internet +” marketing service electronic channel unified identity authentication and service supervision system, to resolve the original e-service channel account decentralized management, users need to be certified in the various electronic service channels, respectively [4]. With the use of services, the data of multiple channels is deeply integrated to complete the opening of multiple channels of accounts, data sharing between channels is achieved, and on the basis of fully guaranteeing the security of internal and external network communications and data transmission, users are provided with “one registration, “All-channel application” is a convenient experience that enhances the ability of multiple electronic service channels to collaborate on services. 3.1

Distributed Security Framework

The distributed security policy framework includes a centralized policy management center, a regional policy management center, and strategic deployment tools, and strategic audit management, integration agents and other components. Strategy identification is the first step in security management. It is the process of judging, classifying, and certifying the nature of security for faced and potential visits [5, 6]. The strategy response is to carry out policy scheduling, authentication, encryption, and execution according to security rules so as to implement control over access events.

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The strategy deployment tool is a means to define policy rules to the policy management center, including policy deployment, addition, deletion, and change. The policy deployment tool is a tool for the unified maintenance, monitoring, and management of the policy center and it is the support means for ensuring the effective operation of the strategy center as shown in Fig. 2 [7].

Fig. 2. Distributed system security policy framework

3.2

Distributed Security Strategy

In the specific security policy design process, we follow the function minimization, security protection, and the principle of least privilege to design the code structure, reduce the potential coding structure and design that can be used; use the latest version of the code compiler to compile the code, use the compiler The built-in defensive features perform security checks; avoid the use of contraband functions; input to all users in the system; validity of data exchanged between systems, verification of legality, filtering of input with security risks, and maintenance of business processes and data between system modules The independence, to prevent different business data mixed with each other, different businesses interfere with each other; focus on preventing login, registration and other key processes of malicious attacks, strengthen the activities of points, sweepstakes, coupons and other vulnerable business applications design review and code detection.

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4 Role-Based Access Control Model State Grid Corporation of China has established a wealth of electronic service channels, including 95598 website, E-Power, Handheld Power, State Grid Mall, E-charging, WeChat, etc. From a strategic point of view, we regard different electronic services as different roles and use unified access control to perform unified management, in this paper, we build the role-based access control model (RBAC) to adjust different role for different businesses for the same user. 4.1

Control Model

The role-based access control model is a set of operation permissions that binds the operation authority and the user to the role. That is, the access authority is assigned to a certain role, and the user obtains the corresponding access authority according to the role. From the perspective of the controlling entity, the access rights are associated with the roles, the rights are mapped to the roles, and the users are mapped to the rightsrelated roles. Role-based access control model as shown in Fig. 3.

Fig. 3. Role-based access control model

RBAC is currently recognized as an effective method to solve the unified resource access control of large enterprises. However, in some cases when a particular user needs to be granted special privileges, RBAC is somewhat inflexible. If you grant a certain role to a user, all users who have the role will have the right or the user who is granted the role. Will have all the permissions of this role, obviously this does not meet the requirements, if it is unreasonable to create a separate role in order to achieve this demand, so the improved access control model combines the theory of RBAC,

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combined with the need for the company to individually grant users With the need for permissions, an access control model that can authorize roles and authorize users individually is designed. 4.2

Role Risk Assessment and Unified Management

When the role is established, the risk assessment of the role is carried out in combination with the business corresponding to the role. If the key risk factor is controlled, the risk-inducing event can be controlled. Through risk assessment, confirm the security and reliability of the role. First establish the relationship between the key elements of the role security strategy, as shown in Fig. 4. From the collection of different key elements, the relationship between the different topics and objects of the role is extracted, and the risk probabilistic model is established according to the related elements of different state spaces.

Fig. 4. Security policy key element relationships

The unified service platform provides unified registration services for multiple network channels, performs unified account management, and stores registered account information in a unified manner. After registering and binding power consumers in one channel, user binding information in other channels can be shared. Electricity business can be mastered. Meanwhile, reorganize, sort out and optimize the existing online channel acceptance business processes with customers as the center, formulate a unified network channel client to handle business processes, and provide customers with unified and clear and easy-to-understand business management guidelines through different channels, reduce the use threshold, and improve Customer usage rate. By including

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technologies such as biometrics, we can achieve secure access authentication for customers, establish a unified role, and achieve unified management of multi-channel access, as shown in Fig. 5.

Fig. 5. Multi-channel unified authentication

5 Summary Because the previous marketing system lacked a platform for unified management and monitoring of various electronic channels, management was decentralized and the means of support were lacking. Therefore, we conducted in-depth research on the user’s unified identity authentication and service supervision in order to optimize the user experience of various Internet electronic channels. Strengthen the national grid company’s Internet service competitiveness; at the same time, it separates and integrates login user information from each business application system into a unified login user management system, achieves a single user data source, implements specialized management, and enhances corresponding security; In the end, the unified account, unified configuration, unified management and control, and unified monitoring of the various electronic channels of the State Grid Corporation will be realized, and the centralized management and control of the multiple electronic channels of the headquarters and the individualized service capabilities of the branches will be enhanced. Acknowledgments. This work is supported by the science and technology project of State Grid Corporation of China under the Grants No. 52110417001D.

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References 1. Shang, C., Tian, Y., Yu, J., et al.: Application of intelligent power service system in power marketing. In: International Conference on Education, Management, Computer and Society (2017) 2. Luo, Y.M., Luo, Y.C., Yan, L.: Application of We Chat public platform in electric power marketing service system. Electr. Power Inf. Commun. Technol. 6, 79–84 (2016) 3. Ning, B.F.: Design and application of unified power grid resource cloud service platform. Electr. Power Inf. Commun. Technol. (2016) 4. Han, S.C., Bai, X., Dong, S.A.: The construction of “Internet + Power Marketing” integrated electronic channel operation system. Telecom Power Technol. (2018) 5. Karahroudy, A.A.: Security analysis and framework of cloud computing with parity-based partially distributed file system. Dissertations and Theses—Gradworks (2011) 6. Pisharody, S., Natarajan, J., Chowdhary, A., et al.: Brew: a security policy analysis framework for distributed SDN-based cloud environments. IEEE Trans Dependable Secure Comput. (2017) 7. Yang, M.: Research on key technologies for security policy in the distributed system environment. Jilin University (2011, in Chinese)

Vegetable Technology Information Visual Service System Based on Knowledge Map Qingfeng Wei1,2, Changshou Luo1,2(&), Jun Yu1,2, Xuezhong Chen3, and Sufen Shun1 1

Institute of Agricultural Information and Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China [email protected] 2 Beijing Research Center of Engineering Technology on Rural Distance Information Service, Beijing, China 3 Changjiang Wuhan Waterway Engineering Company, Wuhan, China

Abstract. To the demand that the vegetable technology information visualization and comprehensive push of related information, through the vegetable instance and attribution recognition, and vegetable technology knowledge map constitution, the vegetable technology information visual service system is developed. The related varieties, cultivation technology, prevention and control of diseases, frequent question and answer the specific vegetable species have retrieved to user systematically and orderly. It helps to vegetable technology information discovery effectively. Keywords: Knowledge map Information service system


Vegetable technology

1 Introduction Currently, simply retrieve by keyword which has the massively results feedback has upgraded to systematic, breadth and depth knowledge services. In agriculture, under the call of the fifteen Central Document No. 1 since 2004, agricultural information resource has been greatly enriched, but information retrieval still stays in the stage of simple matching retrieval. The related and implicit information is difficult to obtain. The information service technical level has to promote. The knowledge map has the advantage that which can show the keynote, display the knowledge system and access to the information directly. It is an effective way for the agricultural information discovery. To the need that the agricultural users find the technology information more effectively, the vegetable technology information visual service system based on the knowledge map is developed. It helps to dig the value of agricultural science and technology information resources fully, so as to promote the effective discovery of agricultural science and technology information.

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2 Knowledge Map The concept of knowledge map was first put forward by intelligence scientist Brooks (B.C.Brooks) in the classic book “Information Science Foundation”. The definition of knowledge map is: on the basis of analysis and screening the logical content of literature, find out the relationship and connection points of the knowledge, express them intuitively and vividly in order to display the organic structure of knowledge like the ordinary maps. Professor Jiasheng He of Taiwan Zhongyuan University has proposed that knowledge map is a knowledge base logo designed by concept and basic model. Dr Dingpeng Liang of Taiwan national Zhongshan University believes that knowledge map is a concept related to knowledge search. It tells where knowledge is located, establish association between knowledge and knowledge, and organize knowledge in a hierarchical manner, so that people can find the required professional knowledge according to the hierarchy of relational hints in searching. It can be seen that experts and scholars have different definitions of knowledge map. There is not any unified definition of knowledge map at present. But their views reveal the essence of knowledge map: It is a form of knowledge guide, not a set of concrete knowledge. For users, most information is in the source origin of knowledge map, it isn’t in the knowledge map itself. Knowledge map not only reveals the location of knowledge, but also reveals the relationship between knowledge, and promotes knowledge discovery and sharing. Therefore, this study holds that knowledge map is a resource organization method which exist in the form of knowledge oriented, in order to explain the relationship between knowledge and the location of resources, and promote the discovery and sharing of knowledge.

3 Vegetable Technology Knowledge Map Construction Analysis of database, makes certain the vegetable plant objects, and constructs the schema of four element. And then, knowledge map is constructed by natural language procession automatically. 3.1

Determination of Vegetable Common Query Objects

According to the FAQ, and audit of agricultural experts, obtain the planting objects. Of vegetable. Finally, the results are potato, squash, cucumber, eggplant, pepper, watermelon, celery, cauliflower, lettuce, cabbage, rape, cowpea, winter gourd, bitter melon, water zizania, onion, garlic, spinach, vegetable beans, carrots, cabbage, mushroom, golden needle mushroom, letinous edodes, straw mushroom, hericium mushroom, Ling Bai mushroom, pleurotus ostreatus, ganoderma lucidum and so on.

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Determination of Vegetable Common Query Objects

According the common vegetable query objects, and the related production problem, the four element framework was constructed to used to describe the technology query rule: AQ ¼ \E; A; N; R[ Among them, E is the query object, such as watermelon, tomato, etc. A is the attribution class, such as variety problem of watermelon, technical problems of watermelon, etc. N is the instance, for example, the instance of watermelon variety is Jingxin NO 1. R is the relationship of resource address and the corresponding instance. 3.3

The Instance Discovery of Vegetable Object Attribution Class

The research material is from the Beijing Agricultural Digital Resource Center (BADRC in short). BADRC is an agricultural digital resource warehouse. Based on the planting database of BADRC, use the index keyword to build varieties, technology, pest and disease vocabulary. Take the vocabulary as the user defined vocabulary list for word segmentation tools, so that the segmentation system can be used to divide the text of agriculture.

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word segmentation,filtering e mpty words and removing sto p word Obtain features word

matching

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YES Store the instance table of the attribute class of the consulting object, and record the the corresponding ID of QA in FAQ database. Attention degree of words Reverse ordering Instance words list of the attribute class of the consulting object Fig. 1. The process of the instance discovery of vegetable object attribution class

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Get the indexing words after the sentence segmentation, filtering and removing stop words. And compared it with the variety words list, technical word list and diseases and insect pests word list. If there is a result, add the word and the corresponding address into the instance table of the attribute class. If there are no results, take the next one for comparison. In the instance table of query object attribution class, the words of object are sorted down according to the clicks amount of FQA which contain the consulting object, to give priority to instances word of higher concern. The process is as the Fig. 1 above. 3.4

Knowledge Map of Vegetable Technology Visualization

According to the query framework, object, attribution class, instance, and establish the relationship between instances and instances, the four level knowledge map is constructed as follows (Fig. 2):

Fig. 2. The knowledge map of the vegetable technology

4 Vegetable Technology Information Visual Service System Development The system is developed based on B/S architecture, which contains distributed network platform, which contains three layers: data layer, business logic layer, presentation layer. The user requests the business logic layer at the presentation layer, IIS receives

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the request and sends it to the ASP.NET engine for processing.ASP.NET call multilevel association retrieval model, and also calls the data in the database through the ADO.NET. Get the results finally and returned to the client. The vegetable technical system can provide visual navigation of agricultural information by application of knowledge map visualization. For example, when user inquires the tomato, the system displays the tomato variety, tomato technology, tomato pest and disease, and common query problems of tomato by visualized navigation map. Hit the keynote of the knowledge map, the related detail information will be listed on the table below. The system page is as follows (Fig. 3):

Fig. 3. The web page of the vegetable technology information visual service system

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5 Conclusion This paper has studied the knowledge map of vegetable technology construction and application. Through the application of knowledge map, the accuracy and experience of users’ access to information are improved. It offers an effective solution for technical information finding of vegetable planting objects. However, the content of agricultural technology consultation is extensive. Part of the problem is not for specific planting objects. And with the popularity of mobile phone, many farmers use pictures to ask questions instead of word queries. In view of this situation, how to effectively provide information retrieval services needs an indepth study. In addition, under the background of the great development of artificial intelligence, how to use the artificial intelligence technology to provide more convenient man-machine dialogue consultation, will be focused in the future. Acknowledgments. The research work was supported by 2018 Beijing excellent talent project: Research on key technology of man-machine conversation in agricultural science and technology consultation and service application of Beijing, Tianjin and Hebei, 2018 international cooperation fund of BAAFS: A comparative study on the agricultural science and technology information service system in China, the United States and Canada, 2018 Beijing financing agricultural funds: Application and demonstration of “Nongkexiaozhi” consulting service robot and WebAPP in agricultural production.

References 1. Author list, paper title, journal name, vol. no. pages, year 2. Tang, Q., et al.: Discriminating related concepts of knowledge map & its research progress. Inf. Stud. Theory Appl. 1, 121–125 (2011) 3. Li, B., et al.: Text classification method based on knowledge map. Command Inf. Syst. Technol. 1, 92–95 (2018) 4. Yang, M., et al.: Fundamental differences and potential interactions among knowledge maps, mapping knowledge domains and google knowledge graph. Inf. Stud. Theory Appl. 5, 121– 126 (2017)

Research on Answerer Recommending Method Based on Online Learning Community Jun-min Ye1, Song Xu1(&), Xiao-min Xu1, Da-Xiong Luo1, Shu Chen1, and Zhi-feng Wang2 1

School of Computer, Central China Normal University, Wuhan 430070, Hubei, China [email protected] 2 School of Educational Information Technology, Central China Normal University, Wuhan 430070, Hubei, China

Abstract. Based on the recommendation of answerers in online learning community, this paper proposes a question answerer recommendation algorithm based on the Interest, specific-length and Attention. The matching degree between the answerer and the question is evaluated from three dimensions, and the appropriate answerer is recommended to the questioner according to the evaluation result. Finally, according to the data set design related experiments, the experimental results show that the proposed method in this paper is effective. Keywords: Online learning community
Answerer’s specific-length Answerer’s attention
Answerer recommendation

1 Introduction In the online learning community, learners communicate through questioning and answering methods to help questioners obtain answers efficiently and accurately and improve learners’ learning efficiency. Therefore, the recommendation based on the online learning community is a very worthy research issue. The recommending answerer is one of the important recommended behaviors based on the online learning community. The recommending answerer refers to recommending the answerer who can quickly and effectively give an answer to the question asked by the questioner. According to answerers’ recommendations, the existing research methods at home and abroad are to assess the participation of the answerers [1], and to predict which learners in the community can answer in time through frequency, knowledge, desire, willingness, and recent participation. Question asked by the questioner to assess the answerer’s knowledge or specific length [2], such as the use of neural network methods to assess the skill level of answerers [3, 4]. Through the modeling method to assess the questioner’s learning behavior and performance [5]; to evaluate the quality of the answer to the questioner’s answer [6]; to use the Bayesian network to predict the deductive reasoning skills of the answerers [7, 8]; will answer the degree of interest in the questions raised is added to the recommended answerers algorithm [9]; the best answerers are recommended through social network relationships [10]. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 572–577, 2019. https://doi.org/10.1007/978-3-030-02804-6_75

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2 Answerer Recommendation Algorithm For the question answerer recommendation algorithm, this article uses the three evaluation indicators of Interest, Special-length, and Attention to assess whether a answerer is suitable to answer a new question. The specific calculation method is as follows. Interest [10] The interest-based recommendation model shows that if the new question has a high similarity with the question that the answerer has answered, indicating that the answerer’s interest in the new question is high, the answerer may be suitable to be recommended to answer new questions. We use formula (1) to calculate answerer ui Interest in new question q: Iðui ; qÞ ¼

Y w2q



   nðw; Qr ðui ÞÞ nðw; NÞ nðw;qÞ ð1  kÞ þk jQr ðui Þj N

ð1Þ

Among them, in the formula (1), nðw; Qr ðui ÞÞ denotes the number of times that the term w in the new question q appears in the questions set has been answered by the answerer ui; |Qr(ui)| denotes the number of all terms in the questions set has been answered by the answerer ui; n(w, N) indicates that the word w in the new question q is the number of times the question has been answered by all answerers; N indicates the total number of terms that the answerer has answered in the question set; Specific-length. If an answerer is good at a field, the answerer will be better at answering questions in his field. This article will use the answerers’ industry and written articles to express the specific length of the answerers. We use formula (2) to calculate the specific-length E of the answerer ui for a new problem q: Eðui ; qÞ ¼ simðprofession; qÞ 

Y w2q

ð1  kÞ

 nðw; Nðui ÞÞ nðw; NðUÞÞ þk Nðui Þ NðUÞ

ð2Þ

Among them, the profession in formula (2) represents the industry in which the answerer resides, simðprofession; qÞ denotes the degree of similarity of the co-rotation of the industry in which the answerer resides with the new problem q, and nðw; Nðui ÞÞ is the expression of the item w in the new problem q in the title of the article written by the answerer ui. N(ui) indicates the total number of terms in all headings that the answerers wrote about the article; n(w, N(U)) indicates the number of times that the word w in the new question q appears in the heading of the article written by all the answerers; N(u) indicates that all answerers have written all the headings in the article. The total number of items. Attention A. If the answerer is concerned about certain issues or topics, it indicates that the answerer may be interested in this type of problem to a certain degree, so he may be more concerned about this type of question (this article refers to it as the degree

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of attention of the answer to the new problem q). We use formula (3) to calculate answerers’ Attention to new issues A: Aðui ; qÞ ¼

1X simðqa ; qÞ qa 2Qa ðui Þ n

ð3Þ

Where n denotes the number of questions the answerer has focused on; Qa ðui Þ denotes the set of questions to be recommended by the answerer ui; simðqa ; qÞ denotes the question qa the answerer has focused on and the cosine similarity of the new problem q. The larger the value of formula (3), the higher the degree of attention of the answerers to the new problem, and the lower the degree of attention to the new problem q.

Function Recommend answerers to questioners Input ew question q Output answerers list recommended Begin 1. Segmentation of new question q and removal of stop words; 2. Compute Score of answerer’s Interest for new question using formula (1); 3. Compute Score of answerer’s Special-length for new question using formula (2); 4. Compute Score of answerer’s Attention for new question using formula (3); 5. Compute total score UR using formula (4); 6. Sort answerers by UR, Select forward answerers for recommendation; End Fig. 1. Question answerer recommendation algorithm.

Based on interest degree, specific length and attention degree, this paper proposes a question answerer recommendation algorithm, as shown in Fig. 1. Input: (1) U ¼ f u1 ; u2 ; . . .; un g where U denotes the set of answerers; (2) ask questions q; output: answerers’ overall recommendation score vector UR; recommend the highest score in UR to questioner q. UR ¼ aIðui ; qÞ þ bEðui ; qÞ þ ð1  a  bÞAðui ; qÞ

ð4Þ

Calculate and get the comprehensive recommendation score vector UR of the answerer, and then recommend the person with the highest score in the UR to the questioner of the problem q; Note: The value of a and b in formula (4) is the weight, and the value is a real number between 0-1. According to the experimental results, the recommendation is best when a = 0.6, b = 0.3.

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3 Experiments and Analysis 3.1

Collection of Experimental Data

The experimental data comes from the industry in which the answerers obtained the “Zhi hu”, which articles were written, which questions were answered, and which questions were asked. Specific experimental data statistics are shown in Table 1. Table 1. Crawled dataset information.

Number

3.2

Potential answerer 2218

Answered questions 35949

Questions asked 6684

Concerned issues 12692

Published articles 17989

Experimental Evaluation Indicators

Accuracy, Recall, and F1 value were used as the evaluation indicators recommended by the answerers. Use Eq. (5) to calculate Precision, use Eq. (6) to calculate Recall, and use Eq. (7) to calculate F1. Accuracy indicates the proportion of valid answerers among all the recommended answerers. Recall indicates the proportion of all recommended answerers who are truly effective answerers among all the recommended answerers. F1 indicates Comprehensive results of accuracy rate and recall rate. Precision ¼ Recall ¼ F1 ¼

jA \ Bj jAj

jA \ Bj jBj

2  Precision*Recall Precision þ Recall

ð5Þ ð6Þ ð7Þ

Among them, A is the recommended answerer. This paper will select the one with the highest recommendation score to recommend the new question; B is all the answerers who can truly answer the new question. 3.3

Analysis of Experimental Results

Using the comprehensive recommendation algorithm based on interestingness, special length, and attention degree, the potential answerer’s comprehensive score is calculated using formula (4), and the answerer is recommended accordingly. The experimental results are shown in Fig. 4.

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Fig. 2. Recommending results of combination algorithm.

Answerers recommend using a comprehensive recommendation algorithm based on interest, special length, and attention, and use formula 4 to calculate the potential responder’s comprehensive score, and based on this, the answerer recommends. The experimental results are shown in Fig. 2. 3.4

Comparative Analysis

In the experiment, different sizes of data sets were used, and the recommended method recommended by this paper was compared with the recommended method proposed in [2]. The experimental results are shown in Fig. 3.

Fig. 3. Precision comparative

It can be seen from the above figure that the recommendation accuracy rate obtained by the recommender algorithm presented in this paper is higher than the accuracy rate of the algorithm proposed in [2] on different sizes of data sets. By comparing the experiments, the validity of the proposed method is further verified.

4 Conclusion In this paper, we have studied the question answerer recommendation, and proposed answerers recommendation algorithm based on Interest, Specific-length, and Attention. This article uses the real data on the “Zhihu” in the online learning community to

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experiment. The experimental results show that the accuracy of the answerers’ recommendation algorithm based on Interest, Specific-length, and Attention is also relatively high. Acknowledgement. This work was supported in part by the National Social Science Fund General Project (17BTQ061).

References 1. Whitelock-Wainwright, A., Tejeiro, R.: What do students want ? towards an instrument for students’ evaluation of quality of learning analytics services. In: International Learning Analytics and Knowledge Conference. ACM, pp. 368–372 (2017) 2. Sindhgatta, R., Marvaniya, S., Sengupta, B., Dhamecha, T.I.: Inferring frequently asked questions from student question answering forums. In: Proceedings of the 10th International Conference on Educational Data Mining, pp. 256–261 (2017) 3. Lin, Y.M., Rao, H., Huang, D.Q.: Personalized question-recommending service in webbased question-answering system. J. Intell. 30(07), 172–177 (2011) 4. Jiang, Z.L., Li, L.X.: Question recommendation mechanism in community question answering systems. Comput. Mod. (08), 89–92 (2015) 5. Bote-Lorenzo, M.L., Gómez-Sánchez, E.: Predicting the decrease of engagement indicators in a MOOC. In: The 7th International Learning Analytics and Knowledge Conference, pp. 143–147 (2017) 6. Ishola, O.M., McCalla, G.: Predicting prospective peer helpers to provide just-in-time help to users in question and answer forums. In: Proceedings of the 10th International Conference on Educational Data Mining, pp. 238–243 (2017) 7. Xu, A.W.: Question answerer recommending in question answering community. Zhejiang University, pp. 23–29 (2011) 8. Du, Q., Wang, Q.X., Huang, D.P.: Question answering system based on social relationship and recommendation of the best answere. J. South China Univ. Technol. (Nat. Sci. Edit.) 43 (01), 132–139 (2015) 9. Duan, L.G., Chen, J.J.: Question recommended technology of integrated sentence structure and semantic similarity. Comput. Sci. 39(01), 203–206 (2012) 10. Liu, Y., Zheng, C.H.: Research on deep network crawler based on scrapy. Comput. Eng. Softw. 38(07), 111–114 (2017)

Study on the Relationship Between Eysenck Personality and Sleep Quality Based on Multiple Ordered Logistic Regression Zhihan Yang(&), Mengge Sun, and Minghui Wang Shandong Normal University, Jinan, China [email protected]

Abstract. Firstly, this paper uses one-way analysis of variance to test sleep quality and two indexes of age and gender, and uses multivariate correlation analysis to test sleep quality and Eysenck’s personality. The result shows that age has a significant effect on the quality of sleep, while gender has no significant effect, and there is no strong correlation between the quality of sleep and each index of Eysenck’s personality. Based on this, we use the multiple ordinal logistic regression model to study the relationship between sleep quality and four indicators of Eysenck personality in different age groups. First of all, according to international standards, Eysenck’s personality and age are divided into 5 grades and 4 grades respectively. Then, we use the multiple logistic regression model to examine sleep quality and Eysenck personality index in different age groups. The result shows that the multiple ordinal logistic regression model can be used to examine the correlation between Eysenck personality and sleep quality effectively. In different age groups, reliability, psychoticism, nervousness and character have different effects on sleep quality. Moreover, with the increase of age, the influence of Eysenck personality on sleep quality shows a decreasing trend. Keywords: Multiple logistic regression model
One-way analysis of variance Correlation test

1 Introduction Sleep quality is an important indicator of life quality, affecting human cognition and social function [1]. It is closely related to stress, emotion, aggression and so on [2–6], especially personality and mental health [7, 8]. Studies have found that mental health, such as loneliness, has a significant effect on sleep quality [9] and depression and anxiety may lead to a decline in sleep quality [10]. Scholars study the problems of mental health and personality tendency with the help of Eysenck personality questionnaire, including emotional stability (neuroticism dimension), internal and external tendency (introverted and extroverted dimension) and psychopathic tendency (psychoticism dimension) [11]. This method is often used to study personality, as well as medicine, justice, education and other fields. Some scholars use SSA to verify the three dimensions of Eysenck personality theory [12]. Others choose the level theory to integrate theoretical construction and empirical © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 578–585, 2019. https://doi.org/10.1007/978-3-030-02804-6_76

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research systematically, and design and analyze the structure of complex categories [12, 13]. In addition, scholars often use multivariate logistic regression analysis method to deal with the similar multidimensional problems [14], such as, using 37 financial indicators to set up a financial crisis warning model [15]. Based on those, we use multiple logistic regression analysis to study sleep quality and Eysenck personality.

2 Data Source The data was collected from the diagnosis records of psychiatric department in one hospital. We selected some indexes including gender, age and the four indicators in the Eysenck personality questionnaire(EPQ) to quantitative the effects on the sleep quality.

3 Data Analysis Considering that age and gender may have an impact on sleep quality, the theory of one-way analysis of variance is used to analyze the results. 3.1

One-Way Analysis of Variance About Age and Sleep Quality

Firstly, we put forward the original hypothesis H0: Changes in the different level of ages have no significant effect on the sleep quality, and it means that the influence of different age is not existed. Then, we choose the F-statistic as the test statistics to perform the one-way analysis of variance about age. The results are shown in Table 1. Table 1. Single factor variance analysis about age Sum of squares df MS F-value Significance level Interblock 158.607 71 2.234 3.360 0.000 Interclass 4173.434 6277 0.665 Sum 4332.041 6348

As is shown in Table 1, the sum of squares of sleep quality is 4332.041. If we only consider the influence of the age factor, the squares caused by the different age groups are 4173.434, and the squares caused by the sampling error are 158.607, that is, the proportion of the age factor in sum of squares of sleep quality is relatively large; their variance is 2.234 and 0.665 respectively. Dividing them, we can obtain the observed value of the F-statistic is 3.360, and P-value is approximately 0. If the significant level a is 0.05, since the P-value is less than a, we should reject the original hypothesis H0. That is the different age groups have a significant influences on sleep quality, and the effect of different age groups on sales is not 0.

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One-Way Analysis of Variance About Gender and Sleep Quality

Same as above, we put forward the original hypothesis H1: the change of gender has no significant influences on the sleep quality, that is the influence of different gender is 0. One-way analysis of variance about age and sleep quality is conducted and the results are shown in Table 2. Table 2. One-way analysis of variance about gender Sum of squares df MS F-value Significance level Interblock 0.235 1 0.235 0.347 0.556 Interclass 3906.775 5771 0.677 Sum 3907.010 5772

As is shown in Table 2, the observed value of F-statistic for the variance of gender and random variance is 0.347, and the P-value is 0.556. Since the P-value is more than apparently the significant level a, and if the significant level a is 0.05, we should accept the original hypothesis, that is, the change of different gender has no significant the influence on the sleep quality. 3.3

Analysis of Correlation Between EPQ and Sleep Quality

Corresponding to N, P, E and L scale respectively, the four indicators of EPQ are nervousness, psychoticism, character and reliability. On the N scale, the higher the score is, the more unstable the participants’ emotions are; On the P scale, the higher the score is, the more unusual the participants’ behaviors are; On the E scale, the higher the score is, the more introverted the participants’ characters are; On the L scale, the higher the score is, the higher the self-closure of the participants, and the smaller the validity of this survey is [16]. In a word, the higher the score, the worse the overall situation of the participant. Next, we analyze the correlation between the indicators of EPQ and sleep quality, and the result of which is shown in Table 3. Table 3. The correlation between the indicators of EPQ and sleep quality Sleep quality Nervousness Psychoticism Reliability Character Sleep quality 1.000 Nervousness 0.0410 1.000 Psychoticism 0.0899 0.2466 1.000 Reliability 0.0253 −0.4278 −0.3996 1.000 Character −0.0307 −0.0628 −0.1942 −0.0081 1.000

As is shown in Table 3, the correlation coefficient between different indexes is small, and the correlation between four indicators and sleep quality also have no significant difference.

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4 Multiple Ordered Logistic Regression 4.1

Rank Division

Firstly, according to Eysenck international rank division standard, the data of the Nervousness, Character, Psychoticism and Reliability indicator can be divided into five ranks. In this way, we can convert the continuous data into classified data. The division standard is shown in Table 4. Table 4. The rank division standard Range of value [0,38.5) [38.5,43.3) [43.3,56.7) [56.7,61.5) [61.5,100.0)

Rank 0 1 2 3 4

At the same time, according to the world health organization classification standard, we also carry on the rank division to the age. Concretely, the age range for young people is between 16 and 44, the middle age range is between 45 and 59, the young elderly age range is between 59 and 75, and the elderly age range is between 75 and 89. 4.2

Multiple Ordered Logistic Regression Model

4.2.1 Based model model

Establishment of the Model on our indexes are both ordered variables, we select the logistic regression to quantize the correlation between EPQ and sleep quality. The mathematical is shown in formula (1). ln½ lnð1  PðY  jÞÞ ¼ aj  ðb1 X1 þ


þ bi Xi Þ

ð1Þ

In which, Y represents the rank of sleep quality, aj represents the constant term, Xi represents the ith index of EPQ, bi represents the regression coefficient of the ith index. Then, we obtain the accumulated predicting probability models of Logit, which can be shown as formula (2) PðY  jÞ ¼ 1 

1 
 exp exp aj  ðb1 X1 þ


þ bi Xi Þ

ð2Þ

4.2.2 Test of the Model Next, we use the ordered logistic regression method to analyze the sleep quality. We regard the “Nervousness, Character, Psychoticism, Reliability” index and age index as independent variables, the sleep quality as the dependent variable and make repeated

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regression analysis of categorical data. Finally, we obtain the fitting information, goodness of fit and Parallel line inspection of the model, as is shown in Tables 5, 6, 7, respectively. Table 5. Model fitting information Age groups Chi-square df Significance [16,44] 162.275 17 0.000 [45,59] 37.546 17 0.003 [60,74] 27.760 17 0.048 [75,89] 41.226 16 0.001

Taking the age group [16,44] as an example, we obtain the parameter estimate table by regression analysis, as is shown in Fig. 1. As is shown in Fig. 1, the significance levels of age, Reliability, Psychoticism and Nervousness are all less than 0.05, which illustrate that these indexes have significant influence on sleep quality. The effect from Gender and Character for sleep quality is relatively weak. Similarly, from parameter estimation table of other age groups (omitted here), we can know that in [45,74] age group, Psychoticism and Nervousness have a significant effect on sleep quality, and effect of the rest of factors is relatively weak. In [74,89] age group, because of the caducity of the body function, the decline of hormone secretion and other objective factors playing major roles, age, gender, reliability, psychoticism, nervousness and character cannot influence the sleep quality of that population obviously. Consequently, the quality of sleep cannot be determined more accurately. 4.2.3 Application of the Model This regression model we establish can be used to calculate the probability of a person’s rank of sleep quality. For example, the levels of disguise, psychoticism, neuroticism and introversion and extroversion of an eighteen-year-old girl were 1, 2, 2, 0 respectively, and her cumulative prediction probability of sleep quality is: 1 exp½expð3:088  ð0:118  0:208  2  0:287  2  0:016  0ÞÞ ¼ 0:1289

^pðsleep quality  0Þ ¼ 1 

^pðsleep quality  1Þ ¼ 1 

1 exp½expð0:878  ð0:118  0:208  2  0:287  2  0:016  0ÞÞ

¼ 0:7159 ^pðsleep quality  2Þ ¼ 1 

1 exp½expð0:507  ð0:118  0:208  2  0:287  2  0:016  0ÞÞ

¼ 0:8302

Study on the Relationship Between Eysenck Personality and Sleep Quality Table 6. Model fit goodness Age groups [16,44] [45,59] [60,74] [75,89]

Significance of Pearson Significant deviation level 0.439 1.000 0.797 1.000 0.125 1.000 0.408 0.509

Table 7. Test of parallel lines Age groups Chi-square df Significance Link function [16,44] 77.601 34 0.000 Complementary Log-log [45,59] 51.969a 34 0.005 Complementary Log-log [60,74] 97.561a 34 0.000 Complementary Log-log [75,89] 66.139 16 0.000 Complementary Log-log Notes: The calculation of chi square statistics is based on logarithmic natural value obtained from the last iteration of generalized model

Fig. 1. Parameter estimation of sleep quality in age group [16,44]

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Based on her cumulative prediction probability of the rank of sleep quality, we can calculate the prediction probability of the rank of sleep quality, which is shown as follows: ^pðsleep quality ¼ 0Þ ¼ ^pðsleep quality  0Þ ¼ 0:1289 ^pðsleep quality ¼ 1Þ ¼ ^pðsleep quality  1Þ  ^ pðsleep quality  0Þ ¼ 0:7159  0:1289 ¼ 0:5870 ^pðsleep quality ¼ 2Þ ¼ ^pðsleep quality  2Þ  ^ pðsleep quality  1Þ ¼ 0:8302  0:7159 ¼ 0:1143 ^pðsleep quality ¼ 3Þ ¼ p^ðsleep quality  3Þ  p ^ðsleep quality  2Þ ¼ 1  0:8302 ¼ 0:1698 As a result, this girl’s sleep quality is most likely to be 1. Similarly, we can predict the probability of the rank of sleep quality in other age groups according to the information of Age, Gender, Reliability, Psychoticism, Nervousness, Character. In conclusion, people of different ages have different Eysenck personality traits, leading to different factors affecting their sleep quality. In [16,44] age group, the factors of age, Reliability, Psychoticism and Nervousness have a significant effect on their sleep quality. In [45,74] age group, Psychoticism and Nervousness have a significant effect on their sleep quality. In [75,89] age group, with the continuous declination of body function, physical aging and other objective factors can significantly affect the sleep quality. Therefore, it is effective to assess the sleep quality of the Eysenck personality scale for a specific age group.

5 Conclusion To find how Eysenck personality impacts on Sleep quality in different age groups, we establish the multiple ordered logistic regression model to analyze the relationship between EPQ and the sleep quality. A serious of valuable conclusions can be obtained from this paper: 1. Multiple ordered logistic regression model can be used to predict the probability of relationship between Eysenck personality and sleep quality. The accuracy of the prediction is high. 2. Different age groups have different Eysenck personality features, and the correlation between sleep quality and four indexes of reliability, psychoticism, nervousness and character is not all the same. Among them, for the age of [16,44], reliability, psychoticism, nervousness, character have significant effect on sleep quality, and for the age of [45,74], psychoticism and nervousness affect sleep quality significantly; and for the age of [74,89] age group, Eysenck personality can not accurately evaluate sleep quality because of large proportion of objective factors.

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References 1. Roth, T., Jaeger, S., Jin, R., et al.: Sleep problems, comorbid mental disorders, and role functioning in the national comorbidity survey replication. Biol. Psychiat. 60(12), 1364 (2006) 2. Nofzinger, E.A., Buysse, D.J., Germain, A., et al.: Functional neuroimaging evidence for hyperarousal in insomnia. Am. J. Psychiatry 161(11), 2126–2128 (2004) 3. Kanaan, S., Siengsukon, C., Arnold, P.M. et al.: Relationship between sleep quality and functional and psychological symptoms in patients with chronic low back pain. In: World Confederation for Physical Therapy Congress (2015) 4. Yuan, Q., Jia, K., Liu, X., et al.: 6 week mindfulness training for men with long prison sentences for aggression and sleep quality. Chin. J. Mental Health 3, 167–171 (2015) 5. Wenyuan, X., Huang, J.: Analysis of sleep disorders in 4 patients with dyskinesia. Chin. J. Neuropsychiatry 10, 624–626 (2014) 6. Zhang, L., Diao, J.: Sleep quality and its related factors in Chinese college students. Chin. J. Clin. Psychol. 14(5), 515–517 (2006) 7. Ireland, J.L., Culpin, V.: The relationship between sleeping problems and aggression, anger, and impulsivity in a population of juvenile and young offenders. J. Adolesc. Health Official Publ. Soc. Adolesc. Med. 38(6), 649 (2006) 8. Alfano, C.A., Kim, K.L.: Objective sleep patterns and severity of symptoms in pediatric obsessive compulsive disorder: a pilot investigation. J. Anxiety Disord. 25(6), 835 (2011) 9. Jiang, R., Chen, S.: Effects of loneliness on depression and sleep quality: mediator of meditation. J. Henan Inst. Sci. Technol. Soc. Sci. Ed. 34(7), 69–72 (2014) 10. Liu, X., Tang, M.: Correlation between anxiety, depression and sleep quality among college students. Chin. J. Mental Health 1, 25–27 (1997) 11. Eysenck, H.J.: Psychoticism as a dimension of personality: a reply to Kasielke. HODDER & STOUGHTON, London (1976) 12. Zhao, S.: Theoretical research on the construct validity of Eysenck personality questionnaire. J. Shandong Norm. Univ. (Humanit. Soc. Sci. Ed.) 4, 146–150 (2012) 13. Zhao, S., Wang, H., Jiang, X., et al.: An effective strategy for testing project compilation and equivalence—level theory. Exam. Res. 2, 64–72 (2007) 14. Tao, X.: Multiple logistic regression analysis of readers’ characteristics and needs. Mod. Intell. 26(6), 144–148 (2006) 15. Li, P., Zhou, Z., Chen, S.: Research on financial crisis early warning based on multiple regression analysis. J. Tongji Univ. (Nat. Sci. Ed.) 34(5), 701–706 (2006) 16. Knorring, L.V., Knorring, A.L.V., Mörnstad, H., et al.: The risk of dental caries in extraverts. Personal. Individ. Differ. 8(3), 343–346 (1987)

Study on Catching-up-Element of Risk in Talent Cultivation Project Xinfa Tang1(&) and Zhuangwen Sun2 1

School of Economics and Management, Jiangxi Science & Technology Normal University, Nanchang, China [email protected] 2 Class 1 Grade 2014 of Financial Management, School of Economics and Management, Jiangxi Science & Technology Normal University, Nanchang, China

Abstract. Catching up the work is a major factor that causes the project risk. About the quality of the personnel training and the quality of the personnel training. The article systematically expounded the connotation of the risk factors for project rushing down in the talent cultivation and the risk element transmission process, established an analytical model for the risk transmission metaphor of the talent cultivation project, explained this relationship between talent cultivation, rush work and talent effectiveness, and proposed measures to rationally train talents. Keywords: Catching-up-risk-element Talent cultivation project


Transfer model

1 Introduction In China, people usually pursue for success and want to enhance their value through certain means. However, during the education process, people may be keen to seek success and choose inappropriate methods and means which are not suitable for causing deviation between the result and the expected goal. The incorrect or unsuitable method is the phenomenon of rushing in the process of talent cultivation. Professor Li Cunbin of North China Electric Power University proposed the concept of the risk element for the first time in 2005. At present, the risk element transfer theory is mainly applied in construction management, production management, and financial management [1, 2], while no academic literature has been applied to personnel training projects. Internationally, there are limited studies on the relationship between sleep and education in economics. Sleep and Allocation of Time written by Biddle and Hamermesh is the first study on the impact of the labor market on sleep. Brochu et al. and Szalontai used the latest data from Canada and South Africa to assess the impact of wage changes on sleep. Bonke studied the effect of two different time on income. These studies provide inspiring insights into how sleep affects work performance [3]. In view of the current circumstance, due to the lack of research on risk factors for talent training projects, and the talent training project is a hot topic in the temporary society, I choose to use the domestic mature risk meta-model analysis method as a theoretical basis and © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 586–592, 2019. https://doi.org/10.1007/978-3-030-02804-6_77

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combined with a mathematical model to quantify analyze the impact of this risk element on the result of the talent training project [4, 5].

2 Catch-up-Risk-Element Related Theory Cost, duration and quality are usually the three major goals of project management. There are numerous factors that cause the three major objectives to deviate from expectations. These risk factors which may result in an increase in the number of projects, delays in the completion of the project or even suspension of the project are considered as risk elements. When there is a causal relationship between variables which have a correlation, the variable that causes the causal relationship may be referred to as an “interpretation variable,” and result variable may be an “interpreted variable.” There are various risk elements in the process of talent training projects. They exist objectively but interact with each other. The names of risk elements in different age groups, occupations, and education levels are different. Catch-up are usually accompanied by day and night. This often leads to a risk of reducing the duration of sleep and leisure. This article will use sleep hours, leisure hours, and working hours/learning hours (primary talent development process for different age groups) as branch risk elements analysis. Among them, sleep hour is “explained variable”, while leisure hour, working hour and learning hour are “explanatory variables”. According to the human resource theory of economics, the sleeping time (S) in the process of talent cultivation may be affected by work time (w), leisure time (r), and study time (l). In the relationship between the independent variable and the dependent variable, the explanatory variables are w, r, l, and S is the explanatory variable. Since the sleeping time of the laborer is mainly affected by the leisure time, study time, and working time.

3 Analyze for Catch-up-Risk Elements Linear Regression Model Given the limited number of samples to be analyzed and selected in a specific environment, related mathematical models which need to be used to analyze the risk element transfer are also required appropriate probabilistic estimates and reasonable assumptions for the risk elements. The probability estimation is mainly to discover the factors that affect the risk element changes in the training process of the talents. The change of the risk elements brings about the change of the parameter distribution in the model to calculate the range and probability of the risk element changes. Probability estimation is a kind of quantitative estimation, which is mainly achieved by quantitative estimation method. It includes time series analysis, analogy, regression analysis, etc. This article uses regression analysis to calculate the degree of interaction between the risk elements that affect the talent training objectives.

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The main task of regression analysis is tantamount to estimate the numerical characteristics of regression coefficients and random errors and determine the regression form of explanatory variables and explanatory variables through statistical tests. Since estimates and statistical tests are performed under assumptions which specific to the relevant variables, some assumptions need to be made on explanatory variables and random errors. The assumptions for variables are: 1 The random error of each set of values of the corresponding variable is a random variable with a mean of zero. 2 The variance of random errors is same. 3 Random errors corresponding to different groups of values are irrelevant. 4 Random error obeys normal distribution 5 Explanatory variables are deterministic variables, and there is no linear relationship or approximate linear relationship between different explanatory variables.

4 Case Analysis of the Transfer Model of Talents Training Project 4.1

Data Acquisition

See Tables 1, 2, 3, 4 and 5. Table 1. Activity time per worker (hours) Total Regular employment 417 Leisure activities 216 Sleep and rest 516

Male 423 232 513

Female 405 188 522

Table 2. Activity time per employee (hours) Total Regular employment 407 Leisure activities 192 Sleep and rest 527

Male 409 216 520

Female 406 170 533

Table 3. Activity time per students (hours) Total Regular employment 547 Leisure activities 163 Sleep and rest 539

Male 544 174 541

Female 551 152 537

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Table 4. Activity time per person (15–19) (hours) Total Regular study 480 Leisure activities 104 Sleep and rest 524

Male Female 480 489 111 97 525 523

Table 5. Activity time per person (20–24) (hours) Total Male Female Regular study 89 94 83 Leisure activities 193 211 176 Sleep and rest 545 544 546

4.2

Calculation of the Association Degree of Risk Elements

Tables 6 and 7 show the results in workers group, it can be demonstrated that the regular employment can explain the 100.0% change in sleep rest, while the regular employment can explain the 97.9% change in sleep rest. The regression coefficient value and P value means that regular employment will have a significant negative impact on sleep rest. The model VII did not pass the F test (F = 46.413, p > 0.05), indicating that formal employment does not have an impact on sleep, therefore we cannot specifically analyze the influence of the independent variable on the dependent variable. Table 6. Coefficients, ANOVA & model summary

Constant Independent variable

p

R2 Adjusted R2

Unstandardized Standardized coefficients coefficients B Std. Beta Error

t

724.5 0 −0.5 0

295245457440.258 0.000** 1 −84573349478.294 0.000**

1

1

Independent Variable: working hours. Dependent variable: sleeping, *p < 0.05, **p < 0.01

Table 7. Coefficients, ANOVA & model summary

Constant Independent variable

p

R2

Unstandardized coefficients B Std. Error

Standardized coefficients Beta

t

2243.286 251.973 −4.214 0.619

−0.989

8.903 0.071 0.979 0.958 −6.813 0.093

Independent Variable: leisure activities, Dependent variable: sleeping, *p < 0.05, **p < 0.01

Adjusted R2

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Table 8 shows the results in employee group, which indicates that the leisure activities can explain the 100.0% change in sleep rest. The regression coefficient value and P value means that leisure activities have a significant negative impact on sleeping. Table 8. Coefficients, ANOVA & model summary

Constant Independent variable

p

R2 Adjusted R2

Unstandardized coefficients B Std. Error

Standardized coefficients Beta

t

581.142 1.056 −0.283 0.005

−1

550.48 0.0001** 1 −51.846 0.012*

0.958

Independent Variable: learning hours, Dependent variable: sleeping, *p < 0.05, **p < 0.01

Tables 9 and 10 show the results in students group. From which we can see that the learning time can explain the 99.3% change in sleep and rest, and the leisure activities can explain the 100.0% change in sleep rest. The model in Table 9 did not pass the Ftest (F = 147.000, p > 0.05), indicating that learning training does not have an impact on sleep rest, and thus cannot specifically analyze the influence of independent variables on the dependent variables. The regression coefficient value and the P value means that leisure activities will have a significant positive impact on sleep rest. Table 9. Coefficients, ANOVA & model summary

Constant Independent variable

p

R2

Unstandardized coefficients B Std. Error

Standardized coefficients Beta

t

Adjusted R2

849.649 25.622 −0.568 0.047

−0.997

33.161 0.019* 0.993 0.986 −12.124 0.052*

Independent Variable: leisure activities, Dependent variable: sleeping, *p < 0.05, **p < 0.01

Table 10. Coefficients, ANOVA & model summary

Constant Independent variable

p

R2 Adjusted R2

Unstandardized coefficients B Std. Error

Standardized coefficients Beta

t

509.364 0 0.182 0

1

1353911417903.5 0.000** 1 78894279547.195 0.000**

Independent Variable: working hours, Dependent variable: sleeping, *p < 0.05, **p < 0.01

1

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Tables 11 and 12 shows the results in 15–19 years old group. The learning training can explain the reason for the 75.0% change in sleep rest, and the leisure activities can explain the 100.0% change in sleep rest. The model VII did not pass the F-test (F = 3.000, p > 0.05), indicating that learning training does not have an impact on sleep rest. However, the leisure activities will have a significant positive impact on sleep rest.

Table 11. Coefficients, ANOVA & model summary

Constant Independent variable

p

R2

Unstandardized coefficients B Std. Error

Standardized coefficients Beta

t

Adjusted R2

604.5 46.478 −0.167 0.06

−0.866

13.006 0.049* 0.75 0.5 −1.732 0.333*

Independent Variable: leisure activities, Dependent variable: sleeping, *p < 0.05, **p < 0.01

Table 12. Coefficients, ANOVA & model summary

Constant Independent variable

p

R2 Adjusted R2

Unstandardized coefficients B Std. Error

Standardized coefficients Beta

t

509.143 0 0.143 0

1

7343188646262.59 0.000** 1 214602695551.17 0.000**

1

Independent variable: learning hours, Dependent variable: sleeping, *p < 0.05, **p < 0.01

Tables 13 and 14 show the results in 20–24 years old group. The leisure activities can explain the 100.0% change in sleep rest, and the leisure activities can explain 99.5% of the reasons for the change in training. The regression coefficient value and P value means that leisure activities will have a significant negative impact on sleep rest and leisure activities will affect learning, which is a positive impact relationship. Table 13. coefficients, ANOVA & model summary

Constant Independent variable

p

R2 Adjusted R2

Unstandardized coefficients B Std. Error

Standardized coefficients Beta

t

556.045 0.183 −0.057 0.001

−1

3043.719 0.000** 1 −60.622 0.011*

0.999

Independent Variable: leisure activities, Dependent variable: learning hours, *p < 0.05, **p < 0.01

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Standardized coefficients Beta

t

p

R2

Adjusted R2

Constant 6.658 0.095 0.995 0.991 Independent 0.998 14.484 0.044* variable Independent Variable: leisure activities, Dependent variable: learning hours, *p < 0.05, **p < 0.01

5 Introduction By using the risk transfer model, the impact of the risk elements and the result of the rushing work in the talent training project is analyzed. During the study, I found that for students, proper leisure activities will be helpful to increase their sleep. For workers, both working hours and leisure time will increase their sleep time. Therefore, in the process of personnel training, if you catch up with work and compress your sleep time, it will be prejudicial to the ultimate cultivation of talent. However, there are some deficiencies in the process of this study. For example, the extraction of sample data is relatively old, so the scope of analysis is limited to a definite period. The selection of quantitative model tools is also relatively simple, and only through the analysis of the linear regression relationship between risk elements to obtain the influence on the results of personnel training projects. In the article, it only uses the mathematical model to analyze the transmission of the risk element in the process of talent training project, but it does not propose relevant solutions and suggestions. If we further analyze the status of data, and combine the qualitative analysis and quantitative analysis, we could provide more novel ideas and methods for solving the problems which are brought by the risk elements in the talent training project.

References 1. Li, C., Liu, Y., Li, S.: Study on model of design risk element transmission in the construction project. Oper. Res. Manag. Sci. 24(6), 145–150 (2015) 2. Cunbin, L.I., Gongshu, L.U.: System dynamics model of construction project risk element transmission. Syst. Eng. Theory Pract. 32(12), 2732–2739 (2012) 3. Gibson, M., Shrader, J.: Time Use and Productivity: The Wage Returns to Sleep. University of California at San Diego Economics Working Paper, vol. 24(2), pp. 5–51 (2014) 4. Borman, K.R., Fuhrman, G.M.: “Resident duty hours: enhancing sleep, supervision, and safety”: response of the association of program directors in surgery to the December 2008 Report of the Institute of Medicine. Surgery 146(3), 420–427 (2009) 5. Biddle, J.E., Hamermesh, D.S.: Sleep and the allocation of time. Soc. Sci. Electron. Publ. 98(5), 922–943 (1990)

Research on Computer Aided Innovation Software Based on Extenics Weitao He1, Rui Fan1(&), Fuyu Ma1, Fuli Chen2, and Bifeng Guo1 1

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Faculty of Software Technology, Guangdong Ocean University, Zhanjiang, China [email protected] Faculty of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, China

Abstract. Extenics is a Chinese original discipline that researches on solving contradictions with its own way. Many Extenics strategy generation systems existing today are only for a certain field, which cannot satisfy common use. Therefore, by studying extension information - knowledge - intelligent formal system we designed Extenics innovation software, a computer aided innovation software, which based on information data, extension service and intelligent recognition. The software is composed of four main modules, the main management module, the knowledge base module, the inference tree module and the forum module. Each module can run independently as well as carry out data interaction to other. Keywords: Extenics strategy Keyword recognition


Knowledge base
Tokenizer

1 Introduction Human society is developing in the process of dealing with all kinds of contradictions. Summing up the manifestations and processing methods of all kinds of contradictions from ancient to present, an original discipline Extenics forms through formalization, logic and mathematics. In the field of computer, the Extenics strategy generation system [1] is the first software of the Extenics. Aiming at making the Extenics innovation method [2] become software based we designed Extenics innovation software which can use software development technology to study and develop creative solutions that can amicably guide people to solve contradictory problems in accordance with the principles and ideas of the extension innovation method. By realizing the core technology of innovation element service materialized and individual innovation component, we build a new innovative service software to solve the urgent problems of common crowd innovation’s lack of guidance and synergy. Users can finally get the solutions to the contradictions even in the case they have no understanding of the principle formula of Extenics. Extenics [4] is a cross-sectional discipline used to solve contradictions. The Extenics strategy generation system [1] is a software that combines Extenics theory and © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 593–600, 2019. https://doi.org/10.1007/978-3-030-02804-6_78

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artificial intelligence theory, it guides users how to use Extenics to solve problems of contradiction and realizes innovation in corresponding fields through Extenics innovation methods. Paper [2] introduces the principle, application and advantages of extension innovation method. Based on the Extenics strategy generation system, we designed a new Extenics software – Extenics innovation software that consist of four modules. In order to query the user’s knowledge quickly, we refer to the Paper [3] and design a knowledge base work for the Extenics innovation software. The paper [8] introduces some cases of single sign-on and its implementation methods. By referring to this Paper, we have realized the single sign-on system of extension innovative intelligent software. In addition, we have to get the keywords from users’ question and use the keyword to get the knowledge that they need, so we have referred to Paper [5], from which we have learned the introduction of a participle kernel – ICTCLAS. In terms of cross-platform software, we used B/S architecture. The paper [6] introduced some advantages of how to implement it through Java and this architecture. Because the software is B/S architecture, needs to be deployed to the server, and directly use Tomcat server may cause the system pressure is too large and collapses, by paper [7], we learned the Nginx some mechanism and method of use, realization of dynamic and static separation by Nginx reduce the pressure of the Tomcat server. At last, based on the case “Analysis Based on Hotel Excess Food in Combination with Extenics” from the Paper [10], we show the application process of the Extenics innovative.

2 Description of Research Work 2.1

The Conditions Around the World

Extenics focuses on the formalization of innovative thinking and puts forward the extension information - knowledge - intelligent formal system. It also provides different ideas for Extenics innovative software models from the perspective of Extenics innovative software architecture. Based on the analysis of the above research status, Extenics innovation software needs to run on different systems and design corresponding configuration information to solve compatibility problems, to adapt to the development of different environments and platforms, and through the decoupling method to develop software to reduce duplication of work, improve the software component reusability. Not only that, the software also needs to provide the corresponding knowledge base [3], so that users can reduce the workload of finding data. 2.2

The Main Research Content of Project

This topic mainly studies the development of the Extenics innovation software. The main contents are as follows: At present, there is no Extenics strategy generation system for the masses, and most of the software learning costs are too high to be popularized. Therefore, the essential task of our software is to reduce the learning cost of users, provide a better service experience and make it easy to learn and use.

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In order to make it easy for users to obtain corresponding knowledge and discussion when solving contradictory problems, we added knowledge base and forum module.

3 Implementation and Key Technologies of Extenics Theory 3.1

Service Code Design of Extenics Basic Theory

The formula of dependent function can better reflect the nature of the basic element and calculate the degree of contradiction problems. Besides, it is available at present; Extension theory also puts forward the theoretical system of innovative methods extensible analysis, extensible transformation and superiority evaluation [4]. In terms of implementation, because the innovation method of Extenics theory system is a more subjective arbitrary, the software mainly puts forward guiding opinions and related knowledge in the innovation method theory system. Therefore, the realization of service codes based on Extenics theory is mainly relative to the calculation of dependent functions. 3.2

Key Technology

For this function point of keyword recognition, we use ANSJ to identify the keywords. ANSJ participle is developing based on the ICTCLAS algorithm [5] of the Chinese Academy of Sciences. Each module of the Extenics innovation software can operate independently and interact with information. The main technology of information interaction is interfacecalling technology. The interface invocation technology I use is the Java open source tool HttpClient. In addition, we use Nginx to achieve dynamic and static separation [6] to reduce the pressure on Tomcat.

4 Design and Achievement of Extensive Innovative Services Community Based on JavaEE Architecture 4.1

Module Design of the Software

In a B/S based architecture [7], the Extenics innovation software is composed of four main modules, the main management module, the knowledge base module, the inference tree module and the forum module. Now we will introduce the main functions by each module. 4.2

Design for the Main Management Module

The main management module consists of into three functional modules (see Fig. 1). The main management module is the portal of the Extenics innovation software. The user can call other module’s interfaces indirectly by the main management module. For

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example, the user can obtain knowledge by the keyword from the inference tree’s node. In addition, we use Swing [8] to generate anti-violence commit verification codes.

Fig. 1. Main management module

4.3

Design for Module of Knowledge Base

The knowledge base module consists of three sub-modules, each of sub-module consist of three function modules (see Fig. 2). This module mainly provides the quick inquiry function of knowledge for the inference tree module such as the user can search knowledge that they need through the inference tree page. 4.4

Design for Module of Inference Tree

The inference tree module consists of three sub-modules, each of sub-module consist of two function modules (see Fig. 3). This module mainly provides the Extenics Tool, which can make deduced tree easily, for users. In addition, users can search knowledge quickly that they need in this module. In order to store the data of the inference tree more conveniently, we use the file store system [9] to store the inference trees’ data. 4.5

Design for Module of Forum

The forum module is developed based on the open source community Symphony. Symphony is a modern community platform, because it implements a content-oriented

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Fig. 2. Knowledge base module

Fig. 3. Inference tree module

discussion forum, the ability to aggregate independent bloggers, to build and share quality resources and 100% open source.

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Based on the Symphony’s code, we made corresponding modifications, and made certain contact with the main management module end of the user login registration [10]. In addition, we fill in the knowledge about Extenics (see Figs. 3 and 4).

Fig. 4. Forum module

5 Case Analysis 5.1

Case Analysis Based on Hotel Excess Food in Based on Extenics [11]

First, according to original problem of the case, we can see that an original problem is divided into two sub-problems, which is called changing its sales mode. If the sub-problem can be divided, the sub-problem will be defined a kernel problem and the system creates three kinds of codes for it. They are goal base element, conditional base element and dependent function. What we are to do is filling the data into the forms of each node of them. Besides, we are to create the dependent functions of each kind of attribute of each element. Then the system will have a calculation for each element attribute and element, which helps users save the time in mathematical calculation. The result of calculation for dependent functions will show in the node of goal base element and conditional base element. If the dependent function value of both goal base element and conditional element are greater than zero, expressing that it is not a contradictory problem. If it is less than zero and it is defined as a contradictory problem so that we can analyze it by Extensible analysis.

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After Extensive analysis, we can have an Extensive transformation [12] for it. Firstly, we can fill the selected conditions in to the editable text and press the button of Extensive transformation. The suitable base element will be selected shown in the right. Finally, we can have a superiority for the transformed base element by means of filling the corresponding condition in the editable text and pressing the button of superiority evaluation (see Fig. 5).

Fig. 5. Process of the extenics innovation software Acknowledgments. This research is supported by Guangdong science and technology project (2014A040402010) and Guangdong college students’ innovation and entrepreneurship project (CXXL2018090).

References 1. Lixi, L.: Extension Strategy Generation System. Science Press, Beijing (2006) 2. Yang, C., Li, X.: Research progress in extension innovation method and its applications. Ind. Eng. J. 15(1), 131–137 (2012) 3. Ohlsson, J., Han, S.: Knowledge base. In: Prioritising Business Processes. SpringerBriefs in Business Process Management. Springer, Cham (2018) 4. Wen, C., Yang, C.: Basic theory and methodology on extenics. Chin. J. 58(13), 1190 (2013) 5. Guo, X.: Research and implementation of ICTCLAS API with Delphi. Comput. Program. Skills Maint. (24), 10–18 (2011) 6. Yang-Bo, W.U., Liang, S.G., University, X.: Optimization of the property of web serverbased on Nginx and Http2.0. J. Xinyu Univ. 22(4), 6–8 (2017) 7. Shi, H.Y.: Discussion on the development process of the application system of Java-B/S structure. Sci-Technol. Inf. Dev. Econ. 18(21), 153–155 (2008) 8. Caponetti, L., Castellano, G.: Java for Image Processing. Fuzzy Logic for Image Processing. Springer, Berlin (2017) 9. Nord, J.H., Hoy, D.R.: Method and a system for responding locally to requests for file metadata associated with files stored remotely: US, US 8131825 B2 (2012)

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10. Mathew, S., Motukuru, V., Martin, M., et al.: Single sign-on between multiple data centers: U.S. Patent Application 10/084,769[P], 25 September 2018 11. Ma, F., Fan, R., Huang, C., et al.: The solution of excess ingredients in hotels deduced by extensible mind mapping. In: 4th Annual International Conference on Wireless Communication and Sensor Network (WCSN 2017), vol. 17, no. 3, p. 03004 (2018) 12. Guan, H.: A New Data Mining Approach Combing with Extension Transformation of Extenics. Future Control and Automation, pp. 199–205. Springer, Berlin (2012)

Analysis and Improvement of User Behavior of Free-Floating Bike Sharing in China Based on Questionnaire Meiyu Li1,2(&), Xifu Wang1, Xi Zhang1, and Yuan Yuan1

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1 School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China [email protected] School of Business, Hebei GEO University, Shijiazhuang, China

Abstract. Free-floating Bicycle sharing (FFBS) system is an innovative bicycle sharing mode. By virtue of the characteristics of dockless operation and random parking, the Chinese FFBS system brings great convenience to users and also brings problems of urban management. Based on the questionnaire, this paper analyzes the performance and reasons of the non-compliant behaviors of FFBS users, and puts forward some strategies, such as product and SC design, contract mechanism design, and refinement management, to regulate the behavior of users, so as to promote the healthy development of FFBS. Keywords: Free-floating bike sharing User behavior
China


Questionnaire
Supply chain

1 Introduction In recent years, people’s consumption model upgrades with the deeper development and revolution in the technology area such as smartphone, 4G internet, mobile payment, internet of things (IoT) and APP. And an increasing number of people have accepted internet shared bike since its operation characteristics such as free floating, internet unlocking, mobile payment, intelligent integration, and optional service which solves the first and last-mile problem. However, every advantage has its disadvantages. One of the main challenges faced by the new mode is the problem of its urban management. Disturbance to the management of the city and its right-of-way environment, such as disordered utilization and haphazard parking, is inevitably caused by users’ non-compliant behaviors. It is helpful to solve the problem by deeply analyzing the causes of the non-compliant behaviors of FFBS users rather than simply attributing it to “human nature”. In this study, we analyzed the performance and causes of user’s non-compliant behaviors through questionnaire, and discussed the unfavorable ecological environment effect of FFBS system SC due to users’ non-compliant behaviors. The improvement strategies of SC Environment for FFBS system based on user behavior are proposed. The significance of this paper lies in the fact that it provides strategies to improve the users’ behaviors and the sustainable development for the FFBS system. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 601–610, 2019. https://doi.org/10.1007/978-3-030-02804-6_79

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The rest of this study is organized as follows: a brief literature review on FFBS system is provided in Sect. 2; Sect. 3 describes our questionnaire survey and discussed the performance and causes of the non-compliant behaviors of FFBS users; Sect. 4 puts forward strategies to improve the users’ behaviors; Sect. 5 draws a conclusion and discusses the extending study.

2 Literature Review of FFBS The modern sharing economy derived from the United States is a new economic mode. It advocates integrating off-line idle resources by using a network as a medium and connecting the supply and demand sides via a sharing platform [1–3]. Bicycle-sharing is not an emerging industry as it has been developing for about 50 years. However, in the last ten years or so, its popularity has significantly increased. It is also known by different names in different regions, e.g. the expression ‘cycle hire’ is used in Britain, ‘bicycle sharing’ in the United States [4], and what is currently referred to as ‘bike sharing’ in China was previously alluded to using the phrase ‘public bicycles’. As an innovative bicycle sharing model, bicycles in the FFBS model can be parked and locked to standalone in a public area, eliminating the need for specific sites [5]. SocialBicycles (Sobi) of the United States, China’s Mobike and Ofo, are well-known operators to provide FFBS services. However, the literature of FFBS is very few for its emerging nature. Caggiani et al., Pal & Zhang, Reiss & Bogenberger discussed the rebalance problem of FFBS [5–7]. Caggiani et al. applied spatio-temporal clusters to forecast the bicycles use trend for FFBS [8]. Caggiani et al. designed a FFBS by distribute the congestion charging revenue [9]. And to our knowledge, no article has reported its impact on FFBS’s supply chain ecological environment from an empirical perspective of users’ non-compliant behavior.

3 Questionnaire Survey In order to explore the influence of user behavior on the supply chain (SC) members of FFBS system, so as to seek the responsible party of the corresponding strategies, before designing the questionnaire, we combined literature analysis and interview method to draw the SC structure chart of FFBS system (Fig. 1). 3.1

Questionnaire Design and Implementation

In order to standardize the behavior of FFBS users and reduce the problems of urban management, a survey on the performance and causes of the non-compliant behaviors of FFBS was designed to explore strategies for improving user behavior. Table 1 shows the part content of the design of the questionnaire. A total of 470 questionnaires were distributed and 450 were recovered, with an effective rate of 96%. The survey included 41 urban administrators, 218 students, 114 corporate office workers, 36 self-employed individuals and 41 other professionals.

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Fig. 1. Structure framework of the FFBS SC with operators forming the core enterprises

Table 1. Sample content of the questionnaire (extract) Factors Basic information

Personal aspects/operator aspects/urban administrators aspects

3.2

Specific issues Gender Occupation Age The performance of the non-compliant behaviors of FFBS users (multiple selection) The reasons of the non-compliant behaviors of FFBS users (multiple selection) The quantity and rhythm of operator’s delivery Negative effects of non-compliant behavior (multiple selection) Main deficiencies of operators (multiple selection) Main deficiencies of urban administrators (multiple selection)

Survey Data Analysis

3.2.1 Performance of the Non-compliant Behaviors of FFBS Users Haphazard and disorderly parked is selected by all interviewees, which indicates that the problem is urgent to be solved (Fig. 2). This is also consistent with reality. Many users randomly park their bikes in some areas where parking is forbidden. For example, they may encroach on freeways for non-motorized vehicles (pedestrians, bicyclists and other non-motorized traffic are allowed to use freeways) and sidewalks intended for the

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blind. It has been reported that some local government departments have confiscated shared bikes that are illegally occupying public resources and some legal-operation departments have also had to repeatedly talk to the operators of the ‘offending’ enterprises.

Fig. 2. The performance of the non-compliant behaviors of FFBS users

3.2.2 Causes of the Non-compliant Behaviors of FFBS Users Figure 3 depicts the causes of the non-compliant behaviors of FFBS users. Unreasonable supply and absence of mechanisms design are the two major reasons. The mechanisms mainly include rewards and punishments mechanism and operation monitoring mechanism. Operators and urban administrator are the main responsible parties for these two factors.

Fig. 3. Causes of the non-compliant behaviors of FFBS users

3.2.3 Negative Effects of Non-compliant Behavior Figure 4 shows the results of the negative effects of non-compliance behavior. The two biggest negative effects of non-compliance behavior are urban pollution and the disturbance of public order and traffic. And all respondents believe that the quantity and rhythm of operator’s delivery is unreasonable.

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Fig. 4. Negative effects of non-compliant behavior

3.2.4 Main Deficiencies of Operators and Urban Administrators Table 2 describes the main defects of operators, and Table 3 expresses the main drawbacks of urban administrators. The operators are responsible for the user’s behavior. However, due to the existence of design flaws of product, surplus supply, and lack of supporting guarantee mechanisms, the operators can’t effectively control the users’ behavior. Meanwhile, because of lagging policies, lack of mechanisms design for the behaviors of operators and users, urban administrators fail to effectively regulate users’ behaviors. Table 2. The frequency distribution of the operators’ main deficiencies Main deficiencies Surplus supply Design flaws of product Lack of supporting guarantee mechanisms Inadequate control Insufficient social responsibility

Number of cases Percentage 441 98.0% 418 92.9% 399 88.7% 325 72.2% 90 20.0%

Table 3. The frequency distribution of urban administrators’ main deficiencies Main deficiencies Lack of mechanisms design Lagging policies and measures Inadequate planning and management for parking areas and bicycle lanes Insufficient specification guidance

Number of cases 414 383 267

Percentage

239

53.1%

92.0% 85.1% 59.3%

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4 Improvement Strategies of FFBS Users’ Behavior Regulating the behavior of users to improve the service ecological environment of the FFBS system requires a joint effort from several entities: the FFBS operators, the enterprises in the SC, government departments, and the users. Therefore, based on the above analysis of user behavior, we construct a mind map for improving the service ecological environment of the FFBS (Fig. 5).

Improve user experience Operator/ supply chain Enhance supply chain efficiency

Product iteration upgrades Intelligent technology innovation Manufacturing upgrades

User awareness promotion Laws and regulations

Refinement operation

Meticulous operation and maintenance Refinement management and service Rational layout and supply

Scientific planning Reasonable supply

Service ecological environment of the FFBS

Access threshold restriction

Publicity and education

Refinement & intelligent operators. Continuous improvement Strengthen requirement supervision and control Parked regional management system Punishment measures against rule - breakers

Refinement supervision

Regulatory authorities

Normalization mechanism

Fig. 5. Idea map for improving the service ecological environment of the FFBS based on user behavior

4.1

Design Improvement of Product and Supply Chain

The FFBS enterprises and SCs need to overcome the flaws in their products and SC design using appropriate technology. They need to enhance the application of intelligent technologies and IoT and strengthen their research and development (R&D) activities. They also need to improve the innovation capability of their software and hardware equipment and the information about their products and SC. In this way, they can guarantee there will be benefits for themselves and their SC participants (a win– win situation) in the design phase. IoT-based big data management, product manufacture and upgrade of the enterprise, and ability to innovate their intelligent technologies are core driving forces responsible for future development of the FFBS industry. Operators, and their upstream cooperative enterprises that play a critical role in improving the design of the FFBS SC or products, need to continually increase their R&D activity. By making improvements to their R&D capability, they can perfect their GPS, IoT, and cloud technologies. In addition, they must be expected to improve the accuracy of their GPSs and the R&D of other patented technologies, so they can solve the problem of obstruction, reflection,

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attenuation, and positioning-deviation of the GPS signals in special scenarios. They need to make the off-line supply and maintenance of bikes interactive and convenient. At the same time, they should enhance their upgrade speed and product quality through precise product and SC design, and also ensure the improvement is sustainable. On the premise of retaining the design advantage of random riding-parking, FFBS system also need to consider designs for ‘intelligent’ parking stations, such as, flexible piles with ‘main control posts’ and electronic fences. In the design of supply chain, we emphasize the intelligent scheme of bicycle accessory facilities. 4.2

Design and Implement Risk-Sharing Contract Mechanism of Noncompliance

Due to the lack of effective mechanism design, operators, who are directly responsible for the management of user behavior, have been unable to implement regulations and measures to curb user violations. In the absence of mechanisms, especially lack of punishment mechanism, the decision of operator’s behavior choice originates from two points: one is lower payment and operation cost from non-active cooperation in the game between the urban manager and the operator; and the other is higher benefits from non-active constraint in the game between the operator and the user. Contract mechanism of urban management cost sharing should be designed and carried out by analyzing the game process between the operator and the urban manager. The inspection probability of urban manager, the penalty cost and penalty rate of operators should be defined scientifically in the contract mechanism. Contract mechanism of user non-compliance behavior risk sharing should be designed and executed by analyzing the game process between the operator and the user. The share threshold of users should be reasonably determined in this contract mechanism. 4.3

Controlling the Quantity and Rhythm of Delivery and Rebalancing Existing Bikes

The operators should cooperate with the government and research institutions to determine an appropriate regional bike supply mechanism. This should be based on an analysis of the big data available according to the city’s characteristics, public demand, and development orientation. For better regulation operators’ behavior, the government can adopt the following strategies: develop a real-time supply indicator system and quantity monitoring equipment, based on real-time data and indicators (such as monthly active rate) of realtime changes to control the operator’s rhythm and quantity. On the other side, surveillance cameras are densely distributed in many parts of China. Therefore, by cooperating with supervision departments, the operators can use these cameras to their advantage. For example, the vehicles of off-line dispatchers can be equipped with on-board computers to allow them to access a real-time monitoring system. In this way, the dispatchers can rapidly acquire information about any bike imbalance at stations in the areas they cover and deal with problems in real-time.

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Cooperative Governance and Refinement Management of SC Members

The FFBS enterprises need to establish a cooperative governance mechanism for the members of the SC. A framework for achieving this is shown diagrammatically in Fig. 6.

Fig. 6. Cooperative governance of FFBS system

The FFBS enterprises can introduce a credit score system to motivate users to adopt appropriate bike use standards. They can also formulate and cooperatively implement a penalty mechanism with the supervision departments. The three parties can be guaranteed to cooperate effectively through the delicate operation of the FFBS enterprises, meticulous supervision of the regulatory departments, and credit score systems. The refinement operation and management of FFBS mainly involves making refinements in the SC businesses, procedures, technologies, management, services (segmentation of customers and service scenarios), SC process, and platform operation. The core contents are shown in Fig. 7. Refinement operation and management Process refinement Business refinement Refinement of the process Technical refinement

Services refinement

Supply chain management refinement

Platform operation refinement

Technical refinement

Refinement of SC cost

Platform operation visualization

Management refinement

Refinement of SC procedures

Online and offline controllable

Interactive services refinement

Refinement of SC collaborative operation

Platform operation predictability

Partner services refinement

Refinement of SC design

Online and offline dataoriented (traceable, manageable)

Fig. 7. Refinement operation of the SC of FFBS system

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In the refinement operation, more attention needs to be paid to the meticulous management of the bike fleets, accurate operation of parking and locking of bikes, and perfecting user interactive experience and refinement of the station network.

5 Conclusions In this study, we use the method of questionnaire survey to analyze the cause and influence of FFBS user’s non-compliance behavior, and propose some measures to take, including improving the design of product and supply chain, establishing the risksharing contract mechanism, controlling the quantity and rhythm of delivery, and developing refinement management, to make a healthy and developing FFBS service ecological environment. The main conclusions are: the non-compliance behavior of FFBS users mainly takes the form of haphazard and disorderly parking, malicious damage, illegal possession, and non-standard riding practices. These behaviors are mainly caused by imbalanced supplying of bikes, design flaws of the SC and products, and an absence of contract mechanism design; various measures, including improving the product design, implementing the risk-sharing contract mechanism, scientifically determining reasonable layout and supply arrangements, cooperative governance, and refinement management, can be used to improve the service ecological environment of the FFBS. The FFBS belong to the field of transportation research which generally involves different disciplines. However, FFBS should not be simply considered as a transportation issue. Rather, we also need to refer to theories and methods appropriate to other disciplines, e.g. marketing, psychology, human engineering, and product and system design. Acknowledgments. This work was supported by the Soft Science Foundation of Hebei Province (18454204D); the Hebei Provincial Social Science Foundation (HB15GL039); the Program for Sciences and Technology of Hebei Province (164576484, 17454209).

References 1. Belk, R.: You are what you can access: sharing and collaborative consumption online. J. Bus. Res. 67(8), 1595–1600 (2014) 2. Kathan, W., Matzler, K., Veider, V.: The sharing economy: Your business model’s friend or foe? Bus. Horiz. 59(6), 663–672 (2016) 3. Shaheen, S.A.: Mobility and the sharing economy. Transp. Policy 51, 141–142 (2016) 4. Ricci, M.: Bike sharing: a review of evidence on impacts and processes of implementation and operation. Res. Transp. Bus. Manag. 15, 28–38 (2015) 5. Pal, A., Zhang, Y.: Free-floating bike sharing: solving real-life large-scale static rebalancing problems. Transp. Res. Part C Emerg. Technol. 80, 92–116 (2017) 6. Caggiani, L., Camporeale, R., Ottomanelli, M., Szeto, W.Y.: A modeling framework for the dynamic management of free-floating bike-sharing systems. Transp. Res. Part C Emerg. Technol. 87, 159–182 (2018)

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7. Reiss, S., Bogenberger, K.: GPS-data analysis of Munich’s free-floating bike sharing system and application of an operator-based relocation strategy. In: IEEE 18th International Conference on Intelligent Transportation Systems (ITSC 2015), pp. 584–589. IEEE (2015) 8. Caggiani, L., Ottomanelli, M., Camporeale, R., Binetti, M.: Spatio-temporal clustering and forecasting method for free-floating bike sharing systems. In: International Conference on Systems Science, pp. 244–254. Springer, Cham (2016) 9. Caggiani, L., Camporeale, R., Ottomanelli, M.: Planning and design of equitable free-floating bike-sharing systems implementing a road pricing strategy. J. Adv. Transp. 2017, 1–18 (2017)

A Study of Learning Effects in MOOC: An Example of Ideological and Political Education Courses in China Tingting Duan(&) School of Marxism, Northwestern Polytechnical University, Xi’an, China [email protected]

Abstract. This paper analyzes the learning effects in MOOC for different kinds of students based on the constructivist learning theory. A comparison experiment is made between classified students and unclassified students in the paper. The results show that by applying different teaching designs to the classified students, the learning effects of classified students in MOOC are significantly improved. Finally, some suggestions are proposed to strengthen students’ learning effects in MOOC. Keywords: Constructivist learning theory Learning effects


MOOC
Experiment design

1 Introduction In the nineteen-eighties, Piaget and other western psychologists established the constructivist learning theory on the basis of cognitive learning theory. Constructivist learning theory emphasizes the initiative, practicality, creativity and sociality of learning. Constructivism emphasizes the learner-centered teaching theory, in which teacher is an instructor, organizer, helper and promoter [1]. And constructivist learning theory holds that situation, collaboration, conversation and meaning construction are the four elements of learning [2]. Nowadays, the teaching mode of MOOCs is widely used in Chinese university courses. Compared with traditional teaching, MOOCs have a positive effect on learning effect and its teaching mode is more in line with the theory thought of constructivism. In the 60’s of 20 centuries, in the face of the situation of popularization of higher education, Burton and Martin divide students into four types which are academic type, social type, professional type and nonconformist type. From the perspective of subculture, Horowitz divides students into four categories: outsiders, social people, new outsiders, and rebels. Kuhn believes that according to the levels of effort, students can be divided as follows: leisure, entertainment, community, social, scientists, individualist, artists, avid, intellectuals and traditional [3]. In recent years, Chinese scholars have also begun to pay attention to the types of students in universities. Based on the two dimensions of students’ satisfactions about the quality of teaching and academic achievement, Chinese scholars divide the students into four types: high efficiency, non-action, positive and negative [4]. This classification method will be adopted in this paper. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 611–619, 2019. https://doi.org/10.1007/978-3-030-02804-6_80

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MOOC is open and extensive online courses. At present, most of course contents in MOOC for different types of students is the same in Chinese university. In the framework of constructivist learning theory, this paper discusses the learning effect after the classification for students, designs an experiment to evaluate the learning effect, and proposes some suggestions according to experiment results. The rest of paper is organized as follows. Section 2 introduces the constructivist learning theory. Section 3 presents a contrastive analysis between the learning effects of classified students and unclassified students in MOOC, by taking the ideological and political education courses as an example. Next, suggestions are proposed in Sect. 4. The conclusions are drawn in Sect. 5 followed by references section.

2 Constructivist Learning Theory Constructivism believes that students’ learning is not the process of transferring knowledge to students. Instead, it’s a process that students construct their own knowledge. Students are not passive receivers of stimulus, and the outside information itself makes no sense. The meaning comes from the construction of the process of mutual interaction between the new and old knowledge. Students encode the new knowledge in the original experience, and constantly adjust the old knowledge, so as to understand the knowledge. The basic contents of constructivist learning theory include the meaning of learning and the methods of learning. 2.1

The Meaning of Learning

Constructivist learning theory holds that situation, collaboration, conversation and meaning construction are the four elements of learning environment. Firstly, the situation in the learning environment should help students to construct the meaning of what they have learned. Secondly, collaboration is very important to the process of learning. That is to say, the subject cannot be directly connected to the external environment, so they must cooperate with each other to lead to the outside world. Collaboration has an important role in the collection and analysis of learning materials, the hypothesis and verification, the evaluation of learning outcomes and the final construction of meaning. Next, conversation is an integral part of a collaborative process. In this process, each learner’s intellectual output is shared for the whole learning community. Last, meaning construction is the ultimate goal of the entire learning process. In order to achieve a deep understanding, it’s vital to help students constructing meaning in the process of learning. In conclusion, under the guidance of constructivism, a new set of effective cognitive learning theory can be formed, and the ideal learning effect can be realized on this basis. In the backdrop of the deepening of education reform and constantly changing of teaching mode, teachers can no longer blindly indoctrination. By gradually developing the students’ ability to think independently, they should make full use of their talents, learn to memorize and actively construct related teaching mode [5]. In the constructivism learning theory, the emergence of the mode of MOOCs represents a spiritual requirement that conforms to the characteristics of the era.

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The Principle of Teaching Design

The principles of teaching design can be summarized as follows. Firstly, the process of teaching design should be as student-centered as possible. Each student’s learning ability is different, because their growth environment and experiences are different. Traditional teaching methods pay little attention to the difference of students’ coding methods due to their different knowledge systems. Although the courses in MOOCs have many advantages, they still lack the pertinence for different types of students in the university. Therefore, this paper will classify the students and different teaching methods will adopt for different types of students. Secondly, the process of teaching design should emphasis on collaborative learning. Constructivism believes that the interaction between learners and their surrounding environment plays a key role in the understanding of learning content (i.e. the construction of knowledge meaning) [6]. For instance, questions teaching and group discussion teaching are popular ways of interaction in China. Thirdly, the process of teaching design should emphasize the design of learning environment. Constructivism holds that learning environment is a place where learners can explore freely and learn independently. In China, universities tend to specify some courses in MOOCs for students. Thus, students are not completely free to choose courses. This is not an ideal learning environment for each type of students. So, in this experiment, some types of students are absolutely free to choose the courses. Fourthly, the ultimate goal of the learning process is to accomplish the meaning construction. In the constructivist learning environment, it is emphasized that the students are the active constructors of the cognitive subject and the meaning. Therefore, students’ meaning construction of knowledge is the ultimate goal of the whole learning process [7].

3 Experiment 3.1

Experiment Design

In this experiment, the research object is all freshmen in a comprehensive university. Two groups of students were selected for the experiment design. Each group is 50 students. Based on the classification above, each group is made up of four types of students. The number of students of each type is the same in the two groups. Table 1 shows the number of students in each type. Table 1. The number of four types Category High efficiency Positive Non-action Negative Number 9 18 19 4

The two groups of students study the ideological and political education (IPE) courses in MOOC. It’s a basic course in university of China. The difference between the two groups is that the first group is the unclassified class. That is, the first group does not conduct classified teaching in the experiment. At the same time, the second group is

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Group 2a Pre-class preparation Specific courses 1 Free selective courses 2 Free selective courses 3 Specific courses 4 Specific courses Interactive learning during Group discussion 1 Problem-based teaching the course of the class 2 Problem-based teaching 3 Heuristic teaching 4 Heuristic teaching Review after class Submit papers Submit papers a 1–4 represents students’ classification. 1 is the type of high efficiency; 2 is the type of positive; 3 is the type of non-action; 4 is the type of negative. Teaching design

Group 1

classified class (i.e. conduct classified teaching). In a word, the teaching design of the two groups is different. Table 2 shows that students in the first group (i.e. unclassified class) learn the same specific courses. Their interaction style is group discussion. This is a common teaching design in Chinese universities. In group 2 (i.e. classified class), based on the principles of constructivism learning theory and the characteristics of all types of students, the curriculum is designed as follows: Firstly, the high efficient students are satisfied with the school’s teaching work, actively cooperate with it and are good at exploring their learning interests; Active students have good academic performance, but they are not satisfied with the teaching work of the school. These students have their own characteristics. So, efficiency and positive students are designed to choose courses freely, and their interaction style is Problem-Based teaching which is operates in the problem situation and aims at solving problems through learners’ teamwork. Secondly, although non-action students have a high degree of satisfaction with school’s teaching work, they have limited improvement in their academic performance which is due to their own problems or improper learning methods; Negative students are not satisfied with school’s teaching work and have poor academic performance. They are passive and don’t have much enthusiasm for learning. Considering their characteristics, these two types of students are suitable for specific courses and heuristic teaching. Heuristic teaching is starting from the reality of students, adopts various ways to inspire students’ thinking, to mobilize students’ initiative and enthusiasm in learning, and to make them study vigorously. Furthermore, both groups are asked to finish and hand in their papers after class. All above design is mainly based on the principles of constructivism learning theory.

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Evaluation of Learning Effect

In the evaluation of learning effect, this paper adopts the combination of process evaluation and result evaluation. To be specific, the effect is evaluated from three aspects: paper writing, final exam and learner’s satisfaction. Firstly, paper writing is graded with the following marking standards: A, B, C. Secondly, the final exam will be conducted when the IPE courses are finally done. Beyond that, students will be investigated on their satisfactions with the courses (i.e., no satisfactory, somewhat satisfactory or satisfactory).

4 Results 4.1

Results of Paper Writing

In this experiment, each student submits 10 papers in the whole courses, and each group submitted 500 papers in total. The comparison results between the two groups are shown in Fig. 1. Students’ papers in the second group get more A than that of the first group. It’s 183 to 130. The number of papers which are marked with B is similar. Papers in the second group get a third less C than the first group. This indicates that the overall performance of papers in the second group is much better than that of the first group. In other words, the learning effects in the second group are much better.

Fig. 1. The comparison of paper writing between the two groups

4.2

Results of Final Exam

At the end of the course, the two groups have their final exams. The full score of the exam is 100. Figure 2 shows the exam results of the two groups. Most significantly of all, students in the second group who scored below 60 are half less than the group 1. In the meantime, the number of students in group 2 who score between 90 and 100 is more than twice as many as the group 1. On the whole, by classified teaching, students’ learning effects in MOOC are much better than that of by unclassified teaching.

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Fig. 2. The comparison of final exams between the two groups

4.3

Results of Satisfaction Survey

Finally, we conduct a satisfaction survey on the students. Figure 3 shows the results of the group 1. Ten percent of the students are not satisfied with the curriculum. Fifty-two percent of the students are somewhat satisfied and thirty-eight percent of the students are satisfied with the courses.

Fig. 3. The satisfaction in group 1

Fig. 4. The satisfaction in group 2

According to the survey, the main reasons why ten percent of the students are not satisfied with the courses are as follows: (1) The teaching videos which are chosen in MOOC cannot stimulate their interests in learning the IPE courses; (2) There is no greater gain after learning this course in MOOC mode; (3) The curriculum lacks a supervisory mechanism for students. In group 2, four percent of the students are not satisfied with the curriculum. Thirtyeight percent of the students are somewhat satisfied and fifty-eight percent of the students are satisfied with the courses. The overall situation can be seen from the Fig. 4. Two students in the second group are dissatisfied with the courses. The reason is that they consider that the length of teaching video is too long.

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In short, the students in group 1 are less satisfied with the courses than that in group 2. Satisfactory students account for about sixty percent of the total number of students in group 2. Somewhat satisfactory students account for half of the total number of students in group 1. This once again demonstrates that it is more helpful for students’ learning effects through the classification teaching in MOOC. 4.4

Changes in Students’ Types After the Experiment

After the experiment, using the same student classification criteria as before, the students type structure of the two groups are all changed. The changes can be seen from the Figs. 5 and 6.

Fig. 5. The changes in group 1

Fig. 6. The changes in group 2

In group 1, the numbers of high efficiency and non-action students are increased. Instead, the numbers of positive and negative students are dropped. In group 2, the number of high efficiency students has a big increase. The numbers of positive students are flat and non-action and negative students are reduced in number. That is, after learning in different teaching design, the learning state of each student is changing. Or rather, through different teaching designs, we can change the learning state of students, thus improving the learning effects of them.

5 Suggestions 5.1

To Provide Guidance According to Different Types of Students

Different types of students in universities need different teaching methods. In the teaching process, students should be taken as the center of teaching. Teachers should pay attention to the motivations and behavior characteristics of all kinds of students. This also conforms to the requirements of constructivism learning theory. Because of the differences in gender, grade, major and the source region, students in universities have different knowledge, experience and intention, which require the teaching

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management departments to consider the needs of different students in the teaching process, so as to improve the quality of the students and the satisfaction of the teaching work. 5.2

To Strengthen the Supervision of Students

Given the students have a limited self-control and widespread procrastination in China, if there is no law of traditional classroom attendance, face-to-face interaction between students or teachers and students, college students are difficult to fully participate in MOOC lessons. Therefore, we are not in favor of that because of some outstanding advantages of MOOC, traditional courses can be completely substituted by MOOC. So, teachers are required to supervise students’ learning process in MOOC through relevant channels. For example, by reasonable and scientific performance evaluation scheme, assess the whole learning process of students. The performances of pre-class, mid-class and aft-class are all included in the final grade, thus fully embodying and improving the students’ learning enthusiasm, and also increasing student’s acceptance with achievement assessment. 5.3

To Make Teaching Video in MOOC More Clear

Video is one of the significant factors in the teaching mode of MOOC. The quality of video is related to the effect of students’ independent learning before class. Therefore, teaching video should be short and concise. The attention span of human’s brain is generally around 15 min. Therefore, video teaching should not be too long and it is best to keep it around 10 min. If the video content is not brilliant enough, longer video would make students lose patience and interests in learning. Meanwhile the production or selection of video in MOOC should take into account the natures of courses and the characteristics of students. It will increase students’ satisfaction with courses, so as to improve the learning effect of students. Acknowledgement. This research was supported by the Fundamental Research Funds for the Central Universities of China (No. 18SZYB07) and Caijingtong Education Industry-University Cooperative Education Project of Ministry of Education of China (No. 201801091012).

References 1. Jonassen, D.H.: Designing constructivist learning environments. Instr. Des. Theor. Models: A New Paradig. Instr. Theor. 2, 215–239 (1999) 2. Savery, J.R., Duffy, T.M.: Problem based learning: an instructional model and its constructivist framework. Educ. Technol. 35(5), 31–38 (1995) 3. Stoddard, C., Kuhn, P.: Incentives and effort in the public sector: have US education reforms increased teachers’ work hours? Econ. Educ. Rev. 27(1), 1–13 (2008) 4. Jianping, L., Luting, M.: The relationship between types of college students and learning behaviors. J. Natl. Educ. Sch. Adm. 13(8), 78–83 (2013) 5. Chen, L.: Empirical research of college English teaching mode based on computer network. Revista Ibérica de Sistemas e Tecnologias de Informação (E9), 77–78 (2016)

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6. Kyriakides, L., Christoforou, C., Charalambous, C.Y.: What matters for student learning outcomes: a meta-analysis of studies exploring factors of effective teaching? Teach. Teach. Educ. 36, 143–152 (2013) 7. Zeidan, A.: Constructivist learning environment among Palestinian science students. Int. J. Sci. Math. Educ. 13(5), 947–964 (2015)

Quality Management Research of the Manufacturing Process Based on Q Company Products Xinmiao Zhou(&) and Shuguang Sun College of Management Science and Engineering, Shandong Normal University, Jinan 250014, China [email protected]

Abstract. Product quality is the core competitiveness of an enterprise. In order to improve the quality and competitive advantage of product, companies must focus on meeting the requirements of product quality which is the design process to the manufacturing process, and keep an eye on the manufacturing process. The paper takes a large transformer manufacturing company (Q company) with high growth as an example, through analyzing the quality management process and activities of the company and its typical products—transformer’s quality problems, and puts forward corresponding measures which refer to human resources, material control, production equipment management, production environment control and other aspects of the product manufacturing process. Keywords: Product


Manufacturing process
Quality management

1 Introduction It is a consensus that quality is designed, and design quality is demonstrated through the manufacturing process. The production and manufacturing activities are the important content and key link of the company’s sustainable operation activities, and the quality of products largely depends on the quality of the process [1], effectively monitoring and managing the quality of the processed objects in the manufacturing process is an important aspect to reduce the scrap rate and improve the product quality and competitiveness [2]. The continuous quality of the process requires a tight cooperation in the process. However, the existing research on product quality control mainly focuses on how to conduct quality control in the design and trial production stage [3] to reflect quality assurance design, and strengthening the quality supervision from the characteristics of quality [4], and research on quality management technology of supply chain [5], and study on the relationship between product quality management and cost control [6]. Research focuses less on the manufacturing process quality, at present, it mainly focuses on the problems existing in manufacturing process and the research on the supervision strength [7]. From the angle of the problem of product fault, it is still extremely insufficient that the study of the people, the equipment, the materials, the environment and the process methods, and proposing some strategies. Therefore, this © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 620–626, 2019. https://doi.org/10.1007/978-3-030-02804-6_81

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article takes transformer manufacturing Q company as an example. It mainly discusses the continuous quality management of products from the manufacturing process. With the investigation and analysis of the transformer quality problems, it eliminates the factors and causes of the quality deterioration, and formulates relevant countermeasures to improve the transformers. The level of quality and reliability management, reduce the economic losses of enterprises, enhance the core competitiveness of enterprises.

2 Theory of Quality Management in Product Manufacturing Process 2.1

Process and Process Management

In quality management, “process” is an activity that defined as the interrelationship or interaction of inputs into outputs. As shown in Fig. 1.

Input

activity

Output

Value-added conversion (Forward conversion) Including people and other resource Fig. 1. Process diagram

As can be seen from Fig. 1 and the process definition, the process has three elements: input, output, and activity. (1) The precondition of the process is input; (2) The result of the process is the output; (3) Activities are the measures taken to achieve the expected objectives by realizing value-added transformation and controlling the fluctuation of the process [8]. It should be pointed out that only the process of realizing forward transformation is an effective process. Process management is to point to: use a set of practical methods, techniques and tools to plan, control and improve the process of effectiveness, efficiency and adaptability. Simply speaking, the process management is according to the requirement of the purchaser, and other related performance goals, and strictly comply with the requirements of process design personnel, in the implementation process and after the implementation of strict monitoring, to determine whether performance goals are met, may need to adjust in the process, in order to fully achieve performance goals.

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Quality Management of Manufacturing Process

After the product is put into production, whether it can meet the design quality standard depends on the technical ability of the manufacturing department and the quality management level of the manufacturing process. The structure of the ISO9001:2000 standard is also a platform based on process management [9]. Dr. M. Morup offers two types of quality theory [9, 10] and the manufacturing process is to expose the internal quality. Competition makes quality, production and delivery become an important factor to measure enterprise production adaptability and competitiveness. Manufacturing process quality management is to achieve quality, quantity and period of integration, with the minimum cost to play the competitiveness of enterprises.

3 Overview on the Present Situation About Q Company Product Manufacturing Process Quality Management 3.1

Product Quality Management Process and Quality Management Activities of Q Company

Q company’s quality management activities run through the whole production process. It is mainly divided into the following three links: Analyze customer needs; Make product design documents; Production and inspection. Q company’s main quality management activities are: raw material inspection, production process inspection [11] (including incoming inspection, semi-finished products inspection, the complete product inspection), site management [12] (including equipment management, personnel management, etc.). 3.2

Analysis of Product Quality Problems of Q Company

We analyze several kind of common faults that can correspond to the transformer manufacturing process, especially the reason can be attributed to the two most main steps of the manufacturing process—iron-core technology and the winding process (including insulation), discusses the main fault in the transformer manufacturing process and the reasons are attributed to controllable factors such as personnel, equipment, materials, environment and process methods. As shown in Table 1.

4 Q Company Product Manufacturing Process Quality Management Strategies Now, combined with the analysis of Q product quality problems mentioned above, the following strategies are proposed from the perspective of human resources, materials testing, equipment management, environmental control and failure prevention system and so on.

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Table 1. Fault and cause analysis of transformer Common faults

Performance of the fault

Direct reasons

1.Surface melting and burning

1.Switch spring deformation

2.Interphase

2.Disconnection switch is not

contact

Switch

discharging or each splice

installed properly

fault

discharging

3. Personnel are not installed in

3.High pressure fuse wire

accordance with the standards

melting and other phenomena

and principle

Deep reasons

Specific reasons

Personnel

relatively low

the quality of human resources is Personnel training is inadequate Materials

Inadequate supervision of suppliers and materials

1.Discharging

Insufficient

2.Increase and

energy

1. Insulation damage, media

reduce

meter reading;

Insulation

consumption

fault

service life

2.

3.Causing winding fault and

process, etc.

Incomplete

insulation

Materials Technology

other phenomena

materials protection

or incoming inspection of materials Incomplete design process Lack of connection and adjustment between design and manufacturing processes

1.

Unreasonable

design

selection;

Incomplete design or lack of key Technology

technical personnel

Equipment

of daily cleaning

Personnel

is not correct

Environment

clean environment

2. The coil welding is not firm

1.Winding earth fault

3. The degree of dryness and

Winding

2.Interphase short circuit

fault

3.Loose joints, open welding and other phenomena

Slow update of equipment or lack

cleanliness of the coil 4. Error in the number of turns

Low quality of personnel or attitude Insufficient supervision of the

during winding 5. Aluminum foil edge is not neat

1.Partial

heating

of

transformer Iron-core

2. Partial iron-core melting

fault

3.Causing the transformer to trip or not working normally, etc.

4.1

1. Silicon steel sheet cutting unqualified 2. The structure of the silicon steel sheet is broken 3. The iron core is unqualified

Personnel Materials Equipment

Cutting personnel quality is low or lack of training Insufficient

materials protection

or inadequate supervision of suppliers Insufficient daily inspection and management

of

machinery

and

equipment

Establish a Complete Personnel Management System

(1) Clear division of labor (1) Clear work tasks and goals. The transformer manufacturing process mainly includes technicians such as slicing and winding, and it is necessary to clearly assign specific tasks to personnel of different types of work. (2) Know the apprentice’s expertise, inadequacy and personality. The technical work in the transformer manufacturing process is often a mentoring system. It is necessary to find insufficiencies and make new ones correct. This will facilitate the mastery and application of technology. (3) To achieve the best match between people and things. The transformer manufacturing process is broken down into many small links, and each link must be allocated to those who are skilled in this technology.

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(2) Improve incentives and reward and punishment system (1) Target incentives. Managers set a medium-high motivation for employees, which will give employees managers a strong sense of purpose and a strong sense of responsibility for their work. (2) Linking rewards and performance [12]. First of all, there must be a clear performance appraisal standard to avoid unfair feelings. Secondly, it is necessary to show more work and more rewards on personnel rewards. In addition, The company can give employees more honor awards, verbal rewards, etc. 4.2

Strengthen Management and Control of Materials

(1) Strengthen the inspection and protection of raw materials, Large-scale equipment is usually a project that is signed after a bid has been tendered. It will be accompanied by a description of the requirements for raw materials. If there is no requirement, the company shall further sample and inspect each batch of materials provided by the cooperative supplier, and sample and test the materials that are prone to problems. (2) Strengthen the management of suppliers. First, divide the supplier’s level. Secondly, for suppliers that provide raw materials or parts, at least one review should be conducted each year. If the suppliers are qualified, companies can maintain a cooperative relationship. If not, they will give up the cooperation. In addition, the company points out the problem to the unqualified supplier and could consider it again after the adjustment was completed. (3) Establish an important parts repository. This mainly takes into account the impact of the environment on the material. For example, some materials are sensitive to changes in humidity, oxygen concentration, etc. At this time, Some special parts can be customized in advance according to customer’s requirements without reserve in order to ensure the best use effect and the longest service life. 4.3

TPM Management of Production Equipment

TPM (Total Productive Maintenance) means “all employees’ production and maintenance”. It is based on the concept of equipment as the center and the participation of all employees in management and maintenance, and thus achieves optimally optimized management techniques. The modern TPM starts from the entire production system and build a “disaster prevention” mechanism for the whole company and the whole enterprise to achieve zero consumption [13]. For large-scale production equipment, periodical overhaul is a difficult task. It can only be partially repaired or even repaired when a fault occurs. Therefore, the maintenance of production equipment is more important, and the maintenance of equipment quality in TPM needs to be implemented.

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Strengthen Management and Control of Production Environment

(1) In the production process of the transformer, the most important thing in the winding process is to do the insulation, so the production workshop should be kept clean and reduce the invasion of dust. (2) Transformer coils and other structures are mostly metal products, and the presence of corrosive gases is strictly prohibited in the production workshop. (3) Control the temperature and humidity. When there is a high relative humidity at high temperatures, the prolific breeding mold affects the spray paint on the surface of the tank. High humidity also affects the corrosion of metals. The drying of machines and coils should be done well in the production process. (4) Avoid environmental problems caused by people’s habits, such as incomplete dust removal before entering the workshop. 4.5

Establish a Complete FRACAS System

FRACAS is the “Failure Report Analysis and Corrective Action System”. The purpose of establishing FRACAS is systematically and comprehensively report, trace, and analyze the product during the design and manufacturing phase. Thus, companies can take appropriate measures to correct mistakes and prevent faults from recurring, so as to improve the quality of products to ensure that the requirements for product reliability and maintainability are met [14]. After the establishment of the FRACAS system, the enterprise should pay attention to and apply this system, especially the multi-angle analysis of the faults that have been generated, use the FRACAS system to accumulate experience, and summarize the information that is relevant for the design and manufacturing process of related products in the later period. As a result, the company can improve the quality of the manufacturing process.

5 Conclusion This article takes Q company as an example to study the quality management in transformer manufacturing process. Through the investigation of Q company, the type of transformer failure was analyzed, and the reasons were attributed to the company’s human, equipment, material, environment, and methods in the quality management of the product manufacturing process, and analyzed the deep reasons of these five aspects. Finally, specific and implementable measures were proposed.

References 1. Qin, X., Liang, G., et al.: Quality Management. Science Press, Beijing (2002) 2. Ma, J., Liang, G., Li, H., Hui, H.: Research on quality oriented manufacture process control management. Mach. Tool Hydraul. 36(2), 17–19 (2008)

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3. Zhang, H.: Design of elevator component R&D project and quality control research during trial production stage – Take elevator control cabinet R&D project as an example. Nankai University (2009) 4. Xie, S.: Study on construction project quality management. Res. Finan. Econ. Issues 6, 180 (2015) 5. Chang, L.: Study on Quality Management Technique under the life cycle of Manufacturing Enterprise Supply Chain. Chongqing University (2012) 6. Yuan, D.: Study on correlation of product quality management and cost control. SME Manag. Technol. (Late edition) 12, 12–13 (2016) 7. Jiang, X.: The analysis on quality control of transformer manufacturing process. Intern. Combust. Engine Parts 8, 152–153 (2018) 8. Ling, T.: Study on Quality Management of Grinder manufacturing process. Anhui Agricultural University (2013) 9. Wu, Z., Yu, Z., et al.: Quality Design. Mechanical Industry Press, Beijing (2004) 10. Ma, F., Shi, X.: Product design quality fuzzy evaluation based on design for quality. Manuf. Technol. Mach. Tool 1, 63–65 (2009) 11. Zhang, X.: The Study on Quality Control in Solar Cell Manufacturing Process of X Company. Shan Dong University (2014) 12. Wang, L.: On performance appraisal and employee incentive measures. Hum. Resour. Manag. 6, 73–74 (2017) 13. Luo, X.: Optimization of TPM management in the enterprise. China Plant Eng. 12, 20–21 (2017) 14. Wang, L.: Failure report analysis and corrective action system. In: Locomotive Diesel Engine Fault Management Application. Railway Quality Control, vol. 11, pp. 24–25 (2014)

Virtual Writing Interactive Display Based on Unity Xuemei Tang(&) and Shuyuan Shang Beijing Institute of Fashion Technology, Beijing 100029, China [email protected]

Abstract. Purposes: Combining virtual reality technology with traditional calligraphy is to try a new writing mode and explore a new way for traditional writing in a digital media environment. Methods: By using the 3dsMAX modeling software and the Unity Engine and the external device HTC VIVE to achieve virtual writing interactive function, it has a certain enlightening effect on the innovation of traditional calligraphy. Conclusions: The realization of the virtual writing interactive display has injected new interest into traditional writing, and also played a certain role in the inheritance of traditional writing. Keywords: Unity


Virtual writing
3dsmax
HTC VIVE

1 Introduction With the arrival of science and technology, young people are writing less and less with pens. Most of them are keyboardists. Writing brushes, ink sticks, paper and ink stones have slowly disappeared from our daily lives. For this phenomenon, a virtual writing method is proposed to make the traditional pen writing form into a virtual writing form in the virtual space. At present, the research on digital calligraphy creation has already started. However, due to the limitations of the technological level and the development of interaction theory, the pace of development has not kept pace with the development of digital life. At this stage, the interactive mode of digital calligraphy creation is divided into visual interaction and pressure interaction. The interaction mode of the visual system is limited by the current interaction mode of the touch screen, and it stays in the two-dimensional interactive mode. The touch-based, sliding, and stretching are the main methods. Most of the pressure-based interactions require auxiliary devices, such as various kinds of electronic writing brushes. This contact also has only two dimensions, direction and strength [1]. The two kinds of interaction methods have not been widely popular, mainly because of too little interaction and lack of immersion. In order to achieve this immersive three-dimensional virtual writing interaction, this paper uses a study room as a virtual space, combines the unity engine to develop HTC VIVE, and continuously debugs the program until the user can freely and smoothly write interaction in the virtual space. This kind of interactive and immersive writing form will be more attractive and interesting to people because the traditional way of writing © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 627–632, 2019. https://doi.org/10.1007/978-3-030-02804-6_82

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is to write in a real environment with a real pen, which is unavoidably monotonous. This form of writing also stimulates the curiosity of the general public and nontechnical people and triggers them to experience.

2 Construction of Study Scene First of all, a four-meter-wide, four-meter-long space was created. There was a bookshelf, a desk, several chairs, several screens, and some writing instruments on the desk. These objects in the scene are basically created using the standard primitives in 3dsmax, such as the cubes and cylinders which are partially adjusted and modified. In order to reduce the rendering pressure, the number of points and faces of the object is reduced to the minimum. The most used commands are shell modifiers, editable polygons, chamfer commands, spline editing, FFD modifiers, extruding commands, image merging commands, and so on. Figure 1 creates one ordinary but concise study scene.

Fig. 1. Wireframe diagram

Fig. 2. Rendering effect

Fig. 3. Import effect in Unity

3 Lighting Mapping and Rendering 3.1

The Use of Light

The light is mainly composed of two parts by ambient light and artificial light. Ambient light is simulated by spotlights. Artificial light is simulated by free point light sources and floodlights. The adjustment of the lighting parameters is a process that requires patience, and repeatedly adjusting the intensity multiplier and subdivision values until a suitable lighting effect is achieved. 3.2

Texture Map

Collect the texture images of the relevant material and paste them. What I want to put forward here is how to make the texture more delicate and detailed. It is not only necessary to adjust the reflection and embossing commands, but sometimes it is also necessary to expand the VU for partial adjustment and other operations. Considering

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the compatibility of unity and 3dsmax I chose the default standard map in 3dsmax instead of using other map modes. 3.3

Rendering of Virtual Scenes

When rendering, the unity compatibility problem is also taken into account, using the default renderer in 3dsmax instead of the V-Ray renderer. Figure 2 is the effect of rendering a real book scene.

4 Implementation of Writing Interaction 4.1

External Devices HTC VIVE

Choosing HTC VIVE instead of KINECT and mouse is because HTC VIVE has a handle that make it look like a real person holding a pen, which has more sense of reality and immense. After the connection to the external device is completed, the system will prompt to install Steam VR and then make Steam VR settings. The hardware part of HTC VIVE mainly includes three pieces: a head-mounted display, two positioners, two interactive handles, and room scale is set to a space of 2  2 m. Utilize unity as the development platform, first create a project file named bookhouse, install two plugins from the asset store which are steam VR plugin and vive input utility, delete the default camera in the scene, and drag the CameraRig and VivePointers from the assets panel into the scene. So the unity and HTC combined development settings are complete. 4.2

Study Scene Import Unity

Exporting files whose suffix names are fbx in the 3dmax and open them from unity because the default units of the two kinds of software are different. It is recommended that they are both unified into the same unit when you start modeling. I am accustomed to setting the software units to meters. Therefore, there is no need to adjust the scale after importing, and import the effect of the scene in Unity software (as shown in Fig. 3). 4.3

The Realization of Interaction

The virtual interactive writing interaction methods mainly includes the following steps: the instantaneous movement of the handle, the interaction of grasping objects, the interaction of holding the pen, and the interaction of the writing, and wherein the interaction of the handle movement mainly realizes that the experiencer can quickly reach the designated object, and the interaction of the grabbing objects mainly realizes that the experiencer takes the brush model effectively, and the interaction of the gripping pen mainly realizes the handle to hold the pen in a natural posture of holding the pen, and the written interaction mainly realizes that the experience person can normally write word and revoke strokes one by one. The first is the interaction of the handle movement. The technical principle of position transfer based on Bezier curve is to use VIVE’s handle controller to launch a

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Bezier curve in a virtual scene, and then the curve will collide with the object of Box Collider, get an intersecting point, and the script will drive the persona Send to this point to achieve the location of the transfer. Using the Bezier curve as the ray emitted from the controller can be transmitted at any height. When the traditional linear ray is higher than the starting point or below and close to the starting point level, the collision transmission point will be displayed inaccurately. The process cannot complete the operation in time and the Bezier curve design can avoid this problem. When the height of the pointing point is less than the starting point, the Bezier curve is a smooth curve, and when the pointing point is higher than the starting point, the second-order Bezier curve will obtain the position of the collision point with the connection of a fixed angle and a fixed length [2]. The Bezier curve emitted by the handle collides with objects such as ground, desktop, and chair that can interact with each other in the scene to obtain the position information of the collision point. Here, in order to allow the user to clearly see the position of the intersection, a cylinder model is created in the scene, and when you press the handle disk key to accurately view the location of the positioning point. In Unity, I use the source file named LaserPointer to realize the instantaneous movement interaction of the handle. The specific optimized core code is as follows (Fig. 4).

Fig. 4. Moving effect of the handle

The object’s grabbing interaction uses an interactive handle to collide with the object. If a collision occurs and the conditions for grabbing (where the name contains the name of the object that contains the maobi) are pulled, the trigger key is used to grab the object. There are a few maobi models, you can grab one of them, and then bind with the handle as a child of the handle, so that the object will follow the handle movement. The implementation of this interaction in Unity is based on the collidingobject object in the C# script-driven scenario. Write a script named ControllerGrabObject in unity. The specific optimized code is as follows (Fig. 5).

Fig. 5. Grasping effect of handle

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In the early stage of debugging, the effect of repeated crawling is poor. After the handle grabs the object, they are always separated by a certain distance. There are two solutions to this problem. One is to adjust the size of the collision body of the maobi model. The second method is to cancel the collision body after grabbing the maobi in the program. After grabbing the object, you want to make the pen point down, in accordance with the normal gripping gesture, instead of writing according to the grasping posture, and you need to rotate the object into the handle’s sub-object before rotating the angle or use the smooth transition method. Here I take the former solution. After the crawling process is completed, the grabbed object moves to the designated position following the hand. At this time, the writing interaction needs to be realized. This process will be more complicated. You need to think clearly about this process. After starting to grasp the object, there are two points in the scene that coincide with each other. When pressing the trigger key and the side key to write, and when the distance is less than a certain value, the next point is not generated, and when the value is larger than a certain value, a new point is generated. When the program runs, a new object pool is created to load the lines generated by the instantiation. Write a name called line script in unity, and the specific optimized code is as follows (Fig. 6).

Fig. 6. Writing interactive effects

When you want to cancel the line operation, press the menu key to undo the previous line. The line will also be removed from the object pool. This design operation can reduce the computer’s endurance. The core code is as follows (Fig. 7).

Fig. 7. Undo interactive effects

In order to meet the different needs of users, the color and width of writing fonts can be set arbitrarily. At this point, the written undo interaction is basically completed. User can smoothly capture the maobi model and perform a writing experience in the virtual space.

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5 Summary The virtual writing interactive display utilizes the unity engine and an external HTC VIVE device to realize the interactive interaction of normal writing in the virtual space. You can set the line thickness and color changes to write any word. But in order to simulate the thickness of the real pen I still need to continue to study. Purpose of this study is to achieve a three-dimensional writing experience, to present a new form of human-computer interaction. Combining traditional calligraphy with modern technology is a good way to save traditional writing. Acknowledgments. This research was supported by the Beijing Philosophy and Social Science Planning Research Base Project (No. 13JDWYA005); BIFT Postgraduate Innovative Research Project in 2018 (No. 120301990122/008).

References 1. Min, W.: The Study of Natural Interaction of Chinese Calligraphy Techniques in Digital Terminal. Beijing University of Posts and Telecommunications, Beijing (2013). (in Chinese) 2. Gong, W.: Design and research for virtual reality sand play therapy based on HTC VIVE. Harbin Institute of Technology (2016) 3. Zhang, M.: The research on sand play therapy design based on spatial augmented reality and its user experience. Harbin Institute of Technology (2014) 4. Huang, X., Dong, H.: Depth-based real-time hand gesture recognition and virtual writing systems. Comput. Eng. Appl. 167–173 (2015) 5. Zhang, J.: Innovative application of virtual display technique in virtual museum. Mater. Sci. Eng. (2017) 6. Hao, Y.: Build teaching methods of body-interactivity virtual reality on the basis of HTC Vive. Shanxi Archit. 256–257 (2017) 7. Zhang, Z.: Researches on computer calligraphy creation and rendering. Zhejiang University (2011) 8. Xu, T., Zhou, H.: A study on calligraphy course virtual community building and dissemination. Educ. Forum, 249–250 (2016)

Extensive Mind Mapping for the Contradiction of the Organic Rice Planting Precautions’ Cost Penghui Liu, Rui Fan(&), Bifeng Guo, Fuyu Ma, and Yongzhao Feng Faculty of Software Technology, Guangdong Ocean University, Zhanjiang, China [email protected]

Abstract. With development of economic, more and more people in China want to enjoy higher quality of food. In such background Organic food become popular in China which let small farmer’s want to increase their income by planting plants. However, the cost of organic planting is more complicated than normal planting. Many farmers hold an opinion that organic planting is hard because the controlling of the growth of weeds, the number of pests and diseases will be expensive. This paper is going to find an economical solution to control disease weeds and pests for small farmer planting Organic rice by using Extenics theory and mind mapping. Keywords: Extenics Precautions


Mind mapping
Organic rice
Planting

1 Introduction As one of the major crop in China, rice planting technology is vital important because the production of the rice plays an important role in the guarding china’s food security [1]. As other crops, rice production also faces with the reduction caused by insects and disease. Pesticides contribute significantly to rice production by limiting to yield losses [2]. However, as we know, the definition of organic clearly says that the Pesticides are forbidden during the production of rice. Which means it is not an option to use Pesticides to control the damage causing by pests. The keys of organic rice planting economic benefit are its cost reduction and yield increase [11]. And the cost of organic rice for defending pest and disease is way more expensive than normal rice [11]. This paper is going to find out a reasonable comprehension solution to decrease yield loss of rice planting from causing by disease and weeds. The principle of Extenics [3] will be used to find a solution to solving this problem. Extenics is a Chinese principle Which was used to solve contradictory problem by using some mathematical methods [4]. Besides, we have some achievement in theory of Extenics for the Innovation software such as the modelling Extenics innovation software by Intelligent Service Components [7]. In order to simulating the human thinking process and convert abstract theory to visual elements, we developed a new web application combining Extenics with mind mapping [5]. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 633–641, 2019. https://doi.org/10.1007/978-3-030-02804-6_83

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2 Development of Extenics Innovation Software At the beginning of the development, we believed that the core theory of the Extenics is mathematical methods [6]. With the mathematical methods we can distinguish the elements’ effect easily, which guide our software development to make a calculator as the target. However, we found out that the software plays a calculating assistant role wasn’t not enough to describe human innovation process. To achieve the new target, we combined Extenics with mind mapping in order to line up the thinking process of the math only. By this new method, we successfully solved some problems. For example, in travel route planning, we solve a self-travel selection by using extensible strategy [7]. In economy area, we found out the best marketing plan of photographing for money deduced by extensible mind mapping [12]. The reason to build the software of Extenics because it has many advantages. Extenics with mind mapping can help the person that learned Extenics to under-standing the problem clearly. Because the Extensible mind mappings can offer a framework of problem which can help human deal information more efficiently. With the software we built, we can concentrate on the problem itself without wasting time of calculation. At the same time, the modeling process of Extenics helps us understanding the relation of data and problem, with the combination of mind map, it helps us to keep our thinking process of the right way.

3 Solving Process with Extenics Innovation Software Here is brief introduction to Extenics with Mind mapping model. It will be finish after following steps. 1. 2. 3. 4. 3.1

Confirm the contradiction, spilt the contradiction to kernel problem. Modeling kernel problem, building comprehensive dependent function Proceeding extension analysis and transformation Evaluate the superiority by superiority evaluation Problem Modeling

From the introduction, we acknowledged that to planting organic rice need to find economical solutions controlling weeds, diseases, and pest which is executable under organic standard. The cost of the solutions is the key of the rice profits and some of them are way too expensive for small farmers. Unlike the farmers in developed country, most of the farmers are small farmers in china. So, it is important to find out a reasonable solution to farmer who wants to change their rice from normal to organic. We split the contradiction into two kernel problems which are the compliance of the standard of organic rice and the solution economic rate of existing solution to control yield loss caused by disease, pests and weeds. There are lots of factors of the standard of organic rice. As the Fig. 1, In order to narrow down the range, according to China organic rice standard [12], we only list out the attributes that will be defer between economical rice and organic rice. Which are the species of the rice, the type of fertilizer, the type of pesticides. These factors were

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Fig. 1. Problem modeling of the compliance of standard

stetted as the attributes of the kernel problem. This is a common situation of the rice planting in China which is contradicting the standard of organic rice. To solve this contradiction, we set up three discrete dependent functions to describe it. The goal we want to achieve is showing in goal element G0 which is according to the standard and the situation for now is showing in condition element L0 as we see from Fig. 1. The definition of those three discrete dependent functions are k0(x), k1(x) and k2(x). If the condition is recommended by organic standard, the value of the condition will be stetted to 1. If it is allowing to exist, the value will be stetted to 0. If it is forbidden by the standard, the will be stetted to 0. By the following discrete dependent functions, we can calculate the values of each attribute. The values of each attribute are 0, −1, −1, that means that the fertilizer and pesticide are incompatible and need to be extended. The solution to normal rice planting is using fertilizer and artificial pesticide. The modeling process of the second kernel problem is showing in Fig. 2. We define a new attribute names cost attribute which is the solution rate is getting by Formula 1. solution rate ¼

cost of control yield loss caused by disease; pests and weeds  100% income ð1Þ

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Fig. 2. Problem modeling of the solution economic rate

We set up a discrete dependent function named k0(x) for the satisfaction from the organic standard. Than we set up a basic-elementary dependent function [2] for solution rate. The functions of basic-elementary dependent function are Formula 2 to 5. According to the paper [11], the normal rice planting solution rate is 20%, after calculation of using Formula 1. We expect the solution to organic planting can be control to the range of 10%–15%.


a þ b

b  a
 qðx; X0Þ ¼
x  ð2Þ 2
2 8 qðx; XÞ  qðx; X0Þ qðx; XÞ 6¼ qðx; X0ÞANDx 62 X0 < Dðx; X0; XÞ ¼ qðx; XÞ  qðx; X0Þ þ a  b qðx; XÞ 6¼ qðx; X0ÞANDx 2 X0 ð3Þ : ab qðx; XÞ ¼ qðx; X0Þ 8
> > > > >
> > > > qðx; x0; X0Þ þ 1 > : 0

637

qðx; x0Þ ¼ qðx; XÞANDx 62 X0 else Dðx; x0; XÞ 6¼ 0; x 2 X Dðx; x0; XÞ ¼ 0; x 2 X0 Dðx; x0; XÞ ¼ 0; x 62 x0; x 2 X

ð5Þ

After the software we developed, the values are showing in the Fig. 2. The value of H1 is −0.5, which also mean there is contrition. 3.2

Extensible Analysis

For kernel problem 1,we can know that the using of the rice species, fertilizer and pesticide in normal rice plant are not conform with organic standard. It is easy for small farmer in China to change their using type of fertilizer form chemical to organic because in Chinese traditional planting ways is using human excrement as the main source of fertilizer which is conforming with organic standard. For pesticide, the type of farmer used to use need to be change. Using organic rice seed can help in this situation because it organic rice have genes that the ability defending some pests are better than ordinary rice. Also, there are some pesticide are allowed to use which is good replacement (Figs. 3 and 4). For kernel problem 2, we know that their way of controlling weeds disease, pests and weeds is economical. But it also not conforms with organic standard. There is some replacement such as releasing the natural enemy of rice pest and using the type of pesticide in the organic standard etc. The cost of these solutions is various and it will be calculated in next step.

Fig. 3. Extensible analysis of compliance of the standard

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Fig. 4. Extensible analysis of the solution economic rate

3.3

Extension Transformation

Though the value of L0b is the same as the L0. a0, the L0. a1 is not possible for executing because pesticide needs to use insect plague, and some of these pesticides produce of natural or biological is safe and harmless for human and plants. So, it is unnecessary to not use pesticide at all for organic planting (Fig. 5).

Fig. 5. Extension transformation of compliance of the standard

For the contrition of kernel problem 2, it is necessary to sure the solution is matching with rice organic standard. According to the paper, there are serval solution match this condition. F1a: Releasing the natural enemy of rice pest This is a kind of biological control. To make this work, the farmer needs to farm insects and frogs to ensure there are enough enemy for pest. The cost of farming them isn’t cheap either. Especially for small farmers, there is big chance that the farmer needs to decrease the planting area in order to farming natural enemy of rice pest, which make the solution rate very high. F1b: Using organic base and Bacillus thuringiensis Both of them are allowing using in organic rice, However, the prize of them isn’t cheaper than artificial pesticides. The prize of organic base is twice as artificial pesticides Which make the solution rate higher than expected.

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L1c: Planting with duck raising It may be the best solution for small farmer. Duck is easy to raise and is easy to sell because Chinese dietary habit. The income by selling duck can significantly reduce the cost of buying duck for raising. Unlike using agriculture chemicals, duck is sustainable resources. At ideal situation, the cost of using duck for eating pest may only be generated by buying feed and the feed may be replaced by using rice’s byproducts which down the solution rate (Fig. 6).

Fig. 6. Extension transformation of the solution economic rate

3.4

Superiority Evaluation

After transformation, we successfully find out the best solution to both kernel question (Fig. 7).

Fig. 7. Superiority evaluation

The comprehensive dependent function of superiority of the original problem is shown as Y(H) = Hp0 + Hp1 = 1 + 0.75 = 1.75 > 0.

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That’s means to conform the standard of organic rice, farmers who want to plant organic rice should use organic seeds for planting, using organic fertilizer and use pesticides which are in the list of the organic standard and the superior solution to control weeds and pest is planting with raising ducks.

4 Conclusion and Evaluation By using Extensible innovation software power by Extenics we successfully find out an economical solution to controlling rice species, fertilizer and pesticide for small farmers to planting organic rice. Raising duck meets Chinese farming system perfectly which also giving the inspiration to explore other plants’ farming ways. Though the result is successful, the process of conducting still need to improvement. The solution to controlling pest disease and weed may affect the production of rice. And the species of rice we choose might have better performance on defending disease. Extenics plays an important role to keep our team’s work in the right way. With the combination of mind map, the Extensible innovation software showing its’s ability to conducting the thing processes which showing the potential of applying in artificial intelligence. Acknowledgements. The research is supported by Guangdong Provincial Science and Technology Project (2014A040402010), Guangdong Province Innovation and Entrepreneurship Training Program for College Students (201710566036) and Guangdong college students’ innovation and entrepreneurship project (CXXL2018090).

References 1. Zhang, Y., Zhu, D., Xiong, H., Chen, H., Xiang, J., Lin, X.: Development and transition of rice planting in China. Agric. Sci. Technol. 13(06), 1270–1276 (2012) 2. Yang, C., Wang, G., et al.: A new cross discipline—extenics. China Sci. Found. (English Edition) 13(1), 55–61 (2005) 3. Cai, W., Yang, C.Y., Bin, H.E.: Several problems on the research of extenics. J. Guangdong Univ. Technol. 2001-01, 1–5 (2001) 4. Chen, Y., Li, W.: Research and implementation of extensible strategy generation system for job hunting problem. J. Guangdong Univ. Technol. 29(1), 88–93 (2012) 5. Yan, S., Fan, R., Chen, Y., et al.: Research on web services-based extenics aided innovation system. Procedia Comput. Sci. 107(C), 103–110 (2017) 6. Fan, R., Peng, Y., Chen, Y., et al.: A method for self-adaptive software formal modeling by extenics. CAAI Trans. Intell. Syst. 10(6), 901–911 (2015) 7. Fan, R.: Modelling Extenics innovation software by intelligent service components. Open Cybern. Syst. J. 8, 1–7 (2014) 8. Kershaw, H.: Mind mapping. Bereave. Care 17(3), 44 (1998)

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9. Guo, B., Fan, R., Huang, C.W., et al.: The best marketing plan of photographing for money deduced by extensible mind mapping. 17(13), 03003 (2018) 10. Huang, J., Qiao, F., Rozelle, S., et al.: Farm pesticide use, rice production, and human health. Eepsea Research Report rr2000051, Economy and Environment Program for Southeast Asia (EEPSEA), revised May, pp. 901–918 (2000) 11. Chen, R., Xi, Y., Xu, X., Wang, H., Yang, J., Fan, W.: Comparison of economic benefit between organic rice and convention rice production. Guizhou Agric. Sci. 37(06), 96–98 (2009) 12. People’s Republic of China Agricultural Industry Standard, NY/T 2410-2013. Technical Specification for Control Organic Rice Production. China Agriculture Press, Beijing (2014)

The Solution of Environmental Damage in Scenic Spots by Extensible Mind Mapping Enna Wu1, Rui Fan2(&), Bifeng Guo2, Fuli Chen2, and Qiubin Liu2 1 Faculty of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China 2 Faculty of Software Technology, Guangdong Ocean University, Zhanjiang, China [email protected]

Abstract. Nowadays, more and more people choose to enjoy life by traveling to the scenic spot with a beautiful ecological environment. However, too many tourists in scenic spot will cause the damage of its ecological condition. Therefore, it is contradictory between human travel activity and the protection of the scenic spots. What we need to consider foremost is to reduce the human’s traveling activity’s impact on the attractions. With the help of Extenics Innovation Software, we find out the best measures to solve the problem based on the Extenics in combination with mind mapping. Keywords: Extenics


Mind mapping
Tourism
Scenic spot

1 Introduction With the development of economy and society, people’s standard of living has been greatly improved. More and more people choose to travel and enjoy life. According to National Bureau of Statistics of China, the total number of domestic visitors increased by 50% between 2010 and 2015. The explosive growth of tourist population have exerted pressure on the ecological environment of scenic spots. In order to facilitate the analysis of specific data, we take Zhangjiajie which is one of the most popular tourist attraction in China as an example to study. As a city arising from tourism, Zhangjiajie has a high degree of Tourism Dependence which is still steady increasing [1]. After nearly thirty years of development, the tourism has been a pillar industry of Zhangjiajie [2]. However, there are some negative impact on Ecological Environment after tourism expansion [3]. At the same time, it will also affect the development of tourism industry [4]. As for the peregrinator, he or she goes to travel to enjoy the beautiful environment of the scenic spots. However, people’s tourism activities will cause damage to the ecological environment of scenic spots. It can be seen that there is a certain contradiction between the tourism behavior of human beings and the ecological environment protection for scenic spots.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 642–649, 2019. https://doi.org/10.1007/978-3-030-02804-6_84

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In this case, finding the best way that coordinates the relationship between the human tourism and the protection of the Zhangjiajie is the key question about it. 1.1

Extensible Mind Mapping

Extensible mind mapping means combining the Extenics and mind mapping to deal with contradictory problem. Extenics is a new cross discipline in China [5]. The discipline studies the contradictory problem by establishing formalized model and taking scientific formula calculus [6]. Up to now Extenics has been widely used in diverse fields to solve various contradictory problem. To make sure that people that are mining and addressing the problem with the extension innovation method will get clear guide, our team is committed to researching and developing an innovative software— Extenics Innovation Software [7]. With the help of the software, the user can solve the problem directly by only knowing the executive stage of extension science, and not knowing the complex principle formula of Extenics [8]. However, the Extenics is a method based on mathematical theory substantially, and the software can’t think in a variety of ways like a human mind. When it takes use of intelligent processing with a computer, the scope of support is so wide that it lacks a clear direction [9]. Mind map is a way to visualize thing which can help people balance the development between science and art, logic and imagination by using the technique of paying equal attention to graphics and text [10]. Thus an innovative approach can be obtained, to solve the contradictory problem with the establishment of a mind mapping analyzing the problem based on Extensive procedure and methods. 1.2

Extenics with Mind Mapping Model

Through the Extenics with mind mapping model (see Fig. 1), we will be more clear and effective in analyzing the problems. Firstly, the original problem will be divided into two sub-problem which will become the kernel problem once it can’t be separated any more. Secondly, for every kernel problem, we establish a model including three parts: conditional base element, goal base element and dependent function. By comparing the differences between the conditional base element and goal base element, we can find

Fig. 1. Extensible procedure mind mapping

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out the root of the matter of this contradictory problem. Thirdly, with the process of extensible analysis and Extensible Transformation, we extend the new base element and the dependent function attribute value. In the end, we get the best way to solve the contradictory problem after superiority evaluation.

2 Procedures of Solving the Problem 2.1

Problem Modeling

It is the heavy ecological burden of over-tourism that make it impossible for Zhangjiajie to maintain a healthy ecological balance. What is the most significance of these is the air contamination. At the same time, Tourism activities also polluted the soil in Zhangjiajie. Therefore, we arrive the Problem Modeling that the original problem is that the negative impact of the Tourism which can divided into air contamination problem and soil impact problem. The reasons are as follows, Air pollution in scenic spots affects visibility which affects visitors’ play to Zhangjiajie, and the soil problem will gradually affect the growth process of plants in Zhangjiajie. As is shown in the Fig. 2, the air pollution problem has three base element attributes included the highest PM_ (10) concentration in 24 h, the highest PM_ (2.5) concentration in 24 h and the highest CO concentration in 24 h [11]. As landscape and famous scenery, the PM_ (10) concentration is prescribed that should be less than 50 lg/m3; the PM_ (2.5) concentration is prescribed that should be less than 35 lg/m3;

Fig. 2. Modeling of air pollution problem

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the CO concentration is prescribed that should be less than 4 mg/m3 by Ambient air quality standards. However, when it is a holiday, the actual observational concentration are not all conforms the statute. In the ideal case, the less the concentration is, the better air quality is. While in usual case, it is considered as extraordinary clear air when the concentration is half of the specified value. Hence we set up the base elements of the first kernel problem. With the help of the software which calculate with the following formula, we arrive the dependent function values.






a þ b

b  a
x  c þ d
 d  c qðx; X0Þ ¼

x   qðx; XÞ ¼

2 2 2
2 8 qðx; XÞ  qðx; X0Þ qðx; XÞ 6¼ qðx; X0ÞANDx 62 X0 < Dðx; X0; XÞ ¼ qðx; XÞ  qðx; X0Þ þ a  b qðx; XÞ 6¼ qðx; X0ÞANDx 2 X0 : ab qðx; XÞ ¼ qðx; X0Þ qðx; x0; XÞ ¼ ¼

kðxÞ ¼

8 > > > > > >
> > > > qðx; x0; X0Þ þ 1 > : 0

x  x0 x 2 \x0; b [ xb xa x 2 \a; x0 [ x  x0

ð1Þ

ð2Þ

qðx; x0; XÞ

ð3Þ

qðx; x0Þ ¼ qðx; XÞAND x 62 X0 else Dðx; x0; XÞ 6¼ 0; x 2 X Dðx; x0; XÞ ¼ 0; x 2 X0 Dðx; x0; XÞ ¼ 0; x 62 x0; x 2 X

ð4Þ

The dependent function values the software calculate are k1(x) = −6.11, k2(x) = −6.9, k3(x) = 0.5. The values of k1 and k2 are less than zero which mean they are incompatibility problems and the values of k3 is greater than zero meaning that it isn’t the contradictory problem. From Fig. 3, there are three effects of tourism activities on the soil impact: soil hardness, soil water content degree and Soil bulk density [12]. Under normal circumstances, it is generally considered that the land hardness of the tourists walking on the edge of the road is 11.4 cm, the water content degree is 18.88% and the soil bulk density is 1.21 g/cm3. We get the dependent function values which are k4(x) = −17.4, k5(x) = −4.1, k6(x) =−4.0. All of the values are less than 0 meaning that they are contradictory problem.

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Fig. 3. Modeling of soil impact problem

2.2

Extensible Analysis

According to the significance and influence power, we evaluate the comprehensive dependent function value with different weights. It is 0.5 for the PM_ (10) concentration, 0.4 for the PM_ (2.5) concentration and 0.1 for CO concentration. As is shown in the following, four measures are mentioned to solve the first kernel problem by changing its conditional base elements (Fig. 4).

Fig. 4. Extensible analysis of the first problem

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L0.a0 is for controlling the car’s numbers in scenic spot. It is the automobile exhaust that as one of the most main origin of PM_ (2.5) and PM_ (10) [13]. When in the tourist season, the number of the cars will have a big increase, and it is likely has a severe traffic jam. Hence this measure will do a great help for the content reduction of PM10. The comprehensive dependent function value is 0.19. L0.a1 is for introducing the tourist season to potential tourist. In order to avoid a booming of the visitor, staff spread the situation in advance with the help of media. The comprehensive dependent function value is −4.369. L0.a2 is for restricting the opening time of scenic spot. It is worth trying to choose a period of time in the year or a few days in a mouth to close the scenic spot. The ecosystem will have an opportunity to ‘relax’ after a continuous reception of tourists. The comprehensive dependent function value is 0.149. L0.a3 is for establishing the administration. The agency’s responsibility is overall planning of the whole area which including the sewage discharge, tail gas treatment and solid waste disposal. The amount of polluting gas emitted by commercial enterprises in scenic spots should not be underestimated. The comprehensive dependent function value is 0.081. When it comes to think about the second kernel problem, the weight of the soil hardness is 0.4, the weight of soil water content degree is 0.4 and the last one is 0.2. The result of the capability is shown in Fig. 5.

Fig. 5. Extensible analysis of the second problem

L1.a0 is for changing the height and canopy of the crown. It can improve the perspectives of tourists by forming a natural viewing channel. This channel not only make tourists enjoy the scenery more conveniently, but also make sure the tourists staying on the road of viewing. The comprehensive dependent function value is 0.158. L2.a1 is for raising the awareness of environmental protection among tourists and local residents. The relevant educational videos should be put into public places such as public transport. The comprehensive dependent function value is −2.232. 2.3

Extensible Transformation

After the Extensible Analysis, we get some new extended elements. According to the effect of implementation and feasibility of the measures, we attach the dependent function value of the Extensible Transformation to 0 for both two kernel problem (Figs. 6 and 7).

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Fig. 6. Extensible transformation of the first problem

Fig. 7. Extensible transformation of the second problem

For the first problem, we reach three better measures L0.a0, L0.a1 and L0.a3. For the second problem, we reach three better measures L1.a0. As shown in the Fig. 2. 2.4

Superiority Evaluation

According to all the procedure we analyzed above, we attach the dependent function value of the Superiority Evaluation to 0.12 for the first kernel problem and for the second kernel problem is 0.15. We pick the measure L0.a0 and L0.a1 for the first kernel problem and measure L1. a0 for the second kernel problem. Here is the dependent function values (Fig. 8).

Fig. 8. Superiority evaluation

As a result, the measure to deal with the first kernel problem has been figured out: controlling the car’s numbers in scenic spot and restricting the opening time of scenic spot. Besides, the way to solve the second kernel problem is changing the height and canopy of the crown.

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3 Conclusion and Future Work Based on the Extenics in combination with mind mapping, we solve the problem with the help of Extenics Innovation Software. We find out the best solution to reduce the scenic spot’s damage that due to the human tourism activity: controlling the car’s numbers in scenic spot and restricting the opening time of scenic spot and changing the height and canopy of the crown. It is the software that combines Extenics and mind mapping neatly. It offer Visual extensible innovative thinking way and powerful automatic computing capability. By means of the software, our research steps have been greatly simplified and the calculation time is saved a great extent. We hold the exception that the software help realize the intelligentization of human innovation in the near future. Acknowledgments. Thanks to the support of Guangdong Provincial Science and Technology Project (2014A040402010), Guangdong Province Innovation and Entrepreneurship Training Program for College Students (201710566036), Guangdong Ocean University Excellent Courses Project for Software Engineering (JP2010007) and Guangdong College Students’ Innovation and Entrepreneurship Project (CXXL2018090) for this research.

References 1. Duan, Z., Xu, C., He, Z.: The dynamic relationship between the change of tourist arrivals and the growth of tourism income: a case study of Zhangjiajie city, China. Tour. Res. 8(2), 68–74 (2016) 2. Wang, Z., Long, L.: Influencing mechanism of tourism on urbanization from perspectives of time and space—the case of Zhangjiajie city. Res. Dev. Market 33(3), 364–368 (2017) 3. Guo, J.: The negative impact of tourism expansion on the ecological environment—case study of Zhangjiajie National Forest Park. J. Huaihua Univ. 29(1), 17–20 (2010) 4. Sajjad, F., Noreen, U., Zaman, K.: Climate change and air pollution jointly creating nightmare for tourism industry. Environ. Sci. Pollut. Res. 21(21), 12403–12418 (2014) 5. Wen, C., Yang, C., Guanghua, W.: A new cross subject-extenics. Bull. Natl. Nat. Sci. Found. China 18(5), 268–272 (2004) 6. Wen, C., Yang, C.: Basic theory and methodology in extenics. Chin. J. 58(13), 1190–1199 (2013) 7. Rui, F., Yan, S., Peng, Z., et al.: A research on software architecture and its application for ESGS. J. Guangdong Univ. Technol. 34(2), 1–5 (2017) 8. Fan, R., Peng, Y., Chen, Y., et al.: A method for self-adaptive software formal modeling by extenics. CAAI Trans. Intell. Syst. 10(6), 901–911 (2015) 9. Jiang, F., Yang, H.: Study of the combined method based on TRIZ and extenics. J. Guangzhou Univ. (Natural Science Edition) 13(6), 59–64 (2014) 10. Buzan, T., Buzan, B.: The Mind Map Book (1998) 11. Guan, R., Zheng, K., Huang, Y.: Analysis on weekend and holiday effects of air pollutants in Zhang-jiajie City. J. Tongren Univ. (3) 2018 12. Shi, Q.A.: The impact of tourism on soils in Zhangjiajie World Geopark. J. For. Res. 17(2), 167–170 (2006) 13. Guo, E., Chen, J., Zheng, M., et al.: Effects of different roads grades on distribution characteristics of PM2.5 and PM10 in Zhengzhou City. J. Henan Agric. Univ. 3, 416–421 (2016)

Prediction of Remaining Useful Life for Equipment Based on Improved Metabolic GM(1,1) Model Liu Yuwen, Cai Hongtu(&), Li Zhiyong, Fang Shidong, and Jiang Min Army Artillery and Air Defense College, Hefei 230031, China [email protected]

Abstract. According to the poor adaptability and the poor effect of medium and long term prediction of the GM(1,1) model, combined with prediction theory of metabolism, applied the equal division function method to optimized the background value constantly, and established a model of GM(1,1) based on metabolism. At the same time, on the basis of sufficient consideration of the random interference and driving factors that entered the model successively, the model of GM(1,1) based on metabolism is further improved and optimized, and established a gradual progressive metabolic grey GM (1,1) which has the optimal dimension to achieve the best balance between the accuracy of prediction and the convergence of prediction. The results of simulation experiments indicate that the metabolic grey GM (1,1) is improved has better forecasting effect and stronger adaptability than the conventional and metabolic GM (1,1) model. Keywords: Remaining useful life


Metabolism
Error analysis

1 Introduction The residual life prediction is the prerequisite and important basis for the realization of condition-based maintenance. It has great significance to improve the system security, combat readiness in good condition and mission success of the equipment. At present, many experts and scholars have carried out related research on this issue. Hu Haifeng and others used Hidden Semi-Markov model (HSMM) to predict the remaining useful life of GaAs laser. Zhou Jianbao and others used FPGA dynamic reconfiguration technologies to design a remaining usefuk life prediction system of reconfigurable satellite lithium-ion batteries based on FPGA. Shen Zhongjie, etc. designed a new method of residual life prediction based on relative characteristics and Multivariable Support Vector Machine (MSVM), and solved the problem that the remaining useful life of rolling bearing is difficult to estimate in the condition of the finite state data. Que Zijun et al. used the prediction method of remaining useful life based on unscented Kalman filter (UKF) to evaluate and predict performance of the bearing. Zhang Lei and others calculate the remaining useful life of the system approximately according to the fault evolution model and certain failure criteria. Jiang Yuanyuan and other used the © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 650–660, 2019. https://doi.org/10.1007/978-3-030-02804-6_85

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method of constructing indirect life characteristic parameters by using the cycle charge discharge monitoring parameters of lithium battery to build the relationship model of and the time prediction model of constant pressure drop discharge time and actual capacity based on ELM. Ghasemi and other used the proportional fault rate model to calculate the average remaining useful life. Li and Kott have studied the prediction method of remaining useful life based on a large number of censored data. All the above methods and models are established under the assumption that the acquired characteristic parameters can fully reflect the state of the equipment, and don’t consider the uncertainty and incompleteness of the equipment state when it is affected by the factors such as noise interference, detection error and incomplete detection information. The grey model has the characteristics of less original sample data, small amount of calculation and no prior information. It has received extensive attention in the prediction of remaining useful life, and it’s an efficient means to solve problems of “small sample”, “poor information” and “uncertainty”. In view of the complexity of the equipment structure of the weapon system, the state characteristic parameters that can be obtained are very limited, and the state information obtained is incompleteness and uncertainty, and it is difficult to realize residual life prediction of equipment. Therefore, we can use grey system theory to forecast remaining useful life of weapons equipment. GM (1,1) is the most simple and commonly used way in the grey theory. The shortterm prediction effect of this model is better, with time going by, the stale content will interfere with the system, which leads to the reduction of prediction accuracy. In light of this situation, this paper combined prediction theory of metabolism, applied equal division function to optimized the background value constantly, and established a model of GM(1,1) based on metabolism. At the same time, on the basis of sufficient consideration of the random interference and driving factors that entered the system successively, the metabolic GM(1,1) model is further improved and optimized, and established a gradual progressive metabolic grey GM (1,1) which has optimal dimension to achieve the best balance between the accuracy of prediction and the convergence of prediction. The results of simulation experiments indicate that the metabolic grey GM (1,1) is improved has better forecasting effect and stronger adaptability than the conventional and metabolic GM (1,1) model.

2 Traditional GM (1,1) 2.1

Model Establishment

Principle of applying the grey theory to the prediction of remaining useful life of equipment is to consider the predicted thing as a whole, using known information to reasoning the characteristics, state and development trend of the agnostic information containing the fault-pattern, and to predict and judge the remaining useful life of the equipment, the process of which is a grey process. GM (1,1) is the most commonly used and the simplest prediction method in the grey system theory, and its modeling process is as follows:

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(1) A cumulative Generation Operator
 Set initial data serials for xð0Þ ¼ xð0Þ ð1Þ; xð0Þ ð2Þ;


; xð0Þ ðnÞ , and cumulative generation operator on xð0Þ , then accumulated data serials is
ð1Þ  ð1Þ ð1Þ ð1Þ x ¼ x ð1Þ; x ð2Þ;


; x ðnÞ . (2) Construction of background value by means of adjacent mean method Calculate the close mean value generation for xð1Þ , the calculation result is background of GM (1,1), that is:   zð1Þ ðiÞ ¼ 0:5 xð1Þ ðiÞ þ xð1Þ ði  1Þ ; i ¼ 2; 3;


; n

ð1Þ

the grey differential equation of GM (1,1) is: xð0Þ ðiÞ þ azð1Þ ðiÞ ¼ b

ð2Þ

(3) Establishing GM (1,1) model The accumulated generation data sequence xð1Þ is fitted by making use of first order single variable differential equation, and obtained the grey dynamic model of grey and white form that is, . dxð1Þ dt þ axð1Þ ¼ b

ð3Þ

Where a is development value, b is action. Order b a ¼ ½a; bT as an argument sequence, order 2

3 1 17 7 ; .. 7 .5

zð1Þ ð2Þ 6 zð1Þ ð3Þ 6 M¼6 .. 4 . zð1Þ ðnÞ

2

3 xð0Þ ð2Þ 6 xð0Þ ð3Þ 7 6 7 Y ¼6 . 7 4 .. 5

1

xð0Þ ðnÞ

According to the least square method, we can get formula (4)  1 b a ¼ MT M MT Y

ð4Þ

Put calculated parameters a and b into formula (2), and solve the resulting differential equations. We can get formula (5). b ai b bx ði þ 1Þ ¼ x ð1Þ  e þ a a ð1Þ



ð0Þ

ð5Þ

First order reduction calculation for the formula (5), GM (1,1) of original serials can be getted.

Prediction of Remaining Useful Life for Equipment

 b ai ð 0Þ bx ði þ 1Þ ¼ bx ði þ 1Þ  bx ðiÞ ¼ ð1  e Þ x ð1Þ  e : a ð0Þ

2.2

ð1Þ

ð1Þ

a

653

ð6Þ

Implementation Steps

The implementation steps of the grey prediction of traditional GM (1,1) are as follows: (1) Accumulate for initial sequence xð0Þ , and generate the data sequence xð1Þ . (2) construct the data matrix B and the data column Y. (3) according to the formula, we get the model development coefficient a and grey action b. (4) establish the time response model, as formula (5). (5) calculate cumulative prediction values and restore cumulative values to predict values based on formula (6).

3 The Metabolic GM (1,1) Model 3.1

Model Establishment

The metabolic GM (1,1) is built on the basis of the conventional method. Its basic idea is to apply initial serials xð0Þ to build model to get the data bx ð0Þ ðn þ 1Þ. And apply data
 serials xð0Þ ð2Þ; xð0Þ ð3Þ;


bx ð0Þ ðn þ 1Þ or add measured data xð0Þ ðn þ 1Þ to xð0Þ and get next value bx ð0Þ ðn þ 2Þ. By analogy, repeated cycles, the model will be updated when calculate each prediction data, and the development coefficient a and grey action b are also changed, making the model parameters have a certain adaptability. 3.2

Implementation Steps

The implementation steps of the grey prediction of metabolic GM (1,1) are as follows: (1) establish GM (1,1) model and get predict data bx ð0Þ ðn þ 1Þ. (2) add bx ð0Þ ðn þ 1Þ or measured data xð0Þ ðn þ 1Þ to the original data sequence, and
 delete xð0Þ ð1Þ, generate the new data sequence xð0Þ ð2Þ; xð0Þ ð3Þ;


bx ð0Þ ðn þ 1Þ or
ð0Þ  x ð2Þ; xð0Þ ð3Þ;


xð0Þ ðn þ 1Þ , and use the new sequence to predict. Recalculate the development coefficient a and the grey action b, and bx ð0Þ ðn þ 2Þ can be obtained. (3) repeat the steps above to predict the cycle until it meets the requirements of model, and outputs the prediction results.

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4 The Improved Metabolic GM (1,1) Model 4.1

Model Improving

(1) update the modeling data In actual prediction, over time, the stale content will interfere with the system, and the prediction accuracy will be reduced. Therefore, to enhance prediction effect, on the basis of the known information, add the new information and delete the old information in time by making use of the idea of the metabolism, reduce the impact and interference of the external uncertainty, so that the modeling sequence can better reflect the characteristics of the data change. It is worth noting that use the measured data first when the new data are added. (2) reconstructing the background value According to the formula (2), the area of the trapezoid in Fig. 1 is the size of the background value. It is obvious that the area of the trapezoidal trapezium formed by the xð1Þ ðtÞ and the transverse axis is the true value of zð1Þ ðiÞ. Obviously, the difference of area is the construction error of background value. Therefore, the construction of the background is improved by using equal function method. Background formula is as follows: i 1 h ðn þ 1Þxð1Þ ði  1Þ þ ðn  1Þxð1Þ ðiÞ 2n

zð1Þ ðiÞ ¼

x( ) ( t )

ð7Þ

1

x (1) ( i + 1)

Δs

x

(1)

x (1) ( t )

(i ) s1

s2

i

sn −1 sn i +1

t

Fig. 1. Background construction

Obtaining the best value n according to the empirical formula: n¼

N X i¼2

!ð1=ðN1ÞÞ Pi

þ ðN  1Þ; i ¼ 2; 3;


; N

ð8Þ

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Where N is the length of the original data sequence, Pi ¼ xð1Þ ðiÞ xð1Þ ði  1Þ. This method approximated the area under the curve to a certain extent, so as to enhance the effect and adaptability of GM (1,1). (3) determination of the dimension of the model In practical applications, the data of the original sequence should be four at least, otherwise the correlation degree is low, so it is not suitable for medium and long term prediction. But the number of measured data is not “more is better”, if the number is too much, it will affect the accuracy and real-time of the metabolic algorithm. Therefore, in practical operation, extend it by idea of metabolic GM (1,1). That is, to eliminate the influence and interference of obsolete information as far as possible and ensure the real-time performance of the prediction algorithm. Then add the measured data or the new predicted data, and delete a number of old data to build the new data sequence. Recalculate a and b according to formula (8). Finally, the original data sequence of different numbers is tested one by one, and the most accurate data sequence is selected as the basis of the improved GM (1,1) analysis to determine optimal dimension. 4.2

Implementation Steps

The steps to the improved metabolic GM (1,1) are as follows: (1) calculate accumulation of the original serials, and use the formula (1) to calculate the background value (2) use the formula (2) to calculated respectively the least squares estimation parameters a and b. (3) calculate formula (4) and formula (5) according a and b, the data bx ð0Þ ðn þ 1Þ is getted when time is n þ 1. (4) add bx ð0Þ ðn þ 1Þ or xð0Þ ðn þ 1Þ to the initial serials, and delete k old original data
ð0Þ x ðk þ 1Þ; xð0Þ ðk þ 2Þ; xð0Þ ð1Þ; xð0Þ ð2Þ;


xð0Þ ðkÞ, form new data serials ð0Þ


; x ðn þ 1Þg, and use formula (8) to recalculate a and b, the data bx ð0Þ ðn þ 2Þ is getted when time is n þ 2. (5) Repeat step (4) for cyclic prediction the original data sequence of different numbers is tested one by one, and the most accurate data sequence is selected as the basis of the model analysis to determine optimal dimension. 4.3

Model Accuracy Test

Prediction accuracy is usually determined by 3 indicators, such as mean relative error, mean variance ratio and small probability error. (1) mean relative error e e¼

n   . 1X xð0Þ ðiÞ  bx ð0Þ ðiÞ xð0Þ ðiÞ ; n i¼1

i ¼ 1; 2;


; n

ð9Þ

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(2) mean variance ratio C C ¼ S2 =S1

ð10Þ

where S1 is variance of xð0Þ , S2 is variance of eð0Þ ðiÞ. (3) small probability error P n o  P ¼ P eð0Þ ðkÞ  eð0Þ \0:6745S1

ð11Þ

Prediction accuracy can be referred to in Table 1.

Table 1. Model prediction accuracy rating table Accuracy rating Mean relative error Mean variance ratio Probability error I 0.0100 0.3500 0.9500 II 0.0500 0.5000 0.8000 III 0.1000 0.6500 0.7000 IV 0.2000 0.8000 0.6000

5 Example Simulation Verification The operating voltage range of a certain type of information system is 27–29 V, with the work hours accumulating, its voltage increases gradually, failure occurs when it is beyond the specified range. According to the actual working condition of the system, the data of the system’s characteristic parameters changing with accumulative working time are monitored, as shown in Table 2. Table 2. Cumulative working time data under normal stress Voltage/V Time/h Voltage/V Time/h

27.10 687 27.80 1706

27.20 963 28.00 1802

27.30 1255 28.20 1899

27.40 1339 28.40 2002

27.50 1386 28.60 2105

27.60 1478 28.80 2159

27.70 1625 29.00 2305

(1) prediction results based on the traditional GM (1,1) model Taking the first 12 measured data as known data, using traditional GM (1,1) model to calculate bx ð0Þ ðiÞ, xð1Þ ðiÞ, eð0Þ ðiÞ, eð0Þ ðiÞ, as shown in Table 3.

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Table 3. Prediction results based on traditional GM (1,1) model i 1 2 3 4 5 6 7 8 9 10 11 12

xð0Þ ðiÞ 687 963 1255 1339 1386 1478 1625 1706 1802 1899 2002 2105

xð1Þ ðiÞ 687 1650 2905 4244 5630 7108 8733 10439 12241 14140 16142 18247

bx ð0Þ ðiÞ 687 1136.8 1212 1292.1 1377.5 1468.6 1565.7 1669.2 1779.6 1897.3 2022.7 2156.5

eð0Þ ðiÞ 0 173.8 43 46.9 8.5 9.4 59.3 36.8 22.4 1.7 20.7 51.5

eð0Þ ðiÞ 0 0.180478 0.034263 0.035026 0.006133 0.00636 0.036492 0.021571 0.012431 0.000895 0.01034 0.024466

(2) Prediction results of metabolic GM (1,1) model Take the thirteenth measurement as input variables, and apply metabolic GM (1,1) to calculate, as shown in Table 4. Table 4. Prediction results based on the metabolic GM (1,1) model i 2 3 4 5 6 7 8 9 10 11 12 13

xð0Þ ðiÞ 963 1255 1339 1386 1478 1625 1706 1802 1899 2002 2105 2159

xð1Þ ðiÞ 963 2218 3557 4943 6421 8046 9752 11554 13453 15455 17560 19719

bx ð0Þ ðiÞ 963 1272.7 1345.2 1421.9 1503 1588.7 1679.3 1775.1 1876.3 1983.3 2096.4 2216

eð0Þ ðiÞ 0 17.7 6.2 35.9 25 36.3 26.7 26.9 22.7 18.7 8.6 57

eð0Þ ðiÞ 0 0.014104 0.00463 0.025902 0.016915 0.022338 0.015651 0.014928 0.011954 0.009341 0.004086 0.026401

(3) Prediction results of improved GM (1,1) Combined with metabolic idea, the background function is reconstructed by the method of equal division function. zð1Þ ðk Þ ¼

h i 1 ð12:27839 þ 1Þxð1Þ ðk  1Þ þ ð12:27839  1Þxð1Þ ðkÞ 2  12:27839

Use the new background to obtain the prediction results, as shown in Table 5.

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xð0Þ ðiÞ 963 1255 1339 1386 1478 1625 1706 1802 1899 2002 2105 2159

xð1Þ ðiÞ 963 2218 3557 4943 6421 8046 9752 11554 13453 15455 17560 19719

bx ð0Þ ðiÞ 963 1275.6 1348.5 1425.6 1507.1 1593.2 1684.3 1780.6 1882.3 1989.9 2103.7 2223.9

eð0Þ ðiÞ 0 20.6 9.5 39.6 29.1 31.8 21.7 21.4 16.7 12.1 1.3 64.9

eð0Þ ðiÞ 0 0.016414 0.007095 0.028571 0.019689 0.019569 0.01272 0.011876 0.008794 0.006044 0.000618 0.03006

(4) Comparison of the prediction results of three models The prediction results of three models as shown in Table 6. As you can see from Table 6, compare with the tradition and metabolic GM (1,1), improved metabolic GM (1,1) has better fitting and better prediction effect. Table 6. Comparison of the prediction results of three models Voltage (V) Measured time (h) Traditional GM (1,1) Result e 27.10 687 687 0 27.20 963 1136.8 0.180478 27.30 1255 1212 0.034263 27.40 1339 1292.1 0.035026 27.50 1386 1377.5 0.006133 27.60 1478 1468.6 0.00636 27.70 1625 1565.7 0.036492 27.80 1706 1669.2 0.021571 28.00 1802 1779.6 0.012431 28.20 1899 1897.3 0.000895 28.40 2002 2022.7 0.01034 28.60 2105 2156.5 0.024466 28.80 2159 Mean relative error 0.030704 Mean variance ratio 0.110712 Small probability error 1 Accuracy rating II

Metabolic GM (1,1) Result e

Improved GM (1,1) Result e

963 0 1272.7 0.014104 1345.2 0.00463 1421.9 0.025902 1503 0.016915 1588.7 0.022338 1679.3 0.015651 1775.1 0.014928 1876.3 0.011954 1983.3 0.009341 2096.4 0.004086 2216 0.026401 0.013854 0.041559 1 II

963 0 1275.6 0.016414 1348.5 0.007095 1425.6 0.028571 1507.1 0.019689 1593.2 0.019569 1684.3 0.01272 1780.6 0.011876 1882.3 0.008794 1989.9 0.006044 2103.7 0.000618 2223.9 0.03006 0.013454 0.048212 1 II

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(5) The determination of the best dimension The larger the N value, the larger the number of original data will shrink. Therefore, in order to make predictions feasible, we stipulate that the minimum number of raw data can be five. According to the original metabolism GM (1,1) model, reduce an outdated value to construct the data sequence when the number of raw data is enough to support the N value. The number of original data is 12 in the example, set N is 1, 2, 3, 4, 5, 6, 7, use matlab to calculate the error respectively, as shown in Table 7.

Table 7. Forecasting effect based on different dimensions Dimension Model accuracy evaluation index e C P Rating 12 (N = 1) 0.013454164 0.048212 1 II 11 (N = 2) 0.013609887 0.057549 1 II 10 (N = 3) 0.013871638 0.065841 1 II 9 (N = 4) 0.009978401 0.073654 1 I 8 (N = 5) 0.006106597 0.048784 1 I 7 (N = 6) 0.006269317 0.060654 1 I 6 (N = 7) 0.006353978 0.062275 1 I

29.00 (V) Prediction 2351 2346.5 2343.4 2327.6 2306.2 2302.1 2292.7

e 0.019956616 0.018004338 0.016659436 0.009804772 0.000520607 0.001258134 0.005336226

The prediction effect is the best when its dimension is 8 (N = 5). Therefore, the 8 dimensional GM (1,1) can be chosen as basic forecasting model.

6 Conclusion This paper combined with the idea of metabolism, established a improved metabolic GM(1,1). The model uses the latest prediction results to replace many old data, and reconstructs the background value by using the equivalent function method. Through cyclic prediction, improved metabolic GM (1,1) based on optimal dimension can be obtained. The results show that compared with the tradition and metabolic GM (1,1), improved metabolic GM (1,1) has higher accuracy and stronger adaptability, which effectively improves the prediction performance of the equipment remaining useful life.

References 1. Zhiwei, L., Kezhao, L.: An optimized weighted non-equidistance GM(1,1) prediction model based on Markov theory. Eng. Surv. Mapp. 25(12), 38–43 (2016) 2. Yanyan, N., Qiuping, W., Yanli, Z.: The predict energy consumption based on the improved GM(1,1) model. Henan Sci. 34(5), 657–661 (2016) 3. Zhaofei, Z., Jianjun, L., Binghua, X., Weihua, M.: Metabolism adaptive muti-parameter prediction method based on grey theory. J. Shanghai Jiao Tong Univ. 51(8), 970–976 (2017)

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4. Tan, G-j, Tan, J-y, Wang, J-y: The reestablishing research of background value in grey system forecasting model GM(1,1). Math. Pract. Theory 45(15), 267–272 (2015) 5. Tian, W.D., Hu, M.G., Li, C.K.: Fault prediction based on dynamic model and grey time series model in chemical processes. Chin. J. Chem. Eng. 22(6), 643–650 (2014)

Supply Chain Managerial Decision-Making Practices Effect Under Various Scenarios Azamat Rajapov(&), Ming Jian, Saidjahon Hayrutdinov, and Botir Ergashev School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China [email protected]

Abstract. Supply chain coordination with revenue-sharing contract has been widely used by many research areas. This paper considering a three-echelon supply chain consisting of a manufacturer, a distributer and a retailer where a single-period, shortage product is produced and sold in demand stochastic market. Product retail price is related to market demand and considered the problem of revenue-sharing contract. Under these assumptions further studies needed the process of revenue-sharing contractual coordination. In this paper, our model allows the supply chain system high efficiency to be successfully achieve as well as it could improve the benefit of all the supply chain parties, by turning the parties contract parameters. The relevant conclusions are proved by simulation analyses. Furthermore, study illustrated the supply chain coordination decision-bias in centralized and decentralized systems. Keywords: Supply chain contract
Revenue-sharing contract Supply chain coordination
Loss-aversion preference
Decision-bias

1 Introduction Research on Supply Chain (SC) management has generally focused on the coordination of Supply Chain where control system is centralized. The centralized system control gets involved the existence of unique supply chain decision-maker. The centralized system control assures the system high efficiency. For decentralized SCs many approaches have been made to improve overall coordination. Research by Cachon and Lariviere has proven that revenue-sharing contract is effectiveness in SCs [1]. In some cases, the incentives make the risk and the much revenue shared by all SC members. Particularly, a decentralized system control of the SC is more appropriate [2]. The decision-making behaviors are identified as loss-aversion, as one of the key features in the Prospect Theory [3]. Additionally, the perception of gains or losses relates to a specific reference point [4]. In the area of vertical SC coordination, a SC leader, such as a large manufacturer, can diversify its assets across multiple firms [5]. In most SC models, decision makers are assumed to be loss-neutral, which maximizes the profit in an uncertain environment [6]. Therefore, it is very important to study the effect of preferences on SC member’s decisions and SC performance, under various centralized and decentralized scenarios. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 661–667, 2019. https://doi.org/10.1007/978-3-030-02804-6_86

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This paper considered a SC with a manufacturer, a distributer and a retailer where a single-period, shortage product is produced and sold. Our model of SC contractual coordination aimed at a three-echelon SC, which is based on the revenue-sharing contract. The relevant conclusions are proved by simulation analyses. Furthermore, study has addressed the SC coordination decision-bias in centralized and decentralized systems. The paper is organized as follows. In Sect. 2, model formulation is given. We propose the model, based on the revenue-sharing contract to coordinate a three-echelon SC in Sects. 3 and 4, the simulation analyses result presented Sect. 5, the paper summarized with conclusion part in Sect. 6.

2 Model Formulation We consider a three-echelon SC consisting of a risk-neutral manufacturer, a risk-neutral distributer and a risk-neutral retailer, in which a single-period product is produced and sold under market demand is stochastic. We consider a three-echelon SC with a single product in single-period. The retailer provides revenue-sharing contract and shares profit quota with distributor and manufacturer. The retailer uses a Newsboy type of commodity. A retailer orders according to the demand forecast before the selling season. The distributor orders the products according to the retailer’s order. The manufacturer produces according to order quantity of distributor.

3 Members Decision Making 3.1

Retailer Decision-Making

In the case of decentralized decision-making, the revenue-sharing ratio given by retailers based on distributors /2 and the retail price x2 . The retailer starts from the perspective of decentralized decision-making the revenue-sharing contract condition with the distributor. Its optimal decision-making is maximized own expected profit. The retailer’s expected profit is: E½pr ðp; q; /2 Þ ¼ ½/2 ðp þ hr Þ  ðx2 þ cr Þq  /2 ðp Zq  v r þ hr Þ F½x  yðpÞdx  /2 hr d

ð1–1Þ

yðpÞ

The first derivative of expected profit: @E½pr ðp; q; /2 Þ ¼ /2 ðp þ hr Þ  ðx2 þ cr Þ  /2 ðp  vr þ hr Þf ½q  yðpÞ @q

ð1–2Þ

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Let,

@E½pr ðp;q;/2 Þ @q

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¼ 0, retailer’s optimal order quantity qr : qr

¼F

1



 /2 ðp þ hr Þ  /2  cr þ yðpÞ /2 ðp  vr þ hr Þ

ð1–3Þ

In the decentralized decision-making the SC coordination operation must meet qr ¼ q . So, let (1-3) formula equals to (1-1) formula, the retailer revenue-sharing wholesale factor /2 and  the retailer’s  h price x2 can i be obtained under the SC coordination. F 1

p þ hc p þ hv

þ yðpÞ ¼ F 1

/2 ¼

3.2

/2 ðp þ hr Þ/2 cr /2 ðpvr þ hr Þ

w2 þ cr pvr þ hr c pv þ h þ vr

; x2 ¼

þ yðpÞ, then, we get:

/2 pvr þ hr c pv þ h

þ vr

 cr :

ð1–4Þ

Distributer Decision Making

Distributor shears his profit with manufacturer under the revenue-sharing contract. Let the revenue-sharing factor /1 with the retailer (1  /2 ) condition. In order to achieve the overall SC coordination, the distributer order quantity is the same as that retailer’s q. Under the decentralized decision, the distributor’s decision its own expectations to maximize profits. The distributer’s expected profit is: Zq E½pd ðp; q; /1 ; /2 Þ ¼/1 ð1  /2 Þðp þ vr þ hr Þq  /1 ð1  /2 Þðp þ vr þ hr Þ

F½x  yðpÞdx yðpÞ

 /1 ð1  /2 Þvr q  /1 ð1  /2 Þhr d þ /1 x2 q  /1 hd s2  ð/1 þ cd Þq

ð1–5Þ First derivative of expected profit we get: @E½pd ðp; q; /1 ; /2 Þ ¼/1 ð1  /2 Þðp þ vr þ hr Þ  /1 ð1  /2 Þðp þ vr þ hr Þ @q  f ½q  yðpÞ  /1 ð1  /2 Þvr  /1 x2  x1  cd Let

@E½pd ðp;q;/1 ;/2 Þ @q

ð1–6Þ

¼ 0, the best order quantity qd :

qd ¼ F 1 ½1 

vr / 1 x 2  x 1  cd þ  þ yðpÞ ðp þ vr þ hr Þ /1 ð1  /2 Þðp þ vr þ hr Þ

ð1–7Þ

Under decentralized decision-making, SC coordination must be satisfied with qd ¼ q . So, let (1-7) formula equals to (1-1), the revenue-sharing coefficient between distributors and retailers /2 , revenue-sharing coefficient between distributor and

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manufacturer /1 . And the distributor’s wholesale price x2 is satisfied the relation/1 x2 x1 cd hc vr 1 ship. F 1 ðpp þ þ hvÞ þ yðpÞ ¼ F ½1  ðp þ vr þ hr Þ þ / ð1/ Þðp þ vr þ hr Þ þ yðpÞ, We have: 1

2

x1 þ c d

/1 ¼

þ vr þ h r ð1  /2 Þðc  vÞ ppv þ h  ð1  /2 Þvr þ x1 ð1  x1 Þ/1 þ cd /2 ¼ 1  þ vr þ h r /1 ½ðc  vÞ ppv þ h   vr

3.3

; ð1–8Þ

Manufacturer Decision Making

In the three-echelon SC, in order to maximize the expected profit, the manufacturer makes optimal decisions. The expected profit of the manufacturer is: E½pm ðp; q; /1 ; /2 Þ ¼ð1  /1 Þ½ð1  /2 Þðps þ vr q  vr s  hr d  hr sÞ þ x2 q  hd d þ hd ðd  sÞ þ ðx1 þ cm Þq  hm ðd  sÞ

ð1–9Þ

As the manufacturer expected profit formula (1-9) is more complex, it is directly used in the following discussion. Analyze and calculate the Eq. (1-10), is Rq s¼q F½x  yðpÞdx, that is the above formula. Calculation as below: yðpÞ

@E½pm ðp; q; /1 ; /2 Þ ¼ ð1  /1 Þð1  /2 Þs þ ½ð1  /1 Þð1  /2 Þðp  v þ hr Þ @p @s @yðpÞ þ ð1  /1 Þhd þ hm   ½ð1  /1 Þð1  /2 Þhr þ ð1  /1 Þhd þ hm  @p @p

ð1–11Þ When the SC achieves the coordination operation, the manufacturer’s optimal price p should meet the following conditions, so that (1-11) is equal to (1-2): @E½pm ðp; q; /1 ; /2 Þ @E½pðp; qÞ ¼ ¼0 @p @p

ð1–12Þ

4 SC Revenue-Sharing Contract Without Loss-Aversion Three-echelon SC coordination and revenue-sharing coefficient satisfied 0\/1  1, 0\/2  1, x1 [ 0 and x2 [ 0. Under those conditions, revenue-sharing coefficient can obtain the three-echelon SC coordination. So, that the three-echelon SC can reach the coordination range of values is: (

1  /1 [ 1  /2 [

cd ðp þ hvÞ ðcvÞðpv þ hr þ hd Þ þ ðvcr Þðp þ hvÞ cr ðp þ hvÞ cðpv þ hr Þ þ vðhhr Þ

ð1–13Þ

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From the (1-13) formula we can get the relation between revenue-sharing coefficients /1 ; /2 for SC coordination: ð1  /1 Þð1  /2 Þðhm þ hd Þ  ð1  /1 Þhd  hm ¼ 0

ð1–14Þ

If the above formula is satisfied, 0\/1  1, 0\/2  1, hm ¼ 0, hd ¼ 0, at this point the SC coordination reaches optimal prices. And manufacturer and distributor do not have stock-loss by the retailers. According to the (1-1), (1-2), (1-3), (1-7), (1-8) and (1-12) formulas can be found that the profit function of manufacturer’s distributors and retailers achieves SC coordination is: Eðpm Þ ¼ ð1  /1 Þð1  /2 ÞEðpÞ; Eðpd Þ ¼ /1 ð1  /2 ÞEðpÞ; Eðpr Þ ¼ /2 EðpÞ ð1–15Þ As can be seen from the (1-15) formula, the different revenue-sharing coefficients determine the manufacturer, distributor, and retailer. The whole three-echelon SC can effectively coordinate between the manufacturer and the distributor by adjusting the revenue-sharing coefficient and wholesale prices.

5 Simulation Analyses To better understand the three-echelon SC coordination with revenue-sharing contracts the procedure can be analyzed by simulation analyses. In this paper, for the convenience of calculation, a Newsboy type is considered, cost is lower, retailers retail price p ¼ 40, manufacturers product cost per unit cm ¼ 6, the product ordered by the distributor (except for wholesale). The distributer’s product cost cd ¼ 2 (except wholesale prices). The retailer’s product cost cr ¼ 2: Total order production costs (except wholesale prices) for the whole SC c ¼ 10 the manufacturer’s shortage due to inventory cost hm ¼ 0, distributor due to shortage goods caused by shortage of cost hd ¼ 0. The retailer’s shortage goods caused by out of stock costs hr ¼ 4 and the whole SC is out of stock costs h ¼ 6, due to retailer’s commodity surplus costs mr ¼ 8. The commodity surplus costs of the whole SC m ¼ 4 market demand the assumption as a uniform distribution. The density function is: f ðnÞ ¼

1

A; 0nA 0; other

ð1–16Þ

The market demand function is: x ¼ yðpÞ þ n ¼ bpk þ n ðb [ 0; k  1Þ

ð1–17Þ

If the parameter substituted in the (1-1), (1-8), (1-9) formulas, can calculate optimal production volume and optimal price combination (20, 1000), the hypothetical value is substituted into the (1-13), (1-14) formulas. And according to the revenue-sharing contract by /1 and /2 the range of SC coordination is 0:5\/1  1; 0:2\/2  1.

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/2 0,2 0,2 0,2 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Eðpm Þ 3020 2265 1510 755 660 566 472 377 283 188 94 0

Eðpd Þ 4531 5286 6041 6796 5947 5097 4248 3398 2548 1699 849 0

Eðpr Þ 1888 1888 1888 1888 2832 3776 4720 5664 6608 7552 8496 9440

EðpÞ 9440 9440 9440 9440 9440 9440 9440 9440 9440 9440 9440 9440

Different value calculated the SC under different revenue-sharing parameter combinations. The profit of the SC members is shown in Table 1. It is clear from Table 1, when the revenue-sharing coefficient is combined (1, 1) the manufacturers and the distributors profit are zero. That is, manufacturers and distributors earn zero from retailers, then the manufacturer and distributor will not be willing to conclude a revenue-sharing contract with the retailer. So, the SC is uncoordinated. In the following analysis, the revenue-sharing contract coefficient enables the SC to achieve coordination when different revenue-sharing contract obtained between manufacturers and distributors and retailers. Assuming that the retailer’s expected profit value remains constant, the trend is still changing. The expected profit of the distributor varies with the trend. The distribution is still unchanged the expected profit as shown in Table 1.

6 Conclusion In this paper considered a three-echelon supply chain consisting of a manufacturer, a distributer and a retailer where a single-period, shortage product is produced and sold in demand stochastic market discussed. It shows the product retail price is related to market demand and considered the problem of revenue-sharing contract. Under these assumptions further studies needed the process of revenue-sharing contractual coordination. In this paper, we proposed a model of a supply chain contract aimed at coordinating a three-echelon supply chain, which is based on the revenue-sharing with risk-neutral preference. The relevant conclusions are proved by simulation analyses. Furthermore, study has addressed the supply chain coordination decision-bias in various centralized and decentralized systems.

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References 1. Cachon, G.P., Lariviere, M.A.: Supply chain coordination with revenue-sharing contracts: strengths and limitations. Manag. Sci. 51(1), 30–44 (2005) 2. Whang, S.: Coordination in operations: a taxonomy. J. Oper. Manag. 12(3–4), 413–422 (1995) 3. Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263–292 (1979) 4. Fisher, M.: Reducing the cost of demand uncertainty through accurate response to early sales author (s): Marshall Fisher and Ananth Raman Source : Operations Research, Vol. 44, No. 1, Special Issue on New Directions in Operations Published by : INFORMS Stable,” vol. 44, no. 1, pp. 87–99 (2016) 5. Wang, C.X., Webster, S.: Channel coordination for a supply chain with a risk-neutral manufacturer and a loss-averse retailer. Decis. Sci. 38(3), 361–389 (2007) 6. Xing, Y., Li, L., Bi, Z., Wilamowska-Korsak, M., Zhang, L.: Operations research (OR) in service industries: a comprehensive review. Syst. Res. Behav. Sci. 30(3), 300–353 (2013)

The Application of Virtual Reality Technology in Logistics Training Yipeng Li, Di Wang, and Yaqi Liu(&) School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wu Han 430073, China [email protected]

Abstract. In recent years, VR technology has been widely used in various fields. Research results of domestic and foreign scholars around VR technology are also quite abundant, but the results of applying them to logistics training have a certain gap compared with other parties. This article elaborates on the application of VR technology and the current situation of logistics training. It conducts research on teachers and students. Based on the results obtained, it analyzes the using of virtual reality technology in logistics training and analyzes the specific directions of this application. With the content, detailed modeling, simulation and optimization of automated warehouses were performed, and the using of virtual reality technology in education was forecasted. This article uses FlexSim software to simulate and demonstrate the logistics supply chain, and applies the normative research method to conduct special research. The innovation of this paper lies in the fact that the virtual reality technology corresponds to the logistics training in education. And put forward the overall operation flow of the simulation design supply chain instead of a single module. For the first time, the idea of students experiencing the role of the virtual enterprise and feel the work atmosphere and content was first proposed. Small and large-scale automated warehouses were simulated and compared. Keywords: Virtual reality technology


Logistics training
Simulation

1 Introduction As early as in the 1960s, there have been proposals that contain virtual reality ideas, but virtual reality technology has been further refined and widely used by people in the past 10 years. At present, although virtual reality technology has been applied in many fields, there are few applications in the field of logistics training that employs largescale equipment and is dangerous. This article discusses and simulates the teaching links and equipment operations involved in logistics training, gets rid of the traditional solidification teaching process, and explores new teaching methods in logistics training.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 668–675, 2019. https://doi.org/10.1007/978-3-030-02804-6_87

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The Status of Foreign Applications of Virtual Reality Technology

VR technology was first born in the United States. Initially used for simulation training for pilots. With the continuous development of science and technology, the United States has promoted VR technology to the general public and has been widely used in four areas: computer user interface, user experience, background software development, and hardware. At present, the United States has established a complete VR education system that can be used throughout the country. Its application research in the aerospace field has yielded amazing results. In Europe, some developed countries, such as the United Kingdom and Germany, have also actively studied virtual reality technology. Their traditional industries, auxiliary equipment and parallel processing are far ahead. 1.2

The Status of Domestic Application of Virtual Reality Technology

Compared with developed countries, China’s research and application of virtual reality technology is still in the stage of enlightenment. However, as VR technology has become so popular around the world, Chinese scholars have also initiated research on VR technology. The degree of recognition and attention of virtual industries in China’s various industries is increasing. Virtual reality technology has been widely used in games, medical care, education, and military. China has also begun to provide professional education in the field of artificial intelligence. For example, Nanjing University has set up an artificial intelligence program. Beijing University of Aeronautics and Astronautics also has authority on virtual reality technology. From this point of view, it is only a matter of time before the virtual reality technology leads the military education industry.

2 Automated Stereoscopic Warehouse 3D Simulation Based on FlexSim 2.1

The Simulation Process of Logistics System

Systematic research, establishment of conceptual models, establishment of simulation models, operation of simulation models, analysis of operational results, output of operational results, analysis of modelling schemes, modification of system parameters, repetition of simulation runs and analysis until end of simulation. Specific steps (Fig. 1):

Fig. 1. System simulation flow chart

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The Overview of FlexSim

FlexSim is a 3D simulation system dedicated to logistics, industrial and manufacturing. It is an intelligent software system integrating computer 3D graphic image processing technology, modeling and simulation technology, data analysis and processing technology. FlexSim can easily and quickly build a three-dimensional model of the logistics supply chain and display it dynamically. Finally, it analyzes and optimizes the established model, and finally obtains a better configuration solution. At present, FlexSim software has successfully modeled and simulated various systems in the logistics field, such as automatic sorting system simulation, picking simulation of distribution centers, inventory control model simulation, logistics node facility layout simulation, and so on. 2.3

The Overview of Automated Warehouse

Automated warehouse is an automatic storage system, also called automatic sorting and storage system. It is a new term in modern logistics storage. Automated warehouse technology makes use of three-dimensional warehouse technology and equipment to make the high-level design of the warehouse tend to be reasonable. The main components of the warehouse are stack cranes, work desks for outbound and outbound warehouses, racks, and automatic transport in (out) and operation control systems. It uses an integrated logistics concept to automate cargo access, improve space utilization and logistics system levels. 2.4

Modeling and Simulation

2.4.1 Determine the Simulation Target This article takes the automated logistics system in logistics training as an example to simulate, build a virtual automated three-dimensional warehouse, make the abstract picture in the book rich for three-dimensional graphics, and demonstrate the animation effect when the warehouse operation. This enables students to grasp the operational flow of an automated warehouse, analyze the rationality, feasibility, and system operation efficiency of the system design, thereby optimizing the management of the warehouse. 2.4.2

Warehouse Equipment Selection

a. Shelf: (1) Shelf Selects the cell in which the steel texture is stored. (2) Formula for quantity calculation: Shelf Number ¼ Warehouse Capacity=Shelf Specifications

ð1Þ

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b. Forklift: (1) According to warehouse picking operation requirements, choose a suitable forklift truck (2) Formula for quantity calculation: Number of forklifts ¼

load of forklifts  average load per pallet  coefficient of relocation ð1:2Þ ðoperating time  duty cycle)  average pallet load

ð2Þ 2.4.3 Automated Warehouse Layout In the Fig. 2, Area A is the cargo storage area; Area B is the cargo inspection area; Area C is the cargo consolidation area; Area D is the pallet packaging and storage area, and the overhead unit type three-dimensional shelf is used; Area E is the cargo loading and unloading operation area; 1 and 2 represent forklifts.

Fig. 2. Automated warehouse layout

2.4.4 Workflow See Fig. 3.

Fig. 3. Automated warehouse operation process

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Large-Scale Automated Warehouse Simulation Model

2.5.1 Scenario Settings The supplier offers a total of 10 different types of goods. The automated warehouse sorts and stores the 10 types of goods in different areas. 2.5.2 Warehouse Equipment Selection See Table 1. Table 1. Equipment table Device name Generator

Quantity 2 10 Synthesizer 10 Conveyor belt 11 Elevator 1 Storage cache 1 10 Shelves 20 Basic fixed entity 2 Stacker 10 Resolver 5

2.5.3

Meaning Tray generator Class 10 cargo generators Goods package Send the goods Deliver goods to high places The goods rose to the buffer area of the second floor shelf 10 types of goods enter the buffer area in front of the shelf Two adjacent shelves store similar goods Two shelf chassis Deliver goods from the holding area to the shelves Decompose second floor pallets and cargo

Equipment Parameter Setting

a. Cargo generators: The types of shipments in the 10 temporary storage areas are sequentially increased from 1 to 10 from left to right, which means that all the goods received from suppliers are distinguished. The colors are randomly assigned and one color represents a class of goods (Fig. 4).

Fig. 4. Cargo generator parameter setting diagram

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b. Tray generator: The type of flow entity generated by the generator is set to pallet, which can generate a pallet (Fig. 5).

Fig. 5. Tray generator parameter setting diagram

c. Temporary storage area: Because the two types of temporary storage areas are transported by elevators and stackers respectively. So when setting up, choose to use the transportation tool (Fig. 6).

Fig. 6. Temporary zone parameter setting diagram

d. Shelf: Shelf is set to 10 floors, 2 m wide and 1 m high (Fig. 7). 2.5.4 Simulation Model See Fig. 8.

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Fig. 7. Shelf parameter setting diagram

Fig. 8. Large automated warehouse simulation

2.6

Analysis and Discussion

The simulation and modeling of the automated warehouse through FlexSim software can make students have intuitive experience and understanding of the operation process of the automated warehouse. Moreover, when planning and managing warehouses, different data sources are obtained for different resource configurations and requirements, which provides a reliable reference for the actual operation of automated warehouses. At the same time, when designing automated warehouses, observers can make adjustments to relevant configurations and optimize models based on postoperation statistics.

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3 Conclusion Through the above discussion and simulation modeling research, we can see the great prospects for the application of virtual reality technology. The application of VR technology in logistics training can refresh existing teaching modes, teaching contents and teaching concepts, give new concepts to the traditional teaching and training, and save many unnecessary processes in an interactive way, so that students can immerse themselves in it and get real feelings. VR technology can truly reproduce the training environment involved in the supply chain, save costs and site resources, reduce the risk of training, and stimulate students’ enthusiasm for learning. It is of great significance to improve the quality of practical training. In future teaching, it is not limited to practical training, and other teaching areas will be integrated with virtual reality technology. It can not only enable students to acquire new learning experiences but also conform to human cognitive processes. The breadth and depth of applications are just a matter of time. Acknowledgments. This research was financially supported by the National Social Science Foundation of China [No. 14CTQ016], Central Universities Education and Teaching Reform Project of China [2018-009], Education Reform Research Project of HuBei Province of China [2016-159].

References Tang, X.Y., Shi, J., Chen, L.C., Yang, L.L., Leng, X.M.: Logistics simulation and optimization design of one production line based on FlexSim. Appl. Mech. Mater. 397–400, 2622–2625 (2013) Wang, Y.R., Chen, A.N.: Production logistics simulation and optimization of industrial enterprise based on FlexSim. Int. J. Simul. Model. 15, 732–741 (2016) Meng, N., Chen, Y., Li, B.: Application of FlexSim CT in port logistics system simulation. Exp. Technol. Manag. (2011) Thurman, R.A., Mattoon, J.S.: Virtual reality: toward fundamental improvements in simulationbased training. Educ. Technol. 34(8), 56–64 (1994) Shi, L.I., Yang, B., Zhen, Y.E., Sun, Z.Q.: Simulation & training system of ship engine based on virtual reality. Acta Simulata Syst. Sin. (2000) Zhao, K.R., Xu, S., Ye, Q., Li, Y.: Design and realization of flight simulation system based on virtual reality technology. In: Control and Decision Conference, pp. 4361–4364. IEEE (2011)

Computer Application Technology Development and Practice Xue Zhao(&) Guangdong University of Science and Technology, Dongguan 523083, China [email protected]

Abstract. At this stage, under the influence of many high-tech developments, China’s computer application technology has been further developed, and thus the dependence of human society on computer application technology has been continuously improved. To a certain extent, computer application technology has become an indispensable and critical tool in current human working life. It is hoped that the research on the development and practice of computer application technology can attract the attention of relevant researchers, and then promote the wider application of computer application technology, and ultimately promote the faster development of modern society. Keywords: Computer
Application technology
Role
Development prospect

1 Introduction The emergence of computer application technology is the most important invention of human beings in the 20th century, and it reflects that human society has moved toward a new social class. The application of computer application technology provides the basic guarantee for the current development of social knowledge economy. With its many application advantages, it has become a key technology in social development and has effectively integrated into all aspects of human society’s production and life. It can be seen that it is necessary to study the specific application of computer application technology and its future development prospects in this context.

2 Key Points of Computer Application Technology 2.1

Lan

Local area network is the most common computer application architecture currently used. The main feature of this application architecture is that the cost is relatively low but the effect is faster. It mainly includes three types of fiber-optic distributed data, Ethernet, and token ring networks. The usage rates of the latter two are relatively high. Ethernet uses its unique topology mechanism and the advantages that site failures do not affect, coupled with strong scalability, so it has advantages over other applications, and transmission materials are selected. Easy to expand management and cheap but easy to bridge and connect the twisted pair, but the use of twisted pair should pay © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 676–681, 2019. https://doi.org/10.1007/978-3-030-02804-6_88

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attention to bus bandwidth and transmission rate. Token ring network is characterized by its strong confidentiality and real-time nature, it can ensure the rapidity of longdistance transmission, and the transmission direction and routing are single, so there will be no information collided. The disadvantages are high cost and complicated protocol. The difficulty of expansion is relatively large, and any site failure at work will cause the entire application to fail. Fiber distributed data has high reliability and controllability, and even exceeds Mb/s when transmitted. It is usually used in campus backbone applications. Figure 1 below shows the LAN control process.

Fig. 1. LAN

2.2

The Internet

The Internet is an application service system composed of many servers and application terminals. It can provide users with file transfer, e-mail, and remote login services, but the most important is through the realization of mutual communication between users. In addition, it can also provide various information inquiries, such as the World Wide Web and electronic bulletin board services [1]. The World Wide Web Service is a global information resource application that integrates text, sound, video, etc., and provides users with a Web-based information window through browsers. Users search the database for the information they need, and they can also share resources. Electronic bulletin board service is a unique online culture that can provide users with important application communication channels. With the continuous development and improvement of the Internet, many new features have emerged, such as application phones, application communities, application relay chat, and ICQ. 2.3

Wireless Network

The basis of wireless network application is wireless communication technology, which mainly includes long-distance and near-distance wireless connection. The main characteristics of relatively wired applications are different media media, wireless applications can ensure that users learn and work anytime, anywhere. According to the different access methods, it can be generally divided into access point connections, bridge connections, HUB access types. Access node connection uses mobile cellular network access. Each mobile station receives information according to the principle of

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proximity, and then transmits the received information to the switching center and then to the wireless receiving station so that the application coverage area can receive signals. In order to achieve roaming communications. Bridge connection applications are used in wireless or wired LAN interconnections. Generally, bridge connections can be applied if the two networks cannot be wired or the connection is difficult. HUB access is to establish a star structure in the LAN through HUB, and then use the intranet switching function.

3 Praction of Computer Application Technology 3.1

Practice in the Field of National Defense

National defense is a manifestation of a country’s defensive capabilities and a symbol of a country’s technological level. Now is the era of peace, but various countries have continuously strengthened their national defense construction. National defense construction needs many indicators to measure, such as the technical content of weapons, the launch status of satellites, and the traditional national defense index measured by the number of weapons and military forces is out of date [2]. The construction requires advanced scientific and technological applications. Computer technology plays an important role in the process of national defense construction. For example, rocket launch control uses computer technology to control and improve computer technology is an important factor in strengthening national defense construction. In the direction of development, national defense construction guarantees an important pillar of national security and territorial integrity. The country’s vigorous development requires the consolidation of national defense construction. National defense construction is the foundation. Only by consolidating national defense construction can we achieve better economic development. According to the needs of national defense construction, computer technology continues to develop and improve to adapt to the needs of national defense construction. At the same time, it can also promote the reform of computer technology and continuously promote the emergence of new technologies. 3.2

The Practice of Daily Life

Computers have gone through more than 70 years of development. Now that computers have gone into the home and into the office, computers are now not using specialized fields such as defense and banking. The application of computer technology has changed people’s lifestyles. Traditional people’s life shopping is to go to a physical store to buy. Comparing one entity to another is more troublesome. It can be said that shopping was inconvenient in the past. In the era of Internet+, the e-commerce industry has developed rapidly. People can make purchases through online platforms. For example, Taobao and JD.com can choose their own suitable products. This is not only convenient, but also generally cheaper than physical stores. The development of computer technology has changed people’s entertainment methods. For example, watching a movie can go to a movie theater before and can be watched at home through a computer. The time is not limited. The application of computer technology also

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promotes the development of film and television production. Now China’s film and television industry Constantly in the development and improvement, improve its practical application effect, enhance the quality of film and television works, enrich people’s lives and improve people’s entertainment channels. Now that computer users are increasing year by year, families, units, and public places are using computers to work. Now that computers have become a part of people’s lives, the development of computer technology has a bearing on people’s livelihood. The continuous development of computer technology is the result of computer development. Social development puts forward new requirements for computer technology and must promote the development of computer technology. 3.3

Practice in Education

Education is the base for cultivating talents. However, college students in China nowadays are not suited to the needs of modern colleges and universities. Therefore, traditional teaching models must be reformed to establish a new type of teaching model to improve students’ practical ability and to cultivate the needs of enterprises. Applied talents. The application of computer technology to teaching reforms has provided strong technical support for teaching reforms, improved teaching reforms, and improved classroom teaching effectiveness [3]. For example, the widely used micro lessons and MU lessons are based on the development of computer network technology, the perfection of digital campuses, and the advent of the era of big data, providing technical support for university teaching reform. All aspects of teaching reform need the support of computer technology. For example, the traditional exam markings are all done manually. In the process of reviewing a large number of examination papers, it is normal to have a problem, but now the marking work must be completed by the computer system, not only to improve the work efficiency also reduces the possibility of errors, and is more scientific and accurate to a certain extent. The application of computer technology in the field of education is the need for the reform and development of education. It is also a new requirement of social development for the development of education, and it also meets the needs of modern education system reform.

4 Development of Computer Application Technology 4.1

Openness and Integration

With the development of science and technology, people’s requirements for computer application technologies have been continuously improved. Under the current social development background, computer application technologies should realize the function of integrating multiple media applications and services so as to ensure the diversification of functions and services. In addition, the automation of the transmission path and information processing requires that its compatibility must also be improved.

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Classification and Refinement

The application fields of computers have become more and more extensive, and the development trend of computer types has become more detailed, such as the application in the field of education, the application in the field of national defense, the application in the industrial field, and the application in the agricultural field. The development of computer technology should provide technical support for the development of other industries in accordance with the needs of the development of the industry. The development in various fields depends on the development of computer technology. At the same time, the innovation of computer technology needs to rely on the support of other industries, no matter how many cloud computing technologies are applied now [4]. All of them are applied in other industries, such as cloud computing applied to agricultural production systems and cloud computing applied to rail transit systems. Therefore, the classification of computer technology needs to be more detailed, and the training of computer technology talents also needs to be classified. It requires computer and technical personnel with certain expertise to meet the needs of social development. The cultivation of talents is a key factor in the development of the industry and the reinforcement of computer personnel. The development of the computer and the enhancement of the application ability of computer talents are in line with the needs of modern industry development for the development of computer technology. 4.3

High Speed and Mobilization

The pace of rapid social development has made people more and more demanding the speed of application transmission. Therefore, the development of wireless applications is even more important. In order to achieve the convenience of access to the Internet, the limitations of the environment are exceeded, and the application of high-speed and mobile development is realized. It is very critical. 4.4

Intelligent

The intelligent development of computer technology is to make computer technology more perfect and meet the requirements of computer technology development in other fields. Intelligent devices are now popular with the masses of the people [5]. No matter whether it is in household appliances or other fields, they are all smart and provide people with more convenience. Therefore, the key development direction of computer technology is to realize the full realization of intelligence. Change. For example, the computer in the future will be more sensitive than the current computer. The development of intelligent computer technology will provide technical support for the development of other fields.

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5 Conclusion In a word, computer application technology plays an important role in improving the overall social development level of mankind. The current application of computer application technology is mainly reflected in the LAN, Internet and wireless network. However, it is believed that with the continuous development of advanced technologies, the application of computer application technology will also be more complete and extensive. Therefore, this requires that the relevant staff must focus on grasping the future direction of development on the basis of clarifying the role of computer application technology and its application points, so as to promote better and more effective development of computer application technology, and further to make it more efficient. Serving the development and progress of human society.

References 1. Yang, T., Shang, M., Ding, C., Luo, Y.: Development status and prospect of computer application technology. Numer. Technol. Appl. (7), 218 (2014) 2. Zhao, M., Wang, Y., Liu, X.: Application of computer application technology and its application in practice. Digit. Technol. Appl. (4), 214 (2015) 3. Wu, Q.: Application of computer network technology in electronic information engineering. Autom. Instrum. (8), 157–158 (2017) 4. Zhang, Y.: Research on application of computer network technology in teaching and management of higher vocational education—review on “The basic of computer network technology”. Educ. Rev. (10) (2017) 5. Yan, X.: Application of network commands in computer software system engineering. Liaoning High. Vocat. Tech. Coll. (7), 80–82 (2017)

Analysis of the Information Demand and Supply of New Occupational Farmers—A Survey Based on Beijing Changshou Luo1,2, Xiaohui Liu1,2, Yaming Zheng1,2, and Sufen Sun1(&) 1

Institute of Agricultural Information and Economics Beijing Academy of Agriculture and Forestry Sciences, Beijing, China [email protected] 2 Daxing District Rural Work Committee, Beijing, China

Abstract. The basic situation, the information demand and supply, and the problems in the information acquisition of the new occupational farmers in Beijing are analyzed in this paper. The results show that, the people under 50 years old are accounted for 70%, and the college degree above is accounted for 57.7%. They are young and highly educated. The information of science and technology, policy and market is the most important for the new occupational farmers, accounting for 80%. Agricultural technology extension departments and agricultural scientific research institutions and agricultural information service department are the primary suppliers, accounting for 84.51%, 74.65% and 61.97% respectively. The Internet, WeChat and QQ have become the most important channels for remote information acquisition, accounting for 80.28% and 70.42% respectively. Face-to-face training is still an effective information channel. Obtain information through training and field observation is accounted for 78.87% and 67.61% respectively. In the process of information demand and supply, the main limiting factors exist on both sides of providers and requester. 38% of new occupational farmers have difficulties in obtaining information. In addition, the shortage of capital and labor is also obstacles. Finally, from the information platform, field training and fund guarantee, some suggestions are put forward to effectively solve the information needs of the new occupational farmers. Keywords: New occupational farmer Supply


Information
Demand

1 Introduction The new occupational farmers are a group of agricultural practitioners who take agriculture as an occupation, has professional skills, and the income comes mainly from agricultural production and operation. The ministry of agriculture “13th FiveYear plan of new occupation farmer development” puts forward the goal: by 2020, the total number of new occupational farmers in the country will exceed 20 million. It has great significance to cultivate new professional farmers and promote the increase of © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 682–688, 2019. https://doi.org/10.1007/978-3-030-02804-6_89

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farmers’ income that how to provide better training and service for the new professional farmers [1]. A scholar takes Shandong as an example to study the training need and selection mechanism [2]. Some others have analyzed the training effects, problems and influencing factors based on the four pilot counties of the west [3]. Taking new professional farmers in Beijing as an example, this paper analyzes the information need and supply status, and puts forward corresponding suggestions for them.

2 Basic Situation of New Occupational Farmers Take Beijing for example, select typical new occupational farmers to investigation from agricultural enterprise, professional co-operatives, family farms, major households, agricultural technician team and so on. After collation and analysis, 171 valid questionnaires were obtained. The investigation personnel are from ten outskirts of Beijing, which can basically represent the situation of new occupational farmers in Beijing. From the age structure, 20–30 is 8.45%, 30–40 is 33.8%, 40–50 is 28.17%, and 50 is 29.58% (Fig. 1). According to age, the new professional farmers are obviously younger than that before. Only 29.58% of them are over 50 years old. The age of traditional agricultural producers is mostly over 50 years old. In terms of gender, men accounted for 49.3%, women accounted for 51.7%, and are balanced basically. From the degree of education, the educational background of new occupational farmers is obviously higher than general farmers. The lowest level of the new occupational farmers is junior high school, accounting for 2.8%. High school and secondary school are accounted for 39.5%, college and above education is accounted for 57.7%. Thus, from two aspects of age and education, the new professional farmers have obvious advantages over the general farmers. Over 50 years old

29.58%

40-50 years old

28.17%

30-40 years old

33.80%

20-30 years old

8.45% 0%

10%

20%

30%

40%

Fig. 1. The age structure of new occupational farmers

3 The New Occupational Farmers Information Demand Science and technology, policy and market information are the most concerned and needed for the new occupational farmers. The survey shows that the four kinds of information demand of agricultural science and technology, technology training, policy

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and market are the largest, accounting for 77.46%, 74.65%, 76.06% and 74.65 respectively. This information has a direct impact on the possibility of their success in agricultural entrepreneurship, so they are most popular. Secondly, it is the information of pest control and the agricultural means of production supply. These information are closely related to the cost of production, accounting for 52.11% and 47.89% respectively. Thirdly, it is the information of weather and disaster forecast, and the processing information of agricultural products, accounting for 32.39% and 30.99% respectively. Finally, it is the job hunting information. Most of the new professional farmers want to do something in the field of agriculture, so the demand for information for migrant workers is the lowest, accounting for only 12.68% (Fig. 2). 30.99%

Agriculutural produƟon processing

12.68%

Go out to work Weather and disaster forecast

32.39%

Pest control

52.11%

Agricultural means of producƟon

47.89% 74.65%

Policy informaƟon

76.06%

Technical training

74.65%

Agricultural science and technology

77.46% 0%

20%

40%

60%

80%

100%

Fig. 2. Information demand of new occupational farmers

4 The New Occupational Farmers Information Demand 4.1

Information Supply Source

From the survey, the agricultural technology extension department, the agricultural scientific research institute and the agricultural information service organization play important roles in information supply. The proportion they provide is 84.51%, 74.65% and 61.97% respectively. Agricultural technicians also play important roles in information provision, is accounted for 47.89%. In addition, some specialized cooperatives organization, agricultural leading enterprises have special information channels, and also has certain information providing capacity, accounting for 32.39% and 26.76 respectively. Finally, although the agricultural means of production market contact with farmer frequently, the ability to provide information is limited, accounting for only 21.13% (Fig. 3).

Analysis of the Information Demand and Supply of New Occupational Farmers

Agricultural leading enterprise

685

26.76%

Farmer cooperaƟve organizaƟon

32.39%

Aricultural agrotechnician

47.89%

Agricultural means of producƟon sales…

21.13%

Agricultural informaƟon service agency

61.97%

Agricultural scienƟfic research insƟtute

74.65%

Agricultural technology extension…

84.51%

0%

20%

40%

60%

80%

100%

Fig. 3. Information supply source of new occupational farmers

4.2

Information Supply Channel

The data show that, the Internet, webQQ and QQ have become the main channel for new occupational farmer to obtain information. The proportion is 80.28% and 70.42%, respectively. In contrast, the role of traditional channels has declined. TV is accounted for 50.70%. The way of mobile phone and SMS is gradually weakening. 29.58% of them use short messages to get information and 21.13% of them get information by mobile phone. The newspaper and CD still have the function of obtaining information, which is 19.72% and 8.45% respectively. Some people get information from their neighbors, relatives and friends, accounting for 5.63%. Due to the complexity of agricultural technology, face to face training and visit and learn on the spot is still effective channels for information obtaining, accounted for 78.87% and 67.61% respectively (Fig. 4). Neighbours, relaƟves and friends visit and learn Train Newspaper CD Internet WebQQ QQ Short message Telephone TV

5.63% 67.61% 78.87% 19.72% 8.45% 80.28% 70.42% 29.58% 21.13% 50.70%

0%

20%

40%

60%

80%

Fig. 4. Information supply channel of new occupational farmers

100%

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5 The Problem of Information Supply and Purchase Matching After the text edit has been completed, the paper is ready for the template. Duplicate the template file by using the Save As command, and use the naming convention prescribed by your conference for the name of your paper. In this newly created file, highlight all of the contents and import your prepared text file. You are now ready to style your paper; use the scroll down window on the left of the MS Word Formatting toolbar. 5.1

Constraints on the Matching of Information Supply and Purchase

Although the education level of new occupational farmers is relatively high, most of them have better income, but some of them still have some difficulties in the process of information acquisition. From the investigation, when there is information demand, 61.97% of the new farmers can solve their problems through effective ways, and 38.03% of the new farmers have limited factors of obtaining information. Among these constraints, one is mainly due to the problem of information supply. For example, the proportion of untimely information transmission is 48.15%, the proportion of information unprofessional is 25.93%, and the proportion of information is useless accounted 11.11%. The others are due to the new farmers themselves. For example, the lacking of computers causes 25.93% failure to get information, 22.22% of them can not understand the information received (Fig. 5). In addition, in the process of investigation, it is also found that all people install WeChat or QQ. In the follow-up work, WeChat and QQ are service channel that needs to be considered.

Others Have no WebChat and QQ

7.41% 0.00%

InformaƟon is incomprehensible

22.22%

InformaƟon is unprofessional

25.93%

InformaƟon is useless

11.11%

Transmission is not in Ɵme

48.15%

Have no computer

25.93%

0%

10%

20%

30%

40%

50%

60%

Fig. 5. The restrictive factors the matching of information supply and purchase

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Other Demands in Agricultural Production

In addition to the information demand, the new occupational farmers’ other demands in agricultural production and entrepreneurship are investigated. The survey shows that the demand for capital is the largest and the proportion is the highest, accounted 88.73%. The technology and product sales issues are also concerned about 80.28% and 77.46%, respectively. In addition, labor shortage is often an obstacle factor, accounting for 42.25% (Fig. 6).

Others

9.86%

Product sales

77.46%

Labor

42.25%

InformaƟon

60.56%

Technique

80.28%

Capital

88.73%

0%

20%

40%

60%

80%

100%

Fig. 6. Other demand factors of new occupational farmers

6 Conclusions and Suggestions 6.1

Concussions

In terms of their own situation, the age of new occupational farmers is younger than general farmers. Their education level is also significantly higher than general farmers. In the aspects of agricultural production, entrepreneurship and information acquisition, new occupational farmers have obvious advantages over the general farmers. In terms of information demand, science and technology, policy and market information are the most concerned and demanded for new occupational farmers. Most of them want to develop in the field of agriculture, so the demand for information on migrant work is the lowest. In terms of information supply, agricultural technology extension departments, agricultural research institutions and agricultural information service department are the main providers. Agricultural cooperatives and enterprises have the ability to provide information. The ability of agricultural means of production market to provide information is limited.

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In terms of information supply channels, the Internet, WeChat and QQ have become the main way. TV, telephone, mobile phone short message, newspaper and CD are weakening gradually. Face-to-face training and technical observation is still the most effective way of information acquisition. In terms of information acquisition constraints, lots of new professional farmers still have difficulties in information acquisition, which is mainly caused by the information providers and the person themself. In addition to the information demand, the shortage of funds and labor is also the main obstacle for them in the agricultural production and entrepreneurship. 6.2

Suggestions

Make full use of modern mulch-channel information service platform. Provide remote information services and technical guidance service by means of modern media such as Internet, WeChat and QQ group. It has the characteristics of convenient, fast, low cost, and can meet the needs of the new occupational farmers. Increase the intensity of field training. Through the establishment of training programs, conduct train for new occupational farmers with the industrial base, high education level and entrepreneurial aspirations, to meet their multi-level, multi form on-the-spot training need. Strengthen the support of policies and funds. Draw up supporting policies, including land circulation, financial credit, agricultural subsidies, agricultural insurance and social security, so as to solve the capital demand of new occupational farmers development. Acknowledgments. The research work was supported by 2018 Beijing financing agricultural funds: Application and demonstration of “Nongkexiaozhi” consulting service robot and WebAPP in agricultural production, the international cooperation fund: A comparative study on the agricultural science and technology information service system in China, the United States and Canada, the Beijing excellent talent project: Research on key technology of man-machine conversation in agricultural science and technology consultation and service application of Beijing, Tianjin and Hebei.

References 1. Author list, paper title, journal name, vol. no. pages-, year 2. Pang, J., Su, M.: New vocational farmers’ training demand and selection mechanism—take Shandong as an example. Investig. World 21–27 (2017) 3. Zhou, B., Dai, L., Di, L.: Analysis on the effect, problems and influencing factors of the training of new professional farmers in China—based on the investigation of four pilot counties in the west China. Rural Econ. 115–121 (2017) 4. Shi, W., Chen, C.: The supply side reform of the new vocational farmers’ training: demand and response—based on the Jiangsu survey. Vocat. Educ. Forum 28, 53–58 (2017)

Internet and Cloud Computing

Construction and Implementation of Information Class Experiment Course Group Based on Cloud Platform Ting Huang ✉ and Peng He (

)

College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei, China [email protected]

Abstract. According to cloud technology, we built a cloud platform for exper‐ imental teaching group. We introduced the design and construction of cloud plat‐ form in detail, and introduced its security and the use of experimental courses group. The advantages of cloud platform are analyzed. Compared with traditional laboratories, the cost of software and hardware is reduced, the maintenance work is reduced, data centralized management and data security are greatly improved, and students’ autonomous learning ability and technological innovation ability have been greatly improved. Keywords: Cloud platform · Information class · Experimental course group

1

Introduction

Zeping [1] proposed an innovative experimental cloud platform based on OpenStack cloud computing. The architecture is divided into basic hardware and software resources, OpenStack cloud management, communication middleware layer, applica‐ tion service layer and application layer, which can satisfy the different demands on computer hardware resources of Colleges and universities’ experimental innovation, which reduce the laboratory management workload, and improve the utilization of labo‐ ratory equipment. This platform is stable in operation and reasonable in function design, which provides effective support for innovative experimental teaching. The laboratory of Alves [2] is a session with certification system in cloud. The plat‐ form has a set of pre-established images, a specific operation system and software for different classes and benchmark images. The “Administrator” of the user type can access all the resources of the cloud and can manage the cloud resources. The “lab manager” of a user type is responsible for one or more experimental classes. User type “student” is a user with less privileges. It can access virtual machine in the laboratory, access software repository, and create and manage persistent data of single volume. The Bazzaza’s [3] paper uses the cloud instead of the traditional computer network laboratory to help students get almost all the key computer network skills. Xu [4] has proposed a cloud based virtual experiment teaching method named VLab platform, which provides an experimental environment for the use of virtualization © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 691–698, 2019. https://doi.org/10.1007/978-3-030-02804-6_90

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technology and the hands-on experiment of OpenFlow switches. The system can be accessed safely through OpenVPN, and students can remotely control the virtual machine and complete the experimental task. The V-Lab platform also provides inter‐ active Web GUI for resource management and social networking sites for knowledge sharing and contribution. V-Lab adopts a flexible and configurable design to integrate the teaching model into the curriculum design, which provides a gradual learning path for network security education. Cloud platform brings new management ideas to the university information class practice teaching and scientific research. The cloud platform can realize the unified management and open sharing of practical teaching and scientific research information, it can realize hardware cloud, software cloud, resources cloud service, can break through the limitation of the space and resources for teaching in practice and research, it can provide a new practice support in teaching and scientific research. The cloud platform can provide virtual information classes experiment, and teachers and students can do the experiment remotely without entering the laboratory. Information experimental courses group based on the cloud platform integrates the existing infrastructure, it can realize full sharing of experimental resources, but also can reduce the cost of laboratory management, improve management efficiency, dynamic client access. The experiment place is no longer confined. It can strengthen the students’ autonomous learning ability, improve the students’ innovation ability of science and technology.

2

The Construction of Cloud Platform

2.1 Server Configuration As shown in Fig. 1, the server cluster (DELL PowerEdge server R730, 16G memory, 4 TB hard disk, CPU frequency 1.7G MHz) builds hardware, cloud server cluster loads cloud platform data, virtual experiment data in the software group built resources cloud, cloud platform data storage common software group of teachers and students build in the soft‐ ware cloud, the switch connects to the server cluster, controls data of server cluster, and manages cloud platform, the data that are needed to access in cloud platform can be connected to the cloud platform through LAN or WAN, laboratory equipment can connect to the cloud platform through the local area network for unified management.

Fig. 1. Server configuration diagram

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The monitor is installed in the laboratory, and the monitoring information is trans‐ mitted to the server cluster to help the lab manager to monitor the laboratory. 2.2 Safety In order to ensure the communication risk of data storage and corresponding data processing, in order to ensure the normal operation of data warehouse, backup servers are prepared in server cluster. Once the primary server is attacked and lose data, the data in backup server can be immediately activated. 2.3 The Use of Cloud Platform Virtual experiment center portal website: a dynamic Web system, which includes center introduction, experimental teaching, experimental team, management mode, equipment and environment, teaching features, Central News/Announcements/notices, etc. Experimental teaching management: the function of the course library, the training plan, the course arrangement and the examination and audit. Experimental teaching management: physical experiment arrangement, virtual experiment arrangement, experiment correction, attendance management, achievement management, experiment report and so on. Pre experiment theoretical study: before the experiment, the students learned the theoretical knowledge through practice, self-test, courseware and other methods. The intelligent guidance of the experiment process: students can ask for guidance in the course of the experiment, and the system gives guidance information. The results of the experiment are automatically corrected: after the students submit the results of the experiment, the system is automatically judged, and the scores and scoring points are given. Experimental teaching resources management: the upload and release of various virtual experiments, simulation software and demonstration animation. Open reservation management of laboratory: laboratory equipment loan, laboratory reservation, experimental reservation, management of station reservation. Interaction and communication between teachers and students: real time answer, online message and so on. The cloud platform provides virtual experimental projects 24 h a day for teachers and students. After class, the teacher log on to the cloud platform to do the experiment, which can open the login authority by the manager. After class time students can log on the cloud platform to do the experiment, which can apply for login by the teacher. When the students finishing the experiment, they can be submitted online test report, save and submit the experimental results, experimental results and comments on the query. The teacher can log cloud platform to check experiment results and give comments on experiment report, automatic correcting and intelligent guidance, statistics and release of students’ scores. The cloud platform provides a high performance computing platform: the hardware system is built into a high performance computing platform through the high perform‐ ance cloud platform computing management system. Users only need to install

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corresponding computing software on the cloud platform to run or directly call the plat‐ form’s existing operation software, and allocate corresponding system resources for them, so that we can get services provided by the cloud platform. 2.4 Experimental Cloud Platform Architecture From the user’s view, the experimental cloud platform is divided into two parts of the management system and the client, and the overall architecture can be divided into 5 layers. (1) The basic hardware resource layer: it includes computing resources (CPU, etc.), storage resources and network resources. (2) Cloud management. It provides and manages the virtual machine and other resources mainly through each component. (3) Communication middleware layer. The main purpose is to connect the cloud management layer and the application business layer, which implements through the interface. Interfaces are divided into public interfaces and private interfaces, and different business logic applications call different interfaces. (4) Application business layer. Different business processes, including courses, users, mirrors, and virtual machines, are given for different requests from a network or a client. (5) Application of the expression layer. Application presentation layers have different forms of expression to different user. Teachers and administrators manage the whole system through the network, and the middle communication part is imple‐ mented through interfaces. Students get resources mainly through the cloud plat‐ form desktop client.

3

The Construction of the Information Class Experiment Course Group

3.1 The Information Class Experiment Course The mutual support and interrelationship between computer class courses are shown in fish bone Fig. 2. Computer courses experimental system is divided into 5 levels, it is in advance and in depth from up to down, it is dependence and relative independence between layers, the down layer is the foundation of learning the up layer. It promotes students’ practical ability gradually improve from up to down through the five stages of learning continues, and achieves the goal of cultivating innovative talents. The first level is to stimulate the students’ interest in learning and to consolidate the foundation. Teaching can be organized with interesting examples, and all new knowledge points are introduced by examples. The second level raises the ability of computer programming and algorithm design, strengthens the application of data structure, and preliminarily grasps the foundation of application and development. Data structure is the foundation of programming, database principle and design, and the development of large scale application system. Its teaching effect will directly affect the training of data abstraction

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ability and programming ability of students. The future level will improve the ability of application and development programming to cultivate the students’ creative ability. After three layers train students analysis and design of computer system, system devel‐ opment and integration capabilities, the ability of engineering practice. The high grade students have the existing software development foundation. We can encourage students to develop practical projects, improve the ability of software development. Project development can be simulated and combined Cognitive ability of computer industry the actual software project development.

Fig. 2. The relational fish bone map of the computer class group

The core courses of information specialty should include four courses: “circuit”, “analog electronic technology”, “digital electronic technology”, and “circuit design and simulation”. These four courses are the prerequisite and foundation for all the students of information majors to learn their professional knowledge. The four courses are combined to form the core curriculum of the information profession. According to the core curriculum group, the electronic, communication experiment and practice course group will be constructed. (1) The electronic communication system design experiment and practice courses (Professional) lay the foundation for later courses:

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Low frequency electronic circuit experiment pulse and digital circuit experiment FPGA and digital logic course design AVR MCU Course Design Electronic communication electronic circuit Measurement and design of electronic system FPGA communication system design embedded system design (2) The information processing and transmission experiment and practice of courses (professional basic courses) initial intake: Curriculum design of MATLAB and signal processing system communication principle experiment DSP and experiment course design of DSP system course design of digital communication system digital image processing experiments (3) The computer experimental practice courses master computer tools course: C class VC++ curriculum design JAVA design experimental computer network experiment

micro computer technology

(4) The experiment of mobile communication direction and practice courses (profes‐ sional direction) cultivate students’ professional skills: Project comprehensive practice 1 (program control, soft exchange) project comprehensive practice 2 (3GWCDMA) graduation practice (mobile phone, SDH) The above is my school to open part of the experimental courses in information. Cloud platform has information experimental courses with virtual experiment form. Courses stored in the cloud, similar courses use the public set of experimental curriculum software, according to the different courses teachers and students visit the experiment by different interface. The students take the experiment course in order. In order to get the next experiment the students master the professional knowledge step by step. For example, “computer network” embedded Cisco packet tracer into the cloud platform, and teachers and students can use this software to do virtual experiments on the cloud platform. The design of MATLAB and signal processing system can embed MATLAB into cloud platform. Teachers and students can log on to cloud platform and complete the system design with MATLAB. 3.2 The Development of the Cloud Platform Experiment Course The cloud platform is landing through the virtual experiment center portal website, and the web site adopts a dynamic web content management system with a comprehensive display function of multimedia information on the Internet. It can satisfy the virtual laboratory center’s introduction, experiment teaching, experiment opening and practice innovation to the drafting, auditing and issuing of columns, management system, teaching staff, equipment environment, network resources and so on. Users can set up multi-level columns suitable for central propaganda business through systematic column management, content management and resource management module, and support multimedia information online editing and publishing, and realize unified management of experimental center and laboratory portal website. The experimental teaching management of the website is based on the experimental class on the course arrangement system, and then the class list of the educational system

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is introduced to the teachers and students in the laboratory. The landing cloud platform for virtual experiment is opened by teachers, and the teachers use the class list of educa‐ tional administration system to open students’ access to the cloud platform. After landing on the cloud platform, the students check the related curriculum and understand the information of the teachers and the teaching arrangements. According to the opening plan of the dean’s office, the teachers maintain the typical experimental library, arrange the experiment, check the students’ experimental progress, correct the experimental results and report, statistics and publish the experimental results. In accordance with the requirements of the teachers, the students preview the experiment, enter the laboratory to complete the experiment and submit the experimental report. The system provides an image, sound, animation, video, interactive exercises, self-testing, mock examinations and other functions, which achieve the timely interac‐ tion between teachers and students, and meet the needs of teaching and management, help to improve the students’ experimental learning efficiency, reduce teacher burden. The system carries out the configuration and maintenance of the subject structure knowledge system of the question bank. The question bank is organized by the subject and knowledge point, which is a tree structure. The system supports the following ques‐ tions: objective questions, multiple choice, multiple choice questions, judgment problem, and etc. Test questions support the following format: pictures, audio, video, table. The system supports intelligent test papers and manual work papers. Intelligent examination paper can extract test questions by knowledge points, questions, difficulties, scores and so on, and compose test papers, and allow adjustment and modification on the basis of composition test papers. Students are examined with a large number of interactive topics covering in all the teaching content, problems encountered can be answered by courseware on demand, and other ways. Through self-testing and exami‐ nation students’ experimental curriculum theory knowledge are further examined. The intelligent guidance of virtual experiment aims at providing intelligence and humanized guidance through the data collection and monitoring of learners’ experi‐ mental process, so as to realize the function of “online teacher”. According to the different conditions of the experiment, it provides targeted experimental guidance to realize the intellectualization of the virtual experiment teaching process. The intelligent guidance subsystem can monitor the learner’s experiment process. When students encounter problems in the process of experiment, students can seek guidance and assis‐ tance through this system, and the system can also prompt students’ improper operation in the experimental process to avoid mistakes. The learner or teacher can ask questions directly to the system, the system searches out the corresponding knowledge information and feedback to the user. After completing the experiment, the students automatically update the subsystem, get the experimental data from the experimental platform, do data processing, and submit useful information to the evaluation reasoning machine. The evaluative inference machine identifies the results of the students’ experimental results by using the pre-recorded samples of the teachers in the knowledge base. After finding the matching example, the reasoning machine continues to make use of the correction rules that the teacher pre-recorded in the knowledge base, and carries on the reasoning analysis to the student’s experimental result information, finally draws the conclusion. The evaluation results are fed back to the experimental platform, and the evaluation

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results are presented to the students. And the results and related information are summed up to the performance management module. In accordance with their interests, students can submit experimental reservations and job reservations for personalized experiments. Applications can also be applied to the laboratory equipment. A teacher can also organize an interest group to make an appointment for an extra‐ curricular experiment, as well as an application to the laboratory for equipment. The system mainly includes real-time online communication, question library search, online message, SMS notification and other functional modules, which aims at building a convenient communication environment for teachers and students, and helps learners solve problems encountered in the experiment process quickly and effectively. The system provides user interaction and communication module, including online realtime answering room, online real-time answering room supporting multiplayer voice, text or video communication, sharing whiteboard, saving chat records, searching problem library, sending offline messages to mailboxes. A short notice function is provided. When the experiment is arranged, students can be informed by SMS in real time, and the students who have not completed the experiment can be urged by text messages.

4

Summary

Based on cloud technology, we built a cloud platform laboratory, which designed and built a cloud platform, and discussed the security and cloud platform instructions, and the construction of information experimental curriculum group. The advantages of cloud platform are analyzed. Compared with traditional laboratories, the cost of software and hardware is reduced, the maintenance work is reduced, data centralized management and data security are greatly improved, and students’ autonomous learning ability and technological innovation ability have been greatly improved.

References 1. Zeping, Y., Chunhua, G., Feng, W., Fei, L., Yaohui, C.: Research of innovative experimental cloud platform based on OpenStack. Exp. Technol. Manag. 33(5), 147–150 (2016) 2. Alves, S., Prata, P.: Using the cloud to build a computing lab. In: 2014 9th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6 (2014) 3. Bazzaza, M.W., Salah, K.: Using the cloud to teach computer networks. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing, pp. 311–314 (2015) 4. Xu, L., Huang, D., Tsai, W.-T.: Cloud-based virtual laboratory for network security education. IEEE Trans. Educ. 57(3), 145–150 (2014)

A Framework for Shop Floor Material Delivery Optimization Based on RFID-Enabled Production Big Data Xin Zhao1, Wei Zhang1, Hengling Meng1, Fangfang He1, and Shan Ren2(&) 1

2

College of Life Science and Technology, Honghe University, Mengzi, Yunnan, China Engineering College, Honghe University, Mengzi, Yunnan, China [email protected]

Abstract. With the wide use of smart sensor devices in production shop floors, a large amount of real-time and multi-source production process big data is being produced. In order to make a better material delivery decision by utilizing these production big data, in this article, a framework for shop floor material delivery optimization (SFMSO) is proposed. Under the proposed framework, the availability of these data and knowledge about production process can be enhanced. Focusing on material delivery process of shop floor, the key enabling technologies are also designed and discussed to facilitate the implementation of the proposed framework. Keywords: Big data


Data mining
Shop floor
Material delivery

1 Introduction The different solutions of material delivery have significant impacts on shop-floor productivity. The existing material delivery processes have several disadvantages such as the field material information, operators’ information, and manufacturing process information cannot be captured and shared in a timely fashion. These lead to the problems of lagged material delivery and lowed equipment utilization, and then increasing the production costs. Therefore, with the transformation of production mode from mass production to small-lot and customization production, traditional material delivery methods cannot be adapted to the new production mode. Thanks to the rapid development of industrial internet and Internet of Things (IoT) technologies (e.g. radio frequency identification, RFID), many manufacturing companies have adopted these up-to-date technologies to conduct real-time traceability in enhancing the performance of shop floor planning and scheduling [1, 2], and a large amount of manufacturing big data are being generated during the production process. In order to improve the production efficiency, material delivery in the shop floor has attracted extensively attention in academic and industry communities. Khayat et al. proposed an integrated method to address the production scheduling and material delivery problems [3]. Boonprasurt and Nanthavanij presented an optimal approach of © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 699–705, 2019. https://doi.org/10.1007/978-3-030-02804-6_91

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the route planning problem for manual material delivery [4]. Zhang et al. presented real-time information capturing and integration architecture of the internet of manufacturing things (IoMT) to provide a new paradigm by extending the techniques of Internet of Things (IoT) to manufacturing field [5]. Zhang et al. proposed an optimization method for shop floor material handling based on real-time and multi-source RFID manufacturing data [6]. Future development directions and trends of manufacturing system in the big data environment are investigated by Zhang et al. [7]. Despite significant effort has been conducted by many researchers in the material delivery field, some research gaps still exist in utilizing real-time RFID-enabled production data driven decision-making to material delivery in an industrial big data environment. For example: How to apply the IoT related techniques to capture the real-time manufacturing big data in the shop floor, how to effectively manage the manufacturing big data, and how to mine useful knowledge from the production big data to provide better decision support for material delivery. The remainder of the paper is arranged as follows. Section 2 proposes a framework for SFMSO based on RFID-enabled production big data. Three key technologies related to the proposed framework are discussed in Sect. 3. Finally, the contributions of this article and the future works are summarized in Sect. 4.

2 A Framework for SFMSO Based on Production Big Data By applying Internet of Things (IoT) technology to production process, a smart manufacturing environment will be created, and the multi-source and heterogeneous data of production process can be gathered. Then big data processing related technologies and methods are used to manage the production process big data and mine the hidden knowledge from them. Based on the mined knowledge, better decision-support for material delivery and shop-floor dynamic scheduling are provided to enterprise managers. Based on the ideas above, a framework of shop floor material delivery optimization based on production big data is designed as shown in Fig. 1. It is described as follows. 2.1

Manufacturing Process Big Data Sensing and Capturing

Deployment of the smart and ubiquitous manufacturing resources is significant to improve the perceptive capability of all manufacturing resources such as machine, assembly line and operator, etc. Under the framework, RFID devices such as RFID tags and Readers will be deployed to manufacturing resources to make the manufacturing resources with the potential of identifying the status of materials, WIP items, operators and locations in a timely fashion. 2.2

Manufacturing Process Big Data Preprocessing and Management

Based on above deployment, the real-time status of manufacturing resources could be collected. Data preprocessing methods including data integration, data cleaning, data

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transformation and data reduction are used to processing the raw big data. In addition, large amount of data need to be stored to provide complete data support for further analysis. Hadoop Distributed File System (HDFS) [8], Extensible Markup Language (XML), and Distributed Data Base System (DDBS) will be applied to store and manage the manufacturing big data.

Big data mining and applying in manufacturing process Big data mining

Mining results applying

Classification Prediction

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Fig. 1. Framework for SFMSO based on production big data.

2.3

Big Data Mining and Applying

By using the theory of data mining and big data analysis, useful knowledge can be released from the manufacturing big data. The mined knowledge can be feedback to production department and production engineer to provide effective decision support during the whole manufacturing stages. Two types of application, namely material delivery trajectory optimization, shop-floor dynamic scheduling are designed. Typical data mining algorithms and modeling methods include cluster analysis, prediction, classification, association, etc.

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3 Enabling Technologies of the Proposed Framework 3.1

Production Process Data Sensing and Capturing

In order to acquire the status data of production process in a timely fashion, it is essential to identify all manufacturing resources, to which RFID technologies (e.g. RFID tags and readers) are used. The RFID are deployed in several ways. In any case, trays of materials are deployed with RFID tags and convert into smart objects that are tracked in the production shop-floor. These RFID tags involve information such as what material is handled and what is the number of the material. Key parts of the work-in-process and critical tools are also tagged, since their critical roles in the subsequent processes (e.g. assembly, transportation packing, etc.). In addition, inventory areas for different materials and finished products are also tagged. Each machine of the manufacturing process is installed with a RFID reader. In the processes of real-life production, the ultra-high frequency RFID devices are recommended, due to their acceptable reading capability and affordable cost. The vehicles that used to carry material trays and finished products are also deployed RFID readers. 3.2

Data Preprocessing and Storage

The big data preprocessing and storage model is designed in Fig. 2. Big data storage solutions DDBS Structured

XML NoSQL/ NewSQL/ HDFS

XML Semi-Structured

Unstructured

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Data reduction (dimensionality reduction/data compression …)

Data transformation (attribute construction/ normalization/discretization)

Data cleaning

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Raw data of production process Material Operators BOM Assembly Warehouse Logistic …

Fig. 2. Big data preprocessing and storage model for production process data.

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3.2.1 Data Preprocessing Firstly, the raw data of production process have a great number of redundancy, thus, a data cleansing operation should be performed to reduce the redundancy. The information structure model can be applied as data cleaning methods because it can support the identification of product-related data for production data tracking and feedback [9]. Secondly, it is critical to carry out the operation of data integration. It is difficult to express the data of production stages in a model, therefore, a unified modeling technology is proposed to integrate the data of production process. The objective is to construct a logically unified description framework to express the data model of production process. Thirdly, in order to effectively facilitate the data mining process, the integrated lifecycle data must be conducted the transformation operation. Five types of data transformation strategies, namely attribute construction, smoothing, discretization, normalization and aggregation, are included. Fourthly, the operation of data reduction should be implemented to generate a reduced representation of the data set that is much smaller in volume. The strategies of data reduction are including numerosity reduction, data compression and dimensionality reduction. 3.2.2 Data Storage Three kinds of data storage solutions for production big data are elaborated as follows: Firstly, for structured data, with more and more structured data collected in the enterprise database, historical and real-time data are stored in the same database, which affected the data processing performance of the system. Hadoop (http://hadoop.apache. org/) and Storm (http://storm.apache.org/) computing framework are separately applied to handle the non-real-time data and real-time data. DDBS can be applied to store and manage the structured data. Secondly, semi-structured data was a type of data which is between structured data and unstructured data. XML is the principal standard for exchanging and expressing semi-structured data or structured data. Therefore, the semi-structured production process big data can be described and expressed by using XLM. Finally, the semistructured big data of production processes are converted into a standardized data format, and stored in Relational Database Management System (RDBMS) or DDBS. Thirdly, unstructured data are those without spatial or temporal constraints. Great amounts of unstructured data are produced by various manufacturing resources. Because RDBMS cannot meet the application requirements of big data in scalability, it is unsuitable to management of unstructured data. Therefore, distributed approaches such as HDFS [8] and not only Structured Query Language (NoSQL) [10] are used to store and manage unstructured big data. 3.3

Data Mining

A four-layer diagrammatic model for mining and analyzing the production process big data is designed as seen in Fig. 3.

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Application layer

Product innovation Supplier selection Cost control Quality control

Shop floor scheduling dynamic optimization Logistics optimization

Meet the demands Data mining result sets

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Data layer is responsible for storing the production process data, such as assembly data, bill of material (BOM) data, logistics data, etc. According to various application requirements, these data are stored in different enterprise databases. Method layer involve different data mining models and methods, including rough set theory, decision tree, neural network, genetic algorithm, and support vector machine (SVM), etc. These models can be used to extract appropriate raw data and mine knowledge from them. Result layer contains a series of data mining result. Based on different demands of shop floor application, appropriate raw data and data mining model are designated to conduct data mining. Finally, various application indexes’ knowledge set for different shop-floor applications is achieved. Useful knowledge of the result layer is used to realize the different enterprise requirements of application layer. The enterprise applications include innovative design of product or service, dynamic scheduling of shop floor, cost control, quality control, etc.

Production process data

Fig. 3. A four-layer graphical model of production process big data mining.

4 Conclusions Recently, Auto-ID technologies have been widely used in shop floor production process. Such a proactive data generation and gathering method produces new challenges, for example, how to apply the muti-source, heterogeneous and real-time production process big data to discovery the hidden knowledge to improve the performance of

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material delivery. In the current article, a new system solution is presented to provide a new mode for manufacturing companies to effectively improve the efficiency of material delivery in shop floor. Three contributions are summarized as follows. Firstly, the framework of SFMSO based on production big data is proposed. Secondly, the big data preprocessing and storage model for production process data are developed. Thirdly, a four-layer graphical model for production process big data mining is designed. Future research works will focus on how to apply the technologies of big data analytics to build the data analysis model and to identify and reveal the potential patterns and real insights from the production process big data for improving material delivery decision.

References 1. Huang, G.Q., Wright, P.K., Newman, S.T.: Wireless manufacturing: a literature review, recent developments, and case studies. Int. J. Comput. Integr. Manuf. 22, 579–594 (2009) 2. Huang, G.Q., Zhang, Y.F., Jiang, P.Y.: RFID-based wireless manufacturing for walkingworker assembly islands with fixed-position layouts. Robot. Comput. Integr. Manuf. 23, 469–477 (2007) 3. El Khayat, G., Langevin, A., Riopel, D.: Integrated production and material handling scheduling using mathematical programming and constraint programming. Eur. J. Oper. Res. 175, 1818–1832 (2006) 4. Boonprasurt, P., Nanthavanij, S.: Optimal fleet size, delivery routes, and workforce assignments for the vehicle routing problem with manual materials handling. Int. J. Ind. Eng. 19, 252–263 (2012) 5. Zhang, Y., et al.: Real-time information capturing and integration framework of the internet of manufacturing things. Int. J. Comput. Integr. Manuf. 3052, 1–12 (2014) 6. Zhang, Y., et al.: An optimization method for shopfloor material handling based on real-time and multi-source manufacturing data. Int. J. Prod. Econ. 165, 282–292 (2015) 7. Zhang, Y., Ren, S., Liu, Y., Si, S.: A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. J. Clean. Prod. 142(Part 2), 1085–1097 (2017) 8. White, T.: Hadoop: the definitive guide. Online 54, 258 (2012) 9. Xu, D.F., et al.: Modelling for product information tracking and feedback via wireless technology in closed-loop supply chains. Int. J. Comput. Integr. Manuf. 22, 648–670 (2009) 10. Han, J., Haihong, E., Le, G., Du, J.: Survey on NoSQL database. In: Proceedings—2011 6th International Conference on Pervasive Computing and Applications, ICPCA 2011, pp. 363– 366 (2011)

An Association Rule Mining Approach for Shop Floor Material Handling Based on Real-Time Manufacturing Big Data Xin Zhao1, Hengling Meng1, Wei Zhang1, Xun Li1, and Shan Ren2 ✉ (

1

)

College of Life Science and Technology, Honghe University, Mengzi, Yunnan, China 2 Engineering College, Honghe University, Mengzi, Yunnan, China [email protected]

Abstract. In recent years, radio frequency identification and smart sensors are widely used by manufacturers to assist their daily production and management. Manufacturing resources such as machines, operators and materials are made smart by configuring with these facilities. As a result, a smart manufacturing environment is created. Under such environment, a large amount of manufac‐ turing big data can be analyzed to support shop floor decisions. In order to get a better decision-making based on the collected manufacturing big data, in this paper, an association rule mining approach for shop floor material handling based on real-time manufacturing big data is proposed to discovery the optimal trajec‐ tory of material handling. An application scenario and a simulation experiment are designed and conducted to verify the availability of the presented approach. Keywords: Big data · Data mining · Shop floor · Material handling

1

Introduction

Shop floor scheduling heavily rely on whether the material arrives in time [1], thus, the logistics route decision-making of material is significant to improve the productivity. In general, the planning of trajectory for material handling is determined by the production capacity of the existing manufacturing system [2]. However, there are some disadvan‐ tages in existing manufacturing system such as lagged material delivery and low equip‐ ment utilization, due to the field data of material consumption and production status cannot be captured and shared by manufacturer enterprise in a timely fashion. As a result, managers and operators involved in production process often make afterwards logistics decisions based on incomplete and inaccurate process data, which has led to inaccurate decisions and operational inefficiencies [3]. In recent years, with the rapid development of Internet of things (IoT) technology [4, 5], many enterprises have adopted these progressive technologies to carry out the real-time traceability in improving the performance of shop floor scheduling [6], and a huge number of production process data has been generated during the production process. Meanwhile, the research topic of material handling in shop floor has attracted many researchers’ attention in academia and industry communities. For instance, a © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 706–713, 2019. https://doi.org/10.1007/978-3-030-02804-6_92

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material flow network was designed to enhance the transport efficiency and reduce the delivery time in a discrete parts manufacturing facility [7]. To address the material delivery and production scheduling problems, an integrated approach was proposed [8]. Frei et al. [9] presented a self-organizing assembly system to spontaneously organize the manufacturing resources in the shop floor to response to the arrival of product order and material, and to manage themselves during production. In order to, get better mate‐ rials handling decision-making based on the multi-source and real-time manufacturing big data, Zhang et al. proposed an active strategy [10]. Recent advances of logistics trajectory discovery in the industrial big data and smart manufacturing environment were investigated by Zhong et al. [11]. Despite a certain degree of progress have achieved in the field of shop floor material handling, some research gaps still exist in big data driven shop floor material handling decision-making: (1) How to achieve effective preprocessing of manufacturing big data to offer available and reliable data for subsequent rule mining, from which reasonable and effective decisions can be made to enhance the material handling efficiency; (2) How to establish a data mining model for valuable rule mining from the large amount of manufacturing data, so as to assist manufacturer to make actionable and more-informed decisions for material handling planning. To address above challenges, in this paper, an association rule mining approach for shop floor material handling based on real-time and multi-sources manufacturing big data is proposed. The rest of the paper is organized as follows. Section 2 discusses the designed data preprocessing method of manufacturing big data. An application scenario and a simulation experiment are developed and conducted in Sect. 3 to illustrate how the proposed method can be applied to the shop floor material handling. Conclusions and future works are given in Sect. 4.

2

Manufacturing Big Data Preprocessing and Mining

In modern manufacturing environment, various sensing devices have been configured to the manufacturing resources. Therefore, real-time status data of the manufacturing resources (e.g. machines, operators, materials, etc.) can be captured during production process. However, there is some ‘noise’ (e.g. redundancy, incorrect and incomplete) included in the raw manufacturing big data, which may not be analyzed directly and may influence the reliability or accuracy of production decision-making. Therefore, the data preprocessing operations such as cleaning, integration, reduction and transforma‐ tion should be implemented so as to offer available and reliable data for following rule discovery. 2.1 Multi-source and Heterogeneous Manufacturing Big Data Preprocessing The data quality is foremost while conducting a data mining or data analysis. Therefore, the preprocessing operation of the original production big data must be done to eliminate the ‘noise’ before the data is stored and analyzed. The data preprocessing solution is designed as shown in Fig. 1.

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Hadoop XML

DDBS Structured RT-DGMIS

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. .Design delivery .Material Shop floor logistic

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.Assembly .Warehouse .Inspection .…

Fig. 1. Manufacturing big data preprocessing solutions

Firstly, the raw manufacturing big data have a great number of redundancy, thus, a data cleansing operation should be performed to reduce the redundancy. The procedure proposed in a recent publication [12, 13] by the authors can be used to carry out the data cleaning operation. Secondly, the cleansed manufacturing big data is still scattered and unusable during shop floor decisions. It is essential to carry out a data integration operation. Due to it is difficult to express the multi-source and heterogeneous data in a model, a data unified modeling method is proposed to integrate the manufacturing big data, and then to construct a logically unified framework to express the model of manufacturing data. Therefore, the theory of meta-model is presented to construct the unified data model. Four types of meta-model are illustrated in Table 1. Take the assembly data meta-model as an example, the modeling procedures are illustrated as follows: (1) Define the data abstraction language, such as association relationship, assembly method and assembly standard; (2) Describe the assembly data (e.g. assembly scheme, attribute data) and their association relationship by the predefined data abstraction language; (3) Establish domain ontology repository to describe the relationships between concept and attributes, and describe the constrains between attributes and relationships; (4) Establish top-level meta-model to define multiple meta-model of various manufacturing data, and the overall association relationships and unified data format; (5) Achieve the model sharing and data integrating by instantiation of meta-models. The other three meta-models are not included as the modeling processes are basically similar.

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Table 1. Four types of data meta-model and functions Meta-model Top-level meta-model Material handling data meta-model Processing data meta-model Assemble data meta-model Inspection data meta-model

Functions Describing the integrated data model Describing the multidisciplinary data and the file information during material handling process Describing the data model of technology and process, and the association relationship between them Describing the data and the file information during assembly process Describing quality data model and the association relationship among them

Thirdly, the integrated data sets are usually still huge. Therefore, data reduction operation should be performed to acquire a reduced representation of the data sets that are much smaller in volume, yet closely maintains the integrity of the original data. Finally, the reduction manufacturing data must be transformed so that the rules found may be easier to be understood. The preprocessed manufacturing big data are transmitted to the enterprise database, meanwhile, shared and applied by various manufacturing stages to optimize the produc‐ tion processes. 2.2 Manufacturing Big Data Mining By using the data mining technologies, valuable rule can be discovered from the manu‐ facturing big data. The management departments of enterprise can adjust and optimize the whole production process according to rule feedback. In addition, by integrating the mined rule, application services such as cost and quality control, shop floor dynamic scheduling, route optimization of material handling, etc. can be achieved. Typical data mining methods include cluster analysis, prediction, classification, association, etc. 2.2.1 Data Mining Graphic Model of Manufacturing Big Data To achieve above-mentioned application services, a data mining graphic model of manufacturing big data is designed as shown in a recent publication [12, 13] by the authors. General models and specific models are involved in this research according to the application requirements of shop floor management. The functions of the general models are introduced as seen in Table 2. The specific implementation steps of data mining graphic model are not repeated due to the limited space here. Interested readers are encouraged to read a recent publication [12, 13] by the authors.

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Model types Clustering Association Classification Prediction

Example functions Formulating the production plan based on customer value analysis Analyzing the quality factor of product and mining the logistics trajectory Customers churn analysis and fault diagnosis Forecasting the order quantity and machine lifetime

2.2.2 An Apriori-Based Model for Material Handling Trajectory Mining The Apriori-based model uses association rules (e.g. support, confidence and lift) to mining the frequent patterns of the material handling logistics. Generally, if these asso‐ ciation rules get higher values, the correlation rules are more important. The association rules can be defined as support(X → Y) =

P(X, Y) num(X ∪ Y) = P(I) num(I)

confidence(X → Y) = P(Y|X) =

lift(X → Y) =

num(X ∪ Y) num(X)

P(Y|X) num(X ∪ Y) = P(Y) num(X)num(Y)

(1)

(2)

(3)

where the X and Y denote the material delivery data sets at different production stages, P(X, Y) means the simultaneous occurrence probability of X and Y in the total data, P(Y|X) means the occurrence probability of Y if X happens, P(Y) denotes the occurrence probability of Y in total data set, num(X ∪ Y) represents the number of data including X and Y simultaneously, num(X) and num(Y) denotes the number of data including X and Y, respectively, num(I) means the total number of data. In order to find the strong correlations of material handling trajectory, some condi‐ tions are satisfied for Apriori algorithm to search the association rules, which can be described as follows. In order to find the strong correlations of material handling trajectory, some condi‐ tions are satisfied for Apriori algorithm to search the association rules, which can be described as follows. support(X → Y) ≥ min_sup

(4)

confidence(X → Y) ≥ min_conf

(5)

lift(X → Y) ≥ 1

(6)

where min_sup denotes the minimum threshold of support, and min_conf denotes the minimum threshold of confidence.

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A Study of Application Scenario

In this section, the production processes of smart manufacturing shop floor scenario are simulated to produce data for material handling trajectory mining. The simulation was performed on a workstation (Intel(R) Core(TM) i7-7700 K CPU @ 4.20 GHz) with 32G of RAM. The operation system is Windows 10 Enterprise with 64-bit. Matlab 2017a and SPSS modeler 18 are used for production simulation and material handling trajectory mining, respectively. There are four stages with different machines in the scenario of smart manufacturing shop floor. In each production stage, a limited buffer with volume of 300 jobs is presented. At any time, a job is exactly assigned on one machine at most, and a machine can only process a job. The orders arrive at the job shop according to the Poisson distri‐ bution, in which the amount of each job is randomly generated. There are five kinds of jobs, whose processing times at every stage are shown in Table 3. The setting times of machine are included in the processing times. Random machine failures are considered in this shop floor. If machine failure happens, the related machine can’t process job until the machine has been repaired. The machine breakdowns and machine repairs are assumed to follow an exponential distribution. Table 3. Processing times of different jobs at stages Job type Job 1 Job 2 Job 3 Job 4 Job 5

Stage 1 65 38 37 52 72

Stage 2 11 60 35 47 21

Stage 3 56 53 58 47 65

Stage 4 31 59 31 16 34

After simulation, a large amount of production data is generated. In order to mine the material handling trajectory of the shop floor, 50 batches of jobs are chosen for simplicity without loss of generality to analyze the logistics knowledge. These batches of jobs go to pass the 4 stages, with 150 jobs contained in each batch. After the data preprocessing and transformation of material delivery-related data, the Apriori-based model is applied to mining the material delivery trajectory of different jobs, depicted in Fig. 2. In the association analysis network diagram, it can be found that the associations between stage 1 and stage 2 are very weak. As the same materials go through every stage, the association degree means logistics load to be assigned to each machine. The weak association between stage 1 and stage 2 imply that there are too many machines set in stage 2, so that the material handling efficiency in stage is lower than other stages. The materials are frequently delivered from stage 1 to stage 2 with a low vehicles’ load. The necessity of assigning such many machines, such as M8, M10 and M11, to stage 2 should be considered. The frequent material handling trajectory of rule 6 shows that the logistics load of this trajectory is very important to the whole production. If machines in the trajectory of rule 6 break down, at least 14.7% of the total logistics volume is

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Fig. 2. Association analysis network diagram of material handling trajectory

influenced, so that maintaining the machines of trajectory of rule 6 is significantly important. Finally, from the mined frequent material handling trajectories, some machines (M1, M8, M10, M11 and M18) have relatively low efficiency than other machines of this shop floor. In Table 4, it shows that six strong association rules mined in the association anal‐ ysis, which means the frequent material handling trajectory of jobs. Job 2, 3 and 4 have two frequent material handling trajectories, respectively, while job 1 and 5 don’t have frequent material delivery trajectory. Besides, the rule 6 gets the highest support of 14.7%. Table 4. Strong association rules of different jobs’ material handling trajectory Rules Rule 1 Rule 2 Rule 3 Rule 4 Rule 5 Rule 6

Job type Job 2 Job 2 Job 3 Job 3 Job 4 Job 4

Assigned machines Stage 1 Stage 2 Stage 3 M3 M7 M13 M2 M6 M13 M4 M9 M14 M5 M7 M16 M5 M12 M14 M4 M9 M15

Confidence (%) Lift Stage 4 M20 M20 M20 M19 M20 M20

80.000 80.000 100 81.818 100 80

3.109 3.109 3.886 3.248 3.886 3.176

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Conclusions

Radio frequency identification and smart sensors have been extensively applied in shop floor management. As a result, the manufacturing resources are made a certain degree of smart, and a large amount of production data can be collected and used to support shop floor decisions. In order to make a better decision-making of shop floor manage‐ ment based on the collected manufacturing big data, in this paper, an association rules mining approach for shop floor material handling based on real-time manufacturing big data is proposed. To verify the effectiveness and availability of the proposed approach, an application scenario and a simulation experiment are also designed and conducted. Future research works will carry out on how to use the latest big data analytics tools to reveal potential insights from the production process big data for optimal material handling decision.

References 1. Ning, T., Huang, M., Liang, X., Jin, H.: A novel dynamic scheduling strategy for solving flexible job-shop problems. J. Ambient Intell. Humaniz. Comput. 7, 72 (2016) 2. Huang, G.Q., Zhang, Y.F., Jiang, P.Y.: RFID-based wireless manufacturing for walkingworker assembly islands with fixed-position layouts. Robot. Comput.-Integr. Manuf. 23, 469– 477 (2007) 3. Jun, H.-B., Shin, J.-H., Kim, Y.-S., Kiritsis, D., Xirouchakis, P.: A framework for RFID applications in product lifecycle management. Int. J. Comput. Integr. Manuf. 22, 595–615 (2009) 4. Perera, C., Liu, C.H., Jayawardena, S.: The emerging internet of things marketplace from an industrial perspective: a survey. IEEE Trans. Emerg. Top. Comput. 3, 585–598 (2015) 5. Jararweh, Y., et al.: SDIoT: a software defined based internet of things framework. J. Ambient Intell. Humaniz. Comput. 6, 453–461 (2015) 6. Huang, G.Q., Wright, P.K., Newman, S.T.: Wireless manufacturing: a literature review, recent developments, and case studies. Int. J. Comput. Integr. Manuf. 22, 579–594 (2009) 7. Herrmann, J.W., Ioannou, G., Minis, I., Nagi, R., Proth, J.M.: Design of material flow networks in manufacturing facilities. J. Manuf. Syst. 14, 277–289 (1994) 8. Khayat, G.El, Langevin, A., Riopel, D.: Integrated production and material handling scheduling using mathematical programming and constraint programming. Eur. J. Oper. Res. 175, 1818–1832 (2006) 9. Frei, R., Şerbǎnuţǎ, T.F., Di Marzo Serugendo, G.: Self-organising assembly systems formally specified in Maude. J. Ambient Intell. Humaniz. Comput. 5, 491–510 (2014) 10. Zhang, Y., et al.: An optimization method for shopfloor material handling based on real-time and multi-source manufacturing data. Int. J. Prod. Econ. 165, 282–292 (2015) 11. Zhong, R.Y., et al.: A big data approach for logistics trajectory discovery from RFID-enabled production data. Int. J. Prod. Econ. 165, 260–272 (2015) 12. Zhang, Y., Ren, S., Liu, Y., Sakao, T., Huisingh, D.: A framework for big data driven product lifecycle management. J. Clean. Prod. 159, 229–240 (2017) 13. Zhang, Y., Ren, S., Liu, Y., Si, S.: A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. J. Clean. Prod. 142(2), 1085–1097 (2017)

Font-End Achievement of Extensive Innovative Services Community Based on Web Qiubin Liu1, Rui Fan1(&), Bifeng Guo1, Zihao Li1, and Enna Wu2 1

2

Faculty of Software Technology, Guangdong Ocean University, Zhanjiang, China [email protected] College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China

Abstract. With the development of technology, computer helps us solve some contradictory problems and it is important to develop a software that helps them have an innovative work. The paper mainly introduces how we develop the software base on web by using font-end technology. To achieve the function of Extensive deduced tree, we use the method of data visualization in combination with Data-Driven Document. Besides, we also achieve the functions of people’s innovative learning and communication by javaScript frameworks to make people have a better way to perform an innovative work. Finally, we also verify the functions by an innovative case and introduce our future work. Keywords: Font-end development


Extenics
Innovative work

1 Introduction As we know innovative work is essential in our daily life. However, it is better for us to perform an innovative work with the help of computer guiding. To solve the problem, it is vital to development a software that can help people have innovative work better. Extensive innovative services community is a software that can not only help people calculate some mathematical innovative formulas but also it provides the functions of learning and communication. The software guides the innovative thinking of people as the form of deduced tree, which trigger both their left and right brain and make them spend more time on innovation. The technology of Data-Driven Document is that making the data visible as a mind mapping [1] shown on web page. To achieve the function of deduced tree, we design according to the theory of Extenics [2]. Extenics is a Chinese cross discipline [3] that provides theory for people to solve contradictory problems. Nowadays, some problems have been addressed according to the theory [4]. Nevertheless, there are many mathematical formulas that is difficulty for people to calculate when having an innovative work [5]. The software’s calculating the complicated mathematical equations makes them save much time [6]. Modules of learning and communication help them learn and communicate when they have difficulties in innovative thinking work.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 714–721, 2019. https://doi.org/10.1007/978-3-030-02804-6_93

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2 Background of Development 2.1

Team Introduction

In order to develop a quality software, we establish a team with the help of teachers to study Extensive innovative work. The tasks for us are explore the laws of people’s innovative thinking and solve the contradictory problems according to the theory. Up to now, we have made some contributions. We firstly develop a software model [7] according to the theory of Extenics. From the exploratory and innovative standpoint, and we solve the contradictory problem of marketing plan [8] and hotels’ excess food [9]. After detailed analysis and discussion, we think there is some complexity in the discipline of Extenics. To simplify the calculation of dependent function of Extenics, we also developed a software based on Android operating system [10]. Besides, we developed a self-adapted software according to the mathematical modeling [11]. Later on, we found a new way that can reflect the whole procedure of people’s innovative thinking [12]. Nevertheless, it can deal with some complicated relationships among base element while performing an innovative work. Consequently, it is necessary to develop a software that can not only help people with complicated calculation but also it helps people learning and innovative communication (Table 1). Table 1. Public Attributes of Component Name of attributes Name Description Period Symbols

2.2

Specification Name of node Description users filled in The times to be defined as kernel problems The special color of node

Difficulties for Development

As for front-end implementations are diverse and complex, it is difficult for us to use proper technology and design proposal that can meet the demands such as how to design a pattern to make each node of the tree in SVG with a table in HTML interact. Generally, figures are printed in SVG while table are displayed in HTML because they are the two independent relationships. What’s more, considering the software can be used off-line and across multiple devices, compatibility becomes vital while developing. In this way, the suitable data structure should be designed to save in both local storage and the cloud server. In terms of system performance optimization, it is hard to control the rate of transforming the data into mind mapping with quantities of nodes in a tree, which needs designing a algorithm structure to speed up the process (Figs. 1 and 2).

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Fig. 1. Mode of work

Fig. 2. Flow chart of event

3 Design and Achievement of the Software 3.1

Architecture

In order to make the developed software extensive, run steady and the code more maintainable, we use the MVC design pattern [13]. Through analyzing

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the whole system, we set three layers of the architecture (see Fig. 3) including Model, View and Controller.

Fig. 3. Flow chart of creating tables

In the pattern, We organize the code in a way that separates business logic, data, and interface, which is convenient for manage the program by layering. The details of the functions in each layer are shown as follows. The layer of controlling is listening for the request of users and inform the layer of model, dealing with the service logic of data. The layer of modeling is used to the judgment of service logic and acquired data from component library are returned to the layer of view, which update the data and display the page to the user. The design of MVC makes us concentrates on the design of view without relying on service logic. In the layer of view, Data Driving Document make the acquired data visible [14], so that users will get feedback and move on to the next step. It can thus be seen that the pattern make the whole framework better to understand. 3.2

Design and Achievement of Functions

3.2.1 Functions in Controller Layer To meet the demands of users, we make the layer of Controller independent so that we can alter the attached events and achieve the corresponding functions as the change of the demands. When users interact with the software, it is the duty of the layer of controller to decide which function will be execute rather than achieve the function. We achieve the controller layer by means of event listening mechanism. We also attached the monitored event to each node of the tree and the layer of controller sends demands and data of users to the layer of model and tell it the ways to accomplish the service when the events are triggered shown in Fig. 2. The node can be connected with corresponding functions according to the layer of model, which shows the convenience for each event. 3.2.2 Function in Model Layer To reduce the system coupling and collaborative development, we design some component in the layer of model. We achieve it by means of prototype chain, which

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can avoid repeat instructions. The prototype object is created with 4 public attributes shown in the following form. Then we create an object to inherit the original object and add private attributes for it to accomplish the encapsulation of the component. According to the requirements, we design nine components in the layer of model including kernel problems, subproblems, goal base elements, conditional base elements, transformed goal base elements, transformed conditional base elements, comprehensive dependent functions, modeling dependent functions, analysis dependent functions and transformation dependent functions. The nine components establish a component library in which the attributes and methods are programmed. In the mind mapping, each node points to an instantiation of the corresponding component. Not only can instantiation decrease the coupling of the codes but also it makes the software develop quickly. 3.2.3 Functions in View Layer We selected Data Driving Document technology to make the data from the layer of model render in the layer of view and it is a great tool with the function of visible data. The technology also sets up a frame that transforms data into calculating of SVG attributes. As for the rendering of all the data in a tree, we use a traversal method started from the root node and print each node according the Data Driving Document. However, there are not only mind mapping tree shown in the web page but also some forms with data users filled in. To solve the problem, we add a table for each node by embedding HTML with foreign-object label. Besides, we design a factory model to solve another problem how to interact the data between node object and the tables. In all, factory model is a common model of instantiated object and we can easily create each table for the node. When data are rendered in the layer of view, the deduced tree traverse the data structure and instantiate the table object and create the corresponding form when a node carrying a form is found. After that, reading each attribute of each object will accomplish the interaction of node and data in form. 3.2.4 Characteristic of Cross Platform To achieve the function of multi-terminal software and considering the terminal we often use, we use the frame of Bootstrap. Besides, we selected the technology of responsive and media queries for the effect of showing and uses in both personal computer and mobile phones. Moreover, the deduced tree of the developed software can be shown quickly with Off-line caching mechanism. Finally, the data format of json increase the proportion of network transmission.

4 Case Analysis To verify our developed software can solve the contradictory problems well, we analyze a classical contradictory problem called Cao Chong weights the elephant. The problem is about how to weight an elephant with a scale whose range is 100 kg. After using the software, the whole procedure of solving the problem are shown in Fig. 4.

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Fig. 4. Figure of achieved page

According to Fig. 4, we first have the original problem divided into two subproblems. After Extensive analysis and Extensive transformation of two sub-problems, we derive a solution of the two sub-problems. By using the deduced tree shown in the mind mapping, we know that we can have an innovative work better and we would spend more time having an innovative thinking, which also triggers both our left and right brains.

5 Evaluation and Future Prospects 5.1

Evaluation

The software we have developed is according to the requirements and design. The familiar user interface makes users work better and they can deduce an Extensive innovative tree according to their thinking, which helps them save much time. The advantages of the software are shown as follows. 1. The software not only simplify the calculation for innovative workers but also it helps them reduce the cost of learning and communication.

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2. The whole procedure of users’ innovative thinking can be shown in a mind mapping. 3. Users spend more time on innovative work rather than perform an innovative work like fragments. 4. The modularity of font-end design makes the codes well-fixed. However, there remain some disadvantages of it. 1. New users have to spend some time learning the theory of Extenics. 2. The system might run slowly when there are too many nodes of mind mapping. 5.2

Future Work

To improve the software, we will provide the users with the function of feedback used to their submitting problems of the software. Besides, we will design the plans of users’ innovative work according to their conditions of using the software. What’s more, it is better for us to find out the laws of people’s innovative thinking after analyzing the collected data of users so that we manage to achieve artificial intelligence of innovation. Acknowledgement. The research is supported by the projects listed as follows. 1. Guangdong Provincial Science and Technology Project (2014A040402010) 2. Entrepreneurship Training Program for College Students (201710566036) 3. Guangdong Ocean University Excellent Courses Project for Software Engineering (JP2010007).

References 1. Deng, Q., Wu, Y., Weng, Y.: The method and application of the extension mind map for the construction of the system structure. Math. Pract. Knowing 45(12), 94–99 (2015) 2. Guo Z, Guo Y, DeptPhysamp. Extenics theoryand its applications a new interdisciplineextenics. J Baoji Univ. Arts Sci.: Natural Sci. Ed. (2014) 3. Cai, W., Yong, S.: The scientific significance and future development of extenics. National Association of Extenics (2006) 4. Yang, C., Cai, W.: Extenics and intelligent processing of contradictory problems. Sci. Technol. Rev. 32(36), 15–20 (2014) 5. Wen, C., Yang, C., Smarandache, F., et al.: Extenics and Innovation Methods (2013) 6. Wen, C., Yang, C.: Basic theory and methodology on Extenics. Chin. J. 58(13), 1190 (2013) 7. Fan, R.: Modeling extenics innovation software by intelligent service components. Open Cybern. Syst. J. 8(1), 1–7 (2014) 8. Guo, B., Fan, R., Huang, C.W., et al.: The best marketing plan of photographing for money deduced by extensible mind mapping, vol. 17, no. 13, p. 03003 (2018) 9. Ma, F., Fan, R., Huang, C., et al.: The solution of excess ingredients in hotels deduced by extensible mind mapping. vol. 17, no. 3, p. 03004 (2018) 10. Yan, S., Fan, R., Chen, Y., et al.: Research on web services-based extenics aided innovation system. Procedia Comput. Sci. 107, 103–110 (2017)

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11. Fan, R., Peng, Y., Chen, Y., et al.: A method for self-adaptive software formal modeling by Extenics. Caai Trans. Intell. Syst. (2015) 12. Hou, Z.S., Wang, Z.: From Model-Based Control to Data-Driven Control: Survey, Classification and Perspective. Elsevier, Amsterdam (2013) 13. Zhang, Y.: Web Dynamic Interactive Visualization of Knowledge Organization Systems with D3.js. New Technology of Library and Information Service (2013) 14. Feng, J.: The significance of web front end MVC Framework and prospect of its development. Comput. Knowl. Technol. (2016)

Software-Defined Data Flow Detection and Control Approach for Industrial Modbus/TCP Communication Ming Wan1,3(&), Yan Song2,3, Yuan Jing1, Zhaowei Wang3, Jianming Zhao3, and Zhongshui Zhang4 1

4

School of Information, Liaoning University, Shenyang 110036, China [email protected] 2 School of Physics, Liaoning University, Shenyang 110036, China 3 Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China CNGC North Automatic Control Technology Institute, Taiyuan 030006, China

Abstract. There is an increasing consensus that software-defined networking may become a successful case to provide fine scalability and availability for industrial Internet, and it also brings new opportunities for the development of industrial cyber security. Aligning with the defense in depth strategy, this paper proposes a software-defined data flow detection and control approach for industrial Modbus/TCP communication. Furthermore, this approach designs a novel security strategy configuration service in SDN controllers to publish the flow control rules, and SDN switches match Modbus/TCP data flows with these flow control rules to detect and control abnormal communication behaviors. Specifically, a flow control rule database which stores all flow control rules of the entire control system is managed by SDN controllers, and a security flow table is maintained by each SDN switch according to different requirements of industrial communication. By using the DPI (Deep Packet Inspection) technology, this approach can run a deep analysis of Modbus/TCP packets according to the protocol specification, and block the improper control commands or undesired technology parameters. The qualitative analysis shows that the proposed approach possesses certain advantages and feasibilities. Keywords: Modbus/TCP Cyber security


SDN
Flow detection and control

1 Introduction SDN (Software-Defined Networking) has been widely studied and discussed by both academia and industry, and its field is growing at a very fast pace [1]. In practice, SDN changes the limitations of current network infrastructures, and presents a new routing architecture of logic control and data forwarding separation [2]. Furthermore, the entire network architecture is separated into the control plane and data plane, and the critical OpenFlow technology is used to control and manage the network routing and forwarding [3]. In the data plane, SDN switches maintain fine-grained flow tables to © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 722–729, 2019. https://doi.org/10.1007/978-3-030-02804-6_94

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forward the data traffic, and guarantee the end-to-end transmission. In the control plane, SDN controllers offer centralized network management, and simplify the policy enforcement and network configuration, such as flow table generation and configuration. By decoupling the control plane and data plane, SDN presents significant benefits, and has an excellent ability to apply in a wide variety of networked environments, including enterprise networks, data centers, infrastructure-based wireless access networks, et al. [1]. In recent years, with the deep integration of information technology and operational technology, ICT (Information Communication Technology) has been emphasized and developed in various network architectures, such as Industrial Internet [4], LTE network [5] and Internet of Things [6]. Due to the significant advantages of SDN, many researchers have developed some new industrial network architectures which are based on software-defined networking [3, 7–9]. In these researches, SDN not only can meet the real-time transmission requirements of industrial applications, but also can overcome the incompatibility problem causing by different network functions. However, although the application of SDN in industrial environments can bring many advantages, it cannot solve the industrial-oriented cyber security problems. Actually, industrial control systems are facing the escalating cyberattacks, and have caused great loss. Statistically, ICS-CERT reported that the number of industrial security incidents had reached 290 in 2016 [10]. Additionally, because these cyberattacks always skillfully steal industrial-oriented properties, the traditional IT security technologies cannot play an active role of industrial security protection [11]. Consequently, both academia and industry start to exploit the intrinsic system weaknesses [12] and develop novel industrial security solutions, including access control [13], vulnerability evaluation [14], intrusion detection [11, 15], et al. Deserved to be mentioned, when a new network architecture or model comes to being, it may also bring new opportunities for the development of security services. In consequence, SDN-based security mechanisms in industrial control systems have started to be explored [16, 17]. In this paper, based on the defense in depth strategy emphasized by NIST (National Institute of Standards and Technology) [18], we propose a software-defined data flow detection and control approach for industrial Modbus/TCP communication, which is an improved approach based on our prior work [19]. Furthermore, this approach designs a novel security strategy configuration service in SDN controllers to publish the flow control rules, and SDN switches match Modbus/TCP data flows with these flow control rules to detect and control abnormal communication behaviors. More specifically, SDN controllers also manage a flow control rule database storing all flow control rules of the entire control system, and each SDN switch maintains a security flow table according to different requirements of industrial communication. Besides, in order to block the improper control commands or undesired technology parameters, our approach also uses the popular DPI (Deep Packet Inspection) technology to run a deep analysis of packets on the basis of Modbus/TCP protocol specification. Finally, we give the qualitative analysis to illustrate that the proposed approach possesses certain advantages and feasibilities.

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2 Software-Defined Data Flow Detection and Control Approach 2.1

Basic Model and Architecture

As shown in Fig. 1, the basic model and architecture of our approach is also composed of two parts: control plane and data plane Control plane

Rule 1:

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Fig. 1. Basic model and architecture of software-defined data flow detection and control approach

In the control plane, one or more distributed SDN controllers generate routing and forwarding strategies of all SDN switches according to the specific network status and user configuration. Moreover, SDN controllers possess the intrinsic basic service and the novel security strategy configuration service, and manage a flow control rule database which stores all user-defined flow control rules. As described by OpenFlow, the basic service provides multiple network management functions, including network topology management, device registration, routing computation, etc. By establishing the whole network view, SDN controllers can compute the routing path of each data flow, and decide the corresponding flow table for each SDN switch. Differently, the security strategy configuration service completes the security function which publishes the flow control rules for all industrial data flows, and sends these flow control rules to the need-related SDN switches. In the data plane, all SDN switches constitute the entire transmission network, and each SDN switch holds one or more security flow tables. Furthermore, the structure of this table mainly including four parts: Flow ID, Packet header information, security control identification and flow control rule. Here, Packet header information covers source IP address, destination IP address, IP protocol, source port, destination port and other optional tuples defined in OpenFlow flow tables. The detailed terms can be defined in Table 1. By using the DPI technology, SDN switches run a deep analysis of all packets belonging to each Modbus/TCP data flow, and match the key contents with

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the flow control rules to detect and block the improper control commands or undesired technology parameters in industrial Modbus/TCP communications. Table 1. Term definition in flow control rules and security flow tables Terms SIP DIP Function code Address range Value range Operation

Timestamp Flow n P-header F-control

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Definition and description Source IP address in one Modbus/TCP data flow or packet Destination IP address in one Modbus/TCP data flow or packet Detailed description in Reference [13] Detailed description in Reference [13] Detailed description in Reference [13] Processing modes in flow control rules Forward: pass the corresponding Modbus/TCP flows or packets; Drop: drop the corresponding Modbus/TCP flows or packets; Alarm: generate an alarm and logging; The lifetime of one flow control rule in SDN switches Representing ID of one data flow Modbus/TCP packet header information Security control identification 1: matching Modbus/TCP data flows with the following flow control rule; 0: processing Modbus/TCP data flows according the following actions Optional actions defined in OpenFlow, such as queuing or modifying

Detailed Executing Process

Figure 2 depicts the detailed executing process to detect and control Modbus/TCP data flow by using our approach. The main contents can be described as follows: Step 1: when one workstation wants to send some control commands to one PLC, it first need establish the initial TCP connection with the PLC, and constructs the Modbus/TCP connection request packet P1 which will be sent to the PLC. Step 2: when the SDN switch receives this request packet P1, it analyzes this packet and get the key information, including source IP address, destination IP address, destination port, et al. After that, the SDN switch looks up its security flow table, and finds whether the corresponding flow ID exists. If it exists, the SDN switch will further process this packet according to the operation of this flow ID; if it does not exist, the SDN switch will construct one flow control rule request packet R1 for this Modbus/TCP data flow, and send it to the SDN controller. Step 3: after the SDN controller receives the request packet R1, it finds the corresponding flow control rule in the flow control rule database, and sends this rule to the SDN switch by using the response packet R2. When the SDN switch receives this packet, it generates new flow ID and stores this rule in its security flow table, and forwards the Modbus/TCP connection request packet P1 to the PLC. Step 4: after receiving the packet P1, the PLC sends the Modbus/TCP connection response packet P2 which is forwarded by the SDN switch, and establishes the TCP connection with the workstation.

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Step 5: when the workstation sends the new Modbus/TCP data a to the PLC by using the packet P3, the SDN switch will match the parsed key contents with the rule belonging to this flow ID. If F-control is 1 and the corresponding operation is Forward, the SDN switch will forward this packet P3 to the PLC. Step 6: when the workstation sends the new Modbus/TCP data b to the PLC by using the packet P4, the SDN switch will match the parsed key contents with the rule belonging to this flow ID. If F-control is 1 and the corresponding operation is Alarm, the SDN switch will log these key contents and generate an alarm. However, it will also forward this packet P4 to the PLC. Step 7: when the workstation sends the new Modbus/TCP data c to the PLC by using the packet P4, the SDN switch will match the parsed key contents with the rule belonging to this flow ID. If F-control is 1 and the corresponding operation is Drop, the SDN switch will drop this packet P4. 2.3

Security Flow Table Generating and Maintaining

In our approach, security flow table is an essential point to offer security network services, because it not only possesses the basic forwarding function defined by OpenFlow, but also implements an effective strategy of deep defense. In order to generate and maintain security flow tables in SDN switches, we suggest the general steps as follows:

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Step 1: when the SDN controller receives the request packet from the SDN switch, it first parses this packet according to the basic service function. According to the parsed packet header, network topology, link state or other information, the SDN controller explores the initial flow items, including Flow ID, P-header, Action, et al. The detailed description of this step can refer to the OpenFlow protocol. Step 2: the security strategy configuration service function in the SDN controller looks up its flow control rule database according to source and destination IP addresses. If some flow control rule matches with these information, go to Step 3; if no flow control rule matches with these information, the SDN controller sets F-control to 0 and send the initial flow items to the SDN switch. Step 3: the SDN controller sets F-control to 1, and sends the initial flow items and the matched flow control rules to the SDN switch Step 4: after the SDN switch receives these information, it generates a new security flow entry and stores it in its table. Additionally, the SDN switch maintains its security flow table by means of the timestamp in each flow control rule. Specifically, the timestamp represents the lifetime of one flow control rule in the SDN switch, and if the timer of one flow control rule changes from the timestamp to 0, this rule will be deleted by the SDN switch.

3 Qualitative Analysis Compared with current industrial control network, the centralized and manageable SDN architecture can provide promising solutions to the problems of traditional industrial networks [3]. Based on SDN technology, our approach puts forward an additional security mechanism to detect and control abnormal Modbus/TCP communication behaviors, and further improves the security of SDN-based control systems. The qualitative analysis on the advantages and feasibilities of our approach is listed as follows: 1. Our approach meets the demands of defense in depth strategy, and can divide industrial control systems into different security enclaves by setting different flow control rules. Additionally, our approach supports the DPI technology according to Modbus/TCP protocol specification, and can dynamically adapt to industrialoriented properties. 2. The flow control rules are based on the centralized management of all SDN controllers, and this situation can facilitate the reconfiguration of industrial network according to various industrial applications. 3. Our approach does not affect the scalability, because our approach can be successfully implemented without changing the basic characteristics of SDN. 4. Based on our prior work [13], the main security defense technologies in our approach are feasible. Additionally, the fine-grained security flow tables are fast and accurate for Modbus/TCP data flows, and the timestamp can avoid wasting resources which is caused by enormous number of entries in the security flow table. 5. Although the end-to-end transmission delay may be increased to some extent, we believe our approach still meets the real-time transmission requirements of

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industrial networks. The causes are chiefly as follows: on the one hand, SDN switches can provide adequate levels of performance to perform deep packet parsing and matching; on the other hand, our prior work [13] has already proven the real-time capability even though the security defense technologies are implemented in the form of network middleware.

4 Conclusion The SDN architecture in industrial control systems can bring new opportunities for the development of security services. From this point, this paper proposes a softwaredefined data flow detection and control approach for industrial Modbus/TCP communication. Furthermore, approach designs a novel security strategy configuration service in SDN controllers to publish the flow control rules, and SDN switches match Modbus/TCP data flows with these flow control rules to detect and control abnormal communication behaviors. Additionally, our approach uses the popular DPI technology to run a deep analysis of data flows according to Modbus/TCP protocol specification, and can support the defense in depth strategy in industrial control systems. Finally, with the help of the qualitative analysis, we show that the advantage and developing prospect of our approach is foreseen. In the future work, we will realize our approach and build the experimental platform, and quantitatively evaluate its performance and defense effect. Acknowledgments. This work is supported by the National Natural Science Foundation of China (Grant No. 61501447), and the General Project of Scientific Research of Liaoning Provincial Department of Education (LYB201616). The authors are grateful to the anonymous referees for their insightful comments and suggestions.

References 1. Nunes, B.A.A., Mendonca, M., Nguyen, X.N., Obraczka, K., Turletti, T.: A survey of software-defined networking: past, present, and future of programmable networks. IEEE Commun. Surv. Tutor. 16(3), 1617–1634 (2014) 2. Kreutz, D., Ramos, F.M.V., Verissimo, P., Rothenberg, C.E., Azodolmolky, S., Uhlig, S.: Software-defined networking: a comprehensive Survey. Proc. IEEE 103(1), 14–76 (2015) 3. Li, D., Zhou, M.T., Zeng, P., Yang, M., Zhang, Y., Yu, H.: Green and reliable softwaredefined industrial networks. IEEE Commun. Mag. 54(10), 30–37 (2016) 4. Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D., Amicis, R., Pinto, E.B., Eisert, P., Dollner, J., Vallarino, I.: Visual computing as a key enabling technology for Industrie 4.0 and Industrial Internet. IEEE Comput. Graph. Appl. 35(2), 26–40 (2015) 5. Zhang, J., Deng, L., Li, X., Zhou, Y., Liang, Y., Liu, Y.: Novel device-to-device discovery scheme based on random backoff in LTE-advanced networks. IEEE Trans. Vech. Technol. 66(12), 11404–11408 (2017) 6. Li, S., Zhang, N., Lin, S., Kong, L., Katangur, A., Khan, M.K., Ni, M.: Joint admission control and resource allocation in edge computing for internet of things. IEEE Network 32 (1), 72–79 (2018)

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7. Hu, P.: A system architecture for software-defined industrial internet of things. In: Proceedings of 2015 IEEE International Conference on Ubiquitous Wireless Broadband, Montreal, Canada, October 2015, pp. 1–5 (2015) 8. Genge, B., Haller, P.: A hierarchical control plane for software-defined networks-based industrial control systems. In: Proceedings of 2016 IFIP Networking Conference and Workshops, Vienna, Austria, May 2016, pp. 73–81 (2016) 9. Gupta, A., MacDavid, R., Birkner, R.: An industrial-scale software defined internet exchange point. In: Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation, CA, USA, March 2016, pp. 1–14 (2016) 10. NCCIC/ICS-CERT, NCCIC/ICS-CERT year in review (2016) https://ics-cert.us-cert.gov/ Year-Review-2016 (2017) 11. Wan, M., Shang, W.L., Zeng, P.: Double behavior characteristics for one-class classification anomaly detection in networked control systems. IEEE Trans. Inf. Forensics Secur. 12(12), 3011–3023 (2017) 12. Ly, K., Jin, Y.: Security challenges in CPS and IoT: from end-node to the system. In: Proceedings of 2016 IEEE Computer Society Annual Symposiumon VLSI, Pittsburgh, USA, Jul. 2016, pp. 63–68 (2016) 13. Wan, M., Shang, W.L., Kong, L.H., Zeng, P.: Content-based deep communication control for networked control system. Telecommun. Syst. 65(1), 155–168 (2017) 14. Kim, S., Jo, W., Shon, T.: A novel vulnerability analysis approach to generate fuzzing test case in industrial control systems. In: Proceedings of 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, Chongqing, China, May 2016, pp. 566–570 (2016) 15. Huo, Y., Hu, C., Qi, X., Jing, T.: LoDPD: a location difference-based proximity detection protocol for fog computing. IEEE Internet Things J. 4(5), 1117–1124 (2017) 16. Ndonda, G.K., Sadre, R.: A low-delay SDN-based countermeasure to eavesdropping attacks in industrial control systems. In: Proceedings of 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks, Berlin, Germany, November 2017, pp. 1–7 (2017) 17. Genge, B., Graur, F., Haller, P.: Experimental assessment of network design approaches for protecting industrial control systems. Int. J. Crit. Infrastruct. Prot. 11, 24–38 (2015) 18. Stouffer, K., Falco, J., Scarfone, K.: Guide to industrial control systems (ICS) security. National Institute of Standards and Technology (NIST). http://nvlpubs.nist.gov/nistpubs/ SpecialPublications/NIST.SP.800-82.pdf (2013) 19. Zeng, P., Shang, W., Li, D., Wan, M., Zhao, J., Liu, J., Yang, M.: Method for controlling transmission security of industrial communications flow based on SDN architecture, USA, US20170339109A1, 23 November 2017

Research on the Application of Block Chain Technology in Internet Finance Qiusheng Zhang(&) and Xingyun Zhang School of Electrical and Information Engineering, Hubei Business College, Hubei 430079, China [email protected]

Abstract. With the rapid development of the Internet, a series of problems, represented by credit risk, have emerged one after another. In this case, blockchain technology emerges as the times require. The characteristics of block chain such as decentralization trace ability reliability and so on can solve many disadvantages in the financial field. Combined with the advantages of block chain technology, this paper analyzes and studies the application of block chain technology in the field of Internet finance. Keywords: Block chain technology Reliability
Internet finance


Decentralization
Traceability

1 Introduction From traditional finance to Internet finance, from indirect finance to direct finance, the development process of finance is a de-intermediation and de-centralization process, and it is also a gradual transition to self-finance and universal finance with the help of new concepts and new technologies. The sharing economy process of point-to-point, end-to-end and end-to-end docking is realized. Block chain, originated from the distributed technology of bitcoin, is the foundation and value core of Internet finance. Bitcoin is an application form of digital currency, which is composed of a series of computer generated codes. It has the characteristics of transparency, low transaction costs, unlimited circulation, less control by financial institutions and governments, and so on. Blockchain can be seen as both a public ledger and a perfect credit system. In particular, blockchain technology is a platform for distributed storage. With the help of functional assets and intelligent contract functions of blockchain, digital money has been in existence since its inception. All the payment, transfer, payment and transaction information of the distributed database are recorded and stored on the “block” in detail, and the related blocks are managed to each other, forming a huge chain. The block chain distributed real time account balancing system combines the separation of money and account in the modern financial system, and has the characteristics of transparency, openness and immutability. On the one hand, it can reduce the error rate of settlement and payment, thus avoiding the post audit. Reduce the cost of enterprises, on the other hand, can real-time monitor the inflow and outflow of each capital, is conducive to financial supervision, prevention and control of financial risks. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 730–735, 2019. https://doi.org/10.1007/978-3-030-02804-6_95

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2 Technology Related to Block Chain Blockchain technology is the underlying technology of bitcoin. Blockchain is a technical scheme to maintain a database through decentralization and trust. The essence of blockchain is a distributed database based on consensus mode. The technology platform is presented as a centric mesh structure, eliminates intermediate links, records data and contracts along a timeline, and all confirmed and certified transactions are linked from the beginning of the chain to the latest block. There is read write function and can not modify delete, which makes the system presents the characteristics of security and efficiency. If a new database is to be added to the shared database, a certain number of nodes running block-chain software need to be recognized in order to reach a consensus. The new database will be upgraded to a transaction block, which will be linked to the existing blocks in the system by password. The complete block chain system must complete the data storage, the network operation, the transaction submission, the verification and so on many functions, the block chain itself is also a cross discipline involves many technologies, mainly has: Cryptography: in order to ensure the security of transactions, block chain uses asymmetric encryption algorithm to generate public and private key pairs to achieve digital signature. In purse bitcoin system, the elliptic curve encryption algorithm of asymmetric encryption algorithm is used. Hash function is also widely used in block chain such as workload proof algorithm. The data in block chain contains not only transaction records but also hash values of the data. Digital signature technology is also used in block chain to determine the sender of the information and prevent the information from being tampered with. Merkle tree: block chain system uses Merkle binary tree to quickly generalize and verify the integrity of block data in order to improve the operational efficiency and scalability of the whole system. P2P network: P2P network is a distributed peer-to-peer network, block-chain network is built on P2P network, all nodes in the network are equal in order to achieve decentralization. Timestamp: timestamp is used to uniquely identify a certain time, each block data will be stamped at the time of generation, ensuring that the data in the block chain can not be tampered with and can be traced back. Distributed database: blockchain itself is a distributed database, all nodes record together to ensure the reliability of the database. Consensus mechanism: block-chain network is decentralized, and to ensure the consistency of each node, which requires that there is no central control to achieve mutual trust between the nodes to reach consensus, which is the consensus mechanism. The current consensus mechanism mainly includes workload proof, equity certification consensus, authorized share certification, space certification, storage certification, acquisitiveness certification and so on.

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3 Advantages of Block Chain Technology 3.1

Decentralization

If the central database is tampered with or stolen, the security and reliability of the entire database will be greatly affected, and the risk is relatively high. Block chain technology is a kind of distributed storage and an application of distributed autonomous architecture (DAC). Distributed autonomous structure, also known as sprawling autonomous system, is an organizational system that operates autonomously without interference and management through a series of fair and open rules. In other words, blockchain can achieve no centralized management, each node has its own independence, even if damaged or ineffective, it will not affect the operation of the entire database. As shown in Fig. 1, the sole center of the network in the (a) no longer exists, and the rights and obligations of all nodes are equal. Each node in the (b) relies on a distributed ledger to record transactions between network participants, sharing, copying, and synchronizing information among network members. This decentralized structure greatly reduces the risk of data, avoiding the risk that the entire database will be paralyzed because of damage to the central data.

Fig. 1. Conversion of Information sharing Mode based on Block chain technique: from Central data Bank to distributed account Book; from Central data Bank to distributed account Book

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Decriminalization

In blockchain, the data exchange between each node need not trust each other and need not be confirmed by the third party, the whole system is open and transparent, all the data is public, and any falsification can not be hidden. Therefore, each node can not deceive each other. This model has greatly reduced the cost of trust, but also to a certain extent to eliminate third-party organizations of data fraud. 3.3

Traceability

Another great advantage of the block chain distributed architecture is the peer-to-peer model. The traditional database is a one-to-many or many-to-many schema, once the data problems can not query the source of the problem, so the reliability and authenticity of the management of the database caused a great deal of trouble. But the pointto-point pattern of block chains can be traced back to the source of the data. This mode greatly improves the transparency and security of the data. 3.4

Collective Maintenance and Sharing

The shared network is maintained by all nodes. Because the node can be participated by anyone, each node can participate in the record and verify the correctness of the other node record results. The maintenance efficiency is greatly improved, and the cost compared with the centralized network system will be greatly reduced. 3.5

Reliability

Each node in the shared network has the latest complete copy of the database. Because the system has the function of automatic comparison, it will filter out the same data records that appear many times to be true. Therefore, it is invalid to modify the database of a single node.

4 Application of Block Chain Technology in Financial Field 4.1

Digital Currency

From the physical exchange to the popularity of physical money to the emergence of digital money, the credit connotation of money has been extended. The issue cost and circulation cost of digital currency are low and transparent, which is propitious to the convenience of economic activities and is more suitable for the era of electronic commerce transaction. If the block-key credit mechanism works on a global scale, digital money will replace physical money as a means of payment for GSM. Take Bitcoin, for example, which can be used as a means of paying for goods in some western countries, and has developed applications such as ATMs for Bitcoin debit cards. Some countries have also introduced trading platforms that support traditional and electronic currency exchanges, such as the US bitcoin exchange network Coinbase, which supports the exchange of US dollars, euros, and sterling with Bitcoin. Exchange

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services for bitcoin and renminbi are also available on a trading platform called OKCoin in China. Central banks have realized that with economic progress, digital money has become the direction of development. It can improve the central bank’s control over the circulation of money and reduce the cost of circulation of money that issues money. At the same time, it makes payment and settlement more convenient and transparent. 4.2

Cross-Border Payment and Settlement

Block chain payment is superior to traditional payment in terms of transaction time, cost and security. In terms of transaction time, traditional methods of payment take a long time, cross-border payment and settlement take 2–3 days due to different settlement procedures in different countries, the procedures are cumbersome and inefficient, and seniority funds in transit take a long time. This has led to a significant increase in investment opportunity costs; after the blockchain technology has been applied, a cross-border payment can be made in a matter of seconds. From the cost point of view, the traditional payment to collect a certain proportion of fees, Nanchang block chain fees can be ignored, it saves the third party involved in the link, the two parties directly involved in the settlement of transactions. Even if an individual node is vulnerable. The entire transaction will not be affected, the realization of 24 h a day service, real-time payment, is a safe, efficient and low-risk payment. 4.3

Stock Exchange

Block chain technology can improve the traditional securities market from issuance to trading subject to complex processes. One is to shorten the transaction time, reduce management costs, so that the transaction time from the day as a unit to an hour or even minutes as a unit. Second, through the flat network to make trading information more open and transparent, to avoid insider trading, and maintain market stability. Goldman Sachs expects blockchain to save more than $11 billion a year in global securities trading. Linq, a block-chain platform built by Nasdaq in partnership with Chain, provides investors with digital securities management. The clearing system designed by Digital Asset Holdings for the Australian Stock Exchange uses block-chain technology to realize real-time asset trading. The Shanghai Stock Exchange of China has also actively explored the business opportunities brought by block chain and formed the China distributed General Ledger Infrastructure Agreement Alliance to promote the process of transaction automation. 4.4

Application on Bills

At present, the international trade document business and the bank bill business because of the artificial participation many, the negotiable instrument fluidity is bigger, has many violations and the operational risk, for example many times appeared the false ticket, duplicates the ticket, the change ticket, the one ticket sells and so on the false bill case for many times. The current transaction of electronic bills requires a third party central bank to keep the transactions between the two sides safe and reliable.

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Under block chain technology, paper bills are no longer used to control and verify the third party, through point-to-point transfer of bill value directly, avoid the influence of human factors, at the same time, it can reduce the cost of bills and improve its security. 4.5

Audit Application

Block chain technology preserves the integrity, immutability and permanence of all the data, and solves the shortcomings of audit in transaction forensics, tracking, association, backtracking and so on. Deloitte has developed a Rubix block chain application platform that provides customers with counter party confirmation, real-time auditing, land registration, loyalty points, and other related services. On the other hand, due to the immutable nature of block chain and the time postmark, Auditors perform all transactions on block chain for clients who need to be audited, so they can reduce the cost of auditing and the inspection risk of auditing. In a word, block chain technology makes the audit work more information step forward.

5 Conclusion With the development of science and technology, block chain, as a new technology, is occupying the financial market with a rapid trend in recent years. The advantages of this technology, such as de-centralization, de-trust, traceability and so on, have exerted great influence on the traditional financial institutions, changed the banking business, improved the settlement system, and effectively managed the risks. Compared with the traditional transaction, it has made a qualitative leap forward in the field of Internet finance. Acknowledgments. Project of Hubei Provincial University Outstanding Young and Middleaged Science and Technology Innovation team: “Research on the Application of big data to Internet Finance” (No. T201838).

References 1. Mei, H., Junhua, M.: Blockchain: changing the financial infrastructure. China International Capital Corporation study report, 2016(5):126–128 (2016) 2. Peng, L., Juan, Z., Juanjuan, Y.: Analysis on the trend of Internet financialization of traditional Financial Instituties Taking Securities companies as an example. Modern Econ. Inf. 13, 233–234 (2015) 3. Greenberg, A.: The little black book of Billionaire Secrets Nakamoto’s Neighbor: my hunt for bitcoin’s creator led to a paralyzed crypto genius. Forbes, April 2017 4. Hoon, C.: Research on internet financial development countermeasures of traditional financial institutions in the era of rule supervision. Southwest Finan. 1, 18–21 (2016) 5. Yili, N., Xiangjun, Q.: Blockchain-the subverter of the rules of the banking game. China Banking White Paper, vol. 5, pp. 89–93 (2016) 6. Sachs, G.: Blockchain: Putting theory to practice (2016)

Categorical Data Clustering Method Based on Improved Fruit Fly Optimization Algorithm Dong Li1(&), Huifeng Xue1, Wenyu Zhang2, and Yan Zhang2 1

School of Automation, Northwestern Polytechnical University, Xi’an 710072, People’s Republic of China [email protected] 2 School of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an 710061, People’s Republic of China

Abstract. K-modes algorithm is a general algorithm for categorical data clustering. It has the characteristics of simple principle and easy implementation. However, K-modes algorithm is vulnerable to the initial cluster centers and falls into the local optimal solution. And K-modes clustering algorithm cannot automatically determine the number of clusters, it needs to be set manually. These problems limit the application of the K-modes algorithm. This paper addresses the two problems by proposing a K-modes clustering algorithm based on the improved fruit fly optimization algorithm (IFOA-K-modes). The IFOA-K-modes algorithm combines K-modes algorithm with the fruit fly optimization algorithm (FOA), and optimizes the number of clusters and the cluster centers by using the improved fruit fly optimization algorithm (IFOA). In this paper, because of the strong local search ability and weak global search ability, the FOA is improved from the search mechanism, coordinate system and dynamic regulation of search radius. At the end of the paper, the IFOA-K-modes algorithm is verified by experiments. And the results show that the IFOA-K-modes has the ability to optimize the number of clusters and cluster centers, and the accuracy of clustering is also improved. Keywords: Clustering Categorical data


K-modes
Fruit fly optimization algorithm

1 Introduction At present, clustering analysis has been widely used in many fields. Clustering algorithm divides a set of instances with multiple attributes into different clusters. Instances in the same cluster have higher similarity than samples in distinct clusters. Clustering algorithms is divided into five types, such as: hierarchical clustering, density-based, partition-based, model-based, and grid-based. K-means is a general algorithm, which is applied in many fields because of its simple principle and easy implementation. However, the categorical data is usually unordered. Therefore, using the k-means algorithm to cluster the categorical data often fails to obtain valuable results, which limits its application range in the real world. To this end, Huang [1] drawed on the idea of k-means and proposed the K-modes algorithm. K-modes algorithm used the Simple Matching Distance (SMD) method to measure the distance of the categorical data. In this way, the clustering method can be used to the categorical data. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 736–744, 2019. https://doi.org/10.1007/978-3-030-02804-6_96

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As a center-based clustering algorithm, K-modes algorithm is easily affected by the initial clustering centers and is easily fall into local optimal solution [2]. And K-modes algorithm cannot automatically obtain the number of clusters and it needs to be set manually. If the number of clusters is not set properly, a large number of sample clustering errors may occur. Facing these problems, scholars began to improve the k-modes algorithm from multiple angles. (1) Initial cluster center optimization. The earliest study of the k-modes cluster center initialization method was professor Huang, who proposed two approaches to get the initial clustering centers for K-modes algorithm [1]. Khan and Ahmad [3] put forward a new approach which was used multiscale data compression to find k initial clustering centers. Cao et al. [4] devised an initial cluster centers algorithm that associated the distance and density measures. The algorithm of Khan and Ahmad [2] determined the initial cluster centres by significant attributes. Peng and Liu developed an initialisation algorithm for K-modes, which combined the distance with density measures [5]. (2) Distance metric optimization. Some experts find the SMD is not a good way to measure similarity. Ng et al. [6] put forward a new difference measurement method for the k-modes algorithm. In the method the frequency of the categorical values in current cluster had used to measure similarity. Cao et al. [7] discussed a new measure method which was to consider the distribution of attribute values. Through analyzing the existing research results, we can find that the majority scholars focuses on the initial clustering coordinates and the sample distance metrics, and there is a lack of overall thinking that combines the optimization of the number of clusters with the optimization of cluster center coordinates. This provides a research entry point for this study. By combining these two methods, it is possible to determine the optimal number of clusters for a set of samples with an unknown number of categories, and to find the optimal cluster center for each cluster, which is very important for clustering research. The swarm intelligence optimization algorithm is an artificial intelligence algorithm that simulates the behavior of biological groups. Fruit fly optimization algorithm (FOA) [8] is a new optimization algorithm, which is inspired by the fruit fly foraging behavior. The FOA uses a population-based global search strategy, group collaboration, information sharing, and has good global optimization capabilities. The swarm intelligence algorithm is widely used in clustering because of its powerful global search capability [9, 10, 11]. In view of the optimization requirements of the K-modes clustering algorithm and the advantages of the FOA, this paper proposes a clustering method based on the FOA. In the paper, the improvement of FOA is firstly discussed, which is aimed at improving the overall search ability. Then, using improved fruit fly optimization algorithm to optimize the number of clustering and cluster center of k-modes algorithm, which can eliminate the effect of initial cluster centers on K-modes algorithm, and reduce the possibility of k-modes algorithm falling into local optimal solution. Because using FOA to search the best number of clusters and the clustering centers need to design a evaluation criterion to evaluate the merits of clustering, we improve the silhouette coefficient and put forward the silhouette coefficient for categorical data. At the end of the paper, the proposed method is verified by experiments. The experiment result shows that the method of this paper has better identification ability of number of clusters and optimization ability of cluster centers.

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2 Background Theory 2.1

K-modes Algorithm for Clustering Categorical Data

The clustering problem of category data can be described as: D is a dataset i.e., D ¼ fx1 ; x2 ; . . .; xn g, where each data point xi , 1  i  N, is described by m categorical
attributes, i.e., xi ¼ ai1 ; ai2 ; . . .; aim , divide it into k isolated groups i.e., D ¼ B1 [ B2 [ . . . [ Bk . Because the sample attribute value is no longer a number but some categorical value, such as gender, skin color, and the like. It is not possible to use the traditional clustering method that determines the sample similarity by the distance between samples, such as k-means. In order to solve this problem, Huang proposed a kmodes method for clustering data with separate types [1]. The specific steps of the kmodes algorithm are as follows. 1. Set the number of clusters k, then stochasticly choose the k objects in the data set D as the initial centers. 2. Calculate the distance between all objects in data set D and k cluster centers according to the SMD and distribute each object to the cluster with the smallest distance. 3. Calculate the frequency of each attribute in each clusters, update cluster centers with the attribute’s highest frequency value. 4. Repeat steps 2 and 3 until the cluster table does not change. 2.2

Fruit Fly Optimization Algorithm

FOA [8] is a optimization algorithm designed based on the behavior of the fruit fly searching for food. Compared with ant colony, particle swarm, and fish swarm optimization algorithm, FOA algorithm is not only simple in implementation, small in computation, but also requires fewer parameters, which can effectively avoid the problem of poor algorithm performance due to improper parameter selection. The FOA process is described in reference [8]. By analyzing the learning mechanism of FOA, the algorithm only takes the best fruit fly as a learning model and emphasizes the search of the optimal individual neighborhood, which results in strong local search ability and weak global search ability. For the sake of improving the global searching ability of FOA, this paper tries to integrate the search strategy of FOA and grid search method, improve the standard FOA, and put forward an improved fruit fly optimization algorithm (IFOA). The specific improvement ideas are as follows: (1) The process is segmented into two major modules: global search and local search. The grid search method is responsible for global search, and the FOA is responsible for local search. (2) In global search, we first use the grid method to divide the search space into many subspaces, and every dimension partitioning method in subspace is shown in formula 2. In formula 2, Nf denotes the number of fruit flies that are preset in the FOA, and d denotes the dimension value of the fruit fly search space. Each dimension in the entire search space can be segmented into M intervals by Eq. 1, and the entire search space can be divided into M d subspaces. After partition, we randomly generate a fruit

Categorical Data Clustering Method Based on Improved FOA ð1Þ

ð2Þ

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ðDÞ

fly Xi in each subspace with the position coordinates ðxi ; xi ; . . .; xi Þ,and calculate the fitness value FSi of Xi as the fitness value FSi of the subspace. Then we sort the FSi of each subspace, and select the optimal subspace Sbest according to the sorting result, and perform a local search in the subspace Sbest . M¼

j pffiffiffiffiffik D N

ð1Þ

f

(3) In the local search, in order to enable FOA to search the same coordinate system with the grid, and also to enable FOA to search the optimal parameters in a negative range, this paper changes the standard FOA algorithm positioning method which based on the two-dimensional coordinate system. In addition, this paper also changes the way to calculate the fitness value by first finding the distance di from the origin of fruit flies and then using the inverse of di as the parameter. Instead of that, this paper unifies the parameters that need to be optimized with the coordinates of the fruit fly, that is, the coordinates of the fruit fly are the parameters that need to be optimized. For the sake of improving the search efficiency, this paper redesigns the default FOA search mechanism with a search range of 1 and proposes a dynamic radius search mechanism, and ðdÞ defines the indicators Df ðiÞ for measuring fitness optimization trends. The calculation ðdÞ

method of Df

ðdÞ

is shown in formula (2). The indicators Df

can describe the optimal ðdÞ

variation trend of j  1 times and j  2 times. According to Df , the search radius of

fruit fly RðdÞ ðjÞ can be adjusted in time. The calculation method of RðdÞ ðjÞ is defined as follows: ðdÞ

Df ðjÞ ¼

fb ðj  1Þ  fb ðj  2Þ ; j[3 fb ðj  2Þ  fb ðj  3Þ

RðdÞ ð0Þ ¼

RðdÞ ðjÞ ¼

8 > < > :

ðdÞ

ð2Þ

ðdÞ

Xmax  Xmin 2M

ð3Þ

ðdÞ

ðdÞ R0 ðj

R0 ð0Þ; j ¼ 1 b ðj2Þ  1Þ  ð1  fb ðj1Þf Þ; 3  j [ 1 jfb ðj2Þj

ðdÞ R0 ðj

 1Þ  ð1 

ðdÞ Df ðjÞÞ;

ð4Þ

j[3

Where j is the local search count variable. fb ðjÞ is the optimal fitness value obtained ðdÞ ðdÞ from the first local search. Xmax and Xmin represents the maximum and minimum values ðdÞ of the dth dimension. R ð0Þ is the initial radius of the dth dimension FOA search. 2.3

Evaluation Function of Clustering

Clustering number and clustering center have great influence on clustering results. If the number of clusters is too small, the data will be coarsened and the effective classification information will be lost. If the number of clusters is too large, the clustering result will be

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large, and the complexity of subsequent data analysis will increase. Therefore, it is necessary to evaluate the results of clustering, so as to select a reasonable number of clustering and better cluster centers. The silhouette coefficient is a common used evaluation criterion which was proposed by Kaufman [12]. In the silhouette coefficient algorithm, the intra-class cohesion degree of the object xi is obtained by calculating the average value of the distance between the object xi and other objects in the class, and is denoted as ai . The calculation of the degree of separation between classes of object xi is slightly complicated. The first is to specify any class C1 outside of the class where object xi is located. Then calculate the distance between the object xi and the class C1 . The calculation method is to calculate the average distance between xi and all objects in C1 , and record it as d1 . The third step is calculate the distance dk between the object xi and other classes. Finally, find the minimum value in dk , denoted as bi . Note: Since the clustering of categorical data is studied in this paper, traditional distance calculation formulas should not be used to calculate distances between objects in a class, but the Simple Matching Distance should be used as the distance between objects. The calculation formula for the i-th object silhouette coefficient si is: si ¼

bi  ai maxðai ; bi Þ

ð5Þ

For clustering evaluation, it mainly refers to the effect of clustering as a whole, rather than the clustering effect of a single object, so usually the calculation of the silhouette coefficient refers to the average silhouette coefficient of all samples, which is defined as follows: Sk ¼

n 1X si n i¼1

ð6Þ

Where n is the number of objects in data set. k is the number of clusters. Sk is the silhouette coefficient, it is also called the average silhouette coefficient. The range of the silhouette coefficient is [–1,1]. When the silhouette coefficient value approaches 1, it indicates that the clustering effect is relatively good.

3 K-Modes Algorithm Based on IFOA The basic idea of the algorithm is: Using the silhouette coefficient as the optimization goal, the IFOA was used to search the best number of clustering and the clustering center. The K-modes algorithm based on IFOA is as follows: 1. Initialization variable: Cmin, Cmax, T, xmax(m), xmin(m). Cmin is the search limit of the number of cluster. Cmax is the upper search limit of cluster number. T is the maximum number of iterations. xmax(m) is the upper limit of the cluster center coordinates in the m-dimensional search interval. xmin(m) is the lower limit of the cluster center coordinates in the m-dimensional search interval.

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2. First, randomly generate the number of clusters k. Then initialize the food source according to the number of clusters k. Finally calculate the fitness function of each fruit fly and find the current optimal solution Gbest. 3. Taking the silhouette coefficient as the optimization goal, the improved fruit fly optimization algorithm is executed to find the optimal number of clusters and the corresponding cluster centers. 4. t ¼ t þ 1, if t  T is true, then go to step 3, otherwise go to step 5. 5. Output the optimal number of clusters, cluster center coordinates, and dataset clustering results.

4 Experimental Simulation and Analysis 4.1

Experimental Simulation Description

In order to verify the algorithm performance of this paper, the experiment is carried out from two levels. (1) Performance simulation of IFOA search for optimal solution. (2) Performance analysis of clustering effects of k-modes algorithm based on IFOA. 4.2

IFOA Performance Analysis

4.2.1 Benchmark Function Description In this paper, a series of common functions are used to compare the performance of IFOA with the FOA, Artificial bee colony (ABC), genetic algorithm (GA) and particle swarm optimization (PSO). The details of these functions are shown in Table 1. Table 1. Benchmark functions Function name

Function expression

Sphere (f1)

f1 ðxÞ ¼

Rosenbrock (f2)

f2 ðxÞ ¼

D P

x2i i¼1 D1 P i¼1

Ackley (f3) Griewank (f4)

Min Minimum position 0 (0, 0, …, 0)

 100ðx2i  xi þ 1 Þ þ ð1  xi Þ2  rffiffiffiffiffiffiffiffiffiffiffi  D D P P 1 2 1 0:2

D

xi

D

i¼1 e f3 ðxÞ ¼ 20 þ e  20e D  D   P Q 1 f4 ðxÞ ¼ 4000 þ1 x2i  cos pxiffii

i¼1

 cosð2pxi Þ

0

(1, 1, …, 1)

0

(0, 0, …, 0)

0

(0, 0, …, 0)

i¼1

i¼1

4.2.2 Related Algorithm Parameter Settings In the experiment, all algorithms were run 30 times, and the population size was 100. In FOA algorithm, the search radius of fruit fly was 1. The GA’s crossover probability is 0.90. The GA’s mutation probability is 0.1. In the ABC algorithm, the numbers of onlooker bees and the numbers of the employed bees was the same, and the number of

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scout bees was 1. In PSO algorithms, c1 = c2 = 2.0. Vmin ¼ 0:1  Lb, Vmax ¼ 0:1  Ub. o decreased from 0.95 to 0.65 linearly according to the iterations. In addition, the number of function evaluations (FEs) was used as a measure criterion. When FEs > 100000, all algorithms were exited. 4.2.3 Simulation Results and Analysis Table 2 shows the mean and standard deviation of the minimum values obtained after each algorithm runs 30 times. As shown in Table 2, we can see that the IFOA performs best on the three benchmark functions, the FOA algorithm performs best on one benchmark function, and the remaining algorithms have slightly worse performance. Comparing the performance of IFOA algorithm with FOA separately, IFOA is superior to FOA in Sphere, Rosenbrock, Ackley, and has obvious advantages. Therefore, we can draw the following conclusion: compared with FOA, ABC, GA and PSO, the performance of IFOA is better. Table 2. Comparison of the effectiveness of IFOA with FOA, ABC, GA and PSO Function Sphere (f1)

Mean Standard deviation Ranking Rosenbrock (f2) Mean Standard deviation ranking Ackley (f3) Mean Standard deviation Ranking Griewank (f4) Mean Standard deviation Ranking

4.3

FOA ABC GA PSO IFOA 1.54E−05 5.74E−04 1.39E+00 2.04E−05 3.91E−10 3.90E−05 5.18E−15 1.56E−01 7.02E−06 2.00E−12 2 4 5 3 1 9.98E−01 2.03E+00 1.32E+03 3.01E+01 1.93E−07 6.76E−02 0.00E+00 6.82E+02 2.51E+01 6.74E−08 2 3 5 4 1 1.24E−01 7.40E−01 1.62E+01 3.22E+00 2.57E−05 3.35E−02 2.53E−05 8.96E−01 5.31E−01 7.69E−07 3 6 5 1 2.87E−06 7.96E−02 4.96E+00 4.22E−02 2.47E−05 3.08E−06 6.41E−05 1.53E+00 1.66E−01 5.42E−06 1

3

5

4

2

IFOA-K-Modes Performance Analysis

4.3.1 Datasets To test the capability of the IFOA-K-modes, we find many pure categorical datasets from the internet. Soybean Small. The dataset consists of 47 instances which characterized by 35 categorical attributes and drawn from four populations. Zoo Data. This dataset consists of 101 instances characterized by 16 attributes and distributed into 7 categories.

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Breast Cancer Data. There are 699 instances in the Breast Cancer Dataset, which has 9 attributes, and divided into four groups. In the data set, it has 9 instances in 2 attributes that contain a missing value. 4.3.2 Evaluation Methods In order to evaluate clustering results, we choosed the Precision (PR), Recall (RE) and Accuracy (AC) as evaluation criterion proposed by Yang [13]. 4.3.3 Experimental Results Table 3 shows the the average of the evaluation criteria obtained after each algorithm runs 30 times on these dataset. As seen from Table 3, IFOA-K-modes and FOA-kmodes are better than the standard k-modes algorithm in each evaluation criteria of each data set. This results shows that the introduction of swarm intelligence optimization algorithm can obviously help K-Modes algorithm improve its accuracy. In addition, IFOA-K-modes is superior to the FOA-k-modes in all evaluation criteria, which proves that the global search ability of the algorithm is improved after redesigning the FOA algorithm, which further validates the effectiveness of the proposed algorithm. Table 3. Comparison of the effectiveness of IFOA-K-modes with FOA-K-modes, K-modes Dataset Performance metrics K-modes Soybean PR 0.9305 RE 0.9483 AC 0.9374 Zoo PR 0.7586 RE 0.6523 AC 0.8356 Breast PR 0.8702 RE 0.7833 AC 0.8461

FOA-k-modes 0.9464 0.9531 0.9672 0.7679 0.7214 0.8612 0.8992 0.8962 0.9003

IFOA-K-modes 1 1 1 0.8312 0.8631 0.9201 0.9439 0.9331 0.9444

5 Conclusion This paper first proposes an improved fruit fly optimization algorithm, which is modified from search mechanism, coordinate system and dynamic adjustment of search radius. By the improvement, the global search ability of the fruit fly optimization algorithm is increased and the convergence rate is optimized. Subsequently, combining the IFOA with k-modes, the IFOA-K-modes algorithm is proposed, which can automatically find the optimal clusters number and the best cluster center coordinates. Finally, the real dataset is used to clustering test to verify the effectiveness of the IFOAK-modes algorithm. The experimental results show that IFOA-K-modes algorithm can find the optimal clusters number and optimize the cluster center.

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Acknowledgments. This work is supported by the 2013 scientific research program of the Shaanxi Provincial Education Department (Grant no. 2013JK0175).

References 1. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Disc. 2(3), 283–304 (1998) 2. Khan, S.S., Ahmad, A., et al.: Cluster center initialization algorithm for K-means clustering. Expert Syst. Appl. 40(18), 7444–7456 (2013) 3. Khan, S.S., Ahmad, A.: Computing initial points using density based multiscale data condensation for clustering categorical data (2003) 4. Cao, F., Liang, J., Bai, L.: A new initialization method for categorical data clustering. Expert Syst. Appl. 36(7), 10223–10228 (2009) 5. Peng, L., Liu, Y.: Attribute weights-based clustering centres algorithm for initialising Kmodes clustering. Clust. Comput. 3, 1–9 (2018) 6. Ng, M.K., Li, M.J., Huang, J.Z., et al.: On the impact of dissimilarity measure in K-modes clustering algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 503 (2007) 7. Cao, F., Liang, J., Li, D., et al.: A dissimilarity measure for the k-modes clustering algorithm. Knowl. Based Syst. 26(9), 120–127 (2012) 8. Pan, W.T.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl. Based Syst. 26(2), 69–74 (2012) 9. Wang, J., Cao, J., Li, B., et al.: Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks. IEEE Trans. Consum. Electron. 61(4), 438–444 (2016) 10. Cagnina, L., Errecalde, M., Ingaramo, D., et al.: An efficient particle swarm optimization approach to cluster short texts. Inf. Sci. 265(5), 36–49 (2014) 11. Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recogn. 33(9), 1455–1465 (2004) 12. Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis. Mach. Des. 74 (1990) 13. Yang, Y.: An evaluation of statistical approaches to text categorization. Inf. Retr. 1(1–2), 69–90 (1999)

Discussion on Computer Network Security Solution Min Xiao and Mei Guo(&) College of Software and Communication Engineering, Xiangnan University, Chenzhou 423000, Hunan, China [email protected]

Abstract. With the rapid development of the Internet, people’s dependence on computers has reached an unprecedented level. Both individuals and businesses use computers anywhere, anytime. Therefore, once the computer network is attacked by the outside world, it will cause the computer to fail to work properly or even cause embarrassment and bring about great losses [1]. Now that the world’s computers can be connected through the Internet, the issue of network security is internationalized, and the content of information security has also changed fundamentally. Therefore, computer network security issues are not just general defense issues, but even international security issues. This article will analyze the computer network security issues and propose solutions to computer network security issues. Keywords: Computer


Network
Security issues
Analysis
Solutions

1 Introduction In the process of development, the security of computer networks has become increasingly serious and has aroused the attention of the general public. This is an important issue that needs to be resolved. The computer network security problems mainly include software, hardware and data information in the computer network system. To ensure the security of the computer network is to protect the information from being illegally read, maliciously tampered or attacked, and ensure that the information is not leaked, so that the computer The network system can operate normally and stably. The network security technology of computers is a comprehensive discipline with a certain degree of connection with multiple industry fields. It is mainly aimed at preventing external malicious programs and viruses from malicious attacks to ensure the safe operation of the network. In the era of a surge of computer network users, the prevention of malicious attacks has a high degree of difficulty. This requires us to accelerate our research in this field, and constantly improve the level of preventive technologies to provide a strong guarantee for computer network security.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 745–751, 2019. https://doi.org/10.1007/978-3-030-02804-6_97

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2 The Importance of Computer Network Security Issues Due to the popularization of computers and the wide application of multiple industries, the increase of data information volume and the increase of the complexity of users have raised higher requirements for computer network security technologies. In order to improve the security and anti-attack performance of the computer network system and reduce the probability of the system being maliciously attacked to ensure the normal operation of the network platform, the use of related network security technologies can achieve better results. If you do not manage and prevent network security, the openness of the network platform itself and the unhealthy motives of some criminals will lead to certain viruses, Trojan horse programs, and hackers. They can arbitrarily read, steal and tamper with the personal information of users. This has great hidden dangers for the property and personal safety of the majority of computer network users; when such security risks involve the vital interests of the broad masses of people, we must strengthen management, adopt computer network security technology, and ensure the normal operation of the network. Based on this, we guarantee the personal information and security of the majority of network users. The combination of network security management system and network security technology can form a relatively complete computer network operation system, continuously improve the security factor of network use, and create a safe and good network environment.

3 Causes of Computer Network Security Problems The existing neural network model can be adapted to specific recognition tasks with modified network architecture and fine turning. The traditional network training method requires a very large number of training samples which is not practical in real application. In real world the training sample is limited to hundreds with reasonable human annotation. Therefore we propose to modify the network architecture to better suit this cross database problem. The CNN is first trained on a large dataset and then tested on another in the experimental section. 3.1

Cyber Hacker Attacks

Cyber hackers belong to a type of people with high computer network technology that are active in the network. These people understand how to exploit the vulnerabilities in computer networks and combine their own technologies to infiltrate the user’s computer network by using illegal means in computer networks. System, and the destruction of the system, leading to the user’s computer system paralyzed. At the same time, these hackers destroy and steal the information and data of the computer network system, which seriously jeopardizes the privacy of the user. Because there are certain loopholes in the computer network itself, this provides an opportunity for hackers to prevent hacking [2].

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3.2

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Network Vulnerability

The computer operating system as a unified interactive platform, in order to better meet the needs of users, the greatest possible convenience for the user to make it in an open state, but the inevitable is the more powerful computer functions, appear The more loopholes, the greater the possibility of cyber attacks. If an operating system is used for a long period of time, its vulnerabilities will be exposed in the eyes of people and the chance of being attacked by the network will increase. Even if there is a strong design compatibility, there will be loopholes. In the process of using the computer, the interaction between the platforms is performed through links, and the effect of network interworking is realized. The presence of links will inevitably cause factors to attack the link, and may even cause attacks on the intercommunication protocol and the conversation link. Because there are certain flaws in the network management strategy, this leads to certain loopholes in the computer network. The loopholes in the computer network are one of the major security risks of the computer. The existence of these vulnerabilities provides an opportunity for illegal intrusion [3]. Intruders can intrude into the user’s computer system through this loophole, thus causing damage to the system. Coupled with the lack of network management, computer security has been severely damaged. In addition, there is a special kind of active attack that is a malicious program. There are several types of malicious programs that pose a serious threat to network security. (1) Computer viruses are harmful to network security. A virus is a piece of computer code that is attached to a program or file. It can be spread from computer to computer, and it can be copied into other programs by modifying other programs. (2) Computer worms harm the network security. Sends itself from one node to another via the network’s communication function and starts the running program. (3) The Trojan horse program is harmful to network security. It performs more than the claimed function, which is used by the user without their knowledge. (4) Logical bombs are harmful to cyber security. A program that performs other special functions when the operating environment meets certain conditions. 3.3

Internet Fraud

Because the network has a certain degree of openness and freedom, and the network also has a certain degree of virtuality, which in turn leads some illegal elements to use the online trading platform or chat software to scam the network. Such criminals often publish some false advertisements on the Internet. Fraudulent users through these fake websites and advertisements. All in all, cyber fraudsters may create a variety of fraudulent means to fraudulently fraudulently take money out of users, causing the user’s own economic losses. 3.4

Users Lack Safety Awareness

There are protection tools such as firewalls in computer networks. However, because many computer users lack a certain degree of network security awareness, some protection measures do not pay enough attention, which makes these protection measures

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difficult to play its role. When users visit some unfamiliar websites, they are vulnerable to virus intrusion, and they also provide convenience for the illegal activities of cyber attackers.

4 Computer Network Security Solutions In terms of technology, computer network security is mainly accomplished by multiple security components such as firewall, anti-virus, and intrusion detection. A single component cannot guarantee the security of the entire system. As far as the current situation is concerned, the most used network security technologies include: firewall technology, anti-virus technology, security scanning technology, and prevention of local area networks [4]. 4.1

Firewall Technology

The firewall carried in the computer system is a security system that is used to prevent network applications and data passing through it. It combines hardware and software to prevent access to unauthorized applications. The main purpose is to protect the system. run. When using firewall technology, there are two factors to consider: security and practicality. When both are available, it is necessary to balance security with practicality. This is mainly because: Security needs to be considered first. At present, most of the firewall technology is designed to use the “disallow operation without permission” strategy. Secondly, when the computer is in a relatively safe environment, it is necessary to enhance the practicality of the firewall. Without a good practical firewall, even if the security is high, it is difficult to put into use. In addition, the access policy as the core of the application of the firewall is the core content of the computer network technology. In the implementation of network strategy, computer network configuration should be regarded as the core content of computer network technology. Through the understanding of computer network technology, it can ensure the effective operation of computer network information, strengthen the function of computer network technology, and form a scientific protection system. In the actual operation of computer network technology, it is necessary to standardize access policies, strengthen the protection of data information access, and ensure the security of computer information access. In the process of accessing data in computer protection, appropriate adjustments to firewall technologies should be strengthened, access plans should be optimized, and computer network security protection technologies should be promoted. Need to record the visit activity, adjust some improper behaviors in network protection, strictly plan the order of visits, and improve the efficiency of network protection. 4.2

Antivirus Technology

Antivirus technology can be divided into three categories: virus prevention, virus detection, and virus cleanup. The prevention of computer viruses mainly through the use of certain technical means to prevent computer viruses from damaging the system.

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Mainly reflected in the operation of the computer disk. The main reason is that if a computer’s disk is attacked, although the system will not be flawed, all the data in the computer will be lost. The application of computer virus prevention includes prevention of existing Ladies virus and prevention of unknown viruses. The detection and clearance of computer viruses is in an inevitable relationship [5]. Through certain technical means, the entire system is tested for viruses. When a virus is found, virus cleanup is an inevitable result. Most of the detection and cleanup viruses used today are performed through certain security software. Figure 1 below shows the design of a company’s anti-virus solution.

Fig. 1. Antivirus solution

4.3

Security Scanning Technology

Security scanning technology refers to the security scan and detection of the computer’s server. It can search for potential hidden dangers in time and discover loopholes in the computer to prevent it from being invaded. Divided into two kinds of active scanning and passive scanning, active scanning Note using the simulation of intrusion or attack behavior, find computer network server in the existing loopholes. Passive scanning is mainly to check the rules and passwords related to security and conduct regular security scans. It is also necessary to use the latest scanning technology according to the actual application of the server to effectively perform the role of scanning for security and improve its quality and level. In addition, it is also necessary to strengthen the management of applications in computer servers, formulate a strict management system, and related personnel must abide by the work procedures. Documents and e-mails that are of unknown origin are prohibited from being received, and they must not be copied using a personal U disk to avoid server attacks. On the virus. We must raise awareness of safety precautions, strengthen the training of safety skills, and give full play to the active and motivating staff to avoid the threat of computer

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network services. Figure 2 below shows the operation of scanning the computer with the port scan tool.

Fig. 2. With port scanning tools

5 Conclusion In summary, computers are widely used in real life. Once there is a security problem, it will cause great losses and affect people’s normal life. Therefore, it is necessary to constantly improve the defense mechanisms of computer network security issues, improve the security of computer networks, establish a good network environment, and make computers work better. Acknowledgments. This paper is funded by Project of School level scientific research project of XiangNan University, Research on network security situation prediction based on data fusion (No. 2017XJ16).

References 1. Jinghong, H.: Analysis of main hidden dangers and management measures of computer network security. Sci. Technol. 11, 98 (2015) 2. Yufei, Z.: Discussion on intrusion and defense technology of computer network server. Biotechnol. Comput. Sci. Res. 24(02) (2016) 3. Jun, Y.: Exploration of network security countermeasures in computer application. Indeed Comput. 32(01) (2014)

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4. Ning, L.: Analysis of main hidden dangers and management measures of computer network security. Sci. Technol. Mark. 1, 81–82 (2015) 5. Yufang, C., Xiangxi, G., Kunfeng, L., Danyang, S.: Application value of firewall technology in computer network security. Coal Technol. 08, 225–226 (2013)

An Internet of Things Framework Based on Upper Limb Rehabilitation Robots for Rehabilitation Qiaoling Meng1,2,3, Hui Zhang1,2,3, and Hongliu Yu1,2,3(&) 1

2

Institute of Rehabilitation Engineering and Technology, USST, Shanghai, People’s Republic of China [email protected] Shanghai Engineering Research Center of Assistive Devices, Shanghai, China 3 Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China

Abstract. The Internet of Things (IoT) in healthcare is anticipated to enable a series of services by connecting all kinds of smart devices. Rehabilitation of stroke survivors has been increasing in importance in recent years with the increasing occurrence of stroke. The rehabilitation robots are becoming part of the rehabilitation medicine in the future. With the development of technology, various sensors have been added to the rehabilitation robots to continuously measure forces, speed, accuracy, repeatability and temporal-spatial features of movement, even the physiological information of the patient, providing the basis for the sensor layer of the Internet of Things. In addition, the employ of wireless communication modules in rehabilitation robots enables the connection between the sensor layer and the network layer of the Internet of Things. So, Rehabilitation robots can not only help patients recover from rehabilitation training, but also collect data and upload data to the cloud platform to help doctors accurately and timely grasp the patient’s rehabilitation status. Through the Internet, doctors can monitor remotely the sort, strength, and duration of patients’ rehabilitation training in the home and community and provide the optimal prescription by adjusting frequency and intensity of rehabilitation training as feedback. Obviously, the inclusion of rehabilitation robots in the IoT system can better provide patients with rehabilitation services, and provide the physician with quantitative assessment data. This paper mainly presents the design and development of building an Internet of Things framework based on upper limb rehabilitation robots for rehabilitation. The architecture and function of the platform are described in detail, and the deployment and test of the development are outlined. Keywords: Internet of things (IoT) Socket communication


Rehabilitation robots
Stroke survivors

1 Introduction The electronic devices, smartphones, and tablets have become the fundamental tool of daily life with the explosive growth of communication and technology, communicating physically or wirelessly [1]. The next generation of the connected world is the Internet of © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 752–759, 2019. https://doi.org/10.1007/978-3-030-02804-6_98

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Things (IoT) which connects smart “things” to the internet by Radio Frequency Identification, sensor technology, positioning system, scanning reader and other information sensing equipment [2]. The Internet of Things can play an important role in all aspects of healthcare, such as barcode patient identity management, mobile doctor’s advice, record of medical signs, mobile drug management, mobile test specimen management, mobile medical record management, data storage and recall, baby burglar protection, nursing procedures, clinical pathways, etc. [3–7]. The purpose of IoT is to enlarge the profit of the Internet with real-time data updating, data sharing, remote control ability and so on [8]. The applications of the Internet of Things in the field of medical and healthcare will facilitate patients to gain the timely medical aid, optimal treatment program, low medical costs, most convenient rehabilitation and most satisfactory service [9]. The Internet of things is the integration of wireless communication and information technology. Implementing the Internet of things system requires knowledge from multiple disciplines. Starting from a generic solution is beneficial to the rapid prototyping of its target system and the effective use of past experience and results [10]. With the increasing number of patients who suffer from stroke and upper limb injuries that require rehabilitation training, patients can’t receive long-term rehabilitation of traditional physical training by physical therapists in hospitals, due to various constraints such as medical resources and costs treatment [11]. Hence, it is effective to continue the rehabilitation training with rehabilitation robots at home or community medical centers after patients discharge [12]. Meanwhile, it is economical to build remote rehabilitation training platforms by means of computer networks for patients and therapists [13]. Many clinical trials have shown that rehabilitation robots can help stroke patients with long-term paralysis regain their ability to control actively their limbs, to a certain extent [14]. With the help of a rehabilitation robot, the patient can perform accurate and repetitive exercises on the affected side of the limb to speed up the rehabilitation process of motor function. According to the theory of cranial nerve plasticity, the rehabilitation training of brain function recombination should emphasize the subjective participation of patients and re-educate the patients according to the scientific method of motor learning to restore their motor function [15]. In addition, the rehabilitation robot can effectively make the patients active by using virtual reality games in the functional recovery training, so that patients can get a better recovery effect. Therefore, this paper proposes an Internet of Thing system for upper limb rehabilitation robots that connected patients, doctors, therapists, rehabilitation robots, and robot administrators. With the help of this system, upper limb rehabilitation robots can send patients’ rehabilitation training data to the cloud database in real time. Doctors can access the patients’ rehabilitation training data; evaluate rehabilitation training results and update rehabilitation prescription through the browser at any time. Patients can choose the nearest hospital booking and implement rehabilitation training, view their own rehabilitation training records and rehabilitation training prescription issued by doctors. The therapist can arrange the work according to the appointment of the patient, use the rehabilitation robot to complete the patient’s rehabilitation training, and make timely assessment feedback on each patient’s rehabilitation training. Robot administrators can take this system to achieve upper limb rehabilitation robot monitoring and management, timely obtain the each robot’s data to facilitate the maintenance and updating the robots.

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The remainder of the paper is arranged as follows: Sect. 2 presents the system architecture and implementation; Sect. 3 provides the experimental results; Sect. 4 concludes the paper and gives the details about the Future work.

2 Proposed System The Internet of Things system for upper limb rehabilitation robots chooses the Browser/Server (B/S) structure which enables cross-platform access compared with Client/Server (C/S) structure, PC terminal, and the mobile terminal can be achieved through a Web browser system access, the system’s development environment is: Windows + Tomcat7 + MySQL5 + Eclipse. The frame diagram of the Internet of Things system for upper limb rehabilitation robots is shown in Fig. 1. Ajax request

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Fig. 1. The frame diagram of the IoT system for upper limb rehabilitation robots

Spring MVC + Spring + MyBatis (SSM framework) is the mainstream architecture in the Java Internet [16]. Based on SSM framework, Maven is used to managing the jar packages’ version of the system architecture. The web page uses Java Server Pages (JSP) technology, the Bootstrap framework is beneficial to the page adaptive design so that users have a comfortable browsing experience in different terminals. Spring MVC in SSM framework stratifies the model, view, and controller to form an organic and flexible system. MyBatis in SSM framework is used for persistent data operations. Spring in SSM framework run through the whole system, its IoC (Inverse of Control) and AOP (Aspect Oriented Programming) technology can greatly reduce the degree of coupling between the various levels of the system, interact using the interface between the various levels to ensure that the system has good portability and scalability. When the users send requests to the web server through a browser, the web server will parse the Uniform Resource Locator (URL), obtain the users’ requests resource from the Java core program, return the corresponding JSP views, translate the JSP views into an HTML page and sent to the browser. If the users’ requests are related to the rehabilitation robots, the web server needs to exchange data with the rehabilitation robots across the Internet and feeds back the HTML page to the user’s browser to complete the control the rehabilitation robots.

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The Internet of Things system for upper limb rehabilitation robots proposed in this paper consist of three layers. 2.1

Sensor Layer

The bottom layer of the system is called a sensor layer which connected things and sensed information by sensors, there are different sensors laying on the upper limb rehabilitation robot, e.g., force sensors, angle sensors, and position sensors. The Force sensor is commonly used to characterize the force in a rehabilitation robot. The position sensor can reflect the current position of the robot. The angle sensor can be used to reflect the angle changes of the upper limbs. In addition, the radio-frequency identification (RFID) tag automatically performs the object identification by reading the tag attached to objects. So we can use RFID to uniquely identify users and robots in the system. 2.2

Network Layer

The Network layer is the backbone of the entire Internet of Things, responsible for the transmission and processing of information obtained by the sensor layer, which plays the key role in communication to connect the devices with wide area network (WAN) using different protocols (TCP/IP), technologies and standards. The sensor information of upper limb rehabilitation robot upload and storage to the web server through the socket connection (TCP protocol). The system needs to start a Server Socket service when the server is started and always monitor a special port in order to receive the command sent by the Client Socket. A Server Socket simultaneously supports multiple Socket Client requests. Rehabilitation robot with communication modules like Wi-Fi, GPRS, etc., as a Client Socket, establishes a Socket connection with the Server Socket of the system [17]. After the Socket is connected, the robot can upload the data of the sensors to the system’s database so that the system can render the data graphically and intuitively. Moreover, the user who owned the privilege of controlling the robot can send an HTTP request to the server by clicking the button on the user interface of the browser, and then the server will forward the corresponding command to the rehabilitation robot’s Socket to achieve remote control of the robot. The process is shown in Fig. 2. The IoT System

User Interface UI Http Request

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Fig. 2. Socket communication block diagram

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2.3

Service Layer

This layer provides data’s direct access to professional medical facilities and stakeholders such as patients, doctors, rehabilitation centers, hospitals, and robot suppliers. The system includes four user roles: patient, doctor, therapist and robot administrator. The user centers are designed according to the permissions of resource access owned by user roles. The functional modules of user roles are described in Fig. 3. Some similar functional modules can be reused when developing. The homepage introduces the related functions of different upper limb rehabilitation robots included in the IoT system so that the user can select the appropriate upper limb rehabilitation robot and implement rehabilitation training, and the working flow of the system for users to better understand and use the system.

Fig. 3. The functional modules of user roles

To summarize, this proposed the Internet of Thing system for upper limb rehabilitation robots has been realized to connect patients, doctors, therapists, rehabilitation robots, and robot administrators. The main functions of the system are as follows: (a) Send patients’ rehabilitation training data from upper limb rehabilitation robots to the cloud database in real time. (b) Enable doctors to access the patients’ rehabilitation training data, evaluate rehabilitation training results and update rehabilitation prescription through the browser at any time. (c) Enable patients to choose the nearest hospital booking and implementing rehabilitation training, view their own rehabilitation training records and rehabilitation training prescription issued by doctors. (d) Enable therapists to arrange the work according to the appointment of the patient, complete the patient’s rehabilitation training by rehabilitation robots, and make timely assessment feedback on each patient’s rehabilitation training. (e) Enable robot administrators to achieve upper limb rehabilitation robot monitoring and management, timely obtain the each robot’s data to facilitate the maintenance and updating the robots.

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3 Experimental Results The proposed the Internet of Things System for Upper Limb Rehabilitation Robots is being deployed and tested on an upper limb rehabilitation robot whose sensor details are entered into the web portal. The homepage design is shown in Fig. 4. The index page of the web portal consists of various tabs including the Login, Registration, Robots, Services, Contact Us. In the “Login” tab, users are divided into four roles: doctor, therapist, patient, and administrator. According to different user roles, different functional modules are designed for each user role.

Fig. 4. The homepage of the system

Different operation rights correspond to different user roles in the platform, such as that, the administrator can view the status information of all robots in real time, the patient can view their rehabilitation status and training data at any time, and so on. These operations must be achieved by reading the field values stored in the database corresponding to the data. The Fig. 5 shows the detailed structure of the database currently hosted on a local host and can be further connected to the whole world via IoT. These data in the database can be plotted through a statistical graph which is used for rehabilitation effectiveness further analysis and tracking.

Fig. 5. The detailed structure of the database

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The IoT technology is applied to the tracking and monitoring system of the state of the rehabilitation robot, and the sensor node continuously monitors the robot’s various operating technical indicators and environmental information using a sensor node, and timely transmits the robot’s state data through a wireless network technology to the robot administrator. The system is finally deployed to multiple upper limb rehabilitation robots and their operating data details are plotted on a chart in real time. The Fig. 6 shows an example of sensor data’s output which the patient’s angle of shoulder activity was being traced in real time.

Fig. 6. The sensor data of upper limb rehabilitation robot upload and display

4 Conclusion In this paper, the main idea of the Internet of Things system for upper limb rehabilitation robots is to give better and effective rehabilitation services to patients by putting a networked information cloud into effect so that patients according to their own circumstances choose the appropriate place and time for rehabilitation training. What’s more, the presented system allows the doctors and therapists to remotely and dynamically view the rehabilitation training data of patients in a Web page and doesn’t need to have any special requirement on the device except an Internet access. With the help of this proposal, robot administrator can fastly and scientifically evaluate the robot’s status and life according to the statistical analysis and comparison of uploaded data, which is beneficial to the maintenance the robot, to a certain extent, reduce the occurrence of medical accidents. In the future, this work can be extended by adding more rehabilitation robots to the existing system. This work is done based on a single robot’s data communication and in future, this can be extended to multiple robots. Standardization needs to enable the interoperability of different devices and data. Network infrastructure needs to be scalable with increasing number of users, lower latencies, and higher bandwidths. The proposed model can also be deployed on the cloud server so that the system becomes more mobile and easy to access anywhere across the globe. Consequently, there will be a lot of work to be done to complete this system in the future.

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References 1. Wasankar, D.B., Gulhane, V.S., Gautam, L.K.: Application of Internet of Things in the field of medical and smart health care: a review (2017) 2. Sidheeque, A., Mar, A.K., Balamurugan, R., Deepak, K.C., Sathish, K.: Heartbeat sensing and heart attack detection using Internet of Things: IoT 3. Zamfir, M., Florian, V., Stanciu, A., Neagu, G., Preda, Ş., Militaru, G.: Towards a platform for prototyping IOT health monitoring services. In: International Conference on Exploring Services Science, pp. 522–533 (2016) 4. Zanjal, S.V., Talmale, G.R.: Medicine reminder and monitoring system for secure health using IOT. Proc. Comput. Sci. 78, 471–476 (2016) 5. Tsirmpas, C., Kouris, I., Anastasiou, A., Giokas, K., Iliopoulou, D., Koutsouris, D.: An Internet of Things platform architecture for supporting ambient assisted living environments. Technol. Health Care 25(3), 391–401 (2017) 6. Santos, A., Macedo, J., Costa, A., Nicolau, M.J.: Internet of Things and smart objects for M-health monitoring and control. Proc. Technol. 16, 1351–1360 (2014) 7. Islam, S.M.R., Kwak, D., Kabir, M.H., Hossain, M., Kwak, K.S.: The Internet of Things for health care: a comprehensive survey. IEEE Access 3, 678–708 (2017) 8. Ullah, K., Shah, M.A., Zhang, S.: Effective ways to use Internet of Things in the field of medical and smart health care. In: 2016 International Conference on Intelligent Systems Engineering ICISE, pp. 372–379 (2016) 9. Misra, P., Simmhan, Y., Warrior, J.: Towards a practical architecture for the next generation Internet of Things. Comput. Sci. (2016) 10. Khakimov, A., Muthanna, A., Kirichek, R., Koucheryavy, A., Muthanna, M.S.A.: Investigation of methods for remote control IoT-devices based on cloud platforms and different interaction protocols. In: Young Researchers in Electrical and Electronic Engineering (2017) 11. Aguirrezabal, A., Duarte, E., Rueda, N., Cervantes, C., Marco, E., Escalada, F.: Effects of information and training provision in satisfaction of patients and carers in stroke rehabilitation. NeuroRehabilitation 33(4), 639–647 (2018) 12. Dobkin, B.H.: A rehabilitation-Internet-of-Things in the home to augment motor skills and exercise training. Neurorehabilit. Neural Repair 31(3), 217–227 (2017) 13. Nordin, N., Sheng, Q.X., Wünsche, B.: Assessment of movement quality in robot- assisted upper limb rehabilitation after stroke: a review. J. Neuroeng. Rehabilit. 11(1), 137 (2014) 14. Kumar, V.: Ontology based public healthcare system in Internet of Things (IoT). Proc. Comput. Sci. 50(6), 99–102 (2015) 15. Douzis, K., Sotiriadis, S., Petrakis, E.G.M., Amza, C.: Modular and generic IoT management on the cloud. Future Gen. Comput. Syst. (2016) 16. Zhu, Z.Y., Wang, H.Q., Guo, B.: Development of mobile web app based on HTML5 and SSM. Comput. Knowl. Technol. (2017) 17. Jiang, Y., Zhang, L., Wang, L.: Wireless sensor networks and the Internet of Things. Int. J. Distrib. Sens. Netw. 2013(2013), 1578–1584 (2013)

Research on Power Big Data Storage Platform Based on Distributed File System Liu Fei1, Pang Hao-Yuan1, Zhang Yi-Ying1(&), Liang Kun1, He Ye-Shen2, Li Xiang-Zhen3, and Liu Zhu4 1

College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300457, China [email protected] 2 China Gridcom Co., LTD., Shenzhen, Guangdong, China 3 Jiaxing University, Jiaxing, Zhejiang, China 4 State Grid Information and Telecommunication Group Co., LTD., Beijing, China

Abstract. With the development of science and technology, the power industry has rapidly transformed into informationization, automation, and intelligent. Information flows, business flows, and power flows are intertwined. Concomitantly, the amount of data generated by each link of the power grid has grown exponentially, and the data types have also shifted from relatively simple structured data to unstructured data. A large variety of structured data, semistructured data, and unstructured data in the power grid constitute power big data. This paper is aimed at power big data, in-depth analysis of a variety of power data characteristics, based on distributed file systems, the development of power big data storage platform. According to different data types, corresponding storage strategies are formulated, and a power large data storage platform based on distributed file systems is developed to provide powerful technical support for the storage of power big data. Keywords: Power big data Storage strategy


Unstructured data
Distributed file system

1 Introduction With the continuous deepening and advancement of power grid construction, the amount of data generated by power grid operation and equipment inspection and monitoring has increased exponentially [1], and the data types have changed from single structured data to semi-structured or unstructured data quickly. The requirements for reliability and real-time performance are even higher, far beyond the traditional grid state monitoring. Most of the domestic traditional power system information platforms are constructed using expensive large-scale servers. Disk arrays are used for storage, and relational database systems are used for databases. Tightly-coupled software packages are used for business applications [2], resulting in poor system scalability and high costs. It is difficult to adapt to the high requirements and high standards of the new

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 760–767, 2019. https://doi.org/10.1007/978-3-030-02804-6_99

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era power grid. Therefore, the power industry has entered the era of big data, and we must re-examine existing storage technologies. Based on the characteristics of power big data, this paper implements different storage strategies for different types of data, and presents a complete set of storage strategies to solve the storage bottlenecks encountered by traditional power system information platforms for different types of data in different application scenarios. And based on the framework of many Java Web development, a set of R & D framework was developed. Technology selection adopts the principles of current mainstream, stable technology and more familiar with the developer for multi-component integration, reducing the developer’s learning cost and difficulty of use, shortening the development cycle, and providing development efficiency.

2 Related Work The current research on power big data storage is not very strong, because the power data are numerous and complex in type, and there is no good unified storage platform in storage. Literature [3] described the difficulties faced by smart grid big data storage and analyzed the application of several typical big data storage systems in smart grids. Literature [4] proposed classification storage and processing technology framework for different requirements of real-time data in different business areas of grid regulation and operation, that is, the data was classified and stored according to different real-time requirements. The literature [5] describes the data characteristics, classification and application value of big data in the power grid. It focuses on the analysis of different storage strategies for unstructured data, and gives different storage strategies for different types of data to allow storage of power data. With further in-depth research, there is still no unified research on power data storage platforms.

3 Distributed File System This platform uses a distributed file system for data storage. The distributed file system has PB-level massive data storage capacity, high aggregate concurrent bandwidth, high scalability, high reliability, easy management, and use. It can effectively solve the massive data storage and I/O bottleneck problems in distributed storage systems, and has become a research hotspot in the storage industry and academia. Distributed file system is an important part of any large-scale distributed computing environment. Its performance directly affects the execution efficiency of the entire distributed computing environment. Distributed file system plays a key role in the storage of massive data. With the development of massive data storage and cloud storage research, the study of distributed file system performance has become increasingly important and urgent. Distributed file systems have features such as high reliability, high availability, high I/O bandwidth, and massive storage capacity, and are considered as effective ways of managing and accessing large amounts of data. With the advent of cloud storage era, research on distributed file systems, especially distributed file system I/O research and performance research, is bound to receive more attention.

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4 Power Big Data Storage Architecture 4.1

Architecture Design

The overall architecture design of the power big data storage platform is shown in Fig. 1. The operation flow is passed from top to bottom for query operations. After compilation, execution, and data management, it reaches the bottom device.

SQL language

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Fig. 1. The architecture design of the platform

The query layer mainly receives user or system query requests for power data, that is, through SQL statements or complex query interfaces, calling related statements to perform operations. The data analysis tool is mainly used to analyze the existing data, for example, draw a graph of equipment operation indicators within a certain period of time and analyze the equipment health status; draw a certain area power consumption trend chart and analyze the power distribution of the next time period happening. The function of the compiler layer is to translate the operation request received from the upper layer into a machine language through a compiler or a parser, and pass it to the next layer for execution. The execution plan generator is used for the distributed storage of data mentioned above and specifies the relevant part of the data storage when the operations involve the distribution of data on different servers. The optimizer is a component that optimizes when the query statement is not efficient, and further improves the query efficiency. Cache management is used for the cache-based performance improvements mentioned above.

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Transaction management mainly deals with the management of data-related additions, deletions, and alterations. The analysis service engine is used for power data query processing and related information mining. The parallel execution engine divides an operation into several independent operations that are executed in parallel. After the execution is completed, the results are summarized to speed up the query rate. The data storage layer is oriented to the physical model of data storage. Data partition management is the management of data storage modes. It is divided into two types, horizontal partition management and vertical partition management, to adapt to the operation of accessing one record or different records of the same attribute to shorten the operation response time. Log management is the management of database operations files for record transactions. Recovery backups are used to recover data when a data disaster occurs. The existence of multi-level index is also to speed up operations. 4.2

Functional Architecture and Storage Method

Based on the functional architecture of the distributed file system power big data storage platform shown in Fig. 2, this platform uses different storage strategies for different types of data, mainly based on unstructured data, various types of data storage strategies are as follows.

Fig. 2. The functional architecture of the platform

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4.2.1

Different File Size Storage Strategies

(1) For the storage of large files, the platform uses a Distributed File System (HDFS). HDFS is the most widely used open source distributed file system [4–7]. HDFS is a highly fault-tolerant system suitable for deployment on inexpensive machines. HDFS distributed file system has a large data set, can store TB or PB level of large data files, can provide a relatively high data transmission bandwidth and data access throughput, accordingly, HDFS open some POSIX must interface, allow Streaming access to file system data. And it has the advantages of high fault tolerance, high data throughput, and streaming data access. (2) For the storage of small files, the platform uses high-end storage SAN [8] and storage area network (SAN). SAN provides an easy way to connect to existing LANs and supports a wide range of physical channels. SCSI and IP protocols used. And adopt the file synthesis strategy, convert many small files into a large file and then store it in the data node, which can greatly reduce the memory consumption, thereby solving the problem of excessive memory consumption when storing large amounts of small files. Because the data is unstructured data and there are many types of data, the improved KNN classification method is used to align and classify associations, and then the classified small files are merged according to the merge algorithm. Using the KNN algorithm, a small file is converted into a corresponding text feature vector value, and then a vector representation of various types of data for each training set and test set is calculated according to Eq. (1). tf ðt; dÞ  logðN=n þ 0:01Þ Wðt; dÞ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ½tf ðt; dÞ  logðN=n þ 0:01Þ

ð1Þ

When the unstructured data is expressed as a vector space model by Eq. (1), the similarity Sim (D1, D2) between various unstructured data types can be calculated. The formula is as follows: Pn k1 W1k  W2k SimðD1, D2Þ ¼ cos h ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  Pn   Pn  2 2 k1 W1k k1 W2k

ð2Þ

In Eq. (2), the smaller h is, the more similar the vectors are. Therefore, when the cosine of the angle between two vectors is smaller than a given threshold, the two data are considered to belong to the same class and there is a corresponding association. 4.2.2

Unstructured Data Storage Strategies with Different Levels of Heat

(1) For the storage of hot data, the platform uses high-end storage [9], and with RAID1, the hot data access efficiency is improved. RAID1 implements data redundancy through hard disk data mirroring to protect data security, and generates data for backup on two disks. When raw data is busy, data can be read directly

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from the mirrored backup, so RAID1 can provide read performance. RAID1 is the highest unit cost in the hard disk, but provides high data security and availability. When a hard disk fails, the system can automatically switch to read/write on the mirrored hard disk, and does not need to reconstruct the invalid data. (2) For the storage of cold data, the platform uses data compression and deduplication to increase the proportion of effective data storage. Cold data adopts association rules when it is stored, because unstructured data cannot be stored in the database as structured data is stored in two-dimensional tables. Therefore, the Apriori algorithm finds the relationship before the data and stores it. The principle is as follows: The storage data set I = {I1, I2, …, Im} is stored, where I1, I2, …, Im are each data items in the data set, and each transaction T is A subset of data set I, i.e. T  I,D is a set of transactional databases, D = {I1, I2, …, Im}, and D = {D1, D2, …, Dn} T, |D| is the number of transactions contained in D; for the association rule X => Y, where X,Y  I and X \ Y = ;. The support degree of X => Y represents the ratio of the number of transactions that satisfy the conditions X and Y at the same time to the total number of transaction data in the transaction data, and is denoted as sup (X => Y), and the expression thereof is as shown in the formula (3): supðX ) Y Þ ¼

countfðX ) Y ÞTg jD j

ð3Þ

The degree of confidence of the association rule indicates the probability of occurrence of transaction Y on the basis of the occurrence of transaction X, and is denoted as conf (X => Y). The minimum confidence of the association rule represents the minimum reliability that the association rule needs to satisfy, and its expression is expressed as (4) shows: conf ðX ) Y Þ ¼

supðX ) Y Þ supðXÞ

ð4Þ

If the rule meets: supðX ) Y Þ  supmin and conf ðX ) Y Þ  confmin

ð5Þ

We call this rule a strong association rule. You can add an index to data that has a strong association when it is stored so that it can be stored and searched. 4.2.3

Data Access Delay Storage Strategy

(1) Low-latency data storage strategy: High-end storage is adopted, and storage network SAN with optical interfaces provides highly scalable, high-performance network storage mechanisms with multiple 4Gbps connections. Fibre switches and fiber storage arrays also provide high throughput. Amount and larger server expansion space. Therefore, high-end storage devices are used to solve the storage problem of low-latency data.

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And in the storage of low-delay data, the platform also introduces entropy to quantify the reliability of the information generated by the resources in the storage strategy, namely the resource entropy, and introduces the entropy measurement in the general case. For a given data x, the platform has a finite set S of mutually exclusive state variables, and S = S1, S2, …, Sn. The corresponding probabilities of these states are P1, P2, P3, …, Pn respectively, then the entropy is defined as: Hx ¼ 

n X

pi lg pi

ð6Þ

i¼1

The high-end storage used by the platform can ensure that the information entropy in the storage strategy is small, thus ensuring the correctness of low-delay data storage. (2) High-latency data storage strategy: Optimizing open source HDFS to meet highlatency storage requirements HDFS is not suitable for applications that require low latency (tens of milliseconds) access because HDFS is designed for largethroughput data. This is at the cost of a certain delay [10]. HDFS is a single master. All requests for files go through it. When there are many requests, there will be delays. Using cache or multimaster design can reduce the client’s data request pressure to reduce latency. 4.2.4

Other Types of Data Storage Strategy

(1) Structured data storage strategy: The column storage structure is adopted, and the field values are sorted and stored according to lexicographic order. Different fields are respectively stored in different positions of the file, and a certain length of data is saved as a single file. (2) Semi-structured data storage strategy: Dynamic tree storage model. Based on depth-first traversal of the object exchange model OEM, and finding all the largest simple path expressions, the maximum path expression obtained by the cumulative counting principle is added to a dynamic tree in turn, thereby generating a storage model. Finally, this model is mapped to a relational table to implement storage and query of semi-structured data in a relational database.

5 Summary This paper analyzes the characteristics of power big data, and focuses on formulating different storage strategies for unstructured data in power big data, and formulates corresponding storage strategies for structured data and semi-structured data in power big data. Based on the distributed file system power big data storage platform, it provides technical support for data storage.

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References 1. Hao, W., Jian, C.: Status and challenges of smart grid big data processing technology. Electron. Technol. Softw. Eng. 1, 188–198 (2016) 2. Wei, L., Shuang, Z., Jiandong, K., et al.: Research on grid big data processing based on hadoop. Electron. Test. 1, 74–77 (2014) 3. Lizhen, C., Yuliang, S., Lei, L., et al.: Application of power big data storage and analysis for smart grid. Big Data 6, 42–54 (2017) 4. Shaoqi, Y.: Research and application analysis of large data storage and processing technology for power grid regulation and operation. Shaanxi Electric Power 11, 47–50 (2016) 5. Ming, L., Yan, C., Jia, W., et al.: Discussion on power data storage strategy based on distributed file system. Autom. Technol. Appl. 35(10), 70–75 (2016) 6. Chunping, Z., Zhicheng, Ma., Qi, Z., et al.: A solution for identity authentication of distributed file system in electric power enterprises. Autom. Technol. Appl. 36(3), 23–26 (2017) 7. Xiaodong, S: Research on Optimizing the Data Storage Performance of Small Files in Hadoop Distributed File System. Beijing Jiaotong University (2017) 8. Luo, Y., Tongliang, W., Genhua, H.: Research on unified backup system based on SAN. Commun. World 14, 42–43 (2017) 9. Thomasian, A., Tang, Y.: Performance, reliability, and performability of a hybrid RAID array and a comparison with traditional RAID1 arrays. Cluster Computing 15(3), 239–253 (2012) 10. Kaushik, R.T., Bhandarkar, M.: GREENHDFS: towards an energy-conserving, storageefficient, hybrid Hadoop compute cluster. In: International Conference on Power Aware Computing and Systems. USENIX Association, pp. 1–9 (2010)

Abnormal Detection Methods of Information Security for Power Big Data Pang Hao-Yuan1, Liu Fei1, Zhang Yi-Ying1(&), Cong Wang1, He Ye-Shen2, Li Xiang-Zhen3, and Liu Zhu4 1

College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300457, China [email protected] 2 China Gridcom Co., LTD., Shenzhen, Guangdong, China 3 Jiaxing University, Jiaxing, Zhejiang, China 4 State Grid Information and Telecommunication Group Co., LTD., Beijing, China

Abstract. Under the big data environment, hi-tech technologies bring great convenience and advantages to the operation of the power system, making the power supply company more convenient and efficient in the process of information processing and using. This paper mainly introduces the application characteristics of power big data, the challenges faced by the information security protection system, and the solutions provided for responding to the information security of big data. The feasibility of information security analysis technology for power big data is discussed by using data mining correlation analysis, sequence analysis methods, anomaly detection and hypothesis testing methods. Security protection technologies and management methods are elaborated in detail to provide guarantees for safe, reliable, economical and efficient operation of power grids. Keywords: Power big data


Data processing
Security methods

1 Introduction A new generation of IT technologies is springing up in the power industry, such as big data, cloud computing, Internet of Things, mobile Internet technologies. The comprehensive construction of intelligent power grids have led to a substantial increase in power data resources, forming a certain degree of scale. In order to cope with the growth of power data, it is imperative to promote the business of the State Grid Corporation of China, strengthen management capabilities, the management level, build a power security information security analysis framework, and improve information security assurance [1]. With the rapid development of power big data, various challenges have also been encountered. This paper systematically analyzes its security analysis methods and provides power companies with information security solutions to deal with big data.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 768–774, 2019. https://doi.org/10.1007/978-3-030-02804-6_100

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2 Application Characteristics of Power Big Data In general, although there is no unified definition for big data, its specific characteristics can be summarized into four characteristics: huge capacity, diverse data types, low value density, and fast processing speed [2]. Relying on these four characteristics, the application of power big data is mainly reflected in the business applications. It estimates the future development direction of the power grid. By selecting the power, space load, a number of indicators for correlation analysis, it could be budgeted and measured. The suitable planning and design could be chose after the comprehensive analysis. It conducts large overhaul in the environment of big data, uses video to monitor the substation, accurately identifies the positions, states, or readings of various meters, switches, and switches in real time, and intelligently analyzes video data. It Inquires about the power grid equipment ledger information, equipment topology information, equipment remote signal telemetering related information forecasting and analyzing the future status. It provides complete suggestions for equipment status management, providing auxiliary decision-making for power grid dispatch, promoting its large-scale operation. It expands for intelligentization and interactive service capabilities that are related to the collection, measurement, charging and service resources of electricity consumption. By this way, it help us get real-time feedback on electricity consumption and electricity purchase could. It is contributed to the market through diversified means and methods.

3 Power Big Data Risks and Challenges 3.1

Power Big Data Risk

Based on electric power big data environment, the rising of the industry, enterprise’s management level and economic benefits of increased, promoted the rapid increase, the quantity and the value of the data but also caused the data in the generation, transmission, processing, storage, interference factors in the application. For example, in the process of generation and transmission, there is a risk of transmission interruptions, malicious eavesdropping, forgery, and falsification. During the process of processing, storage, and application of data, users are authorized to read and write; leakage of sensitive information and privacy occurs; host physical failure and internal transportation. The risk of improper control measures, etc. In addition to the traditional security risks exemplified above, there are new security risks after the application of new technologies. Advanced Persistent Threat (APT) attacks (long-term stealing of data) is one of the new security risks. Its distinctive feature is its long duration. Attackers use various technologies to continuously attack protective devices. Explore and try to find a protective shield. It poses a great threat to the current application of new information technology systems, and also poses a challenge to the traditional information security protection system [3].

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Power Big Data Challenge

As society develops faster and faster, the company’s economic benefits have been significantly enhanced, the number of data has been increasing, the value is getting higher and higher, and the challenges it faces have increased. In conclusion, there are four challenges: data challenges, multiple data fusion challenges, data delivery challenges, and data storage challenges. (1) Data challenge: Power big data is produced with high-quality data accumulation. Some data deviations, incorrect representations can affect, and even change the quality of big data, and sometimes produce erroneous decisions. (2) The Challenge of Multiple Data Fusion: The key to power big data applications is to combine a series of data. We often use professional information systems as the core of information construction, making different professional data independent of each other. In order to solve this problem, it is necessary to combine multiple different professional data such as power generation, transmission, power transformation, power distribution, electricity use, and dispatch, and tap power big data services to power users to increase the value of their economic and social development. (3) The challenge of data and information transmission: The effective way of data value transmission has led to the visualization of power big data, which contains the laws and characteristics of electricity production and service economic and social development. It is generally more abstract and difficult to find. Big data visual analysis will make it easier to discover the laws of big data. It displays the characteristics and rules in massive data. It is convenient for the transmission of data values and the sharing of knowledge. (4) The challenge of data storage: We have a great way to store data for power big data. Power big data analyzes and processes the structured and unstructured data of multiple data sources. It needs to store large amounts of data and provide fast calculation capabilities. Distributed data storage and calculation is an effective way to solve the storage and calculation of power big data [4].

4 Power Big Data Information Security Framework In the era of power big data, it relies heavily on the authenticity, integrity, and reliability of data, transforms the data into information and knowledge, analyzes security risks in the application scenarios of power big data technologies, and maximizes the protection of power. The security of data and information will be the top priority for power grid companies. This paper uses the power big data information security analysis architecture as a solution based on big data security analysis to centralize, store, analyze, and visualize scattered security element information, study the results of the analysis, and schedule tasks for analysis. Various decentralized security analysis technologies are integrated to realize the interaction between various technologies [5]. Through further research on data

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security analysis of power big data information security analysis, the security analysis framework includes five levels: acquisition layer, data layer, analysis layer, management and control layer, and presentation layer, as shown in Fig. 1.

Fig. 1. Power big data information security framework

The five levels of the power big data information security framework complete the collection, preprocessing, storage, analysis and display of a large number of heterogeneous data, and uses correlation analysis, sequence analysis, online analysis and processing, machine learning, malicious code analysis, statistical analysis, etc. A variety of analysis methods comprehensively correlate data, complete data analysis and mining functions, and provide fast and efficient decision support for security analysts and managers.

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5 Power Big Data Information Security Framework Method Different databases can use different data mining analysis algorithms. For example, relational databases and transaction databases can use correlation analysis, sequence analysis, statistical analysis and other algorithms for data mining; data warehouse uses online analysis and processing and other data mining technologies; text databases use automatic aggregation. Data mining techniques such as class, machine learning, pattern matching, and data summarization; and multimedia data mining techniques such as clustering and association analysis. Therefore, this paper uses correlation analysis and sequence analysis as an example to model each index value in each hour according to the correlation of data flow, attack behavior characteristics and time. By Specifying the start and end time of the data, observing the length of the period, using the value of each index value at different times, quantifying the index data through correlation analysis, sequence analysis, calculate the mean and variance of each index value, we make it as the statistical model of the index [6]. By using abnormal detection and hypothesis testing, the abnormity and gradient abnormality of the attack events were detected and verified. 5.1

Abnormal Detection Method

The correlation analysis and sequence analysis algorithms of the application data mining technology detect the abnormal conditions of the benchmark indicators and find the data of the association rules. The Apriori algorithm of association analysis is used to extract the previous frequent item and iteratively generates this frequent item [7]. Then we use the sequence analysis algorithm to associate the correlation between the frequently-concentrated data and the time, and analyze the serial relationship between the data [8], that is to correlate the horizontal data of a record. And then we make a longitudinal arrangement comparison according to the time series. For example, there are association rules in different fields in the same audit record, and then we use sequence analysis to arrange different records in chronological order, so that the correlation between the real-time value of the benchmark index and the historical value under normal circumstances can be extracted. There should not be a big deviation. This means that the correlation index and sequence analysis are used to calculate the real-time value of the current benchmark index as s, and the index model parameter obtained during modeling is Nðl0 ; r0 Þ. Where l0 and r0 represent the mean value and variance of the normal distribution model, the abnormality detection formula is: 8 s  l0 \2:33r0 > > < 2:33r0 \\s  l0 \3:1r0 > 3:1r0 \\s  l0 \3:27r0 > : 3:72r0 \\s  l0

Normal indicators Mild abnormality Moderate abnormality Serious abnormality

ð1Þ

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The judgment thresholds 2.33, 3.1, and 3.72 correspond to the standard normal distributions of 0.01, 0.001, and 0.0001 decile points, respectively [9]. For the security event quantity indicator and the security event propagation indicator, the above judgment method is sufficient; for the address entropy index, it is necessary to judge the js  l0 j, and the address entropy is too large in real-time value (the IP address distribution is too scattered) or is too small (the IP address distribution is too large concentration) is an anomaly, but it is abnormal when other indicators are too large. 5.2

Hypothesis Testing Method

For some attacks that do not cause changes in the benchmark, it is difficult to detect abnormal safety status by means of anomaly detection. However, these attacks will cause the distribution of the indicators to be inconsistent with the distribution of the model. Therefore, hypothesis testing is used to assess the information security status by judging whether the index values still obey the original distribution. To achieve the purpose of active defense, we should forecast and prevent possible security conditions. Assuming that the statistical model parameter of a certain reference index S at the current moment is Nðl0 ; r0 Þ, where l0 , r0 represent the mean value and variance of the normal distribution model, and the current benchmark value is calculated by correlation analysis and sequence analysis. Then construct the U statistic as follows: U¼

s  l0 pffiffiffi n r0

ð2Þ

Where: s represents the average value of all the index values since the model parameters were updated; n represents the number of observations of the index values since the model parameters were updated. U-statistics for abnormality detection are: 8 U\2:33r0 > > < 2:33r0 \\U\3:1r0 3:1r0 \\U\3:27r0 > > : 3:27r0 \\U

Normal indicators Mild abnormality Moderate abnormality Serious abnormality

ð3Þ

The meaning of the threshold is the same as that of the anomaly detection. For the security event quantity index and the security event diffusion index, the above judgment method can be used [10]. For the address entropy index, it is necessary to use js  l0 j to further construct the bilateral statistics. Static information security attack events can be analyzed through anomaly detection methods; dynamic unpredictable information security attack events can be tested through hypothesis testing methods to achieve the purpose of active defense.

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6 Summary With the continuous improvement of big data technology, there are more and more methods for information security analysis of power big data. Comprehensive forecasting of power big data information security events is the result of comprehensive application of multiple analysis methods, and security analysis algorithms based on big data are also constantly improving. The security protection system under the new technology environment has been continuously improved. The data analysis algorithms have been comprehensively used and data sharing modes have been adopted, so that the information security situation assessment of power big data has been better realized.

References 1. Guo-Qi, L., Xiang, G., Xin, A.: Power big data information security. China Sci. Technol. Inf. 42(18), 111–113 (2017) 2. Jie, Z.: New challenges of big data to information security. Softw. Appl. 13, 163–164 (2014) 3. Qian, W., Hong-Feng, Z., Tian-Hua, L.: The status and development of big data security. Comput. Netw. 16, 66–69 (2013) 4. Yan, L.-C., Li, Y.-X., Li, B.-C., et al.: Opportunity and challenge of big data for the power industry. Electr. Power Inf. Technol. 11(4), 1–4 (2013) 5. Liang, C., Wei, H., Xiao-Yuan, C. et al.: Key technologies for large data applications in smart grids. In: Proceedings of the 2012 China Power Engineering Informatization Conference, pp 1–4 (2012) 6. Le, G.: Spatial data mining technology analysis and research. Keyuan 12, 46–47 (2009) 7. Ying-Jie, Z.: Traffic analysis and correlation of communication network based on behavior analysis. University of Electronic Science and Technology, Chengdu (2013) 8. Jian-Bo, Y.: Research on application of data mining technology in abnormal detection of network traffic. Huazhong University of Science and Technology, Wuhan (2006) 9. Shanghai Jiaotong University: Probability theory and mathematical statistics. Shanghai Jiaotong University Press, Shanghai (2002) 10. Zhi-Chen, Z.: Research on information security analysis technology of power big data. Power Inf. Commun. Technol. 13(9), 128–132 (2015)

Research on Data Center Topological Structure Analysis Technology Based on Graph Database Liang Zhu1,2(&), Mingjie Yang3, and He Wang1,2 1

2

Global Energy Interconnection Research Institute Co., Ltd., Nanjing, Jiangsu, China [email protected] State Grid Key Laboratory of Information and Network Security, Jiangsu, China 3 State Grid Gansu Electric Power Co., Ltd., Gansu, China

Abstract. The data center is the main object of operation and maintenance management. The accurate grasp of all the life cycle of all hardware and software resources and the accurate analysis of the relationship between the operation and maintenance data are the cornerstones of the data center IT architecture operation and maintenance, and also the key to the continuity of the data center to guarantee business continuity. The multi-layer topology self discovery mechanism for business characteristics is established according to the library. Through identification, control and maintenance, it can accurately manage the IT resources of power grid enterprises, realize the changing IT infrastructure of efficient control and management, meet the IT service, and respond quickly, efficiently, accurately and flexibly to the needs of the development of the Power Grid Corp business. Keywords: Data center


Graphic database
Topology structure

1 Introduction With the continuous advancement of information construction, the reliance of domestic companies on data centers is becoming more and more strong. More and more attention has been paid to the effective data center management, and the relationship between the topology and the levels of the latest data center IT architecture is critical to the operation and maintenance management. The traditional information network management system only aims at the topology of network devices, lacking the data center’s other hardware and software and business system topology information. In addition, the overall topology of the data center IT infrastructure and its change maintenance are only dependent or over dependent on the manual. Facing the huge volume, complex structure and dynamic characteristics of data centers, the artificial maintenance method can not reflect the overall topology of the data center architecture in real time. Therefore, it is very necessary to study the automatic discovery technology of multi tier topology structure for data center IT infrastructure [1]. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 775–781, 2019. https://doi.org/10.1007/978-3-030-02804-6_101

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The global energy Internet has natural network features, and its physical relations, customer relations and asset relations have formed a huge network topology. At the same time, a large number of power network operation data, state monitoring data and intelligent meter data have been produced on the basis of it. For the huge data systems under the above large-scale power grid environment, they face severe technical challenges in data management and power grid computing [2]. The traditional relational database management system has encountered serious technical bottlenecks in many aspects, such as storage size, query efficiency, scalability, and mass data management. It is difficult to overstep. In the analysis and calculation of giant power grid, the existing computation simulation of hybrid power flow in the AC and DC power grid, the power flow calculation and the multi scene preconceived fault analysis under the on-line condition, and the electromagnetic transient stability analysis and calculation are both faced with the dual challenge of the calculation scale and the calculation speed. Compared with the traditional relational database technology, graph database technology has great technical advantages in the application of network based data structure. It is mainly embodied in: 1. In system modeling and data management, a large number of invalid links can be removed, the required memory is smaller, the inquiry speed is faster, the update data is easier, the table is easier to be updated. The way of reaching is more intuitive; 2. Using a message driven distributed system architecture, the computing efficiency of power systems with more than one million or even hundreds of millions of nodes is greatly improved through a highly parallel algorithm. Using graph data management and computing technology, the natural network features of the power system can be fully explored, the calculation model is closely combined with the grid model, data storage and numerical calculation, and the core technology of large data fields, such as memory computing, distributed parallel computing, decomposition polymerization, is fully utilized, and the scale of data is calculated. Large, fast and efficient advantages, which provide a great potential solution for the data management and high speed analysis and calculation of the super large power grid and even the global energy Internet [3]. It is proved by preliminary experiment that the application of graph database technology can improve the speed of data retrieval of power grid system by 3–5 times, and increase the speed of the power flow calculation of millions of nodes by 1–2 orders of magnitude (10–100 times). This paper studies the self-discovery technology of the data center IT infrastructure resource object, and automatically finds out the network resources, the server resources and the application software resources, and establishes the relational model of the resource objects. Based on the graph database technology, the strong fit resource object has a large number of scenes, which automatically and efficiently displays the relationship between the IT infrastructure resources and resources, and finally realizes the automatic generation and display of the multi-layer topology, such as the network topology, physical topology and application topology of the data center. Seamless switching and automatic updating can be achieved between multi-layered topologies, providing different views of IT topology display for different roles and different business needs.

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2 Research on Automatic Discovery Technology of Data Center IT Infrastructure Multilayer Topology 2.1

Establishing IT Resource Object and Relational Model

By studying the design method of the IT infrastructure resource object model, according to the characteristics of the Power Grid Corp IT architecture, the subdivision strategy of resource objects is formulated, and the depth and breadth of the subdivision of the resource objects are clearly defined. According to the strategy of object segmentation, we study the relationship between resource objects and establish a resource object model with business as the core, suitable for business and granularity. But only the internal relationship between the data center is clear, and the operators can find the relevant entity resources accurately, and can quickly locate the source of the fault and the scope of its influence when the failure occurs, so that all kinds of hidden dangers can be solved quickly. In general, two methods can be used in combing the relationship [4]. From top to bottom, the enterprise usually requires the enterprise to clear the service directory provided first, and then based on the order of service directory, the order of “business service, IT service, IT system, IT component”. From bottom to top, it is upstream, first from the internal IT component relationship, and then gradually mapping the IT component to the IT service logic relationship. It can be understood that what middleware, database users, instances and table spaces are used in the business system, which operating system is running, and what IP addresses are used. The physical relationship can understand which PC server is installed by the business system, which port is connected to the network by the PC server, how the PC server connects to the storage, which cabinet and the machine room is stored by the PC server, and which circuit breaker and UPS power supply by the PC server. 2.2

Self-discovery Technology of Network Topology

The network topology discovery involves all levels of the network architecture. The principle of network layer topology discovery is to combine ICMP, ARP and SNMP to check the active equipment for the designated network, get all the active devices, and then obtain the basic information of the equipment through the SNMP, and determine the type of the equipment according to the basic information, and then root again According to the type of equipment, get the detailed information of the equipment. The principle of link layer topology discovery is to determine the connection between switches based on the cdp neighbor table, port if Index and port corresponding table, and self-learning table. The topology discovery principle of the routing layer is all the device nodes of Trace Route, and the routing topology relationship of the related devices can be obtained according to the returned routing path. There are many ways to discover network topology automatically, but there are three main types of network topology discovery: network topology discovery based on SNMP, network topology discovery method based on general protocol and network topology discovery method based on routing protocol [5].

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Based on the SNMP, ARP, ICMP and other mainstream protocols, the network topology automatic discovery algorithm, combined with the business characteristics of each company, designed the topology algorithm adapted to the data center IT infrastructure of the national network company. The realization of network topology self discovery involves different levels of network architecture, automatically capturing data center network and related equipment. Further integration of SSH, Telnet, JDBC, JMX, WMI, SNMP and other protocols, research data center hardware and software self discovery algorithm to realize the self discovery of soft and hard resources second level. Finally, combined with the resource object model, the data base IT multi layer topology structure is automatically generated [6]. (1) relational data structure: the relational model is represented by two dimensional tables. A two-dimensional table consists of a table frame and a tuple of the table. The table framework consists of multiple named table properties. Each attribute has a range of values called the range of values. Each row of data in a twodimensional table is called a tuple. (2) relationship manipulation: the data manipulation of the relational model is the data manipulation based on the relationship, which generally includes data query (basic unit is tuple component), data deletion (basic unit is tuple), data insertion (basic unit is tuple) and data modification (basic single bit is tuple component). (3) data constraints in relationships: data integrity constraints in relational models, referential integrity constraints and user integrity constraints on three data constraints (Fig. 1).

Configuration Discovery

Configuration Export Configuration Discovery

Configuration discovery extension package

Configuration discovery computer

Configure the self discovery engine

Configuration discovery script

ITinfrastructure

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Network Equipment

Fig. 1. IT infrastructure software and hardware resource configuration self-discovery schematic diagram

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3 Topological Structure Analysis Technology Based on Graph Database Technology 3.1

Graph Database Modeling

Graph database technology is a new model of computing and data management based on the theory of graph theory. It can realize distributed storage and parallel processing with massive data with complex relationships. Based on the function of data center topology analysis system based on graph database, the graph is used to express the relationship between IT resources and resources, including functional module design, performance index design, and communication mechanism. The technology of extracting data table structure data from traditional relational database or file system and the corresponding transformation into data point and side type data are studied [7]. Based on the second section of the data center IT infrastructure, the multi-layer topology automatic discovery technology is used to define the interaction between the “edge” and “point” and “point” of the graph database through the general modeling method of the graph data; for the database search functions, such as breadth search, depth search, path search, and data inspection for the database that needs to be supported eventually [8]. Cable and so on, complete the topology analysis and modeling of power grid data center based on graph database, and finally form a method of map database modeling for grid data center resource management, such as Fig. 2.

Support Function

Mathematical Modeling

Requirement Analysis

Breadth search Depth search

The definition of pointEntity ObjectAssociation attributes

Topology analysis

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The definition of the edgeEntity objectAssociation attributes

asset management

data retrieval

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system operation

Fig. 2. Research on modeling method of graph database for topology of power grid data center

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Key Technology of Data Center Topology Data Storage and Query

This part studies the key technologies to support the storage and querying of topological data in large-scale power grid data center, and the research route is shown in Fig. 3. First, we study the description of structured, semi-structured and unstructured data resources including RDF markup, TLGM data model and key-value, and study the implementation of the data model storage in the distributed environment [9]. On this basis, the segmentation algorithm, index algorithm and storage method of graph data are studied, and then the algorithm of restoring the original map data through subgraph reconstruction and subgraph merging is studied, and the load balancing of data nodes is considered to complete the efficient query and analysis of the multi type data of the large-scale grid data Center topology.

Graph data Descrip tion

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RDFMarkup frame Resou rce definit ion

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Heuristic Graph Segmentation Depth first BFS algorithm

KL/FM algorithm

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Sem antic Web

XML Langua ge represe ntation

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Fig. 3. Research on key technology of topology data representation storage

In the study of the pattern of graph resource description, the data classification will be carried out according to the characteristics of the power grid data center and the application situation. It is divided into: the data object has less attribute and the low density graph which is less connected among the objects; the data object property is more, but the interphase between the objects is independent, that is, the middle density of the ring is not included in the graph [10]. Graph; almost all data objects are located on one or more rings with large number of objects whose attributes are large. RDF resource description framework, TLGM data model and XML language are used to formalize the relevance feature information of large-scale graphs respectively. Then we study the segmentation and index technology of graph data, and use the BFS and KL/FM algorithm to divide the graph by heuristic method, use the Hash + feature tree to index the graph, and use the key-value database to store the graph data in the Hadoop framework.

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4 Summary This paper uses a new database to deal with the management of the power grid data center system, which is different from the usual relational database. The advantage of the graph database in dealing with the massive complex data is obvious. At present, the research at home and abroad is weak. The modeling method of graph database will study the modeling of massive and complex related data in the power grid, and give full play to the advantages of the data processing and analysis of the database, and fully reflect the characteristics of the network of the resource data of the power grid data center. Acknowledgements. First of all, I want to show my most gratitude to my colleague, Gang Wang, who is a respectable, responsible and resourceful research partners, and also he provided me with many meaningful guidance in every stage of the writing of this thesis. Without his assistance, deepest kindness and patience, I would never completed this thesis. Also I shall express my thanks to Mr. Peng for all her kindness help. I would also like to thank all my colleagues who have helped me to develop the essential academic competence. This work was financially supported by the science and technology project to State Grid Corporation’ Research on Key Technologies of Data Center IT Infrastructure Dynamic Perception and Operation Optimization’.

References 1. Flanagan, D.: Developing Metaweb-Enabled Web Applications. Metaweb Technologies, San Francisco (2007) 2. Bunke, H., Vento, M.: Benchmarking of graph matching (1999) 3. Schmidt, D.C., Druffel, L.E.: A fast backtracking (1976) 4. Ullmann, J.R.: An algorithm for subgraph isomorphism. J. ACM 23, 31–42 (1976) 5. Messmer, B.T., Bunke, H.: A decision tree approach to graph and subgraph isomorphism detection. Pattern Recogn. 32(12), 1979–1998 (1999) 6. Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: An improved algorithm for matching large graphs (2001) 7. Bunke, H., Vento, M.: Benchmarking of graph matching algorithms. In: Proceedings of the 2nd Workshop on Graph – Based Representations (1999) 8. Wikipedia (n.d.).: Wikipedia: The Free Encyclopedia. http://www.wikipedia.org 9. Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284, 34–43 (2001) 10. Bollacker, K., Tufts, P., Pierce, T., Cook, R.: A platform for scalable, collaborative, structured information integration. In: Sixth International Workshop on Information Integration on the Web. Association for the Advancement of Artificial Intelligence, July 2007

A Survey of Code Reuse Attack and Defense Bingbing Luo(&), Yimin Yang, Changhe Zhang, Yi Wang, and Baoying Zhang ZhangJiaKou Electric Power Co., Ltd., Zhangjiakou 075000, China [email protected]

Abstract. Code reuse attack is a devastating way of attack. It has great threat and can bypass many kinds of existing security measures and become the mainstream attack mode of attackers. For this reason, research in the field of code reuse attacks is also increasing. This paper briefly describes the origin of code reuse attacks, the way to attack the implementation, systematically summarizes the existing defense mechanisms and evaluates these defense mechanisms. The basic reasons for the code reuse attack are briefly analyzed. Keywords: Code reuse attacks


ROP
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1 Traditional Code Reuse Attacks Code reuse attack is an attack that an attacker can rearrange the program code sequence to form a malicious code fragment. Then the program control flow is transferred to the malicious code fragment to achieve the attacker’s purpose of destroying the system or stealing information. For more than 20 years, code reuse attack is still one of the most common attacks [1]. The earliest code reuse attack is to return the function library attack (RILC) [2, 3]. In the RILC attack, the attacker tamper with the control flow of the program, and make it point to the existing standard library functions in the program. In this way, the attacker can call the system() function to generate a new process, or by calling the mprotect() function to create a writable and executable memory area for bypassing the system W^X defense policy. After such attack method is improved, the malicious code fragment can complete more complex works in the program. However, the impact of this attack is relatively small because the function to be used by the RILC attack is very little in the standard library. In addition, existing defense methods such as Libsafe [4], can effectively defend the attack by replacing the system() or mprotect() function with a specific library function. In 2007, Shacham extended the idea of RILC, and proposed a new attack technology, called Return Oriented Programming (ROP) [5]. In ROP attack, the attacker can search for some short code fragments ended with the ret instruction in the vulnerability program. These short code fragments are called gadget. The address of the first instruction in these gadgets is put into the stack through the memory disclosure vulnerability, and then the control flow of the vulnerability program points to the top of the stack. Thus, the address of the first gadget is extracted to execute at runtime. After the first gadget is executed, the vulnerability

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program will execute the last ret instruction of the gadget. Hence, the address of next gadget in the stack is extracted to execute. According to this method, the vulnerability program will execute all the gadgets and achieve the purpose of the attacker. As shown in Fig. 1.

Fig. 1. Rop attack principle

2 Sophisticated Code Reuse Attacks Compared with the RILC attack, ROP attack doesn’t require the standard library function because there are a lot of code fragments ended with the ret instruction in the program. So the attacker can easily construct an attack payload and make the attack very easy to be implemented. In 2012, Roemer et al. proved that the ROP attack is Turing-complete, meaning that the attacker can perform malicious computations arbitrarily [6]. And because of the characteristics of the ROP attack, the attack ideas have been applied to many architectures, for example, Intel x86 [5], SPARC [7], Atmel AVR [8], ARM [9, 10] and PowerPC [11]. However, because the ROP uses the gadget ended in ret instruction to construct the attack payload, the attack is easily detected. In 2009 and 2010, Francillon and Davi et al. detected the ROP attacks from hardware and software by using this feature [12, 13]. Also in 2009, Chen and Davi et al. detected ROP attacks by detecting the presence of multiple ret instruction sequences [14, 15]. To make up for these defects of ROP attacks, researchers made a lot of improvements on ROP technology. Such as in 2010, Checkoway et al. replaced the original ret instruction with the pop-jmp instruction sequence because the effect of the pop-jmp instruction sequence is the same as the ret instruction [16]. In 2011, Bletsch et al. sought to construct an attack payload ended in jmp instruction sequence. This attack technique is called Jump-Oriented Programming (JOP) [17]. In 2013, Snow et al. exploited memory disclosure to map a program’s

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address space dynamically and find some gadgets. This attack method is called Just-InTime Return Oriented Programming (JIT-ROP) [18]. In 2014, Carlini et al. thought that the target must be a call-preceded instruction when the ret instruction was executed. According to this feature, they proposed an attack method called Call-Preceded ROP. This method makes ROP attacks very common [19]. In February 2015, Davi et al. proposed an improved JIT-ROP method. This method allows an attacker to map the entire address space of a program from the heap or stack, rather than a code pointer in the code segment [20]. In the past the attackers were concerned about the code segment of the program where there are executable instructions. In May 2015, Schuster et al. proposed a code reuse attack called COOP (Counterfeit Object-Oriented Programming) for the C++ programming language [21]. In the method, an attacker can map the address space of the program by hijacking the virtual function of a C++ program, and then construct the gadgets to implement an attack.

3 Defense Methods of Existing Code Reuse Attacks and Deficiencies For these sophisticated attacks, the researchers proposed many new defense measures against them. One of the research hotspots is Address Space Layout Randomization (ASLR) and its improvement. ASLR is a technique that can randomize a program’s address space. In order to prevent an attacker from guessing a program’s address space correctly, ASLR randomly rearranges the address space positions of the program when the program is loaded into the memory [22]. But there are two main drawbacks in the conventional ASLR technology. First, the entropy of the 32-bit system is too low to provide sufficient starting addresses [18]. This makes the technology vulnerable to brute-force attack [23, 24]. Second, because the address space of the program is only randomized one time at load, all ASLR solutions are vulnerable to memory disclosure attacks [25, 26]. In view of the shortage of the conventional ASLR technology, the researchers put forward a lot of improvements. Kil et al. presented a method called Address Space Layout Permutation (ASLP) [27]. ASLP reordered all the functions and data objects to achieve the purpose of the application random by rewriting the ELF file statically. However, the disadvantage of ASLP is that it requires all of the library functions’ relocation information that is not always available for an application. Pappas et al. present a method called In-place code randomization (ICR) [28]. The method can generate a random order for the program by reordering or changing the instruction, or exchanging register contents. Hiser et al. proposed a method called Instruction Location Randomization (ILR) [29]. ILR can statically randomize every instruction in program’s address space. Wartell et al. used a binary rewriting tool called STIR (Instruction Relocation Self-Transforming) to rearrange the basic blocks when the program is loaded into the memory [30]. The drawback of these three methods may lead to a high performance overhead due to modifying instruction. Giuffrida et al. proposed a real-time kernel randomization scheme that was implemented on the MINIX 3 operating system based on micro kernel structure [31]. This scheme is called ASR (Address Space Randomization). In this scheme, the module of the program is again randomized and the whole layout of the program is changed when the kernel

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program runs a specified time. However, this kind of real-time randomization not only generates a great performance overhead, but also applies only to the kernel program and the operating system of the micro kernel structure. Recently, in 2014 and 2015, many researchers are presented with new improved randomization scheme, such as Oxymoron [32], Readactor [33], ASLR-GUARD [34], SeCage [35], Isomeron [36], and TASR [37]. In Oxymoron, the address of the call and jump instruction is identified by a unique index, and the corresponding relationship between the address and index is stored in the RaTTle (Translation Table Randomization-agnostic) table. When the instruction is executed, the program uses this unique identifier in the RaTTle table to find the address of call or jump instruction. At the same time, the RaTTle table is not visible to users and attackers. In Readactor, a new compiler-based code generation paradigm is presented. The code generation paradigm exploits the characteristics of the CPU to make execute-only memory possible and hides code pointers from disclosure to the attacker. In ASLR-GUARD, The main idea is to make the leakage of data pointer unavailable by separating code and data. In this way, ASLR-GUARD can either thwart code pointer leaks or make the leaks harmless. In SeCage, according to the idea of separating control and data, it exploits the VMFUNC mechanism to yield different memory view for different compartments. Isomeron is a strategy to choose one between program’s two execution paths randomly. Two copies of a program are placed in different locations in the address space before the program starts running. Thus, the program uses the method of tossing a coin to randomly select a copy to execute the program in order to implement the randomization at runtime. TASR is a real time randomization method and can rerandomize the program’s address space when there is an output from the program, such as a file write and a console write. These methods can effectively prevent code reuse attacks. However, some defense mechanisms use memory execute-only (XOM) approach, such as Readactor. This approach can prevent programs from reading executable memory. But one challenge in using this approach is that legacy binaries and compilers often scatter code and data in executable memory pages. Thus, it is difficult to actualize the executable memory at page granularity [38]. More importantly, the separation of data and code has been proved to be undecidable [39]. For the Isomeron, It’s obvious that it is a coarse-grained solution. The attacker has still the probability of 50% to guess the path of execution correctly and implement the ROP attacks. For the TASR, it has some limitations: (1) it can only protect compiled binary programs rather than interpreted code, and this makes it not to prevent the JITROP attack; (2) it can’t automatically deal with the code that isn’t compliant with the C Standard and this limits its use; (3) it uses a custom memory allocator that demands the manual addition of the allocator signature into the compilation process. Thus, TASR’s memory allocation must exploit the sizeof() operator and this makes it less flexible; (4) it doesn’t prevent data-only attacks or attacks by using relative addressing. Nevertheless, it is still a critical contribution to prevent code reuse attacks at runtime. There is also a novel defense mechanism. That is Heisenbyte [38]. The Heisenbyte’s concept is destructive code reads. This concept makes code be garbled right after it is read. Garbling the code can deprive the attacker of the ability to exploit memory disclosure vulnerability to carry out code reuse attack. However, its average performance is 16.5% due to the destructive code reads.

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4 Analysis and Conclusion We can draw a conclusion by the review of the attack mode and defense mechanism of the code reuse attack: that is, an attacker must know the address space layout of the program in order to implement the code reuse attack. Consequently, researchers are trying to randomize the address space layout of the program, or to prevent the attacker from knowing the address space layout. As described in the literature [18, 22–38]. In fact, we can think about this issue in another way. We allow the attacker to know the address space layout of the program, but we make the address space layout change constantly; so that the attacker can’t know what the next address space layout is what. That is, this time is A, the next time may be B or other. Based on this concept, a more practical address space randomization approach will be presented to mitigate code reuse attack. This approach can instantaneously and continually randomize the program’s address space at runtime. In this way, an attacker can’t guess the true code address in the program’s address space and the attack will not be implemented successfully. To demonstrate the effectiveness and efficiency of the method, in future a prototype system will be developed. This prototype can randomize the program’s address space layout at runtime. After the program’s address space is randomized, the program still can continue to run correctly. Compared with ASLR, ASLP, ICR, ILR, STIR, Oxymoron, Readactor, ASLR-GUARD and SeCage, this prototype can continually apply randomization to the address space at runtime and doesn’t require additional support, such as modifying the compiler and dynamic linker. Compared with ASR and TASR, this prototype can be applied to any type of program, whether it is an interpreted code, a precompiled binary program, or a kernel program. So it can prevent the JIT-ROP attack. Compared with Isomeron, this prototype can instantaneously and incessantly randomize the address space and the probability that the attacker correctly guesses the address is almost 0.

References 1. Common Weakness Enumeration—Top Software Vulnerabilities. http://cwe.mitre.org/ top25/index.html 2. Nergal, : The advanced return-into-lib(c) exploits: PaX case study. Phrack Mag. 11, 4–14 (2001) 3. Designer, S.: Getting around non-executable stack (and fix) (1997). http://seclists.org/ bugtraq/1997/Aug/63 4. Libsafe (2002) http://www.lst.de/*okir/blackhats/node17.html 5. Shacham, H.: The geometry of innocent flesh on the bone: return-into-libc without function calls (on the x86). In: ACM SIGSAC Conference on Computer and Communications Security CCS (2007) 6. Roemer, R., Buchanan, E., Shacham, H., Savage, S.: Return-oriented programming: systems, languages, and applications. ACM Trans. Inf. Syst. Secur. 15, 2 (2012) 7. Buchanan, E., Roemer, R., Shacham, H., Savage, S.: When good instructions go bad: generalizing returnoriented programming to RISC. In: ACM SIGSAC Conference on Computer and Communications Security CCS (2008)

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8. Francillon, A., Castelluccia, C.: Code injection attacks on harvard-architecture devices. In: ACM SIGSAC Conference on Computer and Communications Security CCS (2008) 9. Iozzo, V., Miller, C.: Fun and games with Mac OS X and iPhone payloads. Black Hat Europe (2009) 10. Kornau, T.: Return oriented programming for the ARM architecture. Master’s thesis, Ruhr-University Bochum (2009) 11. Lindner, F.: Cisco IOS router exploitation. Black Hat USA (2009) 12. Francillon, A., Perito, D., Castelluccia, C.: Defending embedded systems against control flow attacks. In: 1st ACM WORKSHOP on Secure Execution of Untrusted Code, SecuCode 2009 (2009) 13. Davi, L., Sadeghi, A.R., Winandy, M.: ROPdefender: a detection tool to defend against return-oriented programing attacks. Technical Report TR-2010-001 (2010) 14. Chen, P., Xiao, H., Shen, X., et al.: DROP: detecting return oriented programming mallicious code. In: The Proceedings of the International Conference on Information Systems Security ICISS (2009) 15. Davi, L., Sadeghi, A.R., Winandy, M.: Dynamic integrity measurement and attestation: towards defense against return-oriented programming attacks. In: The Proceedings of the 2009 ACM Workshop on Scalable Trusted Computing, ACM STC (2009) 16. Checkoway, S., Davi, L., Dmitrienko, A., et al.: Return-oriented programming without returns. In: ACM SIGSAC Conference on Computer and Communications Security CCS (2010) 17. Bletsch, T.K., Jiang, X., Freeh, V.W., Liang, Z.: Jump-oriented programming: a new class of code-reuse attack. In: 6th ACM Symposium on Information, computer and communications Security ASIACCS (2011) 18. Snow, K.Z., Monrose, F., Davi, L., Dmitrienko, A., Liebchen, C., Sadeghi, A.R.: Just-intime code reuse: on the effectiveness of fine-grained address space layout randomization. In: 34th IEEE symposium on security and privacy S&P (2013) 19. Carlini, N., Wagner, D.: ROP is still dangerous: breaking modern defenses. In: 23rd USENIX Security Symposium (2014) 20. Davi, L., Liebchen, C., Sadeghi, A.R., Snow, K.Z., Monrose, F.: Isomeron: code randomization resilient to (just-in-time) return-oriented programming. In: 22nd annual network and distributed system security symposium, NDSS (2015) 21. Schuster, F., Tendyck, T., Liebchen, C., Davi, L., Sadeghi, A.R., Holz, T.: Counterfeit object-oriented programming: on the difficulty of preventing code reuse attacks in C++ applications. In: 36th IEEE Symposium on Security and Privacy, S&P (2015) 22. PAX TEAM: PaX Address Space Layout Randomization (ASLR). http://pax.grsecurity.net/ docs/aslr.txt 23. Liu, L., Han, J., Gao, D., Jing, J., Zha, D.: Launching return-oriented programming attacks against randomized relocatable executables. In: IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE TrustCom (2011) 24. Shacham, H., Jin Goh, E., Modadugu, N., Pfaff, B., Boneh, D.: On the effectiveness of addressspace randomization. In: ACM SIGSAC Conference on Computer and Communications Security CCS (2004) 25. Serna, F.J.: The info leak era on software exploitation. Black Hat USA (2012) 26. Sotirov, A., Dowd, M.: Bypassing browser memory protections in Windows Vista (2008). http://www.phreedom.org/research/bypassing-browser-memory-protections/bypassingbrowser-memory-protections.pdf 27. Kil, C., Jun, J., Bookholt, C., Xu, J., Ning, P.: Address space layout permutation (ASLP): towards fine-grained randomization of commodity software. In: 22nd Annual Computer Security Applications Conference ACSAC (2006)

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28. Pappas, V., Polychronakis, M., Keromytis, A.D.: Smashing the gadgets: hindering returnoriented programming using in-place code randomization. In: 33rd IEEE Symposium on Security and Privacy S&P (2012) 29. Hiser, J.D., Nguyen-Tuong, A., Co, M., Hall, M., Davidson, J.W.: ILR: Where’d my gadgets go? In: 33rd IEEE Symposium on Security and Privacy S&P (2012) 30. Wartell, R., Mohan, V., Hamlen, K.W., Lin, Z.: Binary stirring: self-randomizing instruction addresses of legacy x86 binary code. In: ACM SIGSAC Conference on Computer and Communications Security CCS (2012) 31. Giuffrida, C., Kuijsten, A., Tanenbaum, A.S.: Enhanced operating system security through efficient and fine-grained address space randomization. In: 21st USENIX Security Symposium (2012) 32. Backes, M., Nurnberger, S.: Oxymoron: making fine-grained memory randomization practical by allowing code sharing. In: 23rd USENIX Security Symposium (2014) 33. Crane, S., Liebchen, C., Homescu, A., Davi, L., et al.: Readactor: practical code randomization resilient to memory disclosure. In: 36th IEEE Symposium on Security and Privacy S&P (2015) 34. Lu, K., Song, C., Lee, B., Chung, S.P., Kim, T., Lee, W.: ASLR-Guard: stopping address space leakage for code reuse attacks. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015) 35. Liu, Y., Zhou, T., Chen, K., Chen, H., Xia, Y.: Thwarting memory disclosure with efficient hypervisor-enforced intra-domain isolation. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015) 36. Davi, L., Liebchen, C., Sadeghi, A.-R., Snow, K.Z., Monrose, F.: Isomeron: code randomization resilient to (just-in-time) return-oriented programming. In: Proceedings of the 22nd Network and Distributed Systems Security Sym (NDSS) (2015) 37. Bigelow, D., Hobson, T., Rudd, R., Streilein, W., Okhravi, H.: Timely rerandomization for mitigating memory disclosures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015) 38. Tang, A., Sethumadhavan, S., Stolfo, S.: Heisenbyte: thwarting memory disclosure attacks using destructive code reads. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015) 39. Wartell, R., Zhou, Y., Hamlen, K.W., Kantarcioglu, M., Thuraisingham, B.: Differentiating code from data in x86 binaries. In: Machine Learning and Knowledge Discovery in Databases, pp. 522–536. Springer (2011)

A Survey of Vulnerability Defense Measures Yang Gao(&), Jing Lou, Changhe Zhang, Hai Wang, and Yijuan Yan ZhangJiaKou Electric Power Co., Ltd., Zhangjiakou 075000, China [email protected]

Abstract. Vulnerability is the main cause of the attack. This paper introduces the main vulnerability detection methods and defense methods. Furthermore, two main methods of vulnerability defense are analyzed: program diversity and control flow integrity. Finally, this paper also introduces the future research direction. Keywords: Vulnerability


Control flow integrity
Program diversity

1 Mitigation Technology of Preventing Attacks Vulnerability Mitigation technology provides solutions to prevent the vulnerabilities from being exploited. In order to illustrate it, mitigation technology is divided into vulnerability detection technology and prevention vulnerability-being-exploited technology. But we declare that there is no clear dividing line between the two concepts and they are not opposite. In fact, vulnerability detection is also to prevent the vulnerability from being exploited. 1.1

Vulnerability Detection Technology

Vulnerability detection will start from detecting errors in the program. The detection process may be static (this moment the program is non-operational state), and the detection object is the program source code or binary code; It may be also dynamic (the program is operational state), and the detection object is the running program and untrusted external data, then we use the reproducible method to determine the vulnerability location in the program. 1.1.1 Static Vulnerability Detection Technology Program vulnerability static detection is a process to find the code snippets that conform to the vulnerability feature by scanning the program source code or binary code. The first task of static vulnerability detection is to establish the corresponding vulnerability feature library according to the features of various vulnerabilities, such as buffer overflow vulnerability feature library, format string vulnerability feature library and integer vulnerability feature library, then according to the established feature library, the program is statically analyzed to detect vulnerabilities in the program. The detection workflow includes the following steps: the program source code model creation, the vulnerability feature library, vulnerability determination and final results analysis. Detection technology includes data-flow analysis and symbolic execution etc. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 789–796, 2019. https://doi.org/10.1007/978-3-030-02804-6_103

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The main detection tools are Fortify SCA [1], Coverity Prevent [2], KLEE [3] and so on. Fortify SCA is a static vulnerability detection tool for the program source code and identifies vulnerabilities in the program from the source code level by analyzing the various paths that the program may execute and provides a complete analysis report of the vulnerabilities that have been identified. In the process of detection, Fortify SCA firstly transforms the source code of the program to an intermediate expression, then uses the five built-in analysis engines (data-flow engine, semantic engine, structure engine, control-flow engine, configuration engine) and the related vulnerability feature libraries to perform a static analysis of the intermediate expressions in the program, thus mines the code snippets in the program that match the vulnerability features (such as buffer overflow vulnerability) and determines the location of the vulnerability with the help of manual analysis. Coverity Prevent, developed by Coverity company, is a source code static vulnerability detection tool, it initially derived from the xGCC system developed by Engler and his students. It uses the basic analysis model that the program vulnerability is established as a state machine model. The state in the state machine model represents the security-related property of the data in the program. In the analysis process, Analysis engine of Coverity Prevent statically traverses the various execution paths of the program and drives the state machine to run continuously according to the semantics of the current running statement. When the program state is in a pre-defined non-security state, it assumes that a suspected vulnerability is detected, then the analysis engine will report the information including vulnerability features and trigger path ect. to the user, and the user finally confirms the vulnerability with the help of vulnerability feature library. KLEE, developed by Daniel Dunbar et al from Stanford University, uses the symbolic execution technology to construct a variety of different test cases to detect vulnerabilities in C language program of Linux system. When KLEE detects vulnerabilities, firstly it constructs a variety of different test cases as symbol parameters to be put into the program. Then, by using symbolic execution and constraint solving technology, it analyzes the range of symbolic parameters for the program key points and checks whether the range of symbolic parameters are within the security range as specified. If the range of the symbolic parameters can’t meet the pre-defined security rules, there are vulnerabilities in the program. 1.1.2 Dynamic Vulnerability Detection Technology Dynamic vulnerability detection technology detects the vulnerabilities that exist in the program through running a program in a real or virtual environment, and records the track while the program is running, then parses the runtime information including the data operation and calling relationship between functions to detect the vulnerabilities in the program. Representative technologies of dynamic vulnerability detection include fuzzing testing technology and dynamic taint analysis technology. Fuzzing testing technology was initially called random testing, later it was called fuzzing testing [4] in the classroom teaching by profession B. Miller. Fuzzing testing basic idea is: to provide the to-be-tested program with a large amount of specially constructed data or random data as an program’s input, then monitor the abnormal situation while program is running and record the input data that cause the abnormal situation, and locate the vulnerability position in the program with the help of manual analysis. Typical tool for fuzzing testing technology is SPIKE fuzzing testing framework released by D.

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Aitel et al. in 2002. SPIKE fuzzing testing framework realizes a block-based testing method to test the network-based application programs. SPIKE not only generates random data as the program’s input, but also contains a library with a variety of special data that can make the program with errors malfunction and trigger the vulnerabilities that exist in the program. In addition, SPIKE framework also has some built-in predefined functions that can help to generate common data in various formats as the program’s inputs. Dynamic taint analysis technology is a dynamic vulnerability analysis technology that gradually popularize in recent years. It monitors real-time data flow and control flow to track and detect the memory data dissemination process while the program is running and finds the vulnerabilities that exist in the program. Dynamic taint analysis technology is divided into analysis based on data flow and analysis based on control flow. Analysis based on data flow detects the vulnerabilities in the program mainly by marking the external data as tainted data and tracking the dissemination process of these tainted data in memory, the main detection tools are TaintCheck [5] and Flayer. Dynamic taint analysis based on control flow is a powerful supplement to analysis based on data flow. On the basis of marking external data as tainted data and tracking tainted data dissemination process, it creates control flow graph (CFG) [6] of the program by analyzing the program control flow and designs a special algorithm to realize the dynamic monitoring and analysis of the implicit tainted data dissemination process, then it finds the vulnerabilities. The representative tool of dynamic taint analysis technology based on control flow is Dytan. 1.2

Vulnerability Prevention Technology

For static vulnerability detection or dynamic vulnerability detection, when the program runs actually, there are many vulnerabilities that can be exploited by an attacker, which shows from another side that vulnerability detection is not perfect and can’t solve all problems. Moreover, the vulnerability detection technology itself has certain limitation. For example, the symbolic execution technology has a problem of path explosion, so it’s impossible to cover all paths during the detection process and there are some false negatives. Fuzzing testing technology requires to construct a large amount of heterogeneous data to trigger vulnerabilities that exist in the program, virtually it’s impossible to completely construct all heterogeneous data to trigger the vulnerabilities in the program, likewise there are false negatives. Besides, the performance issue in the process of vulnerability dynamic detection also bothers the actual deployment of these technologies. Therefore, many researchers focus their attention on preventing vulnerabilities from being exploited in the actual program operation process. Vulnerability prevention technology means that some strategies and measures are used to prevent the vulnerability from being exploited to implement the attack during the program operation process. It should be noted that the so-called program operation process includes the following steps: compile, link, load and run. Preventing the vulnerability exploitation in the program operation process has some advantages that vulnerability detection doesn’t have. Firstly, this process is an actual operation process, which will cause a lot of unexpected problems. These problems that have been exposed make vulnerability prevention technology become more complete and practical. Secondly, the real operation process can detect the actual performance consumption when

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vulnerability prevention technology works and provide different approaches for the continuous optimization of vulnerability prevent technology. Finally, the real operation process can also detect the compatibility capability of vulnerability prevent technology and make it suitable for multiple platforms and different environments. At current time vulnerability prevention technology during the program running process mainly includes the technology based on program control flow integrity and technology based on program diversity. 1.2.1 Technology Based on Program Control Flow Integrity Control flow integrity (CFI) [7] requires the program to run strictly in accordance with the control flow graph of the program, and it does not allow any deviation from the control flow graph. If the execution process deviates from the control flow graph, it can be inferred that the program has been attacked and abnormal situation occurs. The main approach to realize program control flow integrity is to create a complete program control flow graph, then to analyze the program control flow graph to monitor the direct and indirect jump instructions to observe if there is any abnormal jump phenomenon when the program is running. Control flow integrity can be divided into fine-grained type and coarse-grained type. Fine-grained control flow integrity requires the strict control of the destination of every jump instruction and does not allow any deviation from the specified execution process. Coarse-grained control flow integrity is relatively loose, it checks the jump instructions that have been categorized into one class in order to reduce the program performance overhead, but this method will lead to lower security. In theory, the control flow integrity is a deterministic method of preventing attacks, as long as the program execution process deviates from the predetermined orbit, we can determine that the program has been attacked. However, there are many challenges in the process of preventing attacks implementation by using control flow integrity: (1) Obtaining a complete program control flow graph is a difficult issue. The operation process of program is a process of continuous judging and jump. For the program with simple calculation, the relationship between judging and jump is simple and the complete control flow graph can be obtained statically by analyzing the source code. For those complex and huge program, the control flow graph obtained statically is not complete and can’t reflect the real operation process of the program. In the operation process of the program, analyzing and obtaining the complete program control flow graph requires to consider all the matters that occur in the operation process of the program, and the performance consumption is large, all these problems bring great challenges to get a complete control flow graph. Thus, fine-grained control flow integrity technology is almost impossible to achieve because of the absence of complete control flow graph; (2) Because it’s hard to get the complete control flow graph, coarse-grained control flow integrity uses a simplified and low accurate coarse control flow graph. Although this compromise scheme somewhat alleviates performance consumption, because the ability of preventing attacks is not very strong and has been bypassed by many attacks. In this case, the majority of researchers and academic groups have turned their research interest to the technology of program diversity. Compared with control flow integrity, program diversity is a probabilistic method to prevent attacks. By changing the program memory space layout multiple times, the attacker can’t obtain the exact attack information by exploiting the vulnerabilities in the

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program, or the obtained information becomes worthless because of the changes of the memory space layout to prevent exploiting the vulnerabilities to implementing the attack. Realizing program diversity has a lower threshold, what’s more the mature and practical technology has been widely used. Therefore, this article will mainly focus on program diversity to illustrate vulnerability prevention technology. 1.2.2 Technology Based on Program Diversity Program diversity derives from the theory of program evolution. In cryptography, C.E. Shannon [8] proposed two basic ideas of designing the cryptography system: diffusion and confusion. The basic purpose of these two ideas is to raise the difficulty of cryptanalysis so that the cost of cryptanalysis is far greater than the benefits of the successful attack and prevent the attack on the cryptograph. These two ideas are also applied in the theory of program evolution. By implementing measures such as instruction substitution, instruction reordering, variable substitution, garbage code insertion and encryption of instruction coding to add a large amount of disturbing information in the program so that the program appears in different forms and increases the difficulty of attacking. Program diversity is a further concrete implementation of the theory of program evolution. Program diversity means that by some means the components of the program such as instructions, basic blocks and functions show different states during the program operation, thereby increasing the difficulty of the attacker’s attack and preventing the attack. Program diversity is a probabilistic method to prevent the exploitation of the vulnerabilities. By changing the state of the program’s components, the attacker cannot implement the attack because the information about the program obtained previously by some means (such as exploiting memory corruption vulnerability and format string vulnerability) is uncertain. In the process of implementing diversity to the program, an important principle is that for the source program and the program of implementing diversity, the same input must cause the same output. That is, diversity should not damage the semantic information of a program. Program diversity is divided into instruction level, basic block level, function level, program level, and system level according to the granularity of the implementation objects. To realize the diversity of instruction level within the basic block of the program: (1) Substitute one instruction for another instruction or reorder the sequence of instructions after an instruction sequence; (2) Randomize the register allocation in the program; (3) Insert some garbage code into instructions, for example null operations (NOPs). The impact of the diversity is within the basic block of the program and it can prevent attacks by exploiting fine-grained vulnerability, for example CIA, but the premise is not to leak the implementation details of the diversity. A basic block is a sequence of consecutive statements only with one entry and one exit, where the entry is the first statement and the exit is the last statement. That’s to say a basic block is a basic unit of program execution. Therefore, the diversity of the basic block level can be implemented by reordering the sequence of basic blocks. Reordering basic blocks needs to add a jump instruction or a branch function between the basic blocks of convergence sequence to ensure that the basic blocks after the random order still can be executed in accordance with the correct sequence of control flow graph. Therefore, this case also needs to ensure that the implementation details are not leaked.

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To implement the diversification of the function level, it firstly starts from the realization of the stack. It is easy to understand because many attacks start from overlaying the return address of the function on the stack. Diversification of the stack can be achieved by reordering the variables stored on the stack in the program as well as reversing the direction of the stack growth and creating non-adjacent stack frames. In short, the purpose is to break the traditional stack structure to prevent the attacker from implementing the attack by predicting the location of the stack. Secondly, diversification of function level can be realized by dividing existing functions into several functions or by reordering existing functions. In this way, by re-creating the program stack and disorganizing the sequence of functions, we can prevent an attacker from implementing attacks by exploiting the information leak vulnerability such as memory corruption.

2 Research Directions in the Future Software is an important part of the information system, and security vulnerabilities of the software has become a key factor of affecting the security of information system. Moreover, in recent years a large amount of practice also proves that most of information security incidents are implemented by attackers through direct or indirect vulnerabilities exploitation. And this situation becomes more and more intense, and the social impact and harm are also growing. Therefore, the in-depth study of vulnerability defense technology, as well as quickly and effectively preventing attacks by exploiting the vulnerabilities, will have a significant impact on the information security. Vulnerability defense technology from the initial passive vulnerability detection to today’s active vulnerability prevention, is a process of continuous progress: from static vulnerability detection to dynamic vulnerability detection; from the only program diversification to the continuous diversification in the operation process of program. But vulnerability exploitation and vulnerability prevention are like two athletes that participate in the games and continuously transcend each other: new vulnerability exploitation occurs, and new defense method follows; and new defense method will soon be bypassed by new vulnerability exploitation. Therefore, the research on vulnerability prevention technology is an eternal process, only the starting point but no terminal point. Therefore, we believe that the study on vulnerability detection and vulnerability prevention technology in the future are the following three aspects. Research on security coding. As we all know, there are a lot of insecure functions in the program, such as strcpy() and printf() function. Because this kind of functions doesn’t check the array boundary or limit the output format string in the process of the operation, it results in buffer overflow errors and formatted output errors, and these errors generate vulnerabilities that are exploited to implement attacks, which threat the security of the program. Although most of these insecure functions have been rarely used, there are a large number of legacy codes, so this kind of problems cannot be ignored. Therefore, it is urgent to study the secure encoding method and reform the usage of the insecure functions in the program code.

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Vulnerability detection based on cloud computing. The traditional vulnerability detection cannot perform large-scale detection activities due to the limitation of computing power, so the vulnerability mining work is ineffective, resulting in a large number of false negatives and false positives. At present, cloud computing is very mature and plays a tremendous power in a large number of applications. With the help of the superior computing power of cloud computing and in-depth vulnerability mining of the program, we can gradually solve the problems such as the path explosions in the symbol execution, data diffusion in taint analysis and insufficient coverage of test cases in fuzzing testing, which will greatly enhance the vulnerability detection ability. Implementation of comprehensive, real-time program diversity. Despite the program diversity develops rapidly, there are still some shortcomings. Many program diversity implementation schemes diversify the program only once and don’t diversify any more in the operation process of the program, which are almost no resistance against attacks similar to the JIT-ROP. On the other hand, the recent dynamic real-time program diversity technology only changes the key parts of the program, such as the part of the program code, but the data part of the program and the part of the dynamic shared library are not involved. So, the program cannot resist the non-control data attacks and the attacks by exploiting vulnerabilities in the dynamic shared library of the program, so attacks by exploiting the vulnerabilities in the dynamic shared library take a considerable part of all vulnerability attacks. Therefore, it is necessary to conduct the research on a comprehensive real-time program diversity and further improve the program diversity technology.

3 Conclusions For decades, academic and industrial circles have proposed a number of vulnerability mitigation measures. However, so far there is no any final feasible solution. Because vulnerability exploitation and vulnerability mitigation are like a pair of competing rivals, which continue to transcend each other. Under this circumstance, one hand we can continuously improve the existing technologies so that they can be used in the actual application environment. On the other hand, we also should continuously create new research directions to deal with unknown vulnerabilities.

References 1. Arzt, S., Rasthofer, S., Fritz, C., et al.: FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps. In: ACM SIGPLAN Conference on Programming Language Design and Implementation, 5 June 2014, pp. 259–269 (2014) 2. Report, F., Almossawi, A., Lim, K., et al.: Analysis tool evaluation: coverity prevent 3. Cadar, C., Dunbar, D., Engler, D.: KLEE: unassisted and automatic generation of highcoverage tests for complex systems programs. In: Usenix Conference on Operating Systems Design and Implementation, pp. 209–224 (2009)

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4. Sun, X., Yao, Y.Y., Xin-Dai, L.U., et al.: Research and implementation of fuzzing testing based on HTTP proxy. Chin. J. Netw. Inf. Secur. 2, 75–86 (2016) 5. Zhu, G.M., Zeng, F.P., Yuan, Y., et al.: Blackbox fuzzing testing based on taint check. J. Chin. Comput. Syst. 33(8), 1736–1739 (2012) 6. Miller, F.P., Vandome, A.F., Mcbrewster, J.: Control Flow Graph. Alphascript Publishing, Rapid City (2010) 7. Budiu, M., Ligatti, J.: Control-flow integrity. In: ACM Conference on Computer and Communications Security, 7 November 2005, pp. 340–353 (2005) 8. Hao, J.: In memory of C.E. Shannon, the founder of information theory. Telem. Telecontrol (2001) 9. Durden, T.: Bypassing PaX ASLR protection (2002)

Research on the User Fatigue of Household Appliances Human-Computer Interaction Based on Wearable Devices and Bare-Hand Gesture Recognition Yixuan Xue1, Shuxia Li2(&), Jinsheng Lu1, Zhe Tong3, and Hongbo Shan1 1

2

School of Mechanical Engineering, Donghua University, Shanghai 201620, China School of Business, East China University of Science and Technology, Shanghai 200237, China [email protected] 3 School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Hebei 066000, China

Abstract. Based on the analysis of gesture types and gesture recognition technology, this paper divides the function of TV operation interface, designs two sets of gestures based on wearable devices and bare-hand. Using the method of subjective and objective testing, it tests the user fatigue of the two sets of gestures. Finally, experimental results showed that bare-hand gesture interaction technology with its emphasis on natural, comfortable advantage, would become the main way of future home appliance gesture interaction. Keywords: Gesture recognition Fatigue degree


Bare-hand
Wearable devices

1 Introduction With the wide use of gesture recognition technology based on wearable devices and bare-hand, there are many human-computer interaction household appliances. Ahn et al. [1] made a wearable device to record user activities and manage their exercise systematically. The researches on household appliances gesture recognition mainly discuss the recognition speed and accuracy, rarely involve users’ fatigue degree in the process of gesture interaction [2]. However, a lot of product testing and user feedback show that hand fatigue is one of the important factors reducing product user experience [3]. Therefore, the research on user fatigue is significant for guiding gesture interaction design.

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2 Basic Research 2.1

Analysis of Gesture Type

Gestures were divided into static gestures and dynamic gestures [4]. Static gestures are mainly symbolic gestures, such as the most common form of “V” by the index finger and middle finger, meaning victory. Dynamic gestures are mainly symbolic gestures and instruction gestures [5]. The symbolic gestures in dynamic gestures refer to the symbol by hand movements, such as numbers, letters, etc.; instruction gestures refer to the hand movements indicating object position. 2.2

Analysis of Gesture Recognition Technology

Gesture recognition technology can be classified into three categories [6]: 2D hand shape recognition, 2D gesture recognition and 3D gesture recognition. 2D gesture recognition technology only needs X and Y coordinates information, using a single camera to capture 2D image as input to conduct analysis. 3D gesture recognition technology still needs the depth information of Z coordinate, which is mainly applied in motion sensing game and virtual reality technology. Gesture recognition in this paper aimed at household appliances control, so the research was conduct with 2D gesture recognition.

3 Gesture Design 3.1

Household Appliances Control Function Division

Television was selected as the research object, its control types are diverse and regular, which is conducive to the research. “Ali TV assistant” app is a mobile application customized for Alibaba smart TV. It can help users to complete TV remote control. According to the interface, the function division of TV control is divided into 10 items: the up/down left/right choice, power, volume control, menu, ok and return, as shown in Fig. 1.

Fig. 1. Interface function division

Fig. 2. Operation example

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Gesture Design Under the Functional Division

3.2.1 Gesture Design of Wearable Devices The types of wearable devices for gesture control are diverse, including intelligent rings, bracelets and gloves. Intelligent ring was selected due to its portability, which is better combined with TV control interface. According to TV control functional division, the wearable device gesture could be designed as follows: up/down/left/right choice control by index finger swiping; power control by swiping to right arrow shape by index finger; volume control by clockwise and counterclockwise forefinger movement (with the operation example listed in Fig. 2); menu control by swiping letter “M” shape (Menu) by index finger; ok control by ticking “✓” shape by index finger; and return control by swiping to left arrow shape by index finger. The details are shown in Fig. 3.

Fig. 3. Gesture design of wearable devices

Fig. 4. Bare-hand gesture design

3.2.2 Bare-Hand Gesture Design Bare-hand gesture issue instructions through arm movement in X and Y coordinate planes. It could be designed as follows: up/down/left/right choice control by arm swinging; power control by stretching palms, and clenching fists after 1 s; volume control by stretching palms toward right and left; menu control by stretching palms and holding for 2 s; ok control by making “OK” gesture with fingers; and return control by stretching thumb up and down and clenching the other four fingers. The details are shown in Fig. 4.

4 Experimental Design 4.1

The Selection of Experimental Subjects and Site

50 subjects were selected, aged between 20 and 25, healthy, right-handed, and no pain recently. Participants could quickly master gestures contents and the use method of experimental apparatus. The experiment was set in a quiet laboratory, and there was no outside interference except the experimental participants. 4.2

Gestures Difficulty Level Test

The subjects were asked to get familiar with a set of gestures within 2 min and attend memory test immediately. Participants were asked to do corresponding gestures according to the random instructions such as “up, down, power…”. If gesture was

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wrong, they would be prompted until it was right. The time in finishing a whole set of gestures, error times and statistical average were recorded, as shown in Table 1. It showed that the subjects had basically the same memory level for the two sets of gestures, so the two sets of gestures had basically the same difficulty. Table 1. Gesture memory test Wearable device Bare-hand gesture gesture Time (s) Error time Time (s) Error time Average 21.51 0.22 20.63 0.18

4.3

Experimental Auxiliary Equipment and Experimental Apparatus

Existing intelligent ring data showed that 10 g was befitting for this experiment, regardless of their functions. Experimental ring and index finger wear demonstration for wearable equipment gestures operation are shown in Fig. 5. In the objective fatigue tests, motion stabilizer and timing counter were selected as the experimental apparatus, shown in Figs. 6 and 7. Motion stabilizer test is constitutive of nine-hole test, slot wedge test and curve test. If the testing needle contacted with the edge of the trough after moving, the buzzer would alarm. Error times and time were recorded simultaneously in optional timing counter. Stability indicator could be expressed by the product of edge touching number and time.

Fig. 5. Experimental ring

4.4

Fig. 6. Motion stabilizer

Fig. 7. Timing counter

Subjective Fatigue Degree Test

Subjective fatigue degree test adopted sense estimate method, referred to Gunnar Borg’s subjective Rating of Perceived Exertion (RPE), the subjective fatigue assessment scale was designed. The preliminary tests confirmed that the subjects could obviously feel the first level 13 of fatigue, thus the following scale was designed, as shown in Table 2. At the start of the experiment, the subjects were asked to have comfortable sitting posture, and begin gesture operation by imitating teaching video, the time was computed. The subjects repeated the operation of a complete set of gestures. According to the subjective fatigue degree assessment scale, they were asked for many times, and their fatigue degree time of each stage was recorded. When they reached the final fatigue degree, the test stopped.

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Table 2. Subjective fatigue assessment scale Level 6 13 15 17 19 20

4.5

Fatigue degree Quiet A little fatigued Fatigued, but not painful enough to give up Fatigued, began to feel painful and give up Quite fatigued, needed to persist to keep current status, wanted to rest Exhausted

Objective Fatigue Degree Test

Objective fatigue degree test was consisted of three steps: hand stability test under nonfatigued status, hand stability test of wearable devices gestures fatigue and bare-hand gestures fatigue. Firstly, the subjects were asked to conduct 3 sets of tests (nine-hole, curve slot, and slot wedge test) to test hand stability under non-fatigued status Their error times and time were recorded, the product of error times and time was regarded as an indicator to measure the hand stability. After resting adequately, the subjects were asked to conduct wearable devices gestures hand stability test. It was required to be consistent with the subjective fatigue degree test. Nine-hole, curve slot, and slot wedge test were conducted in order; error times and time were recorded. After a day of rest, the subjects were asked to conduct bare-hand gesture fatigue hand stability test. The experiment steps were the same as wearable devices gesture fatigue hand stability test. Error times and time were recorded.

5 Experimental Analysis 5.1

Data Analysis of Subjective Fatigue Degree Test

As shown in Table 3, comparing the time reaching fatigue of the two groups, the percentages of longer time for bare-hand gestures were 78%, 66%, 62%, 64% and 90%. More than 60% of the subjects entered fatigue state later when doing bare-hand gesture Table 3. The proportion of fatigue time Level Longer time for wearable device Number Percentage 6 13 11 22 15 17 34 17 19 38 19 18 36 20 5 10

Longer time for bare-hand Number Percentage 39 33 31 32 45

78 66 62 64 90

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than wearable device gestures. And 90% of the subjects had longer time for bare-hand gesture when reaching the final fatigue. It showed that the fatigue of wearable device gestures was greater than that of bare-hand gestures. 5.2

Data Analysis of Objective Fatigue Degree Test

SPSS was used for T test to analyze the hand stability. 5.2.1 SPSS Data Processing: Paired Sample T Test Paired sample T test was carried on, analyzing the significant differences of hand stability between non-fatigue state and operating wearable devices gestures, nonfatigue state and bare-hand gestures. The outputs are shown in Tables 4, 5 and 6. Table 4. The nine-hole non-fatigue state test results Paired difference

Pair 1 Pair 2

Nine-hole wearable Nine hole bare-hand gesture

t SE mean

SD

−12.3514

11.63300 1.64515 −15.65746 −9.04534 −7.508 49 .000

−5.47560 12.68317 1.79367

95% Confidence interval of difference Lower Upper limit limit

df Sig. (Bilateral)

Average

−9.08012 −1.87108 −3.053 49 .004

Table 5. The wedge slot non-fatigue state test results Paired difference Average

Pair 1 Pair 2

Wedge slot wearable Wedge slot barehand gesture

SD

t SE mean

95% Confidence interval of difference Lower Upper limit limit

df Sig. (Bilateral)

−5.09580 4.55612 .64433 −6.39064 −3.80096 −7.909 49 .000 −2.35720 4.65165 .65784 −3.67919 −1.03521 −3.583 49 .001

Table 6. The curve slot non-fatigue state test results Paired difference Average

Pair 1 Pair 2

SD

t SE mean

95% Confidence interval of difference Lower Upper limit limit

df Sig. (Bilateral)

Curve slot wearable −4.72700 4.08043 .57706 −5.88665 −3.56735 −8.192 49 .000 Curve slot barehand gesture

−1.79360 3.68186 .52069 −2.83997

−.74723 −3.445 49 .001

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In the nine-hole test, the paired sample T test results of non-fatigue state and operation wearable devices gesture hand stability were as follows: t = −7.508; p = 0.000 < 0.01, the difference was extremely significant; the paired sample T test results of non-fatigue state and bare-hand gesture hand stability were as follows: t = −3.053; p = 0.004 < 0.01, the difference was extremely significant. Thus, subjects obviously felt tired after operating wearable device gestures and bare-hand gestures, and more fatigue after operating wearable devices gestures. Similarly, based on the test results of wedge slot and curve slot, the conclusion was the same. 5.2.2 SPSS Data Processing: Independent Sample T Test An independent samples T test was conducted. The significant differences between operating wearable device gestures and bare-hand gestures hand stability were analyzed. The outputs are shown in Tables 7, 8 and 9. In the nine-hole test, independent sample T-test results were as follows: F = 1.818; P = 0.181 > 0.05, homogeneity of variance; t = 2.936; p = 0.004 < 0.01, showing there was significant difference. The subjects had worse hand stability and more obvious fatigue after operating wearable equipment gestures than bare-hand gesture, Similarly, based on the test results of wedge slot and curve slot, the conclusion was the same. Table 7. The nine-hole test results Levene test of variance equations F

Assuming variance is equal Assuming variance is unequal

Sig

t

df

The test of mean equations

Sig. Mean Standard (Bilateral) difference error values

95% Confidence interval of difference Lower Upper limit limit

.004

6.8758

2.3415

2.2291 11.5225

2.936 95.286 .004

6.8758

2.3415

2.2275 11.5241

1.818 .181 2.936 98

Table 8. The wedge slot test results Levene test of variance equations F

Assuming variance is equal Assuming variance is unequal

Sig

t

df

The test of mean equations

Sig. Mean Standard (Bilateral) difference error values

95% Confidence interval of difference Lower Upper limit limit

.003

2.73860

.885814

.98073 4.49647

3.092 94.165 .003

2.73860

.885814

.97984 4.49736

1.772 .186 3.092 98

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Assuming variance is equal Assuming variance is unequal

Sig

t

df

3.153 .079 4.517 98

The test of mean equations

Sig. Mean Standard 95% Confidence (Bilateral) difference error values interval of difference Lower Upper limit limit .000

2.93340

.64940

1.64470 4.22210

4.517 82.064 .000

2.93340

.64940

1.64156 4.22524

In conclusion, the subjects had more subjective fatigue degree when operating wearable devices gestures than when operating bare-hand gestures. Based on the analysis results of the objective fatigue degree test, after operating the two kinds of hand gestures, their hand stability became worse than no gesture was operated, especially after operating wearable devices gestures. The subjective fatigue degree test, and objective fatigue degree test had the consistent conclusions, so the rationality of the experiment was proved.

6 Conclusion Bare-hand gesture interaction technology with its emphasis on natural, comfortable advantage, would become the main way of future home appliance gesture interaction. The subjects in this study were right-handed, so whether the left-handed users have the same fatigue status is still to be studied. It is hoped that the research results provide reference for the design and evaluation of household appliances gesture interaction. Acknowledgement. This paper is sponsored by Natural Science Foundation of Shanghai (No. 18ZR1409400).

References 1. Ahn, T., et al.: Physical training gesture recognition using wristwatch wearable devices. Int. J. Multimed. Ubiquitous Eng. 11(6), 427–434 (2016) 2. Trigueiros, P., et al.: Hand gesture recognition for human computer interaction: a comparative study of different image features. Commun. Comput. Inf. Sci. 449, 162–178 (2014) 3. Sun, W., et al.: Experimental study of fatigue degree quantification for multi-feature fusion identification. High Technol. Lett. 20(2), 146–153 (2014) 4. Wexelblat, A.: Research challenges in gesture: Open issues and unsolved problems. Lecture Notes in Computer Science, vol. 1371. Springer, Heidelberg (1998) 5. Linqin, C., et al.: Dynamic hand gesture recognition using RGB-D data for natural human– computer interaction. J. Intell. Fuzzy Syst. 32(5), 3495–3507 (2017) 6. Arsenault, D., et al.: Gesture recognition using Markov Systems and wearable wireless inertial sensors. IEEE Trans. Consum. Electron. 61(4), 429–437 (2015)

Research on Precision Marketing Model of Beijing Agricultural Products Under Big Data Environment Xiangyu Chen and Jing Gong(&) Institute of Agricultural Information and Economics, Beijing Academy of Agriculture and Forestry Sciences, Key Laboratory of Urban Agriculture (North China), Ministry of Agriculture, Beijing 100097, People’s Republic of China [email protected] Abstract. Traditional agricultural sales mode has been unable to adapt to the rapid development of Beijing agricultural economy, and there exist prospects for precision marketing of Beijing agricultural products under big data environment. This paper constructs an effective precision marketing model of Beijing agricultural products by adopting big data technology, and discusses the key aspects along with matters in the model establishment, according to the characteristics of agricultural products and the current marketing situation in Beijing. With this model, it is possible to achieve efficient docking of agricultural production and sales, and further to meet the growing high-quality demand of consumers. Keywords: Big data


Agricultural products
Precision marketing

1 Introduction Traditional sales mode of agricultural products seriously hinders the rapid development of agricultural economy, due to the mismatch between supply and demand, planting and sales, and the difficulty in selling agricultural products, etc. Furthermore, that traditional agricultural marketing model emphasizes on winning by quantity to cover up the inefficiency of rough marketing process, leads to the producers and marketers of agricultural products staying in the stage of low profit level for a long time. Precision marketing, first proposed by Philip Kotler (2005), refers to a marketing model in which enterprises could occupy a target market and obtain high rate of return by means of measurable and accurate marketing methods. In the application of precision marketing model, marketers should attach importance to the accuracy of marketing actions and the high efficiency of marketing results, and highlight its precise value in terms of audience selection, marketing effectiveness and marketing expenses. Big data is another important technological change following the Internet of things and cloud computing in the information technology industry, which has also accelerated the process of transformation from traditional agriculture to modern agriculture, digital agriculture and information agriculture, and especially agricultural product network marketing is the crucial link in this process. Meanwhile, big data analysis technology provides a favorable technical foundation for precision marketing of agricultural products. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 805–812, 2019. https://doi.org/10.1007/978-3-030-02804-6_105

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Beijing agriculture shoulders the important responsibility of providing safe agricultural products in the capital, but meanwhile there exist severe constraints such as land, water, ecological environment and other resource conditions. Under big data environment, it is of great necessity and urgency to construct an effective precision marketing model of them by employing big data technology, in view of the characteristics and marketing status of Beijing agricultural products. In addition, the implementation of Beijing precision marketing strategy based on big data technology, will not only be helpful to improve the ability of the Beijing agricultural product marketing system to meet the diversified and personalized needs of consumers, but also be helpful to improve the market function of Beijing agricultural products, and provide new ideas and methods to solve the difficult income increasing problem of farmers.

2 The Current Situations of Beijing Agricultural Products and the Influence of Big Data on Its Precision Marketing Located in the northwestern part of the North China Plain, Beijing is surrounded from west to north by majestic and steep Taihang Mountain along with Yanshan Mountains, the valleys of which are peculiar and deep. Moreover, Yongding River and Chaobai River look over the Beijing suburbs, scattered with mineral springs as well as magnificent and mysterious caves. Blessed and unique natural environment conditions have given birth to rich and diverse animal and plant resources for Beijing. 2.1

Characteristics of Agricultural Products in Beijing

(1) The variety of agricultural products is rich and the quality is high. Beijing is located in the warm zone continental monsoon climate and the four seasons are distinct, so that species resources are very rich, and natural advantages are obvious. Relying on its own advantages of science and technology, capital, information and market, Beijing agriculture continuously optimizes its industrial structure and production structure. The main goal of agricultural production is to meet the needs of metropolitan areas, with emphasis on developing high-quality agriculture and laying particular emphasis on high-grade agriculture. High valueadded agricultural products. At present, Beijing produces more than 100 kinds of fresh and live agricultural products and processed products, including meat, eggs, vegetables, milk, fish, fruits and other products, and has more than 1,300 leisure picking parks for the citizens to experience the fun of farming. (2) The local features are obvious. The unique natural ecological environment, historical and humanistic factors in Beijing have brought up rich and diverse varieties of agricultural products with Beijing geographical characteristics, and have obvious advantages in many modern consumption concepts such as ecology, health, delicacy and so on. At present,20 special agricultural products, such as Yan Qing Guoguang Apple, Tong Zhou Cherry, Miaofeng Mountain Rose, HaiDian YuBada apricot and so on, have passed the National Geographic indication Certification.

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Among them, JingBai Pear, AnDing Mulberry, SiJiashui Toon, HaiDian YuBada apricot, FangShan MoPan persimmon, DaXing watermelon, HuaiRou chestnut, JingXi rice and MaoShan HouFoJianXi pear, all were royal tribute. Beijing is rich in tourism resources, the combination of characteristic agricultural products and sightseeing and leisure tourism, on the one hand, has given Beijing agricultural products the label of “native products”, on the other hand, through the form of tourist souvenirs and gifts amplify the impact of its local characteristics. (3) Demand for precision marketing is being improved. With the rapid development of Beijing’s social economy and changes in the consumption patterns of residents, consumers demand for agricultural products is characterized by diversification, individuation, freshness and greening. It is required that the suppliers of agricultural products be able to provide agricultural products which are rich in variety and reliable in quality in a timely manner. On the other hand, in terms of the characteristics of agricultural products, due to the timeliness of production, marketing of agricultural products, the characteristics of seasonality, and intolerance of storage, there are high requirements for the environment and facilities in the logistics links of picking, transporting, storing and trading of agricultural products, such as cold chain storage and transportation, product bar code identification, quality traceability, etc. which require the rapid sharing of information network to be realized in each link of agricultural products supply chain. Through big data analysis the sensitivity of producers to market demand will be improved. And producers can forecast consumption demand and timely adjust the state of inventory and production planning to achieve accurate marketing. 2.2

Present Situation of Precision Marketing of Agricultural Products in Beijing

With the rapid development of internet of things and cloud computing, the application of large data technology is on the rise in the field of precision marketing of agricultural products in Beijing. Several representative cases of precision marketing of agricultural products in Beijing are selected to do some research. (1) Xinfadi Fresh Net is marketed precisely. The supply chain integration platform from the planting base to the end users has been established by fresh net of newly developed area in order to mobilize social resources, and reduce circulation costs, and provide products to users more economically. Its statistics cover real-time prices for the year. Through data analysis price purchase can be guided more accurately, and more reliable product suppliers can be selected. In addition, through the analysis of data, the vegetable base of origin can be adjusted immediately according to the market price, and the production can be ordered by sales. In 2016, taking a canteen as an example, the comprehensive cost dropped by more than 15 percent after using Xinfadi Fresh Net for two months.

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(2) Renwo online Electronic Commerce co. Ltd. is marketed precisely of Beijing. The company has created the internet direct marketing support system based on internet, mobile internet and internet of things. Data sharing, remote control and internet of things data acquisition, across four industries, such as production, retail, logistics, e-commerce four industries. By making full use of big data analysis to promote the docking between producers and consumers, the producer realizes to sell and order production, effectively avoiding overcapacity and wasting. At the same time, the consumers’ diversification, individuation, high-end demand are excavated and satisfied. (3) Beijing Agricultural Information Technology Co Ltd. is marketed precisely. The company developed series of large data products and do analysis with them, such as “Rural public opinion monitoring and management platform system and mobile client”, “Producer price rapid acquisition system”, “Data center application system of science and technology economic policy”, and “Electronic management system of agricultural machinery certification project files”, which serve Information Center of Ministry of Agriculture, Township Enterprises Bureau of Ministry of Agriculture, etc. The government can make agricultural production and management decisions using large data analysis based on these systems. 2.3

The Influence of Big Data on the Marketing Environment of Beijing Agricultural Products

Big data has greatly changed the marketing environment of agricultural products. Consumers are no longer as closed as the past, and the extensive and timely information obtained from various channels make them more active. Consumers are more fully aware of Beijing’s agricultural products, and their choice is also more diverse and targeted. Through big data technology, marketers can also collect more comprehensive information at lower costs and analyse consumers’ interests and preferences constantly. So accurate marketing of agricultural products in Beijing can be carried out. (1) Marketing information and marketing methods are promoted precisely. Big data technology enables Beijing agricultural product marketers to obtain large quantities of agricultural product purchase information, web browsing records and feedback of consumers from domestic and foreign e-commerce platforms and social media in a timely manner. This technology can quickly be used to analyse and identify consumer characteristics, preferences and potential consumer needs. Then consumers are accurately subdivided, target market is selected and positioned precisely. Combined with the regional characteristics of Beijing agricultural products, product characteristics, and using the shopping account, mobile phone, E-mail, microblogging, WeChat and other channels, producers can provide more accurate services and personalized products to consumers in real time and achieve precision marketing of agricultural products to target markets at home and abroad.

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(2) Marketing decision and marketing strategy design are assisted. Through data collection and mining, producers can find the target customer group and design targeted marketing activities according to the objectives of target customer group marketing activities, including product, price, channel and promotion. They evaluate the options to select the best ideas and form a final marketing plan. A good marketing approach must focus on a target customer base. If there is no data support, then the marketing plan and marketing decision will inevitably lack of scientific rationality, and can’t really focus on the users. According to relevant research, thank to the use of big data, a producer’s average marketing cost can be dropped by 50%, with its profit increased by 60%. It is very important to reduce the marketing cost for the sales of large and thin agricultural products. (3) Forecasting ability of agricultural products market consumption is improved in Beijing in the future. People need not only to know what is happening in Beijing agricultural product market, but also to use the data to predict what will happen in Beijing agricultural product market, and then they can make some active preparations in action. In the context of big data, by mastering the huge consumption data resources of the agricultural products market, the behavior of all related consumers will be accurately associated and people can predict the consumption of the future agricultural products market.

3 Construction of Precision Marketing Model of Beijing Agricultural Products Under Big Data Environment Under big data environment, it is possible to pick up information about markets, consumers and products, and to realize the analysis, processing and utilization of large scale data which is needed by the precision marketing of agricultural products in Beijing. Meanwhile, it also provides mining and precision push about advantage in cultural connotation, ecology, health and environmental protection of special agricultural products in Beijing. 3.1

Precision Marketing Model of Beijing Agricultural Products Under Big Data Environment

Based on the theory of precision marketing, combined with the theories of customer value, customer relationship management and database marketing, and took the big data platform as the technical support, the paper which was aimed at the characteristics, customer characteristics and preference of agricultural products in Beijing, built precision marketing model of agricultural products using massive data acquisition, storage, data mining, statistical analysis and processing, push and other related technologies that the platform has (Fig. 1).

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Fig. 1. Precision marketing model of agricultural products in Beijing under big data environment

(1) Generation, collection, storage, and processing of data. The core task of this link is to collect information and data about consumers, prices, channels, logistics and distribution and agricultural products market consumption in Beijing through SNS, Weibo, Wechat, forums, posts, e-commerce platforms, fan groups, web pages, data terminals, and traditional agricultural products online platforms on the basis of five main sources of information generation. Relying data storage technology and database technology of big data platform, the original data is stored, and the existing data and real-time data are preliminarily preprocessed to form the next comprehensive primary database. (2) Analysis and depth mining of data. According to the relevant agricultural product market consumption data information captured in real time from various channels by the primary database and big data platform, the statistical analysis and deep mining of the primary data and real-time data are carried out. By means of MapReduce, OLAP online analysis and other technologies, through the thirdparty big data analysis and processing platform, the diversity of real-time raw data and primary data related to consumers, markets and agricultural products are statistically analyzed and deeply excavated, and on this basis, the customer feature model and Beijing agricultural products feature model are built. (3) Precision marketing model of agricultural products is structured. According to customer characteristics, preferences and Beijing agricultural products feature model, the agricultural products market is accurately subdivided, accurately selected and accurately positioned, and the Beijing agricultural products precise marketing model is constructed. Relying on the agricultural products market consumption big data platform to establish the precise marketing business scene,

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and combining the marketing situation to develop the precise marketing scheme, so as to realize the mutual matching between the content library and the marketing scheme. Through personalized customization and multi-channel access, with the help of SNS, online accounts, Email, DSP and so on, personal information push technology is adopted. And big data technology is effectively used to carry out accurate marketing of agricultural products in Beijing. (4) Precision marketing effectiveness is evaluated. According to the monitoring data of Beijing agricultural products precision marketing implementation process of big data platform, and real-time record and analysis processing of the implementation effect index data, and comprehensively evaluate the application effect of Beijing agricultural products precision marketing mode. According to the evaluation results, the main problems existing in data analysis and mining, characteristics and preference model, marketing situation construction, precision marketing model and precision marketing execution link are analyzed qualitatively and quantitatively Also, the solutions are found out accurately, which provides scientific decision-making basis for the improvement and optimization of Beijing agricultural products precision marketing mode in the next stage. 3.2

Matters Needing Attention in Key Links

(1) When collecting and analyzing the data of agricultural products and consumers in Beijing, on the one hand, we should pay attention to the quality of data and the effective choice of collecting channels, which can lay the foundation for improving the accuracy of big data analysis, mining and pushing work. On the other hand, the data collection should focus on the consumers of agricultural products, this main line of Beijing agricultural products, to prevent the collection of a large number of useless, invalid, worthless data, resulting in human, material and financial waste. At the same time, in the process of data mining and analysis, we should pay attention to the development and utilization of the third big data platform, the method of data analysis and processing, and the rationality of technology selection. We should not blindly pursue the advantages of our own platform and the advancement of analytical processing technology, but ignore the feasibility, validity, economy and readability of the analysis results of our own platform construction and analytical processing technology. (2) In the process of subdividing, selecting and positioning about the precision marketing of agricultural products in Beijing, we should pay attention to the trend of consumption such as individualization, diversification of current consumer market for agricultural products. Also we should highlight the advantages of B(4) rket competitiveness have been further improved. At the same time, the differences in consumer consumption level, and the particularity of agricultural products in storage and logistics transportation should be paid attention to Beijing’s technology, capital, information and market advantages should be taken full account of and a high-end route in the selection and positioning of target markets for agricultural products should be taken.

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(3) When the implementation process and effect of precision marketing of agricultural products in Beijing are evaluated under big data environment, the factors affecting the marketing effect and the complexity of the impacting degree should be paid attention to. Among the many factors that affect the effect of precision marketing of agricultural products, there are not only the dominant factors such as consumer attributes, behavior, consumption ability, and the quality and characteristics of agricultural products, etc. There are also many hidden factors, such as consumer psychology, personality, consumption expectation and so on, which affect the effect of precision marketing. And the big data platform is mostly used for data processing and quantitative analysis of dominant factors. Therefore, when the effect of precision marketing is evaluated, marketers should take these two factors into account, and pay attention to the effective combination of qualitative and quantitative evaluation methods, and constantly improve the precision marketing model.

4 Conclusion According to analysis of the relationship between big data technology, Beijing agricultural products and precision marketing, through analyzing the current situation of Beijing agricultural products and the impact of big data on its precision marketing. Furthermore, the practical precision marketing model of Beijing agricultural products based on big data technology is constructed, by combining the features of Beijing agricultural products, consumer characteristics and behavioral preferences, along with integrating specific marketing situations. This model provides a reference for the development and application of precision marketing based on big data in Beijing agricultural products field. Under the big data environment, the following significant contents, such as formation mechanism and evaluation of effect on precision marketing of Beijing Agricultural products as well as the integrated development and utilization of big data platform will be the direction of further research and exploration.

References 1. Gantz, J., Reinsel, D.: Extracting value from chaos. IDC iView 1142, 1–12 (2011) 2. Kwon, O., Lee, N., Shin, B.: Data quality management, data usage experience and acquisition intention of big data analytics. Int. J. Inf. Manag. 3, 387–394 (2014) 3. Wang, Z.: Prediction of customers consumption patterns based on two-stage behavior analysis model-taking precision marketing of telecommunications data services as examples. In: Intelligent Information Technology Application Association. Proceedings of 2011 International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE 2011 V2). Intelligent Information Technology Application Association, p. 8 (2011)

Mobile and Wireless Communication

A Layered Secure Communication Model for Cooperative Vehicle Infrastructure System Yao Zhang(&) and Qun Wang School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China [email protected]

Abstract. Because of the unique characteristics in road traffic environments and wireless communication conditions, it is difficult to guarantee communication security of transmitting traffic related messages in Cooperative Vehicle Infrastructure System (CVIS). We propose a three layered secure communication model for CVIS, which integrates identity authentication, channel access control and encryption communications. The functions of each layer are defined, and the implementation methods are discussed. The proposed scheme gives consideration to both reliability and efficiency in CVIS, and is compatible with Wireless Access in Vehicular Environment (WAVE) standard. Keywords: Vehicular Ad-Hoc Network (VANET) Cooperative Vehicle Infrastructure System (CVIS)
Communication security Layered model
Wireless Access in Vehicular Environment (WAVE) standard

1 Introduction In future Cooperative Vehicle Infrastructure System (CVIS), high efficient and coordinated transportation management can be implemented based on Vehicular Ad-Hoc Networks (VANETs). VANET is the basic unit of CVIS, which is consisted of Road Side Unit (RSU) and plentiful On Board Units (OBUs) equipped with microprocessor, wireless communication devices and varying traffic sensors. So that real time traffic related messages or application messages can be collected and exchanged between VANET nodes and transportation management center. However, the complex road traffic environments and wireless communication conditions make it difficult to guarantee security of communications in CVIS [1, 2]. In this paper, we propose a three-layered communication framework to improve communication security in CVIS, including identity authentication layer, channel safe access layer and encryption communication layer. The functions of each layer are defined, and the implementation methods are respectively discussed in detail. The proposed scheme gives consideration to both reliability and efficiency of communications in CVIS, and is compatible with Wireless Access in Vehicular Environment (WAVE) standard [3].

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2 Layered Secure Communication Model for CVIS According to WAVE standard promulgated by IEEE in 2010 [4], we propose a threelayered secure communication model for CVIS. The structure is as shown as Fig. 1.

Fig. 1. Layered structure of secure communication model for CVIS

In identity authentication layer which is corresponding to WAVE Short Message Protocol (WSMP) layer, the indicator of credibility degree is introduced to evaluate OBU’s identity. OBU’s credibility degree can be calculated by RSU and transportation management center through monitoring and analyzing their usual communication behavior characteristics. In order to perform reliably identity authentication, each OBU can obtain other CVIS member’s credibility degree from transportation management center. In the protocols of WAVE Media Access Control (MAC) layer, seven wireless channels of 75 MHz bandwidth at 5.850 GHz–5.925 GHz are assigned to CVIS, including one control channel (CCH) for transmitting traffic related messages and six service channels (SCHs) for transmitting application messages. In our proposed scheme of channel safe access layer, channels’ access time is divided into three slots, containing channel reservation period, control frame transmitting period and service frame transmitting period. In order to maintain high channel utilization, the length of time slots can be dynamically adjusted according to network load status of different data frames in VANETs [5]. In the protocols of WAVE physical layer, to reduce the effects of Doppler spectrum spread and Inter Symbol Interference (ISI) caused by multi-path propagation of wireless channels, 64 sub-carriers’ Frequency Division Multiplexing (OFDM) technique is employed, which can provide data rate of 3M bit/s up to 27 Mbit/s with 300 m–1000 m communication distance. 52 sub-carriers are used for data transmission consisting of 48 data sub-carriers and 4 pilot sub-carriers. The pilot signals are used for

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tracing the frequency offset and phase noise. In proposed encryption communication layer, we present a design scheme of communication system of physical layer, which combines chaos scrambling and frequency hopping with OFDM [6].

3 The Implementation Scheme of Three-Layered Secure Communication Model 3.1

Implementation Scheme of Identity Authentication Layer

In identity authentication layer, credibility degree is introduced to perform OBU’s identity authentication, packet loss rate and packet sending frequency are used as characteristic parameters to evaluate OBU’s credibility degree [7]. Based on Hermes model [8], OBU’s credibility degree can be calculated by formula 1 and formula 2. qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðti  1Þ2 =x2i þ ðci  1Þ2 =y2i pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Ti ðti ; ci Þ ¼ 1  ði ¼ 1; 2Þ 1=x2i þ 1=y2i T ¼ aT1 þ bT2

ð1Þ ð2Þ

In above formulas, T1 is OBU’s credibility degree relevant to the event of packet loss rate, T2 is OBU’s credibility degree relevant to the event of packet sending frequency, T is OBU’s comprehensive credibility degree, xi, yi, and a, b are weight coefficients. Furthermore, let A1 be the number of successfully forwarding packets of OBU, B1 be the number of lost packets of OBU, A2 be the average value of packet sending frequency of OBUs in VANET, B2 be the maximum of packet sending frequency of OBUs, and then we have. The trust values relevant to packet loss rate and packet sending frequency are t1 ¼

A1 A1 þ B1

ð3Þ

A2 B2

ð4Þ

t2 ¼ 1 

The confidence values relevant to packet loss rate and packet sending frequency are c1 ¼ 1 

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 12A1 B1 ðA1 þ B1 Þ2 ðA1 þ B1 þ 1Þ

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 12A2 ðB2  A2 Þ c2 ¼ 1  B22 ðB2 þ 1Þ

ð5Þ

ð6Þ

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In general, Ti (0.5, 0) is used as threshold to distinguish trustworthy OBUs. To verify availability of identity authentication scheme, an example to calculate OBU’s credibility degree is shown in Table 1. Table 1. An example to calculate OBU’s credibility degree Parameter x1 (y1) x2 (y2) a (b) A1 A2

Value 0.6 (0.4) 0.6 (0.4) 0.5 (0.5) 950 packet 620 packet/s

Parameter B1 B2 t1 t2 c1

Value 280 packet 1800 packet/s 0.7724 0.6556 0.9586

Parameter c2 T (0.5,0) T1 T2 T

Value 0.9612 0.1230 0.8691 0.8063 0.8377

In identity authentication layer, following functions are defined: • OBU’s credibility degree analysis. In CVIS, RSU is responsible for monitoring and calculating OBU’s credibility degree, and submitted it to transportation management center. Transportation management center is responsible for collecting and integrating OBU’s credibility degree, and recording it into identity authentication database. • OBU’s credibility degree request and acknowledgement. RSU can obtain all VANET members’ credibility degrees by exchanging credibility degree request packet and acknowledgement packet with transportation management center. Besides, OBU can also obtain credibility degree of other OBUs in the same VANET by exchanging credibility degree request packet and acknowledgement packet with RSU. • OBU’s credibility degree announcement. RSU timely broadcasts all members’ credibility degrees in VANET, so that each OBU can verify other OBU’s identity according to their credibility degree. 3.2

Implementation Scheme of Channel Safe Access Layer

3.2.1 The Scheme of Channel Access Control The proposed channel access scheme is shown in Fig. 2, coordination method between CCH and SCHs is refers to Universal Time Coordinated (TUC) scheme [9]. In our scheme, channel’s access time is divided into different synchronizing cycles, containing channel reservation period, control frame period and service frame period. In order to improve channel utilizations, the duration of control frame period and service frame period can be dynamically adjusted according to network load conditions in CCH and SCHs. During fixed length channel reservation period, OBUs want to transmit data frames in current synchronizing cycle must send CAR (Channel Access Request) to RSU through idle channel among one CCH or six SCHs. When received CAR of OBUs, RSU assigns sub-channels to OBUs according to the statuses of credibility degree, length of control frame period or service frame period and load status in CCH and SCHs. At last, RSU broadcasts CAB (Channel Assignment Broadcast) to all VANET

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Fig. 2. Channel coordination scheme between CCH and SCHs

members at the end of channel reservation period through CCH, CAB packet contains the information of channel assignment results and time lengths of current control frame period and service frame period. During subsequent control frame period and service frame period, permitted OBUs can transmit data frames through assigned sub-channels, and data frames can be demodulated by receiver from corresponding sub-channels. 3.2.2 Performance Analysis of Channel Safe Access Scheme Let kC be arrival rate of CCH CAR frames, kS be arrival rate of SCH CAR frames, V be data transmission rate, LCR be length of CAR frames, LCF be length of control frames, LSF be length of service frames, NCF be the number of permitted OBUs in control frame period, NSF be the number of permitted OBUs in service frame period, and then we can obtained  NCF ¼ int  NSF ¼ int

7TCR
V kC  LCR kC þ kS

7TCR
V kS  LCR kC þ kS

TCF ¼ TSF

NCF
LCF V

NSF
LSF 6V

 ð7Þ  ð8Þ ð9Þ ð10Þ

Table 2 shows performance analysis results of channel safe access scheme in different communication conditions. It can be clearly observed that the length of timeslots can be dynamically adjusted with arrival rates of control frames or service frames, so that high channel utilizations can be maintained both in CCH and SCHs. On the other hand, the data frame length or network load must be restricted to avert overlong synchronizing cycles.

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Table 2. Analysis results of channel access layer (V = 6Mbit/s, LCR = 140bytes, TCR = 6 ms) kC (frames/s) kS (frames/s) 20 80 160 240 320 50 80 160 240 320 80 80 160 240 320

3.3

LCF (bytes) 500 1000 1500 2000 500 1000 1500 2000 500 1000 1500 2000

LSF (bytes) NCF 800 45 1600 25 2400 17 3200 13 800 86 1600 53 2400 38 3200 30 800 112 1600 75 2400 56 3200 45

NSF 180 200 207 211 138 171 186 194 112 150 168 180

TCF (ms) 30 33.3 34 34.67 57.3 70.7 76 80 74.7 100 112 120

TSF (ms) 32 71.1 110.4 150.04 24.5 60.8 99.2 138 19.9 53.3 112 128

Implementation Scheme of Encryption Communication Layer

Figure 3 shows the constitution of designed encryption communication system in physical layer, it combines OFDM modulation with frequency hopping technique. In order to achieve encryption communications, the frequency hopping patters are controlled by two-dimension chaotic sequence [10].

Fig. 3. The constitution of designed system in encryption communication layer

During control frame period, each OFDM sub-carrier in CCH is further divided into NCF time-slots. These multiple sub-channels divided by time and frequency are averagely assigned to permitted OBUs according to frequency hopping patters, and then data sent by source OBUs is modulated to specified sub-channels, it can be demodulated from corresponding sub-carriers and time-slots by receivers. The process of service frame period in SCHs is analogous to control frame period.

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For example, suppose the number of OFDM sub-carriers is 8 (from f1 to f8), NCF = 6, a feasible scheme of sub-channel assignment can be described as matrix A 2

3 5; 2; 4; 1; 6; 3 6 3; 6; 1; 5; 4; 2 7 6 7 6 6; 1; 3; 2; 5; 4 7 6 7 6 4; 2; 6; 3; 1; 5 7 7 A¼6 6 1; 3; 2; 5; 4; 6 7 6 7 6 6; 4; 1; 2; 5; 3 7 6 7 4 3; 2; 6; 4; 1; 5 5 6; 5; 3; 4; 2; 1 The row of matrix A denotes number of OFDM sub-carriers, the column denotes number of time-slots, and elements in matrix A denote number of permitted OBUs. Table 3 is frequency hopping pattern corresponding to matrix A. It can be observed that each OBU is assigned eight sub-channels of time division and frequency division. Table 3. An example of feasible frequency hopping pattern for OBUs (NCF = 6, the number of OFDM sub-carriers = 8) OBU1 OBU2 OBU3 OBU4 OBU5 OBU6

Time-slot1 f5 – f2, f7 f4 f1 f3, f6, f8

Time-slot2 f3 f1, f4, f7 f5 f6 f8 f2

Time-slot3 f2, f6 f5 f3, f8 f1 – f4, f7

Time-slot4 f1 f3, f6 f4 f7, f8 f2, f5 –

Time-slot5 f4, f7 f8 – f2, f5 f3, f6 f1

Time-slot6 f8 f2 f1, f6 f3 f4, f7 f5

4 Conclusions Based on WAVE standard, we present a three-layered communication model to improve the security of CVIS. Furthermore, the implementation schemes of each layer are discussed. In identity authentication layer, the indicator of credibility degree is introduced to perform OBU’s identity authentication. In channel access layer, access control scheme is proposed. In order to improve efficiency and safety in CCH and SCHs, our scheme integrates channel reservation and dynamic time-slot adjustment with time division multiplexing. In encryption communication layer, we propose a design scheme of encryption communication system of physical layer, in which OFDM modulation and scrambling of two-dimension chaotic sequence are combined with frequency hopping technique. As our further research work, we will focus on geographic routing protocol for multi-hop VANET based on node location prediction, high efficient channel access mechanism with priority control, complex simulation framework for communication performance test under unban transportation environments, and more accurate credibility degree detection algorithm of OBUs in CVIS.

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References 1. Yao, J., Yang, X., Wu, D., Shi, B.: Urban traffic control tentative exploration in vehicleinfrastructure integration environment. In: Proceedings of International Conference on Intelligent Computation Technology and Automation, pp. 1110–1113 (2010) 2. Festag, A.: Cooperative intelligent transport system standard in Europe. IEEE Commun. Mag. 52(12), 166–172 (2014) 3. IEEE Standard for Wireless Access in Vehicular Environments (WAVE)-Multi-Channel Operation, IEEE Std.1609.4 (2010) 4. Guo, X.: The implementation of WAVE standard’s MAC layer. Master Dissertation of Beijing Jiao Tong University of China (2013) 5. Campolo, C., Cozzetti, H.A., Molinaro, A., Mscopigno, R.: Overhauling ns-2 PHY/MAC simulation for IEEE 802.11p/WAVE vehicular networks. In: Proceedings of IEEE International Conference on Communications, pp. 7167–7171 (2012) 6. Ding, L.: System design and MATAB realization of OFDM. Commun. Technol. 41(11), 63– 65 (2008) 7. Wang, H., Zhang, Y.: On the security of an anonymous batch authenticated and key agreement scheme for value-added services in VANETs. In: Procedia Engineering-2012 International Workshop on Information and Electronics Engineering, vol. 26, pp. 1735–1739 (2012) 8. Zouridaki, C., Mark, B.L., Hejmo, M., Thomas, R.K.: Robust cooperative trust establishment for MANETs. In: Proceedings of the Fourth ACM Workshop on Security of Ad Hoc and Sensor Networks, pp. 23–34 (2006) 9. Murali, A., Bhanupriya, K., Snitha Shekar, B., Narendra Kumar, G.: Performance evaluation of IEEE 802.11p for vehicular traffic congestion control. In: Proceedings of International Conference on ITS Telecommunications, pp. 732–737 (2011) 10. Wang, X.: Synchronization of Chaotic System and Its Applications in Secure Communications. Science Press, Beijing (2012)

A Relay Protocol in AF Relaying Wireless Energy Harvesting Network Xian Li1,2, Yulong Han2, Qiuling Tang2(&), and Jiahao Shi2 1

2

School of Automation, Southeast University, Nanjing, China School of Computer, Electronics and Communication, Guangxi University, Nanning, China [email protected]

Abstract. In this article, we consider the energy harvesting system of wireless sensor networks with amplify and forward (AF) relay nodes. In this model, the working mode of the relay node is divided into two parts: first, the relay node collects the energy signal of the source node. Then, the data is sent to the target sensor through this energy. We put forward the relay cooperation protocol based on the relay forward protocol. We also calculate the mathematical expression of the interruption probability of the protocol and investigate the value of outage and throughout. Finally, the experimental simulation is carried out by MATLAB, the performance of relay cooperation protocol is better than that of relay protocol. Keywords: Radio frequency energy harvesting
AF relay Channel interruption probability
Network throughput


Relay protocol

1 Introduction The bottleneck problem in the application of wireless network is that the energy of nodes is limited. Nodes are usually powered by battery, the periodicity of the battery replacement will greatly increase the cost of network maintenance and it is hard to replace the battery in some special environments. Over the past ten years, wireless portable communication system has attracted wide attention of experts from home and abroad, and quickly become a research hot spot [1–3]. Simultaneous wireless information and power transfer (SWPT) technology is a new type of wireless communication. In SWPT technology, radio frequency signals can be used to transmit information as well as to transmit energy, which provides a durable energy supply for the energy constrained network. In recent years, the research involved in SWPT technology mainly includes SISO system [4], SIMO system [5], MISO system [6], and MIMO system [7]. Unlike [4–7], [8–10] study the three-node SWPT system. The relay forward protocol is proposed in [8]. The influence of different system parameters on decode and forward (DF) relay network is analyzed in [9]. The time switching based protocol with block-wise transmission is considered in [10].

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This paper considers the three-node SWPT system based AF relaying. We propose the relay cooperation protocol: on the basis of relay forward protocol, joined the direct link between source node and destination node. Finally, according to the SNR, the outage probability and the mathematical expression of the network throughput are derived and analyzed, the performance of relay cooperation protocol is better than that of relay protocol. The structure of this article is as follows: In Sect. 1, we propose the system model of this paper and make assumptions about it. A mathematical formula for the probability of interruption and the throughput of the network is given in Sect. 2, and make a mathematical analysis of it. The numerical results and simulation analysis of the algorithm are given in Sects. 3. Finally, Sect. 4 concludes this paper.

2 Relay Cooperation Protocol In the model of relay cooperation protocol system in article, the SWPT system consists of three parts: source node, S, a relay node, R and a destination node, D. As shown in Fig. 1, the relay node collects the energy signal of the source node, then, the data is sent to the target sensor through this energy. Apart from the relay cooperation link, the direct communication between D and s is also considered.

Fig. 1. Relay cooperation protocol model

In Fig. 1, channel gain between S, R and D is block fading and frequency non selective parameters, denoted by h, g and l, respectively. In the unit time T, the channel parameters do not change with time, and satisfies independent and identically distributed in each unit time T. In this wireless channel, the non selective Rayleigh block fading is the main consideration. The channel parameters adopted in this paper are consistent with the literature [4, 9]. The transmission block of the wireless communication system has been given in Fig. 2. a represents the score of block time T and the block time is composed mainly of three parts: aT, ð1  aÞT=2 and ð1  aÞT=2. In the first part time, R harvests energy from source RF signal; in the second part time, S transmits information to both R and D; in the third part time, the energy consumption of R sending messages to D comes from R’s own RF energy collection. The performance parameter analysis of the system is described in detail in the following section.

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Fig. 2. The relationship between energy harvesting and information forwarding in time allocation in relay cooperative protocol

2.1

Outage Probability of the S - R - D Link

The probability of outage between S - R - D is given in [8]: c

bkh pSRD uK1 ðuÞ out  1  e

ð1Þ

where, a ¼ d1m d2m r2nrd ð1  aÞc0 b ¼ 2gPs a c ¼ 2gd1m r2nsr ac0 qffiffiffiffiffiffiffiffi u ¼ bk4ah kg The distances between S and R, R and D are expressed by d1 and d2 , respectively. and r2nrd is the variance of overall Additive White Gaussian Noise at R and D, respectively. Ps is the source transmission power. g 2 ð0; 1Þ is the energy conversion efficiency. c0 ¼ 2H  1 is the threshold value of Signal Noise Ratio (SNR) for correct date detection at D, and H denotes the source transmission rate. kh and kg are the mean values of the exponential random variables jhj2 and jgj2 respectively, and K1 ð
Þ represents second classes of first order modified Bessel’s functions [11]. r2nsr

2.2

Outage Probability of the S - D Link

In the transmission link of the S-D, the receive signal at D, ysd ðkÞ, is given by 1 pffiffiffiffiffi ysd ðkÞ ¼ pffiffiffiffiffimffi Ps lsðkÞ þ nd ðkÞ d3

ð2Þ

where d3 is the S to D distance, sðkÞ is the sampled and normalized information signal from S, nd ðkÞ represents the overall Additive White Gaussian Noise at D, where r2nsd is the variance of nd ðkÞ.

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Using (1), the SNR, csd , is given by csd ¼

Ps jlj2 d3m r2nsd

ð3Þ

Thus, the outage probability between S and D can be given by pSD out ¼ pðcsd \c0 Þ   d3m r2nsd c0 2 ¼ p jlj \ Ps

ð4Þ

2

¼ pðjlj \dÞ ¼ 1  ed=kt where d ¼ d3m r2nsd c0 =Ps , Fjlj2 ðdÞ ¼ pðjlj2 \dÞ ¼ 1  ed=kt is the cumulative distribution function of the exponential random variable jlj and kt is the mean value of an exponential random variable jlj. 2.3

Throughput Analysis

According to (1) and (4), System outage probability can be made by    c pout ¼ 1  e bkh uK1 ðuÞ ð1  ed=kt Þ

ð5Þ

where, a ¼ d1m d2m r2nrd ð1  aÞc0 b ¼ 2gPs a c ¼ 2gd1m r2nsd ac0 d ¼ d3m r2nsd c0 =Ps qffiffiffiffiffiffiffiffi u ¼ bk4ah kg Given the transmission rate, H bits/s/Hz, and the effective communication time, ð1  aÞT=2, the throughput, s, at the destination is given by s ¼ ð1  pout ÞH

ð1  aÞT=2 ð1  pout ÞHð1  aÞ ¼ T 2

ð6Þ

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3 Numerical Results In the third part of this paper, numerical simulation is proved mainly through MATLAB to verify the excellent performance of the relay cooperation protocol proposed in this paper. If there is no special explanation, the parameters commonly used in this paper are installed as follows: η = 1, Ps = 1 J/s, m = 2.7, H in Figs. 1 and 2 are set to 3 bits/s/Hz, Noise variance in the process of propagation, r2nsr , r2nrd and r2nsd are set to 0.01, respectively. Among them, the distance parameters d1, d2, d3 are set to 1, 1, 2. Other simulation parameter values in this article, |h|2, |g|2 and |l|2, kh, kg and kl are installed with 1. Figure 3 describes the changes in the function of pout and a under different protocols. Obviously: in the same protocol, a changes from 0 to 1, and as a increases, outage probability becomes smaller; in the same a, the outage probability performance in relay cooperation protocol is better then relay forward protocol.

Fig. 3. Outage probability changes of a in TSR scheme.

Figure 4 describes the changing trend of throughput s with time block fraction a under various protocols. As shown in Fig. 2: for any given a, the throughput performance in relay cooperation protocol is better then relay forward protocol; for both two protocols, there exist an a to achieve the optimal throughput. Figure 5 plots the optimal throughput s versus transmission rate H. As shown in figure, the optimal throughput of both two protocols are close in a high transmission rate, but at relatively low transmission rates, the optimal throughput in relay cooperation protocol is better then relay forward protocol.

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Fig. 4. Achievable throughput s versus a.

Fig. 5. Optimal throughput s versus transmission rate H.

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4 Conclusion This paper proposes a relay cooperation protocol in AF relaying wireless energy harvesting network. At the same time, a detailed mathematical deduction and proof of outage probability is given, the numerical relationship between outage and ergodic in relay cooperation protocol is also analyzed. The experimental results of the algorithm show that the functions of the presented relay cooperation algorithm in this paper is better than that of the relay forward algorithm. Acknowledgement. This work was supported by National Natural Science Foundation of China (Grant No. 60974120), Guangxi Natural Science Foundation under Grant (No. 2014GXNSFAA118373).

References 1. Zhou, X., Zhang, R., Ho, C.K.: Wireless information and power transfer: architecture design and rate-energy tradeoff. In: IEEE GLOBECOM (2012). https://doi.org/10.1109/glocom. 2012.6503739 2. Liu, V., Parks, A., Talla, V., Gollakota, S., Wetherall, D., Smith, J.R.: Ambient backscatter: wireless communication out of thin air. In: ACM SIGCOMM (2013) 3. Zungeru, A.M., Ang, L.M., Prabaharan, S., Seng, K.P.: Radio frequent energy harvesting and management for wireless sensor networks. In: Venkataraman, H., Muntean, G.-M. (eds.) Green Mobile Devices and Networks: Energy Optimization and Scavenging Techniques. CRC Press, Boca Raton (2012) 4. Liu, L., Zhang, R., Chua, K.C.: Wireless information transfer with opportunistic energy harvesting. IEEE Trans. Wirel. Commun. 12(1), 288–300 (2013) 5. Liu, L., Zhang, R., Chua, K.C.: Wireless information and power transfer: a dynamic power splitting approach. IEEE Trans. Commun. 61(9), 3990–4001 (2013) 6. Xu, J., Liu, L., Zhang, R.: Multiuser MISO beamforming for simultaneous wireless information and power transfer. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vancouver, Canada, pp. 4754–4758 (2013) 7. Park, J., Clerckx, B.: Joint wireless information and energy transfer in a two-user MIMO interference channel. IEEE Trans. Wirel. Commun. 12(8), 4210–4221 (2013) 8. Nasir, A.A., Zhou, X., Durrani, S.: Relaying protocols for wireless energy harvesting and information processing. IEEE Trans. Wirel. Commun. 12(7), 3622–3636 (2013) 9. Nasir, A.A., Zhou, X., Durrani, S.: Throughput and ergodic capacity of wireless energy harvesting based DF relaying network. In: IEEE ICC, pp. 4066–4071 (2014) 10. Nasir, A.A., Zhou, X., Durrani, S.: Wireless-powered relays in cooperative communications: Time-switching relaying protocols and throughput analysis. IEEE Trans. Commun. 63(5), 1607–1622 (2015) 11. Gradshteyn, I.S., Ryzhik, I.M.: Table of Integrals, Series, and Products, 4th edn. Academic Press, Cambridge (1980)

Information Sharing Technology in Device-to-Device Cellular Networks Min Wang(&), Qiaoyun Sun, Shuguang Zhang, and Yu Zhang Beijing City University, Beijing 100083, China [email protected]

Abstract. Device to device communication (Device-to-Device, D2D) technology can increase the spectrum utilization of cellular networks. It can significantly improve the performance of data distribution system, such as increasing network throughput, improving spectrum efficiency and reducing network load. The idea of information sharing in D2D cellular networks is that a cluster is formed by multiple mobile terminals connected to the cellular network in close geographical location. In D2D cellular networks, the content distribution can be realized by means of communication between mobile terminals with content caching ability, which can improve the efficiency of content distribution, reduce the traffic load of cellular network and decrease the time delay of content acquisition. Keywords: Device-to-device Content distribution


Information sharing
Cellular networks

1 Introduction With the popularity of social video, real-time streaming media, 360° immersion experience and VR/AR applications, the mobile multimedia service is becoming more and more important for the users. Nowadays, the video and other multimedia data have become the main components of mobile network data traffic. Strategy Analytic predicts that by 2021, the penetration rate of mobile multimedia will reach 36%, and the total number of mobile multimedia users will exceed 2 billion. How to make good use of limited network resources to achieve high-speed multimedia data distribution has become one of the most important topics in the field of wireless communication. Device to device communication (Device-to-Device, D2D) technology can increase the spectrum utilization of cellular networks, and to some extent solve the problem of the lack of spectrum resources in wireless communication systems. As an efficient information transmission scheme, D2D is considered to be one of the most promising largescale data distribution technologies in the mobile internet field, which can meet the growing demand for mobile multimedia services in the future.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 830–835, 2019. https://doi.org/10.1007/978-3-030-02804-6_108

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2 Device-to-Device Technology 2.1

Fundamental Theory of D2D Technology

The core idea of D2D technology is to achieve efficient point to point or point to multipoint transmission by using the advantages of high D2D communication rate, high spectral efficiency and low power consumption. D2D networking can significantly improve the performance of data distribution system, such as increasing network throughput, improving spectrum efficiency and reducing network load. D2D technology is introduced into the traditional cellular network system to form D2D cellular network. One of the typical applications of D2D cellular network is information sharing. Data dissemination services in traditional cellular networks, such as data downloading and multimedia sharing, can improve efficiency through D2D technology. The basic idea of information sharing in D2D cellular networks is that a cluster is formed by multiple mobile terminals connected to the cellular network in close geographical location. Each mobile terminal in the cluster receives part of the data content through the base station downlink respectively, and each mobile terminal in the cluster sends the data blocks received by each of the mobile terminals to the other mobile terminal in the cluster. The use of D2D communication to share information between mobile terminals in the cellular network can reduce the traffic load of the cellular network back link, and improve the efficiency of wireless resource utilization and content distribution. The cluster information sharing in D2D cellular network includes two parts: D2D discovery and D2D communication. They both require the management control and the downlink signaling support of the cellular base station, including the cluster management based on terminal ID, the terminal location of data downloading, the terminal receiving status notification, and the wireless resource allocation. 2.2

Key Problems of D2D Technology

Address oriented management architecture has a large control overhead. If IP address or terminal ID is addressed in the process of information sharing in D2D, the content information base needs to be maintained. The IP/ID of the content is determined, and the whole network information needs to be synchronized and continuously broadcast. The scalability of the network is poor, and the management and control overhead is large. The storage and forwarding control of data content is complex. The terminal cluster needs to be generated and maintained in the process of sharing information in D2D, and the storage and forwarding of data content need to be carried out in the cluster. However, the mobility of the terminal and the uncertainty of the wireless link make the terminal cluster change, and the complexity of content storage and forwarding in the maintenance cluster will be greatly increased, which affects the acquisition of information content and the reliability of information transmission.

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3 Research on D2D Network Information Sharing For the traditional cellular communication system, the two mobile terminals must carry out information interaction and must transmit information through their base stations. However, for some relatively close users, such as those in the same office, they need to share information with each other. If the traditional cellular communication mode is still forwarded through the base station, the resources of the cellular system will be wasted. The cellular controlled D2D communication system will effectively solve this problems. D2D communication system is a short distance communication technology, which can completely change the working mode under the above environment, that is, the data is transmitted directly between the users without the base station forwarding. When the distance between users is closer, the channel quality between them is better, and the transmission rate is high. The application scenarios of D2D network are mainly divided into two aspects: public security and commercial application. The application of public security means that when the natural disasters such as fire and earthquake occur, and when the traditional cellular network is not working properly, it allows the terminal to communicate directly without the control of the cellular network. Business application scenarios can include: information push for target users, information sharing among mobile users, and information interaction for the internet of things. With the development of D2D technology, its advantages have been recognized by more and more people. D2D technology can be widely used in IMT-Advanced systems and the next generation cellular network (5G) [1]. The 5G cellular network is expected to obtain 1000 times the unit area data capacity, 10-100 times the number of access devices and user data rate, 10 times the battery life, and reduce the transmission delay by 5 times. Because D2D technology can reduce the consumption of system resources, reduce transmission delay and expand coverage, it has important theoretical value and wide application prospects, and its research has aroused great concern in the academic and industrial circles. International well-known universities, scientific research institutions and companies, such as University of Helsinki, University of Waterloo, Qualcomm Corp, PHILPS, NOKIA Research Center, etc. have actively invested in the research of D2D and have obtained some important research results. In China, Tsinghua University, Shanghai Jiao Tong University and Electronic Science and Technology University have also studied D2D and made some progress. At present, a lot of research on D2D network mainly focuses on the following aspects: transmission mode selection [2], interference coordination [3], power control [4] and resource allocation [5]. 3.1

Analysis of D2D Communication

Introducing caching technology into cellular networks to achieve multimedia content distribution helps reduce network load and improve network performance [6]. Therefore, in view of cellular networks, including heterogeneous cellular networks and D2D cellular networks, the research of cache technology has become one of the key research points of 5G in the recent two years. [7] analyses the theoretical performance index of the content access probability and service rate in the heterogeneous cellular networks of

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small base stations, relays and caching terminals. In document [8, 9], the approximate scaling characteristics of D2D network cache performance are analyzed, the relationship between cooperation range and interference is studied, and the best transmission range is given to achieve maximum frequency reuse. The above research is mainly based on theoretical performance analysis. Based on the caching capability of mobile terminals in cellular networks, the performance gain of cellular networks is analyzed. [10] designs and optimizes the algorithm from the perspective of D2D network cache technology. [11] presents a markup based opportunity sharing mechanism in mobile social networks, which selects mobile terminal clusters based on content influence and user mobility for D2D communication. [12] studies the problem of mobile link instability in D2D content sharing, and proposes a D2D link allocation algorithm. [13] studies the influence of user selfish behavior on the performance gain of content sharing in D2D network, establishes a game model to represent the selfishness of users, and then proposes a network formation algorithm, which determines its own content sharing strategy based on historical information. [14] studies the communication scope of the terminal when D2D networks share content. Combined with the characteristics of the community, the strategy of building long connection edges is given. 3.2

Analysis of Content Caching

However, the main consideration of D2D communication range or the establishment of terminal cluster, D2D communication routing and link allocation problem [15, 16] is in the related research. The research is within the scope of communication and does not study the implementation of D2D content sharing from the cache itself. The above studies do not specify how to implement content cache and forwarding management and control in D2D cellular networks. The caching strategy based on content attributes is to use the popularity of the content (access frequency) as a caching decision condition and to cache the content [17–19] with high popularity. Based on node attribute cache decision, node attributes are used as caching decision conditions to cache [20] content at nodes with higher node attributes. [6] gives different schemes for the deployment of content caching in mobile core networks and wireless access networks. By designing cache strategies for content popularity, timeliness, diversity, and replica locations, the performance gain of minimizing both the traffic and the user delay can be achieved. [21] studies the network performance of base station configuration data storage function in heterogeneous cellular networks. 3.3

Analysis of Control Management

In document [22], the protocol overhead is reduced by simplifying the data structure of the interest package and the data packet. A probabilistic selection transmission mechanism based on energy aware is proposed to preset residual energy threshold [23]. [24, 25] studies the information central network architecture in D2D cellular networks. The document [24] performs content caching in local users with sharing the cached copies of the cached data across users through the D2D communication between users, thus it effectively reduces the base station load. The literature [25]

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compares the performance difference between cellular access and D2D transmission mode, and designs the selection criteria for transmission mode in D2D cellular networks with the aim of minimizing the energy dissipation of the unit’s successful transmission.

4 Conclusion In D2D cellular networks, the content distribution can be realized by means of communication between mobile terminals with content caching ability, which can improve the efficiency of content distribution, reduce the traffic load of cellular network and decrease the time delay of content acquisition. With the popularity of intelligent terminals, the scale of mobile cellular network users has expanded rapidly. Mobile internet services enrich people’s social activities, and the users’ dependence on mobile social networks is growing. 5G is a new generation of mobile communication system for the development of mobile communication demand in 2020. 5G mobile communication will be closely combined with other wireless mobile communication technologies to form a new generation of ubiquitous mobile information network, which can meet the development demand of 1000 times more mobile internet traffic in the next 10 years. As a typical application scenario of 5G, the content sharing in hot spots will be the key problem that 5G should need to provide the solution. D2D is expected to further evolve to D2D communication technology in 5G cellular networks on the basis of 4G standardization.

References 1. Khoshkholgh, M.G., Zhang, Y., Chen, K.C., Shin, K.G., Gjessing, S.: Connectivity of cognitive device-to-device communications underlying cellular networks. IEEE J. Sel. Areas Commun. 33(1), 81–99 (2015) 2. Liu, Z., Peng, T., Xiang, S., Wang, W.: Mode selection for Device-to-Device (D2D) communication under LTE-Advanced networks. In: 2012 IEEE International Conference on Communications (ICC), pp. 5563–5567. IEEE (2012) 3. Min, H., Lee, J., Park, S., Hong, D.: Capacity enhancement using an interference limited area for device-to-device uplink underlaying cellular networks. IEEE Trans. Wirel. Commun. 10 (12), 3995–4000 (2011) 4. Erturk, M.C., Mukherjee, S., Ishii, H., Arslan, H.: Distributions of transmit power and SINR in device-to-device networks. Commun. Lett. IEEE 17(2), 273–276 (2013) 5. Phunchongharn, P., Hossain, E., Kim, D.I.: Resource allocation for device-to-device communications underlaying LTE-advanced networks. IEEE Wirel. Commun. 20(4), 91– 100 (2013) 6. Wang, X., Chen, M.: Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun. Mag. 52(2), 131–139 (2014) 7. Yang, C., Yao, Y., Chen, Z., Xia, B.: Analysis on cache-enabled wireless heterogeneous networks. IEEE Trans. Wirel. Commun. 15(1), 131–145 (2016) 8. Golrezaei, N., Dimakis, A.G., Molisch, A.F.: Scaling behavior for device-to-device communications with distributed caching. IEEE Trans. Inf. Theory 60(7), 4286–4298 (2014)

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9. Golrezaei, N., Dimakis, A.G., Molisch, A.F.: Wireless device-to-device communications with distributed caching. In: IEEE International Symposium on Information Theory Proceedings (ISIT), pp. 2781–2785 (2012) 10. Jiang, J., Zhang, S., Li, B., Li, B.: Maximized cellular traffic offloading via device-to-device content sharing. IEEE J. Sel. Areas Commun. 34(1), 82–91 (2016) 11. Wang, X., Li, X., Leung, V.C.M.: TASA: traffic offloading by tag-assisted social-aware opportunistic sharing in mobile social networks. In: IEEE International Workshop on Local and Metropolitan Area Networks, pp. 1–6 (2015) 12. Wang, L., Wu, H., Han, Z.: Wireless distributed storage in socially enabled D2D communications. IEEE Access 4, 1971–1984 (2016) 13. Wang, T., Sun, Y., Song, L., Han, Z.: Social data offloading in D2D-enhanced cellular networks by network formation games. IEEE Trans. Wirel. Commun. 14(12), 7004–7015 (2015) 14. Zhao, Y., Li, Y., Mao, H., Ge, N.: Social community aware long-range link establishment for multi-hop D2D communication networks. In: IEEE International Conference on Communications (ICC), pp. 2961–2966 (2015) 15. Alicherry, M., Bhatia, R., Li, L.: Channel assignment and routing in multi-radio wireless mesh networks (2016) 16. Darehshoorzadeh, A., Grande, R.E.D., Boukerche, A.: Toward a comprehensive model for performance analysis of opportunistic routing in wireless mesh networks. IEEE Trans. Veh. Technol. 65(7), 5424–5438 (2016) 17. Haipeng, L.I., Nakazato, H.: Two-level popularity-oriented cache replacement policy for video delivery over CCN. IEICE Trans. Commun. E99.B(12), 2532–2540 (2016) 18. Mangili, M., Martignon, F., Capone, A.: Performance analysis of content-centric and content-delivery networks with evolving object popularity. Comput. Netw. 94, 80–98 (2016) 19. Sermpezis, P., Spyropoulos, T.: Effects of content popularity on the performance of contentcentric opportunistic networking: an analytical approach and applications. IEEE/ACM Trans. Netw. PP(99), 1 (2016) 20. Izquierdo, L., Hanneman, R.: Introduction to the formal analysis of social networks using mathematica (2016) 21. Zaidi, R., Ghogho, M., McLernond, C.: Information centric modeling for two-tier cache enabled cellular networks. In: International Conference on Communication Workshop (ICCW), pp. 80–86. IEEE (2015) 22. Kuniyasu, T., Shigeyasu, T.: A study on implementation of NDN to WSN. In: IEEE, International Conference on Advanced Information Networking and Applications, pp. 392– 398. IEEE (2017) 23. Zou, Y., Liu, W., Yang, Y., et al.: Energy-aware probabilistic forwarding in wireless content-centric networks. In: International Conference on Information and Communication Technology Convergence, pp. 270–275 (2016) 24. Chandrasekaran, G., Wang, N., Tafazolli, R.: Caching on the move: towards D2D-based information centric networking for mobile content distribution. In: The 40th Conference on Local Computer Networks (LCN), pp. 312–320. IEEE, Clearwater Beach (2015) 25. Xu, Y., Wang, S.: Mode selection for energy efficient content delivery in cellular networks. IEEE Commun. Lett. 20(04), 728–731 (2016)

The Weakness of the Self-encryption Mechanism for Authentication of Roaming Services Min-Shiang Hwang1,2, Song-Kong Chong3, and Cheng-Ying Yang4(&) 1

2

4

Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan 3 Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan Department of Computer Science, University of Taipei, Taipei 100, Taiwan [email protected]

Abstract. In this paper, we will show that the authentication protocol for roaming service proposed by Hwang-Chang is vulnerable to Denial-of-Service attack. Most important we also will show that the protocol may cause the mobile communication system to become insecure because the home network is fully trusted and because the security responsibility of each party is unclear. Keywords: Authentication PCS


Denial of service
Global communications

1 Introduction Mobile communications have been widely used in personal applications [1–3]. Previous researchers have proposed various schemes to protect in mobile communications. These schemes include in authentication [4–8], secure routing [9–15], roaming [16– 18], key management [19, 20], and attack model [21–26]. In this paper, we will show that the authentication protocol for roaming service proposed by Hwang-Chang is vulnerable to Denial-of-Service attack. Most important we also will show that the protocol may cause the mobile communication system to become insecure because the home network is fully trusted and because the security responsibility of each party is unclear. The next section of this paper is devoted to an overview of self-encryption mechanism for authentication of roaming users proposed by Hwang-Chang [27]. In Sect. 3, we will discuss the weaknesses of Hwang-Chang’s mechanism. Finally, a conclusion for this paper is given in Sect. 4.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 836–841, 2019. https://doi.org/10.1007/978-3-030-02804-6_109

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2 Hwang-Chang’s Mechanism Hwang-Chang’s self-encryption mechanism for authentication of roaming [27] is showed in Fig. 1. Ui, V, H indicate the identity of the roamer, the visited network, and the home network, respectively. Below is the description of the protocol: Ui firstly generates a random number r0 and sends the messages Ui, H, EKuh(Kuh || r0) to V in Step 1, where Kuh denotes the long-term shared secret key between Ui and H. After receiving the messages, V generates r1. V passes EKvh(Ui || r1 || t) and EKuh(Kuh || r0) to H in Step 2, where Kvh denotes the long-term shared secret key belonging to the V and H, t denotes the current date and time. In Step 3, H checks the validity of Ui through a secret one-way function f, if Ui is valid, H passes EKvh(r1) and C = EKuh(r0 || r1 || V) to V. If DKvh(EKvh(r1)) = r1, then V believes Ui is an authorized user, V sets r1 as the authentication key and passes C received from H to Ui in Step 4. In Step 5, the legitimate Ui will obtain the correct authentication key Kauth = r1 if the examination to r0 is passed. After that, Ui sends EKauth(r1) to V to confirm that he has received the correct authentication key Kauth. In Step 6, if V validates r1, then V records the authentication key Kauth for Ui.

Fig. 1. Hwang-Chang’s authentication protocol for roaming service

The lower portion of Fig. 1 demonstrates the establishment of the session key after the upper portion of Fig. 1 is completed. For a detailed description of authentication protocol for roaming service, we refer the reader to [27].

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3 The Weaknesses of Hwang-Chang’s Mechanism Because the roamer Ui and V have not shared a secret key before, the authentication process of V to verify the validity of Ui requires the assistance of Ui′s home network H. The weaknesses of the protocol are that the authentication key Kauth or r1 used to verify Ui in subsequent authentication processes (when Ui requires a service from V), is known by the roamer’s home network H in the initial authentication process. Since Kauth is a critical authentication key for V to identify a particular roamer Ui, V can charge the related fees to H according to the service requests of Ui to V, we advise that it should be kept as a secret between V and Ui; it should not be known by a third party. Another weakness of Hwang-Chang’s protocol is its vulnerability to DoS attack. An intruder can replay the messages generated by a legitimate Ui in Step 1 and cheat H easily. The weaknesses of [27] can be summarized as follows: (1) A malicious system operator of a home network may profit by compromising the Kauth to other roamers through the authentication loophole of the protocol, in which the home network knows the Kauth in the initial authentication process. It leads to illegal roamers using the services of the visited network illegally. Fees will be recorded to the bill of the legitimate roamer, since the visited network believes that a legitimate roamer requires the services. (2) The session key establishment will employ the Kauth mentioned above to first make a mutual authentication between Ui and V. The malicious intruder may eavesdrop all of the communication contents of the legitimate roamers since he/she knows the authentication key from the home network. Therefore an intruder has the capability to decrypt the EKauth(r′0 || Ks || V) and obtain the session key Ks. (3) By replaying Ui, H, EKuh(Kuh || r0) eavesdropped in Step 1 to any other visited network V′ which is not currently attached by Ui, an intruder can launch a serious DoS attack to a particular roamer Ui. In the GSM, the home network H of Ui will maintain the current location of the mobile. The home network H will then route a call to the visited network V where Ui is currently located. It is easy to find that after H authenticates Ui in Step 3, the current location of the Ui will be updated accordingly. The authors suppose that the later authentication to Ui by V will pass deservedly if Ui is able to produce a valid EKuh(Kuh || r0) to H in Step 2. However, a serious DoS attack can be launched by an intruder by using this security weakness. As mentioned before, when an intruder replaying Ui, H, EKuh(Kuh || r0) which eavesdropped in Step 1 to any visited network V′, V′ will then transmit the related messages to H. Because the timestamp t present by V′ is fresh, and the verification to the EKuh(Kuh || r0) is positive, the home network H will believe that Ui is currently attach to V′. Therefore, the current location of Ui will be updated by H accordingly. In the meantime, if a call is coming for Ui, H will route the call to the improper visited network V′ and consequently cause the Ui who currently attach to a visited network V missing the call. Although it can be argued that V′ will inform H about do not update the new location of Ui if the authentication to Ui is failure.

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However, there still exists a problem. According to the security analysis in [27], an intruder is able to cut off the communication between V′ and H. In accordance with this capability, the intruder can also intercept the warning message sent by V′ to H in our attack. Therefore, H will believe the remaining authentication processes are working properly, and feel relieved to route any calls to the improper V ′. Since the calls will never be received by Ui, therefore the DoS attack is succeeded by the intruder. (4) After eavesdropping and gathering many legal authentication messages Ii′ = EKuh(Kuh || r0) of Ui in Step 1 of the protocol, an intruder can launch the DoS attack to the roaming service by replaying them to V persistently in great quantity. V will consider that a legitimate roamer is requiring services. V sends the messages to H for verification. The Ii′ will pass the examination of H and related information will be sent back to V. Finally, the replaying will fail in Step 5 because the intruder cannot decrypt C to obtain the authentication key Kauth = r1. Unfortunately, the goal of the intruder is achieved. A large number of garbage authentication messages may exhaust the memories of V due to r1 and related roamer information needed to be stored in its memory over a long period of time. Since there are no mechanisms in Hwang-Chang’s protocol to resist this attack as soon as possible, it causes the garbage information to be stored from Step 2 until Step 6. The weakness of the protocol causes the DoS attack on memory can be succeeded and can only be discovered in the final step. The prior efforts become wasted. From the viewpoint of efficiency, this weakness is serious and intolerable. Since our exposition of DoS attack is quite straight and easy to understand, we will just make a detailed discussion about the first and second weaknesses of the protocol in the following. When a roamer, says Alice, moves to a visited network, after authorizing herself to the visited network used the authentication protocol proposed by HwangChang, she will obtain an authentication key Kauth. Now Alice needs to make a call. The visited network will use the Kauth issued previously to identify Alice. After the visited network authorized Alice, it will generate a session key Ks to Alice. Each time Alice requires a service, the visited network may use the Kauth to verify the identity of Alice and record the services that Alice uses. Since the visited network has made a contract with Alice’s home network previously, the visited network may charge Alice’s home network according to the services used by Alice. Alice’s home network will charge these fees to her based on the record obtained from the visited network. Such a scenario demonstrates the operation of global mobility services. The second weakness is based on the previous one. Once the authentication key used for validating the identity of Alice is compromised to other illegal roamers by Alice’s home network, the illegitimate roamers may eavesdrop all of the communication contents of Alice by just decrypting EKauth(r′0 || Ks || V) sent by V in Step 2′. The illegitimate roamers will then obtain the session key Ks which is used for communications privacy accordingly. The problem is unable be warded off because the establishment of a session key is based on Alice’s authentication key.

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4 Conclusions In this paper, we have shown that Hwang-Chang’s authentication mechanism for roaming service is vulnerable to Denial-of-Service attack. We also show that their mechanism may cause the mobile communication system to become insecure because the home network is fully trusted and because the security responsibility of each party is unclear. In future, it’s important to study an improvement to the protocol to avoid its security flaws. The protocol should be strived to make the computation performance of the roamer become more efficient during the authentication phase. Acknowledgements. This work was partially supported by the Ministry of Science and Technology, Taiwan, under grant MOST 106-3114-E-005-001, MOST 106-2221-E-468-002, and MOST 106-2221-E-845-001.

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14. Li, C.-T., Yang, C.-C., Hwang, M.-S.: A secure routing protocol with node selfishness resistance in MANETs. Int. J. Mobile Commun. 10(1), 103–118 (2012) 15. Jiang, C.-L., Wu, S.-L., Gu, K.: New kind of delegation-based anonymous authentication scheme for wireless roaming networks. Int. J. Netw. Secur. 20(2), 235–242 (2018) 16. Guo, D., Wen, F.: A more robust authentication scheme for roaming service in global mobility networks using ECC. Int. J. Netw. Secur. 18(2), 217–223 (2016) 17. Le, H.-D., Chang, C.-C., Chou, Y.-C.: a novel untraceable authentication scheme for mobile roaming in GLOMONET. Int. J. Netw. Secur. 17(4), 395–404 (2015) 18. Xie, Q.-Q., Jiang, S.-R., Wang, L.-M., Chang, C.-C.: Composable secure roaming authentication protocol for cloud-assisted body sensor networks. Int. J. Netw. Secur. 18 (5), 816–831 (2016) 19. Hwang, M.-S.: Dynamic participation in a secure conference scheme for mobile communications. IEEE Trans. Veh. Technol. 48(5), 1469–1474 (1999) 20. He, L., Yuan, C., Xiong, H., Qin, Z.: An efficient and provably secure certificateless key insulated encryption with applications to mobile internet. Int. J. Netw. Secur. 19(6), 940–949 (2017) 21. Rana, A., Sharma, D.: Mobile ad-hoc clustering using inclusive particle swarm optimization algorithm. Int. J. Electron. Inf. Eng. 8(1), 1–8 (2018) 22. Al-khatib, A.A., Hammood, W.A.: Mobile malware and defending systems: comparison study. Int. J. Electron. Inf. Eng. 6(2), 116–123 (2017) 23. Binlin, C., Jianming, F.: Social bots detection on mobile social networks. Int. J. Netw. Secur. 19(1), 163–166 (2017) 24. Mas’ud, M.Z., Sahib, S., Abdollah, M.F., Selamat, S.R., Huoy, C.Y.: A comparative study on feature selection method for N-gram mobile malware detection. Int. J. Netw. Secur. 19(5), 727–733 (2017) 25. Choudhury, H., Roychoudhury, B., Saikia, D.Kr.: Security extension for relaxed trust requirement in non-3GPP access to the EPS. Int. J. Netw. Secur. 18(6), 1041–1053 (2016) 26. Janani, V.S., Manikandan, M.S.K.: An outlook on cryptographic and trust methodologies for clusters based security in mobile ad hoc networks. Int. J. Netw. Secur. 20(4), 746–753 (2018) 27. Hwang, K.-F., Chang, C.-C.: A self-encryption mechanism for authentication of roaming and teleconference services. IEEE Trans. Wireless Commun. 2(2), 400–407 (2003)

Cryptanalysis of the Serverless RFID Authentication and Search Protocols Chia-Hui Wei1,2, Cheng-Ying Yang3, and Min-Shiang Hwang4,5(&) 1

National Central Library, Taipei, Taiwan Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 3 Department of Computer Science, University of Taipei, Taipei, Taiwan 4 Department of Computer Science and Information Engineering, Asia University, Taiwan, 500, Lioufeng Rd., Wufeng, Taichung 41354, Taiwan [email protected] 5 Department of Medical Research, China Medical University Hospital, China Medical Univesity, Taichung, Taiwan 2

Abstract. In this article, we analyze Tan et al.’s serverless RFID authentication and search protocols. Tan et al. proposed a serverless RFID system to solve this problem without a central database while protecting the data in the portable reader. Their protocol based on serverless RFID system includes the design of a secure protocol to protect the data of the portable reader. Even if the portable reader is lost, the adversary still cannot obtain useful information from it. However, we will show that their protocol was not designed to resist denial of service, de-synchronization, and tracking in this paper. Keywords: Authentication


RFID
Security
Serverless

1 Introduction RFID systems have been widely used in various applications [1–4]. The privacy and security problem are concerned in RFID systems [5–9]. Previous researchers have proposed various cryptographic operations in security [10–13], privacy [14–17] and attack models [18–22] in RFID system. However, traditional RFID systems [23–26] assumed that the reader would always connect with the backend central database to identity tag’s data. However, the tag’s data stored in the portable reader could be exposed if the portable reader is lost or the adversary has stolen the portable reader. Such scenario could actually occur in the real world, but most of the proposed RFID systems [23–27] cannot solve this problem. Therefore Tan et al. [28] first proposed a serverless RFID system to solve this problem without a central database while protecting the data in the portable reader. Their protocol based on serverless RFID system includes the design of a secure protocol to protect the data of the portable reader. Even if the portable reader is lost, the adversary still cannot obtain useful information from it. However, we will show that their protocol was not designed to resist denial of service, de-synchronization, and tracking in this paper.

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The next section of this paper is devoted to an overview of Tan et al.’s serverless RFID authentication and search protocol [28]. In Sect. 3, we will discuss the weaknesses of Tan et al.’s authentication protocol. Finally, a conclusion for this paper is given in Sect. 4.

2 Review on Authentication Protocol of Serverless RFID In this session, authentication protocol of serverless RFID system is reviewed in detailed steps, and then we will point out the lack of it. In Table 1, we listed the notations that are used in authentication protocol of serverless RFID. Table 1. Notations and indexing terms. CA Ri ri Li Tj idj tj h(x) f(x,y)

Trusted party, responsible for authenticating readers and deploying tags A RFID Readeri ID of a RFID Reader Ri Access list for RFID Reader Ri RFID Tagj ID of a RFID Tag Tj Temp value for RFID Tag Tj One-way hash function Concatenate x and y, then applying h(.), i.e., h(x||y)

The serverless RFID framework [28] involves three entities: A Certificate Authority (CA), a portable reader, and a tag. The CA is a trusted party responsible for deploying the tags and authorizing the readers. The portable reader obtains the access list Li from CA via secure channel. Li = f(ri, t1), id1


f(ri, tj), idj . The Li has a unique identifier ri of ith portable reader, a unique identifier idj of jth tag, while tj is jth tag secret, and f(
) is a hash function. The portable reader knows only idj and the outcome from f(ri, tj). In other words, the portable reader does not know secret tj of the tag. The steps of serverless authentication protocol are described in Fig. 1. Step 1: The portable reader sends request to the tag. Step 2: After the tag receives request, the tag generates a random number nj, and then sends nj to the portable reader. Step 3: The portable reader generates a random number ni, and then sends its unique identification ri and ni to the tag. Step 4: The tag generates h(f(ri, tj))m and h(f(ri, tj)||ni||nj)♁idj and then sends it to the portable reader, where identification idj and tj are pre-stored in the tag. The portable reader finds a matched entry in the pre-stored Li, and then checks if h(f(ri, tj))m and h(f(ri, tj)||ni||nj)♁idj are computed successfully with a match. Otherwise, it will fail.

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Fig. 1. Serverless authentication protocol [28].

3 The Weaknesses of the Authentication Protocol of Serverless RFID Private concern may be with the surveillance modalities of RFID system since most tags are able to uniquely identify individual items or location of a product type. Although the communication data are encrypted between the tag and the reader as authentication to each other, the attacker could still track the tag. For example, an attacker could recognize and trace the tag because it always responds the same

Fig. 2. Weak Point in Authentication Protocol of Serverless RFID

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encrypted messages in authentication at any location and time. One of the goals of serverless RFID’s design is un-traceability. It is claimed that the authentication protocol of serverless RFID improves the optimization of searching time and resists tracking of the portable reader at the same time. However, this study points out that the protocol is vulnerable to tracking attack in Step 4 because the tag always sends constant value, h(f(ri, tj))m, where ri is the ID of the reader, and tj is the secret data of the tag. h(f(ri, tj))m is a unique identifier of the tag, so the attacker can recognize the identification of the tag thus tracks the tag. For example, the attacker can obtain the message of the same tag replying from two separate locations, and then infer that the user of the tag has been to these two locations. This scenario therefore proves that the attacker can track the tag as shown in Fig. 2.

4 Conclusions Although, Tan et al.’s serverless RFID system could need not a central database to protect the data in the portable reader. Even if the portable reader is lost, the adversary still cannot obtain useful information from it. However, in this paper, we have shown that their protocol was not designed to resist denial of service, de-synchronization, and tracking in this paper. In future, it’s important to study an improvement to the protocol to resist traceability of the tag and additional resistance against denial of service and synchronization. Acknowledgments. This work was partially supported by the Ministry of Science and Technology, Taiwan, under grant MOST 106-3114-E-005-001, MOST 106-2221-E-468-002, and MOST 106-2221-E-845-001.

References 1. Chen, C.L., Lai, Y.L., Chen, C.C., Deng, Y.Y., Hwang, Y.C.: RFID ownership transfer authorization systems conforming EPCglobal class-1 generation-2 standards. Int. J. Net. Secur. 13(1), 41–48 (2011) 2. Chiou, S.-Y., Ko, W.-T., Lu, E.-H.: A secure ECC-based mobile RFID mutual authentication protocol and its application. Int. J. Netw. Secur. 20(2), 396–402 (2018) 3. Wu, H.-J., Chang, Y.-H., Hwang, M.-S., Lin, I.-C.: Flexible RFID location system based on artificial neural networks for medical care facilities. ACM SIGBED Rev. 6(2), 12 (2009) 4. Chikouche, N., Cherif, F., Cayrel, P.-L., Benmohammed, M.: Improved RFID authentication protocol based on randomized McEliece cryptosystem. Int. J. Netw. Secur. 17(4), 413–422 (2015) 5. Naveed, M., Habib, W., Masud, U., Ullah, U., Ahmad, G.: Reliable and low cost RFID based authentication system for large scale deployment. Int. J. Netw. Secur. 14(3), 173–179 (2012) 6. Hwang, M.S., Wei, C.H., Lee, C.Y.: Privacy and security requirements for RFID applications. J. Comput. 20(3), 55–60 (2009)

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7. Wei, C.-H., Hwang, M.-S., Chin, A.Y.-H.: A secure privacy and authentication protocol for passive RFID tags. Int. J. Mobile Commun. 15(3), 266–277 (2017) 8. Wei, C.-H., Hwang, M.-S.: Chin AY-H: Security analysis of an enhanced mobile agent device for RFID privacy protection. IETE Tech. Rev. 32(3), 183–187 (2015) 9. Hwang, M.-S., Wei, C.-H., Lee, C.-Y.: Privacy and security requirements for RFID applications. J. Comput. (Taiwan) 20(3), 55–60 (2009) 10. Xie, R., Jian, B.Y., Liu, D.W.: An improved ownership transfer for RFID protocol. Int. J. Netw. Secur. 20(1), 149–156 (2018) 11. Xie, R., Ling, J., Liu, D.-W.: A wireless key generation algorithm for RFID system based on bit operation. Int. J. Netw. Secur. 20(5), 938–950 (2018) 12. Zhang, X., King, B.: Security requirements for RFID computing systems. Int. J. Netw. Secur. 6(2), 214–226 (2008) 13. Hwang, M.S., Li, L.-H.: A new remote user authentication scheme using smart cards. IEEE Trans. Consum. Electron. 46(1), 28–30 (2000) 14. Lin, I.C., Yang, C.W., Tsaur, S.C.: Nonidentifiable RFID privacy protection with ownership transfer. Int. J. Innov. Comput. Inf. Control 5(5), 2341–2351 (2010) 15. Garfinkel, S.L., Juels, A., Pappu, R.: RFID privacy: An overview of problems and proposed solutions. IEEE Secur. Priv. 3(3), 34–43 (2005) 16. Wei, C.-H., Hwang, M.-S., Chin, A.Y.-H.: An authentication protocol for low-cost RFID tags. Int. J. Mob. Commun. 9(2), 208–223 (2011) 17. Chen, Y.-C., Wang, W.-L., Hwang, M.-S.: Low-cost RFID authentication protocol for anticounterfeiting and privacy protection. Asian J. Health Inf. Sci. 1(2), 189–203 (2006) 18. Cao, T., Shen, P.: Cryptanalysis of two RFID authentication protocols. Int. J. Netw. Secur. 9(1), 95–100 (2009) 19. Li, C.T., Wei, C.H., Lee, C.C., Chin, Y.H., Wang, L.J.: A secure and undeniable billing protocol among charged parties for grid computing environments. Int. J. Innov. Comput. Inf. Control 6(11), 5061–5076 (2010) 20. Li, C.T., Wei, C.H., Chin, Y.H.: A secure event update protocol for peer-to-peer massively multiplayer online games against masquerade attacks. Int. J. Innov. Comput. Inf. Control 5(12(A)), 4715–4723 (2009) 21. Wei, C.-H., Yang, C.-Y., Hwang, M.-S., Chin, A.Y.-H.: Cryptanalysis of Li-Wang authentication protocol for secure and efficient in RFID communication. In: Recent Developments in Intelligent Computing, Communication and Devices, Advances in Intelligent Systems and Computing, vol. 752. Springer (2018) 22. Khedr, W.: On the security of Moessner’s and Khan’s authentication scheme for passive EPCglobal C1G2 RFID tags. Int. J. Netw. Secur. 16(5), 369–375 (2014) 23. Wei, C.-H., Hwang, M.-S., Chin, A.Y.-H.: An improved authentication protocol for mobile agent device in RFID. Int. J. Mobile Commun. 10(5), 508–520 (2012) 24. Wei, C.-H., Hwang, M.-S., Chin, A.Y.-H.: A mutual authentication protocol for RFID. IEEE IT Prof. 13(2), 20–24 (2011) 25. Cui, P.Y.: An improved ownership transfer and mutual authentication for lightweight RFID protocols. Int. J. Netw. Secur. 18(6), 1173–1179 (2016) 26. Qian, Q., Jia, Y.L., Zhang, R.: A lightweight RFID security protocol based on elliptic curve cryptography. Int. J. Netw. Secur. 18(2), 354–361 (2016) 27. Chen, P.Y., Chen, W.T., Tseng, Y.C., Huang, C.F.: Providing group tour guide by RFIDs and wireless sensor networks. IEEE Trans. Wireless Commun. 8(6), 3059–3067 (2009) 28. Tan, C.C., Sheng, B., Li, Q.: Secure and serverless RFID authentication and search protocols. IEEE Trans. Wirel. Commun. 7(4), 1400–1407 (2008)

Near Ground UWB Channel Modeling in Different Terrain Surface Shihong Duan, Jiacan Si, Cheng Xu, Junluo Yin, and Jie He(&) School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China [email protected]

Abstract. In the GPS-denied environment, Ultra-Wideband (UWB) technology has great potential to solve the problem of relative localization among nearground mobile robots. Our goal is to establish a near-ground UWB pathloss channel model with antenna height, distance and surface type. In this paper, we built up an on-site UWB signal measurement set, with antenna heights of 0– 30 cm and signal frequencies from 3 to 8 GHz. The proposed model is compiled with a lognormal shadow path attenuation model, there is not only a logarithmic function relationship between the path loss parameters and the antenna height, but also surface type with different roughness play a key role on pathloss variance. The model was validated against multiple experimental data sets obtained in open grazing areas. Keywords: Near ground channel modeling Relative localization


Path loss
Mobile robot swarm

1 Introduction Mobile near-earth robots with local sensing and communication capabilities have recently attracted attention from the robotics community, especially collective action in a potentially unknown environment [1]. The robot group shown in Fig. 1 needs relative distance and position information; relative position is defined as the distance denoted by d and angle to the north denoted by a between two robots. An absolute positioning system (GPS-like system or overhead camera system) usually is used to obtain angle, a communication channel can be obtain distance. However, this solution generates higher communication over-head and lower scalability than solutions in view of relative positioning systems [2]. Also, robots will work in an unknown environment with no GPS reception. Ultra-wideband (UWB) signal is employed in this paper for ranging and finding direction [3], due to its ability to alleviate multi-path effects [4]. In order to correct the ranging error and design the rotational direction-finding algorithm it is necessary to know the exact channel propagation characteristics. In military, agriculture monitoring and landslides monitoring, Mobile robots often operate at the ground level with antenna height less than 20 cm; for instance, it’s 7 cm above the plane of the horizon that the antenna phase-center height in the Self-Healing Minefield system [5]. However, near-ground channel models are scarce [6]. The characteristics of channel in [6–8], such as channel impulse response, power delay © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 847–855, 2019. https://doi.org/10.1007/978-3-030-02804-6_111

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profile, the number of paths, delay spread and path loss exponent for near-ground wireless communication were preliminarily statistically analyzed based on field measurement. [7] defined that the transmit and receive antennas were positioned near ground and concluded that lowering the antennas shrinks the range of entire system, significantly decreases the amount of receive power; but did not give the quantified channel model. [8] proposed a two-slope log-normal path loss model for a WSN at 868 MHz in a barrier-free area. Our UWB application requires a new channel model with a very low antenna and short communication range.

Fig. 1. Near-ground UWB communication and localization application

In our study, we built up Network-Analyzer based platform to take measurement of channel characteristics of very low antenna on rubber and brick ground, and established path loss model, we can obtain path loss by the path-loss model and get the path noise by antenna height in the path noise model. The proposed model is verified to be consistent with measurement results. Channel signal fluctuation is obviously affected by the diverse terrain surface, geometric features formed by antenna height and distance, bandwidth and etc.

2 Near-Ground Channel Measurement Platform and Scenario In this section, near-ground UWB channel measurements are performed on open rubber and brick ground, by using a vector network analyzer (VNA). 2.1

Measurement Platform

VNA-based measurement platform can facilitate our measurement, VNA accomplishes the waveform transmission and records the channel profile, then the post processing program will parse the channel profile to extract the channel characteristics for modeling.

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Our measurement platform consists of a agilent E8363 VNA as transmitter and receiver, a 30 dB power amplifier, a pair of long low loss cable and UWB antennas. The power amplifier has been added at the transmitter port of the VNA to achieve better SNR and guarantee the peak detection at the receiver side. A pair of low loss RF cables have connected a pair of omnidirectional antennas to transmit (TX) and receiver (RX) port of the VNA. The TX and RX antenna are respectively tied to liftable wooden poles with same height. The channel forward gain S-parameter S21, which is known as frequency transfer function, can be measured by VNA with 1601 stepped frequency points in the range of 3 GHz to 8 GHz. The received signal in frequency domain can be expressed by the following Eq. (1). YðxÞ ¼ HðxÞXðxÞ þ NðxÞ

ð1Þ

Where H(x) is the channel impulse response in frequency domain, and N(x) means the addictive white Gaussian noise. The post-processing program applied Hanning windows to the recorded frequency domain spectrum profile in order to limit side lobes and detect more multipath components. The Hamming widow is defined by the Eq. (2).  xðnÞ ¼

0:54  0:46 cos 0;


2pn N

; 0nN otherwise

ð2Þ

Then, base band complex inverse fast Fourier transform (IFFT) is executed in postprocessing program to transfer the frequency domain profile to time domain profile. the first detectable peak is logged as first path with selecting proper threshold. Figure 2 shows the typical time domain channel profile with first peak and fist path loss.

Fig. 2. Typical time domain channel profile with threshold, power of the first path

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Measurement Use-Case

Our application targets are mobile robots, the antenna height is very low, so the ground is always in the first Fresnel zone. Nonetheless, in most UWB channel analysis, in the derivation of the model, we assume that the ground is flat and perfectly conductive, taking no notice of effects of terrain roughness and electrical properties on channel transfer characteristics. Some research proposed, in regard to realistic ground surfaces, the ground wave attenuates generally by up to 6 dB in physical statistics of the terrain roughness in the model. From the perspective of scenario-based approach, our measurement should take consideration of roughness with antenna height and distance. Test case set can be denoted by: Case ¼ fh; d; stg Where subset h is the antenna height of Tx and Rx, d is the distance between transmit (Tx) and receiver (Rx), and subset st is the surface type. Settings of h, d, and st are shown in Table 1. In order to guarantee accuracy and validity of measurement and channel modelling, over 500 snapshots are obtained in each case. Table 1. Settings of near-ground channel measurement. h(cm) d(m) st(surface type) {6, 8, 10, 12, 14, 16, 18, 20} {0.25, 0.5, 0.75, 1, 2, 3, 4, 5, 6, 8} 1: rubber; 2: brick;

First path loss is the measurement energy value of the first arrival signal path. Total pathloss with different distance d is the integration of pathloss over the entire band, not the time domain channel profile. We abstained the total pathloss basing on the following equation: Ns X f 1 1X jH p ðnÞj N N i¼1 n¼1 i N

Ptotal ðdÞ ¼  20 log10

! ð3Þ

where Ptotal(d) denotes the total pathloss at d; Ns is number of snapshots in each case (hi, dj, stk) which is 500 in this paper in each case, Nf is number of frequency sample points in each snapshot which is 1601 here, and Hip ðnÞ is the S21 reading at each sample in ith point from the VNA.

3 Statistical Model Empirical Statistical Channel Model There are a few researches focused on revealing the theory of near-ground UWB signal attenuation. [9] described the near-ground radio waves by using the formula: Ground Wave = Direct Wave + Reflected Wave + Surface Wave. Direct wave refers to the wireless signal wave directly from the transmitting antenna to the receiving antenna under the condition of line-of-sight, which has highest strength and the shortest propagation

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time. Surface waves mainly associated with the diffraction of electromagnetic waves [10], which is considered in the long-distance communications. Because of near-ground multipath effect, the ground reflected wave is the main influence on the receiving end signal. As for Traditional two-ray path loss model [11], near-ground scenarios is applicable, however, It is defective that no considering the statistical characteristics of ground roughness shown in Fig. 3 [12]. [6, 13] explained excess path loss in Fresnel zones. [13] calculated the critical value for the antenna height at which the near-ground antenna path loss became significant. [6] exploited the Fresnel zone principle divides the path loss model into three parts. The LOS ray dominates the signal transmission in the first region, in the second region, the received energy is affected by direct and ground reflection signals. In the third region, insufficient path gap results in diffraction loss being a supplement to the reference loss. The said theoretical models were not verified in very near-ground mobile robots application scenario. R0

Transmitter

ht Free Space:ϒ0 Dissipative uneven Ground:ϒ1

Multi-Reflected path Surface wave

Direct path

Reflected path

R1

Receiver

hr

horizontal distance = d

Fig. 3. Multi-reflected paths between near-ground transmitter and receiver.

In Sect. 3, firstly, concerning the propagation characteristic of the near ground UWB channel based on slow fading model, we consider model as the superposition of path loss and random shadow variation. As a general-purpose model, using typical log distance path loss model to forecast the propagation loss for various environments. The path loss is the average of the signal attenuation related to the distance d between the Tx and Rx, with path loss exponent related to different environment. Shadowing effect on channel is described by a zero-mean Gaussian distributed random variable (in dB) with standard deviation (r), which can produce signal fluctuation. Therefore, first path loss model and total path loss model is given by Eq. (4) PLðd Þ ¼ PLðd0 Þ  10n log10

d þ Xr d0

ð4Þ

where PL(d) is path loss in dB at an arbitrary distance d. PL(d0) is path loss in dB at a distance d0. N is path loss exponent for modelling the slope. Xr is random shadow variation with standard deviation r. N and Xr are all related to various environment factors. Least squares method is employed to estimate N and Xr, that is minimizing the sum of the squared deviations of the measured path loss from the estimated path loss given by fitting formula. Figure 4a, b show the fitting results of the first path loss and total path loss with different antenna height. We can conclude that the model shown in Eq. (4) can accurately describe the first pathloss and total pathloss characteristics. But the parameter of model is related with the antenna height, distance and different surface type.

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From Fig. 4, we can see antenna height and different surface type are major factors to affect path loss in the near-ground application scenario. The lower of the antenna, the greater of the slope of the fitting curve, and the bigger of the N. The optimal fitting value of Nfirst and Ntotal with different antenna height and different ground surfaces are shown in Table 2. Least squares fitting method is used to give the Eqs. (5) and (6). Linear logarithmic relationship exists between path loss exponent and antenna height, as shown in Fig. 4a, b. R Nfirst ¼  2:4217  log10 ðhÞ þ 0:5233

ð5Þ

R Ntotal ¼  1:8325  log10 ðhÞ þ 1:1926 B Nfirst ¼  1:9945  log10 ðhÞ þ 0:7911

ð6Þ

B Ntotal ¼  1:6280  log10 ðhÞ þ 1:0504

-10

-10 h=2cm fit1 h=4cm fit2 h=8cm fit3 h=15cm fit4 h=20cm fit5

First Path Loss(dB)

-30 -40 -50 -60

-30 -40 -50 -60

-70

-70

-80

-80

-90 0

1

2

3

4 d (m)

5

6

7

h=2cm fit1 h=4cm fit2 h=8cm fit3 h=15cm fit4 h=20cm fit5

-20

Total Path Loss(dB)

-20

8

-90 0

1

(a)

2

3

4 d (m)

5

6

7

8

(b)

Fig. 4. Statistical summary of signal path loss at different antenna heights. (a) The first path loss (b) the total path loss

Table 2. Path loss exponent with different antenna height and surface. st Rubber_Ntotal Rubber_Nfirst Brick_Ntotal Brick_Nfirst

6 3.3788 3.3913 3.1105 3.1260

8 2.8818 3.0234 3.0572 2.8647

10 3.4450 3.2293 2.8998 2.5043

12 2.9608 2.9971 2.9118 2.6013

14 2.5643 2.7843 2.2086 2.3751

16 2.1005 2.5074 1.9828 2.1940

18 2.6359 2.8779 2.6166 2.4680

20 1.9776 2.1676 2.1676 2.2086

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Table 3. Standard deviation of random shadow variation with different antenna height and surface. st h(cm) 6 Rubber rfirst 0.5199 rtotal 0.4000 Brick rfirst 0.7794 rtotal 0.4210

8 0.4080 0.4287 0.4290 0.3398

10 0.3730 0.4073 0.4906 0.2947

12 0.4492 0.2900 0.4176 0.2399

14 0.2981 0.2357 0.3213 0.2441

16 0.3596 0.2797 0.3859 0.2847

18 0.2581 0.2599 0.7669 0.4302

20 0.3010 0.2967 0.2924 0.2151

From analyzing our sampling data, we found Random shadow variation does obey Gaussian distribution with different standard deviation r because of different antenna height, as shown in Table 3. rfirst and rtotal respectively describe random shadow variation of first path loss or total path loss. We use least squares fitting method to get the relationship between r and antenna height, which is described by Eqs. 7 and 8. R rfirst ¼  0:4087  log10 ðhÞ  0:0037

ð7Þ

R rtotal ¼  0:3365  log10 ðhÞ þ 0:1629 B rfirst ¼  0:4195  log10 ðhÞ þ 0:1009 B rtotal ¼  0:1967  log10 ðhÞ þ 0:1284

ð8Þ

Also, Table 4 shows that PL(d0) is related with h and surface type, and the fitting function PL(d0) is defined as Eqs. 9 and 10. R Pfirst ðd0 Þ ¼ 29:17  h  21:19 R Ptotal ðd0 Þ ¼ 52:28  h  19:37

ð9Þ

B Pfirst ðd0 Þ ¼ 19:52  h  18:93

ð10Þ

B Ptotal ðd0 Þ ¼ 17:35  h  14:86

Table 4. PL(d0) with different antenna height and surface type. st

h(cm)

Rubber PLfirst(d0) PLtotal(d0) Brick PLfirst(d0) PLtotal(d0)

6 − − − −

8 19.796 18.059 19.796 15.132

− − − −

18.542 14.043 16.697 13.283

10

12

− − − −

− − − −

18.223 14.742 15.130 13.008

14

16

18

20

18.192 − 15.854 − 17.768 − 14.735 − 16.043 13.642 − 8.7991 − 10.457 − 9.648 − 11.179 16.444 − 15.854 − 14.984 − 17.576 − 14.631 12.847 − 9.963 − 10.504 − 14.670 − 11.463

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4 Model Validation In Sect. 4, we compare our proposed model with actual near-ground measured value in order to verify the accuracy of the data predicted by the model discussed in Sect. 3, the data for verification is not used for modeling. Over 500 snapshots are obtained in each measurement case. 400 snapshots are used to build the channel model, the other 100 snapshots are used for verification. Also, the 500 snapshots in the case of antenna height is 6 cm are used for verification. Predicated path loss data by the empirical model shown in Sect. 3 are compared with measurement data. The results are shown in Fig. 5a, b. The high degree of coincidence of test data and model data indicates that our model has credibility and good predictability.

Fig. 5. Statistical channel model validation results. (a) First path loss; (b) total path loss

5 Conclusion Empirical near-ground is presented to facilitate highly accurate near ground network simulations. It is observed that the proposed models are accurate and feasible for very near ground applications (antenna height is less than 20 cm). Our actual results including: Antenna height and surface are key factor to decide the channel models. Also, terrain roughness will affect the accessible range and connectivity. In our models, the terrain roughness is not described explicitly. In our future work, we will focus on research of impact of different terrain surface roughness on channel signal attenuation characteristics.

References 1. Chamanbaz, M., Mateo, D., Zoss, B.M., et al.: Swarm-enabling technology for multi-robot systems. Front. Robot. AI 4, 12 (2017) 2. Pugh, J., Martinoli, A.: Relative localization and communication module for small-scale multi-robot systems. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pp. 188–193. IEEE (2006) 3. Mani, V.V., Bose, R.: Direction of arrival estimation of multiple UWB signals. Wirel. Pers. Commun. 57(2), 277–289 (2011)

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4. Guo, K., Qiu, Z., Meng, W., et al.: Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments. Int. J. Micro Air Veh. 9(3), 169–186 (2017) 5. Merrill, W.M., Liu, H.L.N., Leong, J., et al.: Quantifying short-range surface-to-surface communications links. IEEE Antennas Propag. Mag. 46(3), 36–46 (2004) 6. Torabi, A., Zekavat, S.A.: Near-ground channel modeling for distributed cooperative communications. IEEE Trans. Antennas Propag. 64(6), 2494–2502 (2016) 7. Hugine, A., Volos, H.I., Gaeddert, J., et al.: Measurement and characterization of the nearground indoor ultra wideband channel. In: Wireless Communications and Networking Conference, 2006. WCNC 2006. IEEE, vol. 2, pp. 1062–1067. IEEE (2006) 8. Martinez-Sala, A., Molina-Garcia-Pardo, J.-M., Egea-Ldpez, E., Vales-Alonso, J., JuanLlacer, L., Garcia-Haro, J.: An accurate radio channel model for wireless sensor networks simulation. J. Commun. Netw. 7, 401–407 (2005) 9. Parameswaran, A.T., Husain, M.I., Upadhyaya, S.: Is RSSI a reliable parameter in sensor localization algorithms an experimental study, field failure data analysis workshop (F2DA), New York, NY, September 2009 10. Dagefu, F.T., Sarabandi, K.: Analysis and modeling of near-ground wave propagation in the presence of building walls. IEEE Trans. Antennas Propag. 59(6), 2368–2378 (2011) 11. Pahlavan, K., Krishnamurthy, P.: Principles of Wireless Access and Localization. Wiley, Hoboken (2013) 12. Xu, C., He, J., Zhang, X., et al.: Toward near-ground localization: modeling and applications for TOA ranging error. IEEE Trans. Antennas Propag. 65(10), 5658–5662 (2017) 13. Aslam, M.I., Zekavat, S.A.R.: New channel path loss model for near-ground antenna sensor networks. IET Wirel. Sens. Syst. 2(2), 103–107 (2012)

Research on Visible Light Communication Hongwei Zhu1(&), Shuguang Zhang1, Hui Jia2, and Bo Zhou1 1

2

Beijing City University, Beijing, China [email protected] China Mobile Communications Group Co., Ltd., Beijing, China

Abstract. Visible Light Communication (VLC) is a way of communication that uses visible light as information carrier to transmit optical signal in the air without wired channels. VLC is attracting more and more attention from the industry because of its good performance in electromagnetic radiation, environment and safety. However, it’s still a tough challenge to achieve high accuracy in real applications, due to the complexity of the environment and the limitations of devices and technology of VLC system. In this paper, the VLC systems’ developments and applications in indoor positioning are summarized, providing a reference to the further study of VLC systems. Keywords: Visible Light Communication Indoor positioning
Modulation


Channel coding

1 Introduction Visible Light Communication (VLC) is a way of communication that uses visible light as information carrier to transmit optical signal in the air without wired channels. It has obvious advantages in electromagnetic radiation, spectrum resources, energy loss and safety [1–4]. With the breakthrough of experimental research, Visible Light Communication technology has broad application prospects in intelligent transportation, intelligent medical treatment and indoor location in recent years. VLC is the technology that modulates the electrical signal to the emitting light source, then the light signal changes according to information of the sender, so that the information is received by the detector and converted to the electrical signal through the transmission medium. VCL has many advantages. The width of visible spectrum that VCL uses is quite large, so single data channel can have high bandwidth or it can contain more channels for parallel transmission. In that case the speed of data transmission can increase significantly to the peak of several hundred MB/s. In addition, VCL has high security because light cannot travel through the wall, which means the information indoor is safe for it will not be leaked outside the room. Besides, currently the cost of light source is quite low and the related technology is mature, which can largely reduce the cost of the application of VCL. Therefore, the importance of VCL technology is obvious since the demand for high speed and economical Internet is surprisingly huge [5].

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 856–861, 2019. https://doi.org/10.1007/978-3-030-02804-6_112

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2 VLC System Model The circuit structure of VLC system is composed of Transmitter device and Receiver device as shown in Fig. 1. The Transmitter part mainly consists of modulation circuit and LED light sources. Generally the modulation circuit is composed of CPUs, preprocessing unit, LED driving unit. The light signal carries information of the transmitter and finally the information is received by the receiver. The receiver mainly includes photoelectric conversion device, electrical signal processing circuit and CPUs used for data processing. The photoelectric conversion device is usually realized by photoelectric sensor, such as silicon photodiodes, which can detect light signal and convert it to electrical signal. The circuit of Receiver part is often more complex than Transmitter part’s, which is responsible for filtering, amplification, decoding, and demodulation and sends information to the end-user. In this way, wireless communication can be accomplished.

Visible light transmission channel LEDs

Photoelectric conversion device

Modulation circuit

Demodulation circuit

Transmitter

Data processing center

Receiver

Fig. 1. VLC system structure diagram

3 Applications of VLC in Indoor Positioning In recent years, Visible Light communication technology has been used in indoor wireless network, intelligent transportation, underwater communication [5], intelligent medical treatment and indoor location. Recently indoor positioning with high accuracy and reliable real-time performance needs to be developed urgently and has become one of the most exciting issues of the next generation wireless systems [6, 7].

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Traditional satellite positioning method is hard to accomplish the precise positioning of indoor mobile users. The use of infrared, ultrasonic, wireless LAN and Bluetooth technology to achieve indoor positioning is being explored [8–10]. However, due to the high requirements for the application of environmental conditions and the need to add additional devices, increasing location cost, applications of these technologies have been restricted to different degrees. Based on the advantages of VLC discussed above, it is considered an effective choice to apply it to indoor positioning. The indoor positioning methods based on visible light communication are mainly as follows: proximity, image positioning method, scene analysis and triangulation method so far. Proximity method uses arrays of reference points, which have known locations. This method is considered to be co-located with the reference points when a mobile target collects signal from it. Proximity method is very simple but provides accuracy no more than the resolution of the grid itself. Image positioning uses image sensors to detect the target’s locations based on visible light communication technology. Scene analysis method is known as fingerprints analysis. This method refers to finding out target’s location by matching real-time measurements with the fingerprints that collection in information base [11]. Based on the geometric properties of the triangle, the triangulation method estimates the target’s location by Trilateration algorithms. Each of these methods has its own advantages and disadvantages. The triangulation method and the principle were focused on in this part. Trilateration is the basis of localization techniques, which is a primary building block of many complicated localization systems. If three points’ locations and distances between the unknown point and these three points are given, one can uniquely determine the coordinates of the unknown points. Figure 2 shows a circuit structure of an indoor VLC positioning system. The scheme is based on VLC system model discussed above. In this application, we use at least three white light LEDs as visible light source because white LED lights not only have the advantages of low working voltage, long life and miniaturization, but also have the characteristics of high speed modulation and short response time. White LED light signals are modulated by the corresponding driving circuit at different time. When the frequency of the transmission signal is greater than 60 Hz, the white light LED does not appear obvious scintillation phenomenon, which guarantees the basic lighting. The receiver is placed on the point to be measured in the bottom. It receives the light signals from different LEDs separately, and converts light intensity information to the corresponding electrical information by the silicon photodiodes on it. According to the feature of silicon photodiodes, we know that the smaller the distance between light sources and the receiver, the greater the intensity of light, the greater the electrical level and the converse is also true. We use d1, d2, d3 to represent distances between the receiver and LED1, LED2, LED3 respectively, and they can be derived from the electrical levels measured by the receiver. Then we can figure out the receiver’s location by Trilateration Algorithms.

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Fig. 2. Indoor VLC positioning system structure diagram

4 Modulation and Demodulation Technology In the indoor positioning VLC system discussed above, the system sends signals to the three LED lights at different time separately in order to avoid confusion when receiver is receiving multiple LED light signals at the same time. We set a ID number to each light source to solve problems of signal recognition of the receiver. Data information send by different LED lights contain their own ID information. The data transferred includes starting code, ID code, ID negative code, ending code. The starting code consists of a high level and a low level, which lasts 250 ls in total. The ending code is a low level signal, lasting 150 ls. The negative code is to improve the accuracy and reliability of the information transmission. Currently modulation techniques used in VCL include Orthogonal Frequency Division Multiplexing (OFDM), Carrier-less Amplitude and Phase Modulation (CAPM) and Nyquist Single Carrier (N-SC). OFDM is a kind of multi-carrier data communication technology. It divides high-speed data stream into parallel low-speed data stream, and uses low rate multi-status symbols to modulate mutually orthogonal subcarriers, eventually forms a transmission system that transmits multiple low rate

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symbols in parallel. CAPM is a multi-dimensional and multi order modulation technology. By using two orthogonal digital filters, the complex signal of electricity or light is removed to the real signal conversion, which has lower algorithm complexity and higher spectral efficiency [13].

5 Limitations and Challenges VLC is attracting more and more attention from the industry because of its good performance in electromagnetic radiation, environment and safety. However, it’s still a tough challenge to achieve high accuracy in real applications, due to the complexity of the environment and the limitations of devices and technology of VLC system. VCL has the following technical limitations: Firstly, unlike in the optical fiber media, visible light will encounter various obstacles and scattered particles in the air, and the communication distance can only reach a few meters when systems work at a higher transmission rate. Secondly, in order to achieve two-way transmission, it is necessary to integrate transmitter and receiver at both ends. However, it is difficult to implement reverse link design in VLC system currently. With the vigorous development of research institutes, communications industry and equipment manufacturers in various countries, VLC equipment and technology research have been developing rapidly in the past ten years. The technology involves many fields, such as communication, lighting and electricity. However, the process of industrialization is relatively slow, which is restricted by industrial chain, market positioning and policy. In terms of industry chain, although research institutions and equipment manufacturers are actively involved, there is a lack of terminal manufacturers and chip factories. The whole industry chain has not yet been formed. In terms of technology, although there are interference from source and non-source, a large number of technical certification work needs to be improved.

6 Conclusions Visible Light Communication can both be used for lightning and communication, and it has the advantages of high transmission speed, high security, no electromagnetic interference and so forth. In this paper, the features of VLC technology were introduced and the VLC system model was proposed firstly. Then Applications of VLC was introduced and an indoor positioning system based on the model was discussed in details. After presenting the coding and decoding techniques used in this indoor positioning VLC system, the modulation and demodulation technology was discussed thoroughly. At last, Limitations and challenges of VLC were discussed, providing a reference to the further study of VLC systems. Acknowledgments. This work was supported by the National Natural Science Foundation of China (Grant Number 51775051).

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References 1. Tuncer, S., Tuncer, T.: Indoor localization with bluetooth technology using artificial neural networks. In: IEEE, International Conference on Intelligent Engineering Systems, pp. 213– 217. IEEE (2015) 2. Chen, H., Guan, W., Li, S., et al.: Indoor high precision three-dimensional positioning system based on visible light communication using modified genetic algorithm. Opt. Commun. 413, 103–120 (2018) 3. Hsu, C.W., Wu, J.T., Wang, H.Y., et al.: Visible light positioning and lighting based on identity positioning and RF carrier allocation technique using a solar cell receiver. IEEE Photonics J. 8(4), 1–7 (2017) 4. Chaabna, A., Babouri, A., Zhang, X.: An indoor positioning system based on visible light communication using a solar cell as receiver, pp. 43–49 (2017) 5. Yinfan, X., Huang, X., Rongling, L., et al.: Research on indoor positioning algorithm based on LED visible light communication. China Light Light. 4, 11–15 (2014) 6. Gu, Y., Lo, A., Niemegeers, I.: A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutor. 11(1), 13–32 (2009) 7. Zheng, D., Cui, K., Bai, B., Chen, G.: Indoor localization based on LEDs. In: IEEE International Conference on Control Applications, vol. 19, pp. 573–578 (2011) 8. Thmas, Q., Wang, Y., Ahmet, S., et al.: Analysis of an optical wireless receiver using a hemispherical lens with application in MIMO visible light communications. J. Lightwave Technol. 31(11), 1744–1754 (2013) 9. Monica, S., Ferrari, G.: An experimental model for UWB distance measurements and its application to localization problems. In: IEEE International Conference on Ultra-Wideband, pp. 297–302. IEEE (2014) 10. Ajmani, M., Sinanović, S., Boutaleb, T.: Optimal beam radius for LED-based indoor positioning algorithm. In: Students on Applied Engineering, pp. 357–361. IEEE (2017) 11. Wang, C., Wang, L., Chi, X., et al.: The research of indoor positioning based on visible light communication. China Commun. 12(8), 85–92 (2015) 12. Lee, Y.U., Kavehrad, M.: Hybrid positioning with lighting LEDs and Zigbee multihop wireless network. Proc. SPIE Int. Soc. Opt. Eng. 8282(1), 16 (2012) 13. Liang, L., Cai, X., Zichuan, L.I., et al.: Research on visible light communication system based on 8-ASK modulation. Opt. Commun. Technol. (2018)

Sampling Redundancy Removal Algorithms for Stepped Frequency Continuous Wave Yue Pan, Ming Diao, and Zengmao Chen(&) College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China [email protected]

Abstract. High resolution one-dimensional range image can be acquired with stepped frequency continuous wave, from which the information of targets derived. But sampling redundancies caused by oversampling result in failure to identify the targets in the one-dimensional range image. Nowadays, the existing solutions of sampling redundancy removal include of abandonment method, selection maximum method and maximum confidence method, which are complex in computing for stepped frequency continuous wave. In this paper, two methods are proposed to simplify the computation, namely, end-point method and full-point method. The end-point method obtains the onedimensional range image by performing the inverse fast Fourier transform of the sampling points of the last group. The full-point method obtains the onedimensional range image by performing an inverse fast Fourier transform of all the sampling points. The simulation shows that two methods can both reduce the amount of computation in the condition of guaranteeing the accuracy. Keywords: Stepped frequency signal Sampling redundancy


One-dimensional range imaging

1 Introduction Stepped frequency signal is constructed by a series of pulses whose frequency are increased by a constant frequency increment. Hence, a large effective bandwidth can be attained to acquire high resolution [1–3]. Stepped frequency signal is divided into stepped frequency pulse signal and stepped frequency continuous wave (SFCW) whose pulse period equal to pulse width. SFCW technique enables the detection of different targets points along the radar’s line of sight through high resolution one-dimensional range image obtained by performing the inverse Fourier transform (IFFT) of the SFCW [4, 5]. An accurate one-dimensional range image can be obtained when sampling interval equal to the period of SFCW. However, some points in the same frequency are sampled during sampling, i.e. over sampling, which will cause sampling redundancy, in order to ensure the accuracy and completeness of information during signal processing. Some solutions of redundancy removal for SFWC have been found, e.g. abandonment method [6], selection maximum method and maximum confidence method [7]. The basic principle of abandonment method is: extract the range image with a distance of Rs from the IFFT results of each group of sampling points, splicing the © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 862–869, 2019. https://doi.org/10.1007/978-3-030-02804-6_113

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extracted range image into a complete one-dimensional range image. Selection maximum method obtain the one-dimensional range image by choosing the maximum point compared with the information with the distance of Rs from the IFFT results of each group of sampling points. The principle of maximum confidence method is: comparing the peak from the IFFT results with the threshold value in descending order until it is lower than the threshold value, drawing the distance represented by which the peak value above the threshold on the one-dimensional range image. The fundamental defect of abandonment method is that SNR will decrease because the first few sampling points are selected. Selection maximum method need overcome the shortcoming of heavy computing. High complexity of algorithm is the disadvantage of maximum confidence method. The methods above have a large amount of computation for SFCW compared with the new methods proposed in the paper.

2 Step Frequency Signal 2.1

Stepped Frequency Signal Performance Parameters

Radar performances are determined by stepped frequency signal parameter settings. The period of signal Tr determines the maximum range of radar Rmax . The maximum range of radar can be calculated from: Rmax ¼ cTr =2

ð1Þ

Where c is speed of light in free space. The time duration s of pulse determines the maximum length of the target detected by radar Rs which can be expressed as: Rs ¼ cs=2

ð2Þ

The constant frequency increment Df determines the maximum unambiguous range Ru which can be expressed as: Ru ¼ c=ð2Df Þ

ð3Þ

The sampling interval Ts determines the resolution of sampling Rs which is the minimum distance represented by a sampling point. Rs ¼ cTs =2

ð4Þ

The number of pulses is N, which together with the constant frequency increment Df determines the bandwidth of the emitted signal B and the minimum distance Dr between two points which can be differentiated. The minimum distance is given by: Dr ¼ c=ð2NDf Þ

ð5Þ

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The Influence of the Relationship Between s and Df on the OneDimensional Range Image

When sDf [ 1, Ru \Rs , the distance range of the one-dimensional range image after IFFT is a little less than the actual distance range, So it is impossible to identify target information in the missing image. When sDf ¼ 1, Ru ¼ Rs , the distance range of the one-dimensional range image after IFFT is equal to the actual distance range. Therefore, the target information can be correctly identified. When sDf \1, Ru [ Rs , the distance range of the one-dimensional range image after IFFT is longer than the actual distance range. The target information can be correctly identified for SFCW. In summary, in the case of sDf  1, the one-dimensional range image after IFFT can represent the actual distance information to correctly identify the target information, which is called wide constraint. 2.3

Stepped Frequency Signal Processing

The emitted stepped frequency signal is: xð t Þ ¼

N 1 X

 rect

i¼0

 t  iTr  2s  cosð2pðf0 þ iDf ÞtÞ s

ð6Þ

The echo signal reflected by the target is: r ðtÞ ¼

N 1 X i¼0

    t  iTr  2s  2R 2R c rect  cos 2pðf0 þ iDf Þ t  c s 

ð7Þ

In radar receiver, the reference signal is: yð t Þ ¼

N 1 X i¼0



 t  iTr  2s rect  expðj2pðf0 þ iDf ÞtÞ s

ð8Þ

Two orthogonal (in-phase, quadrature) signals obtained after mixing and low-pass filtering can be expressed using the following equations, respectively. yI ð t Þ ¼

N 1 X i¼0

yQ ð t Þ ¼

    t  iTr  2s  2R 2R c rect  cos 2pðf0 þ iDf Þ c s

ð9Þ

    t  iTr  2s  2R 2R c  sin 2pðf0 þ iDf Þ c s

ð10Þ

N 1 X i¼0

rect

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In the condition of Ts ¼ s, at the time nTs , the sampling signal of sub-pulse ði ¼ 1; 2; 3; . . .; N  1Þ can be given by:     2R 2R Y ðiÞ ¼ exp j2pf0 exp j2piDf c c

ð11Þ

For a stationary target, the first index item of the above equation is a constant, and the second index item can be regarded as a frequency-domain signal at a point in time 2R=c whose frequency increases linearly. Therefore, the result of IFFT of the sampled and demodulated signal can be given by:   N 1 1X 2p Y ðiÞ exp j il yð l Þ ¼ N i¼0 N

ð12Þ

         sin p l  NDf 2R 1 2R N1 2R c    yðlÞ ¼ exp j2pf0  exp jp l  NDf  N c N c sin Np l  NDf 2R c ð13Þ Equation (13) can be simplified as:    sin p l  NDf 2R c    jyðlÞj ¼ N sin Np l  NDf 2R c

l ¼ 0; 1; . . .N  1

ð14Þ

When l ¼ ½NDf 2R c  mN, ([ ] indicates rounding operations), Eq. (14) reaches the maximum. The range solution R can be calculated from: R¼

lc mc  2NDf 2Df

ð15Þ

3 The Cause of Sampling Redundancy Ideally, an accurate one-dimensional range image can be obtained by performing the IFFT of the mix-filtered echo signal in the condition of the time duration s of pulse equal to the sampling interval Ts . Actually, time duration s of pulse is 3–5 times as long in time as sampling interval Ts in order to ensure the completeness and accuracy of information of targets. However, the sampling redundancies will increase with the increase of the sampling frequency. The fact that the number of sampling points of the same pulse is more than 1 in the condition of Ts \s means that the same target information is sampled more than one time, so that the same target will appear in different IFFT results. The case that over sampling lead to fail in targets identification, is named sampling redundancy.

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2Rs

3Rs

2Rs

3Rs

4Rs

3Rs

4Rs

5Rs

Fig. 1. Cause of sampling redundancy.

The result of the IFFT of three sampling points is shown in Fig. 1. Since the time difference between the two adjacent sampling points is Ts , the distance difference between the two range images is Rs [8]. And two range images represented by different sampling points maybe contain the same information of targets. It is impossible to obtain the accurate one-dimensional range image directly because of sampling redundancies. In order to obtain the authentic distance information, it is necessary to remove sampling redundancies.

4 Proposed Methods for SFCW Sampling Redundancy Removal 4.1

End-Point Method

In the case of wide constraint, the number of sampling points of signal in a period is the same as the number of sampling points of a sub-pulse for SFCW. The distance represented by the first sample point in the first sub-pulse of the emitted signal is Rs . The distance range represented by the second sampling point is 2Rs . And the distance range represented by the last sampling point is Rs . f original f

t

echo 1 f

t

echo 2 t

Fig. 2. The principle of end point method.

In Fig. 2, within the scope of the radar range, 2R=c\Tr , it is unable to determine that if the target within the distance range of the first few sampling points represented, but can be guaranteed that the target is within the maximum length of the target represented by the last sampling point. Therefore, an accurate one-dimensional range image can be obtained by performing the IFFT of the last group of sampling points simply.

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Full-Point Method

In the case of wide constraints, assume that there are m sampling points for each subpulse in SFCW, and N  m sampling points altogether. The result of the IFFT of all sample points can be expressed as:   X 1 Nm1 2p lk yðlÞ ¼ Y ðkÞ exp j N  m k¼0 Nm

l ¼ 0; 1; . . .N  1

ð16Þ

The signals sampled at the sample points of the i-th pulse are all signals whose frequency is iDf , which can be regarded as ai , (i = 0, 1, 2 … N−1).   2R 2R ai ¼ exp j2pf0  expðj2pði  1ÞDf Þ c c

ð17Þ

Now divide Y ðkÞ sequences into N groups with m elements each group.  2p    N 1 1  exp j Nm lm 1X 2p  2p   lmi yðlÞ ¼  ai exp j N i¼0 N m l m 1  exp j Nm

ð18Þ

Equation (17) into Eq. (18) can be combined as:  2p    1  exp j Nm lm 1 2R  2p  exp j2pf0 yð l Þ ¼  c m 1  exp j Nm l N        sin p l  NDf 2R N1 2R c   exp jp l  NDf  p  N c sin N l  NDf 2R c When m is given, the first part of Eq. (19)

ð19Þ

2p 1expðjNm lmÞ can be regarded as a 2p lÞÞ ð1expðjNm

constant.    sin p l  NDf 2R c    jyðlÞj ¼ C  mN sin Np l  NDf 2R c

l ¼ 0; 1; . . .N  1

ð20Þ

According to the stepped frequency signal processing, it can be inferred that the full-point method can also obtain an accurate one-dimensional range image with the mathematic derivation. jyðlÞj is related to the number of sampling points m, the fullpoint method compensate for jyðlÞj by multiplying factor in order to ensure the amplitude of output signal.    sin p l  NDf 2R c     m  jyðlÞj ¼  N sin p l  NDf 2R c =N

ð21Þ

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Fig. 3. Comparison of resulting one-dimensional range image using three different methods. a Abandonment method b end-point method c full-point method

5 Simulation When s ¼ 1 ls, Df ¼ 1 MHz, TS ¼ s=3, N ¼ 256, SNR = 40 dB, two stationary target points are set with distances of r1 ¼ 50 m, r2 ¼ 100 m. Figure 3 shows the simulation results of the abandonment method, the end point method and the full-point method respectively. It can be seen from the Fig. 3 that both the end point method and the full-point method can obtain an accurate one-dimensional range image and remove the sampling redundancy to extract the correct target information. The abandonment method needs M  N 2 multiplication operations, M  N  ðN  1Þ additions, 2 M remainder operations and M intercepting operations. The end-point method needs N  N multiplication operations and N  ðN  1Þ additions. The full-point method needs M  N 2 multiplication operations and M  N  ðN  1Þ additions. From the point of view of computational complexity, the end-point method and the full-point method are superior to the abandonment method. In terms of the amplitude of output signal, the full-point

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method is optimal. So the two methods can simplify the computation in the condition of guaranteeing the accuracy.

6 Conclusion Some solutions have been found to remove the sampling redundancy caused by over sampling, which have a large amount of computation. In this paper, two methods of sampling redundancy removal for SFCW (end-point method and full-point method) have been proposed by analyzing the imaging process of the one-dimensional range image of the stepped frequency signal and the cause of the sampling redundancy to simplify the computation. Compared with the abandonment method by simulation, the new methods can reduce the amount of computation in the condition of guaranteeing the accuracy. Acknowledgments. The authors acknowledge the support from the National Natural Science Foundation of China (Grant No. 61401196) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20140954), and also acknowledge Central University Basic Operating Expenses Project of Harbin Engineering University (Grant No. 201749), (Grant No. GK2080260144).

References 1. Wehner, D.R.: High Resolution Radar, 2nd edn, pp. 102–104. Artech House, Norwood (1995) 2. Paulose, A.: High Radar Range Resolution with the Step Frequency Waveform. Naval Postgraduate School Monterey, California (1994) 3. Skolnik, M.I.: Introduction to Radar Systems, 2nd edn. McGraw-Hill, New York City (1980) 4. Seyfried, D., Schoebel, J.: Stepped-frequency radar signal processing. J. Appl. Geophys. 112, 42–51 (2015) 5. Sheng, Y., Xu, P.: The improvement of displacement measurement algorithms by Step Frequency Radar. In: 2013 International Conference on Wireless Communications and Signal Processing, Hangzhou, pp. 1–4 (2013) 6. Long, T., Li, D., Wu, Q.: Design methods for step frequency waveform and the target pick-up algorithm. J. Syst. Eng. Electron. 23(6), 26–31 (2001) 7. Wang, F., Long, T.: A new targets pick-up algorithm for step freguency radar signal. J. Proj. Rocket. Missiles Guid. 26(2), 135–137 (2006) 8. Zhao, B., Li, G.P., Quan, T.F.: A novel algorithm for target 7S redundancy removal of stepped frequency radar. Mod. Radar 32(8), 39–43 (2010)

The Performance of Chirp-BOK Modulation in the Time Fading Channel Zhiguo Sun, Shiming Li, Zengmao Chen, and Xiaoyan Ning(&) College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China [email protected]

Abstract. In this letter, the performance analysis of chirp-BOK modulation is carried out and the bit error rate (BER) function is derived and the parameters that influence error performance are defined in Additive White Gaussian Noise (AWGN) channel. In wireless communication, Doppler shift is introduced due to the relative movement of transmitter and receiver terminal, it severely affected the performance of general communication system. Based on the parameters analyzed, the closed-form BER performance of Doppler channel is derived and evaluated by simulation. The simulation result indicate that the simulation BER is depend on the time and signal to noise ratio (SNR) and demonstrate the derivation is validity. Keywords: Index terms-chirp modulation
Doppler shift
Bit error rate (BER)

1 Introduction In recent years, a class of signals, namely wideband time-varying signals has been developing rapidly. In contrast to the narrowband time invariant counterpart, this class of signals is characterized by its frequency varying over time and having wide bandwidth [1, 2]. The application of wideband time-varying signals can be found in many fields due to its merits of interference suppression and enabling multiple signals inhabiting the same frequency band to be transmitted simultaneously without interfering with each other [3]. The wideband time-varying signals also has been used extensively in military communication systems due to its desirable features in terms of low probability of interception and low probability of detection [4]. Chirp signal, as a representative wideband time-varying signal [5], has been utilized in many applications such as sonar and radar systems. In radar application, it is used to estimate multipath components and Doppler effect [6, 7]. As for sonar echo ranging, it provides more accurate estimation for Doppler resolution and simultaneous range than other pulses such as sinusoids [8]. Recently, chirp signals have also been used in wireless communication systems as modulation signals [9]. Chirp signal was first employed in a binary data communication system by Winker [10] as a spectrum spreading technique. In the literature, the following features of chirp signals were explored including jamming resistance, not needing synchronization [9], possessing extremely negative cross-coherence characteristics when compared with sinusoids, and inherent insensitive against fading and Doppler effect due to a moving receiver [11]. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 870–878, 2019. https://doi.org/10.1007/978-3-030-02804-6_114

The Performance of Chirp-BOK Modulation in the Time Fading Channel

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Although there are several studies have concentrate on Chirp modulation, the BER performance of Chirp modulation in fading channel is not available. The closed-form BER performance of Chirp modulation in fading channel is quite significant in the communication system design and analysis. The paper is organized as follows. In Sect. 2, introduce the Chirp-BOK modulation theory. The system model and closed-form bit error rate (BER) performance of ChirpBOK modulation is derived in Sect. 3, In Sect. 4 the influence parameters of Chirp modulation in fading channel is analyzed and derived. The paper is concluded in Sect. 3.2.

2 Chrip-BOK Modulation Theory Chirp signal whose instantaneous frequency varies linearly with time. Mathematically, the instantaneous expression of chirp signal can be written as sðtÞ ¼ aðtÞ cosð2pfc t þ plt2 Þ

ð1Þ

Where a(t) represents the envelop of chirp signal, when jtj  T=2, a(t) = 1. fc is the carrier frequency. l is the positive or negative chirp rate, l > 0, calls up-chirp which represents data bit 1, l < 0, calls down-chirp which represents data bit 0. Chirp signal has good autocorrelation performance, the autocorrelation function is defined as [12] h  i jtj pffiffiffiffiffiffi sin pBT 1  T cosð2pf0 tÞ qðtÞ ¼ BT pBT

ð2Þ

pffiffiffiffiffiffi When t = 0, the envelop of q(t) can get maximum peak value BT ; t ¼ 1=B, the envelop value of q(t) is zero. The signal energy concentrate in main-lobe width is 2/B.

3 Chirp-BOK Modulation Model 3.1

Chirp-BOK Modulation Model

For the good orthogonality between up-chirp and down-chirp, they can be used to BOK system. The block diagram of Chirp-BOK modulation system is plotted in Fig. 1.

Fig. 1. Block diagram of Chirp-BOK modulation system

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Assumed the signal s(t) is transmitted over AWGN, the received signal s(t) can be expressed as: rðtÞ ¼ sðtÞ  hðtÞ þ nðtÞ

ð3Þ

Where the mean of n(t) is zero and the power density (PSD) is N0/2. The coherent detection is used in this paper. The receiver signal is passed through matched filter then is sent to decider. The decider decide which data bit is sent based on the peak value output of each matched filter. 3.2

The BER Performance of Chirp-BOK

The expression for the up-chirp s1(t) and down-chirp s2(t) can be described as [13].   s1 ðtÞ ¼ cos 2pfc t þ plt2   s2 ðtÞ ¼ cos 2pfc t  plt2

ð4Þ

s1(t) pass through AWGN channel, enter into correlation, the output of matched filter is: Z

T

y1 ð t Þ ¼

Z

0

Z

Z

Z

T

s1 ðtÞs2 ðtÞdt þ

0

T

s1 ðtÞnðtÞdt ¼ E þ

0

T

y2 ð t Þ ¼

T

s1 ðtÞs1 ðtÞdt þ

s1 ðtÞnðtÞdt

ð5Þ

s2 ðtÞnðtÞdt

ð6Þ

0

Z

T

s2 ðtÞnðtÞdt ¼ qE þ

0

0

RT 2 Where E represents the signal energy, E ¼ 0 si ðtÞdt, q denotes the cross-correlation coefficient between up-chirp and down-chirp. The decision variables written as: l ¼ y1  y2 ¼

Z T pffiffiffiffi 1 E ð1  qÞ þ pffiffiffiffi ½s1 ðtÞ  s2 ðtÞnðtÞdt E o

ð7Þ

When l < 0, bit ‘1’ is sent, l < 0, bit ‘0’ is sent. According to the formula (7), E[l], D[l] can be expressed as: pffiffiffiffi E ð1  qÞ

ð8Þ

D½l ¼ N0 ð1  qÞ

ð9Þ

E ½l ¼

When s1(t) is sent, the probability density function of l is: 1 pðljs1 Þ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2pN0 ð1  qÞ

Z

T

e 0



ffiffi

p 2 E ð1qÞ 2N0 ð1qÞ

½l

dl

ð10Þ

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When s2(t) is sent, the probability density function of l is: 1 pðljs2 Þ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2pN0 ð1  qÞ

Z

T

e



ffiffi

p 2 Eð1qÞ 2N0 ð1qÞ

½l þ

dl

ð11Þ

0

So, the theory BER of chirp-BOK in AWGN channel is written as 1 pe ¼ pðejs1 Þ ¼ pðejs2 Þ ¼ erfcð 2

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Eb ð1  q ÞÞ 2N0

ð12Þ

where Q represents the Gaussian distribution function, Eb indicates the signal energy, and q denotes the signal correlation coefficient determined by the time-bandwidth (TB) product. When the TB increases, q will decrease accordingly. Therefore, Pe will decrease as well. The effect of TB product on the error performance can be observed in Fig. 2.

Fig. 2. Error performance for binary chirp modulation with different TB products.

4 Chirp-Bok Modulation in Fading Channel According to formula (12), the BER performance of binary chirp modulation depend on the signal energy Eb and the cross-correlation q. In this section, the BER of chirp modulation in fading channel is derived by analyzing the parameter Eb and q. Now, the variation of parameter Eb and q is analyzed. 4.1

Autocorrelation Performance in Doppler Channel

Assumed the Doppler shift is Δf, the received signal r(t) is defined as   r ðtÞ ¼ cos 2pðfc þ Df Þt þ plt2

ð13Þ

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The up-chirp matched filter output is obtained as     r ðtÞ ¼ cos 2pðfc þ Df Þt þ plt2  cos 2pfc t þ plt2 T=2  pffiffiffiffiffiffi Z    ¼ 2 BT cos 2pðfc þ Df Þs þ pls2 cos 2pfc ðt  sÞ  plðt  sÞ2 ds T=2

¼

ð14Þ

pffiffiffiffiffiffi sinðpðlt þ Df ÞðT  jtjÞÞ cosð2pfc tÞ BT pðlt þ Df Þ

The Doppler shift leads to the autocorrelation performance degrade, the output . The waveform produce deviation in time domain, the deviation Dt ¼ Dfl ¼ TDf  pffiffiffiffiffiffi B Df  amplitude of the waveform also decreases, the amplitude is 1   l  BT . So, the autocorrelation performance depends on the Doppler shift and chirp rate. The autocorrelation function with different BT product in Doppler channel is shown in Fig. 3a and b.

Fig. 3. The autocorrelation function with different BT products in Doppler channel

Derived from the previous text, we can see that the energy of the chirp signal is RT 2 determined by the autocorrelation function, E ¼ 0 si ðtÞdt. Compared with the AWGN channel, the autocorrelation performance of the signal is degraded by the Doppler shift, therefore, the energy of the signal at the receiving terminal also degrade.

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Cross-correlation Performance in Doppler Channel

The received signal through down-chirp matched filter output is derived as     r ðtÞ ¼ cos 2pðfc þ Df Þt þ plt2  cos 2pfc t  plt2 T=2  pffiffiffiffiffiffi Z    ¼ 2 BT cos 2pðfc þ Df Þs þ pls2 cos 2pfc ðt þ sÞ þ plðt þ sÞ2 ds T=2

      1 p pffiffiffiffiffiffi pffiffiffi 2 p pffiffiffiffiffiffi pffiffiffi 2 ¼ pffiffiffiffiffiffi C BT  ljtj BT  ljtj þ S cosð2pfc tÞ 2 2 BT ð15Þ It can been seen from formula (14), the cross-correlation coefficient is small, so compared with autocorrelation, the Doppler shift has little effect on cross-correlation. The cross-correlation function with different BT product in Doppler channel is shown in Fig. 4a and b.

Fig. 4. The cross-correlation function with different BT products in Doppler channel

4.3

Numerical Results

The performance of the chirp-BOK modulation in time fading channel is evaluated using Eqs. (14) and (15). 1. In this condition, it correspond to no spread spectrum, the BER is extremely high whatever SNR is big. The improved performance of chirp spread system is demonstrated by this condition (Fig. 5). 2. In the limiting condition which the bandwidth B is increase, the system performance improve. This curve give a method to improve the BER performance of system.

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Fig. 5. Error performance for binary chirp modulation with different B

Figure 6 depicted BER cure under different values of fd. In this diagram fd = 0 corresponds to the condition that Doppler shift is zero, the channel is AWGN channel, with the increase of Doppler shift, the system performance decreases. However, when the Doppler shift exceeds ð1=2BÞl, the performance of system seriously reduced whatever value of SNR is big.

Fig. 6. Error performance for binary chirp modulation with different fd

In Fig. 7, the BER curve under various values of B for T is keep constant. It can be seen that the values of T has no obvious influence on the BER of system.

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Fig. 7. Error performance for binary chirp modulation with different B

5 Conclusions In this paper, the performance of chirp-BOK modulation is analyzed in Doppler channel. The performance analysis is not only demonstrated by matlab simulation but also supported by the mathematical model deduction. The influence of Doppler shift can be highly reduced when the bandwidth B is extremely sufficient. It can be concluded through massive numerical examples. At the same time, we can know that changing the duration T of the system has no effect on the performance deterioration 1 caused by Doppler shift. when the Doppler shift exceeds 2B l, the performance of system seriously reduced. Therefore, considering further employs the error control coding techniques to improve the performance is necessary. Acknowledgments. This work is supported by the National Natural Science Foundation of China (Grant No. 61401196) and also acknowledge Central University Basic Operating Expenses Project of Harbin Engineering University (Grant No. 201749).

References 1. Cohen, L.: Time-frequency analysis: theory and applications. J. Acoust. Soc. Am. 134(5), 4002 (1995) 2. Papandreou-Suppappola, A.: Applications in time-frequency signal processing. J. Res. Child. Educ. 27(2), 127–152 (2003) 3. Proakis, J.G.: Digital Communications, 3rd edn. McGraw-Hill, New York (2001) 4. Gupta, C., Mumtaz, T., Zaman, M., et al.: Wideband chirp modulation for FH-CDMA wireless systems: coherent and non-coherent receiver structures. In: IEEE International Conference on Communications, vol. 4, pp. 2455–2459. IEEE (2003) 5. Dixon, R.C.: Spread Spectrum Techniques, pp. 1–14. IEEE Press, New York (1976) 6. Cook, C.E., Bernfeld, M.R.: Radar Signals: An Introduction to Theory and Application. Artech House, Norwood (1993)

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7. Bernfeld, M.R.: Chirp Doppler radar. Proc. IEEE 72, 540–541 (1984) 8. Ziomek, L.J., Chamberlain, S.: Underwater acoustics: a linear systems theory approach. Phys. Today 40(12), 91–92 (1987) 9. Winkler, M.R.: Chirp signals for communications. In: IEEE WESCON Convention (1962) 10. Bemi, A.J., Gregg, W.D.: On the utility of chirp modulation for digital signaling. IEEE Trans. Commun. 21(6), 748–751 (1973) 11. Tsai, Y.R., Chang, J.F.: The feasibility of combating multipath interference by chirp spread spectrum techniques over Rayleigh and Rician fading channels. In: Proceedings of the IEEE International Symposium Spread Spectrum Techniques Applications, pp. 282–286 (1994) 12. Cook, C.E.: Linear FM signal formats for beacon and communication systems. IEEE Trans. Aerosp. Electron. Syst. 10, 471–478 (1974) 13. Dutta, R., Kokkeler, A.B.J., Zee, R.V.D., et al.: Performance of chirped-FSK and chirpedPSK in the presence of partial-band interference, pp. 1–6. IEEE (2011)

An Improved Direct Sequence Spread Spectrum Signal Detection Algorithm Zengmao Chen, Fangpeng Wan(&), and Shiming Li College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China [email protected] Abstract. This paper presents an improved algorithm for detecting low signalto-noise ratio (SNR) direct sequence spread spectrum (DS) signal and estimating Pseudo-Noise (PN) sequence in non-cooperative communication. As the noise intensifies, the correlation peak of the received signal will be submerged in the noise, which makes it impossible to detect the PN sequence. At the same time, the imbalance of information code will also affect the cancellation of the correlation summit of the received signal. Therefore, adaptive noise canceller - segment and amplitude accumulation joint method is used to improve the traditional time domain auto-correlation algorithm. Keywords: Adaptive noise canceller
Direct sequence spread spectrum signal Estimation of PN sequence
Signal detection

1 Introduction Spread spectrum technology is one of the most advanced radio technologies in today’s information society. It is widely used in the third-generation mobile communication system. It not only has good anti-interference performance, low interception rate and low detection rate, but also can reduce the influence of frequency selective fading caused by multipath effect in digital communication system [1]. Therefore, it is more important to study the detection and parameter estimation of DS signals in low SNR. Among them, the auto-correlation detection method is widely studied, and it was the first time that Kuehls proposed pioneered the time-domain correlation method in 1990 [2], the sharp auto-correlation characteristic of PN sequence similar to white noise was taken as the feature quantity to judge whether the DSSS signal exists or not. After autocorrelation processing of spread spectrum signal, in addition to its sharp rate line feature, its rate line also presents periodicity, and its periodic fundamental frequency corresponds to the PN sequence, thus it is easy to detect the PN sequence [3]. But its obvious disadvantage is that when the SNR is low, the auto-correlation peak is not easy to extract, which makes it difficult to intercept and detect. Aiming at the problem that auto-correlation peak is not easy to be extracted when SNR is low, this paper adopts adaptive noise canceller - segment and amplitude accumulation method to improve existing algorithms, which makes auto-correlation peak obvious when SNR is low. Computer simulation shows that this method can effectively detect the DS signal and estimate the PN sequence in low SNR, and it is also suitable for non-cooperative fields. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 879–886, 2019. https://doi.org/10.1007/978-3-030-02804-6_115

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2 DS Signal Model The DSSS-BPSK signal x(t) received by the receiver is: xðtÞ ¼ sðtÞ þ nðtÞ ¼ AdðtÞ cosðx0 t þ uÞ þ nðtÞ

ð1Þ

In the formula: s(t) is a direct spread signal, n(t) is the noise in the channel. Among them, the d(t) compound code after the information code is spread spectrum modulated by the PN sequence, that is dðtÞ ¼ aðtÞcðtÞ. The auto-correlation function of x(t) is Rx ðsÞ ¼ E ½xðtÞxðt  sÞ ¼ RS ðsÞ þ RN ðsÞ ¼

A2 Rd ðsÞ cosðx0 sÞ þ RN ðsÞ 2

ð2Þ

The PN sequence c(t) has P a sharp auto-correlation property, and after the spread spectrum sequence dðtÞ ¼ ni¼1 ai c½t  ði  1ÞTc , the sequence of the information sequence after one-time spread modulation is essentially a splice of cðtÞ. Therefore, the auto-correlation function Rd ðsÞ also has the correlation property of the PN sequence s ¼ kNTc ðk ¼ 0; 1; 2; . . .; nÞ, that is, the peak appears at the integral multiple of the spreading code. While the noise is at s 6¼ 0; RN ðsÞ  0. Therefore, we can detect the DS signal, and estimate the PN sequence according to the interval between adjacent correlation peaks [4].

3 Structure of Adaptive Noise Canceller and LMS Algorithm In practical application, when the SNR is high, the correlation peak of signal autocorrelation is obvious and easy to extract. However, when the SNR is very low, the correlation peak is submerged in noise, which makes it difficult to identify and extract it accurately, which makes it difficult to estimate the PN sequence of the DS signal in low SNR. At first, this paper uses adaptive noise canceller to denoise the received signal, mainly based on the structure of adaptive filter and LMS algorithm. 3.1

Adaptive Noise Canceller

The core of the adaptive noise canceller is the adaptive filter. The adaptive algorithm controls its parameters to achieve the best filtering [5]. Because this method uses a reference noise as the auxiliary input, and the better effect of noise reduction can be obtained. Especially in auxiliary input noise and the noise in signal relevant completely, the adaptive noise cancellation method can completely eliminate the randomness of noise [6]. The principle of adaptive noise canceller is as follows is depicted in Fig. 1.

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Fig. 1. Functional block diagram of adaptive noise canceller

In Fig. 1, there are two input channels, one is a main input channel, it contains the signal s that carried information and unrelated noise n0. Another is the reference input channel, it is related noise n1, and is unrelated to the useful signal, its function is to detect noise, and through the adaptive filter to adjust tap weight x1, making the output y close to main channel’s noise n0 in the sense of minimum mean square. Thus, the best estimation of useful signal can be obtained by subtracting the adaptive output y from the main input channel d [7]. 3.2

LMS Algorithm

LMS algorithm is proposed through Wiener Filter based on the minimum mean square error criterion and steepest descent algorithm, and it is an approximate simplification of gradient descent algorithm. The basic theory of the algorithm is to adjust the parameters of the filter itself to minimize the mean square error between the output signal of the filter and the expected output signal, and the system output is the best estimation of the useful signal [8]. The algorithm process is as follows: *

1. Initialize tap weights xð0Þ ¼ 0 2. Calculate the output of the FIR filter at time n *H

*

yðnÞ ¼ x ðnÞlðnÞ

ð3Þ

3. Estimate the current n-time error *H

*

eðnÞ ¼ dðnÞ  x ðnÞlðnÞ

ð4Þ

xðn þ 1Þ ¼ xðnÞ þ le ðnÞlðnÞ

ð5Þ

4. Tap weight update *

*

*

5. Correct the error. If it meets the error criteria, stop iterating. If it is not satisfied, taking the next step. 6. Calculate the weight at the next n + 1 moment. Repeat the above steps so that the conditions are met.

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Among them, l is a fixed step factor, used to control the convergence rate and stability. Obviously, the above algorithmic process does not need to know the statistics of the signal in advance, and the weights obtained by using their instantaneous estimation instead of the algorithm are just an estimation value, but as the adjustment weights, these estimation values gradually increase and the filter adapts to the signal characteristics more and more. The final weights converge, and the convergence condition is ð6Þ

0\l\1=kmax

In the formula, kmax is the maximum eigenvalue of the input data variance matrix [9]. 3.3

Simulation Analysis

Combining MATLAB simulations to further explain the adaptive noise canceller. In the simulation, the spreading code uses a 127-bit m-sequence, the information code length is 10, the PN sequence rate is 5 Mbit/s, the carrier frequency is 15 MHz, the sampling rate is 90 MHz, and the adaptive filter order is 12, the step length factor is 0.0008. The following figure shows when the SNR = −12 dB, the DS signal including Gaussian white noise is directly subjected to auto-correlation operation and compared with the spectrum obtained through the adaptive noise canceller. As shown in Fig. 2a, the DS signal will be submerged in the noise when the SNR is −12 dB, and the spectrum peak of the DS signal at the carrier frequency cannot be observed. When adaptive noise canceller is used, Fig. 2b can be seen to have a clear denoising effect. It can show the approximate profile of the DS signal in the frequency domain and the peaks appear at the carrier frequency, indicating that the adaptive noise canceller is effective to denoise the DS signal.

Direct spread signal spectrum

Direct spread signal spectrum

600

2500

500 2000

400

Amplitude

Amplitude

1500

300

1000

200

500

100

0 -5

-4

-3

-2

-1

0 Frequency/Hz

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2

3

(a) Spectrum of DS signal canceller

4

5 7

x 10

0 -5

-4

-3

-2

-1

0 Frequency/Hz

1

2

3

4

5 7

x 10

(b) Spectrum of DS signal after adaptive noise

Fig. 2. Comparison of spectrum of DS signal

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4 Segment Correlation and Amplitude Accumulation Method After the signal is filtered by the adaptive noise canceller, in order to make the correlation peak appear more clearly, using segment correlation and amplitude accumulation method 4.1

The Theory of Segment Correlation and Amplitude Accumulation

Firstly, we divide the received signal into several non-overlapping segments of the short signal, requiring each segment contain at least several complete PN sequences. Secondly, the autocorrelation function is obtained for all short-term signals, and the absolute value of the autocorrelation result is squared on this basis. Finally, all the results of the second-order moments of several short-term signals are accumulated, and then calculate the average and search the peaks. Its functional diagram is shown in Fig. 3. This method has two main advantages: First, the probability of concealed correlation peaks can be reduced by means of segmentation-accumulation-average, and the probability of occurrence of correlation peaks can be increased. Second, it can smooth the noise curve effectively and reduce the effect of noise on correlation peaks.

Fig. 3. Functional block diagram of segment correlation and amplitude accumulation

Assuming that the received signal is x(t), its autocorrelation function is Rm xx ðsÞ

1 ¼ T

ZT xm ðtÞxm ðt  sÞdt

ð7Þ

0

In the formula: T is for each segment of the signal duration, M is for the number of segments, xm ðtÞ is for the segment m to receive the signal. Rm xx ðsÞ is for the segment m of the signal autocorrelation function. And the greater the number of signal segments (accumulation times), the greater the degree of suppression of noise.

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Simulation Analysis

As shown in the Fig. 4, the signal after adaptive noise canceller directly performs autocorrelation calculation and the result obtained by autocorrelation calculation after segment correlation and amplitude accumulation is compared. From the Fig. 4, it can be seen that the signal after segment correlation and amplitude accumulation has larger peaks on integer multiples of code period, and the peak amplitude increases, which makes it easier to extract, thus facilitating the detection of DS signal and the estimation of PN sequence.

Fig. 4. Comparison of the two algorithm

5 Improved Algorithm Performance Analysis Simulation conditions as mentioned above, the performance simulation of the traditional time-domain autocorrelation algorithm, segment correlation and amplitude accumulation method, the algorithm after the adaptive noise canceller - segment correlation and amplitude accumulation is carried out to detect the DS signal, and 200 Monte Carlo simulations are carried out respectively under the condition that whether there are equal-width spectral peaks separated by PN sequence is taken as the judgment condition, and the relative error of PN sequence is less than 1% as the correct detection, and the results are as follows. As shown in Fig. 5, the traditional time-domain autocorrelation algorithm can realize the DS signal detection with more than 90% accuracy when the SNR is at the lowest of −2 dB. The segment correlation and amplitude accumulation algorithm can achieve the same effect when the SNR is −8 dB. Through the adaptive noise canceller segment correlation and amplitude accumulation joint method, the correct detection effect of 0.9 can still be achieved when the SNR is −11 dB, the lower SNR tolerance can be achieved, the effective detection of the DS signal in low SNR can be achieved, and at the same time, the estimated value of PN sequence can be obtained.

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Performance of three algorithms 1 0.9

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0.8 0.7 0.6 0.5 0.4 0.3 0.2 segment correlation and amplitude accumulation traditional time domain auto-correlation adaptive noise canceller-segment and accumulation

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-6 -5 SNR/dB

-4

-3

-2

-1

0

1

2

3

Fig. 5. Performance comparison of three algorithms

6 Conclusions Aiming at the problem that the PN sequence can’t be accurately extracted in low SNR by the traditional time-domain autocorrelation method, this paper proposes an improved autocorrelation algorithm of adaptive noise canceller - segment correlation and amplitude accumulation joint algorithm. Firstly, the adaptive noise canceller is added in the front of the signal to reduce the influence of noise on segment correlation and amplitude accumulation method and improve the anti-noise performance of the system. Then the segment correlation and amplitude accumulation method can enhance the peak line, relatively reduce the noise power spectral density, and solve the problem of noise inundation in low SNR. Simulation results show that under the condition of low SNR, this algorithm can get clear and obvious autocorrelation peak, improving the detection performance of DS signal to some extent, and at the same time realize the estimation of PN sequence. It is of great significance to successfully detect the DS signal under non-cooperative conditions. Acknowledgements. The authors acknowledge the support from the National Natural Science Foundation of China (Grant No. 61401196) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20140954), and also acknowledge Central University Basic Operating Expenses Project of Harbin Engineering University (Grant No. 201749).

References 1. Zhu, J.K.: Spread Spectrum Communication and Applications, pp. 16–42. China University of Science and Technology Press, Anhui (1993) 2. Bendat, J.S., Piersol, A.G.: Engineering Applications of Correlation and Spectral Analysis, p. 315. Wiley, New York (1980) 3. Burel, G., Bouder, C.: Blind estimation of the pseudo-random sequence of a direct sequence spread spectrum signal. Century Military Communications Conference Proceedings (Milcom 2000), vol. 2, pp. 967–970. IEEE (2000) 4. Lopes, W.B., Al-Nuaimi, A., Lopes, C.G.: Geometric-algebra LMS adaptive filter and its application to rotation estimation. IEEE Signal Process. Lett. 23(6), 858–862 (2016)

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5. Tian, Y.J., Zuo, H.W.: Application of adaptive noise cancellation. J. Qingdao Inst. Archit. Eng. 26(1), 77–80 (2005) 6. Shen, F.M.: Adaptive Signal Processing, pp. 211–222. Xi’an University of Electronic Science and Technology Press, Xi’an (2001) 7. Cao, L.Y.: Application of LMS algorithm in adaptive filter. J. Instrum. 26(z2), 452–454 (2005) 8. Pei, B.N.: Convergence and step selection of LMS algorithm. J. Commun. 4, 106–111 (1994) 9. Zhou, Z., He, J., Liu, Z.W.: FPGA-implementation of LMS adaptive noise canceller for ECG signal using model based design. In: 2011 International Symposium on Bioelectronics and Bioinformatics (ISBB), pp. 127–130. IEEE (2011)

Research on Non-contact Palmprint Recognition Positioning Method in Mobile Terminal Chunyu Zhang1(&) and Chenghao Zhang2 1

Qiqihar Institute of Engineering, Qiqihar, China [email protected] 2 Harbin Engineering University, Harbin, China

Abstract. In order to solve issues including invalid palmprint image, the acquisition of blurred image and palm deformation in non-contact palmprint acquisition, which will result in degradation in the performance of the recognition system, the author combines the characteristics of the mobile terminal itself and put forward a new solution. Using the open-end function of the mobile terminal, a positioning model was drawn on the camera interface of the mobile terminal. The SURF algorithm was used to extract the feature points. Finally, the Euclidean distances between the feature points were used for matching and classification. The self-built 100-person (500-image) palmprint library test results showed that the effectiveness of the images acquired by the acquisition method was 97%, the clarity was 91%, and the deformation was reduced to 3%. Compared with the traditional non-contact palmprint acquisition method, the ratio was increased by 26% and 28% respectively, while the deformation was reduced by 20%. The use of a hand-shaped frame positioning method ensures the quality of the picture and meets the requirements of the palmprint recognition on images, showing that this method is effective and provides a feasible way for non-contact palmprint recognition. Keywords: Non-contact acquisition


Palm print
Hand frame positioning

1 Introduction The non-contact palmprint recognition devices [1] in the world today are too bulky and costly. The simple non-contact device is very small, but it cannot achieve the desired results. The primary task of extracting palmprint features is to acquire good palm images [2]. The proposal of non-contact acquisition meets the health concepts of contemporary society. At the same time, the safety of biological information is extremely high, which promotes the exploration of non-contact palmprint recognition [3]. The pixels of mobile phone cameras have been continuously improved, which has ensured the feasibility of mobile phones for palm capture. However, the problem of non-contact acquisition devices still exists [4]. Using non-contact palmprint recognition to capture images is extremely demanding for users. The user needs to place the palm at the camera’s shooting center and fix it every time. The distance between the camera and the palm needs to be specified within a certain range, and it is difficult for ordinary © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 887–894, 2019. https://doi.org/10.1007/978-3-030-02804-6_116

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users to grasp the range of depth of focus. It’s hard to guarantee the degree of palm spread and whether the palm is parallel to the camera at each shot [5]. Summarize the above questions: problems such as unacceptable palmprint acquisition, blurred images, palm deformation and palmprint rotation. These problems directly affect the extraction of regions of interest in the later period, so solving these problems can be a good way to achieve subsequent feature extraction and matching.

2 Palm Positioning in Mobile Terminal 2.1

Palmprint Validity Determination

The acquisition of non-contact palmprint images is very different from the contact image acquisition. The contact acquisition method fixes the position of the palm, while the non-contact acquisition method does not have a fixed acquisition panel. It may happen that the palm is off-center and the acquired palm image has no fingers or parts of fingers, which causes troubles for the later extraction for interested parts in preprocessing, as shown in Fig. 1a. It is also possible that only partial palms are acquired or no palms are acquired, as shown in Fig. 1b, c, so that an effective ROI cannot be extracted.

(a) Part of the finger (b) Part of the palm (c) No palm

(d) Effective palm

Fig. 1. Plam acquisition

In order to ensure that the position of the palm is fixed during each acquisition, the center of the palm and the camera does not deviate, the acquisition of the palm is complete, and at the same time, the pixels of the palm can be recognized, a positioning shooting by using a hand-shaped frame is proposed in this paper, as shown in Fig. 1d. By placing the palm in the hand-shaped frame, the palm is in the center of the camera without any deviation; the hand-shaped frame also fixes the position where palm is placed each time, and bid farewell to placing the palm by intuition; placing the palm in the hand-shaped frame will not result in situations such as no fingers in the image or the missed palm. The hand-shaped frame guarantees the validity of the captured image.

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Palmprint Definition

Since the palm of the collector is in a non-contact state without reference objects, it is normal for the palm to shake and the distance is based on personal feeling. The collector is not a professional photographer and it is easy to shoot beyond the depth of field, so the blurred image appears. The blurred palmprint Fig. 2a reduces a large number of original features of the palmprint, therefore the recognition rate decreasing.

(a) Motion blur

(b) Shooting distance exceeds depth of field (c) Clear palm diagram Fig. 2. Blur and clear image

The hand-shaped frame positioning method can solve the above problems well. With the hand-shaped frame Fig. 2b as a reference, the palm is completely placed in the frame, and the collector can control the relative motion of the palm and the camera in a short time, thereby solving the problem of the relative movement of the palm to the camera. Since the size of the hand-shaped frame is fixed, if the hand is put into the hand frame, the palm and the hand-shaped frame are almost coincident, and the obtained distance between the palm and the camera is fixed. The distance between the palm and the camera is within the depth of field, and the distance between the palm and the camera is almost constant each time, which solved the issue of shooting distance. Figure 2(c) is taken after the hand-shaped frame shooting. After extracting the palm of HSV complexion, the texture of the image is clear. 2.3

No Palm Angle is Determination

When the non-contact palmprint acquisition device collects palms, there may be a problem that the plane of the palm and the plane of the camera form a certain angle, and the palmprint image taken is deformed. The direction of this deformation may be up and down, as shown in Fig. 3a, or it may be left or right, as shown in Fig. 3b. The spatial relationship between each pixel of the palm has changed, and features that may have been erroneously extracted during feature extraction have been extracted, resulting in a decrease in the subsequent matching recognition rate. The reason why palm tilt occurs is that there is no frame of reference when the image is taken, making the hand palm free. The hand-shaped frame can be used as a reference system for palm placement. Figure 4a shows a palm image with a dip. By contrasting the hand-shaped frame, the collector himself adjusts the angle of the palm until the palm coincides with the hand-shaped frame as much as possible. Figure 4b is an image of the palm after the automatic adjustment.

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(a) Tilt up and down (b) Tilt to left and right Figure Fig. 3. Hand with dip

(a) Presence of tilt (b) After adjustment Figure Fig. 4. Palm pitch adjustment.

3 Palmprint Feature Extraction and Matching 3.1

SURF Algorithm

1. Building a Hessian Matrix The Hessian matrix is the core of the SURF algorithm and is denoted as H. The Hessian matrix of a pixel in the image is as follows: " H ðf ðx; yÞÞ ¼

@2 f @x2 @2 f @x@y

@2 f @x@y @2 f @y2

# ð1Þ

Taking into account the effect of scale changes on the extraction of feature points, Gaussian filtering is applied to the Hessian matrix before it is constructed. In this way, the Hessian matrix is calculated after filtering, and the formula is as follows: Lðx; tÞ ¼ GðtÞ
Iðx; tÞ

ð2Þ

L(x,t) is an image representation at different resolutions, G(t) is a Gaussian kernel, and I x is an image function. The formula for the Gaussian kernel G(t) is: GðtÞ ¼

@ 2 gð t Þ @x2

ð3Þ

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2. Building the Gaussian Pyramid Figure 5 shows the pyramid built by using traditional method. The size of the image is varied. Gaussian filters are used to conduct smoothing. While the SURF algorithm maintains the original image size and only changes the filter size. The SURF algorithm eliminates the downsampling process.

Fig. 5. Constructing the Gaussian pyramid

3. Positioning Feature Points Hessian matrix processing is performed on each pixel, and the processed pixel points are associated with upper and lower layers. The 26 points are compared in size, and when it is the maximum or minimum of these 27 points, the original feature point is reserved. 4. Determine the Main Direction of Feature Points The SURF algorithm performs statistics on Harr wavelet features in the feature point area. The Harr wavelet response is assigned a Gaussian weight coefficient, and the smaller the weight coefficient is, the further away from the feature point is, and the response is within the range of 60°. The weighted sum becomes the new vector. Rotate the fan area to find the longest vector. The direction of this vector is set as the main direction of the feature point. Figure 6 shows a schematic diagram of this process.

Fig. 6. Main direction of feature points

5. Construct SURF Feature Point Description Operator After the feature point is determined, a square frame with a side of 20 s is taken around the point. The direction of the box is the main direction of the feature point. The box is divided into 16 sub-areas. In each sub-area, 25 Harr wavelet features parallel to the main direction and perpendicular to the main direction are counted. The harr wavelet feature sums the horizontal and vertical values and sums the corresponding absolute values. Figure 7 shows a schematic of this process.

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Fig. 7. Schematic diagram of feature point description operators

3.2

SURF Feature Matching

The SURF feature descriptor is a 64-dimensional real-valued vector, which can use Euclidean distance to calculate the descriptor distance. Determine whether the feature points of the two palmprint images match by using Eqs. 3–4. dij \t
mindik ; k ¼ 1; 2;


; N; k 6¼ j

ð4Þ

where dij and dik represent the Euclidean distance between the descriptors pi and qj, and pi and qk respectively. T is the set threshold.

4 Experiment Analysis 4.1

Effectiveness, Clarity, and Deformation Comparison Tests

The hand-framed positioning of the palm of the hand is compared with the no-hand frame, and the recognition method is the SURF algorithm. Palms and regions of interest are extracted from the acquired palm images and stored in two image libraries (500 images for 100 persons). The histogram equalization enhancement is performed on the image, and the validity, clarity and deformation of the palmprint image are counted. The test results are shown in Table 1. Table 1. Using a hand frame versus not using a handframe Used data Use a palm print image of a positioning frame Palmprint image without positioning frame

The effectiveness of the hands (%) 97

Clear picture (%) 91

Deformation image (%) 3

71

63

23

It can be clearly seen from Table 1 that the use of a hand-shaped frame can improve the effectiveness of the acquisition of the palm, ensure the clarity of the picture, and reduce distortion. Using the positioning frame can promote the extraction of the region of interest. The palm’s region of interest can well reflect the palmprint features and reduce unnecessary processing. The area of interest reduces the requirements for storage space to include more features in a very small storage space. The experiment confirmed that the proposed hand frame is effective.

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Recognition Accuracy Test

As shown in Fig. 8, the distribution of the scores of the recognition match between the user and the impersonator is shown. The distribution of matching scores has two different peaks. A peak at the position of the abscissa 0.6 corresponds to the user’s recognition match score, and another peak at the abscissa 0.3 or so corresponds to the counterfeiter’s recognition match score. The separation effect of the two peaks is obvious and the intersection point of the distribution curve is very small. Therefore, a good image can be acquired by using the acquisition mode of the positioning frame, so as to achieve a certain recognition effect.

Fig. 8. Match score distribution

5 Inconclusion There are drawbacks in non-contact palmprint recognition, and there are many problems with the inability to position the palm of the hand: acquisition of palm ineffectiveness, palm distortion, and palm hand-shake. This paper proposes the positioning method of the hand-shaped frame and locates the palm in the hand-shaped frame. Through experiments, it has been verified that this method not only solves the problem of non-contact palmprint positioning, but also improves the effectiveness of the captured image, makes the image clearer, and reduces the hand-type. The distortion also facilitates the extraction of the outline of the later image, reduces the requirements of the feature extraction algorithm, and improves the recognition rate.

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References 1. Lin, S., Bai, Y., Tang, Y.: Design of online non-contact palmprint recognition simulation system. In: International Congress on Image and Signal Processing, Biomedical Engineering and Informatics, pp. 686–690. IEEE (2017) 2. Wu, X.Q., Pu, W., Zhao, Q.S.: Non-contact palmprint recognition method based on palmprint image registration. CN 103440480 A[P] (2013) 3. Weiqi, Y.: Simulation system of improved non-contact on-line palmprint recognition. Acta Opt. Sin. 31(7), 0712003 (2011) 4. Yang, J., Zhang, D., Yang, J.Y.: “Non-locality” preserving projection and its application to palmprint recognition. In: International Conference on Control, Automation, Robotics and Vision, pp. 1–4. IEEE (2007) 5. Wu, Q.E., Chen, Z., Han, R., et al.: A Palmprint recognition approach based on image segmentation of region of interest. Int. J. Pattern Recognit. Artif. Intell. 30(2) (2016)

Android Palmprint Recognition System Design and Implementations Chunyu Zhang1(&) and Chenghao Zhang2 1

Qiqihar Institute of Engineering, Qiqihar, China [email protected] 2 Harbin Engineering University, Harbin, China

Abstract. At present, the requirements for user information security protection are getting higher and higher, and palmprint recognition is widely used as one of the most important means in biometric identification. The Android system is widely used on various smart devices, so it is of great significance to add the palmprint recognition function to the Android system. This paper designed and implemented a solution to add palmprint recognition to Android devices. Keywords: Palmprint recognition


Android devices
Android system

1 Introduction With the widespread adoption of social payment applications, information security has received more and more attention from the society. The existing solution is to authenticate the user’s identification through name, password and fingerprint. However, this method has some problems: the password is easy to be lost, the fingerprint is easy to forge and the fingerprint is easily blurred. So new methods are needed for identity authentication. The palmprint feature is the only identifiable biometric of each individual. Palmprint [1] is textures on the palm that are caused by sags and crests. These patterns differ from person to person in patterns such as breakpoints and intersections [2]. Palmprint areas are larger than fingerprint and feature points are more. Android is the operating platform on mobile devices developed by Google in the United States. This platform is based on the embedded linux operating system and a wealth of services and interfaces are added to the platform to facilitate the development of various applications at the upper level. The combination of Android and palmprint recognition is very important for the protection of user information [3].

2 System Implementation Flow Chart Figure 1 shows the flow chart of application implementation. The implementation is divided into two phases: user registration and login. During the registration process, the user needs 3 acquisitions of palmprint, after which the image is preprocessed to determine the image quality and the best image is stored in the database.

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(a) Palmprint image registration

(b) Palmprint image match

Fig. 1. Palmprint recognition system flow chart

At the identity recognition stage, the user collects and pre-processes the image, extracts the image of the interested region registered in the database and extracts and matches features of the image the interested region extracted at the recognition stage, and then returns a matching result after the judgment. The palmprint recognition system on the Android smart phone is divided into four modules, which are: registration login module, preprocessing module, feature algorithm implementation module, and feature matching module. The following introduces the design philosophy and implementation of registration login, the design and implementation of preprocessing modules, the design and implementation of palmprint feature extraction modules and the design and implementation of palmprint matching modules. All of the following experiments are done on the phone.

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3 Acquisition Registration Module There are two ways to implement the camera function of Android camera: call the build-in camera of the phone or use the custom camera. The system uses a custom camera. (1) Declaration of authority. The permissions that need to be set are: check whether the camera exists, the camera call permission, the flash call permission and the photo storage permission. (2) Detect and turn on the camera. Before opening the camera, you need to use the PackageManager to check if the camera is available, and once the camera is damaged, the program will not work. Use the Camera.open() method to obtain the camera. In this method, it is necessary to acquire the camera abnormality to prevent the camera from being occupied or absent. (3) Preview. In order to observe the photographed palm, a real-time dynamic preview window is created. (4) Save photos. Call the takePicture() method to achieve the camera function. The camera captures the saved image in the format of jpeg. (5) Find the image. Open the gallery to find the photo you have taken. You need to save the path lastPicPath of the previous photo. When the application is started for the first time, lastPicPath is given null and it is not possible to view previous photos. (6) Drawing of a hand-shaped frame. Android uses the graphics class to display 2D graphics. This class includes Canvas, Paint, Color, Bitmap and other classes. Graphics has features such as drawing points, lines, colors, 2D geometry, and image processing. The Paint class and Canvas are mainly used in this article. Brush color, brush style, brush aliasing effect, and more are set in the Paint class. Canvas is a canvas that provides methods for drawing lines, rectangles, arcs, and scallops. (7) The user’s registration interface design is very important. This application requires user registration before matching and recognition. After clicking the Register button, the system generates a folder for containing user information and prompts you to take a picture three times. After three shots, click “Entry” and the system will switch to the entry screen with the title “Image Name”. This dialog box contains: Name, ID, and OK and Cancel buttons. Click on The OK button completes registration. Figure 2 shows the effect diagram for the camera module. Figure 3 shows the system registration interface.

Fig. 2. Camera module effect picture

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Fig. 3. System registration screen

4 Palmprint Image Preprocessing Module Grayscale the captured palmprint image, then use the captured image under complex background. Skin color detection in the HSV space extracts the palm area, then uses the OTSU [4] algorithm to segment the palmprint image, and then equalizes the palmprint image to enhance the contrast. Specific steps are as follows: (1) Segmentation and graying of palmprint images. Figure 4 is the graying after the acquisition of the palmprint image to segment the region of interest. (2) Calculate the gradient field. After extracting the region of interest from the already captured palmprint image, calculate its gradient field as shown in Fig. 5.

Fig. 4. Gradient image of palmprint

Fig. 5. Orientation field of palmprint image ROI

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(3) Image enhancement. Use histogram equalization for enhancement. Figure 6 shows the enhanced effect of the palmprint image.

Fig. 6. Palmprint image enhancement

5 Palmprint Image Feature Extraction Module Feature extraction uses the SURF [5] algorithm, which is implemented in OpenCV. The specific steps of SURF feature extraction are as follows. (1) SURF feature point detection. Concrete steps: Define a series of vectors; allocate space for dets and traces, and initialize sizes, sampleSteps, and middleIndices; and use SURFFindInvoker to find extrema. (2) SURF feature point principal direction and descriptor calculations. Create SURFInvoker’s constructor, initialization parameters. Calculate the contrast between the main direction and the descriptor. (3) SURF algorithm determinants and trace calculations. The specific steps are as follows: First, define three class Haar templates with a block size of 9; generate class-like Haar templates; use the integral image and haar-like template to quickly calculate the determinants and traces of the Hessian matrix. (4) SURF search feature points. Finally, use the SURFFindInvoker class-like to search for extreme points in the scale space. Taylor fitting is performed on valid extreme points to determine their position and size. Figure 7 shows the palmprint feature extraction result graph.

Fig. 7. Palmprint feature extraction result graph

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6 Palmprint Image Feature Matching Module In the registration process, after the feature extraction is completed, the system prompts you to name the collected palmprint. Named feature points will be saved in the phone. In the process of identity recognition, after the feature extraction is completed, the user will enter the recognition process, and then the matching of palmprint images will be explained. The system uses FLANN matcher to match. Figure 8 shows the matching result of the FLANN matcher. The FLANN matcher can perform feature point matching very well. The FLANN matcher has a fast matching speed and is very suitable for mobile phones.

Fig. 8. FLANN matcher match result map

Figure 9 shows the effect picture of successful matching and failed matching. Title is Result, if the match is successful, it will show: This palmprint belongs to: If the match fails, it will show, “The match score is too low and palmprint recognition failed.” Through the dialog box, the user can visually see the matching result, which is user-friendly.

(a) Picture for success matching

(b) Match Failure Effect

Fig. 9. Match result map.

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7 Key Technology Experiments of Palmprint Recognition on Android Phone Through the above steps, we have implemented palmprint recognition implementation on the Android system. The following is a detailed test of the overall system functionality. The specific functional tests are as follows: 1. Interface testing The testing of the system interface in software development can help developers find bugs in time to solve them and the system can be optimized. Table 1 mainly tests whether the overall interface function of the system meets the design requirements. Table 1. Shows the system interface evaluation table Functional description Purpose Test items

Interface operation display function

1 2 3 4 5 6 7

Whether the window switch is normal Is each interface text normal? Is the status of each interface element normal? Is the order of operations reasonable? Whether the operation has bullets If there is any error in the operation Is the layout of each interface element reasonable? Whether the background color is soft

8

Test all system pages Test result (yes/no) Yes Yes Yes Yes Yes Yes Yes Yes

2. function test In order to analyze the completion of the system’s function implementation, this paper conducts a comprehensive functional test of the system. Tests are shown in Table 2. 3. system identification efficiency test This system has tested 500 groups of users on recognition efficiency after their registration and login. Among these 500 sets of valid data, there were 15 errors in the test. After removing the 15 sets of error data, the user registration time measured data and the user login time measured data shown in Table 3 were obtained, including the test results of the average error comparison. It can be seen that the application has reached the palmprint recognition function.

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Functional description Purpose Input/action

System installation, registration, identification function test status is good Test the system function Expected output

1

Apk to phone

2

Display identification information after successful system identification Click Register to enter the registration interface Tap the screen to take a picture when registering Click on initialization for contour extraction Click to open flash, flash on

3 4 5 6 7 8 9

Click on the entry, pop-up database save prompt Click on match to identify the identity Failed dialog when authentication failed frame

Display system icon Normal display of name card normal normal normal normal normal normal normal

Test results meet the goal meet deadline meet deadline meet deadline meet deadline meet deadline meet deadline meet deadline meet deadline

Table 3. Comparison and analysis of a large number of actual test data and trip signals 100 tests User registered to collect photos User login to capture photos User registration User login

Measured time Error rate Average error 8s 9.1% 7.3% 5s 7.1% 5.1% 19 s 7.9% 8.7% 13 s 6% 7.3%

8 Conclusion Firstly, according to the implemented functions, draw the system flow chart. The system is divided into four modules: acquisition registration module, preprocessing module, feature extraction module, and feature matching module. Describe separately the functions implemented by each module and the methods used to implement them. Through the overall experimental analysis of the program, it is proved that the functions of each module are well-functioning and the palmprint recognition and recognition rate of the Android system meets the recognition requirements.

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References 1. Wang, R., Zhang, F., Li, D., et al.: Android privacy protection system based on palm print recognition, CN105323355A (2016) 2. Karar, S., Parekh, R.: Palm print recognition using zernike moments. Int. J. Comput. Appl. 55 (16), 15–19 (2012) 3. Shreyas, K.K.M., Rajeev, S., Panetta, K., et al.: Comparative study of palm print authentication system using geometric features. In: SPIE Commercial+Scientific Sensing and Imaging, p. 102210M (2017) 4. Gudadhe, S.S., Thakare, A.D., Dhote, C.A.: Parallel palm print identification using fractional coefficients of palm edge transformed images on GPU (2018) 5. Kaur, S., Rai, P.: An efficient and robust palm print recognition system. In: International Conference on Optical and Wireless Technologies (2017)

A Cache-Aware Multicast Routing for Mobile Social Networks Xia Deng(&), Shuxian Bao, Yu Lin, and Zhishuang Xu School of Computer Science and Educational Software, Guangzhou University, Guangzhou, Guangdong, China [email protected]

Abstract. Recently, mobile social networks have attracted considerable attention due to their wide application areas, e.g., community message distribution, campus information transmission etc. Many of these applications require of highly efficient group communication, which makes multicast a critical building block in the design of mobile social networks. In this paper, we design a cacheaware multicast routing scheme CMRMSN that utilizes the sociality of nodes and manages small-size node cache at the same time. Multicast social metric is used to select promising forwarding nodes to carry messages. At the same time, the node cache is managed based on the cache time and replication number of messages. Through simulation-based experiments, it is verified that a combination of these strategies achieve high delivery ratio, small transmission cost and low delay at same time. Keywords: Mobile social networks


Multicast
Cache management

1 Introduction Mobile social networks is widely used in many applications, such as community message distribution, campus information transmission, etc. [1–3]. Because of its flexibility and fast networking mode, it enables efficient communication where the reliable network infrastructure, e.g., LTE, 4G, is absent [4–6]. In mobile social networks, nodes often need to work together to accomplish group activities, and thus multicast in mobile social networks has its important application value [7–9]. Nodes use the opportunity connections to distribute messages in groups with same interests; in campus, teachers and students use the opportunistic connections to deliver messages to the class. In mobile social networks, nodes move frequently, connect with each other intermittently, and the network topology changes fast. Despite such hostile features, however, mobile social networks are composed of personal equipment, and demonstrate strong social characteristics. Based on these social characteristics, the study on multicast in mobile social networks has attracted considerable attentions [8, 9]. In mobile social networks, data forwarding may be in the store-carry-forward mode, and the mobile device cache is usually with limited capacity. Therefore, it is important to design a reasonable cache management strategy. The study of cache management strategies of mobile social network multicast is still in the early stage. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 904–913, 2019. https://doi.org/10.1007/978-3-030-02804-6_118

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In [10], E_DBCM is proposed based on caching time and limited arrival rate. E_DBCM predicts the maximum transmission probability under the maximum caching time, and forwards data according to the transmission probability. The results show that E_DBCM improves the delivery performance of multicast routing. In [11], QBMR is proposed to determine message quota based on the average cache occupancy. The results show that QBMR can effectively reduce the data transmission overhead through trace-driven experiments with real data. In [12], a caching strategy for multicast, SCBMR, is proposed based on social centrality. When the cache is full, SCBMR discards the messages with a large number of replicas and small TTL. The results show that SCBMR improves the data delivery ratio with low overhead. However, SCBMR does not fully consider other social characteristics, such as similarity, which is important in group communication. Moreover, the cache time of messages is also important for cache management design. This paper proposes a cache-aware multicast routing (CMRMSN) which considers social centrality and similarity, and uses two important attributes of messages, cache time and replication number, to design a cache management strategy that greatly improves the performance of multicast protocol.

2 Social Characteristics In mobile social networks, topology may change fast and connections occur intermittently. These characteristics bring great challenge to the design of multicast routing protocols. However, such networks also show relatively stable social characteristics. There are also some active nodes which show strong social centrality, and some friendly nodes have high social similarity between each other [13–17]. 2.1

Multicast Social Similarity

A group of members in multicast may perform a task together or be interested in same topics, so multicast members usually have stronger social connections or similarities than unicast nodes. Therefore, it is possible to optimize multicast routing performance utilizing such social similarity. This study adopts the definition of social similarity in [14]. Social similarity is defined as the contact probability between two nodes in the future based on the historical information of the number of common neighbors among them. At time t, node x meets node y, the encountered nodes of node x and node y are N (x), N (y) respectively, the contact probability of node X and Y in the future is calculated using formula (1). Pðx; yÞ ¼

N ð xÞ \ N ð yÞ N ð xÞ

ð1Þ

The larger the N(x) and N(y), the larger the P(x, y), indicating that x and y encounter more frequently in the past, and the probability of future encountering will be greater. Considering that multicast communication has multiple destinations, we adopts the multicast similarity in [18]. A group of D has k members, which are recorded as {d1, d2, …, dk}, the similarity value vector of node i to the K members in group D is

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fSimilarityi;d1 ; Similarityi;d2 ; . . .. . .Similarityi;dk g. We use the average value of the vector as the multicast social similarity between node i and group D, MSSi;D , which can be calculated using formula (2). MSSi;D ¼ ð

2.2

Xj¼k j¼1

Similarityi;dj Þ=k

ð2Þ

Multicast Social Centrality

The social centrality of a node measures the activity of a node in the network. If the node has a high centrality, it means that the node has high connection probabilities with other nodes and plays an important role in data transmission. In this paper, we adopt the betweenness centrality CB(Pi ) proposed by Daly et al. [14]. The centrality of the node is defined as formula (3). CB ðPi Þ ¼

XN Xj1 j¼1

k¼1

gjk ðPi Þ=gjk

ð3Þ

N represents the number of nodes in the network, gjk is the number of all paths between node Pj and Pk, and gjk(Pi) is the number of all paths through node Pi. According to the definition of social centrality, each node can calculate its centrality locally in the network. In the message transmission process, messages can be forwarded to the nodes with high centrality, which can improve the data delivery ratio to the multicast destinations. 2.3

Multicast Sociality

Based on the definition of multicast social similarity and centrality, we further define the social metric of multicast, SMetric, in formula (4) SMetric ¼ a  MSSi;D þ b  CB ðPi Þ

ð4Þ

SMetric considers both social similarity and centrality. Different a and b values can be used to meet different needs of the network. Here, we set a and b to 0.5. The larger the SMetric is, the stronger social characteristics the node indicates.

3 The Proposed Cache Management Strategy In mobile social networks, mobile nodes have limited cache space. When the cache is limited, how to discard the buffered messages in the cache is critical [19]. In this study, we consider the cache time and number of replicas, and discard the messages with long cache time and large number of replicas.

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Cache Time

Cache time of a message is the residence time of a message in the cache on a node. When the cache becomes full, the message with long residence time is preferentially dropped. Because the longer the message stays, the longer the node has the message and the message is better distributed to other nodes. Discarding such messages can provide more cache space for new messages, and thus provides more transmission opportunities for new messages and improves the average lifetime of messages and the data delivery ratio in the network. The cache time of message m in node i is denoted as Cachetimeim . 3.2

Message Replication Number

Message replication number is the number of messages transmitted in the network. The message replication of messages m in node i is denoted as Nmi . The larger the Nmi is, the more times message m is transmitted in the network. Therefore, when the node cache is full, the message with a large Nmi value should be dropped in priority. It makes more cache space for poorly-replicated message, and thus improves the overall data delivery rate in the network. Considering the multi-destination of multicast, we adopt the definition of multicast replication number in [12]. Given message m, the destination is multicast group, the members number of multicast group G is n, MNmi is calculated in formula (5). MNmi ¼ Nmi =n

ð5Þ

When the node cache is full, CMRMSN takes into account the cache time and replication number. The priority of discarding messages in the cache is given to those with large cache time and replication numbers.

4 The Proposed Cache-Aware Multicast (CMRMSN) Algorithm This section describes the algorithm of CMRMSN. When two nodes encounter, the values of the multicast sociality metric, SMetric, are calculated concerning the centrality and similarity of the two nodes using formula (1)–(4). If the meeting node has a higher SMetric, the node will forward the data to the meeting node, expecting a higher probability to reach the destinations. Then, when a node receives a message, it first checks whether there is enough cache space for receiving the message. If the answer is yes, it accepts the message. Otherwise, the cache time and replication number of the messages are inspected in the cache, and the message with large cache time and replication number is discarded, and then the new message is received and stored. The details of the algorithm are shown in Fig. 1.

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Fig. 1. The algorithm of CMRMSN

5 Performance Evaluation 5.1

Simulation Setup

In this study, we conduct simulation-based experiments to evaluate the performance of CMRMSN using the opportunistic network simulation environment (ONE) [20]. The Helsinki city map is used to simulate the real world, which is a 4500 * 3400 area. The message size varies from 0.5 to 2 KB. The message generating interval at each node is 1 s. To simulate the community-based mobile pattern, we select three regions in the map as the hop spots. Each multicast group has a region as its major region. The nodes in a multicast group prefer to stay in their major region. The visiting probability of each region is {0.7, 0.1, 0.1, 0.1}, where the probability to visit the major region is 0.7. There are three multicast groups, each of which has 5 members. The velocity of the members is from 0.5 to 4 m/s. In addition, we assign high centrality values to four nodes in the network. These nodes move between the three regions with a high speed varying from 7 to 14 m/s, like the buses in the city in real world. The entire simulation time is 6 h, and the TTL of messages is 120 min. We vary the cache size following {0.3, 0.5, 1, 1.5, 2} MB.

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Three multicast routing strategies are simulated, namely CMRMSN, SMRMSN and Epidemic [21]. CMRMSN is the proposed algorithm of this study, which combines sociality-based routing and cache management. SMRMSN is the routing strategy that considers social similarity and centrality with a FIFO caching strategy. Epidemic is a flooding-based algorithm proposed by Vahdat et al. 5.2

Evaluating Metrics

Data delivery ratio: the ratio of the number of received messages by the destinations to the messages that expect to arrive. Transmission cost: the total number of forwardings in the network minus those arriving at the destinations, divided by those arriving at the destinations. Average delay: the average time spent from the source to the destinations. 5.3

Simulation Results

Figure 2 illustrates the data delivery ratio with varying cache sizes of the three algorithms. As the cache size increases, the performance of all the three algorithms improves. When the cache size is limited, our CMRMSN performs the best data delivery ratio. When the cache size is smaller than 1 MB, CMRMSN improves the performance by 50% compared with Epidemic, and 20% with SMRMSN. It is worth mentioning that the improvement is more evident with a smaller cache. Compared with Epidemic, CMRMSN and SMRMSN takes advantage of sociality. Compared with SMRMSN, CMRMSN manages the cache based on the message cache time and replication number. These factors make CMRMSN outperform the other two algorithms in terms of data delivery ratio. When the cache size increases, there are a smaller number of messages discarded due to cache limit, and thus all the algorithms demonstrate desirable data delivery performance. 0.95 0.90

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The transmission cost of all algorithms is shown in Fig. 3. Epidemic adopts a flooding-based distribution method, and thus incurs huge transmission cost. The performance is especially worse with a small cache size. CMRMSN demonstrates a

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transmission cost comparable to SMRMSN, but the delivery ratio is much higher. This verifies that CMRMSN improves the delivery performance without increasing the transmission cost.

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Figure 4 shows the average delay of the three algorithms. When the cache size increases, the average delay of all the three algorithms will increase. This is because a greater size can accommodate more messages, and the number of messages arriving at the destinations will increase, making the average delay bigger. As Epidemic always selects the shortest path to distribute messages, the delay is shortest, yet with a great transmission cost. CMRMSN demonstrates the longest delay, as it makes space for messages with a smaller number of replicas, through considering the message cache time and replication number.

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However, the average delay is less efficient in verifying the comprehensive performance, as it does not consider the fact the data delivery ratio of CMRMSN is far better than SMRMSN and Epidemic. Therefore, for a fair comparison among the three algorithms, we need to retrieve the delay under the same delivery ratio. To achieve that, we sort the delay of all successfully received messages in the ascending order, and

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select those messages until a certain delivery ratio is reached. Then the average delay is calculated based on these filtered messages, referred to as filtered delay. We fix the cache size to 1 MB, and plot the corresponding filtered delay in Fig. 5, which clearly demonstrates that CMRMSN achieves a lower delay under the same delivery ratio. This indicates that the messages with high delay in CMRMSN are those discarded by SMRMSN and Epidemic and never reach the destination. Therefore, we draw the conclusion that CMRMSN in fact performs better than SMRMSN and Epidemic in terms of the delay of messages. 3600 3400 3200 3000 2800 2600

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6 Conclusion and Future Work In this paper, we proposed a cache-aware multicast routing in Mobile social networks, i.e., CMRMSN. In the distribution process of messages, CMRMSN selected in priority the nodes with distinct social characteristics to improve the data delivery efficiency. At the same time, to solve the problem that such nodes are easily overloaded and tend to discard a large number of messages, CMRMSN carefully manipulated the node cache through setting the message cache time and replication number as the cache replacement metrics. When the cache was full, messages with a longer cache time and larger replication number would be discarded. Through extensive simulation-based experiments, we verified that CMRMSN achieved the best delivery performance, with a small transmission cost and delay. Our future work will involve performance evaluation of CMRMSN with real data traces, e.g., Sigcomm2009. We will also study the imbalanced interests of multicast groups to different locations or messages [22], and design efficient multicast routing algorithms with proper cache management strategies. Acknowledgments. This work was supported in part by the National Natural Science Foundation of China (Grant No. 61702127), Science and Technology Program of Guangzhou (Grant No. 201804010461), Undergraduate Innovation Training Program of Guangdong Province (Grant No. 201811078115), Undergraduate Innovation Training Program of Guangzhou University (Grant No. CX2017132, CX2017126, CX2018083).

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References 1. Chakchouk, N.: A survey on opportunistic routing in wireless communication networks. IEEE Commun. Surv. Tutor. 17(4), 2214–2241 (2015) 2. Hu, X., Chu, T.H., Leung, V.C., Ngai, E.C.-H., Kruchten, P., Chan, H.C.: A survey on mobile social networks: applications, platforms, system architectures, and future research directions. IEEE Commun. Surv. Tutor. 17(3), 1557–1581 (2015) 3. Wei, K., Liang, X., Xu, K.: A survey of social-aware routing protocols in delay tolerant networks: applications, taxonomy and design-related issues. IEEE Commun. Surv. Tutor. 16 (1), 556–578 (2014) 4. Mao, Z., Jiang, Y., Min, G., et al.: Mobile social networks: design requirements, architecture, and state-of-the-art technology. Comput. Commun. 100, 1–19 (2016) 5. Gao, C., Cheng, Q., Li, X., Xia, S.: Cloud-assisted privacy-preserving profile-matching scheme under multiple keys in mobile social network. Clust. Comput. 1–9 (2018) 6. Luo, E., Liu, Q., Abawajy, J.H., Wang, G.: Privacy-preserving multihop profile-matching protocol for proximity mobile social networks. Future Gener. Comput. Syst. 68, 222–233 (2017) 7. Patra, S., Saha, S., Shah, V., et al.: A qualitative survey on multicast routing in delay tolerant networks. Commun. Comput. Inf. Sci. 162, 197–206 (2011) 8. Galluccio, L., Lorenzo, B., Glisic, S.: Sociality-aided new adaptive infection recovery schemes for multicast DTNs. IEEE Trans. Veh. Technol. 65(5), 3360–3376 (2016) 9. Deng, X., Chang, L., Tao, J., Pan, J., Wang, J.: Social profile-based multicast routing scheme for delay-tolerant networks. In: Proceedings of 2013 IEEE International Conference on Communications (ICC). IEEE, pp. 1857–1861 (2013) 10. Jiang, G., Chen, J., Shen, Y.: Delivery ratio- and buffered time-constrained multicasting for delay tolerant networks. J. Netw. Comput. Appl. 44, 92–105 (2014) 11. Lo, S., Luo, N., Gao, J., et al.: Quota-based multicast routing in delay-tolerant networks. Wirel. Pers. Commun. 74(4), 1329–1344 (2014) 12. Deng, X., Wang, J., Chang, L.: Sociality-based comprehensive buffer management for multicast in DTNs. Int. J. Inf. Commun. Technol. 2(3), 263–277 (2015) 13. Hu, J., Yang, L.-L., Hanzo, L.: Distributed multistage cooperative-social-multicast-aided content dissemination in random mobile networks. IEEE Trans. Veh. Technol. 64(7), 3075– 3089 (2015) 14. Daly, E.M., Haahr, M.: Social network analysis for information flow in disconnected delaytolerant MANETs. IEEE Trans. Mob. Comput. 8(5), 606–621 (2009) 15. Peng, S., Yang, A., Cao, L., Yu, S., Xie, D.: Social influence modeling using information theory in mobile social networks. Inf. Sci. 379, 146–159 (2016) 16. Qin, Y., Jia, R., Zhang, J., Wu, W., Wang, X.: Impact of social relation and group size in multicast ad hoc networks. IEEE/ACM Trans. Netw. 15(7), 1661–1673 (2015) 17. Animesh, R., Tamaghna, A., Sipra, D.: Social-based energy-aware multicasting in delay tolerant networks. J. Netw. Comput. Appl. 15(87), 169–184 (2017) 18. Deng, X., Wang, J., Liu, Y., et al.: A social similarity-aware multicast routing protocol in delay tolerant networks. Int. J. Simul. Process Model. 8(4), 248–256 (2013) 19. Qiu, L., Cao, G.: Cache increases the capacity of wireless networks. In: IEEE International Conference on Computer Communications (INFOCOM), IEEE, pp. 20–27 (2016) 20. Keränen, A., Ott, J., Kärkkäinen, T.: The ONE simulator for DTN protocol evaluation. In: Proceedings of 2009 International Conference on Simulation Tools and Techniques, p. 55 (2009)

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Applications

Research and Design of Expert System Based on Oil-Gas Field Energy Saving Information Platform Yidong Guo(&), Yufeng Lu, Xiaomei He, and Tongyang Zhang Research Institute of Petroleum Exploration and Development-Northwest (NWGI), PetroChina, Lanzhou, China [email protected]

Abstract. Oil-gas enterprises as the source of oil and gas resources exploration, storage and transportation processing, and the high consumption industries of various energy sources, face with the increasing double pressure of energy saving and cost reduction. The exploration in oil-gas field enterprises Comprehensive energy consumption monitoring, energy efficiency benchmarking, and energy saving expert knowledge database system, to build the integrated framework which take the integration of the comprehensive energy consumption monitoring, energy efficiency real-time benchmarking and energy saving policy pushing as the main body, to realize the monitoring of energy efficiency data and equipment operation situation in the oil-gas production process, and the energy efficiency benchmark warning mechanism and the flexible analysis and evaluation of energy efficiency, will certainly have very important significance for the control of enterprise energy consumption, the promotion in energy-saving technological progress and meticulous management, the implementation of the opening up source and the flow regulating, the cost reduction and the efficiency increase. In this paper, based on the research of the system architecture on energy saving information platform and expert system for oil and gas field enterprises, deployment strategy functional module, the application of expert system, we put forward some suggestions in the future direction of development, and provide a new model for energy saving by means of expert system in oil and gas enterprises launched a comprehensive assessment of energy consumption analysis and instant interventional management. Keywords: Energy-saving monitoring Energy efficiency real-time benchmarking Optimization of energy saving technology Energy saving expert system

1 Preface The exploration and development of oil and gas field is not only the process of energy production, but also a huge consumption process of energy. Energy efficiency monitoring and energy efficiency as a practical activity to improve the level of enterprise energy use is shouldering the supervision of enterprise energy utilization level, by © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 917–927, 2019. https://doi.org/10.1007/978-3-030-02804-6_119

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monitoring the activities to find and analysis the crux of the problem and put forward constructive proposals, promote energy conservation measures effectively in the production process, in particularly, the effective introduction of mature and applicable advanced technology to save energy, have become an important means for enterprises energy efficiency to improve and the implementation of fine management. In the field of information system assistant management, some oil and gas companies having a monitoring system to build their own monitoring mechanism construction, take the energy saving monitoring data management system in a certain oilfield built in 2015 as an example, which realized a preliminary analysis of the system to complete the data results of on-site detection/monitoring personnel as well as historical data and application analysis, generate analysis report of evaluation results, and the online audit, implement the energy-saving monitoring project decomposition, from the schedule detection/monitoring instruments out of storage, detection/monitoring data entry to the online analysis process management evaluation report. Some enterprises in a specific period of time spontaneously with the brotherhood to carry out some out-line of energy efficiency benchmarking. These favorable factors, to a certain extent, satisfy the energy saving monitoring and energy efficiency benchmarking business in oilfield enterprises, improve the work efficiency and standardize the business process management, and play a good role in the reference of energy saving new technologies. But on the other hand, we noticed that in addition to the above mentioned enterprises, most enterprises have not yet deployed related information systems to support their energy-saving monitoring, energy efficiency benchmarking and new energy saving technologies. This leads to the failure of the historical data of energy efficiency monitoring and energy efficiency to be converted into effective storage management of digital assets. The best energy saving technology can only be applied in a very limited range by the out-line selection of the best energy saving technology. Even with the support of information system between enterprises, as for the lack of effective communication channels, their own self-building information systems are not unified, the monitoring and evaluation standards are different, and that the underlying reference system and index definitions cannot expand effective horizontal contrast, which cannot form the effective data sharing, valuable data and valuable benchmarking system, and which became a formation of information island. In the introduction and application of energy-saving technology, the current oil and gas enterprises take the project of energy-saving technological transformation measures (technical innovation project) as the starting point to achieve the energy saving milestones. It will be very important for the energy management effect of oil field enterprises and the future development of enterprises, whether or not the selection of technical measures is applicable, whether or not economic benefit assessment meets expectations. However, in the implementation of energy-saving technology reform process, it affect the comprehensive benefits of the implementation of energy-saving transformation projects, that the selection of energy saving technology has a certain blindness, the various kind of some good and some bad energy saving technology in the market, each enterprise lack of a comprehensive assessment of the actual energy saving and potential effects of energy saving technology, and there are few application results analysis and lessons learned sharing in the case of the same type of enterprise. Therefore, it is necessary to take into account the universality and particularity of

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energy-saving work in different technological conditions for the production of oil and gas under development, to optimized integrate the energy saving on-line monitoring efficiency, real-time energy efficiency benchmarking, and expert system auxiliary decision and energy-saving technical improvement measures to establish a set of convenient deployment and application of energy-saving expert system, to support the oil and gas enterprises energy-saving monitoring, energy efficiency benchmarking and energy-saving new technology introduction work and data sharing more information about asset value, as to meet the management requirements of oil and gas field enterprises energy saving management means.

2 Architecture of Energy Saving Information Platform and the Functional Design Established objectives of the building of the energy saving information platform are as follow: Firstly, to meet the needs of every oil-gas field enterprise by using the unified information platform, we can carry out energy saving monitoring, energy efficiency benchmarking and expert system aided decision-making tasks according to their own business characteristics. Secondly, according to the accumulation of historical data, we can provide real-time data query, comparison and analysis of energy saving monitoring results/parameters, provide early warning and correction measures for enterprises, push forward potential energy saving technological transformation measures, and tap potential of energy saving, and provide data support. The third is to provide the various types of oil and gas fields to carry out the evaluation of energy conservation after the rectification, case tracking analysis and implementation effect evaluation of the best energy-saving practice application effect and evaluation of implementation effect, and practical reference and guidance for oil and gas field enterprises to learn and implement energy saving technical reform measures. Fourthly, combined with the existing enterprise networking, DCS (Distributed Control System), SCADA (Supervisory Control and Data Acquisition) system data sharing, the implementation of the deployment or supplement of the data acquisition and monitoring equipment, we establish the real-time online monitoring system of key energy consumption units and equipment. By obtaining real-time data information, we improve the timely control of energy consumption and the capacity of strain treatment in production operation. Fifthly, it is convenient for the competent departments of energy conservation at all levels to grasp the operating state and energy level of the enterprise energy equipment in time, and to dynamically grasp the efficiency of energy use. Through all kinds of energy saving monitoring data, analysis and evaluation conclusions and constructive rectification proposals, it will provide data support for energy saving monitoring plan’s release, work deployment, target setting, rectification and tracking, and energy saving technology transformation investment direction.

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Platform Architecture Design

The framework of energy saving information platform includes acquisition layer, data storage layer, data processing layer, display application layer, and that the platform architecture designed, as shown in Fig. 1. Among these, the data acquisition layer implements the collection of data sources for smart meters, intelligent water meters, flowmeters, temperature transmitters and other remote terminal data sources by using Internet of things technology and applications, SCADA/DCS system and terminal acquisition devices, provides basic data support for the whole comprehensive energy consumption monitoring. The data storage layer provides the required classification data support to the data processing layer by improving the storage and off-line detection and monitoring of the structured data, to improve the data logical processing performance. The data processing layer provide data support with the establishment of energy-saving monitoring model for energy consumption, operation of all aspects of the production of oil and gas enterprises by data mining and classification of sub metering/statistics, historical data analysis and benchmarking online expert system and other means of online and offline analysis of energy consumption. The display application layer based on the statistics and analysis of energy efficiency and energy efficiency benchmarking diagnosis, control, monitoring and early warning threshold field analysis function tracking, filing, and rectification measures push the skills and system management, to achieve information interaction with the user through the system function interface, multi-dimension visualization.

Fig. 1. Platform framework design

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Platform Architecture Design

According to the actual work of energy saving management, the platform functions include monitoring project management, monitoring instrument management, monitoring data collection, monitoring data analysis, energy efficiency real-time benchmarking management, energy saving measures management, system management and other main functions. The role function permissions are configured according to different management requirements at different levels. See Fig. 2.

Fig. 2. Function design of energy saving information platform

1. Project management monitoring: Monitoring requirements, plans, implementation and results of the energy conservation monitoring all business process management. The effective communication and information tracing and the tracing of the regional Corporation and the monitoring institutions are realized. 2. Management of the assets of the monitoring: Asset management of special monitoring instruments and instruments, Warehousing-to-use management of the equipment, Management of equipment status. 3. Monitoring data acquisition: By monitoring tasks, on-line monitoring instrumentation or related systems will share data storage energy consumption monitoring collected parameters to the system platform database, including real-time data and relational database data conversion, storage management, access to relevant data collection and timely presentation on a monthly, quarterly and annual data report. 4. Monitoring data analysis: Monitoring data analysis of the background of oil and gas business model in accordance with the relevant standards and evaluation criteria of monitoring analysis, according to the time series trend analysis, outlier detection, energy consumption, energy efficiency map trends and historical comparative

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analysis chart, comparative analysis of results analysis and early warning threshold obtained a series of enterprise, provide the comprehensive data analysis ability for the enterprises to find out the abnormal energy point of energy consumption, the potential energy saving techniques and the energy saving potential. 5. Management of real-time energy efficiency benchmarking: In the time of configuration definition, we choose the current 30 min average value by default, which is used as a real-time [1] or a selected time history data. Diagnosis and detection of potential energy consumption change trend is easy to take timely measures to correct. 6. Management of energy saving measures: According to the energy monitoring and benchmarking analysis by quantitative index trend feature, the configuration of the selection principle of quantifiable indexes, and push related energy conservation information display, it is easy to take immediate corrective measures by warning or measures of technical reform measures of systematized energy saving. 2.3

The Technical Implementation Route and Deployment Strategy of the Platform

The building of energy saving information platform should not only meet the macro management needs of group companies, but also meet the management objectives of production management, energy conservation and consumption reduction by oil and gas field enterprises through online energy saving monitoring. Platform construction adopts B/S architecture, SSH framework, J2EE design and development; Selecting ORACLE database management relational data in database management, we uses PHD real-time database synchronized with existing oil and gas well networking projects to complete real-time data collection, storage and real-time conversion of data; the transmission of data and security of data communicate through the one-way secure protection measures based on wide area network and production network. In order to satisfy the business management needs of different levels and different users and the difference of data acquisition frequency, the whole platform is constructed by hierarchical management and distributed deployment strategy. With the deepening of energy control and propulsion, directly facing the working unit of production at the end of the energy consumption monitoring data provided by real-time online monitoring timely deal with energy consumption anomalies, requires that the production management end adopts distributed deployment method to complete the deploy mission of the real-time data acquisition, processing and analysis results timely shown. However, the monitoring institutions, the regional Corporation and the group company is more need to grasp the scale, to find out the global energy consumption energy consumption link in macro background. In the centralized deployment mode we collect and analyze a certain period of time the data to predict by historical comparison, data comparison, the trend over the same period, with the continuous development of science and technology in the field of energy saving, and that we use advanced energy conservation measures to improve production efficiency, by the high energy consumption of key energy consuming unit, rely on the support of decision data. Based on The Cloud Architecture with centralized and distributed hybrid deployment model, on the one hand, it effectively solves the problem of timeliness of data collection, which ease the

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pressure on network bandwidth of real-time data transmission, and that is conducive to the enterprise energy management immediate intervention work; on the other hand, the establishment of the headquarters system and the enterprise system of centralized summary analysis and improvement the data, will further enrich the energy consumption monitoring and benchmarking data of oil and gas enterprises, expand such basic data range, and will be more conducive to the depth mining and analysis of data. Enterprises not only own their own data assets, but also share the valuable benefits of data analysis brought by other enterprises. The deployment architecture of the oil and gas energy saving information platform is shown in Fig. 3.

Fig. 3. Deployment architecture diagram

Data acquisition frequency according to the demand of different levels for flexible customization, with the main monitoring parameters of steam injection heavy oil boiler as an example, the fuel consumption of key production data, the water flow, water temperature, water pressure frequency collection is carried out to implement the production management demand of real-time alarm and fast response. With the management level by the production unit to enhance the management layer unit, data acquisition conversion type by real-time data to relational data in statistical analysis, the underlying data acquisition frequency is sufficient to meet the requirements of production management based on the gradual summary for the day, week, month, season and year on-demand mode, and through the data drill function ready access to real-time data of fine particle size.

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The Application of Expert System in the Field of Energy Saving Management

By using AI technology and computer technology, the concept of expert system is introduced, and all kinds of energy saving monitoring types are acquired by various feasible ways, after processing, the expert system is transformed into knowledge intensive repository. Gain the support of energy saving control by forward reasoning [2]. Based on the different energy monitoring domain knowledge and experience provided by one or more experts, the reasoning and judgment, simulating the decision process of human experts, undoubtedly can make use of all kinds of energy-saving monitoring expert knowledge and the method to deal with the domain problem of comprehensive energy consumption monitoring data which is multisource and hierarchical, not only accumulate of the precious wealth of knowledge and experience of experts, fast response was encountered during the actual production process, but also to some extent alleviate the business impacts which result from the training difficulty, long training period and post retirement due to change and the loss of talent. The structure of the expert system is shown in Fig. 4. User

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Fig. 4. The Structure of expert system

The establishment of expert system construction depends on the knowledge system and maintenance, the advanced knowledge engineers can card the fundamental database based on the professional knowledge, such as the operation system, water injection system, power supply and distribution system, gathering and transportation system, heating system, natural gas processing system and so on, to set up rule parameter datum according to the standard of series analysis and evaluation, which is “Oil field production system energy saving monitoring standard” GB/T 31453-2015, “Standard for economic operation of mechanical oil production system” SY/T 63742016, “Energy efficiency limit value and energy efficiency grade of industrial boiler” GB 24500-2009, “Oil field production system energy saving monitoring standard”

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GB/T 31453-2015, and “Energy saving monitoring standard for oil and gas pipeline system” SY/T 6837-2011, and to build the main solution strategy of the knowledge database. When the energy consumption monitoring equipment detect a fault warning threshold or energy leakage, the manual or active triggering of knowledge requests will be triggered, according to the real-time information consumption device, to make decisions according to the analysis of a position, case, or suggestion of the expert system, to make necessary control of energy consumption equipment to avoid a large amount of energy waste caused by the abnormal monitoring of energy consumption, so as to implement the monitoring target of the enterprise energy saving management and control. The application of expert system and the process of knowledge updating are shown in Fig. 5.

Fig. 5. The Application of expert system and the Knowledge base update flow chart

The expert system of the operation depends on the data accumulated and matching rules will continue to improve, along with the expert system in operation, the knowledge expert team clarifies the edge of the fuzzy knowledge in their respective fields; the matching rules are refined and perfected; the response of the reasoning mechanism is more accurate; the solution of the new knowledge request further renewing and perfecting the reserve of knowledge; after go and return to a certain degree of maturity, it could achieve effective management of energy efficiency. Some of the specific practices and accumulation of oil and gas enterprises in various specific areas based on the energy efficiency benchmarking arrangement and energy-saving technology best practice base, especially as an improvement in specific areas of a certain direction will be further in the expert system based on the analysis of location, will provide the reference guide to take a more practical action. As for the construction of expert team, team building and maintenance are not only dependent on the unilateral

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factors of information technology, but also depend on the synchronous follow up or advance of administrative management. In the actual operation process, in addition to the administrative establishment of expert support center, inviting industry experts to exercise responsibilities, energy-saving monitoring technical exchanges, yearly training and new technology transformation which depend on the management institutions held to enrich and constantly improve the degree of knowledge database, as to the system will involve more broad areas, knowledge recommendation conformance and effectiveness, and achieve a long-term and effective promotion. When carrying out the recommendation of the best energy-saving technical measures case, fitting degree of the case should be fully considered: firstly, determine the technically feasible energy-saving projects according to the energy-saving target and the existing technological conditions or equipment conditions; and then establish a scientific and rational evaluation index system of energy-saving technical measures: selects and optimize the TOPSIS method, Euclidean distance value function method, gray correlation degree and other appropriate evaluation methods to research various energy-saving technical measures, determine the priority of each item, and prioritize the optimal project to designed and implement [5–7]. The evaluation index system of the energy-saving technology measures is shown in Fig. 6.

Fig. 6. Evaluation index system of energy-saving technical measures

3 Future Work Prospect Cut costs, lowering the efficiency depends on the three ways of the structural adjustment, technological progress and strengthening the management, which is based on effective management. Energy saving monitoring as the main means and carrier [3] management technology for saving water, through the network monitoring and management efficiency of the data, change the original energy efficiency monitoring data management and application method of the backward situation, through inter enterprise data barriers, improve the work efficiency of energy-saving tube, to promote technological progress in energy conservation and achieve the energy conservation of the purpose of fine management.

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At present, the majority of oil and gas enterprises still rely on manual meter reading, which meter reading time cycle is long and a large fluctuations, and that is unable to master the fine and effective real-time data of energy consumption. With the vigorous implementation of the oil and gas production network engineering of the group and the increasing enthusiasm of the enterprise’s energy efficiency control, on the basis of the establishment and improvement of the real-time monitoring system for energy consumption, the enterprise can accurately grasp the energy consumption state of the enterprise in the whole process of the production from multiple dimensions, and implement the monitoring of energy efficiency data and equipment operation situation in oil and gas field production process, and the flexible analysis and evaluation of instant benchmarking to control the energy consumption of enterprises, promote the technological progress and fine management of energy saving, which has a very important significance for the implementation of the opening up source and the flow regulating, the cost reduction and the efficiency increase. It will provide a new model for the comprehensive energy consumption assessment and analysis of oil and gas field enterprises with the help of energy saving monitoring.

References 1. Xu, C., Liu, L., Li, Y.: Development and application of Benchmarking information management system for combined real-time energy-saving in regional power grid company and power plants. Thermal Power Gener. 2012(5), 12–17 (2012) 2. Yan, T.: Dissertation Submitted to Zhejiang University of Technology for the Degree of Master. Zhejiang University of Technology, vol. 17, pp. 32–41 (2012) 3. Yu, L., Yuan, J., Zhang, X.: Research and application of comprehensive energy consumption monitoring system based on multi energy. Electron. Technol. Softw. Eng. 2016(19), 215 (2016) 4. Zengliang: Research on real time monitoring and intelligent control technology of energy consumption in industrial enterprises, vol. 2013, pp. 67–69 (2013) 5. Jie, X., Qiu, Z.: Optimal decision of energy-saving technologies base on TOPSIS method and grey retional degree. Energy Sav. Technol. 2017(2), 142–176 (2017) 6. Huang, J.: The study of multi-attribute optimization of the program based on the integrated Model of the Improved TOPSIS method, Chengdu University of Technology, vol. 2010, pp. 18–32 (2010) 7. Su, N.: Research On Oilfield Enterprises Energy Consumption Evaluation and Optimize Decision-making, China University of Petroleum (Hua Dong), vol. 2008, pp. 103–116 (2008)

A New Training Sample Selection Method Avoiding Over-Fitting Based on Nearest Neighbor Rule Guang Li(&) School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China [email protected]

Abstract. Sample selection is an important task. Now, there are many sample selecting methods using nearest neighbor rule. But most of them never consider the over-fitting problem. For overcoming this disadvantage, this paper gives a new sample selecting method. This method uses pruning tactics and crossvalidation to avoid over-fitting. It divided the original sample set to some disjoint subsets. Every time, a subset is used as validation sample set to prune samples selected from other subsets. All the subsets take turns as validation set. And the final result was gotten by combining all the selected sample sets. The experiments show that, compared with the existing methods, the new method can get smaller selected sample set and better classifiers can be trained on its selected samples. Keywords: Sample selection Cross-validation


Nearest neighbor rule
Over-fitting

1 Introduction Sample selection [1, 2] is the process of finding representative samples. Assuming D is the original sample set, after sample selection, another sample set D′, which is a subset of D, will be gotten. D′ contains the important samples and can replace D to be used. In general, D′ have much less elements than D. With the continuous improvement of the human capacity for data collection, the sample set becoming larger and larger. So, the sample selection is very important now. Using sample selection can get a smaller and more representative sample set and can saving computing resources. One important kind of sample selection method is based on the nearest neighbor rule. Recently, many such methods have been proposed [3–8]. All of these methods never consider the over-fitting problem [9]. They all want to find samples which can represent the original samples, and never consider if these samples having generalization. That means they never consider if the selected sample set can represent another independent sample set. But in fact, the generalization is very important and must be considered carefully. For example, all the classifiers are trained to predict the category of the samples gotten in the future and have no class labels. So if the over-fitting © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 928–935, 2019. https://doi.org/10.1007/978-3-030-02804-6_120

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appears for the selected sample set, it can not represent the future samples very well and then can not be used to train a good classifier. For solving this problem, a new sample selection method has been proposed in this paper. This new method used pruning tactics and cross-validation [10, 11] to overcome over-fitting. Samples are selected from training set and pruned by using validation set. The training and the validation sets are generated by a cross-validation process. Experiments show that, compared with existing methods, this new method can get smaller selected sample set and on which better classifiers can be trained. The organization of the rest paper is shown as below. Section 2 is about the related work, which includes the existing methods for sample selecting using nearest neighbor rule and cross-validation. Section 3 describes the new sample selected method proposed by this paper. Section 4 describes the experiment result. The conclusion is in Sect. 5.

2 The Related Work 2.1

Sample Selection Methods Using Nearest Neighbor Rule

There are many sample selecting methods using nearest neighbor rule. The most basic one of them is the CNN (Condensed Nearest Neighbor) algorithm [3]. In the CNN algorithm, assuming the original sample set is D and the selected sample set is T, the

Input: The original sample set D = {x1, x2,…, xn}. The parameters n. Output: The selected sample set T. procedure CNN (D, n) T = empty set NotEnd = TRUE T = T + x1 D = D – x1 while NotEnd NotEnd = FALSE for x in D y in T is the nearest sample of x in T if Class(x) is not equal as Class(y) NotEnd = TRUE T=T+x D=D–x end if end for end while return (T) end procedure Fig. 1. The detailed process of CNN algorithm.

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initial value of T is an empty set. In the first, one sample is moved from D to T. And then, the samples in D are checked one by one. If the sample x, which in D, has different class label with its nearest neighbor in T, it will be moved to T from D. The algorithm is stopped when there is no sample can be moved from D to T. The detailed process of CNN algorithm can be found in Fig. 1. CNN algorithm has many improvements and modifications. WCNN (Weighted CNN) method [4] runs CNN algorithm many times, and calculates the weight for every selected sample. Only the samples with greater weight will be selected. Reference [5] turns sample selection problem to be minimal consistent set problem. It constructs a new fitness function and use tabu search [12] to get the result. Reference [6] finds the centroid of each category and then uses the Voronoi diagram [13] to get the result. GCNN (Generalized CNN) algorithm [7] likes the CNN algorithm but selects samples more cautious. Reference [8] selects samples for the situation that some samples do not have category calibration. In summary, some sample selection methods based on nearest neighbor rule have been proposed recent years. But never of them consider the over-fitting problem. They all want to find samples which can represent the original samples, and never consider if these samples can represent other independent samples. 2.2

The Cross-Validation

Cross-validation [10, 11] is a useful kind of model validation technique. It can judge if a statistical analysis results can be generalized to another set of data. It is widely used in machine learning in order to decide if a model can perform well in practice. In machine learning, the data set is usually divided into two parts: the training data set and the testing data set. The model is trained by using training data set, and is tested by using testing data set. The purpose of cross-validation is to give the data set to evaluation the model in the training phase, in order to prevent from some problems like over-fitting. In one round, cross-validation will divide the data set to two subsets. The model will be trained on one subset, and be validated on the other subset. Usually, it will be performed many rounds. And different partitions will be used in each round. The final results are averaged over these rounds. In this paper, a technology named k-fold stratified cross-validation is used. In it, the whole sample set is divided to k equal parts randomly. Every time, one part will be validation sample set and the other samples will be the training sample set. After k times, every part have been validation sample set exactly once. The final result will be averaged over these k times. The cross-validation is stratified means that every part have the same category distribution as the whole sample set. If there are two types of class labels, it means each part has almost the same proportions of these two categories.

3 New Method For overcoming the disadvantage that current sample selection methods never consider the over-fitting problem, this paper gives a new sample selection method using the nearest neighbor rule. The detailed process of this new method is shown in Fig. 2.

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Input: The original sample set D. The parameters k, m, p and q. Output: The selected sample set T. procedure NewMethod (D, k, m, p, q) for i in 1:m D was divided to k equal parts D1, …, Dk for j in 1:k Tij = SelectAndPruning(D – Dj, Dj) end for Num1(x) = |{A|A in {Ti1,…,Tik} and x in A}| Ti = {x|Num1(x) > p} end for Num2(x) = |{A|A in {T1, …,Tm} and x in A}| T = {x|Num2(x) > q} return (T) end procedure procedure SelectAndPruning (D, P) T = CNN(D) NotEnd = TRUE while NotEnd NotEnd = FALSE for x in T r and tr are classification accuracy of P using nearest neighbor method with training set as T and T – x respectively if tr >= r NotEnd = TRUE T=T–x end if end for end while return (T) end procedure Fig. 2. The detailed process of the new method.

The basic idea of the new method is using pruning tactics and cross-validation to overcome over-fitting. For pruning tactics, two independent sample sets have been prepared. One of them is for training, and the other is for validation. The pruning tactics has two steps. Firstly, samples are selected by using the training set. And then, the validation set is used to pruning these selected samples. That means delete some one from the samples selected in the first step. In our new method, these two sample sets will be generated by cross-validation process. In our method, the whole sample set D is divided to k equal parts randomly. These subsets take turns as validation set, so there are k rounds processing. When one of these subsets is validation set, the other samples compose the training set. In each round,

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firstly, samples are selected using CNN algorithm from the training set. Then, validation set P is used to prune the selected samples. Assuming T is the sample set selected from the training set, for each x in T, r and tr are classification accuracy of P using nearest neighbor classifier trained on T and T – x respectively. If r is not larger than tr, x will be deleted. If a sample x have been selected more than p rounds in all the k rounds processing, x will be selected. Obviously, p should be smaller than k. Repeat above process m times. If a sample x have been selected more than q times in all the m times, x will be the final selected sample. Obviously, q should be smaller than m.

4 Experiments 4.1

Databases

In experiments, three real-life databases are used. They all come from the Machine Learning Repository of UCI (the University of California at Irvine). The names of these data sets are Iris, LD (Liver Disorders) and WBC (the original Breast Cancer Wisconsin). We only use complete and unrepeated samples. The final size of each data set is shown in Table 1. In experiments, all the three databases are divided to two parts. One is training data set, which includes 80% of the samples. The other is testing data set, which includes the other 20% of the samples. Table 1. The size of each data set Data set WBC Iris LD The number of attributes 9 4 7 The number of samples 449 150 345

4.2

Experiment Result

This paper repeats the experiment 50 times. Every repeat time will get a result. The final result is the average value over all of them. The comparative methods are the CNN [3] algorithm and WCNN [4] algorithm. In experiments, samples were selected using CNN algorithm, WCNN algorithm and our new method respectively. Then, two classifications are trained on the original training set and these three selected sample sets respectively. These two classifications are NN (the nearest neighbor) and the J48 decision tree [14]. The testing set will be used to get the accuracies of these classifiers. When using our method, 10-fold stratified cross-validation is used, and let m = 3, p = 6 and q = 1, which are all the parameters in our new method and all can be found in Sect. 3. Figure 3 shows the number of samples selected by different sample selecting method. Figure 3 does not contain the WCNN method, because the WCNN method let users to decide how many samples will be selected. Figure 3 shows that our new method will select fewer samples than the CNN method.

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Fig. 3. The number of samples selected by different methods.

Figure 4 shows the errors of the J48 decision tree and the nearest neighbor (NN) classifier. Equation (1) shows how to get the error, where Ro and Rp are the accuracies of classifiers trained on the original training set and the selected sample set respectively. When using WCNN, the number of selected samples is equal to the number of samples selected by our new method. Error ¼

Rp  Ro Ro

ð1Þ

Fig. 4. The errors of different classifications.

Figure 4 shows that all the classifiers trained using the original samples have better performance than that trained using selected samples. And the classifiers trained using the samples selected by our method have higher accuracy than that trained on the samples selected by CNN or WCNN algorithm.

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In summary, compared with the CNN and WCNN algorithm, our new method can select fewer samples, and these samples are more representative. Using the sample set selected by our method, better classifiers can be trained than using samples selected by the comparative methods.

5 Conclusions Currently, there are many sample selecting methods using nearest neighbor rule. But all of them never consider the over-fitting problem. They all want to find a smaller sample set which can represent the original samples, but never consider if they can represent another independent sample set. For solving it, this paper gives a new sample selection algorithm, which uses pruning tactics and cross-validation to overcome over-fitting. Samples are selected on training set and pruned using validation set. The training and validation sets are generated by a cross-validation process. This process will repeat some times. Every time, a selected sample set will be gotten. And the final selected samples are gotten by combining all these selected sample sets. Experiments shows that, compared with existing methods, our new method can select less samples and better classifiers can be trained on them. Acknowledgments. The Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016JQ6078), and the Fundamental Research Funds for the Central Universities of Chang’an University (300102328107, 0009—2014G6114024).

References 1. Zhou, X., Jiang, W., Tian, Y., Shi, Y.: Kernel subclass convex hull sample selection method for SVM on face recognition. Neurocomputing 73(10–12), 2234–2246 (2010) 2. He, Q., Xie, Z., Hu, Q., Wu, C.: Neighborhood based sample and feature selection for SVM classification learning. Neurocomputing 74(10), 1585–1594 (2011) 3. Hart, P.: The condensed nearest neighbor rule. IEEE Trans. Inf. Theory 14(3), 515–516 (1968) 4. Hao, H., Jiang, R.: Training sample selection method for neural networks based on nearest neighbor rule. Acta Autom. Sin. 33(12), 1247–1251 (2007) 5. Cerveron, V., Ferri, F.J.: Another move toward the minimum consistent subset: a tabu search approach to the condensed nearest neighbor rule. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 31(3), 408–413 (2001) 6. Angiulli, F.: Fast condensed nearest neighbor rule. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 25–32 (2005) 7. Chou, C.-H., Kuo, B.-H., Chang, F.: The generalized condensed nearest neighbor rule as a data reduction method. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 02, pp. 556–559 (2006) 8. Sogaard, A.: Semisupervised condensed nearest neighbor for part-of-speech tagging. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, vol. 2, pp. 48–52 (2011)

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9. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929– 1958 (2014) 10. Karkkainen, T.: On cross-validation for MLP model evaluation. Lecture Notes in Computer Science, vol. 8621, pp. 291–300 (2014) 11. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2010) 12. Escobar, J.W., Linfati, R., Toth, P., Baldoquin, M.G.: A hybrid granular tabu search algorithm for the multi-depot vehicle routing problem. J. Heuristics. 20(5), 483–509 (2014) 13. Yan, D.M., Bao, G., Zhang, X., Wonka, P.: Low-resolution remeshing using the localized restricted Voronoi diagram. IEEE Trans. Vis. Comput. Graph. 20(10), 1418–1427 (2014) 14. Witten, I.H., Frank, E., Hall, A.M.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)

A Safety Analysis Method for FGS Based on STPA Tao Feng(&), Lisong Wang, Jun Hu, and Miaofang Chen College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China [email protected]

Abstract. Flight Guidance System (FGS) plays a vital role in the avionics system. It is one of the main components of the Flight Control System (FCS). Because of its safety and inherent complexity, it becomes particularly important to analyze its safety properties. Nowadays, the safety analysis of Flight Guidance System mainly based on formal modeling and the validation of safety requirements. However, in terms of how to capture the safety requirements is not too much. Therefore, this paper proposed a set of safety analysis method based on System Theoretic Process Analysis (STPA), that implements safety analysis of Flight Guidance System and capture the safety requirements from the aspect of system interaction. Follow the steps of System Theoretic Process Analysis, first of all, we build control structure diagram, and then add process model variables, finally get the safety requirements of Flight Guidance System by establishing context table. Keywords: Safety requirements
Flight guidance system System-Theoretic Process Analysis (STPA)

1 Introduction Flight Guidance System is a kind of software, which is responsible for gathering various data of flight status and environment status from sensors to generate roll or pitch guiding variables for Flight Control System (FCS). As shown in Fig. 1, we can find that FCS is composed of FGS, Flight Director (FD), Autopilot (AP) and Autothrottle (AT). And there are two elements, Mode Logic and Control Laws, exist inside FGS. Control Laws is a continuous function that compare the current state of flight with the target state and then generating guiding commands so that it can reduce differences between current state and target state. And Mode Logic is a disperse algorithm used to select appropriate Control Laws when system is active. However, the growing complexity and integration issues associated with these advanced technologies also increase the potential for errors that could have a direct impact on safety. With the high integration of the system, system engineers find that interactions in the system are almost impossible to work as designed and expected, and unfortunately, these interactions in system have a great impact on the safety of the aircraft.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 936–944, 2019. https://doi.org/10.1007/978-3-030-02804-6_121

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Fig. 1. The high level architecture of an avionics system

Formal method is the mainstream method to do the safety analysis of FGS. In 2002, the work done by Alan C. Tribble, Steven P. Miller et al. in literature [1] mainly focused on safety analysis of FGS and was supported by NASA Aviation Safety Plan. Based on this, Anjali Joshi et al. of the Department of Computer Science and Engineering of the University of Minnesota studied the internal mode confusion of FGS through formalization based on the work of Alan C. Tribble [2]. In 2013, Alan C. Tribble, Steven P. Miller and others wrote the requirements specification of Mode Logic in FGS by RSML-e language with the support of NASA Langley Research Center, Rockwell Collins and the Key Systems Research Group of the University of Minnesota [3]. These works generated safety requirements based on the functional requirements of FGS, and didn’t describe the details about how to capture the safety requirements of FGS. With the development of software technology, many critical systems become more and more complex. And it is impossible to evaluate the system through traditional Fault-Failure Model analysis methods (Fault Trees Analysis, etc.). The SystemTheoretic Process Analysis (STPA) method [4–6] proposed by Nancy G. Leveson of the Massachusetts Institute of Technology emphasizes the system as a whole, and safety of system is decided by the interactions of system components. Finally, we capture the safety requirements by constraining the behavior of the single component and the interactions among the components.

2 Methodology 2.1

Introduction to STPA

System-Theoretic Accident Model and Process (STAMP) was proposed by Nancy G. Leveson of Massachusetts Institute of Technology in 2004. STAMP is a new accident causality model based on system theory and expanding on traditional accident model. The traditional Failure-Event Model regards component failure as the cause of the accident, while STAMP treats the system as a whole, and the safety of system is decided by interactions of all components in system.

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Based on STAMP, Leveson proposed a specific hazard analysis method named System-Theoretic Process Analysis (STPA). STAMP emphasizes that hazards are caused by unsafe control actions, while STPA focuses on how to identify unsafe control actions that lead to hazards and find the primary causes of system hazards through a series of steps. When STPA is used to design under security guidance, there are only system-level requirements and constraints at the beginning of the process [4]. However, these requirements and constraints are refined and traced to each system component through iterative design and process analysis. STPA has two main steps: (1) Identify improper control of systems that may cause dangerous status. Dangerous states are caused by inappropriate controls and the implementation of security constraints. The reasons for their occurrence are as follows: • • • •

Controls not provided or not complying with safety requirements. Provide an unsafe control. Provide early or late safe control, and wrong timing. The safe control ended too quickly or the duration of action was too long.

(2) Determine how unsafe control action may occur in step 1. 2.2

Functional Requirements of FGS

First, functional requirements are the starting point for safety analysis. In order to generator the safety requirements, we start with the functional requirements of FGS. The software function of the FGS is mainly responsible for four parts: The first part, FGS should calculate the guiding commands by Control Laws. The second part, FGS’s Mode Logic should select and indicate the flight guidance mode. The third and fourth parts, FGS should control Flight Director (FD) and Autopilot (AP). (1) Calculate guiding commands Whenever the current flight mode is, the FGS must calculate the roll and pitch values to update the guidance for pilot. This requires that the Control Laws must produce the correct values according to the current mode. Control Laws itself is not analyzed in this paper, so we can presume that the guidance produced by Control Laws always correct. (2) Select and indicate the flight guidance mode The FGS must correctly select the mode of operation and correctly identify the flight mode. These are all controlled by software, which is also the important part of this project’s safety analysis. The choice of mode is based on the Boolean values received from other systems. Mode should be identified by pilot through output to the display and instrumentation panel. The main work of the safety analysis is to ensure the correctness of the selected mode based on the input, and the correctness of the display based on the output mode.

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(3) Control FD and AP The pilot selects different flight guidance mode and then Control Laws calculate the pitch and roll guiding commands that the FGS should display based on current mode. These commands will be displayed in Primary Flight Display (PFD), which instructs the pilot to control the flight of the aircraft. The Autopilot translates the FGS flight guidance instructions into actuator commands for FCS to act on the Control System Actuator (CSA). Second, FGS has two types of modes: lateral and vertical. Lateral modes can control the horizontal movement of the aircraft by adjusting the roll of aircraft. The vertical modes can control the aircraft’s vertical motion by adjusting the pitch of the aircraft. Horizontal mode is divided into: Roll (ROLL), Approach (APPR), Go Around (GA), Heading (HDG), Navigation (NAV). The vertical mode is divided into: Pitch (PITCH), Altitude Hold (ALT), Altitude Select (ALTSEL), Approach (VAPPR), Flight Level Change (FLC), Go Around (VGA), Vertical Speed (VS). This shows that there are too many FGS models and it is not easy to perform an overall analysis at one time. Therefore, we chose the Altitude Select mode in the vertical mode as the entry point for our analysis.

3 Case Study of Safety Analysis for FGS Based on STPA In this paper, we analyzed the roll guidance and pitch guidance provided by FGS and then act on Autopilot in ALTSEL mode. As shown in Fig. 2, we assume that the pilot presses the ALTSEL button, and then Flight Control Panel (FCP) will tell FGS that the pilot has selected ALTSEL mode. Therefore, FGS will obtain real-time flight status and send different control signals. First, ALTSEL will be set to Armed. And then, after the target point has been calculated by FGS, it will set to Capture. Finally, when the aircraft reaches the target point, ALTSEL mode is set to Track. It is worth noting that the only way to exit active, track, or capture state is to cancel the mode selection because it is impossible to go directly from the Track state to the Capture state or from the Active state to the Armed state.

Fig. 2. Rules of state changes inside ALTSEL mode

In this situation, Control Laws will send different roll guidance and pitch guidance according to the state of ALTSEL mode. Therefore, the FGS controller can be divided into two parts: inside and outside. Inside, Mode Logic sends different control signals (Cleared, Armed, Capture, Track) to the ALTSEL mode according to the current aircraft state. Outside, Control Laws calculates the real-time Roll Guidance and Pitch Guidance instructions to the Autopilot based on the current state of ALTSEL mode.

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Control Structure Diagram

The first step of STPA method is to build a control structure diagram. As shown in Fig. 3, the control structure diagram shows the relationship and interaction between the FGS and other subsystems.

Fig. 3. Control Structure Diagram of outside interactions

3.2

Identify Potential Unsafe Control Action

In Sect. 3.1, we can clearly find the control loop between FGS and other subsystems. According to the control signal sent by FGS, we can identify potential unsafe control actions under the following guide words: “not provided”, “not provided”, “wrong timing” and “end too early or too late”. In FGS internal Mode Logic, we can know that Control Laws will provide four control signals for Mode Logic: Cleared, Armed, Capture, Track. Therefore, we first analyze the unsafe control actions corresponding to different control signals under the four guide words and then establish the safety constraints correspond to the unsafe control actions: SC1.1: ALTSET can be selected when VAPPR and VGA and ALT are inactive, otherwise it should keep Cleared state. SC1.2: If ALTSEL has been selected, ALTSEL should be set to Armed. SC1.3: If we deactivate the ALTSEL, ALTSEL should be set to Armed. SC1.4: Before the target point has been calculated by Control Laws. ALTSEL should keep Armed state. If the calculation is over, ALTSEL should be set to Capture immediately.

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SC1.5: If the current ALTSEL state is Capture, the previous state must be Armed. SC1.6: If the aircraft achieves the target point, ALTSEL should be set to Track immediately. Before the new target altitude, ALTSEL should keep the Track state. SC1.7: If the current ALTSEL state is Track, the previous state must be Capture. SC1.8: When the pilot selects a new target altitude, ALTSEL should be set to Armed. In FGS outside Mode Logic, Control Laws will continuously update the roll or pitch guiding commands based on the current state of the FGS. Therefore, we also establish the safety constraints correspond to outside control signals: SC2.1: If ALTSEL is Capture state, FGS should continuously update the values of Roll Guidance and Pitch Guidance. SC2.2: If ALTSEL is Armed state, FGS shouldn’t update the values of Roll Guidance and Pitch Guidance. SC2.3: If ALTSEL is Track state, FGS shouldn’t update the values of Roll Guidance and Pitch Guidance. 3.3

Generate Safety Constraints

Till now, we analyze the situation in which the control action may lead to hazards under the four guiding words, and generate corresponding safety constraints. However, the unsafe control actions and safety constraints we analyzed are ambiguous and inaccurate. It can’t provide assistance for the formalization and system modeling for some people do the safety analysis, so we need to add the process model inside the controller. By adding some process model variables to help FGS modeling and facilitate the formal modeling and model checking later. For inside control signals, add the following process model variables based on the functional requirements of Mode Logic (Table 1): Table 1. Process model variables for inside control signals Name Mode VAPPR VGA ALT

Type

Range

Boolean False, True Boolean False, True Boolean False, True

Logic status Target Alt Boolean False, Changed True Capture Boolean False, ALTSEL True Track ALTSEL Boolean False, True

Physical Interpretation True and False present that VAPPR is active or inactive True and False present that VGA is active or inactive True and False present that ALT is active or inactive

Target Alt Changed means a new request about ALTSEL Capture ALTSEL means Control Laws has calculate the target point Track ALTSEL means the aircraft achieves the target point

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According to the process model variables, we can clearly describe what kind of environment the unsafe control action occurs in Sect. 3.2. Therefore, the context table can be established by performing a Cartesian product of the process model variables of the inside and outside control signals. We use the Cleared signal as an example, as shown in Table 2: Table 2. Combination of Cartesian product Mode

VAPPR

T

T

T

F

F

F

F

F

F

F

F

VGA

F

F

F

T

T

F

F

F

F

F

F

ALT

F

F

F

F

F

T

T

T

F

F

F

Logic

Target Alt Changed

T

F

F

T

F

T

F

F

T

F

F

Status

Capture ALTSEL

F

T

F

F

T

F

T

F

F

T

F

Track ALTSEL

F

F

T

F

F

F

F

T

F

F

T

.

.

.

At the same time, according to the requirements of the FGS, we set up the requirements table, and then act on the context table to find out the context that lead to unsafe control action. Now we use Cleared signal as an example to illustrate how the requirements table acts on the context table and then find out the context that may lead to hazard (Table 3). Table 3. Requirements table for provide Cleared signals

By creating the requirement table and acting on the context table, we can find out all the unsafe control actions and the refined context. Finally generate refined safety constraints, as follows: SC1: if (VAPPR = True OR VGA = True OR ALT = True) then ALTSEL = Cleared. SC2: if (Capture ALTSEL = False) then ALTSEL = Armed.

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SC3: if (Target Alt Changed = True) then ALTSEL = Armed. SC4: if (Capture ALTSEL = True AND Track ALTSEL = False) then ALTSEL = Capture. SC5: if (ALTSEL = Capture) then Previous (ALTSEL) = Armed. SC6: if (Track ALTSEL = True) then ALTSEL = Track. SC7: if (ALTSEL = Track) then Previous (ALTSEL) = Capture. SC8: if (ALTSEL = Armed) then (Update Roll Guidance = False AND Update Pitch Guidance = False). SC9: if (ALTSEL = Capture) then (Update Roll Guidance = True AND Update Pitch Guidance = True). SC10: if (ALTSEL = Track) then (Update Roll Guidance = False AND Update Pitch Guidance = False).

4 Conclusions and Future Work This paper uses the STPA method to capture safety requirements for FGS. The results show that: (1) The STPA method can find the safety requirements more rationally, and with the help of process model variables, it is more convenient to determine the refined safety constraints. (2) The STPA method considers and analyzes the Mode Logic into the overall context to identify dangerous causes. It can provide guidance for software engineer to design and eliminate these unsafe scenarios during the requirement analysis phase. Next, we are ready to build formalized model for FGS on the existing basis, formalize the safety constraints as temporal logic formulas and then verify them. Finally, we will propose a complete set of safety analysis methods, including the capture of safety requirements, formal modeling, and verification of safety constraints.

References 1. Tribble, A.C., Lempia, D.L., Miller, S.P.: Software safety analysis of a flight guidance system. In: 2002 Proceedings of Digital Avionics Systems Conference, vol. 2, pp. 13C1-1– 13C1-10. IEEE (2002) 2. Joshi, A., Miller, S.P., Heimdahl, M.P.E.: Mode confusion analysis of a flight guidance system using formal methods. In: 2003 Digital Avionics Systems Conference, DASC 2003, vol. 1, pp. 2.D.1–21-12. IEEE (2003) 3. Miller, S.P., Tribble, A.C., Carlson, T.M., et al.: Flight guidance system requirements specification. Department of Information Systems the University of Melbourne Melbourne, pp. 31–41 (2013) 4. Leveson, N.: Engineering a Safer World: Systems Thinking Applied to Safety. MIT Press, Cambridge (2011)

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5. Leveson, N., Dulac, N., Zipkin, D., Cutcher-Gershenfeld, J., Carroll, J., Barrett, B.: Engineering resilience into safety-critical systems. In: Resilience Engineering–Concepts and Precepts. Ashgate Aldershot, pp. 95–123 (2006) 6. Leveson, N.: An STPA Primer - Version 1, June 2015. http://psas.scripts.mit.edu/home/wpcontent/uploads/2015/06/STPA-Primer-v1.pdf 7. Thomas, J.: Extending and automating a Systems-Theoretic hazard analysis for requirements generation and analysis. Office of Scientific & Technical Information Technical Reports (2013) 8. Howard, G., Butler, M., Colley, J., et al.: Formal analysis of safety and security requirements of critical systems supported by an extended STPA methodology. In: IEEE European Symposium on Security and Privacy Workshops. IEEE (2017) 9. Abdulkhaleq, A., Lammering, D., Wagner, S., et al.: A systematic approach based on STPA for developing a dependable architecture for fully automated driving vehicles. Procedia Eng. 179(41), 41–51 (2017) 10. Thomas, J., Suo, D.: STPA-based method to identify and control feature interactions in large complex systems. Procedia Eng. 128, 12–14 (2015)

Hybrid Evaluating Method for Battlefield Support Capability of Field Petroleum Pipeline Unit Based on Support Vector Machine Wen-ming Zhou(&), Yao-wu Wu, Lin Zheng, Xiang-sen Yu, Peng Jia(&), Yun-he Wang, and Shi-bin Lan Joint Service Command and Training Centre, Joint Service College, National Defense University of PLA, Taiping Rd, No. 23, Beijing 100858, China [email protected] Abstract. The field POL (Petroleum, Oils, and Lubricants) service is very important for maintaining and enforcing battle effectiveness of unit. Key factors and important links of field POL support are studied. The efficiency evaluation index system is constructed. Thanks to much merits that the algorithm support vector machine (SVM) held, based on it, a hybrid evaluating method for the field POL support capability is proposed. It strongly support the POL service information system construction and POL service decision. Keywords: Field POL support


Evaluating
Support vector machine

1 Introduction Field petroleum pipeline unit is the main and important POL service force in battlefield. Its basic task is to supply in time or previously the POL to battlefront units [1]. The field POL support affects directly the battle process and result. At present, study about field POL service capability evaluation is less, big simulation system [2, 3] of American simulate the petroleum pipeline unit and its POL service support operation, but it didn’t embody support capability evaluation function. Owing to much merits that the algorithm support vector machine (SVM) held, it is widely used in pattern recognition and classification evaluation. By analyzing the main proceeding, key factors and important links of field POL support, this article establishes a battlefield support capability evaluation method for field petroleum pipeline unit. Evaluation examples of ten units are presented to verify its rationality and validity.

2 SVM Modeling SVM [4, 11] established on statistical learning theory is an effective method for solving non-lie problems. Even through less training samples, it can also get the characteristics of generalization. SVM make sure structural risk minimization (SRM) according to Vapnik and Chervonenkis (VC) dimension theory rather than other machine learning algorithms only thinking empirical risk minimization (ERM). © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 945–953, 2019. https://doi.org/10.1007/978-3-030-02804-6_122

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Basic Theory of SVM

Supposed a set fðxxi ; yyi Þgli¼1 , where xxi ¼ Rn yyi 2 f1g i ¼ 1. . .l. It is used for training. The algorithm based on SVM substantively is a process of optimizing weight parameters. The purpose is to ensure SRM, and maximization of the distance between classification hyper planes. When training set is line and classifiable, exist (w, bb), we can get that: w
xxi þ bb  1; 8xxi 2 AA

ð1Þ

w
xxi þ bb   1; 8xxi 2 BB

ð2Þ

Here AA, BB are results of classification. Finally get that: fw;bb ¼ signðw
xx þ bbÞ

ð3Þ

Translate to optimization problem as follows: Max

1 1 or Min kwk2 2 kw k

s:t: yyi ðw
xxi þ bbÞ  1 i ¼ 1; . . .l

ð4Þ

Construct Lagrange function (5) to solve it. l X 1 ai ½yyi ðw
xxi þ bbÞ  1 Lðw; bb; aÞ ¼ kwk2  2 i¼1

ð5Þ

Give the answer to the optimization problem by the saddle point of Lagrange function. Making decision function is transfer to that: f ðxxÞ ¼ sign

l X i¼1

Where bb ¼ yyi  w
xxi

w ¼

l P i¼1

! yyi ai ðxx
xxi Þ þ bb

ai yyi xxi

ð6Þ

ai is the optimal solution to dual

quadratic programming as follows: MaximizeFðaÞ ¼

l X i¼1

ai 

l   1X ai aj yyi yyj xxi
xxj s:t: 2 i;j¼1

l X

ai yyi ¼ 0; ai  0 ð7Þ

i¼1

Use slack variable set and penalty factor to solve line non-classification problem. Map training samples xx to a certain higher dimensional characteristic-space /ðxxÞ, and execute line classification in the space. Thus line non-classification and non-line problems can be solved.

Hybrid Evaluating Method for Battlefield Support Capability

MaxFðaÞ ¼

l X

ai 

i¼1

l   1X ai aj yyi yyj /ðxxi Þ
/ðxxj Þ 2 i;j¼1

947

ð8Þ

Supposed Kðxxi ; xxj Þ ¼ /ðxxi Þ
/ðxxj Þ function (8) transfer to that: MaxFðaÞ ¼

l X

ai 

i¼1

l   1X ai aj yyi yyj K xxi ; xxj 2 i;j¼1

ð9Þ

Making decision function is transfer to that: f ðxxÞ ¼ sign

l X i¼1

! yyi ai Kðxx


xxi Þ þ bb



ð10Þ

Where Kðxx
xxi Þ is a kernel function. Use it to substitute for vector product in higher dimensional characteristic-space. Three kind of kernel function is as follows: line kernel function Kðxx; xxi Þ ¼ xxT xxi ; polynomial kernel  function Kðxx;  xxi Þ ¼ p

ðcxxT xxi þ r Þ ; c [ 0; radial basis function Kðxx; xxi Þ ¼ exp ckxx  xxi k2 ; c [ 0

SVM training focuses on solving the problem of quadratic programming. This paper use sequential minimal Optimization (SMO) put forward by Platt [5]. 2.2

Sort Making Decision Model of SVM

Use dichotomy SVM to construct multi-element sort method as follows: one against one, it need NðN  1Þ=2! designed classifiers; one against rest, it also need n classifiers. This paper combine SVM with binary tree making decision method [6], and it need n − 1 classifiers as Fig. 1.

SVM-one Sort one

SVM-two

Sort two

SVM-three

Sort three Fig. 1. Multielement sorter

Sort four

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3 The Index System of Field POL Service Support Capability Evaluation The field POL service support is a multi-factor and multi-index complex system. Its support element and feature index are a little much, and it easy bring dimension disaster. Based on establishing index system principle, such as: objectivity, systematicness, completeness, independence and measurability, and information compress method correlative to information theory [7], this paper analysis the process of field POL service support [1, 8], establish and optimize the field POL service evaluating index system as Fig. 2. The full rate of POL equipment ( x1 ) The readiness rate of POL equipment ( x2 ) POL storage level ( x3 ) Protection capability ( x4 )

Efficiency of field POL service support

Mobility ( x5 ) Personnel ( x6 ) Refueling capability ( x7 ) Information level ( x8 )

Fig. 2. Efficiency index of the field POL service support

4 Computing Index Value Method Based on established optimized index, adopt following methods to comput its value: 4.1

Mathematics Definition

Definition 1. The full rate of POL equipment (x1 ) is defined as follows: x1 ¼

n

v

nw

1;

; nv  nw nv [ nw

ð11Þ

Where nv is the number of POL equipment, nw is the allocated number. Definition 2. The readiness rate of POL equipments (x2 ) is defined as follows: x2 ¼

n

1

n2

1;

; n1  n2 n1 [ n2

ð12Þ

Hybrid Evaluating Method for Battlefield Support Capability

949

Where n1 is the number of intact POL equipment, n2 is the allocated number. Definition 3. The index of POL storage level (x3 ) is defined as the rate of practical reserves (nm ) to need for battlefield usage (nd ). It is as follows: x4 ¼

n

;

m

nd

1;

nm  nd nm [ nd

ð13Þ

Where nm is the practical storage level, nd is the battlefield usage need level. 4.2

Operations Research Method

The index (x5 ) of mobility of the field petroleum pipeline unit includes mobility mode (m1 ), personnel mobility (m2 ), mobility environment (m3 ), mobility distance (m4 ), carrying load (m5 ) etc. mi ði ¼ 1; 2; . . .; 5Þ is confirmed by experts. x5 ¼

5 X

ai m i

ð14Þ

i¼1

Where ai is weight coefficient, and a1 þ a2 þ


þ a5 ¼ 1. Factor of POL service support personnel always plays the most important role in POL service support. POL service staff with adept skill and higher degree will mostly increase the POL service efficiency. Personnel is expressed with x6 . Personnel index [9] includes three aspects: knowledge, quality, skill and capacity structure. The respective secondary indexes are omitted here. The index value of it is calculated with fuzzy comprehensive evaluation [10]. POL service information level is specified by x8 . It includes command and control information capability, information communication capability, information management capability of POL maintenance equipment warehouse, and specialist knowledge database level, etc. 4.3

The Method of Examination

Through inspection and examination method, The value of protection capability (x4 ), refueling capability (x7 ) are all calculated by it. Where x4 includes individual military all-around development and command capability. Where x7 includes theory level and operation skill.

5 Classification Pattern Set up classification evaulation pattern of POL service support with Delphi method, classify and evaluate and synthesize the eight indexes respectively. Full mark is set according to the one thousand and fifty mark examination system. According to performance, it is sorted as four grades: excellent (A), good (B), moderate (C) and inferior (D), as can be seen in Table 1.

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W. Zhou et al. Table 1. Index classification pattern of field POL service support Comprehensive index

Four grade pattern A B [95 100] [85 95] Full rate of POL equipment (x1) Readiness rate of POL equipment (x2) [95 100] [90 95] POL storage level (x3) [95 100] [90 95] Protection capability (x4) [95 100] [90 95] Mobility (x5) [45 50] [40 45] Personnel (x6) [400 450] [350 400] Refueling capability (x7) [90 100] [80 90] Information level (x8) [45 50] [40 45]

C [75 85] [80 90] [85 90] [80 90] [35 40] [300 350] [70 80] [35 40]

D [0 [0 [0 [0 [0 [0 [0 [0

75] 80] 85] 80] 35] 300] 70] 35]

6 Evaluating Example 6.1

Data Acquisition and Preprocessing

To set up a stable SVM evaluating model, through mathematics definition, operations research method and examination method, the training data of field POL support capability of units are collected and processed. Sixty five samples are acquired. Fifty five of the samples are as training samples to set up the evaluating model, the rest ten are as test samples to verify it. Due to the limit to paper length, only part training and test samples can be seen in Tables 2 and 3. Visual training data is given for comparing the difference between respective sample-values as Fig. 3.

Table 2. The value of training samples Sample Index x1 x2 1 96 96 2 95 95 3 98 97 4 97 99 5 99 98 6 100 97 7 99 96 8 97 99 9 96 100 10 95 100 … … … 55 72 75

x3 97 98 99 98 99 100 96 95 97 98 … 81

x4 95 97 96 95 97 95 96 98 97 96 … 70

x5 47 46 45 48 47 49 48 46 45 46 … 29

x6 420 425 430 444 447 410 415 409 426 433 … 290

x7 95 93 94 96 93 97 98 96 95 90 … 60

x8 46 47 48 45 46 48 49 47 46 45 … 34

Table 3. The value of test samples Sample Index x1 x2 1 97 96 2 95 98 3 88 93 4 87 92 5 90 91 6 92 94 7 81 85 8 73 78 9 79 86 10 91 91

x3 94 96 94 91 93 92 88 80 87 91

x4 96 97 93 92 94 91 86 75 89 93

x5 46 47 43 42 45 41 38 33 37 42

x6 429 435 387 378 369 390 327 289 338 376

x7 93 95 88 83 85 82 78 65 77 82

x8 46 45 43 40 41 44 38 33 39 41

Hybrid Evaluating Method for Battlefield Support Capability

60

0 50100 sample attribute 6 450

45

400

40 35 30

0 50100 sample

350 300 250

0 50100 sample

90 80 70 60

80 70 60

0 50100 sample

80

90 80 70

70

0 50100 sample attribute 7 100 90

90

attribute 4 100 index value

70

0 50100 sample attribute 5 50 index value

index value

1

80

index value

2

90

attribute 3 100

0 50100 sample attribute 8 50 index value

index value

class-label

3

attribute 2 100 index value

attribute 1 100 index value

class 4

951

0 50100 sample

40 30 20

0 50100 sample

Fig. 3. Visual training data of model

In order to eliminate data level and reduce error of evaluation, this paper adopts maximum-minimum normalization method to normalize the samples data as follows: f :x!y¼

x  xmin xmax  xmin

ð15Þ

Where x; y 2 Rn , xmin ¼ minðxÞ, xmax ¼ maxðxÞ. 6.2

Evaluating Field POL Support Capability

This paper adopts SOM method to train the four sort making decision pattern and get the optimization making decision hyperplane. Adopt radial basis function as kernel function as follows: jxx  xxi j2 Kðxx; xxi Þ ¼ exp  r2

! ð16Þ

Input test samples value to the trained evaluating model, the classification result is as Fig. 4, among ten test sample, only one is classified error, accuracy is up to ninety percent.

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W. Zhou et al.

4

3.5

class-label

3

2.5

2

1.5

1

0

10

20

30

40

50

60

training and test sample set

○: training set classification; *: test set classification; practical classification

:

Fig. 4. Training and prediction set classification

7 Conclusion By the evaluating example, the proposed hybrid evaluating POL service capability algorithm for POL service Unit is applicable and valid. It strongly supports the logistics information system optimum design and command decision making of the POL service support.

References 1. Hua, H., Nan, Z.Q., Wang, S.Q.: Logistics Support. Chinese Encyclopedia Publishing House, Beijing (2007) 2. U.S. Joint forces command joint warfighting center: JTLS (3.4.3.0) Version Description Document [EB/OL]. http://www.rolands.com/jtls/j_vdds./vdd_3200.pdf. Accessed 08 June 2010 3. George, F.S., Gregory, A.M.: The joint warfare system (JWARS): a modeling and analysis tool for the defense department. In: Proceedings of the Winter Simulation Conference, pp. 78–99 (2001) 4. Haykin, S.: Neural Networks: A Comprehensive Foundation. China Machine Press, Beijing (2004). (Ye S W, Shi Z Z, trans.) 5. Platt, J.C.: Sequential minimal optimization: a fast algorithm for training support vector machines. In: Scholkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge (1999) 6. Fu, Y.G., Shen, R.M.: Learning effect evaluation system based on support vector machine. Comput. Eng. 30(8), 15–16 (2004) 7. Chen, YS: Simple in Complex and Certainty in Uncertainty [EB/OL]. http://survivor99.com/ entropy/chen21.htm

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8. Sun, X.D., Huang, C.L., Zhang, Q.: Science of Military Logistics. The Publishing House of PLA, Beijing (2002) 9. Ren, H.Q.: Science of Military Command Decision. National Defense University Publishing House, Beijing (2007) 10. Shi, H.P., Han, T.: Evaluation in support ability of equipment maintenance personnel based on fuzzy comprehensive evaluation. Mod. Electron. Technol. 246(1), 96–98 (2008) 11. Zhou, W.M., Chen, J.S., Song, J.X., et al.: Prediction algorithm for the support capability of armament technical preparation based on support vector machine. Syst. Eng. Electron. 35(9), 1903–1907 (2013)

The Computer Formula of the Smarandache Dual Function Liu Miaohua(&), Song Xiuchao, and Jao Hongying Air Force Engineering University Foundation Department, Xi’an, China [email protected]

Abstract. We define the F. Smarandache dual function S
ðmÞ as the largest natural number n, that m is devided by n factorial. This article mainly uses the method elementary with analyse to study the compute formula of Q
of combining S ðdÞ, here of S
ðmÞ is the Smarandache dual function d=m

Keywords: Smarandache dual function

 Formula

1 Introduction It is well known that the Smarandache problems is very important in the study of number theory, and they relate to many famous number theoretic problems. Therefore, any fundamental progress in this field will contribute to the development of elementary number theory. We define the F. Smarandache dual function S
ðmÞ as the largest natural number n, that n factorial divide m, namely is S
ðmÞ ¼ maxfn : n 2 N; n!jmg Many academics are very interested in the nature of the number theory function of S
ðmÞ, see [1–3]. As for this function, in the literature [4], Sandor has speculated that S
½ð2n  1Þ!ð2n þ 1Þ! ¼ Q  1; Finally, about this speculation, Le Maohua has given proof in the literature [5]. Liu Miao Hua has also studied the number theoretic function of S
ðmÞ and obtained an interesting formula in the literature [6], that is 8 > > < ðb1 þ 1Þðb2 þ 1Þ    ðbk þ 1Þ; X
s ðdÞ ¼ ð2b þ 1Þðb1 þ 1Þðb2 þ 1Þ    ðbk þ 1Þ; > > d=n : ð2b þ 1 þ b1 þ 3bb1 Þðb2 þ 1Þ    ðbk þ 1Þ;

b

b

b

n ¼ q1 1 q2 2    qk k b

b

b

n ¼ 2b q1 1 q2 2    qk k ; q1 6¼ 3 n¼

b b 2b q1 1 q2 2



b qk k ; q1

¼ 3; b ¼ 1; 2

Where qi is the different odd prime numbers and bi ; b is positive integer.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 954–957, 2019. https://doi.org/10.1007/978-3-030-02804-6_123

:

The Computer Formula of the Smarandache Dual Function

955

P In
the literature [7] Jie used elementary method to study the properties of S ðmÞ and got a good asymptotic formula:

mX

X

S
ðmÞ ¼ eX þ Oðln2 Xðln ln XÞ2 Þ

mX

Related to the property of the F. S dual functions are also available in the literature [8–10]. The main purpose of this article is that we will get Q an
exact compute formula using analytic method to study the compute problem of S ðdÞ. d=m

Theorem. For all positive integer m we have the compute formula Y

S
ðdÞ ¼

d=m

8 b b > 1; m ¼ p1 1    pj j ; pi  3 > >
> > : 2bðb2 þ 1Þðb3 þ 1Þðbj þ 1Þ 3bb1 ðb2 þ 1Þðb3 þ 1Þðbj þ 1Þ ; m ¼ 2b 3b1 pb2    pbj ; p  5; b ¼ 1or2 1 j1 i

Where Pi is the different odd prime numbers and bi ; b is positive integer.

2 Theorem Proving In this part, we’re going to prove it directly. In fact for any positive integer m [ 1, also b

b

express as the product of the prime number, that is m ¼ p1 1 . . .pj j , then we discuss it according to different case. 2.1

b

b

When m ¼ p1 1 . . .pj j

In the first place, we discuss the simpleness form, that the factorization of m is squareb

b

free number. That is m ¼ p1 1 . . .pj j (where 3  p1 \p2 \. . .pj ), we know that Y

S
ðdÞ ¼

d=m

2.2

b

Y

S
ðdÞ ¼ 1

b b d=p1 1 pj j

b

When m ¼ 2b p1 1 . . .pj j

In the next place, the factorization of m comprise square divisor, that is b

b

m ¼ 2b p1 1 . . .pj j , we know that

956

L. Miaohua et al.

Y

Y

S
ðdÞ ¼

b

b

d=m

Y

S
ðdÞ ¼

b

d=2b p1 1 pk k b1

¼ 1  2dðp1

b

Y

S
ð2dÞ    a

a

b

d=p11 pj j

d=p1 1 pj j

pj j Þ

Y

S
ðdÞ

b

S
ð2b dÞ b

d=p1 1 pj j

b b1 pj j Þ

   2dðp1

¼ 2bðb1 þ 1Þðb2 þ 1Þðbj þ 1Þ

2.3

b

b

j When m ¼ 2b 3b1 p1 2 . . .pj1

b

b

j In the end, let m ¼ 2b 3b1 p1 2 . . .pj1 , (here 5  p1 \. . .pj1 ; b ¼ 1or2), we know that

Y d=m

Y

S
ðdÞ ¼

bj b d=3b1 p1 2 pj1

Y

¼1

d=pb2 p

Y

1

bj d=pb2 pj1 1

Y

S
ðdÞ 

bj b d=3b1 p1 2 pj1

S
ð2dÞ b

j j1

S
ð2b dÞ

Y

S
ð2dÞ   

Y

S
ð2  3dÞ   

bj d=pb2 pj1 1

Y

Y

S
ð2  3b1 dÞ   

bj d=pb2 pj1 1

Y

S
ð2b  3dÞ   

bj d=pb2 pj1 1

S
ð2b dÞ

bj b d=3b1 p1 2 pj1

S
ð2b  3b1 dÞ

bj d=pb2 pj1 1

¼ 1  2ðb2 þ 1Þðb3 þ 1Þðbj þ 1Þ 3b1 ðb2 þ 1Þðb3 þ 1Þðbj þ 1Þ    2ðb2 þ 1Þðb3 þ 1Þðbj þ 1Þ 3b1 ðb2 þ 1Þðb3 þ 1Þðbj þ 1Þ ¼ 2ðb2 þ 1Þðb3 þ 1Þðbj þ 1Þ 3bb1 ðb2 þ 1Þðb3 þ 1Þðbj þ 1Þ

So we have completed the proof.

3 Summary In the book “Only problems, Not solutions” published by the American Research Press in 1991, Professor F. Smarandache put forward 105 unsolved mathematical problems and conjectures about special numbers, arithmetic functions, etc.. The Smarandache dual function is one of the functions, and many scholars have studied this function deeply and obtained the research results with theoretical value. In this paper, a progressive study of dual functions is presented and formula is obtained. It is more convenient for us to further study other problems involving dual functions. Some properties and conclusions of the dual functions of other Smarandache functions are discussed

References 1. Smarandache, F.: Only Problems, Not Solutions, vol. 23. Xiquan Publishing House, Chicago (1993) 2. Sandor, J.: On certain generalizations of the Smarandache function. Notes Numb. Th. Discr. Math. II, 45–51 (1999) 3. Xue, X.: On the Smarandache dual function. Scientia Magna 3(1), 29–32 (2007)

The Computer Formula of the Smarandache Dual Function

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4. Sandor, J.: On certain generalizations of the Smarandache function. Smarandache Not. J. II, 202–212 (2000) 5. Le, M.: A conjecture concerning the Smarandache dual function. Smarandache Not. J. 14, 153–155 (2004) 6. Liu, M.: A calculation formula of Smarandache dual function. J. Air Force Eng. Univ. 14(5), 92–94 (2013) 7. Jie, L.: On Smarandache dual function. Scientia Magna 2(1), 111–113 (2006) 8. Gorski, D.: The Pseudo Smarandache functions. Smarandache Not. J. 12, 104–108 (2000) 9. Sandor, J.: On additive analogues of certain arithmetic function. Smarandache Not. J. 14, 128–132 (2004) 10. Sandor, J.: On additive analogues of the function. Smarandache Not. J. 13, 266–270 (2002)

Safety Risk Assessment for Formamide in Yoga Mats Fang Jia1, Xia Liu2(&), Wenjian Xie1, and Xiaolei Feng3 1

3

Guangzhou Research Institute of Quality Supervision and Inspection, Guangzhou, Guangdong, China 2 China Institute of Standardization, Beijing, China [email protected] Zhejiang Institute of Product Quality and Safety Inspection, Hangzhou, Zhejiang, China

Abstract. Gas chromatograph- mass spectrometry method is adopted to carry out quantitative analysis on formamide content in 100 batches of Yoga mat products, and carry out environmental chamber release and perspiration migration migration simulation test. The inhalation exposure model and skin contact exposure model are established to analyze risks in accordance with EU’s calculation model for chemical substance exposure quantity and in combination with use characteristics of Yoga mat. After comparing hazardous degree of formamide to different people in different scenarios, the research finds that the daily average exposure quantity to children in the same scenario is higher than that to adults; for the same group of people, the daily average exposure quantity in Yoga Clubs is higher than at home. Thus, it is necessary to avoid taking children to venues with intensive yoga mats such as Yoga Clubs. Keywords: Yoga mat


Formamide
Risks
Exposure model

1 Introduction Yoga mats as a kind of foaming products, the formamide content in Yoga mat is from foaming agent azodicarbonamide, which will generate formamide by decomposition and restructuring under high temperature. Formamide is easy to permeate through skin, or inhaled to human body due to its volatility. Formamide is of reproduction toxicity, and has been listed to REACH SVHC List. Ruan Liqin reported statistical data of hazardous substance detection test for formamide in exported shoes and countermeasures [1]. Zhong Wenhan et al. reported main formamide detection methods currently, including headspace gas chromatography, accelerated solvent extraction-gas chromatography method, ultrasonic extraction method and climate chamber method [2]. Huang Kaisheng, et al. studied the method to measure formamide in EVA foaming materials with gas chromatography- mass spectrometry. The method is of good selectivity, precision and low detection limit [3]. Xiao Zhaojing et al. formulated the method to detect acetophenone and formamide contents in foaming plastic products with simultaneously accelerated solvent extraction-gas chromatography method [4]. Sun Duozhi et al. studied headspace gas chromatography measurement method for © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 958–965, 2019. https://doi.org/10.1007/978-3-030-02804-6_124

Safety Risk Assessment for Formamide in Yoga Mats

959

formamide content in children’s products made from acetic acid - vinyl acetate copolymer, and adopted NP detector to rapidly and accurately confirm the quantity [5]. This paper aims to detect and study acrylamide content, volatilization and perspiration migration volume in 100 batches of yoga mat products with GC-MS (gas chromatography-mass spectrometer) and HPLC (high performance liquid chromatograph), assess the safety of formamide content in yoga mats, and draw conclusions about precautions for consumers in use.

2 Methodology 2.1

Sampling

Totally 100 yoga mat samples are purchased from the market. The sampling site distribution can be seen in Table 1. Table 1. Sampling site situations Product type Sampling site Sampling batch Proportion % Yoga mat Supermarket/shopping mall 15 15 Wholesale market 15 15 Exclusive shop 13 13 Network 57 57

2.2

Experimental Instruments and Materials

The extracting solutions used in this paper contain pure acetone and acid perspiration (prepared according to Article 4.4 in GB/T 3922). The pre-processing instruments are analytical balance, Termovap Sample Concentrator, ultrasonic extractor and smallscale environmental chamber. Detection instruments contain GC-MS and HPLC. 2.3

Test Method

2.3.1 Measurement of Formamide Content in Yoga Mats Take representative samples, cut into pieces smaller than 5 mm  5 mm and mix evenly. Extract for 1 h with ultrasonic extractor by acetone. Filter the extracting solution to centrifugal tube, and wash the residues twice with acetone. Mix the washing solution and extracting solution. Extract liquid nitrogen and concentrate to less than 1 mL, and then fill in acetone to 1 mL. The concentrated solution will be filtered by 0.45 µm membrane, and measured with GC-MS. 2.3.2 Measurement of Formamide Release Volume in Yoga Mats After putting the yoga mat to environmental climate to release for 24 h, and take gas sample for 60 min with 0.5 L/min flow; put absorption liquid to 10 ml colorimetric tube fully, set constant volume with water to the scale line of 10 mL, and shake evenly;

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filter with 0.22 µm aqueous phase filter membrane; abandon 2 mL initial liquid and collect the filter liquor to 2 mL brown sample bottle, measure with GC-MS. 2.3.3 Measurement of Formamide Migration Volume in Yoga Mats Take representative samples, cut into pieces smaller than 5 mm  5 mm and mix evenly. Accurately weigh and take samples to conical bottle. Add acid perspiration to fully soak the samples, and dip into 37 °C thermostatic water bath for 60 min; after cooling to room temperature, filter the soak solution, measure with GC-MS.

3 Results Analysis 3.1

Analysis on Detected Formamide Content

Formamide is detected in 85 out of 100 batches of yoga mats. Results show as foaming agent of foamed plastic products, formamide is universally used in yoga mats. According to Taiwan Standard CNS 15493-2015 Safety requirements for splicing plastic mats, formamide content shall be no more than 200 mg/kg. Formamide content in 33 batches of yoga mats exceeds the standard. Distribution of detected amount can be seen in Fig. 1.

formamide content (mg/kg)

1200 1000 800 600 400 200 0

5

10

15

20

25

30

35

sample no.

Fig. 1. Detection results of formamide content

3.2

Analysis on Detected Formamide Release and Migration Quantity

Formamide release is detected in 24 out of 33 batches samples, migration is occurred to all 33 batches samples after being soaked by perspiration. The data proves approaches, either through skin contact or inhalation, of hazards caused by formamide to human body. The detected amount of formamide release and migration can be seen in Figs. 2 and 3.

2

formamide volatile quantity (mg/(m h))

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35 30 25 20 15 10 5 0 -5

0

5

10

15

20

25

sample no.

Fig. 2. Detection results of formamide volatile quantity 800

formamide migration (mg/kg)

700 600 500 400 300 200 100 0 0

5

10

15

20

25

30

35

sample no.

Fig. 3. Detection results of formamide migration

4 Risk Assessments 4.1

Hazards Recognition

In accordance with EC No 1272/2008, formamide is categorized as 1B reproductive toxicity, which may cause irreversible injury to infants after long-term exposure. In this risk monitoring, among 100 batches of samples, formamide contents in 33 batches yoga mats are detected higher than that in common splicing soft foamed mats pursuant to Taiwan Standard CNS 15493-2015 Safety requirements for splicing plastic mats. Therefore, formamide in yoga mats has safety risks. In measurement of release quantity and migration after synthetic perspiration soaking of samples by simulating actual applying environment, both release and migration are detected. Results show during use, formamide in yoga mats may bring inhalation risk and skin exposure risks to human body.

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4.2

Dose-Response (Concentration) Analysis

The formamide is of reproductive toxicity. Its toxicological data shows for contamination through stomach, LD50 6.1 g/kg to large mice and LD50 3.15 g/kg to small mice. The acute toxic symptom is featured by nervous system damage, including respiratory disturbance and conjunctivitis, tonic convulsion, and death in 3–4 days. The threshold concentration of chronic inhalation shall be 6 ± 4 mg/m3. The maximum allowed concentration in operating environment shall be 30 mg/m3 (20 ppm), based on US regulations. No reference dose or concentration for formamide is found from IRIS database. 4.3

Exposure Assessment Model

Since formamide in Yoga mats can enter human body through either respiratory tract or skin, the calculation models for exposure quantity through inhalation and skin absorption are established. 4.3.1

Calculation Model for Exposure Quantity Through Inhalation C¼

W  Q  Fv  D  T Vroom

ð1Þ

ET  ED AIR  ATd BW

ð2Þ

Ex ¼ C  Among aforesaid two formulas,

In Formula (1): C: Exposure concentration of each substance, mg/m3; w: Release quantity of substances in product, mg/m2h; Q: Product usage quantity, m2 product; Fv: Volatility coefficient, %; D: Dilution factor; T: Duration of exposure, h; Vroom: Space volume of application scenario, m3; In formula (2): Ex: Average daily exposure through inhalation, mg/(kg
d); ET: Exposure time, h/d; ED: Exposure duration, d; AIR: Respiratory rate, m3/h; BW: Body weight, kg; ATd: Average exposure duration (by days), d.

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4.3.2 Calculation Model for Exposure Quantity Through Skin Absorption EXskin ¼

C  S1  Q  ABS  n S  BW

ð3Þ

In formula (3): EXsink: Daily exposure of chemical substances through skin exposure, mg/(kg
d); C: Concentration of each substance in the product, mg/kg; S1: Touching area between products and skin, m2; Q: Product usage quantity, kg; ABS: Mass fraction of each substance to human body through skin; n: Exposure frequency per day, time/d; Superficial area of product, m2; BW: Weight, kg. 4.4

Exposure Quantity Calculation

4.4.1 Confirmation of Simulative Scenes According to consumer complaint and risk early waning, the yoga mats are mainly applied to two scenes: Scene 1: home, generally one yoga mat; Scene 2: Gym and Yoga clubs, supposing half of the area is paved by yoga mats. Moreover, due to extensive scope of yogis, covering people from 6 to 50 years old, the risks are assessed for two age groups, including children (6–14) and adults. 4.4.2

Parameter Setting for Inspiration Exposure Model

(1) Volatility coefficient Fv and dilution factor D Since the structure of yoga mats is usually simple, and suitable for volatilization of harmful substances. The volatility coefficient Fv shall be 1. The dilution factor D in Scene 1 and Scene 2 shall be 1 according to the principles of the worst angle of risk assessment to human body. (2) Product usage quantity Q and exposure space Vroom The area of single room in china shall be about 5–30 m2, and the number shall be 15 m2 in this assessment. The storey height is generally no higher than 2.8 m, and no lower than 2.4 m for bedrooms and living rooms. Thus, the room height shall be 2.5 m. Then, the volume of common room shall be Vroom1: 15  2.5 = 37.5 m3. Generally, the area of yoga clubs shall be about 100 m2, the height shall be 2.5 m. Then the volume of yoga clubs shall be Vroom2: 100  2.5 = 250 m3. The area of yoga mats is usually 1.36 m2. Supposing half of the area in Yoga clubs is paved with yoga mats, the product usage shall be 50.32 m2. (3) Exposure time T Yoga practice per time shall be 1 h usually, so T = 1 h. (4) Respiratory rate AIR In accordance with researches, the short-term exposure respiratory rate of children under medium active state shall be 1.2 m3/h, and of adults shall be 1.6 m3/h.

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(5) Exposure time ET, exposure duration ED and average exposure time ATd The exposure time ET refers to hours exposed to people every day. According to yoga practice frequency and time of common people, ET shall be 1 h/d; when applied to non-carcinogenic model assessment, ED equals to ATd. 4.4.3

Parameter Setting for Skin Exposure

(1) Contacting area between product and skin S1 and product area S The thickness of yoga mats is usually 0.004–0.008 m, The surface area should be S = 2.75 m2. When practicing yoga, the contact area with yoga mats is the largest when yogis are lying on mats, i.e. upper limbs and legs are touching the mats simultaneously. Based on the principle of strictness for risk assessment, the area shall be S1. According to research, S1children = 0.226 m2 and S1adults = 0.412 m2. (2) Product quality Q Through measurement, the weight of every yoga mat is about 0.8–1.8 kg, and the mean value, 1.3 kg, is taken. (3) Mass fraction of formamide migrated to human body through skin ABS Since substance concentration in the model is the migration of formamide after simulating perspiration soaking, ABS shall be 50%. (4) Exposure frequency per day n Usually, people will practice yoga once a day, so the exposure frequency per day shall be 1 time/d. (5) Weight BW The average weight of children shall be 28 kg, and BW of adults shall be 55 kg. 4.4.4 Selection of Product Detection Results For the purpose of simplified analysis, only samples with detected formamide content are analyzed in this assessment. The median value, P50, of quartile steady method shall be used as the mean value of compound content in products, and minimum and maximum value detected shall be used as the scope. Specifically see Table 2. Table 2. Median value, minimum value and maximum value of detected results Substance Median value Minimum value Maximum value 2 0.046 34.225 Formamide release [mg/(m h)] 4.596 Formamide migration (mg/kg) 72.6 14.8 741.7

4.4.5 Calculation Results of Exposure Quantity Through confirmation of each parameter, the exposure assessment results for harms caused by formamide in yoga mats to users can be calculated. Specifically see Table 3.

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Table 3. Hazard assessment results of formamide in yoga mats Exposure model

Parameters

Inhalation exposure model

Ex, mg/ (kg
d)

Skin exposure model

Ex, mg/ (kg
d)

Scene Family Yoga clubs –

Formamide Children 7.1  10−3 7.1  10−5−5.3  10−2 4.0  10−2 4.0  10−4−3.0  10−1 1.4  10−1 2.8  10−2−1.4

Adult 4.8  4.8  2.7  2.7  1.3  2.6 

10−3 10−5−3.6  10−2 10−2 10−4−2.0  10−1 10−1 10−2−1.3

5 Conclusion According to exposure risks analysis of formamide in yoga mats, on the same scenes, the exposure quantity to children is higher to that to adults for inhalation exposure, which means children are easier to get harmed; for the same group of people, exposure quantity in Yoga mats is higher than that at homes, which means yoga practice in yoga clubs is highly risky in health relating to number of yoga mats paved in specific space of yoga clubs. For skin exposure, the exposure amount to children is slightly higher than that to adults because of different skin areas and weights that the yoga mats contact with. Consumers shall check product identification when purchasing, or unpack to smell whether there is pungent smell. Do not purchase those with serious pungent smell. After buying yoga mats, place and air it for a few days before use; clean the body after using yoga mats. Do not take children to venues with intensive yoga mats such as yoga clubs. Acknowledgments. This paper has been funded by the national key research and development project “Research on key technical standards for quality and safety control of consumer goods” (2016YFF02022600), and “Research on Consumer goods Safety Hazard Identification and risk Assessment based on scenario Simulation” (552018Y-5928-2018).

References 1. Ruan, L.: Statistical data of hazardous substance detection test for formamide in exported shoes and countermeasures. Light Text. Ind. Fujian 2, 53–54 (2015) 2. Zhong, W., Che, X., Hu, T., et al.: Research status of formamide detection method in children’s products. Furniture 38(4), 100–105 (2017) 3. Huang, K., Lin, H., Xu, D., et al.: Measurement of formamide in EVA foamed materials with GC-MS method. Phys. Test. Chem. Anal. Part B Chem. Anal. 51(10), 1465–1467 (2015) 4. Xiao, S., Li, G., Lu, J., et al.: Measurement of acetophenone and formamide in plastic mats for children with ultrasonic solvent extraction-gaschromatographic mass spectrometry method. Plast. Sci. Technol. 44(12), 73–76 (2016) 5. Sun, D., Lu, C., Zuo, Y., et al.: Measurement of formamide in EVA copolymer plastics for children with NPD. J. Insp. Quar. 23(3), 19–20 (2013)

A Model of Probabilistic Event for CPS Based on Hidden Markov Model Cheng Zhou1, Youqian Feng1, Zhonghai Yin1, Yali Wang1(&), and Yunmin Huang2 1

2

Department of Basic Science, Air Force Engineering University, Xi’an, Shaanxi, China [email protected] Aviation Maintenance NCO Academy, Air Force Engineering University, Xi’an, Shaanxi, China

Abstract. Cyber-physical system is a cooperating system combining with physical and cyber worlds, in which all kinds of prime event can be processed to be complex events to connect this two worlds. However, the uncertainty will be occurred from the noises of sensors or environment, which gives the impetus to a method for processing probabilistic events. In this paper, a method of event model based on HMM is proposed to obtain the probability of complex events. At last, a simulation is executed in MATLAB to verify the effect of our method. Keywords: Cyber-physical system HMM


Probability
Complex event

1 Introduction Cyber-physical system is an area of emerging cooperating system combining with both physical and cyber world, in which event can be regarded as a pathway to connect this two worlds [1]. Usually, all kinds of prime events can be detected and sensed by several kinds of devices, such as sensors, GPS, etc. Recently, many scholars have huge interests in CPS, which has extensively been used in many fields about sensor-based systems, such as autonomous cars, smart grid, robotics, and wireless sensor networks [2, 3], etc. A large amount of systems require that data are precise and certain, in which data is deterministic in nature. However, in many real-time CPS, the uncertainty is usually treated with probabilities for the data with noises from sensors or environment. Therefore it is important to research events with probability in CPS. In recent years, some scholars have huge interests in probabilistic complex event [4–6]. Most of these methods process probabilistic events based on Markov chain. In a world, a key challenge should be solved in processing the probabilistic event stream: how to calculate the probability of a complex event from correlated uncertain events. This paper is structured as follows: Sect. 2 introduces some concept for event operators. In Sect. 3, several models of probability events are presented. Furthermore, evaluation and simulation analysis are presented in Sect. 4. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 966–973, 2019. https://doi.org/10.1007/978-3-030-02804-6_125

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2 Concepts Definition 1. The prime event is the event which is generated by collecting changes of physical world through sensors. It can be denoted by eðAttr; T; L; PÞ. Where Attr is the attribute of event. T and L are the occurrence time and location of event. P is the occurrence probability of event. Additionally, a prime event can be simplified as e without confusion. Definition 2. The set of prime event is consisted of two or more prime event, which can be denoted by E ¼ fe1 ; e2 ;


en g. Definition 3. A composite event is constructed by several prime event through composition operators of disjunction “_”, conjunction “^” or negation “–”. “_” and “^” are binary operators and “–” is unary operator, specified as following. For 8a; b 2 E, the binary operators “_” and “^” are defined, (1) Disjunction “_”: The disjunction ða _ bÞ occurs if either event a or b occurs. (2) Conjunction “^”: The conjunction ða ^ bÞ occurs if both a and b occurs, regardless of the order. Similarly, the unary operator “–” is defined as follows. (3) Negation “–”: a means that a will not occur.

3 Models of Probability Events In this section, sever models of probability events will be introduced in detail according to the independence or not between prime events 3.1

Probabilistic Events with Absolute Independence

This model assumes that all events are absolute independence for each other, i.e., for 8a; b 2 E, we have Pða; bÞ ¼ PðaÞPðbÞ:

ð1Þ

(1) The probability of composite event ða _ bÞ can be computed by Pða _ bÞ ¼ PðaÞ þ PðbÞ  Pða; bÞ ¼ PðaÞ þ PðbÞ  PðaÞPðbÞ ¼ 1  PðaÞPð bÞ

ð2Þ

(2) The probability of composite event ða ^ bÞ can be computed by Pða ^ bÞ ¼ PðaÞPðbÞ:

ð3Þ

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(3) The probability of event a can be computed by PðaÞ ¼ 1  PðaÞ:

3.2

ð4Þ

The Model of Joint Probability Distribution

It is unrealistic that all events are absolute independence for each other. The model of absolute independence has a limited utilization in real world. Thus, a model of joint probability distribution is introduced in this section. The joint probability distribution can be regarded as a knowledge base, in which the probability of composite event can be deduced. Example 1. Table 1 shows a joint probability distribution for three prime events a, b and c. Try to deduce the occurrence probability of events ða ^ bÞ, a.

Table 1. The joint probability distribution for abc b c c a 0.108 0.012 a 0.016 0.064

b c c 0.072 0.008 0.144 0.576

(1) For composite event ða ^ bÞ, it means that both a and b are obtained. Thus, we have Pða ^ bÞ ¼ Pða; b; cÞ þ Pða; b; cÞ ¼ 0:108 þ 0:012 ¼ 0:12

ð5Þ

(2) For event a, it means that all relevant event of a should be added together. Thus, we have PðaÞ ¼ Pða; b; cÞ þ Pða; b; cÞ þ Pða;  b; cÞ þ Pða;  b; cÞ ¼ 0:108 þ 0:012 þ 0:072 þ 0:008 ¼ 0:2

ð6Þ

However, the table of joint probability distribution can not usually be obtained in practice. Furthermore, the joint probability distribution can be deduced by condition probability distribution. Thus, the joint probability distribution of x1 ;


xn is computed as Pðx1 ;


xn Þ ¼ Pðxn jxn1 ;


x1 ÞPðxn1 jxn2 ;


x1 Þ


Pðx2 jx1 ÞPðx1 Þ n Y Pðxi jxi1 ;


x1 Þ ¼ i¼1

ð7Þ

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Additionally, a definition of conditional independence is introduced to simplify the formulations above Definition 4. For any random variable X and Y, the conditional independence of X and Y for a third random variable Z is defined as PðX; YjZÞ ¼ PðXjZÞPðYjZÞ:

ð8Þ

Thus, the joint probability distribution of x1 ;


xn can be further computed by a conditional independence of random variable xZ , specified as Y Pðx1 ;


; xz ;


; xn Þ ¼ Pðxz Þ Pðxi jxz Þ: ð9Þ i6¼z

Example 2. In Table 2, the condition probability distribution of three prime events a, b and c can be further obtained by Table 1. Additionally, let event a be the conditional independence for events b and c, i.e., Pðb; cjaÞ ¼ PðbjaÞPðcjaÞ. Table 2. The condition probability distribution for abc pðaÞ pðbjaÞ pðbjaÞ pðcjaÞ pðcjaÞ

0.2 0.12/0.2 0.08/0.2 0.18/0.2 0.02/0.2

= = = =

0.6 0.4 0.9 0.1

Thus, the joint probability distribution of a, b and c can be computed as Pða; b; cÞ ¼ PðbjaÞPðcjaÞPðaÞ : ¼ 0:6  0:9  0:2 ¼ 0:108

3.3

ð10Þ

The Model of Probabilistic Event with HMM

In a realistic CPS, all events are obtained by various devices, such as sensors, GPS, cameras and so on. Thus, the observer is introduced in the model of events to detect all possible events in physical world. Furthermore, the model of Hidden Markov Model (HMM) are introduced to process all events in this section. The HMM is the simplest structure of dynamic Bayesian network. It is a model with directed graph and has a widely application in processing time series data, for example, speech recognition [7], natural language processing [8], etc.

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The event model of time series with HMM is shown in Fig. 1, which mainly contains two sets of variables. First is the set of states variables Y ¼ fy1 ; y2 ;


; yn g, where yi 2 Y is the i th state in systems. Second is the set of event variables X ¼ fx1 ; x2 ;


; xm g, where xi 2 X is the interest event for systems. Thus, the values range of yi are n possible values in discrete space of states fq1 ; q2 ;


; qn g. Similarly, the values range of xi are m possible event fe1 ; e2 ;


; em g.

q1

q2

e1

e2

...

qn

1

qn

em

1

em

Fig. 1. The event model with HMM

Additionally, the arrows represent the relations between all variables. It should be note that the observation variables only depend on event variables, i.e., yt is only determined by xt , which is unrelated with other event variables or state variables. Thus, the state variable yt of t th only relies on state yt1 of t  1 th, regardless of any other state of t  2 th. It can be specified as Pðyt jyt1 Þ ¼ Pðyt jy1 ;


; yt1 Þ:

ð11Þ

Similarly, the event variable xt of t th only relies on state yt of t th, regardless of any other state. It can be specified as Pðyt jxt Þ ¼ Pðyt jx1 ;


; xt Þ:

ð12Þ

In order to specify the event model of HMM with time series precisely, three terms of parameters need to be determined. (1) The condition probability between states is specified as follows. Usually, it is   denoted by A ¼ aij nn , specified as   aij ¼ P yt þ 1 ¼ qj jyt ¼ qi ; 1  i; j  n:

ð13Þ

It means the probability of occurrence state qi to next state qj . (2) The probability of interest event of system is specified as follows. Also, it is  denoted by B ¼ bij NM , where   bij ¼ P xt ¼ ej jyt ¼ qi ; 1  i  n; 1  j  m:

ð14Þ

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(3) The model of initial state. It means the probability of initial events in systems, denoted by p ¼ ðp1 ; p2 ;


pN Þ, where pi ¼ Pðy1 ¼ qi Þ; 1  i  n:

ð15Þ

Proposition 1. Let the set of events with time series be ðe1 ;


; et Þ, and the set of states is ðq1 ;


; qn Þ. Assume all events in ðe1 ;


; et Þ obey the model of time series with HMM, denoted by khmm . Furthermore, the joint probability distribution of all variables can be denoted by Pðe1 ;


; et jkhmm Þ ¼ Pðq1 ÞPðe1 jq1 Þ

t Y

Pðqi jqi1 ÞPðei jqi Þ:

ð16Þ

i¼2

Proof. The proof can be obtained by formula (10) and (11) straightforwardly. Proposition 2. An arbitrary set of events are divided into two subsets of T and S, where T is set of events obeyed independent distributed; while S obeys the model of time series with HMM. Hence, the joint probability distribution of all variables can be denoted by ! Si Y   Y Y Pðe1 ;


; et jkhmm Þ ¼ P qj Pðqi1 ÞPðei1 jqi1 Þ Pðqm jqm1 ÞPðem jqm Þ : Ej 2T

Si 2S

m¼2

ð17Þ Proof. The proof can be obtained by formula (10), (11) and (16) straightforwardly.

4 Experiment An example is presented to verify the effect of the event model with HMM. The scenario is to supervise the working process of boiler in a smelting factory. The states of the working process can be divided into three kinds, fully operation (FO), half operation and maintenance (HOM), and fully maintenance (FM). As the working process of devices can not be judged directly, some sensors can be used to collect the temperature of environment to control the working process of devices. Additionally, the interest events of systems can be divided into four kinds, extraordinarily high temperature (EHT), high temperature (HT), appropriate temperature (AT), low temperature (LT). Assume that all states and events obey the model of HMM, shown in Fig. 2. Similarly, a sequence of states and events is shown in Table 3.

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q0

q1

qt

e0

e1

et

Fig. 2. An example of sequences of states and event with HMM Table 3. A sequence of states and events t 1 2 3 4 5 …

Events AT EHT HT LT AT …

FO 1 0 1 0 0 …

HOM 0 1 0 0 1 …

FM 0 0 0 1 0 …

The method of Baum-Welch is utilized to train the data in Table 3 to obtain the maximum likelihood estimate for HMM. The simulation is executed in MATLAB and the value of maximum likelihood estimate of model is depicted by Fig. 3. -400

-450

MLE

-500

-550

-600

-650

0

10

20

30

40

50 Iteration

60

70

80

90

100

Fig. 3. The maximum likelihood estimate for training

Let the initial probability of each state be pi , thus we have pi ¼ ½pi ðHO), pi ðHOM), pi ðHM) ¼½0:63; 0:17; 0:2

ð18Þ

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Furthermore, let the probability of state transition be ptr , thus we have 2 6 FO Ptr ¼ 6 4 HOM FM

FO 0:5 0:25 0:25

HOM 0:375 0:125 0:375

3 FM 0:125 7 7: 0:625 5 0:375

ð19Þ

Additionally, let the probability of observation be pob , thus we have 2 6 FO Pob ¼ 6 4 HOM FM

AT 0:6 0:25 0:05

LT 0:2 0:25 0:1

HT 0:15 0:25 0:35

3 EHT 0:05 7 7: 0:25 5 0:5

ð20Þ

Thus, a danger event ed is defined as follows, ed ¼ The event of EHT occurs in three consecutive times t: According to formula (15), the probability of danger event can be labeled by pðed Þ, specified as ! Pðed Þ ¼ PðEHT; EHT; EHTjkhmm Þ ¼

Si Y   Y Y P qj Pðqi1 ÞPðEHTjqi1 Þ Pðqm jqm1 ÞPðEHTjqm Þ

Ej 2T

Si 2S

m¼2

¼ 0:0153

Hence, the probability of danger event is 0.0153.

References 1. Lee, E.A.: The past, present and future of cyber-physical systems: a focus on models. Sensors 15, 4837–4869 (2015) 2. Zhou, C., Feng, Y., Yin, Z.: An algebraic complex event processing method for cyberphysical system. Clust. Comput. 21, 1–9 (2018) 3. Zhou, C., Yin, Z., Feng, Y.: Events algebra of triggers. Comput. J. 1, 1–10 (2017) 4. Wang, Y., Cao, K.: A proactive complex event processing method for large-scale transportation internet of things. Int. J. Distrib. Sens. Netw. 4, 1–8 (2014) 5. Wang, Y.H., Cao, K., Zhang, X.M.: Complex event processing over distributed probabilistic event streams. Comput. Math. Appl. 66(10), 1808–1821 (2013). https://www.sciencedirect. com/science/article/pii/S0898122113004677 6. Shi, D., Elliott, R.J., Chen, T.: Event-based state estimation of discrete-state hidden Markov models. Automatica 65(C), 12–26 (2016) 7. Xue, S., Jiang, H., Dai, L., et al.: Speaker adaptation of hybrid NN/HMM Model for speech recognition based on singular value decomposition. J. Signal Process. Syst. 82(2), 175–185 (2016) 8. Paul, A., Purkayastha, B.S., Sarkar, S.: Hidden Markov model based part of speech tagging for Nepali language. In: International Symposium on Advanced Computing and Communication, pp. 149–156. IEEE (2016)

Abnormal Condition Analysis and Preventive Measures of a 220 KV Transformer Neutral Point Bushing Zhou Yuxiao1(&), Han Honggang1, Guo Tie1, Liu Lu2, Chen Hao1, Liu Yang1, and Song Yundong1 1

Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, Liaoning, China [email protected] 2 State Grid Liaoning Electric Power Co., Ltd., Metering Center, Shenyang 110006, Liaoning, China

Abstract. This paper introduces the abnormal condition of a 220 kV transformer neutral point bushing. The structure and characteristics of the neutral sleeve are expounded. According to the condition that the state test data are not up to standard, the bushing is inspected, tested and disassembled. The root cause of the anomaly is humidity and moisture from the top of the oil inlet into the bushing body. The valuable suggestions for the field installation, operation and maintenance of the bushing and the measures to prevent such incidents are then put forward. Keywords: Neutral point bushing
Capacitive bushing
Preventive measures

1 Introduction The bushing is the main insulation equipment outside the transformer box. The leading line of the transformer winding must pass through the insulated bushing, which plays an important role of the insulation between leading lines and acts as a fixed leading line [1]. High voltage bushing needs a great process, as it is very easy to damage the insulation when high voltage bushing is affected with damp [2] or internal defect [3] or improper maintenance occurs [4]. In recent years, due to the development of the power grid, the demands for transformer bushing have increased dramatically. There is a phenomenon that the production process of the manufacturer [5] and the field installation are not up to the standard [5, 6], which leads to constant defects in the operation and sometimes even threatens the transformers [7, 8].

2 Basic Information and Structure Form of the Bushing The model number of 220 kV transformer neutral point bushing is BRLW-126/630-4 and the rated capacitance is 335 pF. It came out in 2011 and was put into operation in 2012. In 2013, the conclusions of the dissolution test indicated that the acetylene © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 974–984, 2019. https://doi.org/10.1007/978-3-030-02804-6_126

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(C2H2) in oil exceeded the standard. Then it was taken back to the manufacturer for repairing. However, the bushing is not disposed. Then, the transformer was put into operation in 2014 until the abnormal condition occured. The bushing of the transformer is an oil paper capacitive cable sleeve, which is composed of capacity core, top sleeve, oil conservator, upper porcelain bushing, middle flange, CT cylinder, lower porcelain bushing, voltage equalizing ball and other accessories. The capacity core acts as the major insulation, while the porcelain sleeve acts as a container for external insulation and protection of the core. The structure of the bushing is fully sealed. The strong springs contacts the capacity core, the installation flange, the porcelain sleeve, the oil conservator, etc. The connections are made of high quality sealing pads and reasonable sealing structure. The structure of the bushing is shown in Fig. 1.

Fig. 1. The bushing structure

3 Analysis of Condition Based Maintenance Test In April 19, 2018, the transformer was overhauled and tested by the testers from the substation maintenance department of power supply company. It was found that the measuring value of the insulation resistance of the end capacity screen of the bushing was 0 MX, the capacitance was 401.6 pF and tangent value of the dielectric loss angle was 0.027. Then, the insulating oil was taken from the top of the bushing and was tested. The breakdown voltage of insulation oil was 19.6 kV. The results of gas dissolved in oil are shown in Table 1. The standard values [9] are based on DL/T 7222014 Guide to the analysis and the diagnosis of gas dissolved in transformer oil.

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Z. Yuxiao et al. Table 1. The results of gas dissolved in oil (unit: uL/L) CH4 C2H4 C2H6 C2H2 CO CO2 Total hydrocarbon Component H2 Data 47 1.20 0.00 2.35 0.00 21 2218 3.55 Standard  500 – – – 2 – –

< @l ¼ r2 ðln xi  uÞ ¼ 0 i¼1

@Lðl;r2 Þ > > : @r2 ¼  2rn 2 þ

1 2r4

n P

ð7Þ

ðln xi  uÞ2 ¼ 0

ð8Þ

i¼1

The estimated values of l and r can be obtained by the Eq. (9). 8 n P > > ^ ¼ 1n ln xi

> 2 1 :r ^Þ2 ¼ 1n ^ ¼ n ðln xi  l ln xi  1n ln xi i¼1

i¼1

ð9Þ

i¼1

1 ^ and n1 ^2 are the minimum variance unbiased estiIn the normal distribution, l r 2 1 ^ and n1 ^2 are also the minimum variance mates of l and r respectively. Therefore, l r unbiased estimation of the lognormal distribution parameters l and r2, respectively.

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5 Case Analysis 5.1

Case Description

The fault data of a certain type of aeronautical equipment components [9] are shown in Table 1. According to experience, it is known that the life of the aeronautical equipment component is a bathtub curve, so its life is in accordance with the Lognormal distribution. The lognormal distribution model is constructed according to the statistical fault data, and the reliability life analysis of the aeronautical equipment components is analysed. Table 1. Failure data of a certain type of aeronautical equipment component No. 1 2 3 4 5 6 7 8 9 10 Fault time/h 350 380 400 430 450 470 480 500 520 540 No. 11 12 13 14 15 16 17 18 19 20 Fault time/h 550 570 600 610 630 650 670 730 770 840

5.2

Distribution Type Confirmation

According to the fault data, make the corresponding probability plot of exponential, normal, Weibull and lognormal distributions, as shown in Fig. 2.

Fig. 2. Probability plot of exponential, normal, Weibull and lognormal distributions

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According to the probability plots, it can be intuitively seen that the normal, Weibull and lognormal distributions form a straight line, indicating that these probability distributions fit the data well. We can also further compare the Anderson-Dailing test value to determine which probability distribution is most suitable for the data. The smaller the Anderson-Dailing test value, the better the fit of the probability distribution. As can be seen from the Table 2, the lognormal has a Anderson-Dailing value of 0.724, so it can be assumed that the aeronautical equipment component failure fellows lognormal distribution. Table 2. Goodness-of-Fit No. 1 2 3 4

5.3

Probability distribution Exponential distribution Normal distribution Weibull distribution Lognormal distribution

Anderson-Dailing (adj) 5.842 0.802 0.845 0.724

Parameter Estimation

The parameters of the lognormal distribution are estimated using the maximum likelihood method (95% confidence interval) to obtain the probability distribution parameters, as shown in Table 3. We further give its probability distribution characteristics, as shown in Table 4. Table 3. Parameter estimates (ML estimates) Parameter Estimate Standard error 95.0% normal CI Lower Upper Location 6.29589 0.0517861 6.19440 6.39739 Scale 0.231594 0.0366183 0.169880 0.315729

5.4

Life Prediction Analysis

We can analyze the probability distribution of the airborne equipment according to the lognormal distribution law by using the statistical analysis method of probability plot. The average life or fault interval of the airborne equipment is 557.082 h, and the standard deviation of life or fault is 130.766 h. Therefore, in the maintenance of equipment, the inspection should be strengthened when the work of the airborne equipment is close to the average life or fault interval, and the hidden problem will be eliminated in time to ensure the reliable use of the equipment components and improve the level of flight training and actual combat.

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Estimate Standard error 95.0% normal CI Lower Upper Mean (MTTF) 557.082 29.2333 502.633 617.428 Standard Deviation 130.766 23.3480 92.1544 185.557 Median 542.340 28.0857 489.995 600.278 First Quartile (Q1) 463.908 26.6164 414.567 519.121 Third Quartile (Q3) 634.034 36.3773 566.598 709.495 Interquartile Range (IQR) 170.126 28.5129 122.492 236.282

The lognormal distribution parameters of aeronautical equipment components have been obtained, and the reliability function is:     ln t  l ln t  557:082 RðtÞ ¼ 1  FðxÞ ¼ 1  U ¼U r 130:766

ð10Þ

In order to further discuss the reliability characteristics parameters of fault data, such as probability density function, reliability function, fault function and so on, we can give an overview of probability distribution (as shown in Fig. 3), which will help us to intuitively understand the various indexes or characteristics of the aeronautical equipment components life or fault distribution data [10].

Fig. 3. Distribution overview plot for fault time

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6 Conclusion The analysis results show that the use of probability plots to determine the type of probability distribution is simple and feasible. The maximum likelihood estimation method is used to estimate the fault distribution function and data characteristics, and the fitting accuracy is relatively high. The use of lognormal distribution model effectively solves the problem of aviation equipment component life prediction, and has a good application prospect in the field of aviation equipment support.

References 1. EI-Shaarawi, A.: Log-normal distribution. Am. J. Phys. 14(6), 445 (2016) 2. Hantson, S., Pueyo, S., Chuvieco, E.: Global fire size distribution: from power law to lognormal. Int. J. Wildland Fire 25, 403–412 (2016) 3. Information on https://en.wikipedia.org/wiki/Log-normal_distribution 4. Xavier, H.S., Abdalla, F.B., Joachimi, B.: Improving lognormal models for cosmological fields. Month. Not. R. Astron. Soc. 459(4), 3693–3710 (2018) 5. Monteiro, M.J.: Fitting molecular weight distributions using a log-normal distribution model. Eur. Polym. J. 65, 197–201 (2015) 6. Jiang, R.Y.: Two probability plots of the three-parameter lognormal distribution. J. Donghua Univ. 31(6), 757–759 (2014) 7. Jiang, R.Y.: Study on probability plot correlation coefficient of the log-Weibull distribution. J. Shanghai Jiaotong Univ. (Sci.) 20(3), 298–301 (2015) 8. Huang, C.: Parameter estimation of the lognormal distribution. Stud. Coll. Math. 18(4), 19– 20 (2015) 9. Yang, Y., Gao, Y., Zhang, R.: Statistical Analysis and Application of Quality Management, pp. 164–171. Tsinghua University Press, Beijing (2015) 10. Yang, Y., Ding, Y., Zhao, Z.: Fault distribution analysis of airborne equipment based on probability plot. In: 2017 3rd IEEE International Conference on Control Science and Systems Engineering, pp. 239–242 (2017)

Application Analysis of Engine Emission Technology Fan Yang(&) Guangdong University of Science and Technology, Dongguan 523083, China [email protected]

Abstract. The combustible mixture of automotive engines produces harmful gases such as HC, CO, and NO during the combustion process, which has a great influence on our living environment and human health. Therefore, the technicians understand the structure and principle of the engine exhaust system and study the engine. Emission control technologies, as well as the exploration of new technologies for engine emissions and pollution from vehicle emissions, are of great significance to China’s environmental protection. In the research process of this article, first specify the hazards of automobile emissions to the environment and humans. Then study the influencing factors of engine emission generation, and finally discuss the engine emission control technology based on the appeal research. This article aims to contribute a certain amount of research value to environmental science. Keywords: Environmental pollution Control technology


Engine
Exhaust system

1 Introduction According to relevant data, there are many types of pollutants in automobile exhaust. Among them, solid aerosols and carbon monoxide are the most abundant contaminants. It is reported that an ordinary family car can emit about three times its own harmful gas per year. No matter in which country, the number of people who die from air pollution is much higher than the number of people with serious traffic accidents. Therefore, it is a good breakthrough to study the engine emission technology from harmful substances in automobile exhaust.

2 Harm of Engine Emissions Engine emissions refer to the exhaust gas emitted by motor vehicles or other equipment during the work process. In modern civilization, automobiles have become indispensable transportation tools for human beings. However, with the rapid development of the automotive industry, and the increase in vehicle production and possession, the automobile has also brought atmospheric pollution, that is automobile emissions pollution. Among the major second and third tier cities in China, the main cause of air pollution is automobile exhaust. According to statistics, the total number of cars in © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1046–1052, 2019. https://doi.org/10.1007/978-3-030-02804-6_135

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Shanghai is equivalent to only 1/12 of that in Tokyo, but the total amount of CO, HC, and NO emitted from cars in the air is basically the same. The amount of automobiles used in China will increase substantially with the rapid economic development and increasing social needs. This shows that the urgency of reducing vehicle exhaust emissions. The following hazards of engine emissions are described in the following four points. 2.1

Global Warming

A large number of automobile exhausts are not harmless. Gasoline releases nitrogen oxides and other pollutants during the combustion process. The most direct consequence of this is global warming. The rise in global temperatures directly leads to the formation of tsunamis, hurricanes and large typhoons. We all know that human beings have weak resilience in the face of natural disasters, and property and human losses are even more incalculable. Therefore, as a part of humanity, we must minimize the chances of these natural disasters and reduce our losses. Figure 1 below shows the possible consequences of global warming.

Fig. 1. Tsunami

2.2

Cancer

Carbon monoxide is a harmful gas that is very harmful to the human body. If carbon monoxide enters the human body, it will first be absorbed into the bloodstream, affecting the blood transmission of the human body, and it is highly likely to induce cardiovascular and cerebrovascular diseases [1]. In addition, hydrocarbons produce a highly toxic chemical smog that is extremely harmful to humans and poses a carcinogenic potential.

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Damage to Livestock and Articles

The white smoke generated by the engine damages livestock, fruit and rubber products and buildings. 2.4

Acid Rain

The main substance formed by acid rain is nitrogen oxides, whose main hazard is harm to human lungs and eyes. The lungs are the main organs of the respiratory system. The eyes are the main organs of the visual system. If the two organs of human beings are damaged, the rest of the life will be very painful. In addition, acid rain can also cause death in large areas of plants, and it also changes the acidity and alkalinity of the soil, resulting in no vegetation in the area in the future. Figure 2 below shows the trees that were corroded by acid rain.

Fig. 2. Forests corroded by acid rain

3 Influencing Factors of Engine Emissions 3.1

Ignition Advance Angle

From the moment of ignition until the piston reaches the compression top dead center. The smaller the ignition advance angle, the higher the pre-discharge temperature, the HC in the discharged exhaust gas is burned off in the exhaust pipe and the muffler [2]. When the ignition advance angle is outside the normal range, the engine will explode. At this time, under the action of high temperature and high pressure, N and 0 are easily combined to generate NOx, and at the same time, the HC emissions are increased due to incomplete combustion. Figure 3 below is a schematic diagram of ignition advance angle adjustment.

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Fig. 3. Ignition advance angle adjustment

3.2

Mixture Concentration

When the concentration of the mixture is too high, the exhaust gas contains carbon particles, causing black smoke due to insufficient combustion of the mixture. When the concentration of mixed gas is reduced by concentration, the emission of CO and HC will be less and less. 14.7:1 is the normal flammability ratio of the mixture concentration. Exceeding this ratio or lower than this ratio will generate harmful gases. For HC, when the mixture air-fuel ratio is changed to 16-17:1, the exhaust gas contains the least HC content. Since then, the more the mixture ratio, the more HC. Nitrogen oxides are composed of N0, N02, and the like. When the air-fuel ratio is 16: 1, the NO content in the exhaust gas is the highest. 3.3

Air-Fuel Ratio

The air-fuel mixture ratio is the air-fuel ratio. In order to optimize the exhaust gas catalytic rate (more than 90%), it is necessary to install an oxygen sensor in the exhaust pipe of the engine and realize closed-loop control. The working principle is that the oxygen sensor will measure the concentration of oxygen in the exhaust gas and convert it into an electrical signal and then send it. For the ECU, the air-fuel ratio of the engine is controlled in a narrow, near-ideal area (14.7:1). If the air-fuel ratio is large, although the conversion rate of CO and HC is slightly increased, the conversion rate of NOx is sharp. The drop is 20%, so the best air-fuel ratio must be guaranteed, and the best airfuel ratio is achieved. The key is to ensure that the oxygen sensor works properly. If the fuel contains lead and silicon, the oxygen sensor will be poisoned. In addition, improper use can also cause malfunctions such as carbon deposition of the oxygen sensor, ceramic chipping, heater resistance wire burnout, and internal line breakage. The failure of the oxygen sensor causes the air-fuel ratio to be out of alignment, the exhaust condition deteriorates, and the efficiency of the catalytic converter decreases, which shortens the service life of the catalytic converter for a long time. Mixtures with air/fuel ratios greater than the theoretical value are called lean mixtures. They have less gas, more oil, complete combustion, lower fuel consumption, and less pollution, but have lower power. The air-fuel ratio of less than the theoretical value of the gas mixture

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is called rich gas mixture, gas less oil and more power, but the combustion is not complete, high fuel consumption, pollution.

4 Engine Emission Control Technology 4.1

Particle Capture Technology

The diesel particulate filter is a wall-flow type honeycomb ceramic which is made of cordierite or silicon carbide. The exhaust gas contained in the diesel engine is mainly composed of carbon particles. The particulate trap is installed in the diesel exhaust system. Among them, a device for reducing particulate matter (PM) in exhaust gas by filtration has a filtration efficiency of more than 90% for carbon particles, and a soluble organic component SOF (mainly high-boiling HC) can also be partially trapped [3]. How the particle trap works: By alternately blocking the ends of the pores of the honeycomb porous ceramic, the ceramic wall holes are used to filter and remove the PM. Figure 4 below shows a schematic cross-section of the particle trap.

Fig. 4. Particle trap

4.2

Twin Turbo Technology

Twin turbo is one of the turbocharged ways. For turbine turbocharged turbo-hysteresis, connect two large and small turbines or two parallel turbines in parallel. When the engine is running at low speed, less exhaust gas can drive the turbine to rotate at high speed to generate enough intake air. Pressure reduces the turbo lag effect. The common turbochargers are single turbocharged, mechanical turbocharged, exhaust turbocharged, and compound turbocharged. Mechanical turbocharging is the direct operation of the turbocharged engine. The advantage is that there is no turbo lag. The disadvantage is that the lost power and boost value are low. Exhaust turbocharger is relying on the remaining kinetic energy of the engine exhaust to drive the turbine rotation. The advantage is that the high turbine speed and the large boost value increase

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the power obviously. The disadvantage is turbo lag, that is, the engine has a low speed (usually at 1500—Less than 1800 rpm) The kinetic energy of the exhaust gas is small and cannot drive the high-speed rotation of the turbine to produce the effect of increasing the intake pressure [4]. At this time, the engine power is equivalent to naturally aspirating. When the speed is increased, the turbocharger will work suddenly. Promote. 4.3

In-Cylinder Direct Injection Technology

Fuel Stratified Injection is a technique for direct injection of fuel from a nozzle into a cylinder. This technology can further improve the thermal efficiency of gasoline engines and reduce gasoline engine emissions. This set of technology derived from diesel engines has now been widely used. FSI technology actually refers to a mixture of two different combustion modes. These two combustion modes refer to the homogeneous combustion mode and the stratified combustion mode. Homogeneous combustion mode completes mixing with air during the intake stroke and compression stroke, and the formation of a relatively uniform mixture in the cylinder at the time of ignition to ensure stable ignition. Stratified combustion mode refers to the injection of fuel in the compression stroke. As the compression stroke progresses, the fuel and air are mixed until the moment of ignition. From the spark plug to the cylinder wall, the fuel concentration is from strong to thin, ensuring effective ignition. Flame propagation also occurs. Normal, thereby improving fuel economy. Direct injection engine fuel and air mixing mainly in three ways, namely, jet guidance, wall guide and airflow guidance. The airflow guidance of such an engine is mainly jet guidance, and the jet guidance is easy to form a spark required for ignition, and is also more economical. In addition, in addition to jet guidance, there is also a wall guiding method. This way, the injector is placed on the side and the direction of movement of the oil bundle is guided by the special shape of the instrument [5]. In this way, a large area of flammable area can be formed around the spark plug. The air flow guidance method also uses the side of the injector and the top of the spark plug, and uses the tumble flow formed during the intake to intensify the mixture of oil and gas. The wall guide method and the air guide method are similar in structure and are used in the homogeneous combustion mode. They can be converted from traditional PFI engines, and can share the combustion chamber and the cylinder head blank with the PFI engine, thus achieving the platform and modularization of the engine.

5 Conclusion China’s car ownership is still rising, which is not only a huge challenge to the environment, but also a sharp consumption of fossil energy. Therefore, research on engine emissions technology is a problem that we must pay attention to at present. In the face of increasingly stringent emission standards in various countries of the world, we also need to put a lot of effort and hard work.

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References 1. Ye, J., Zhibo, L., Lixin, W., Ning, W.: Study on the emission of fuel engine. In: Science and Technology Progress and Socio-economic Development in the 21st Century (2009) 2. Jian, L.: Research direction and status quo of engine low pollution emission control technology. Bull. Technol. Res. (2015) 3. Zhou, H., Zhang, X., Han, Y.: Gas engine in-cylinder direct injection lean burning technology (GDI). Bull. Technol. Res (2007) 4. Xu, Y.: Study on air flow and gas flow in the air intake and cylinder of four-valve gasoline engine. Tianjin University (2014) 5. Yu, S.: Simulation study on ignition timing and failure mode of gasoline engine. Sichuan University (2015)

Research and Application of Dual Active Disaster Preparedness System Based on Redo Read-Write Separation Cluster Qianjun Wu(&), Qingquan Dong, Guangxin Zhu, and Jun Yu NARI Group Corporation, No. 19, Chengxin Avenue, Jiangning District, Nanjing City, Jiangsu Province, China [email protected]

Abstract. At present, the security and reliability of information systems have directly related to the development of various businesses of the State Grid Corporation. In order to solve problems such as data security, business continuity, and performance pressure, and avoid the impact of system disruption or data loss caused by a disaster on the business, it is necessary to research the solution of active-active disaster recovery. In this paper, based on a thorough study of the principle of read-write separation and its technical advantages in solving the performance bottleneck of high-concurrency business systems, a read-write separation cluster based on redo is designed in the data layer of the active and standby systems, and at the same time, the information system The active layer, the application layer, the data layer, and the storage layer of the architecture are designed for active-active layer by layer and a set of remote active-active disaster recovery systems is constructed. Considering that Zhejiang Power’s OMS system is a highly concurrent business system and the standby system is a cold standby with full service capabilities, this design scheme is used to implement transformation and upgrade of Zhejiang Power’s OMS business system. Through comparative testing, the new system is capable of providing services after a disaster. Interrupted, data zero loss recovery capability, and system response speed increased by more than 50%. Keywords: Information system
Disaster recovery Read-write separation cluster
Active-active disaster recovery

1 Introduction With the successful construction of the SG186 project of the State Grid, the company has built an information support platform based on an integrated enterprise-class information integration platform, eight business applications, and six security systems [1–3]. The dependence on data and network applications for company and users is increasing. The greater the time, the more irresistible disasters (such as earthquakes, tsunami and other destructive events and human factors, etc.) have a huge impact on data and business of the entire information system, and even cause a devastating blow to the company. Therefore, it is an inevitable requirement for the State Grid Corporation to maintain the security and reliability of its information system business within a long-term sustainable operation to © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1053–1063, 2019. https://doi.org/10.1007/978-3-030-02804-6_136

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build a disaster-tolerant system [4–8]. In this paper, after analyzing and researching on some existing disaster recovery architecture and the characteristics of the intelligent dispatching management system of the Zhejiang Provincial Electric Power Company of the State Grid [9], an active-active disaster recovery system with read/write separation function was constructed through by integrating the REDO read/write separation clustering technology. Now, the disaster recovery system is successfully implemented in Zhejiang Power’s original intelligent dispatch management system (OMS system) and improves system performance [10, 11].

2 The Basic Principle of the Read and Write Separation Cluster Technology The basic principle of the read and write separation is that the database transactional operations and query operations access different database servers, usually containing a main database and multiple standby databases [12]. The main database is mainly responsible for providing write operations, and the standby database mainly is primarily responsible for providing read operations. The user’s query requests are distributed to multiple standby databases which is effectively improving the system’s ability to load balance. This method is scalability for read query operation, and reduces the pressure on the database and I/O devices. When the standby database is writing, the data must be copied to the main database synchronously, so as to effectively guarantee the consistency and integrity of the data [13, 14]. The OMS system is a high-concurrency transactional system. When the proportion of write transactions is relatively small compared to read transactions, the read-write split cluster technology of the Da Meng 7 database [15] can be used. 2.1

Implementation Principle

The basis for the realization of read-write separation clusters is real-time archiving which has the following features, as shown in Fig. 1: 1. The main database first writes the log into Redo log file of the local online and then sends it to the standby database. 2. There are two options to replay time for the standby database: the transactionconsistent mode and the high-performance mode. The transaction-consistent mode requires the standby database to respond to the main database after replaying the Redo log. The high-performance mode immediately responds to the main database after receiving the Redo log. 3. The synchronization mechanism for real-time archiving can ensure that the Redo logs of the standby database will not be more than the Redo logs of the main database. Therefore, the standby database directly starts the redo log to replay.

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Fig. 1. The process of the real-time archiving

4. When the standby database is faulty, or the network between the active and standby databases is faulty, the main database immediately modifies the real-time archiving to the Invalid state and switches to the Suspend state. 5. Then the state of real-time archiving is changed into Invalid, the user session that has a shadow session on the standby database is forcibly disconnected immediately. This prevents read-only operations from being distributed to the standby database, resulting in inconsistent query data. Therefore, the basic idea of implementing read-write split clustering is that all operations are sent to the standby database first to take advantage of the standby database to provide read-only service and cannot modify data characteristics when the host split ratio is zero. Once an error occurs during the standby database is executed, it is Send back to the main database and executed again. By “trial and error” operation of the standby database, the read-only operation is naturally offloaded to the standby database. Meanwhile, the “trial and error” operation of the standby database is automatically completed by the interface layer and is transparent to applications. When the host split ratio is greater than zero, it is automatically allocated according to the proportion of transactions performed by the database. The host directly performs the assigned transaction. The standby machine is continue by using the abovementioned “trial and error” mode. The connection process of the read and write separate cluster database as following (as shown in Fig. 2): 1. The user initiates a database connection request. 2. Interfaces (JDBC, DPI, etc.) are configured to log in to the main database based on the service name. 3. The main database picks an IP/Port that is valid for the standby database and returns it to the interface.

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4. The interface initiates a connection request to the standby database based on IP and Port information of the returned standby. 5. The standby database returns connection success information. 6. The interface responds to the user database connection created successfully. The connection created by the interface on the standby database is created automatically by the read/write split cluster and a database connection is created on the main database for the user, in the following, a read-write cluster configured with two standby databases is taken as an example to illustrate the connection creation flow between read and write separated clusters (as shown in Fig. 3).

Fig. 2. Read and write split connection creation

Fig. 3. Read and write separation cluster statement distribution process

The statement distribution process of Read-write split cluster as following: 1. The interface receives the user’s request. 2. The interface sends SQL to the standby database first. 3. The standby database return the execution result. If the interface receives an execution success message, go to step 6, or if the interface receives an execution failure message, go to step 4. 4. The failed SQL is Send back to the main database and executed again. As long as the SQL in step 3 fails to execute in the standby database, all subsequent operations (including read-only operations) for the same transaction are sent directly to the main database for executing. 5. The main database executes and returns the execution result to the interface. Once the write transaction executed on the main database is submitted, the execution will continue from step 1 next time. 6. The interface responds to the user and returns the execution result to the user.

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Applied to Active/Standby System to Improve Performance

From the read-write separation statement distribution process, it can be found that the read-only transactions are in most cases in an application system, there may be high load and high pressure in the standby database, and at the same time, the main database is rather idle. In order to achieve load balancing and make better use of the hardware resources of the active and standby databases, database interfaces such as JDBC provide configuration items that allow a certain percentage of read-only transactions to be distributed to the main database for execution. Therefore, users should flexibly adjust the distribution ratio of the interface based on the load of the active and standby databases to obtain the best database performance. The number of standby databases is an important factor that affects the performance of read-write and split clusters. The more standby databases, the fewer tasks each standby database must undertake, which helps improve the overall system concurrency efficiency. But at the same time to consider another factor, with the transactionconsistent mode, the main database do not respond to the user until all standby databases have replayed the Redo log. Although the Redo log replay of the standby database is performed in parallel, the Redo log is sent serially. With the increases of the standby database, the time of real-time archiving will become longer. Eventually it reduces the response speed of non-read-only transactions. Therefore, the number of deployed databases must also be comprehensively considered in terms of hardware resources, system performance, and other factors. Another effective way to promote read-write split clustering is to configure it into a high-performance mode. If the application system does not require high real-time for query results, and the operation of modifying data in a transaction does not depend on the query result in the same transaction, then, it can greatly improves the overall system performance to configure the read-write split cluster into high-performance mode. In addition, it can also achieve better performance based on the read-write separation feature and with a reasonable planning of the application’s transaction logic.

3 Design for Active-Active Data Center 3.1

Overall Architecture Design

In this paper, active-active architecture design for Zhejiang OMS based on the standby system hardware and software using above methods. As shown in Fig. 4, from the access layer, application layer, data layer to the storage layer, each layer is designed by using active-active architecture which ensures each layer mutually redundant. At last, The DMC 7 data read-write automatic allocation technology is adopted for real-time synchronization of master and standby data and intelligent allocation of system resources.

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Fig. 4. Overall architecture design of active-active system

3.2

Access Layer Design

The access layer adopts the global load balancing technology and server load balancing technology. Global load balancing makes users access to the master or standby OMS system using intelligent DNS and proximity. Then the user’s access is accessed through server load balancing through the actual OMS application server. 1. Global load balancing access technology A global load balancer is used at the access layer, and the user is assigned to the primary and secondary OMS systems using the intelligent DNS and the nearest access feature. In the process of domain name resolution, the provincial company’s DNS is used as the resolved root domain name. Two NS records are configured on the provincial company’s DNS, which related to the two global load balancers for the primary and standby OMS. The global load balancer uses the functions of the intelligent DNS to resolve domain names, and the parsed access is allocated to the main call OMS system or the standby OMS system according to the configured proximity strategy. 2. Server load balancing Server load balancing adopts the bypass mode. The client’s request packets pass through the load balancing device, and the packets returned to the client are returned directly through the switch. This reduces the burden of server load balancing and prevents server load balancing from becoming a bottleneck in the network. At the same time, the load sharing algorithm is used by the source address hashing. Write a script to access the database. If it can be accessed normally, the OMS application server is normal. Otherwise, the OMS application server is “deadly dead” or down, and the OMS application cluster should be removed in time.

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Application Layer Design

The application layer adopts intelligent data discovery technology. When the database is unavailable, the OMS application can automatically switch to another database. In order to achieve OMS business can automatically switch to another available database in case of the database downtime, it needs to add the intelligent service discovery module on the application server, create the IP address configuration file for the main and standby database, and modify the parameter value of the file to the format required by the Dream Database. So that even when the database accessed by the business system goes down, it can actively detect the available database, and automatically switch to the available database which is transparent to the application. 3.4

Data Layer Design

The main and standby databases adopt read-write separation to form a cluster. The main and standby databases perform real-time synchronization of data through redo logs. Read-write split cluster is a high-performance database cluster based on real-time archiving. It not only provides basic functions such as data protection and disaster recovery, but also has the features of automatic read and write operations separation, load balancing, etc. All operations are send to the standby database preferentially by using characteristics of a read-only service and unable to modify the data. Once standby database display the implementation error, then the main database to re-execute. 3.5

Storage Layer Design

No matter what physical storage device, the storage virtualization device can be used to virtualize the storage device. The server and its application system display are logical images of its physical devices. The use of storage virtualization for achieving storage activity solves two core issues: 1. It realizes real-time synchronization of data between two data centers, so as to guarantee zero data loss under abnormal conditions; 2. It provides storage shared volumes for simultaneous accessing by two data center hosts which implement cross-site deployment of host application clusters. This ensures that applications can switch automatically under the situation of an exception time.

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4 Switching and Recovery in Disaster Situation After the active-active design from the access layer, application layer, data layer, to the storage layer of the prime and standby systems, an active-active disaster recovery system with a read/write separation function has been built. It takes the successful implementation of intelligent dispatching management system (OMS system) of the State Grid Zhejiang Electric Power Company’s as an example. Its main system is located in Hangzhou and is called provincial deployment. The standby system is located in Huzhou and is called standby database. Under normal circumstances, User access requests are connected to the provincial deployment and standby by using the domain name and proximity strategy which realizes the activeactive of the active/standby system. Therein, the prime database is responsible for writing, 35% for reading when 65% reading for standby database, and second level replication of prime and standby databases, as shown in the following Fig. 5.

Fig. 5. Normal oms dual-active system

Fig. 6. Prime database downtime

If a disaster occurs in the primary database, the standby database will take over the database service within 10 s to undertake the read and write tasks. In addition to the database, all other hardware and software resources saved by the province will continue to provide services and improve resource utilization. In this case, the user is almost unaware. As shown in Fig. 6. Similarly, when the OMS server is unavailable, the roles of the prime and standby databases do not change. When the global load balancing performs domain name resolution, it will issue the expiration time of the domain name. After the expiration time of the domain name, the user will return to the standby server and load balancing address when accessing the domain name. The standby server provides services. The entire active-active system continues to provide services for users.

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5 Implementation Effect After the detailed design of the access layer, application layer, data layer, and storage layer, the original OMS prime-standby system of Zhejiang Power was upgraded to an active-active system with read-write separation function. The new live-active system has improved on all levels than the original system: 1. In the access layer, the original system has a single export route, exists a single point of failure, and the same link is used for data and applications. The new system is fully routed, eliminating single point of failure and physically isolating the data line from the application. 2. In the application layer, the original data stream is single and the failure recovery is slow, and there is a phenomenon of staging. But the new system uses load balancing to ensure security and reliability, and faults are switched at the second level. 3. In the data layer, the original system is Da Meng 6, and the standby database is the cold standby database resulting in that the main and standby data is not synchronized. In the new system, it is upgraded to Da Meng 7 database, and realizes the read-write separation, and the data implementation synchronization which ensures the data more Safe and reliable. 4. In storage layer, the original system is centralized storage which performance is low and faults cause system-wide defects. But the new system uses distributed storage clusters and virtualization technology to eliminate system paralysis. Caused by a single point failure of physical storage. 5. In terms of user operation experience, the new active-active system has achieved a tremendous improvement over the original system. In this regard, we clicked and tested the main pages of the various functional modules of the OMS system to measure the system response time, as shown in the following Table 1. Table 1. Comparison of page response times of the active-active and the original system Serial number

Functional

Page/status

Response speed of system(s) Original ActiveActive

1

Plan

Login system to the main console is refreshed The power outage planning management/application form finished The power outage planning management/View the flow chart The power outage planning management/show log The power outage planning management/New repair sheet finished Follow-up personnel selection

2.99

2.07

44%

2.7

2.27

19%

1.28

0.83

54%

1.9

0.57

200%

1.94

1.64

18%

2.1

1.22

72%

User: P75293030

Percentage of promotion

(continued)

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Serial number

Functional

Page/status

Response speed of system(s) Original ActiveActive

2

Regulationdispatching User:

Login system to the main console is refreshed The user opens the scheduling page until the refresh is completed Select “Scheduling Staff “ for a total of 246 orders Login system to the main console is refreshed From the fixed value list Enter the task

3.30

2.52

31%

5.59

2.31

104%

1.23

0.86

43%

3.03

1.75

73%

1.18

0.68

74%

Login system to the main console is refreshed Task page loading completed

3.69

1.72

114%

0.95

0.78

21%

Login system to the main console is refreshed

3.25

1.67

95%

P75293027 3

System

4

User: P75294014 Relay

5

User: P75295016 Automation

Percentage of promotion

From the data in the Table 1, it can be seen that the new active-active system has a significantly shorter response time on the page, and some operation page times have been reduced by a factor of two. From the feedback of user experience, users indicated that the new system operates more smoothly than the old system without any cotton phenomenon. More importantly, it is almost 7 * 24 h available. There is no longer any situation where the old system could not be used because of a single failure.

6 Conclusion A set of highly active and disaster-free active-active disaster recovery systems has been built based on the read-write separation cluster technology of Da Meng 7 database through researching the disaster recovery technologies of information systems in each province of the State Grid and active-active designing from the access layer, application layer, data layer, to the storage layer of the information system. The system successfully transformed the Zhejiang OMS primary cold disaster recovery system into an active-active system, which improved the resource utilization of hardware and software and performance of system. The new system has the capability of not interrupting services and recovering data in seconds at the time of disaster, and effectively improves the system response speed. The successful implement of the disaster recovery has certain guiding significance for each business system of other provincial companies.

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Acknowledgements. This research was supported by the Science and technology projects of state grid corporation of China (No. 500409081).

References 1. Xin, L., Kaijin, Z.: Research on data replication mode of data backup center of State Grid Corporation of China. Silicon Val. 14, 94–95 (2012) 2. Xiang, W., Hong, O., Lijuan, D., et al.: Research and application of marketing business application system of State Grid Corporation of China. Electr. Power Inf. 9(2), 49–54 (2011) 3. Cheng, Z., Li, H., Su, Z., et al.: Design and research of disaster recovery center of centralized information system. Electr. Power Inf. 9(2), 77–80 (2012) 4. Li, C., Sun, Y., Chen, X., et al.: A preliminary analysis of the “11.4” blackout accident in Western Europe and the measures to prevent large-scale blackouts in China. Grid Technol. 30(24), 16–21 (2006) 5. Zhang, X., Jiang, X., Zhou, X., et al.: Application of disaster recovery system in electric power enterprises. Guangdong Electr. Power 19(2), 67–69 (2006) 6. Yonghua, H., Yutao, C.: Yunnan Power Grid Corporation Disaster Recovery System Research and Construction. Electr. Power Inf. 8(5), 29–31 (2010) 7. Bin, Z., Yutian, J., Yonglian, Z.: An overview of Shanghai Disaster recovery center construction of State Grid Corporation of China. Supply Use 28(2), 10–12 (2011) 8. Feng, Y., Huafeng, Z.: Research and application of disaster recovery technology for Power Grid Enterprise Information System. Power Inf. 10(1), 76–79 (2012) 9. Xiao-qiang, L.U.: Dual live data center network architecture. Financ. Technol. Time 7, 63– 65 (2013) 10. Ji-bo, M.A.: Analysis on the transformation of data center to “doublelive” cloud data center. Shandong Soc. Sci. S1, 244–245 (2013) 11. Chenyang, S., Hongyu, S.: Future-oriented city network architecture of the twin city. Financ. Comput. 11, 52–54 (2013) 12. Xie, P., Zeng, M., Hang, C., et al.: Discussion on the application of enterprise-level application of double-living in the same city of Guangxi Electric Power Grid. Guangxi Electr. Power 41(1), 51–55 (2018) 13. Jia, B., Zhang, J., Yang, F., et al.: Research and design of electric marketing business application system in the same city. Electr. Power Inf. Commun. Technol. 15(2), 66–70 (2017) 14. Congqiang, D., Bingtang, S.: Application of DB2 database read/write detachment technology in bank core system. Inf. Technol. Inf. 12, 51–53 (2017) 15. Min, W.: SQLAlchemy’s dream database dialect design and implementation. Comput. Netw. 4, 45–50 (2015)

Analysis on the Governance Path of Financial Fraud of Listed Companies from the Perspective of New Media Yueming Cheng1(&), Mengge Gu1, Yi Cheng2, Xiaoping Hu3, and Kang Cheng4 1 School of Economics and Management, Jiangxi Science and Technology Normal University, Nanchang 330013, China [email protected] 2 School of Economics and Management, East China University of Technology, Nanchang 330013, China 3 Affiliated Middle School, Jiangxi University of Technology, Nanchang 330000, China 4 School of Management, Jiangxi University of Technology, Nanchang 330000, China

Abstract. The issue of financial fraud of listed companies has been frequently exposed in the media, which has caused wide public concern. Financial fraud of listed companies will not only greatly harm the interests of investors, but also reduce the investment enthusiasm of shareholders, and will hinder the standardization of securities industry. Although the operation and development of securities industry is gradually normalized and institutionalized, and relevant laws and regulations are also gradually improved, there are still many listed companies with a sense of luck and knowledge of law violations. In order to prevent this phenomenon continue, social networks, media has become the important means of external management mechanism, this article analyze the financial fraud of listed company under the new media sphere of governance path. Keywords: Listed company financial fraud New media
Media supervision


Corporate management

1 Introduction It is known to all that financial fraud of listed companies not only greatly harms the interests of investors, but also reduces the investment enthusiasm of shareholders. On the macro level, it will hinder the healthy development of securities industry and pollute the social atmosphere. However, in recent years, there have been a lot of media reports on financial fraud of listed companies. Although the operation of securities market is gradually normalized and institutionalized, and relevant laws and regulations are also increasingly perfect, it is still the crux of the problem that blocks the normal development of the capital market, and it is imperative to eradicate it. Studies on the financial fraud of listed companies in the past generally focus on the general discussion of board structure, administrative supervision and civil litigation. In today’s new media © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1064–1071, 2019. https://doi.org/10.1007/978-3-030-02804-6_137

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era, the media’s supervision of the capital market has attracted attention. Results show that most of these problems are reported and to attract the attention of stakeholders, and investigating and processing by relevant departments, such as Sanlu milk powder incident, and the south stake in financial fraud case. The author thinks that it is of innovative significance to explore the management path of financial fraud of listed companies from the perspective of new media. As the main body of capital market economy, listed companies are directly related to the operation and further development of securities market. And Frequent occurrence of financial fraud of listed companies severely damaged the normal order of the capital market, has now become a social problem that must be eradicated. Early research is out of the rapid development of the age, in today’s new media era, media supervision has already become another tool to promote capital market normal operation, but the related research are few and far. The author consulted relevant literature and materials, collected some cases of financial fraud of listed companies and classified them, and believed that media plays a significant role in the management of financial fraud of listed companies. Under this background, the research in new media era on financial fraud of listed company management path should how to keep pace with the age, will help to safeguard the investors interests, maintain a sustained and healthy development of the securities market.

2 Overview of Financial Fraud of Listed Companies from the Perspective of New Media In short, financial fraud is the violation of accounting standards by business leaders or financial accounting personnel in the course of general accounting. According to incomplete statistics, in 2017, due to financial fraud through media exposure and received the SRC (Securities Regulatory Commission) on the administrative penalty of A total of 11 a-share listed companies, the problem of counterfeiting is becoming more and more serious. Throughout the major cases, the author summarizes the basic characteristics of financial fraud of listed companies from the following aspects. 2.1

Typical Manifestations of Financial Fraud of Listed Companies from the Perspective of New Media

2.1.1 Fabricating Transaction Operation The purchase and sale business of the enterprise is fabricated by financial workers, which is a fictitious transaction, also called a sham operation. Enterprises manipulate profits by falsely operation, falsely increasing revenues and profits. Some listed companies make use of the falsification of sales objects, business books, false invoices and warehouse receipts to improve their business performance in order to reach the listing conditions or obtain the qualification of rights issue. As a common way of financial fraud of listed companies, fictitious transaction will lead to inaccurate reflection of the financial situation and problems of enterprises, and it is easy for enterprises to indulge in the habit of fraud. The fictitious transaction business will face a loss of fame and fortune, abandoned by consumers and investors.

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2.1.2 Participate in Related Transactions The definition of related transactions refers to whether there is a connection between the parties that transfer resources or obligations and generate actual collection of price. The existing laws of our country do not have the clauses to prohibit related transactions, and there are also some cases of related transactions that do not violate the market rules of our country. In the actual operation process, some listed companies without self-discipline consciousness often realize the control of profits through affiliated party transactions, or become a means for major shareholders to occupy the company’s funds, which leads to data fraud and false information. In addition, nonstandard information disclosure, inappropriate use of accounting policies and shell companies will also affect the authenticity of listed companies’ finance and lead to financial fraud. The general approach of the listed companies participate in related transactions is summarized as follows. Firstly, listed companies use related parties including the parent company and subsidiaries to realize purchase and sale of assets, entrusted management, cost sharing, etc. Secondly, asset restructuring or debt restructuring of listed companies often take the parent company and its subordinate companies as the main objects to transfer good assets to listed companies, which can greatly improve the operating performance of listed companies in a short period of time. Once again, when the parent corporations are reassembled internally, the companies alter the profit by using the accounting individual changes to increase the surplus or reduce the loss of subsidiary companies when consolidating accounting statements. Finally, the companies de-correlate the related transactions by using the company that does not disclose the related relations, to control the purchase or sales transactions, and deliver the benefits in the dark. 2.1.3 Conduct Accounting Without Strict Compliance with Relevant Laws and Regulations In addition to the two types of typical listed company counterfeiting, the remaining listed companies are exposed to accounting phenomena that are not strictly followed by legal regulations. For example, not accurately accounting equity investment, not in accordance with the rules the depreciation and impairment, not according to the matching principle affirm income and cost, virtual assets hang up, etc. 2.2

The Main Means of Financial Fraud of Listed Companies from the Perspective of New Media

2.2.1 Fabricating Cash Hidding security, hidding non-operating financial transactions, hidding banker’s acceptance bill and bank loan are their commonly methods. To be specific, understating liability account or concealing illegal possession of capital or debt by hedging of assets and liabilities to hide limited use of funds, these practices are particularly detrimental to the quality of listed companies.

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2.2.2 Not Reveal Important Matters in Time The information disclosure system, which is that the public corporation has to disclose its own business and financial changes to the relevant securities departments and the public, to accept the accounting system of social supervision and ensure that investors fully understand the situation. In the existing cases of financial fraud of listed companies, the disclosure of major matters is not timely and opaque, which accounts for a large proportion. Among them, major project cooperation agreement has not been disclosed in accordance with regulations, a significant claim and debt agreement, an asset subject have not been disclosed in accordance with regulation and have not been entered into accounts timely are frequently occurring. The above situation will lead to omission and false disclosure of information, which is a common means of fraud of listed companies. 2.2.3 Hidding of Related Transactions It is also a common means of financial fraud to disassociate related party transactions and to conceal related relations. Some fraudulent listed companies use undisclosed relationships with affiliates to control transactions to achieve expected financial results. As it involves the business independence of the issuer, transaction auditing requires that the affiliated transactions of enterprises should be reduced gradually and there should be no significant related transactions that affect the business independence during the reporting period. Therefore, there are also some enterprises that transfer and cancel related party companies in order to meet the audit requirements. 2.2.4 Retaining Profits in Liabilities Some listed companies use retained profits in liabilities to keep their future profits stable. Specific practice is that listed companies in advance overcharge account payable, other accounts payable or employee pay payable and other liabilities, identified as the cost of the current period so as to reduce profits, no longer accrue when the actual payment or settlement happen in the future, so as to reduce the cost of the company and release profits during the actual payment period. This is called profit reserve, which can increase the stability of the company’s profits.

3 Analysis of Reasons for Financial Fraud of Listed Companies 3.1

Analysis of Internal Reasons

The financial fraud of China’s listed companies is mainly caused by performance pressure, private interests, vanity driving, failure of internal governance and moral integrity, which can be classified into two aspects. 3.1.1 Lack of Self-discipline Awareness In the face of a poorly regulated capital market, the self-discipline of the enterprises is particularly importance, while the lack of self-discipline makes the financial counterfeiting of a public corporation overrun. In the fierce market competition environment,

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listed companies are hungry for profits to ensure stock returns. To achieve self-purpose, the Stockholders of public corporations use the hype and various ways to stimulate the market, in order to boost the confidence of the shareholders and reward them. 3.1.2 Failure of Internal Governance Structure The key figure of financial fraud of listed companies is the financial practitioner. At present, due to the low threshold of accounting work, the professional skills and professional ethics of financial staff are generally low, and high-end talents are insufficient, so there is a gap for financial fraud to be multiplied. There are even some financial personnel who have been colluded with the public’s management for their own personal gain, to fabricate financial information, damaging the interests of investors. 3.2

Analysis of External Reasons

In addition to the above internal reasons, the deeper reasons are the institutional factors. If the capital market system, the management mechanism of the certified public accountant industry, the accounting standards, the accounting laws and regulations, and the supervision and law enforcement system all have imperfect problems, those are the main external reasons for the continuous financial fraud of listed companies. 3.2.1 Possibly Imperfect Capital Market System At present, no matter in the system or structure, there are many defects in capital market. The system in the capital markets can’t restrain the management of the public corporation, which has caused our capital market to be the land of the “money encirclement”. In addition, the imperfect investor protection mechanism is an important reason for the current regulatory penalty dilemma. Many industry insiders call on to speed up the construction of the investor’s claim mechanism in the capital market and safeguard the interests of investors. 3.2.2 Possible Shortcoming in Accounting Standards and Accounting Regulations Accounting standards are the tools to guide enterprises in choosing accounting policies, measuring methods and preparing financial statements. Listed companies have a lot of autonomy in the application of certain standards, while current accounting standards and accounting regulations have more flexible choices, which is an important prerequisite for listed companies to implement financial fraud.

4 The Governance Path of Financial Fraud of Listed Companies from the Perspective of New Media As is known to all, Government has continuously strengthened the system management, improved the government supervision mechanism, and deeply cracked down on their financial violations. Nevertheless, financial fraud cannot be eradicated. The current research on the financial counterfeiting of public corporations, mostly in the

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perspective of a few years ago, even though many scholars begin to pay attention to the media industry supervision and utility for the financial fraud, but financial fraud governance research in the new media age is rarely heard. As to governess of listed companies, the continuous development of the Internet is not only a change of the traditional governance path, but also an innovation. 4.1

Governance Measures for Financial Fraud of Listed Companies from the Perspective of New Media

The financial fraud of listed companies is the crux of preventing the normal development of capital market. The utility of media supervision to the management of financial fraud can be discussed from the following aspects. 4.1.1 Information Dissemination: Reduce the Information Asymmetry Barrier of Stakeholders Due to the influence of 5W factor in the process of information transmission, information asymmetry is widespread in the capital market, making it difficult for investors to obtain information, let alone judge the authenticity of information and make correct investment decisions. Media can use their own advantages to complete news writing and disseminate company information, and weakening the information advantage of insiders. the research results show that if a company has financial fraud problems, the media can timely transmit information to the public, reducing information asymmetry and effectively monitor financial fraud. By virtue of its professional and extensive advantages, financial media can quickly and accurately convey all kinds of information, effectively reducing the cost for investors to obtain information, to enable stakeholders to fully understand the situation of listed companies, and promote investors to make correct decisions. 4.1.2 Agenda Setting: To Urge the Government to Revise Regulations and Supervise Law Enforcement It is well known that financial medium, with its good professionalism and insight, reveals various illegal phenomena such as financial counterfeit and encroachment on the investor’s interest, by means of field investigation and secret investigation, which have attracted wide public attention, and can effectively urge relevant government departments to take decisive action, so as to achieve the effect of governance and purify the operating environment. Because of the agenda setting function of the media, continuous reports or series reports on a certain event during a period of time can make the public have a great cognitive effect on the importance of such events and guide the public opinion to produce. For the government, public comments and evaluations can affect the society. Therefore, in order to avoid the bad influence caused by the enterprise violation behavior, the government will actively take various measures, such as the disposal of event related enterprises, perfecting the revised laws and regulations. Media coverage of events is usually the end of the story, and subsequent enforcement of the government can be monitored at the end of the event.

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4.1.3 Guide Public Opinion: Force Enterprises to Maintain Public Image and Reputation The continuous improvement of network technology make the age of globalization and informationization, every media coverage of great value can throughout every corner of the country and even the world in a short period of time. The department of public relations has been an integral part of the business and management of the enterprise, and it is due to the various aspects of the public opinion that has been produced by the media coverage about the industry, like the rising of the stock price, the sale of products, and they are responsible for the dissemination of good information, and minimizing the impact of the negative information on the business. In order to ensure the normal operation of the company, enterprises must maintain the public image and social reputation of themselves and senior managers, and create a positive corporate image. Listed companies usually be known by shareholders as well as other social public, they will all become very concerned and press by public opinion, which will be more effective in medium oversight. 4.2

Innovation in the Governance of Financial Fraud of Listed Companies from the Perspective of New Media

In addition to the transformation of the above traditional governance path, the innovation path brought by new media is also really desirable. The author summarizes the following two points through analysis. 4.2.1 Increase the Number of Disseminators and Supervise the Utility Upgrading The change of network media, mobile media and other new media has enabled the audience to rise from the right of communication to power. In the past, media was more of a one-way communication for the audience, and the audience had few channels to disseminate information. New media enables everyone to send messages and get attention. Many popular events of recent years are the first to spread information at the grassroots level, then to get the attention of media and opinion leaders, and then to further spread the information comprehensively. Dealing with the financial fraud problem of listed companies, the expanding main body of communication makes the external supervision more strict and meticulous, any problems of the enterprises may be discovered, and the effectiveness of supervision will be correspondingly upgraded. 4.2.2 Increasing News Sources to Prevent Financial Fraud In addition to the change in the level of information transmission caused by the expansion of the communication subject, the information sources, namely news clues, have also increased. In the era of traditional media, newspapers, radio and television are divided into three groups, and their news clues usually come from mass hotlines, reporters’ discovery or instructions from superiors. The channels are indeed limited. Nowadays, the Internet not only brings new media, but also enriches the information sources of media. Looking back on the financial cases of listed companies, many of them originate from journalists’ tracking of news clues on the Internet, which can regulate the behaviors of enterprises and also make potential financial cases hard to escape.

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5 Conclusion To sum up, media supervision indeed has a strong external governance effect, and this is multifaceted. In the era of new media, the means of financial fraud of listed companies are constantly changing, and the governance path of our country must keep pace with the age, promoting the old and bringing forth the new, and strictly to control the financial problems of enterprises. Only through the full cooperation of media supervision and government supervision the external supervision mechanism be improved, so as to maintain the healthy development of capital market and the normal operation of the securities market.

References 1. Yi, C., Yueming, C., Ting, C.: Research on the governance of financial fraud of listed companies—based on the case analysis of Yunnan green land. J. JiangXi Norm. Univ. Sci. Technol. 3, 64–69 (2017) 2. Kang, W.: Analysis and management of financial fraud of listed companies. Acc. Acc. Stud. 11, 59 (2017) 3. Pin, W.: Analysis on the effectiveness of media reports in supervising financial fraud of enterprises. Jiang Xi Univ. Finance Econ. (2017) 4. Anqi, L.: Case Study on Financial Fraud of ShenXianYuan Listed Company on the New Third Board. Shenyang University of Technology, Shenyang (2017) 5. Ruoqiong, Z.: Analysis on the governance of financial fraud of China’s listed companies. Times Finance 24, 167–168 (2016) 6. Liang, D.: Causes, means and influences of financial fraud of listed companies—taking the case of financial fraud of China southern textile corporation as an example. New Acc. 05, 26–29 (2016) 7. Li, M.: Research on the Supervisory Role of Media Attention on Financial Fraud of Listed Companies. Overseas Chinese university, Taichung (2015) 8. Hao, Z.: Case Study on Financial Fraud of Wanfu Shengke. LiaoNing University, Shenyang (2015) 9. Yuejun, H.: Media Supervision, Financial Fraud and Corporate Governance. ZheJiang University of Technology, Quzhou (2015) 10. Zhenzhen, C.: Research on Financial Fraud of China’s Listed Companies. XiaMen University, Xiamen (2013) 11. Deming, Y., Jing, L., Can, Z.: Media supervision and financial scandal—case study on ZiXin pharmaceutical co LTD. Manag. Res. 7(04), 36–56 (2012) 12. Yang, Yu.: Manifestations and governance measures of financial fraud of listed companies. Education 16, 111–112 (2010)

Research on the Application of Financial Sharing Service Center Information System Lei Xia(&) School of Management, Wuhan University of Science and Technology, Wuhan, China [email protected]

Abstract. Big data, cloud computing and mobile Internet technology have promoted the development of financial informatization, and traditional means of financial computerization cannot meet the needs of financial modernization of enterprises. Financial shared mode is an effective means to help enterprises in transition. This paper analyzes the application status of Chinese enterprise financial sharing service, analyzes the application of the information system of the Financial Sharing Service Center (FSSC), constructs the structure of the financial information system, points out the shortcomings of the construction of the Chinese Financial Sharing Center information system, and puts forward some suggestions for the development of the future Financial Sharing Service Center information system. Keywords: Financial Sharing Service Center (FSSC) Financial informatization


Information system

1 Introduction Since twenty-first Century, the application of information technology, such as cloud computing, big data and mobile internet technology, has promoted the development of financial informatization. On the one hand, the financial process is more real-time and efficient, on the other hand, the work of financial personnel is more inclined to the transformation of management decision-making. First of all, the bank and enterprise interconnection connects the financial system, ERP system and bank account; Secondly, Tax reform, such as tax declaration, online certification invoice, electronic invoice, electronic payment tax, and other tax reforms have promoted the process of financial informatization; Finally, the accounting archives can be archived in electronic form. These changes have promoted the transformation of computerized accounting to financial informatization, and financial shared mode is an effective means to help financial transformation [1]. Andersen (1997) [2] believes that the financial sharing service is the product of the combination of advanced financial management concept and advanced information technology. The normal operation of the Financial Sharing Service Center (FSSC) is supported by the strong information system in the background. Chen (2014) [3] believe that the influence of information technology on the value of financial sharing service is mainly embodied in two aspects: Firstly, information technology has set up a broad platform for financial sharing services. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1072–1079, 2019. https://doi.org/10.1007/978-3-030-02804-6_138

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Based on this platform, the application of advanced technology such as cloud storage and cloud computing can greatly improve the level of financial information. Secondly, information technology can make some internal control, reduce the interference of human factors, and can effectively improve the level of risk management of the FSSC.

2 Current Situation of Financial Sharing Service Center in China In early 1980s, Ford established the first FSSC. By 2003, more than 50% of the global Fortune 500 enterprises had established FSSC, such as GSB (Global Services Business) of DuPont and FSO (Financial Services Operation) of GE (General Electric). By 2013, the proportion of global Fortune 500 companies applying for FSSC was more than 70%, and the proportion of preparing for the implementation of the FSSC was 50%. The construction of the FSSC can improve the efficiency of financial operation, improve the customer satisfaction, reduce the financial cost, optimize the financial process, monitor the financial status of the molecular company, and finally realize the expansion of the enterprise. The application of Chinese financial sharing mode is relatively late, and as for the mode of financial shared services, both theoretical and practical sessions have been actively explored. In 1999, Motorola set up a global accounting service center in Tianjin and this became the first FSSC in China. In 2005, ZTE began to establish a FSSC. Then, China Mobile, China Telecom, China Ping An, Suning, Vanke and other enterprises have established FSSC. According to the global survey of ACCA in 2012, among the 249 questionnaires for Chinese enterprises, nearly half of the Chinese enterprises are carrying out financial sharing services. The enterprise expects to improve the overall financial management level of the enterprise through the transformation of the financial sharing service. Enterprises with annual sales exceeding 3 billion US dollars have adopted over 70% of financial shared services. It shows that the larger the scale of enterprises is, the more willing they are to adopt the mode of financial shared services [3] (Fig. 1).

Fig. 1. The current situation of FSSC in China

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The global survey of ACCA in 2012 shows that the high value process usually adopts the non shared management mode. Such as: financial planning and analysis, forecasting and budgeting, management accounting report, tax analysis, tax declaration, capital operation, internal audit, corporate governance, etc. The transactional business process is more easily incorporated into the FSSC, for example, accounts receivable, accounts payable, total accounts, employee compensation, expense reimbursement, fixed assets and capital management, and the proportion of accounts payable to FSSC is more than 70% [3] (Table 1). Table 1. Workflow of FSSC Num 1

Type Business processes that are often incorporated into financial sharing services (transactional business process)

2 3 4 5 6 7 8 9 10 11 12 13 14

Business processes that are not often incorporated into financial sharing services (high value process)

Flow Accounts receivable/accounts payable Expense reimbursement General ledger Employee salary Fixed assets Fund management Financial planning and analysis Management accounting report Tax analysis Tax return Capital operation Internal auditing Corporate governance Budget and forecast

3 Framework of Financial Sharing Service Information System According to the global survey of ACCA in 2012, 69% of the respondents think that the process and technology can be well matched to be an important indicator of the results of the FSSC. Therefore, the matching of information technology is the most important guarantee for the successful implementation of the financial sharing service. Y. He, F. Zhou (2013) empirically studied the key factors of the success of Chinese FSSC [6]. The order of impact is strategic planning, information system, process management, organizational structure design, performance management and personnel management. Information system is only after strategic planning, and its importance can be seen. The rapid development of information technology symbolizes the arrival of the third wave of

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industrial revolution. The combination of information technology and financial reform makes the financial sharing service present an beyond imagination. Information system plays an important role in the construction of the FSSC. The information system transforms the preceding business data into financial information, supports the financial operation process and business process, and improves the decision-making ability of management [4]. Chen Hu (2017) divides the financial information system into four levels: business level, accounting level, management level and decision level [5]. The information system fully supports the efficient operation of the three major cycles, namely, the business cycle, the financial cycle and the management cycle. Enterprise finance will become a big data center, and finance needs to cooperate with all dimensions of sales, research, production and human resources. Therefore, based on large data technology, finance will build large data center of enterprise, deal with enterprise financial data, non financial data, structured data and unstructured data, carry out deep mining and management, realize the real-time presentation of financial data, and provide decision-making basis for enterprises. Financial information will connect business layer (CRA/ERP/SCM/PLM/HRM), accounting layer (accounting system), management layer (budget management system, cost management system, performance management system, risk control system), decision layer (business decision support system) system. and the information system is connected with the three party platforms such as banks, suppliers, customers, tax departments and so on, and gradually realizes the integration of business and finance (Fig. 2).

Fig. 2. Information system framework of FSSC

A survey by Accenture shows that the most widely used information technologies in financial sharing services include workflow technology, document image, data analysis, data warehouse, electronic reimbursement, and so on. Generally speaking, this unified information platform includes four main modules: image scanning, financial accounting, network reimbursement, bank and enterprise interconnection. It has laid a

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solid foundation of information technology for the implementation of financial shared services. According to the survey of He and Zhou in 2013, the general use of information technology in the implementation of shared services is ERP system, employee self-help reimbursement system, data analysis system, electronic bill system and electronic image system, etc. A large number of information systems include electronic reimbursement system, electronic payment system, data warehouse and so on. The adoption rate of the customer relationship management system is only 16.36%, because most enterprises are in the basic pattern stage, and the service objects are mainly from the internal group, so the demand for customer relationship management is small. When the financial sharing service goes to the market model, the customer relationship management will become an important business activity of the FSSC, and the adoption rate of the customer relationship management system will be greatly improved. In general, 11 international information technology widely used in financial shared services, only 6 information technology has been widely applied in Chinese business groups. This indicates that the application of information technology in the process of implementing financial shared services in Chinese enterprises group is not mature enough, and it needs to be promoted in the future development process (Fig. 3).

Fig. 3. The application of the information system of the FSSC

4 Problems in Application of Information System in Financial Shared Service Center According to the survey on the enterprise application of financial sharing services by He and Zhou in 2011, the survey on the attitude of internal control and risk management shows that the risk of IT system is the most concerned risk of the enterprise, of which 98.18% of the enterprises attach great importance to it, while 1.82% of the enterprises are more serious, and no enterprises remain neutral and not pay attention to them [6]. The data indicate that the risk of IT system is the main concern of enterprises.

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Enterprises applying financial shared service mode should pay attention to IT risk and take precautions against risks. The main problems of Applying Financial Shared Information System in Chinese enterprises are: i. The Efficiency of the Information System is Insufficient Taking Suning as an example, Suning has applied the information systems of enterprise resource planning system, network reimbursement system, bill image system, fund management system, archives management system, asset management system and so on. It adopts the management mode of full value chain to promote the unified management of financial information. However, there are still some problems, such as too complex operation interface, incomplete form data, bad information docking, and so on, which affect the ability and efficiency of the system data processing [7]. ii. The Operation Process is Separated from the Actual Business The process design is designed by the personnel of the FSSC. Because the personnel of the sharing center are lack of understanding of the actual business, and the operators of the information system do not understand the process, the flow of data is not smooth. Separation of process and business leads to low financial efficiency and low customer satisfaction. iii. The Flow of Personnel is Large, and the Stability of the Department is Low The work in FSSC is simple, repetitive and boring, resulting in a high turnover rate. This interruption will cause data errors, and the mobility of personnel will also increase the workload of enterprise training.

5 The Future Direction of the Development of Financial Information System The application of information system will make the financial work develop in the direction of intelligence, paperless, automation and cloud. The development direction of the future financial sharing enterprise information system is as follows: i. The Traditional Standardized Financial Work will be Replaced by Intelligent Information System The connection of the financial information system and the business and the automatic collection of the whole process data will greatly improve the efficiency of the work, and realize the automatic collection of bills, the automatic generation of vouchers, the automatic accounts receipts, and the intelligent tax declaration. Business data automatically generates vouchers, registers accounts, and compiles reports to achieve intelligent financial analysis. Traditional process finance operations will be replaced by computers.

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ii. The Adoption of Information Technology will Promote the Process of Electronic Archives and Achieve Paperless Office Work The information systems are connected to each other to achieve the connection between the interconnected system of silver and enterprise, the contract management system and the financial system, the electronic data transfer, and the slowly disappearing of paper documents. At the same time, the application of electronic invoices has also promoted the process of paperless office. iii. The Financial Information System is Applied to the Mobile Intelligent Terminal to Realize The Mobile Office Through the mobile intelligent terminal, the functions of financial reimbursement, business examination and approval, financial audit and report inquiry are realized. The financial work breaks through the limits of the office space and makes the financial work and management accessible. iv. The Financial System is Converted to Cloud Services In the future, small and medium-sized enterprises do not need to build their own financial information system. Instead, they can pay on demand, through the cloud services provided by the third party, to meet the needs of enterprises and improve their financial efficiency [8]. The global economic situation is changing with each passing day. The traditional accounting model can not meet the needs of the enterprise. The managers are reconstructing the financial process through continuous financial process, and gradually realize the transformation of financial centralization, financial cooperation and financial sharing to the “financial cloud service”.

6 Conclusion This paper analyzes the situation of ACCA in 2012 and He and Zhou’s enterprise survey in 2011, analyzes the application status of FSSC in China, and analyzes the application status of the information system of the FSSC of Chinese group enterprises. Chinese financial sharing services have developed rapidly in large enterprises, but the application of information technology in the process of implementing the financial sharing service is not mature enough for the Chinese enterprise groups to improve in the future development process. The main problems in the application of financial sharing information system in Chinese enterprises are insufficient efficiency of information system, separation of operation process from actual business, large flow of personnel and low stability of department, which need to be improved. The application of future information system will make financial work develop towards intelligent, paperless, automatic and cloud oriented.

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References 1. Bergeron, B.: Essentials of Shared Services, pp. 1–20. Renming University of China Press, Beijing (2014) 2. Andersen, A.: Insights on European shared services operations. Am. Econ. Rev. 2, 253–256 (1997) 3. Chen, H., Chen, D.S.: Case Set of Financial Sharing Services, pp. 195–214. China Financial and Economic Publishing House, Beijing (2014) 4. Li, W.Y. et al.: Study on services quality of financial shared service center. Account. Res. 59– 65 (2017) 5. Chen, H.: Finance is IT-Enterprise Financial Information System, pp. 46–55. China Financial and Economic Publishing House, Beijing (2017) 6. Liu, Y., He, F.: An empirical study on the key factors in the implementation of financial shared services for Chinese Enterprise Group. Account. Res. 59–66 (2013) 7. Wang, Z.H., Wang, Z.K.: Under the Financial Sharing Service Model of Management Accounting Information Implementation Strategy Research-Based on Suning Cloud Business Group Case, pp. 34–38. Chinese Certified Public Accountants, Beijing (2018) 8. Gill, R.: Why cloud computing matters to finance. Strateg. Finance 1, 43–48 (2011)

Research on the Teaching Effects of Flipped Class Model Based on SPOC Binghui Wu1(&) and Tingting Duan2 1

2

International Business School, Shaanxi Normal University, Xi’an City, China [email protected] School of Marxism, Northwestern Polytechnical University, Xi’an City, China

Abstract. With the widespread popularity of MOOC, SPOC generally becomes a hot spot of educational reform. This paper introduces the development processes of SPOC at first, then analyzes the teaching process of SPOC according to some famous SPOC experiments, next designs a flipped class model based on SPOC, finally discusses the teaching effects of flipped class model at a western university in China. The results show (1) students are easier to ignore the self-previewing before class and self-reviewing after class at the beginning of term; (2) students pay greater attentions to the self-previewing before class and self-reviewing after class at the end of the term; (3) students like watching videos more than reading literature; (4) students’ grades are obviously improved in flipped class model. Keywords: SPOC


Learning effect
Blended learning
Flipped class model

1 Introduction With the rapid development of information technology, traditional teaching way is facing with unprecedented challenges. Especially after the emergence of MOOC, many universities have begun to recognize the importance of e-learning. MOOC is short for Massive Open Online Courses, which is an e-learning platform and rises in the United States since 2012. About MOOC education, the famous e-learning platform includes Udacity (udacity.com), Coursera (coursera.com) and edX (edx.org) in the world. Although Udacity and Coursera are profitable websites, edX is a nonprofit website. The partner organizations of Udacity mainly concentrate on technology companies in the computer field, such as Microsoft, Google, Autodesk, Nvidia, Cadence, and Wolfram Alpha. But, Coursera and edX are more focused on the cooperation with top universities in the world, such as Stanford University, Harvard University, Massachusetts Institute of Technology, Princeton University, University of Michigan and University of Pennsylvania. Though the emergence of MOOC changes the traditional teaching way, these massive online courses have brought new learning problems. Among them, the most obvious problem is MOOC teaching separates the relations between teachers and students. In order to achieve the goal of face-to-face teaching in e-learning, a small and efficient e-learning way, SPOC, is generally attached a great deal of attentions. As one of blended teaching patterns, SPOC not only stays open e-learning way in MOOC, © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1080–1087, 2019. https://doi.org/10.1007/978-3-030-02804-6_139

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but also combines the advantages of face-to-face classroom teaching. In addition, according to a research from University of California at Berkeley, MOOC has less effect on solid teaching in universities [1]. In 2013, Prof. Fox firstly advanced the idea of SPOC, which is short for Small Private Online Course. In SPOC, the original MOOC resources are used in a small-scale and particular group. For university education, the concrete form of SPOC relates to MOOC video, online scoring network and internet forum in traditional classroom teaching [2]. In brief, SPOC is a combination of MOOC resource and classroom teaching, which can take advantage of MOOC and fill in the gap of MOOC and traditional teaching. The reminder of this paper is organized as follows. In Sect. 2, some famous SPOC experiments are introduced. Subsequently, Sect. 3 analyzes the teaching effects of flipped class based on SPOC. Finally, the conclusions are drawn in the final section.

2 SPOC Experiments The core contents of SPOC contain two parts: online learning and offline discussion. The first part requires students to perform some online tasks, such as watching video and online discussion. The second part emphasizes a face-to-face communication between teachers and students. In this communication, teachers can answer questions that students may meet in online learning, and students can develop the aspect of knowledge through direct face-to-face contact. About related studies of SPOC, some famous SPOC experiments are described as follows. 2.1

The SPOC Experiment in University of California Berkeley

“Software engineering” course, as a famous brand of the major curriculum in University of California Berkeley, was offered to students in the pattern of SPOC by Prof. Fox on edX platform [3]. The central feature of this course was the auto grading system, which allowed students to submit their assignments multiple times for the sake of getting high scores. Besides, this system could give the grade immediately, once the student submitted the assignment. Because of the high efficiency of SPOC in teaching, students could use it to test the mastery of knowledge in course. Consequently, the application of SPOC significantly improved teaching effects for teachers and learning efficiencies for students. In University of California Berkeley, SPOC teaching model attracted more and more students, who graded their teacher higher ratings after the completion of course. Then Prof. Fox introduced this new teaching model to other universities, and suggested teachers in other universities watch the MOOC video of California Berkeley, adopted the question in the MOOC and used the auto grading system [1]. 2.2

The SPOC Experiment in Harvard University

In 2013, Harvard University adopted SPOC in three different schools, which were Law School, Kennedy school of Government and Graduate School of Design [4]. At Law School, the “Copyright” course that imitated the traditional law classroom, appeared in edX platform. This online course divided students into two groups, and guided the

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members in different groups to discuss the teaching contents. By the end of the course, these students took a 3-h exam. If there was a good grade, the student would obtain a certificate. At Kennedy School of Government, the online course was “Central challenges of American national security, strategy and the press: an introduction”. This course required students to watch videos of lectures, read related literature at least 75 pages one week, finish their homework, and take part in discussions. At the end, the students who achieved the level required for their course, were awarded the HarvardX certificate. At Graduate School of Design, new graduate students were required to learn the SPOC course, which was “The Architectural Imaginary”. In short, the SPOC courses were welcome by students in the above schools of Harvard University. 2.3

The SPOC Experiment in University of Massachusetts Boston

In University of Massachusetts Boston, “Basic Biology” was a major basic course for a long time, which was taught to students by Prof. White by lectures and experiments [5]. In the fall of 2013, Prof. White began attempting to use SPOC in the course of “Introduction to Biology”. Some learning goals were set in this course, such as to make good use of time for students, to play a greater role in helping students with their studies for teaching assistants, to design more experiments in course teaching, and motivate student to take initiatives in learning. In order to attain the teaching objectives of SPOC, a part of course contents was assigned to self-study after class. This teaching reform was helpful in improving the students’ constructive abilities and problemsolving skills. In the aspect of curriculum assessment method, Prof. White designed some exercises in edX platform and 10 classroom tests. For teaching contents, the original course was divided into 30 independent units. Every unit included one video lecture and several self-testing questions. Students were required to spend more than 10 h in learning this course after class every week. Then, many questions in self-study after class were solved in the next classroom teaching. In University of Massachusetts Boston, this new teaching method optimized the original curriculum structure and improved the teaching result of course. 2.4

The SPOC Experiment in San Jose State University

In the fall of 2012, Prof. Ghadiri started “Circuit Analysis” course in edX platform, and applied the SPOC teaching method to the parts of course contents [6]. As a specialized basic course in engineering profession, “Circuit Analysis” had some difficulties for students’ learning. The grade of this course was usually poor, and about two thirds of students only got a C grade. So, SPOC teaching was recommended to apply to this course. In the process of SPOC teaching, students were required to watch instructional videos outside of classroom, and then had group discussions inside of classroom. More specifically, students filled in a questionnaire after self-teaching in order to record the key points and difficult problems firstly, and then the teacher combed out main knowledge points and discussed the above questions with students. At the end of the term, the pass rates of this course were over 90%. This meant that SPOC gained the wide acceptance from the students. By SPOC teaching method, students could learn course contents without restrictions of time and place. Besides, this teaching method

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motivated the students’ learning enthusiasm and strengthened the interaction between teachers and students. The results showed that compared with the traditional learning method, the attendance, participates degree and self-confidence of students had gratefully been improved. Thereafter, SPOC learning method was applied in more and more courses in San Jose State University.

3 Teaching Effects of Flipped Class Based on SPOC In order to reflect the teaching effects of flipped class based on SPOC, juniors who study “International Finance” course at a university located in the west of China, are chose as the research objects in this paper. There is one natural class including 56 students whose major is finance. The process of study is from March 2017 to June 2017, that is, spring term in 2017. This course is taught in the first sixteenth weeks. And in seventeenth week, a final test is planned. This course is scheduled in a weekly class lasting 2 h. In the teaching process of this course, a flipped class model based on SPOC is design to research students’ learning effects. 3.1

The Teaching Design

Based on SPOC teaching method, the course learning can be finished inside and outside of the classroom. In the classroom, the learning process mainly includes teacher lecture and discussion between teacher and student. Out of the classroom, the learning process contains self-previewing lessons before class and self-reviewing lessons after class. Thus, the course learning out of class is divided into two parts: the learning before class and the learning after class. This paper designs a teaching model of flipped class based on SPOC, which can be seen in Fig. 1. Figure 1 shows the process of course learning under flipped class model, which happens in the classroom and out of the classroom. Firstly, Stage 1 emphasizes selfpreviewing before class. Students should log in SPOC platform, and watch videos of lecture. After reading relevant literature, students are required to finish the self-testing questions. Secondly, Stage 2 emphasizes teacher’s guidance in class. According to the results of self-testing questions in SPOC platform, teachers arrange group discussion and solve the puzzles in self-previewing. At the end of each lecture, teachers sum up the intellectual dots. Thirdly, Stage 3 emphasizes self-reviewing after class. When students finish a lesson, they are required to spend some time to review the course contents and rethink the difficult points of course. In addition, students are suggested to spend 30 min to watch videos, 30 min to read literature and 20 min to do self-testing questions in a week. 3.2

Teaching Results

In order to reveal the learning situation after class, a questionnaire about selfpreviewing and self-reviewing is designed, which shows the completion status of selfpreviewing and self-reviewing.

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Fig. 1. The process of course learning

Although teachers require student do self-previewing before class and selfreviewing after class, several students probably don’t complete these tasks at times, because of various reasons. After analyzing questionnaires, not all of students finish their homework in self-previewing and self-reviewing each week. In other words, several students may only complete a part of homework in the previewing and reviewing of weekly course. Figure 2 shows the number of students previewing before class, and Fig. 3 indicates the number of students reviewing after class.

Fig. 2. The number of students previewing before class

Fig. 3. The number of students previewing after class

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From Figs. 2 and 3, it can be seen that several students don’t finish their homework in self-previewing before class or self-reviewing after class at the beginning of term. These learning performances are obvious in the first two weeks. This is because students are unfamiliar with the contents, features and objectives of the new course at the beginning of term. As the class goes on, most of the students gradually begin adapting to the SPOC teaching method, and paying more attentions to self-previewing before class or self-reviewing after class. Especially in the final two weeks, all students finish their homework in self-previewing and self-reviewing, because the final exam is coming. In Stage 2, teachers mainly guide students to understand course contents and solve the puzzles rising in Stage 1. And in Stage 3, the process of self-reviewing plays an important role in strengthening the students’ learning effects. However, the keys of flipped class model are instructional videos, course materials and self-testing questions in Stage 1, because this stage is a beginning of entire flipped class, deciding the teachers’ teaching effects in the latter stages. Thus, the status of self-previewing before class is analyzed in the following aspects: (1) watching videos; (2) reading literature; (3) doing self-testing questions. According to the questionnaires, the average of study time in watching videos, reading literature and doing self-testing questions can be shown in Figs. 4, 5 and 6.

Fig. 4. The average of study time in watching videos

Fig. 5. The average of study time in reading literature

Fig. 6. The average of study time in doing self-testing questions

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From Figs. 4, 5 and 6, the average of study time always changes in different weeks. In the aspect of learning contents, some students fail to strictly comply with the teacher’s requirements, which require students to complete different previewing links within a suggested time. Thus, the statistics characteristics of study time are presented in Table 1. It is seen that the average of study time in watching videos is 31.25 min every week. And it is higher than the suggested time (30 min). However, the average of study time in reading literature is 28.81 min every week, which is lower than the suggested time (30 min). At last, the average of study time in doing self-testing questions is 20.94 min, which is approximately equal to the suggested time (20 min). In addition, the standard deviation of weekly study time is in turn 4.07, 4.98 and 2.54 from watching videos to reading literature to doing self-testing questions. After comparing the average of study time in self-previewing before class, it is found that students prefer watching videos to reading literature. As for doing self-testing questions, almost all students do them well. Table 1. The statistics characteristics of study time The status of self-previewing The weekly study time Mean Standard deviation Watching videos 31.25 4.07 Reading literature 28.81 4.98 Doing self-testing questions 20.94 2.54

After the final exam, the students’ grades are summarized in Table 2. The students’ scores are divided into 5 intervals, which are 90 * 100 (represented as A), 80 * 89 (represented as B), 70 * 79 (represented as C), 60 * 69 (represented as D) and 0 * 59 (represented as E). In flipped class model, the entire teaching process is based on SPOC teaching method. Table 2 shows students’ overall grades are improved obviously. Especially, the number of students achieving grade A is 13 in flipped class model, and this number in traditional class model is only 3. Besides, in flipped class model, no one fails in the final exam. But in traditional class model, there are 3 students failing in the final exam. Table 2. The grade in the final exam Grade Flipped class model* Traditional class model** The number of students Percentage The number of students Percentage A 13 23.21% 3 5.36% B 24 42.86% 15 26.79% C 12 21.43% 21 37.50% D 7 12.50% 11 19.64% E 0 0.00% 6 10.71% *The data of flipped class model came from the final exam in July 2017. **The data of traditional class model came from students’ performances in the previous year, when students was taught the same course by conventional teaching way.

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4 Conclusions This paper designs a flipped class model based on SPOC, which includes 3 stages: selfpreviewing before class, teacher’s guidance in class and self-reviewing after class. Firstly, through data analysis, some students don’t tend to complete their homework in self-previewing before class or self-reviewing after class at the beginning of term in first two weeks, as they are unfamiliar with this course. But in the final two weeks, with the approach of the exam, all students complete their homework before class and after class. Secondly, for self-previewing before class, students likes watching videos more than reading literature, because the visually appealing of the former is high relative to that of the latter. However, all students show no distinct preference in doing self-testing questions. This is because Chinese students have a learning habit of consolidating knowledge by doing self-testing questions. Thirdly, the students’ grades have significant improvements in the final exam when the flipped class model is adopted. All students succeed in passing the final exam, and almost a quarter of students receive grade A. Acknowledgments. This research was supported by the Fundamental Research Funds for the Central Universities of China (No. 18SZYB07) and Caijingtong Education Industry-University Cooperative Education Project of Ministry of Education of China (No. 201801091012).

References 1. Fox, A., Patterson, D. A., Ilson, R., Joseph, S., Walcott-Justice, K., Williams, R.: Software engineering curriculum technology transfer: lessons learned from MOOCs and SPOCs. UC Berkeley EECS Technical Report. https://www2.eecs.berkeley.edu/Pubs/TechRpts/2014/ EECS-2014-17.pdf (2014) 2. Xu, W., Jia, Y., Fox, A., Patterson, D.: From MOOC to SPOC: lessons from MOOC at Tsinghua and UC Berkeley. Mod. Dist. Educ. Res. 4, 13–22 (2014) 3. Fox, A.: From MOOCs to SPOCs: curricular technology transfer for the 21st century. In: Proceedings of Ubiquity Symposium on MOOCs and Technology to Advance Learning and Learning Research, pp. 1–4 (2014) 4. Kang, Y.: An analysis on SPOC: post-MOOC era of online education. Tsinghua J. Educ. 35 (1), 85–93 (2014) 5. Zhao, D., Liang, X.: The practical research of the computer-based courses in university. In: Proceedings of International Conference on Frontier Computing, Siena, Italy, May 15–17, 2017, pp. 94–103. Springer, Singapore (2017) 6. Pomerol, J.C., Epelboin, Y., Thoury, C.: MOOCs: Design, Use and Business Models. Wiley, Hoboken (2015)

A Comparative Analysis of the Utilization of FDI in Six Central Provinces Min Cheng(&) and Lan Liu College of Literature Law and Economics, Wuhan University of Science and Technology, Wuhan, China [email protected]

Abstract. This paper points out the problems existing in the six provinces of Central China in these aspects through the horizontal comparison between the development of FDI in the six central provinces, investment patterns and industrial distribution. Then using qualitative and quantitative methods, using R3.3.1 software with principal component analysis to explore the main factors affecting FDI in six provinces. Finally, based on the results of empirical analysis, this paper puts forward countermeasures and suggestions on the utilization of FDI in the six central provinces of China Keywords: Central six provinces


FDI
Principal component analysis

1 Introduction FDI (Foreign Direct Investment) has played an incomparable role in China’s economic development. Since the reform and opening up, China has made tremendous leaps in its work with FDI. The rapid economic growth in the Southeast can be said to be based on foreign-oriented foreign-funded enterprises. Therefore, under the background of the rise of Central China, it is particularly critical to study the use of FDI in the six provinces of Central China to achieve the purpose of coordinated development among the six provinces. However, due to differences in the reform and opening up process, economic development policies, and regional conditions in various regions, foreign investors have invested mainly in the southeast region for many years. In order to change the current disparity in the distribution of FDI regions, the Chinese government has formulated a lot of support for the central government. At the same time, there is an extremely large gap between the FDI amounts in the six central provinces. For example, in 2015, Shanxi Province actually used 20% of the FDI, which is less than Henan Province. Therefore, according to the current situation of FDI development in the six central provinces, relevant countermeasures are proposed to achieve a healthy economic development, has a strong practical significance. There are also many studies of FDI by Chinese scholars. Wei (2012) used space panel measurement methods to examine the influencing factors of FDI location distribution in 31 provinces and regions in China between 1998 and 2007. The study found that FDI in one province is affected by FDI and other unmeasured factors in its © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1088–1095, 2019. https://doi.org/10.1007/978-3-030-02804-6_140

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neighboring provinces and regions. The large market size and convenient transportation infrastructure are all conducive to attracting FDI. High wages, low marketization, and excessive government intervention have a significant negative impact on the entry of FDI; Qingping (2012) believes that FDI is one of the reasons for the increase in the economic growth rate and regional economic disparity in the eastern, central and western regions of China; Shoudong and Fengyuan (2012) concluded that FDI through cointegration test and Granger causality test. There is a long-term equilibrium relationship between investment and China’s exports, and on the basis of this empirical study, a detailed analysis is made from the increase in FDI and the contribution of the two parts of the inventory to the upgrading of China’s industrial structure, and then a series of improvements have been proposed. China’s Export Commodity Competitiveness, Promotion of FDI, and China’s Export Trade, Policy Suggestions; Handan University, Daxue and Liyao (2015) We believe that there are some problems in Jiangxi Province such as the low level of allocation of FDI resources, the relative concentration of sources, and the shortage of hardware and software for the use of foreign investment, and analyze the role of foreign investment in the economic growth of Jiangxi Province, and the measures that Jiangxi Province should adopt, such as actively attracting foreign investment. Encourage foreign investors to invest in hightech industries and service industries, optimize regional distribution of foreign capital in Jiangxi Province, optimize resource allocation, and make use of FDI to maximize its effectiveness. Hongqing (2011) used quantitative methods to explore the factors influencing the use of FDI in the six provinces of the Central China, including cumulative FDI, per capita GDP, infrastructure level, market size, and other factors, and proposed policy recommendations for the use of FDI in the six provinces of the Central China.

2 Analysis of Existing Problems of FDI in Six Central Provinces 2.1

Uneven Regional Distribution

According to Fig. 1, it can be seen that the distribution of FDI in the six provinces of Central China from 2002 to 2015 is uneven, especially in Shanxi Province. The actual use of FDI in 2015 was less than 20% of the year in Henan Province. Since the accession to the WTO, the actual use of FDI in the six provinces of Hubei and Jiangxi in the central region has increased year by year. However, from a national perspective, the growth rate of FDI in the six central provinces is far below the national average. This imbalance in regional development is not conducive to the improvement of the overall average level of FDI utilized by our country. 2.2

Single Investment Method

As shown in Fig. 2, after comparing the FDI investment patterns in the six provinces of central China in 2015, the six provinces used FDI to invest basically in a certain two or three forms. After summarizing, they found that they focused on foreign-owned sole

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14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% 2 0 0 22 0 0 32 0 0 42 0 0 52 0 0 62 0 0 72 0 0 82 0 0 92 0 1 02 0 1 12 0 1 22 0 1 32 0 1 42 0 1 5

Hubei

Hunan

Henan

Shanxi

Jiangxi

Anhui

Fig. 1. The proportion of actual interest in the six provinces in the central part of the country. Source: Statistical Yearbook of the Central 6 Provinces, 2003–2016

proprietorship and joint ventures. Many methods such as BOT (Build-OperateTransfer) and its derivative models are also rare in the six central provinces. The single investment mode has restricted the scale of the use of FDI in the six provinces of Central China to continue to expand.

Fig. 2. Comparison of FDI investment methods in six central provinces. Central Statistical Yearbook》

Source: 《2015

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Unbalanced Distribution of Industries

Because of the large gap in the ability of the six central provinces to attract foreign investment, the distribution of FDI among the three major industries in the provinces is not balanced, with the majority being dominated by the secondary industry, and the amount of investment flowing into the tertiary industry is low. The “2-1” industrial pattern has a certain gap from the formation of the “3-2-1 industry structure” dominated by the tertiary industry.

3 An Empirical Analysis of the Influencing Factors of Using FDI in Six Central Provinces 3.1

Indicator Selection and Data Sources

In order to find out the influencing factors that affect the FDI in the six provinces of Central China, the analysis process is completed on the R3.3.1 software. This article examines the GDP of the six central provinces of 2009–2016, average employee wages, transport capacity, degree of dependence on foreign trade, the proportion of research activities in GDP, and human capital, and the meaning and data of each selected indicator. The source is as follows: 1. GDP (X1). 2. The average wage of employees (X2). 3. Transportation capacity (X3): The ratio of road mileage, railway mileage and inland river mileage to regional area is a measure of the transportation capacity. 4. Foreign trade dependence degree (X4): The degree of dependence on foreign trade equals the proportion of total import and export trade and regional GDP. 5. The proportion of research activities in GDP (X5). 6. Human capital (X6): The quality of laborers in a region cannot be directly measured. This paper uses the human capital as a measure of this. 3.2

Principal Component Analysis

Generally, the number of principal components is determined based on the cumulative contribution rate of the principal component of at least 85%, so that the data can retain the interpretability of the original data as much as possible after dimension reduction. As can be seen from Table 1, the cumulative contributions of the four provinces of Hubei, Jiangxi, Hunan, and Shanxi Province reached more than 85% under the second principal component, and the cumulative contributions of Anhui and Henan Province under the first principal component. The rate has reached 85% or more. Taken together, in order to make the results of the analysis consistent, the number of principal components in each province is chosen on the basis of the selection of principal components. After the number of principal components of each province is obtained by the above table, the linear expression of the original variable that can be written out according to the factor load matrix for each type of principal component is as follows.

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Hubei

Comp.1 Comp.2 Shanxi Comp.1 Comp.2 Standard deviation 1.8694485 1.1809664 1.8561566 0.9898380 Proportion of Variance 0.6795518 0.2711881 0.6699228 0.1905126 Cumulative Proportion 0.6795518 0.9507398 0.6699228 0.8604355 Anhui Comp.1 Comp.2 Henan Comp.1 Comp.2 Standard deviation 2.1070275 0.70555630 2.1752508 0.47386703 Proportion of Variance 0.8632487 0.09679633 0.9200559 0.04366249 Cumulative Proportion 0.8632487 0.96004503 0.9200559 0.96371843 Jiangxi Comp.1 Comp.2 Hunan Comp.1 Comp.2 Standard deviation 1.9133949 0.9234977 2.0865386 0.7780689 Proportion of Variance 0.7118767 0.1658316 0.8465418 0.1177150 Cumulative Proportion 0.7118767 0.8777082 0.8465418 0.9642567

Since this article selects two principal components, we only look at the data in the two columns Comp.1 and Comp.2 in Table 1. From Table 2, it can be seen from the principal component expression that only the first type of principal component in Jiangxi Province is composed of five variables X1, X2, X3, X4, X6, etc. This shows that there is a strong correlation between the first main components and X1, X2, X3, X4, X6. The first type of principal components in the other five provinces consists of six variables X1, X2, X3, X4, X5 and X6, and their load coefficients are similar, indicating that these variables have a strong correlation with the first principal component. The second principal component of the provinces is slightly different. The second principal component of Hubei Province is composed of X4, X5, and X6, reflecting factors such as the degree of foreign trade dependence, the proportion of research activities in GDP, and the human capital. According to its load factor, It can be seen that the dependence on foreign trade and the proportion of research activities in GDP have a strong correlation with the second principal component; the second principal component of Anhui Province is composed of X3, X4, and X5, reflecting the transport capacity and foreign trade. According to factors such as dependence degree and the proportion of research activities in GDP, it can be seen that the dependence degree of foreign trade and the proportion of research activities in GDP have a strong correlation with the second principal component; the second principal component of Jiangxi Province. It is composed of X4 and X5, reflecting factors such as the degree of dependence on foreign trade and the proportion of research activities in GDP. Among them, the load factor of X5 is relatively large, which means that the proportion of research activities in GDP and the second principal component have a strong correlation; The second principal component of Shanxi Province is composed of X4 and X6, reflecting the strong correlation between factors such as foreign trade dependence and human capital and the second principal component; the second principal component of Henan Province is composed of X1, X2, X5, and X6, reflecting factors such as GDP, employee average wage, research activity, GDP, and human capital, among which the higher load factors are X2 and X5, which means that the average wage of employees and the proportion of research activities in GDP have a strong correlation with the

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second principal component; the second principal component of Hunan Province is composed of X2, X3, X4, X5, and X6, reflecting the average wage of employees, transportation capacity, and dependence on foreign trade. The factors such as the proportion of GDP, human capital, and research activities, among which the load factor of X3 and X4 are relatively large, indicating that the transport capacity and dependence on foreign trade have a strong correlation with the second principal component. Table 2. Factor Load Matrix Hubei X1 X2 X3 X4 X5 X6 Anhui X1 X2 X3 X4 X5 X6 Jiangxi

3.3

Comp.1 −0.493 −0.492 −0.484 0.157 0.146 −0.484 Comp.1 −0.436 −0.438 −0.423 −0.349 −0.369 −0.425 Comp.1 −0.483 −0.48 −0.481 −0.317

Comp.2 Shanxi

0.699 −0.7 −0.121 Comp.2 Henan

−0.223 0.739 −0.625

X1 X2 X3 X4 X5 X6 X1 X2 X3 X4 X5 X6

Comp.2 Hunan X1 X1 X2 X2 X3 X3 X4 −0.209 X4 X5 −0.973 X5 X6 −0.445 X6

Comp.1 −0.482 −0.487 −0.487 0.331 −0.371 0.213 Comp.1 −0.416 −0.409 −0.424 −0.417 −0.391 −0.392 Comp.1 −0.442 −0.436 −0.367 −0.367 −0.41 −0.42

Comp.2

−0.594 0.796 Comp.2 0.394 0.539

−0.681 −0.292 Comp.2 0.188 −0.586 −0.578 0.425 0.316

Empirical Conclusion

Based on the above analysis, the following conclusions can be drawn: The first principal component has a greater impact on FDI in the six provinces of central China. The main factors affecting Hubei and Anhui province are foreign trade dependence and scientific research activities; the major factors affecting FDI in Jiangxi are research activities in GDP. The main factors affecting FDI in Shanxi Province are the dependence on foreign trade and human capital; the main factors affecting FDI in Henan Province are the average wages of employees and the proportion of research activities in GDP; the impact of FDI in Hunan Province The main factors of the amount are the transportation capacity and dependence on foreign trade.

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4 Suggestions on the Use of FDI in Six Central Provinces 4.1

Promote the Optimization and Upgrading of Industrial Structure

Through the comparative analysis of the foreign investment industries in the six provinces of the central provinces, it has been found that the use of FDI in the six provinces of the central provinces shows an overall shift to the second and third industries, but actually the using of FDI in the tertiary industry in some provinces such as Henan, Jiangxi and Hubei has been a decreasing trend in recent years. Therefore, the six central provinces, especially Henan Province, Jiangxi Province, and Hubei Province, should formulate appropriate industrial policies to guide the direction of foreign investment to complete industrial upgrading. 4.2

Increase Research and Education Investment

According to empirical results, scientific research input is one of the main influencing factors of FDI in Hubei, Anhui, Jiangxi and Henan Province, and human capital is one of the main influencing factors of FDI in Shanxi Province. Therefore, these five provinces should devote more financial expenditures to scientific research, formulate appropriate strategies for strengthening the country through talents, actively introduce advanced technologies, actively cultivate outstanding talents, and improve the level of innovation of enterprises. 4.3

Optimizing the Standards for Staff Wages

The empirical results show that the average wage of employees on the job is one of the major factors affecting FDI in Henan Province, and has a certain positive effect on the introduction of FDI in Henan Province. Therefore, the wages of employees in Henan Province should be appropriately increased. In raising the wages of workers in Henan Province, the speed should not be too fast or too much, nor too little or too slow. When the wages increase too quickly, the cost of the enterprise will increase, and the profits of the enterprise will decline or even lose money; if the speed is too slow, then we must not increase the enthusiasm of workers’ production and the inflow of FDI. Therefore, Henan Province should formulate an appropriate system of employee wages and a reasonable system of welfare policies to ensure that the wage level of workers can be reasonably and steadily increased. 4.4

Strengthening of Infrastructure Construction

From the analysis of the principal components, we can see that transport capacity is one of the major factors affecting FDI in Hunan Province. Therefore, it is necessary to strengthen infrastructure construction in Hunan Province and improve the investment hard environment. The same areas of infrastructure construction, such as supporting medical and educational fields, should also be improved and developed at the same time. The environment in Hunan Province should be improved. FDI should be attracted to the greatest extent so as to bring into play the role of FDI and promote the economic development of Hunan Province.

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References Wei, J.: The location decision of foreign direct investment in China: a spatial econometric analysis based on the “third party effect”. World Econ. Study 1, 75–80 (2012) Qingping, W.: Influence of foreign direct investment on regional economic disparity in China and its countermeasures. J. ABC Wuhan Train. Coll. 1, 62–67 (2012) Shoudong, C., Fengyuan, Z.: Analysis of the influence of foreign direct investment on China’s export trade. Theor. Investig. 01, 83–87 (2012) Daxue, K., Liyao, J.: The present situation, problems and countermeasures of utilizing foreign capital in Jiangxi Province. J. Nanchang Norm. Univ. 2, 14–17 (2015) Hongqing, W.: Research on the present situation, influencing factors and countermeasures of foreign direct investment in Henan Province——based on the comparison of six provinces in Central China. J. Int. Trade 5, 80–87 (2011) Zhipeng, Z.: Research on the use of FDI in Henan Province—based on the comparison of six provinces in the central part of China. China University of Geosciences, Beijing. Dissertation, pp. 11–42 (2013)

Solid Edge’s Application in Vertical Mill Design Kunshan Li1(&) and Yang Li2 1

School of Mechanical Engineering, University of Jinan, Jinan, China [email protected] 2 Shandong Jinfengda Machinery Co., Ltd., Jining, China

Abstract. Vertical grinding is an indispensable key equipment for cement industry grinding system, the performance of the vertical mill depends on the design of its internal structural parameters, And directly affect the technical and economic indicators of the grinding system and make a decisive role in the quality of the grinding product. To this end, many pulverizer workers and technicians from manufacturing companies have undergone a lot of changes in the structure of vertical mills, and achieved some results, but there are still many problems, the main reason is the unknown and uncertainty of structural parameter transformation, at the same time, exploratory transformation also wastes a lot of energy and financial resources due to uncertainty. With the continuous development of computer technology, using virtual design method to transform the internal structure of vertical mill is very effective, In this vertical mill design we have adopted Solid Edge. Keywords: Vertical mill


Solid edge
3D design

1 The Structure and Working Principle of Vertical Mill 1.1

The Working Principle of Vertical Mill

The material enters the vertical mill from the upper feed inlet, the material is sent between the grinding roller and the grinding disc by the sealing device, the disc is driven by the motor and reducer and the roller depends on the friction generated by the material between the disc and the grinding roller to drive the rotation, then the he material is ground and crushed by the gravitational force of the grinding roller and the external force and friction force exerted by the hydraulic cylinder. The ground material enters the upper classification zone driven by the bottom-up airflow, after classifying the rotor, the finished product enters the collection system for collection, finally, Unqualified meal falls and continues to grind until qualified (Figs. 1, 2).

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1096–1103, 2019. https://doi.org/10.1007/978-3-030-02804-6_141

Solid Edge’s Application in Vertical Mill Design

Fig. 1. Vertical mill structure

1.2

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Fig. 2. Internal structure of the vertical mill

Vertical Mill Structure Features

(1) The relative sliding difference between the core plate and the grinding roller is small and the speed of the disc is very fast, roller working pressure also very high all this cause relatively high yield and Power saving effect is obvious. (2) Grinding process is simple and it can dry, grind, and select powder at the same time with low noise. (3) Suitable for wind sweeping and part of the material outside the cycle grinding system, can greatly reduce the system’s ventilation power consumption. (4) Grinding inside the ventilation ring gap can be flexibly adjusted according to the distribution of the surface material (5) For four-roller vertical mills, the grinding roll can be pressed symmetrically or simultaneously with four rolls at the same time to adapt to the needs of the change of the material bed, which facilitates the grinding of raw materials with different grindability. (6) Grinding roller can be lifted automatically to realize no-load starting, and the motor starting torque is greatly reduced (7) The roller mill can be pulled out of the casing by the action of the roll cylinder and it is easy to maintain. (8) The high-efficiency separator is designed in the upper part of the machine body, the product fineness adjustment is sensitive, the load adapts strongly and the powder selection efficiency is high; (9) Both the roll surface and the grinding disc are designed with wear protection, low metal consumption, low operating and maintenance costs

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2 Introduction to Solid Edge Solid Edge is a three-dimensional solid modeling software developed on the basis of UG software. It has powerful parametric design and surface modeling functions, highperformance assembly functions and finite element structure analysis functions. It can make designers to design mechanical products, processed products, and electromechanical products more quickly and it allowed designers product document, and share product knowledge convenient and creating easy-to-use mockup standards. Because vertical mill design involves different product specifications, due to technical limitations, it is impossible to form a standardized design. Obviously, 2D design cannot meet the requirements of individual product design and product performance. However, Solid Edge 3D design can solve these problems.

3 Solid Edge 3D Design in Vertical Mill Design 3.1

Spill Tray

In the design of the spreader tray of the vertical mill grader, the problem encountered is that the leaves of the spreader tray are curved surfaces. The two-dimensional design drawing is very difficult, and it is hard to form a standardized design through the twodimensional map, the surface can not expressed clearly. Using Solid Edge 3D solid design product surface design to achieve the desired results, will be based on the threedimensional design editing program input CNC machining system can easily solve the splatter disk curved blade processing problems, three-dimensional solid distribution tray was shown in Figs. 3 and 4. 3.2

Cage Rotor

In the three-dimensional design process of the grader rotor, the design scheme provided by Solid Edge is an intuitive three-dimensional design. In this way, the designer can obtain complete information of every detail of the product during the stage of solution demonstration, and thus can effectively propose improvements and Suggestion. Solid edge software has parametric design and size-driven functions. It can quickly and easily generate a second solution based on the first solution. It can improve product design based on customer requirements and user’s recommendations. The cage rotor components of the vertical mill grader have been partially improved according to the user’s requirements and user’s recommendations during the program discussion stage and optimized accordingly in order to realize the personalized customization of products and components in the true sense, Fig. 4 is a cage rotor designed using Solid edge 3D solid software.

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Fig. 3. Spill tray diagram

Fig. 4. Cage rotor

3.3

Design of Roller Core Roller

With the continuous development of science and technology, the requirements for products are getting higher and higher, the product specifications are getting bigger and bigger, the structure is becoming increasingly complex, and the product development cycle is getting shorter and shorter, facing the new situation, the technology development work is facing severe challenges. The vertical grinding disc diameter of this design exceeds five meters and is a large-scale equipment in the industry. In order to shorten the product development cycle as quickly as possible and reduce the development cost, we use the solid edge three-dimensional solid design software in the vertical mill design work. Practice has proved that the use of software greatly improves the efficiency of design work, especially in the design of roller components for vertical mills, the successful application of three-dimensional solid modeling design has solved its functional principle analysis, calculation of structural mechanics and Using finite element analysis to solve the difficult problems in the design (Figs. 5, 6).

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Fig. 5. Vertical grinding roller

Through the interference inspection of the assembled three-dimensional software model, the interference problem of the parts can be easily checked, and the problem of unreasonable dimensions can be solved in the design stage. 3.4

Vertical Mill’s Master Plan and 3D Design

A complete set of vertical mills mainly includes foundations, transmissions, roll plates, pressurizing devices, rocker arms, housings, three lock air valves, sealed blower systems, water spray devices, and separators (separator). These partial optimization combinations can realize the perfect design of vertical mill (Figs. 7, 8). The foundation of the vertical mill consists of the main engine base, the speed reducer base, the motor base, the anchor bolt compartment and the tensioner lower support and so on. It is the cornerstone for accurate vertical mill installation and stable operation. The main components of the roller mill are the roller and the disc. The disc base is mounted on the main reducer and is tightened with bolts and pins to transmit torque. The replaceable disc lining is supported by a disc block which is divided into several segments and tops the wedged edge of the outer edge of the disc block. The inner ring lining plate is fixed with a pressure plate. The vertical grinding roller is hydraulically pressurized, with a buffer for the animal implement, and the adjustment control is very convenient. The rocker arm is a connecting device for pressurizing the grinding roller and is subject to a large torque. The upper part of the upper rocker arm is connected with the bearing, the middle part is connected with the lower rocker arm and the lower part is connected with the rocker arm mandrel. The middle of the lower rocker arm is fixed with the mandrel through the cover and the lower part is connected with the pressurized oil cylinder. Both ends of the mandrel are provided with bearing seats and rolling bearings, and the bearing seats are bolted to the frame. An upper and lower limit switch is provided at the bearing seat to control the movement of the grinding roller. The upper part of the rocker arm is provided with a scale plate with a fixed pointer to read the thickness of the upper layer of the platen.

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Fig. 6. Vertical roll grinding explosion

Fig. 7. Vertical grinding structure

The separator is an important part of the vertical mill. Whether its structure can reasonably affect the mill’s output and product fineness. Through the analysis of the main structure of the vertical mill, we use the assembly function of the solid edge three-dimensional solid software to implement the assembly link of the main components of vertical mill, carried out the anti-jamming analysis, and solved the problems in the assembly, overall design of vertical mill was completed well and achieved the expect of design.

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Fig. 8. Vertical grinding structure

4 Comprehensive Analysis and Conclusion The standardization of mechanical design makes the application of three-dimensional design easy to promote, and at the same time greatly improves the design efficiency. Because vertical mill design involves different specifications, it is difficult to form a standardized design, which makes the difficulty and workload of 2D design greatly improved. Obviously, in the vertical mill design, three-dimensional design techniques and three-dimensional modeling methods are applied to the main components, which can easily solve the problems of two-dimensional design. At the same time, the threedimensional model can also be imported into the structural analysis software for finite element analysis. Three-dimensional software can directly use three-dimensional models to generate two-dimensional engineering drawings, freeing designers from the dilemma of drawing various views with imagination. The size of parts and components of building materials machinery products is generally large and the structure is complex. When drawing engineering drawings, it often takes more than a dozen views to express clearly. Drawing drawings of these large parts is a very heavy work for engineers. The Solid edge can automatically generate drawings. Each view in the drawing is generated automatically by the 3D model and is very accurate; These 2D drawings are associated with 3D models and can be mutually driven in size; These functions ensure the correctness and unity of the design. At the same time, the software can use assembly drawings to automatically generate a list of various assembly materials. The parts information is consistent with the three-dimensional model, which is faster and more accurate than the two-dimensional software. The animation and pictures demonstrated by the three-dimensional production plan of the vertical mill fully demonstrate the product’s structure and performance characteristics. Solid Edge 3D software provides tools for transparency, cutaways, explosion views, renderings, and animations. Using these tools, you can fully display every detail

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of the design scheme and generate model images for various demonstration scenarios. These pictures and animations allow the user to intuitively obtain product information in new product promotion and project bidding, and leave a deep impression on the product.

References 1. Li, K.: The design and development of the non-ball mill. Miner. Mt. Mach. 07, P24–P27 (2002) 2. Li, K.S.: The characteristic and theory of LKS non-ball mill. In: Symposium on Refractories, p. 316 (2005) 3. Li, K.S., Yimin, M.O.: The design and development of non-ball mill for superhard refractories. China’s Refract. 16 (2007) 4. Boger, Z.: Application of neural networks to water and wastewater treatment plant operation. ISA Trans. 31, 25–33 (1992) 5. Leschinski, K.: Classification of particles in the submicron range in an impeller wheel air classifier. KONA Powder Part. 14, 52–60 (1996) 6. Shouren, W., Haoran, G., Kunshan, L.: Fabrication and abrasive wear properties of metal matrix composites reinforced with three-dimensional network structure. Rare Met. 25(6), 671 (2006) 7. Li, K.: Simulation of failure detection based on neural network for no-ball mill. Manuf. Sci. Eng. 201-203, 627–631 (2011) 8. Li, K.: Rare earth superfine powers fabrication and characterization by novel non-ball Miller. Mechatron. Mater. Process. 328-330, 648–653 (2011) 9. Li, K.: The re-design of centrifugai classifier. Mater. Metall. Eng. 402, 820–823 (2012) 10. Li, K.: Classifers’environmental friendly(green)re-design. Manuf. Sci. Eng. 468-471, 658– 662 (2012) 11. Li, K.: The use of solid Edge in the classifiers design. Manuf. Sci. Eng. 472-475, 2080–2083 (2012) 12. Li, K.: The application of the non-ball mill for superhard refractories. Appl. Mech. Mater. 239-240, 1577–1580 (2013) 13. Li, K.: The assembly design of non-ball mills. Adv. Mater. Res. 605-607, 65–68 (2013) 14. Li, K.: Design of non-ball mill based on the green conception. Adv. Mater. Res. 605-607, 73–76 (2013)

Analysis on Development Trends of Research Topics in Agricultural Sciences Changshou Luo1, Liying Zhou2, Qingfeng Wei1, and Sufen Sun1(&) 1

Institute of Agricultural Information and Economy, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China [email protected] 2 Library of China Agricultural University, Beijing, China

Abstract. In this paper, the highly-cited cited papers and the corresponding citing papers are used as data sources for analysis in the field of ESI agricultural science. Coupling-enhanced co-word analysis methods are used to probe important research topics in the field of agricultural sciences. Based on the analysis of the internal connections and the half-life period of the citations, the analysis identifies the frontiers of research in this discipline. By comparing the changes of research frontiers in different periods, this paper also analyzes the development trend of agricultural science in recent years, Finally, based on the analysis results of the change trend of research topics, research frontiers and research subjects in the field of agricultural science, this paper puts forward suggestions for the research topic layout of relevant institutions or personnel. Keywords: Agricultural science Research frontier


Highly cited paper
Research topics

1 Introduction In recent years, ESI (Essential Science Indicators) has been widely used in trend analysis and benchmarking analysis of subject development and scientific research competitiveness by various intelligence agencies. The relevant studies mainly focus on the number of subjects entering the TOP 1% and TOP 1‰, the number of academic papers and their influences, the highly cited papers and the highly cited scholars [1]. Based on the 22 major subjects, ESI defines highly-cited papers as the papers whose citations are ranked in the top 1% of all papers in their published years. The selection of highly-cited papers eliminated the error of the paper’s influence caused by the research field and the published years, and the selected papers had a better representation of subject research topics. In the fourth round of subject assessment of the Ministry of Education, the index of ESI highly-cited papers was incorporated into the scientific research level evaluation system. The Chinese academy of sciences, together with Clarivate Analytics, conducts frontier detection of academic research based on ESI highly-cited papers every year [2]. Therefore, ESI highly-cited papers has become an important source in the subject intelligence research.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1104–1112, 2019. https://doi.org/10.1007/978-3-030-02804-6_142

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This paper takes highly cited papers and their citing papers in ESI agricultural sciences as the analysis objects. Coupling-enhanced co-word analysis methods are used to probe important research topics in the field of agricultural sciences. Based on the analysis of the internal connections and the half-life period of the citations, the analysis identifies the frontiers of research in this discipline. By comparing the changes of research frontiers in different periods, this paper also analyzes the development trend of agricultural science in recent years, providing references for relevant institutions and personnel in this field to carry out research topic layout.

2 Data and Methods 2.1

Data Sources

ESI divides the journals collected by SCIE and SSCI in Web of Science into 22 subject areas, of which 337 are included in the field of agricultural sciences. First, based on journals, all papers in the field of agricultural Science from 2008 to 2017 are retrieved in the core collection of Web of Science, and the highly cited papers are selected and named as highly cited paper collection A. Then search the citing papers of these highly cited papers, the high cited papers are selected again and named as highly cited paper collection B. Finally, the collection A and the collection B were integrated for the exploration of important research topics in the field of agricultural science. Figure 1 shows how to build a basic data set for the exploration of important research topics in the agricultural sciences.

Fig. 1. Methods for the construction of basic research data sets for important research topics in agricultural science

Table 1 shows the number and the annual citation frequency threshold of highly cited papers and citing papers in the field of agricultural science from 2008 to 2017.

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Table 1. Summary of highly cited papers and the corresponding citing papers in agriculture science 2008–2017 Item Cited threshold Highly cited papers The number of citing papers in highly cited papers

2.2

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Total 120 99 98 77 65 52 40 27 15 5 319 334 353 376 386 392 383 441 443 454 4165 144 280 442 555 616 718 796 919 903 880 6277

Research Methods

(1) Important research topics detection methods Citation-coupled and enhanced co-word analysis methods [3] combine the relevance of keywords in both co-word and citation coupling, making some of them less closely related in the co-occurrence dimension, but more closely related to the citation coupling dimension. The keyword pair was also excavated and incorporated into a research topic. Citation-coupled enhanced co-word analysis method, the core of which is to analyze keyword relevance based on the co-word relationship of citation-coupled enhancement. The specific implementation process is as follows: The frequency of co-occurrence of keyword statistics and the distribution of citations of keywords. The key frequency co-occurrence matrix and keyword citation frequency distribution matrix are generated. Based on the frequency of co-occurrence of keywords, Pearson correlation coefficient is used to analyze the co-occurrence of keyword co-occurrence, and a keyword co-occurrence correlation matrix is generated. Based on the distribution of keyword citations, the Pearson correlation coefficient was used to analyze the coupling of keyword citations and generate the keyword citation coupling correlation matrix. If the two keywords i and j have a co-word correlation of CWi,j, and a citation coupling correlation of CRi,j, then the correlation of these two keywords is Ri,j = Max (CWi,j, CRi,j). A systematic cluster analysis of the keyword correlation matrix with Ri,j as a matrix element is performed to identify important research topics in the field. (2) Research Frontier Identification Method Density is an important index [4] used to characterize the degree of internal correlation of the research subject in co-word analysis, which is defined as: clustering constitutes the mean of all key words correlation on the subject of research. The greater the density, the closer the internal structure of the research topic is, the more focused its research content is. On the contrary, it indicates that the more loose the internal structure of the research topic, the more dispersed its research content.

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Citation half-life period [5] refers to the length of time of publication of the newer half of the paper’s references, and is often used to evaluate the knowledge update rate of a paper. The shorter the half-life period of the citation, the more recent the references quoted in the paper, the faster the knowledge update; on the contrary, the longer the half-life period, the older the references quoted in the paper, the slower the knowledge update. Based on the density and half-life period of citations, this paper evaluates the important research topics in the subject area and defines those research topics with high density and short citation half-life period as research frontiers (3) Research methods of trend analysis in topic change Based on the co-occurrence relationship of the research topic in the papers of the newer period, the proportion of co-occurrence frequency of research topics in the older period and the newer period of the research topic in the newer period topic research papers is defined as the continuation of research topics. Indicators to evaluate the development trends of research topics in older periods. Assume i is the topic of the older time period, j is the topic of the newer time period, Cij is the co-occurrence frequency of i and j in the newer time period, and Cj is the relative amount of the papers for the newer time period topic. The calculation formula for the topic’s continuity index study is: Si ¼ Cij=Cj  100%

ð1Þ

The higher the index of continuity is, the stronger the relationship between the research topic and the research topic in the new period is, and the development trend is good. When the continuity index reaches 100%, it indicates that it is fully integrated into the new timeline research topic; otherwise, the weaker the relationship between research topics during the time frame, the study has shown a trend of decline. When the continuity indicator is 0%, it indicates that it has completely disappeared.

3 Results and Analysis 3.1

Important Research Topics in Agricultural Science

The papers in the basic data set are divided into the publication years. High-frequency keywords with frequency higher than 10 in each time period are selected, and text clustering is performed using the coupled-citation-enhanced co-word analysis method to identify the major research themes in 2008–2012 and 2013–2017, the results are shown in Tables 2 and 3. The order of the keywords in each research topic in the table is determined by the average relevance of keywords to other keywords in the topic.

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Number T01

T02 T03 T04 T05 T06 T07

T08 T09 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20

Keywords Antioxidant activity, Antioxidant capacity, Total phenolic content, phenolic content, antioxidant properties, Radical scavenging, Phenolic compounds, beta-carotene Soil respiration, Climate change, Carbon cycle, soil moisture Fatty acids, docosahexaenoic Acid, Omega-3 fatty acids, fish oil, polyunsaturated fatty acids Crop yield, Zea mays, Crop productivity, Triticum aestivum Physical activity, systematic review, sedentary behaviour, older adults Drought tolerance, Abiotic stress, Oryza sativa Microbial community, microbial biomass, Soil microbial community, community structure, soil organic matter, Phospholipid fatty acids, Organic matter, priming effect, Enzyme activity Greenhouse gas emissions, greenhouse gas, nitrous oxide, global warming Gut Microbiota, type 2 diabetes, high-fat diet Carbon sequestration, organic carbon, black carbon, soil organic carbon, soil carbon, soil fertility, soil quality Essential oils, antimicrobial activity, Chemical composition, mechanical properties Oxidative stress, reactive oxygen species, Free radicals Food security, food packaging, high pressure Biological activities, bioactive compounds, Dietary Fiber, Health Benefits, Phenolic acids, By-products, human health, in vitro Environmental impact, life cycle assessment, Land use, food production Insulin Resistance, metabolic syndrome, adipose tissue, weight loss, nonalcoholic Fatty Liver Disease Genomic selection, single nucleotide polymorphism, plant breeding Physical properties, Spray drying, Physicochemical properties, green Tea Mycorrhizal fungi, arbuscular mycorrhizal fungi, soil microorganisms Bioactive peptides, functional properties, Mass spectrometry

Papers 262

96 66 106 90 62 100

51 61 105 72 52 49 111 53 64 37 50 23 39

According to Table 2, 20 important research themes were identified from 2008 to 2012, among which T01 (the key words with Antioxidant activity, Antioxidant capacity, Total phenolic content, etc.) is the largest, including 262 related papers. And T04 (Key keywords include crop yield, Zea mays, Crop productivity, etc.), T07 (Key words with Microbial community, microbial biomass, Soil microbial community, etc.), T10 (with carbon sequestration, organic carbon, black carbon, etc.) and T14 (biological keywords, bioactive compounds, Dietary Fiber, etc.) These four research topics are also relatively large, and the number of relevant papers is greater than 100.

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Table 3. Key research topics for 2013–2017 Number F01 F02 F03 F04 F05 F06 F07 F08 F09 F10 F11 F12 F13 F14 F15 F16 F17 F18 F19 F20 F21 F22 F23 F24 F25 F26 F27 F28

Keywords Antioxidant activity, phenolic content, Antioxidant capacity, Total phenolic content, Phenolic compounds Meta-analysis, systematic review, prospective studies, Cohort studies Gut Microbiota, intestinal microbiota, short chain fatty acids, inflammatory bowel disease, Irritable Bowel Syndrome, gut-brain axis Blood Pressure, Risk Assessment, physical activity, cardiovascular disease Crop modelling, Climate change, food production Fatty acids, polyunsaturated fatty acids, fish oil, Omega-3 fatty acids Reactive oxygen species, oxidative stress, Free radicals Sustainable intensification, food security, crop yield, ecosystem services, sustainable agriculture Oryza sativa, grain yield, Triticum aestivum, Zea mays Edible film, antimicrobial activity, Essential oils, food packaging, mechanical properties Type 2 diabetes, diabetes mellitus, metabolic disorders, Metabolic disease Soil organic matter, Organic matter, priming effect Insulin Resistance, nonalcoholic Fatty Liver Disease, adipose tissue Microbial community, microbial biomass, Phospholipid fatty acids, Soil microbial community, community structure, Enzyme activity Organic carbon, soil organic carbon, soil carbon, carbon sequestration Greenhouse gas, nitrous oxide, greenhouse gas emissions, N2O emission Human health, chronic diseases, nitric oxide By-products, Phenolic acids, bioactive compounds, in vitro Heavy metals, Soil contamination, Activated carbon Sedentary behaviour, sedentary time, Public Health Delivery systems, Functional foods, Whey protein Mediterranean diet, dietary patterns, olive oil, diet quality Stress tolerance, drought stress, Abiotic stress, drought tolerance Lignocellulosic biomass, ionic liquids, Physicochemical properties Ectomycorrhizal fungi, Nitrogen cycling, Forest soil, Carbon cycle, nutrient cycling Soil amendment, Crop productivity, soil quality, black carbon, carbon dioxide Spotted wing drosophila, Drosophila suzukii, invasive species, biological control Genotyping-by-sequencing, single nucleotide polymorphism, Nextgeneration sequencing, genomic selection

Papers 236 185 174 185 92 79 87 109 138 97 79 52 87 71 79 48 60 65 49 47 49 45 52 49 59 53 29 43

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According to Table 3, a total of 28 important research topics were identified in 2013–2017, among which the F01 (with key words such as Antioxidant activity, phenolic content, and Antioxidant capacity) has the largest scale with 236 papers. And F02 (Key words such as meta-analysis, systematic review, prospective studies, etc.), F03 (based on Gut Microbiota, intestinal microbiota, short chain fatty acids, etc.), F04 (Blood Pressure, Risk Assessment, physical activity, etc.), F08 (sustainable intensification, food security, crop yield, etc.) and F09 (core keywords such as Oryza sativa, grain yield, and Triticum aestivum) are also relatively large, the amount of relevant papers is more than 100. 3.2

Research Frontiers in Agricultural Science

The density and citation half-life period of different research topics during the two periods of 2008–2012 and 2013–2017 have calculated. Taking the mean values of the density and half-life period of all the topics in the same period as the origin, and the density and citation half-life period as horizontal and vertical coordinates, the coordinate graph of the key topics in the same period was drawn. The results are shown in Fig. 2. In 2-a and 2-b, those research topics located in the first quadrant in the upper right corner of the figure have relatively large density and relatively short citation halflife period, which conforms to the characteristics of the research frontier. It can be seen that: T01 (core key words such as Antioxidant activity, Antioxidant capacity, Total phenolic content), T03 (key words based on fatty acids, docosahexaenoic Acid, Omega-3 fatty acids, etc.), T06 (Key words such as drought tolerance, Abiotic stress, Oryza sativa), T07 (Key words with Microbial community, microbial biomass, Soil microbial community), T10 (Key words with carbon sequestration, organic carbon, black carbon, etc.)and T19 (key words for mycorrhizal fungi, arbuscular mycorrhizal fungi, soil microorganisms, etc.) are the frontiers of research from 2008 to 2012; F01 (key words with Antioxidant activity, phenolic content, Antioxidant capacity, etc.), F06 (key words with fatty acids, polyunsaturated fatty acids, fish oil, etc.), F12 (keywords with soil organic matter, Organic matter, priming effect, etc.), F14 (keywords with Microbial community, microbial biomass, Phospholipid fatty acids), F15 (key words such as organic carbon, soil organic carbon, and soil carbon) and F16 (key words such as greenhouse gas, nitrous oxide, and greenhouse gas emissions) are research frontiers in 2013–2017. 3.3

Trends in Research Topics in Agricultural Science

The continuity of the research topics from 2008 to 2012 in 2013–2017 was calculated, and based on the indicators of the continuity of each research theme, the trend chart of the change of agricultural science research topics was drawn with Ucinet. The results are shown in Fig. 3. The circular nodes represent the research topics of 2008–2012, the circular ring nodes represent the research frontier of 2008–2012, the square nodes represent the research topics of 2013–2017, the square ring nodes represent the research frontier of 2013–2017, the connection line indicates that the research topic has a high continuity (greater than 49%), and the arrow indicates the evolution direction of the research topic. It can be seen from the above that the continuity of most of the

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research topics in 2008–2012 is strong, among which T01, T03, T12, and T16 are fully integrated into the research topics of the new time period, and five topics (T05, T07, T09, T14, T10) are divided into a number of new timeframe research topics, only four research topics (T15, T18, T19, T20) have shown a negative trend in 2013–2017. Most research topics in 2013–2017 are evolved from 2008–2012, only six research subjects (F19, F21, F22, F24, F25 and F27) have a weak relationship with the research topics of 2008–2012, which are the new topics of this area.

(2-a) 2008-2012

(2-b) 2003-2017

Fig. 2. Key research topics comparison chart

Fig. 3. Trends in research topics in agricultural science

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4 Summary and Suggestion Based on ESI highly-cited papers in agricultural sciences and their corresponding citing papers, this paper uses citation-coupled and enhanced co-word analysis to detect key research topics in the two periods of 2008–2012 and 2013–2017. Based on the density of the research topics and citation half-life period, the frontiers of research at different time periods are identified. And based on the index of continuity of each research topic, the change trend of agricultural science research topic is analyzed. The results show that: There are 20 key research topics in the field of agricultural sciences from 2008 to 2012, of which T01, T03, T06, T07, T10 and T19 are the research frontiers. There are 28 key research topics in the field of agricultural sciences from 2013 to 2017, of which F01, F06, F12, F14, F15 and F16 are the research frontiers. In 2008–2012 research topics, four topics (T01, T03, T12 and T16) are fully integrated into the research topics of the new time frame, five topics (T05, T07, T09, T14 and T10) are divided into several new time periods, and four topics (T15, T18, T19 and T20) are showing negative trends. Most of the research topics of 2013–2017 have been developed from 2008–2012. Only six research topics, including F19, F21, F22, F24, F25 and F27, have a weak relationship with the research topics of 2008–2012, which are the new topics of this area. Based on the analysis of research topics and development trends in the field of agricultural science in this paper, relevant institutions or personnel can focus on and lay out the six research frontiers of F01, F06, F12, F14, F15 and F16 in 2013–2017. If the existing layout has strong correlation with these four research topics (T15, T18, T19 and T20), the research direction should be adjusted. Acknowledgments. The research work was supported by 2018 international cooperation fund of BAAFS: A comparative study on the agricultural science and technology information service system in China, the United States and Canada, 2018 Beijing excellent talent project: Research on key technology of man-machine conversation in agricultural science and technology consultation and service application of Beijing, Tianjin and Hebei, 2018 Beijing financing agricultural funds: Application and demonstration of “Nongkexiaozhi” consulting service robot and WebAPP in agricultural production.

References 1. Zheng’e, D., Huilan, C.: Investigation into library service model of university discipline evaluation on the basis of ESI and in cites. Libr. J. 33(11), 23–28 (2014). https://doi.org/10. 13663/j.cnki.lj.2014.11.005 2. Wenyue, B., Haiming, W., Ying, X., et al.: Analysis of nanoscience and technology development based on ESI research fronts. Bull. Chin. Acad. Sci. 32(10), 1150–1158 (2017). https://doi.org/10.16418/j.issn.1000-3045.2017.10.014 3. Liying, Z., Fuhai, L., Wenge, Z.: Research on the improvement of the co-word analysis method based on citation coupling. Inf. Stud. Theory Appl. 38(11), 120–125 (2015). https:// doi.org/10.16353/j.cnki.1000-7490.2015.11.023 4. Weijin, Z., Jia, L.: The research of co-word analysis (2). J. Inf. 06, 141–143 (2008) 5. Junping, Q., Jinyan, S., Zunyan, X.: Information resource management: comparative study based on bibliometrics. J. Libr. Sci. China 05, 37–45 (2008). https://doi.org/10.13530/j.cnki. jlis.2008.05.00

The Impact of China Manufacturing on the Environment Meng Li1, Gang Yu1(&), and Liang Yang2 1

Shenzhen University, Shenzhen 518060, Guangdong, China [email protected] 2 College of International Exchange, Shenzhen University, Shenzhen 518060, Guangdong, China

Abstract. This paper bases on the Chinese manufacturing industries, evaluates the impact of commodity manufacturing on the environment from the perspective of commodity production. We mainly use I–O models to measure the energy and raw materials that are directly or indirectly needed by the production sector and the impacts on the environment, these impacts include the discharge of pollutants in the production of goods and the environmental impact of the production of intermediates. Keywords: Manufacturing industries


Pollutants emission
Energy

1 Introduction With the development of economy, the problem of environmental destruction becomes more and more serious. The destruction of manufacturing industry deserves our deep consideration, and the input–output method can give a better evaluation of the final products and intermediate products in the process of commodity production. First of all, we must consider the environmental impact of energy consumption in the production process, and secondly, consider the consumption of energy and raw materials by intermediate products, and finally make a comprehensive assessment of the environmental impact. Carbon dioxide emissions are the main issue of our research during the analysis, but similar analyzes of other pollutant emissions are possible. Similarly, the scope of this article concentrates on the impact of manufacturing industry in China on the environment, we can also extend the scope of this article to the world.

2 Model Analysis The input–output model can well measure the interdependence of inputs and outputs of various parts of the system. This analysis starts with Leontief and it is also called the Leontief model. The basic idea of this model: Sector j outputs Xj , and the corresponding output of sector i is Xij , and the coefficient between the two is defined as aij . Xij ¼ aij Xj ;

i; j ¼ 1. . .n:

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1113–1116, 2019. https://doi.org/10.1007/978-3-030-02804-6_143

ð1Þ

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For sector i, we get the relationship between total supply and total demand Xi ¼

n X

xij þ Fi ;

i ¼ 1. . .n,

ð2Þ

j1

where Fi is final demand of sector i, and xij is the intermediate of sector i. Combine Eq. (1) with Eq. (2): The total demand and the total supply of change are also corresponding DXi ¼

n X

aij Dxj þ DFi ;

i ¼ 1. . .n;

ð3Þ

j1

Where DFi represents the changes in the final demand of sector i, DXi represents the changes in the production of sector i. Assuming that the final demand of sector i changes by F, we can know the final supply changes X, and F = X. The increase in output also increased all intermediate inputs, in order to produce intermediate demand requires a second round of intermediate inputs, this process will continue until the final round of zero input. In order to facilitate the calculation, we will write Eq. (3) in matrix form X ¼ AX þ F ) ðI  AÞX ¼ F ) X ¼ ðI  AÞ1 F

ð4Þ

In this equation, X is the output vector of each sector, F is the vector of the final demand of each sector, and A is the coefficient matrix of the intermediate inputs. I is the identity matrix. For our analysis, we take the j-th unit’s impact on the environment as ej, and obtain the following matrix 0 B e¼@

e1

..

0

0 .

1 C A:

The matrix of Environment

ð5Þ

en 0

1 f1 B.C We assume that F ¼ @ .. A is the final demand of each sector’s products. The fn production of goods and services has a direct impact on the environment and expressed as vector e
f. We call X the production-induced vector so that we get the overall environmental impact of the production of goods and services. e
DX ¼ e
f þ e
Af þ e
A2 f þ


¼ e
ðI  AÞ1 Df :

ð6Þ

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3 Overall Effect Evaluation The results of the data analysis show in Table 1, we can see that the CO2 emissions per unit of manufacturing reached 4.2 tons (10.0%). We can also know: In terms of carbon dioxide emissions, coal industries emissions is more than natural gas, electricity, oil industry, respectively 888 kg, 421 kg, 183 kg, 148 kg. The manufacturing industry’s direct impact on the environment is 105 kg, far less than the indirect effect. When the energy price went up, we came to the conclusion that GDP would drop by 0.9%. When the energy price went up by a large margin, the demand for energy was also decreasing. The decrease in demand also affected actual consumption and investment. In fact, the drop in real consumption and investment far outweigh the decline in real GDP, where rising energy prices had a positive effect on the efficient use of energy. It is clear that the development of China’s economy needs to pay attention to the coordinated development of environment and economic growth. To prevent pollution from reaching its critical mass (see Tisdell 2010), China must upgrade manufacturing and adopt more effective pollution reduction strategies. Table 1. Manufacturing production and carbon dioxide emissions. Rate of change of energy demand relative to the base period (%) Energy prices remain Energy prices rose Energy prices rose unchanged 5% 10% 6.0 6.3 6.6 6.0 6.2 6.5

Coal Intermediate demand Final demand 8.4 9.1 9.5 Natural gas 1.6 0.8 −0.05 Intermediate 1.5 0.7 −0.1 demand Final demand 3.1 2.6 1.7 Electric power 0.4 −0.8 −2.0 Intermediate 0.2 −1.0 −2.2 demand Final demand 2.2 1.2 0.1 Oil 0.3 −0.9 −2.1 0.3 −0.9 −2.1 Intermediate demand Final demand 2.1 1.0 −0.5 CO2 10.0 8.0 7.0 Source: Calculated from Input–Output Table of China (2007), China Statistical Yearbook (200711), China environment Statistical Yearbook (2007-11), and China Commerce Yearbook (200711) data.

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4 Conclusions and Recommendations In the past 40 years, the development of manufacturing industry has greatly boosted China’s economy. However, with the prominence of environmental issues, China should pay more attention to environmental issues and its potential adverse effects, promulgate new laws and regulations, and upgrade manufacturing as one of the feasible solutions. In recent years, China adopted a series of international top-level technical practices, greatly reducing its impact on the environment. At present, China’s manufacturing industry is also making continuous efforts to transform itself. It hopes to transform the dirty industry into a clean industry through technological progress and technology introduction. Acknowledgements. This article is supported by the following agencies: (1) “Discipline and Professional Construction” Project of Scientific Research of the GuangDong Province, (No. 2013WYXM0102). (2) Project of China Postdoctoral Science Foundation (No. 2013T60217). (3) Project of Scientific Research of the Chinese Ministry of Education (No. 15YJA790033). (4) 2014 Key platform construction Project & Development Program for Major Projects and Achievements (No. 2014 Guangdong Provincial Department of Education).

References Tisdell, C.A.: Free trade, globalisation, and environment and sustainability: major positions and the position of WTO. Economics, Ecology and the Environment Working Paper, No. 39 (2010) Tisdell, C.A.: China’s environment problems and its economic growth. In: Tisdell, C.A., Chai, J. C.H. (eds.) China’s Economic Growth and Transition: Macroeconomic, Environmental and Social Regional Dimensions, pp. 295–316. Nova Science Publishers, Commack (2007) Vermeer, E.B.: Industrial pollution in China and remedial policies. China Q. 156, 952–985 (1998) Tisdell, C.: Capital/natural resource substitution: the debate of Georgescu-Roegen (through Daly) with Solow/Stiglitz. Ecol. Econ. 22(3), 289–291 (1997) Li, X.N., Jiao, W.T., Xiao, R.B., et al.: Soil pollution and site remediation policies in China: a review. Environ. Rev. 23(3), 263–274 (2015)

The Influence of Cultural Creativity on Beijing Textile and Clothing Industry Analyse Sun Jie(&) and Xi Yang Beijing Institute of Fashion Technology, Beijing, China [email protected]

Abstract. In view of the new national policy of developing culture and creativity Beijing has ushered in a new development, which needs to be transformed and upgraded. The development of culture and creativity can further implement the promise of design capital. This paper analyzes the input-output table of textile and clothing in Beijing through direct and complete consumption coefficient, and concludes that cultural creativity affects the development of textile and clothing. It is found that design is an important factor by regression analysis on Beijing textile and clothing. Therefore, the textile industry is analyzed from the design point of view. Keywords: Beijing textile and clothing industry Input-output table
Regression equation


Cultural and creative

1 Introduction Beijing is center of the economic and cultural in our country, reaching in resources and talented people [1]. China is a large country with a large population, and the textile and clothing industry is a pillar industry in the national economy and people’s livelihood [2]. In recent years, Beijing has paid great attention to the development of cultural creativity, and the textile and clothing industry has witnessed unprecedented historical opportunity [3]. Therefore, this paper demonstrates the relationship between them through quantitative and qualitative research, and puts forward some suggestions for the development of Beijing textile and clothing industry. 1.1

Status of Textile and Clothing Industry in Beijing

The reform of state-owned enterprises is on the fast track in Beijing and the traditional textile manufacturing industry is withdrawing from the capital city. The textile industry will explore the integration between civilization and the fashion industry and inject new vitality into Chinese national crafts, it’s repositioned as fashion holding and transformed to modern urban services [4]. Up to now, the textile manufacturing industry has basically been cleared up and transferred in Beijing, and the clothing manufacturing industry left is basically small scale in Beijing, and it is also in the process of unwinding [5].

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Input-Output Analysis of Textile and Clothing Industry in Beijing

Input-output analysis is an economic quantitative method to study the interdependence between input and output in various industries, which can be reflected by direct and complete consumption coefficient. 1.2.1 Direct and Complete Consumption Factor Direct consumption coefficient refers to the consumption of products from one sector to other sectors in the process of production, that is, aij (i, j = 1, 2, …, n). When there is no major change in production technology, it can be assumed that it is a stable coefficient, which is calculated in the following terms: aij = Xij/Xj (i, j = 1, 2, …, n) [5]. Its matrix is represented as A. The greater the aij value, the greater the direct dependence of J on sector I, conversely, the dependency is small. The complete consumption coefficient can more comprehensively and profoundly reflect the interdependent quantitative relationship between departments. In general, the full consumption coefficient is recorded as bij (i, j = 1, 2, …, n). Its matrix is represented as B and its formula is B = (I − A)^(−1) − I. The A is a direct consumption coefficient matrix in the formula, I is a unit matrix, and B is a complete consumption coefficient matrix [7]. The data from Beijing Statistical Yearbook, In the input-output table for sector 42, the textile and clothing industry falls into two categories: One is the textile industry with a product division code of 07, and the other is the garment, leather, down and its products industry with a product sector code of 08 [6]. As in Tables 1 and 2, the complete consumption coefficient of textiles and leather, down and down of textile, clothing, shoes and hats and their products are presented. Table 1. Complete consumption coefficient of textiles Trade Agricultural, forestry, pastoral and fishery products and services Textile Textile, clothing, shoes, hats, leather, down, down and its products Chemical products Nonmetallic mineral products Metal Transport and communication facilities Water production and supply Wholesale and retail Transportation, warehousing and postal services Accommodation and catering Information transmission, software and information services Finance Real estate Culture, sports and recreation Cultural creativity

2002 0.04520

2007 0.043798

2012 0.004357

0.28553 0.12651

0.327634 0.155544

0.369144 0.126773

0.06327 0.00244 0.00398 0.00187 0.00209 0.02291 0.01190 0.00541 0.00847

0.057698 0.001263 0.004280 0.001968 0.001458 0.020008 0.003829 0.004936 0.009362

0.017587 0.000511 0.002060 0.000209 0.000786 0.000434 0.068125 0.004090 0.007893

0.00372 0.02674 0.00208 0.01055

0.008545 0.016469 0.001650 0.011012

0.009022 0.004014 0.000403 0.008296

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Table 2. Complete consumption coefficient of leather, down and its products for textile, clothing, shoes and hats Trade Agricultural, forestry, pastoral and fishery products and services Textile Textile, clothing, shoes, hats, leather, down, down and its products Chemical products Nonmetallic mineral products Metal Transport and communication facilities Water production and supply Wholesale and retail Transportation, warehousing and postal services Accommodation and catering Information transmission, software and information services Finance Real estate Culture, sports and recreation Cultural creativity

2002 0.19409

2007 0.249440

2012 0.131551

0.50490 0.15160

0.649650 0.203880

0.841527 0.149553

0.27532 0.01307 0.02167 0.02450 0.04606 0.08509 0.03132 0.02370 0.04806

0.351440 0.015410 0.026628 0.029186 0.033069 0.080309 0.014388 0.023544 0.051621

0.266072 0.010232 0.019584 0.018012 0.017038 0.002216 0.198006 0.024649 0.025714

0.01262 0.05221 0.00586 0.05392

0.018881 0.040662 0.005692 0.057313

0.078460 0.021849 0.005017 0.030731

1.2.2 Influencing Factors of Beijing Textile and Clothing Industry According to the national economic classification table, the cultural and creative industries include culture and art, press and publication, radio, television, film, software, network and computer services, advertising and exhibition, art trade, design services, tourism and entertainment, and other ancillary services. The Beijing Statistical Yearbook provides an input-output table of 42 departments, and does not contain all the industries in which culture and creativity are subdivided, but information transmission, software and information technology services, and culture, sports and entertainment are cultural and creative industries. So add and calculate the consumption coefficient of them. From the above analysis, we can know: agricultural, forestry, animal husbandry, fishery products and chemical products have a great impact on Beijing textile and clothing industry. Agriculture, forestry, herding and fishing are the basic industries of the country, which can provide raw materials for the development of the textile and clothing industry. The influence of chemical products on the textile and garment industry is nothing more than the development and production of new fabrics. The cultural creativity also has the influence to the Beijing textile clothing.

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2 The Development of Cultural Creativity 2.1

Status of Cultural Creativity

Cultural creativity is a new industry based on technology, creativity and industry. The textile and clothing industry should keep up with the pace of the times and turn its eyes to the direction of creativity. The cultural and creative industries have taken on an allout trend, which has not only been strongly supported by national policies, At the same time, the rapidly growing cultural consumption market is also becoming more and more attractive [8]. Its development has been accelerated from basic material consumption to spiritual and cultural consumption [9]. 2.2

A Summary of Beijing’s Cultural Creativity

Cultural creativity is not only a cultural product for the society, but also an important platform for innovation and development. Beijing issued a plan for strengthening cultural construction in the 13th Five-Year Plan period, actively promoting the cultural reform and development of the capital city, and providing a strong support for the construction of Beijing. The Beijing Municipal Government has listed cultural practices that are not in line with the development of culture and creativity, labor-intensive manufacturing and cultural practices that occupy more land as a restricted and prohibited catalogue. With the introduction of a series of national policies to stimulate the innovation and entrepreneurial vitality of cultural and creative industries, Beijing’s cultural creativity faces greater opportunities and challenges. 2.3

The Trend of Beijing Cultural Creativity

Cultural creativity creates and promotes cultural resources with the aid of high technology. Beijing regards cultural and creative industries as new economic growth points and strategic industries [10]. In order to further explore the development trend of cultural and creative industries in Beijing, it relies on a large number of data. This paper deeply analyzes the current situation of the development of Beijing’s cultural and creative industries, and obtains the data from the Beijing Statistical Yearbook, such as Table 3: From the analysis of the table, we can see that the scale of Beijing’s cultural and creative industries has steadily increased, and the added value of software networks and computer services accounts for the highest proportion of the cultural and creative industries. The proportion of advertising exhibition is relatively high, and the other development speed is basically consistent with the overall development speed of cultural and creative industries [11].

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Table 3. The added value of the cultural and creative industries in Beijing (billion yuan) Project Regional GDP Cultural creative industry Culture and art Press and publication Radio, television, movies Software, network and computer services Advertising exhibition Art trade Design service Travel, leisure and entertainment Other ancillary services

2012 17879.4 2205.2 76 208.3 177.6 1190.3 168.6 59.2 97.4 83.4 144.4

2013 19800.8 2578.1 96.7 241.4 191.1 1421.8 206 60.5 130.6 94.1 135.9

2014 21330.8 2826.3 115.6 239.7 200.3 1605.2 220.2 56.2 127.7 99.7 161.7

2015 23685.7 3253.8 138.9 281.9 225 1900 217.4 64.3 134.9 107.7 183.5

2016 25669.1 3581.1 161.2 322.8 231.5 2109.4 221.8 65.6 163.5 119.1 186.2

3 Regression Analysis on Beijing Textile and Garment Industry by Cultural and Creative Industries The textile and clothing industry in Beijing belongs to the secondary industry. Therefore, through the analysis of the output value of the secondary industry and the value added of the culture and creativity industries, the influence of each industry on the textile and clothing industry in Beijing can be obtained. The data comes from the Beijing Bureau of Statistics. Assume that the linear regression equation is Y = b0 + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6X6 + b7X7 + b8X8 + b9X9 + l, Among them, X1 stands for culture and art, X2 for press and publication, X3 for radio, TV, movies, X4 for software, network and computer services, X5 for advertising, exhibition, X6 for art trade, X7 for design services, X8 for travel, leisure and entertainment, X9 for other ancillary services, l for random interference items. The analysis results are as follows, under the confidence of 95%, X7, X8 and X9 have a significant influence on Beijing textile and garment industry, so it is the factor that has a great influence on Beijing textile industry by passing the test (Figs. 1, 2 and 3).

Fig. 1. Regression analysis

Fig. 2. Regression analysis

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Fig. 3. Regression analysis

4 General Theory The textile and clothing industry in Beijing is a traditional industry with a history of thousands of years. It is not a sunset industry. At different stages of development, the traditional industry is constantly innovating and transforming through its own. But today we also see the inherent requirements of the textile industry’s own development [13]. Cultural creativity has a great impact on the textile and clothing industry in Beijing, This will provide a very good basis for promoting the transformation and upgrading of our textile and clothing industry [11]. In view of the construction of the capital, the promotion of the international influence of the Beijing Design brand, and the new stage of development [12], we must adapt to the new normal of the economy, lead key breakthroughs with innovation, and with the strong support of the government and relevant trade associations, The development system of urban clothing industry, which takes fashion creativity, design, research and development and brand marketing as the leading factor, will make positive contributions to meet the demand of consumption upgrading, accelerate the adjustment of textile industry structure and promote the development of related fields of national economy.

References 1. Li, L., Li, B.: Research on the Development of Beijing’s Cultural and Creative Industry based on Input-Output Analysis, Beijing 2. Yu, Q., Lu’an: Research on the Development of China’s Textile and Garment Industry based on Input-Output (2018) 3. Ma, Q.: Research on the Development Strategy of Beijing Garment Industry Driven by Cultural Creativity 4. China - New Network, Textile Exploration and Wenchuang, Fashion Industry Convergence Traditional Textile Manufacturing Industry Exit Beijing (2016) 5. China Textile Newspaper: Transformation of Beijing textile service industry to “high precision” (2014) 6. Wang, L., Chen, Y., Wang, J., Cheng, Y.: Research on the Measurement of Vertical Division of Labor in China’s Textile and clothing Industry based on Input-Output Table (2008)

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7. Zhang, Q., Jiang, Y.: Input-Output Analysis of Textile and clothing Industry (2009) 8. Benefit Analysis of Cultural and Creative Industry agglomeration by Jiang Ling and Wang Liling: a case study of Beijing Cultural and Creative Industry (2016) 9. Information on www.chinabgao.com (2017) 10. Li, L., Li, B.: Research on the Development of Beijing’s Cultural and Creative Industry based on Input-Output Analysis: award-winning. In: Proceedings of the 16th Beijing Statistical Science Symposium. Beijing statistical materials discussion association (2011) 11. Chang, L., Zhuo, X.: An empirical Analysis of the influence of China’s Textile Industry based on the Input-Output method (2009) 12. Textile and garment industry should strive to promote transformation, upgrading and development—Long Guoqiang’s speech at the “2017 China Fashion Hangzhou summit” 13. Beijing Garment Industry Development report 2005–2014. China Textile Press (2016)

Application Traceability in the Food Supply Chain Gang Liu(&) School of Economy and Management, Tianjin Agricultural University, Jinjing Road 22 in Xiqing District, Tianjin 300384, China [email protected]

Abstract. Food safety issue has attracted more and more attention from consumers. Traceability systems can play an important role in food quality control. Implementing end-to-end traceability along food supply chain is quite a challenge. The paper aims to analyze how food practitioners establish the traceability systems in order to ensure food safety. This study analyzes the risk of quality in food supply chain, and identifies three key dimensions for the establishment and operation of traceability systems: standard system, technology system, management system. Keywords: Traceability system


Food supply chain
Food safety

1 Introduction Food safety has become a growing concern of consumers. Some food crises in past years have had a great negative impact on consumers trust, especially melamine scandal in 2008. Sub-standard food has not only harmed physical health of consumers, but also the development of the food industry. Food supply chain is long and complex which travels from farmers to consumers; therefore, keeping quality control along food supply chain has become a challenge. The timely and accurate traceability of food and production activities in supply chain is very important for food quality control. Establishing the traceability system will become a prerequisite for enterprises to gain competitive advantage in future. Traceable food is also easier to get consumers’ trust.

2 Literature Review Traceability is considered as a effective quality-monitoring tool which can improve food quality in supply chain (Kher et al. 2010). Traceability is defined as: “the ability to trace the history, application or location of an entity, by means of recorded identifications” (ISO, 1995). Wilson and Clarke (1998) consider food traceability as the information of production history which is from farm to consumer’s plate. Traceability system is not only used to precisely log the history, but also reinforce the level of coordination between members in supply chain (Dabbene and Gay 2011). Golan et al. (2004) propose that traceability is an effective tool to solve selected market failure. They analyze the development of traceability in three sectors: fresh agricultural © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1124–1129, 2019. https://doi.org/10.1007/978-3-030-02804-6_145

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products, beef, and oilseeds. Jansen-Vullers et al. (2003) suggest that traceability system including four elements, which are physical lot integrity, process data collecting, process linkage and product identification, data analysis and reporting. Duan et al. (2017) identifies six elements for implementation of traceability including government support, consumer approving, communication and management, support from vendor and senior manager, information system, standards system and regulations. Pappa et al. (2018) suggest that perceived costs and perceived control are important factors which affect the installation and operation of traceability in food supply chain. In this paper, we focus on the implementation of traceability for food supply chain in order to ensure food safety.

3 The Risk of Quality in Food Supply Chain There is a series of transformation processes in food supply chain, these continuous transformation processes which are from the grower to the consumer including the following components: (1) The environment of producing areas will have an important impact on the quality of agricultural products. Nature input is the first step in determining the quality of food, such as cleaner water and soil for plant. (2) The quality of agricultural products is mainly determined by farmers’ behavior in agricultural production. Whether the grower use the production input such as chemical fertilizer and pesticide in a standardized way will directly affect the quality of agricultural products. Especially the residue of pesticides or chemical fertilizer is the most important index to measure the quality of food. (3) There is a great risk of food safety in food processing. The main factors affecting the quality of food in processing activity include hygienic plant or machinery, correct use of food additives, hazardous material control in processing. (4) Logistics process also affects the quality of food. Certain factors such as temperature variation, biological or chemical contamination in warehousing and distribution will bring down the quality of food. Fresh agricultural products may be sold directly to consumers via supermarket or electronic commerce without processing; therefore logistics is more important for fresh food. (5) The risk of food safety in retail is mainly from expired food. Expired food may breed a lot of bacteria which harm physical health of consumers.

4 Traceability in Food Supply Chain The risk of food safety can be downwards along the food supply chain. Quality problems from any link within food supply chain will ultimately lead to the production of inferior food. To ensure food safety, any risk in different stage of food supply chain must be eliminated. Traceability is an important tool of quality control which can monitor the whole food supply chain. The paper develops a model of traceability system for food supply chain (Fig. 1).

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Fig. 1. Traceability system for food supply chain

4.1

Standard System

The standard system includes series of management concept and method. It is the basis of data collection and threshold monitoring in traceability system. Standard systems in practice mainly include good agricultural practice (GAP), hazard analysis critical control point (HACCP), good manufacturing practice (GMP), and ISO 22000 (food safety management systems). GAPs can be defined as practices to decrease microbial contamination in agro-production process such as cultivation, harvesting, packing and storage operations for agricultural products. HACCP is an important quality control tool to prevent food from being polluted by chemical or biological hazards. GMP is concerned with the general standards, policies, procedures, processes and other precautions which are required to consistently produce safe foods of uniform quality. ISO 22000 is a food safety management system developed by International Organization for Standardization (Blank, 2006). ISO 22000 is a management standard which strengthens the HACCP. The basic principles of these standards are presented in Table 1. 4.2

Technology System

Traceability is a complex system which involves all stages of food supply chain such as production, processing, storage and distribution. Traceable information is not only for products, but also the process of movement and transformation. Therefore, traceability

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system needs to be supported by appropriate technology. Support technology for traceability mainly includes the following: Table 1. Basic principles of traceability standards

(1) Sensor. Sensor is a detection device that can feel the information which is need to be measured, and transform this information into electrical signal or other required form in order to meet the requirements of information transmission, processing, storage, display, record and control. An important application of the sensor is to gather temperature information of different stages in food supply chain.

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(2) QR code. QR code is a kind of matrix two-dimensional code which can store more data than ordinary bar code. It has the characteristics of security encryption, high density coding, low cost, high reliability, and strong fault tolerance. QR code can be used to trace food safety information from food supply chain especially with the development of intelligent terminal. (3) Radio Frequency Identification (RFID). RFID is a radio frequency technology that users can radio waves for identifying objects automatically. The identification process is done by storing specific information such as serial number in a microchip which is installed on an antenna. Then the identification information is transmitted to users through the microchip and antenna. The reader can translate the radio wave which is reflected back from the RFID into digital information that can be transmitted to the information system of firm. (4) Management information systems (MIS). MIS is mainly used to manage the information needed, including recording related information and dealing with the recorded data. MIS for food traceability maybe comprised of safety production management, physical distribution management, information inquiry system et al. 4.3

Management System

The implementation of traceability system requires support from management system. It is an assurance system includes the following: (1) Responsibility and authority. Appoint the quality officer who is in charge of traceability system. (2) Operation management. Drawing the flow chart of food supply chain, including sources of material inputs such as seed, feed and ingredients, and establishing the operational programs. (3) Performance assessment. Testing the effect of traceability system. (4) Continual improvement. Updating the traceability process for food safety and improving the ability of personnel through training.

5 Conclusion Traceability is an important quality control tool for food supply chain. Traceability system can help to increase the transparency of food supply chain and increase the added value of food. The paper develops a model of traceability for food supply chain which is composed of standard system, technology system and management system. Standard system mainly includes a series of quality management ideas and standards such as GAP, HACCP, GMP, and ISO22000. Technology system is the basic for the implementation of traceability which is made up of sensor technology, QR cord, RFID and MIS. Management system is used to support effective operation of traceability system for food supply chain. It contains a number of management functions such as operation management, organization and performance assessment et al. This research has management implications for food practitioner. Firstly, food practitioner should regard the establishment of traceability system as important social responsibility of

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food safety and a way of gaining competitive advantage. Secondly, the establishment and operation of traceability systems is a system project which is supported by standard system, technology system and management system. Thirdly, food firm should strengthen cooperation with supply chain members in the field of traceability for food safety. Acknowledgments. This work was supported by 2018 philosophy and social science planning project in Tianjin(TJGL18-030), “Research on development path and supporting policy for agricultural products E-commerce based on E-commerce ecosystem in Tianjin”.

References Kher, S.V., Frewer, L.J., Jonge, J.D., et al.: Experts’ perspectives on the implementation of traceability in Europe. Br. Food J. 112(3), 261–274 (2010) Wilson, T.P., Clarke, W.R.: Insights from industry food safety and traceability in the agricultural supply chain: using the Internet to deliver traceability. Supply Chain Manag. 3(3), 127–133 (1998) Dabbene, F., Gay, P.: Food traceability systems: performance evaluation and optimization. Comput. Electron. Agric. 75(1), 139–146 (2011) Golan, E., Krissof, B., Kuchler, F., et al.: Traceability in the U.S. Food Supply: Economic Theory and Industry Studies. Agricultural Economic Report, Washington DC (2004) Jansen-Vullers, M.H., Van Dorp, C.A., Beulens, A.J.M.: Managing traceability information in manufacture. Int. J. Inf. Manag. 23(5), 395–413 (2003) Duan, Y., Miao, M., Wang, R., et al.: A framework for the successful implementation of food traceability systems in China. Inf. Soc. 33(4), 226–242 (2017) Pappa, I.C., Iliopoulos, C., Massouras, T.: What determines the acceptance and use of electronic traceability systems in agri-food supply chains? J. Rural Stud. 58(2), 123–135 (2018) Yamao, M., Hosono, K.: Factors affecting the implementation of good agricultural practices (GAP) among coffee farmers in Chumphon Province, Thailand. Am. J. Rural Dev. 2(2), 34–39 (2014) Mortimore, S.E.: How to make HACCP really work in practice. Food Control 12(4), 209–215 (2001) Kelepouris, T., Pramatari, K., Doukidis, G.: RFID-enabled traceability in the food supply chain. Ind. Manag. Data Syst. 107(2), 183–200 (2007)

Research on Irregular Block Spatial Scheduling Algorithm in Shipbuilding Yongjian Zhang(&) and Huiyue Ci Harbin Institute of Technology (Weihai), Weihai 264209, Shandong, China [email protected]

Abstract. This paper addresses the spatial scheduling problem for irregular blocks with purpose of determining the optimal schedule of a given set of irregular blocks and the layout of workplaces by designating the blocks’ workplace simultaneously. Workspace and irregular blocks are described using two-dimensional homogeneous grid. On this basis, the problem is formulated as an optimization model with objectives to minimize duration and to balance workloads over workplaces. A hybrid spatial scheduling algorithm based on genetic algorithm and simulated annealing algorithm is proposed in which the heuristic decoding strategy using vertex positioning and bottom-left moving rules are used. The comparison and computational results shows the proposed hybrid algorithm solves the irregular block spatial scheduling problems effectively and efficiently. Keywords: Spatial scheduling Hybrid algorithm


Irregular block
Heuristic rule

1 Introduction The blocks, which are the basic units of the shipbuilding, are assembled through several processes and finally erected to a ship in a dry dock. During this process, spatial scheduling problems occur in various working areas, such as cutting and blast shops assembly shops and outfitting shops, due to the limited space. These complicated NPhard problems both involve assigning blocks to specific work areas while simultaneously determining the start times and locations within the assigned work area for each block. To solve these problems, many methods and algorithms were proposed from different perspectives [1–3]. Koh et al. [4] proposed a Largest Contact Area (LCA) policy to select a suitable location for blocks. Kwon et al. [5] developed a novel mixed integer programming model considering various tough constraints in practice and a two-stage heuristic algorithm using dispatching priority rules and a diagonal fill space allocation method. Zhang and Chen [6] proposed an agglomeration algorithm (AA) which used 3D classification to cluster blocks linked closely in time and space into virtual blocks. Shang et al. [7] introduced a scheduling system for block spatial scheduling combining best contact algorithm with genetic algorithm. By now the real efficiency of block spatial scheduling is still low, and most of the methods simplify the blocks into rectangles. In this paper, model of spatial scheduling problem based on irregular block is proposed, a hybrid spatial © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1130–1136, 2019. https://doi.org/10.1007/978-3-030-02804-6_146

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scheduling algorithm is introduced and discussed. This paper is organized as follows: In the next section, spatial scheduling model on irregular blocks is given in detail. Next, a hybrid spatial scheduling algorithm called SAGA using simulated annealing algorithm and genetic algorithm is proposed, in which the heuristic decoding strategy based on vertex positioning and bottom-left moving rules are used. In application section, the effectiveness and efficiency of the proposed algorithm is verified. In the last section, the paper is concluded.

2 Irregular Blocks Spatial Scheduling Model The purpose of irregular blocks spatial scheduling is to determine the optimal schedule of a given set of irregular blocks and the layout of workplaces by designating the blocks’ workplace simultaneously. This problem introduces the constraints related to time dimension based on the static spatial scheduling problem, aiming to obtain an ideal scheduling scheme within a given period. The problem can be described as follows: known the earliest start time ESTi, latest start time LSTi, and processing duration of all blocks, also a two-dimensional rectangular workspace with the length L and the width W. Workspace and irregular blocks are described using twodimensional homogeneous grid. Matrix Gi is used to describe the position of block bi is the workspace and when block bi takes up the workspace position, the corresponding element in Gi takes the value of 1. The rotation angle of the block is selected from 0°, 90°, 180°, and 270°. Once the position of the block is determined, it is fixed until the block is processed and removed. Under the premise of satisfying the relevant constraints, n pending blocks are arranged in a rectangular site in a certain chronological order (usually in days) and are immediately evacuated when the processing is completed. The free area after evacuation will be used to arrange the right-sized blocks to be processed. The waiting time for removal is negligible with respect to the entire duration. Therefore, the completion time CTi of the block bi is the sum of actual start time sti and duration di. Ati is the area occupied by the blocks on the tth day. The objectives of the model are to minimize the total duration primarily and to balance the workloads over workplaces per day secondly. The total duration f1 is calculated by the difference between the latest completion time and the earliest start time. Load of the workplace f2 is measured by minimizing the sum of distance between space utilization and average space utilization. The smaller the value, the more balanced the load of the workplace. To balance the two objectives, weight coefficients a and b are introduced. Therefore, the final optimization objective function can be formulated by Eq. (1). f ¼ a
f1 þ b
f2

ð1Þ

f1 ¼ maxðsti þ di Þ  minðsti Þ

ð2Þ

f2 ¼

max ðCTi Þ X t¼minðsti Þ

ðSCRt  avgSCRÞ

2

ð3Þ

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SCRt ¼ avgSCR ¼

max ðCTi Þ X

X

 Ati ðL  W Þ

SCRt =ðmaxðCTi Þ  minðsti ÞÞ

ð4Þ ð5Þ

t¼minðsti Þ

Equations (6)–(7) are boundary constraints which guarantee the block to be allocated within the workplace. Where sizeðGi Þð1Þ and sizeðGi Þð2Þ are the number of rows and columns of the matrix Gi. At the same time, the model requires no overlapping for any blocks that are allocated to the same workplace. Equation (8) constrains the earliest and latest starting time for each block. Equation (9) ensures there is no overlapping in time dimension for pre-existing blocks and unprocessed blocks considering the restriction of processing order where block bi is the predecessor of block bj. xi  1 and xi þ sizeðGi Þð2Þ  1  L=m

ð6Þ

yi  1 and yi þ sizeðGi Þð1Þ  1  W=m

ð7Þ

ESTi  sti  LSTi

ð8Þ

stj  sti  di

ð9Þ

3 Hybrid Spatial Scheduling Algorithm Obtaining global optimum solutions is easy to fall into the local optimum and very time consuming, considering the larger solution space due to the increase of time dimension constraints. A hybrid spatial scheduling algorithm called SAGA was proposed based on simulated annealing algorithm and genetic algorithm. In this algorithm, when the temperature keeps falling, the acceptance probability of the excellent individuals generated by the crossover mutation is much greater than that of the average individual, so that the superior individual’s advantage is more obvious. The algorithm process is shown in Fig. 1. 3.1

Coding and Initial Population Generation

The blocks are arranged inside the workplace according to a certain time sequence. The scheduling sequence of the block is directly encoded to form a chromosome, and then the final scheduling result under the current scheduling sequence is obtained through the heuristic decoding operation. Chromosomes Ij ¼ ða1 ; a2 ; . . .; ai ; . . .; an Þ are formed by randomly sorting the block numbers which are (1, 2, 3… i, … n). ai represents a block number, whose order in the chromosome represents the scheduling order of the block. Combining EST rules with processing order constraints to generate initial populations. Firstly, globally search for the earliest start time ESTi of all blocks, and the

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blocks with the same ESTi are grouped into a set Bt. The internal sequence of the set may be randomly arranged, then the set of scheduling objects under different start times is determined. Secondly, A scheduling sequence I is obtained by sorting the resulting sets from small to large according to the value of t. Finally, determine whether the sequence satisfies process constraints. If so, the scheduling sequence is completed. Instead, the block is removed from the current set and added to the set that satisfies the constraint, resulting in a new scheduling sequence I as Initial population. 3.2

Heuristic Decoding Strategy Based on Vertex Positioning

The purpose of heuristic decoding based on vertex positioning (HDVP) is to obtain a feasible scheduling result that satisfies the dynamic scheduling constraints through the scheduling sequence. The blocks in the workplace are arranged according to the vertex positioning rules and bottom-left heuristic rules. When the timing constraints are met, the actual start time and the specific machining position and angle of the block are solved. Due to the space-time coupling of dynamic scheduling problems, the scope of the solution space is huge. Based on the use of scheduling sequence coding, the use of vertex positioning rules can further narrow the scope of algorithm search and improve the solution efficiency. Heuristic decoding process is illustrated in Fig. 2. Initial Placement Strategy: For the first block in the workplace, place it at the bottom left corner of the workplace. Vertex-Based Spatial Positioning Rules: The vertex positioning rule refers to selecting the bottom right corner and the top left corner of the arranged enveloping rectangle blocks as the positioning reference point of the subsequent block, where the bottom right corner point is the priority reference point, and if the bottom right corner does not satisfy the constraint, the top left corner point is considered. Figure 3a shows the scheduling order of 4 blocks based on vertex positioning rules. “*” is the remaining optional reference position. Block 1 is arranged at the lower left corner of the work site, its lower right corner is selected as the reference position of block 2, and the lower right corner of block 2 does not meet the placement requirement of block 3, so the upper left corner of block 1 is selected as the reference position, and for block 4 The lower right corners of blocks 1, 2, and 3 do not meet the placement requirements. Therefore, the upper left corner of block 2 is selected as the reference position. Combining the discretized geometric information, two reference positions can be generated after each block bi is positioned: ðxi þ sizeðGi Þð2Þ; yi Þ and ðxi ; yi  sizeðGi Þð1ÞÞ. Bottom-Left Heuristic Rules: To reduce the space gap caused by the irregular shape of the block, the currently positioned block is moved leftwards or downwards by the heuristic move operator to be close to the positioned block. Only the current positioning block is moved, and the rest of the block positions remain unchanged in this process (see Fig. 3).

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Y. Zhang and H. Ci Start Generate initial population Decoding to calculate individual fitness

Start Generate initial position

Get new individuals by selection, crossover and mutation

Arranging subsequent blocks

Current block start time delays No one day Is it located? and update scheduling Yes sequence of the day. Update spatial resource and reference position set

Decoding to calculate new individual fitness Get a new population by replacing the current worst individual with optimal individual in the last evolution Determine ultimate population by Metropolis rule

No

No

Is it up to the maximum evolutionary generations?

Are the blocks of the day all arranged? Yes Time step plus one

Yes Remove finished blocks and update spatial resource and reference position set

dropping in temperature

Is it up to the termination temperature?

No

No

Are all the blocks arranged? Yes

Yes Stop

Fig. 1. Hybrid algorithm process

Stop

Fig. 2. HDVP decoding process

Fig. 3. Vertex positioning and bottom-left rules

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Evaluation Function and Genetic Operation

The evaluation function is used to calculate the fitness of the genetic algorithm and as the basis of replacement for the new and old individuals of the simulated annealing algorithm. Its value takes the reciprocal of the objective function, the larger the evaluation function, the better the scheduling result. Genetic operations include selection, crossover, and mutation. The crossover operation swaps the order of two blocks in the same earliest start time set. The mutation operation is implemented by randomly generating the blocks in all sets. The selection part uses the roulette selection strategy. After the mutation is completed, the worst individuals in this evolution are replaced by the best individuals in the last evolution.

4 Application and Discussion To demonstrate the effectiveness and efficiency of the proposed algorithm, 25 irregular blocks are simulated in the workplace where the length L is 50 m and the width W is 50 m with a programming environment MATLAB R2010b. Part of the shape information of blocks used in the experiment is obtained from ESICUP (EURO special interest group on cutting and packing), another part of the data is generated artificially. In this simulation experiment, the duration of the block is set between 2 and 6 days, the grid precision m = 1 m. The algorithm parameters are set as follows: Population size pop = 50, maximum evolution generations maxgen = 100, crossover probability pc = 0.7, mutation probability pm = 0.2, initial temperature T0 = 1000, temperature update constant q = 0.9, termination temperature Tend = 0.001. Figures 4, 5 and 6 are convergent curve of algorithms, it can be concluded that SAGA algorithm’s solution quality is better than GA and SA when solving irregular blocks spatial scheduling problem. The algorithm SAGA proposed in this paper can quickly converge to a better solution in the early stage, which is similar to GA, but it converges to the optimal solution after the 60th generation and tends to be stable. Table 1 shows the space utilization rate obtained by the SAGA algorithm is better than that of GA and SA, and the total duration obtained is shorter. Figure 7 shows the spatial coverage variation curve of SAGA. It can be seen from the figure that the spatial

Fig. 4. Convergent curve of GA

Fig. 5. Convergent curve of SA

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Fig. 6. Convergent curve of SAGA

Fig. 7. Spatial coverage curve of SAGA

Table 1. Computational results of SAGA, GA and SA algorithms Algorithm SAGA GA SA

Optimal solution avgSCR maxSCR 15.44 60.42% 74.84% 16.16 55.38% 70.64% 17.10 51.12% 63.80%

Total duration 11 12 13

coverage fluctuates greatly from the third day to the seventh day but declines significantly after the seventh day. The results show that compared to GA and SA, SAGA has better global and local search ability, and can obtain more optimal solutions. Acknowledgments. We are grateful to the reviewers and the editors for their constructive suggestions. This work was supported by the Subject Construction Foundation of Harbin Institute of Technology, Weihai (Grant No. WH20160102).

References 1. Cho, K.K., Chung, K.H., Park, C., Park, J.C., Kim, H.S.: A spatial scheduling system for block painting process in shipbuilding. CIRP Ann. Manuf. Technol. 50(1), 339–342 (2001) 2. Liu, Z., Chua, D.K.H., Wee, K.H.: A simulation model for spatial scheduling of dynamic block assembly in shipbuilding. J. Eng. Project Prod. Manag. 2(1), 3–12 (2011) 3. Shin, J.G., Kwon, O.H., Ryu, C.H.: Heuristic and metaheuristic spatial planning of assembly blocks with process schedules in an assembly shop using differential evolution. Prod. Plan. Control 19(6), 605–615 (2008) 4. Koh, S., Eom, C., Jang, J., Choi, Y.: An improved spatial scheduling algorithm for block assembly shop in shipbuilding company. In: 2008 3rd International Conference on Innovative Computing Information and Control, p. 253 (2008) 5. Kwon, B., Lee, G.M.: Spatial scheduling for large assembly blocks in shipbuilding. Comput. Ind. Eng. 89, 203–212 (2015) 6. Zhang, Z.Y., Chen, J.: Solving the spatial scheduling problem: a two-stage approach. Int. J. Prod. Res. 50(10), 2732–2743 (2012) 7. Shang, Z.Y., Gu, J.A., Ding, W., Duodu, E.A.: Spatial scheduling optimization algorithm for block assembly in shipbuilding. Math. Probl. Eng. 2017, 1–10 (2017)

Study on the Cutting Force in Elliptical Ultrasonic Vibration Cutting Chengmao Zhang(&) School of Mechanical and Vehicle Engineering, Linyi University, Shandong 276005, People’s Republic of China [email protected]

Abstract. In this paper, the main cutting force, feed force and thrust force of model in elliptical ultrasonic vibration processing research is carried out. Mechanism of ultrasonic vibration processing of ellipse, and the analysis of the main cutting force, thrust force and feed force has been cut down by means of applying elliptical ultrasonic vibration is carried out in theory. Finally, Experimental results show the result of elliptical ultrasonic vibration processing on main cutting force, feed force and thrust force. Keywords: Elliptical ultrasonic vibration processing Cutting force


Stainless steel

1 Introduction Shamoto and Moriwaki first proposed elliptical ultrasonic vibration processing (EUVC) [1]. It has been proved by the elliptical vibration that friction force between metal chip and cutter front face is reversed or cut down, as consequence, the cutting energy and the cutting force are decreased remarkably [1, 2], the regenerative chatter and burr are subjected to suppression [2–4], and a good quality of processing with good surface finish is produced and extremely lengthening tool life can be performed [5–13]. But, references have shown that few people have done research so far on cutting force of hard metal materials by elliptical ultrasonic vibration processing. Most of these studies achieved in copper [1] or aluminium (52S) [14, 15], etc. The main cutting force, thrust force and feed force model in elliptical ultrasonic vibration processing of threedimensional separating type is took up here. The influence on elliptical ultrasonic vibration processing on the main cutting force, thrust force and feed force is theoretically combed. In short, the influence on the main cutting force, thrust force and feed force is verified in ordinary cutting and elliptical ultrasonic vibration processing of hard metal materials by comparison of experimental results. Moreover, the impact of cutting depth on the cutting performance by elliptical ultrasonic vibration processing has been studied.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1137–1142, 2019. https://doi.org/10.1007/978-3-030-02804-6_147

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2 Theoretical Analysis In EUVC method, the tip of the knife moves in an oval track by the use with a ultrasonic elliptical transducer (Shamoto and Moriwaki 1995; Shamoto et al. 2002; Moriwaki and Shamoto 1994; Suzuki et al. 2003, 2004, 2007). Figure 1 to illustrate that a diagram of an electrical of the EUVC approach. A tool is set by the tail of the apparatus and some PZT transducer are fixed into a metal ring [9, 16]. When exciting power supply are used, the apparatus is resonated longitudinal-bending mixed ultrasonic vibration with two actuator by double bending mixed ultrasonic vibration. The elliptical vibrational trajectory can be produced simultaneity by changing the amplitudes of the phase shift and the two driven voltages between them in two ultrasonic driving systems. In this method of cutting, selection of the cutting speed should be lower than the size of the maximum vibration velocity. In every cyclic vibration process, the separation of metal chips and tool cutters is to assure.

Fig. 1. Cutting mechanism of EUVC (Suzuki 2007).

In the first half of the vibration period of ultrasonic vibration, the workpiece and tool are moving vibrate back and forth in opposite direction relatively. It hence starts processing and chips are forming. When work-piece and the tool are moving in the same direction, in the next half cycle, the front angle of the cutter helps to pull the chip back. As a consequence, friction of the workpiece and tool is reverse so that it reduces cutting energy or force significantly. Because of the cutting force is greatly reduced, the service life of the tool has been greatly improved through this method [9].

3 Experimental Discussion and Results 3.1

Experimental Equipment and Conditions

The elliptical ultrasonic vibration equipment was carried out the test of elliptical ultrasonic vibration processing. A tool holder of this device was fasted to the tool carrier, which fixed to a turning machine, which contains stacked PZTs. The machine

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equipment is illustrated in Fig. 2. Measuring the cutting force signal through a dynamometer to a Kistler three channel digital charge application amplifier, a Kistler 3-component dynamometer was attached in the lathe machine with the elliptical vibrator (Kistler 9254).

Power source

Amplifier

Chuck Feed direction Elliptical vibration tool Computer

kistler dynamometer Fig. 2. Experimental equipment

Table 1 shows the elliptical ultrasonic vibration processing experimental conditions. Experimental work piece is stainless steel (0Cr13Ni8Mo2Al) with hardness HRC39. 3.2

Experimental Results and Conclusions

Figure 3 shows that cutting forces were measured at different processing times. Compared with ordinary processing was shown in Fig. 3, The experiment is very obvious that the main cutting force, feed force and thrust force are reduced significantly, about 1/10, 1/30 and 1/40 in elliptical ultrasonic vibration processing are relative to ordinary cutting, that is because the reverse and the characteristic of separation between front knife surface of metal chip and tool of frictional force direction referred to Sect. 2. Figure 4 illustrate effect on cutting forces by depth of cut. By contrast to ordinary cutting in Fig. 4, we see clearly that the main cutting force, feed force and thrust force are reduced significantly in elliptical ultrasonic vibration processing at small processing depth. Cutting forces increase with depth of processing in both the processes, which means that the elliptical ultrasonic vibration processing method accords in the ordinary cutting with same regulation.

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140

ordinary cutting

principal force feed force thrust force

120

Force N

100 80 60

ultrasonic elliptical vibration cutting

40 20 0 0

10

20

30

40

50

60

-20

Time [s] Fig. 3. Cutting forces measured at different time (Depth of cut 0.05 mm and Vc 3.39 m/min)

Study on the Cutting Force in Elliptical Ultrasonic Vibration

Fmocf Fmevcf Fmocp Fmevcp Fmocc Fmevcc

180 160 140 120

Force N

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100 80 60 40 20 0 0.0

0.1

0.2

0.3

Depth of cut (mm)

0.4

0.5

Fig. 4. The effect of cutting depth on cutting forces (Vc 3.39 m/min)

4 Conclusions The main cutting force, thrust force and feed force model is set up in elliptical ultrasonic vibration processing, and the result is analyzed theoretically on the main cutting force, the thrust force and the feed force of elliptical ultrasonic vibration processing in turning. Cutting experiments by ordinary processing and elliptical ultrasonic vibration of processing are carried out. Both experimental results and theoretical analysis of ultrasonic cutting vibration prove that the main cutting force, the thrust force and the feed force can be reduced significantly in elliptical ultrasonic vibration processing compared with ordinary processing. Acknowledgements. The author is very grateful to editors for his hard work and the editor’s rationalization proposal.

References 1. Shamoto, E., Moriwaki, T.: Elliptical vibration cutting. Ann. CIRP 43, 35–38 (1994) 2. Shamoto, E., Ma, C., Moriwaki, T.: Elliptical vibration cutting (the third report). J. Jpn. Soc. Precis. Eng. 65, 586–591 (1999) 3. Ma, C., Shamoto, E., Sattayawuthipho, B., Moriwaki, T.: Suppression of chatter in turning by elliptical ultrasonic vibration. Memories of Graduate School of Science and Technology, Kobe University 17-A, pp. 47–62 (1999) 4. Ma, C., Shamoto, E., Moriwaki, T., Zhang, Y., Wang, L.: Suppression of burrs in turning with elliptical ultrasonic vibration processing. Int. J. Mach. Tools Manuf. 45, 1295–1300 (2005)

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5. Shamoto, E., Ma, C., Moriwaki, T.: Ultraprecision ductile cutting of glass by elliptical ultrasonic vibration processing. In: Proceedings of the 1st International Conference of European Society Precision Engineering and Nanotechnology, pp. 408–411 (1999) 6. Shamoto, E., Moriwaki, T.: Ultaprecision diamond cutting of hardened steel by applying elliptical ultrasonic vibration processing. Ann. CIRP 48, 441–444 (1999) 7. Brinksmeier, E., Gläbe, R.: Elliptical vibration cutting of steel with diamond tools. In: Proceedings of the 14th Annual ASPE Meeting, USA, pp. 163–166 (1999) 8. Nath, C., Rahman, M., Neo, K.S.: Machinability study of tungsten carbide using PCD tools under elliptical ultrasonic vibration processing. Int. J. Mach. Tools Manuf. 49, 1089–1095 (2009) 9. Nath, C., Rahman, M., Neo, K.S.: A study on elliptical ultrasonic vibration processing of tungsten carbide. J. Mater. Process. Technol. 209, 4459–4464 (2009) 10. Suzuki, N., Yokoi, H., Shamoto, E.: Micro/nano sculpturing of hardened steel by controlling vibration amplitude in elliptical vibration cutting. Precis. Eng. 35, 44–50 (2011) 11. Kim, G.D., Loh, B.G.: An elliptical ultrasonic vibration processing device for micro Vgroove machining: kinematical analysis and micro V-groove machining characteristics. J. Mater. Process. Technol. 190, 181–188 (2007) 12. Suzuki, N., Haritani, M., Yang, J., Hino, R., Shamoto, E.: Elliptical vibration cutting of tungsten alloy molds for optical glass parts. Ann. CIRP 56(1), 127–130 (2007) 13. Song, Y.C., Park, C.-H., Moriwaki, T.: Mirror finishing of Co–Cr–Mo alloy using elliptical vibration cutting. Precis. Eng. 34, 784–789 (2010) 14. Ma, C., Shamoto, E., Moriwaki, T., Wang, L.: Study of machining accuracy in elliptical ultrasonic vibration processing. Int. J. Mach. Tools Manuf. 44, 1305–1310 (2004) 15. Ma, C., Shamoto, E., Moriwaki, T.: Study on the thrust cutting force in elliptical ultrasonic vibration processing. Mater. Sci. Forum 471–472, 396–400 (2004) 16. Brehl, D.E., Dow, T.A.: Review of vibration-assisted machining. Precis. Eng. 32, 153–172 (2008)

The Influence of Climate in Lingnan Area on the Architectural Design of Library and Its Countermeasures Zhenwei Wang(&) Guangdong University of Science and Technology, Dongguan 523083, China [email protected]

Abstract. The external environment of the library refers to the architectural modeling of the library and the surrounding environment. The library is generally a landmark of a school and directly affects the outsider’s overall sense of the school. At the same time, the architectural style of a library can also affect readers’ reading mood and reading behavior. This paper analyzes the climatic characteristics of Lingnan region from the macro, meso, and micro perspectives by studying the climatic characteristics of Lingnan region and the characteristics of previous buildings. Then from the two aspects of creating an ecological space and setting up thermal buffer layers, it proposes an ecological strategy for library building design [1]. Due to the limitations of access to knowledge, my views and ecological strategies may be somewhat biased. Keywords: Library Influence


Architectural design
Climate in Lingnan region

1 Introduction One of the issues designers must consider during the design process is the local climate. Architecture and climate are closely linked, because the reason why humans need to build is to better adapt to environmental changes. It can be said that the climate environment has a decisive influence on the design of buildings.

2 Climate Characteristics of Lingnan Area Lingnan belongs to the south of the East Asian monsoon climate zone and has the characteristics of tropical and subtropical monsoon oceanic climate. Most of Lingnan is of subtropical fortunate monsoon climate. The Leizhou Peninsula, Hainan Island and South China Sea Islands are tropical climates. The Tropic of Cancer passes through the middle of the south of the Lingnan Mountains, and its high temperature and heavy rainfall are the main features of the climate. Most regions have short summers and long winters, but they do not see frost and snow all year round. The amount of solar radiation is more and the sunshine duration is longer. Take Guangdong Province as an example, the average sunshine hours across the province is between 1450 h and 2300 h. © Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1143–1148, 2019. https://doi.org/10.1007/978-3-030-02804-6_148

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Lingnan is a typical monsoon climate zone, and the wind direction changes with the seasons. In the summer, the winds from the south to the southeast are dominant, and the wind speeds are small. In most of the winter, the winds from the north to the northeast are predominant, and the wind speeds are relatively large. In spring and autumn, the season is alternate, and the wind direction is not as stable as winter. Due to the high temperature throughout the year and the abundant rainfall, the forests are lush, evergreen, and flowers blossom. Plant resources are very abundant. Forest plants also provide favorable conditions for the growth of animals. Lingnan has more animal species and is one of the most prosperous regions in the country.

3 Architectural Features of Lingnan Area Lingnan architecture preserves both the ancient side and the Chinese side and the west side. Lingnan coastal areas, especially in Guangzhou, have access to sea transportation. Cultural exchanges between China and foreign countries are very active. The Qing Dynasty folk architecture used Western decoration techniques and decorative materials, reflecting the development of changes in Chinese and foreign exchanges. At the end of the Ming Dynasty, there were European-style churches and residences built by the Portuguese in Macao. Afterwards, there were 13 businesses in Guangzhou, including the “Yingguan”, commonly known as the “Yangguan” and “Yangruo Painting”. This was the earliest “western building” that appeared in mainland China. In the Qing Dynasty, after the Opium War, a large number of Western-style large-scale public buildings such as commercial, financial, customs and post offices appeared in Guangzhou’s Changdi and Xidi areas, and more advanced reinforced concrete or I-beam construction materials were used. The Shamian Concession has concentrated hundreds of various types of Western buildings, including consulates, banks, foreign banks, churches, schools, etc., in the form of neo-classical styles, vouchers, imitation gothic styles, etc.; in other places in Guangzhou There are churches (including the largest stone Gothic churches in the Far East), church schools and hospitals, and western-style villas. It also affected official buildings (such as the Guangdong Provincial Office of the Advisory Council), private houses, and factories (such as the Tu Min soil factory that had become the generalissimo government office). It has become a French concession in Guangzhou Bay, opened in the city of Shantou, Haikou, but also a four-style building. In various places, there are churches and outbuildings built on foreign missions. In the hometown of overseas Chinese, the overseas Chinese returned to their hometowns to build houses, and had the architectural style of living in their home countries. Modern buildings in Lingnan show more features of the combination of Chinese and Western styles. The letters sent from Guangzhou on April 11, 1860 in the French Bulletin describe the visits by a Frenchman to a wealthy house in Guangzhou. The floor is marble, and the house is also decorated with marble columns, extremely tall mirrors, and valuables. The furniture made of wood is painted with Japanese paint, velvet or silk carpets decorate the rooms one by one, and the chandelier with precious stones hangs down from the large flower board. In the late Qing Dynasty and early Republic of China, the arcade buildings that began to become popular in the towns of the south of the Five Ridges retain the traces of the real-estate emptiness that stemmed from the gantry

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architecture. The column and the side of the street have also introduced western vouchers and columns, which are called “western storefronts.” The eclectic mix of Chinese and Western decorative techniques, such as cased glass, coiled iron window bars, bottle railings, arched doors and windows, and geometric pools, were once popular in Lingnan towns and became an indispensable part of modern architecture in Guangdong. Figure 1 below shows the typical architectural appearance of the Lingnan area.

Fig. 1. Typical architectural appearance of Lingnan area

4 Climate Suitability Strategy in Lingnan Region In the process of urban construction in Lingnan, it provides people with a comfortable and livable living environment. According to the climatic characteristics of Lingnan, after referring to the past architectural features of Lingnan area, the Lingnan library architecture design proposes the following strategies. 4.1

Macro Perspective

4.1.1 Plan Structure with “Generally Distributed and Moderately Concentrated” “Generally dispersed” means that there should be enough green space between city groups to control the scale of built-up areas, increase the ecological contact area between the built-up areas of cities and the external natural environment, and facilitate the air exchange between the inner and outer areas of the city to avoid The accumulation of urban heat reduces the heat island effect; “moderate concentration” refers to the proper compact layout within the urban cluster to meet the demand of urban development for land use, but at the same time to avoid excessively high building density and lead to long-term high thermal stress levels in urban areas.

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4.1.2 Using a Concave Space Pattern in the Longitudinal Direction In the south of the Lingnan area, urban space should be strengthened as far as possible in the southward and southeast direction of the summer dominant wind. Therefore, in the overall layout of the building, it should follow the direction of the dominant wind direction, that is, the southernmost layout of the low-rise building type, In the north, the height of the building is gradually increased to form a concave shape that caters to the dominant wind [2]. 4.2

Meso-angle

4.2.1 Suitable Ventilation Corridor The planning and design of urban ventilation corridors generally combines natural channels such as natural rivers, lakes, and green spaces. At the same time, the main roads along the city are also important ways to form ventilation corridors. However, the high-rise buildings on both sides of the road often form a “canyon effect”. The wind speed can be increased by 15% to 20%. Although the basic requirements for ventilation and heat dissipation have been reached, the street space has too much wind to affect the comfort of outdoor activities. Therefore, the direction of major roads should maintain a certain angle with the prevailing wind direction. In addition, relevant studies have shown that on the urban scale, the ventilation corridor can reach an ideal width of 150 m. Therefore, the use of low-intensity development on both sides of the main ventilation corridor can be considered to enhance ventilation. 4.2.2 Inclined Block Layout In Lingnan area, the densely populated urban areas generally have more heat storage and are difficult to distribute. Therefore, ventilation in the neighborhood is particularly important. When the street is perpendicular to the wind direction, the layout of the strips along the street should be avoided as much as possible, because such a model tends to hinder the ventilation of the city and reduce the wind speed and the speed of the street above the roof of the building. When the street is parallel to the wind direction, the street wind speed is relatively large, but the buildings on both sides are poorly ventilated. The ideal street direction should be 30–60° with the wind direction, taking into account the ventilation effect of the street and the buildings on both sides. 4.2.3 Reasonable City Interface The urban interface has an important influence on the comfort of urban climate, especially the effect of green space on the cooling effect of the surrounding built environment is very significant. Therefore, increasing the proportion of green space can effectively reduce the temperature of the city. However, there is a distance limitation problem in the cooling effect of greening vegetation [3]. When the general distance exceeds 400 m, even if the green area is doubled, the cooling effect is not improved obviously. In the layout of green land, in order to improve the overall cooling efficiency, it is recommended that small and medium-sized green space should be the decentralized arrangement, and that all green areas must be concentrated. In addition, in the main colors of the city, it is recommended to use light-colored tones to avoid excessive depth. Tones absorb too much radiation and cause unnecessary urban warming.

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Microscopic Perspective

4.3.1 Optimizing the Portfolio of Buildings It is advisable to maintain a sufficient distance between buildings and should be staggered so that as many buildings as possible are well ventilated. In order to increase the degree of ventilation of a building, space can be reserved between buildings, between the platform and its upper floors, and between different floors of the same building to ensure ventilation of the building behind the building. In addition, the height of the stair-type building helps to improve the ventilation of the building and can guide the air flow from the sky to the ground. 4.3.2 Optimizing Building Monolith Design The monolithic building design encourages the use of new energy-saving materials, reducing building heat dissipation through architectural construction and threedimensional afforestation design. The shape design of high-rise buildings should take into account the influence of windward facing the air flow and reduce the upwind area index [4]. At the same time, consideration should be given to adopting a building shape that generates a small air flow vortex, and the cool air is introduced as far as possible into the building and its surrounding area.

5 Climate Suitability Ecological Strategy in Lingnan Region 5.1

Creating Ecological Space

Lingnan has a warm and rainy climate with lush vegetation. During the architectural design of the library, consideration should be given to adapting the design of the library to the surrounding natural environment. The core of library architecture design is from nature, applied to nature. Ultimately zero pollution and zero emissions. 5.2

Setting a Hot Buffer Layer

The function of the thermal buffer layer is similar to that of the check valve, which equalizes the pressure in the pipeline and relieves the effect of the flow concrete pressure. In short, buffering can be understood as a spring [5]. When the stroke of the concrete pump starts, the concrete receives upward pressure and the stroke ends, and gravity affects the concrete to become a large downward pressure. Severe pressure changes, the pump is easily damaged, and can not eat. With the buffer layer transition, a portion of the pressure is absorbed by the buffer layer and the upward pressure in the pump tube can be maintained.

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6 Conclusion The climatic condition of Lingnan is an invisible program for the construction of libraries in the Lingnan region. A good space design is conducive to improving the microclimate of the library, and a good microclimate contributes to the comfort of readers. Therefore, “form following the climate” should be the same as “form following function” and become an important part of library architecture design. Acknowledgment. This work was financially supported by Guangdong Book and Cultural Information Association (GDTWKT2017-52): “The Influence of Climate in Lingnan Area on the Architectural Design of Library and Its Countermeasures.”

References 1. Xiao, Y., Wang, J., Qi, B.: Reflections on the heat protection mode of exterior skin of buildings under hot and humid climate. South China Archit. (2010) 2. Zeng, Z.: Ventilation method of traditional residential buildings in Guangfu and its application in modern architecture. South China Univ. Technol. (2010) 3. Li, A.: Discussion on ventilation and heat dissipation design of house buildings. Urban Geogr. (2014) 4. Yao, Y.: Research on passive design strategies for Lingnan Office buildings. South China Univ. Technol. (2011) 5. Ren, T.: Integrated photovoltaic design for non-closed visor heat-sink surface construction in the hot and humid areas in Guangzhou. South China Univ. Technol. (2011)

Designed of Ball Rolling Control System Based on STM32 Si-Lian Xie1(&), Zhou Yue1, and He-Ping Li2 1

2

Information Institute, Hunan University of Humanities, Science and Technology, Loudi 41700, China [email protected] Loudi Vocational and Technical College, Loudi 41700, China

Abstract. This design is a control system that uses a series MCU of STM to control the small ball can run along the specified track to the destination point and stable. It is used STM32F407 and STM32F103 as a control center for the control of signal acquisition and control of the steering wheel. The STM32F407 and the camera OV7670 form the information collection system module. The information that is collected is processed coordinate former by STM32F407, which is sent to another to receive the Bluetooth module by Bluetooth. Then the coordinates are received by STM32F103, which controls the rotation of the two steering gears according to the coordinate information. To control the rigid ball in accordance with the specified trajectory movement and to reach the specified time of stay. When the ball reaches the specified coordinates, use the Positional PID control to stabilize the ball at the specified point. Keywords: Steering gear
Information acquisition Bluetooth
Positional PID


Track
Coordinate

1 Introduction On a square plate with a smooth side length of 65 cm, there are nine circular areas with outer diameter 3 cm, numbered 1–9 respectively. The position is shown in Fig. 1. The designed control system, by controlling the tilting of the plate, enables the ball with a diameter less than 2.5 cm to complete all kinds of actions on the plate according to the specified requirements. And to start the timing and display from the action. When actions start, the time begins and displays.

2 System Scheme Design 2.1

System Architecture Diagram

The architecture of the whole system consists of 2 planks, 2 single chip microcomputers, 2 Steering gears, 1 camera, etc. The diagram is shown in Fig. 2.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1149–1156, 2019. https://doi.org/10.1007/978-3-030-02804-6_149

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Fig. 1. System position diagram

Fig. 2. System architecture diagram

2.2

System Block Diagram

The system is composed of signal acquisition system and control system [4], and the communication between the 2 systems is carried out by wireless module. The whole scheme also includes steering gear driving module, power module, key control module and LCD dynamic [5] tracking display small ball module, etc. The system block diagram is shown in Fig. 3.

3 Theoretical Analysis and Design The system collects the information of the small black ball through the camera OV7670, and preprocesses the collected image, such as grey level transformation, wave filtering, edge detection, then sends it to the main control system in the form of coordinates through Bluetooth in real time. The master chip controls steering arm to rotate and pull the pull rod to make the board move around. When the ball reaches the specified coordinates, the PID is used to control the ball to stabilize at the specified point.

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Fig. 3. System block diagram

3.1

Measurement and Calculation of the Position of the Rolling Ball

Select a roll ball with a large difference in color from the base plate (the design uses white and brown ball), and collect the image through the camera OV7670. The OV7670 converts the brightness of the board and the ball into the gray degree of the pixel, and passes it to the single chip computer via the data bus, processing it into a binary image. SCM began to scan the binary image pixel points, find the ball in the picture, and use coordinates to express the position of the ball. The OV7670 continuous transmission of gray image to the single chip microcomputer can get the ball position at all times. Method of grayscale treatment [7, 8]: Rafter ¼ Rbefore  0:3 þ Gbefore  0:59 þ Gbefore  0:11 Gafter ¼ Rbefore  0:3 þ Gbefore  0:59 þ Gbefore  0:11 Bafter ¼ Rbefore  0:3 þ Gbefore  0:59 þ Gbefore  0:11 R is the value of the red variable, G is the value of the green variable, and B is the value of the blue variable [6]. The below after indicates after value of grey. The below before indicates the before value of processing. Binaryzation [9] is to have the gray value of each pixel in the image dot matrix of the image be 0 (black) or 255 (white), which is to make the whole image appear only black and white [10]. The range of the gray value in the grayscale image is 0–255, and the range of gray value in the binary image is 0 or 255. Black: R ¼ G ¼ B ¼ 0; B ¼ 0 White: R ¼ G ¼ B ¼ 255

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Design of the PID Controller

When the structure and parameters of the controlled object are not fully grasped or the exact mathematical model is not available, the structure and parameters of the system controller must be determined by experience and field debugging. The PID control technology [2, 3] is the most convenient. The PID controller is calculated according to the error of the system by proportion, integral and differential. This system uses the position-type PID algorithm to control the speed of stepping motor rotation. After the motor began to work, the position acquisition module collected the current position of the rolling ball continuously, and compared with the previous position of the rolling ball, which made the bottom plate tilt change gradually. The PID algorithm controller [1] consists of ball position error ratio P, ball position error integral I and ball position error differential D. The relation between the input e(k) and the output U(k): X Uðk) ¼ Kp  eðkÞ þ Ki  eðiÞ þ Kd  ½eðkÞ  eðk  1Þ In the ball system, the parameters of pid are obtained by repeated: KP ¼ 3

TI ¼ 0:2

TD ¼ 38:

4 Hardware Circuit and Program Design 4.1

Information Acquisition System and Control Circuit

The camera OV7670 is used to collect the ball data, and then it is transmitted to the STM32F407 [11]. The MCU is sent to the receiver’s Bluetooth through the Bluetooth in the form of coordinates. The received data are sent to STM32F103 [12] for analysis and processing in order to control and rotate the steering gear and make the plank rotate accordingly. The circuit is shown in Fig. 4. 4.2

System Flow Chart

The camera collects the position information of the rolling ball constantly and sends it to the MCU. The processed position information is transmitted to the PID algorithm and the motor is controlled by the PID algorithm to accomplish the purpose. The flow chart is shown in Fig. 5.

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Fig. 4. Circuit schematic diagram of information acquisition system

START

POSITION ACQUISITION

CHANGE THE POSITION OF THE BALL

TRANSMISSI ON TO SCM

CHANGE THE TILT ANGLE

EXTRACT LOCATION INFORMATION

TRANSMIT PULSE TO MOTOR

N

DELIVERY TO PID ALGORITHM

WHETHER TO ACHIEVE THE SET GOAL Y RETURN

Fig. 5. System flow chart

4.3

Rolling Ball Position Acquisition and Processing

The grayscale image collected by the camera is transmitted to the single-chip microcomputer, which processes the image into a binary image. The position of the ball is determined by pixel scan by interlacing, and the position information is transferred to the control algorithm in the array. The processing flow chart is shown in Fig. 6. 4.4

Design of PID Algorithm

Using position PID algorithm. The algorithm needs to read the fixed value and input value and calculates the two-value deviation. According to Positional PID algorithm, it can calculate U(k) and store U(k). Move the storage data address to the right to access the new current input value, and then return to the initial step. PID algorithm flow chart is shown in Fig. 7.

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START

END

THE CAMERA PICKS UP IMAGES

PROCESSING INTO BINARY IMAGE

SCM

TRANSMISSION TO CONTROL ALGORITHM

DETERMINE THE VERTICAL COORDINATES OF THE BALL

Y

PIXEL SCAN BY INTERLACIN

WHETHER IT IS A SMALL BALL

N

Fig. 6. Rolling ball position acquisition and processing flow chart

START

READ THE GIVEN VALUE

END

READ INPUT VALUE

CALCULATES THE BINARY VALUE DEVIATION

e k-1 ? e k-2 e k ? e k-1

CALCULATE U k

STORE DATA

Fig. 7. PID algorithm flow chart

5 Data Test and Analysis 5.1

Test Content 1

(1) Place the ball in area 2 and control the ball to stay in the area for not less than 5 s. (2) In 15 s, let the ball enter area 5 from area 1 and stays in area 5 for not less than 2 s. (3) Control the ball from area 1 to area 4, stay in area 4 for not less than 2 s; Then entering area 5, the ball stays in area 5 for not less than 2 s. The ball stays in area 5 for not less than 2 s. The total time to complete two actions does not exceed 20 s. (4) For 30 s, let the ball enter area 9 from area 1 and stay at area 9 for not less than 2 s. 5.2

Test Result Analysis 1

After the system is designed, the performance is tested step by step, as shown in Table 1. 5.3

Test Content 2

(1) Within 40 s, let ball start from area 1, then enter area 2 and area 6, stop in area 9 and maintain not less than 2 s. (2) Within 40 s, let the ball start from area A, enter area B and area C, stop in area D; The test uses the keyboard to set the number A, B, C, D in turn to control the ball to complete the action. (3) The ball starts from area 4 and moves around area 5 (not entering), After more than 3 weeks of the movement, the ball stops in area 9, and maintains not less than 2 s.

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Table 1. Test result analysis 1 Request Number of tests ① ② ③ ④ ⑤ (1) >5 s >5 s >5 s >5 s >5 s (2) 7.2 s 8.3 s 7.1 s 6.4 s 9.3 s (3) 24.1 s 15.9 s 16.2 s 17.8 s 14.6 s (4) 7.2 s 8.9 s 8.5 s 7.8 s 8.3 s Test result analysis: The above data show that the function can be achieved, but the time is not stable, fluctuating around a value. After many researches, the reason for the unstable time is the dead zone of the steering gear. The constant wobble of the ball in this dead zone causes uncertainty about the stopping time.

5.4

Test Result Analysis 2

After the system is designed, Test in accordance with the above requirements, as shown in Table 2. Table 2. Test result analysis 2 Request Number of tests ① ② ③ (1) 10.4 s 12.2 s 12.8 s (2) 29.7 s 30.6 s 26.9 s (3) 32.6 s 34.3 s 28.4 s Test result analysis: Data testing requirements.

④ ⑤ 13.5 s 11.0 s 27.3 s 30.2 s 31.7 s 31.3 s can meet the

6 Conclusion The system takes the STM single chip microcomputer as the control core, controlling the signal collection and the steering gear rotation, respectively. The collected information is processed by STM32F407 and sent to another receiving Bluetooth module by Bluetooth in the form of coordinates. According to the coordinate information, the rotation of the 2 steering gears is controlled to drive the rod to move around the board in order to control the ball to achieve balance according to the specified trajectory. The designed control system, by controlling the tilting of the plate, can make the ball complete all kinds of prescribed actions on the plate according to the specified requirements. This system can be widely used in robot self-balancing and 2 wheeled vehicle balancing system.

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Fund Project. National Natural Science Fund Project (661571188), Hunan Natural Science Fund Project (2018JJ5048).

References 1. Li, S., Wan, C., Li, H., Lin, R.: Design and manufacture of ball rolling control system based on PID controller. Equip. Manuf. Technol. 11, 68–70 (2017) 2. Zhao, J., Zhao, Y.: Research of scrolling control system based on STC89C52. Telecom World 14, 283–284 (2017) 3. Shu, H., Chen, W.: Intelligent floating embroidery textile control system based on ARM and Zigbee technology. Comput. Meas. Control 2(7), 2359–2361 (2015) 4. Zeng, W., Cai, L., Han, B.: Design and implementation of ball control system base on 51 small single-chip. Electron. Des. Eng. 25(08), 68–71 (2017) 5. Zhang, Y., Zhang, L., Luo, Y.: Stability analysis and verification of self-balancing control system. J. Chongqing Univ. Posts Telecommun. 26, 501–506 (2014) 6. Zhang, J., Zhang, Q.: Image fusion algorithm based on wavelet transform. Comput. Eng. Appl. 12, 74–76 (2007) 7. Xia, J.: Design of Computer graphic image and visual communication in virtual reality environment. Electron. Technol. Softw. Eng. 18, 94–95 (2016) 8. Li, C.: Design and implementation of image preprocessing system based on FPGA. Xi’an University of Electronic Science and Technology (2014) 9. Castlemen, K.R.: Digital Image Processing, pp. 170–187. Electronic Industry Press, Beijing (1998) 10. Li, L.: A study on image denoising method. Yangtze River University, Hubei (2012) 11. Gao, L.: Design of two-wheeled self-balance system based on posture sensor. Shandong Ind. Technol. 12, 40–41 (2015) 12. Ding, L., Song, Z., Xu, M., Tao, C.: Design of embedded measurement and control system based on STM32. J. Cent. South Univ. (Science and Technology) 44, 260–265 (2013)

Influencing Factors of Government Microblogs’ Communication Effects: A Research Based on User Behavior Manrui Zhang1(&) and Wenbo Liu2 1 School of Literature, Law and Economics, Wuhan University of Science and Technology, Wuhan, China [email protected] 2 Hubei SME Research Center, Wuhan University of Science and Technology, Wuhan, China

Abstract. As an important carrier of e-governance, government microblog has an important influence on the government’s public management activities. This paper uses statistical methods based on the influence evaluation method of user behavior to calculate the influence of microblogs, and then analyzes the correlation between microblog characteristics and user behaviors, and draws the influencing factors of government microblogs’ communication effect. Through the above research, this paper provides some feasible suggestions for the development of government microblogs. Keywords: E-governance


User behaviors
Communication effects

1 Introduction In recent years, great progress has been made in the construction of e-governance in China. As an important carrier of e-governance, government microblog has gradually become a hot spot. More and more government agencies and government officials have opened microblogs, using microblog as a channel for information dissemination and a place for interaction between government and citizens. To maximize the role of government micro-blogging, we need to improve the influence of government microblogs to have a better communication effect. This paper combines quantitative analysis with qualitative analysis, and combines the evaluation method of microblog influence put forward by authoritative scholars in the computer field. It makes up for the shortcomings of other scholars who use retweet number as the impact measurement standard. We analyze the time of microblog release, the length of microblog, whether the microblog is original, the topic of microblog. Through these four characteristics of microblog content and the relevance of user behavior, we can find the factors that can affect user behavior, and thus put forward suggestions to improve the effect of microblog communication. It also has a better theoretical and practical significance for the study of the development of our government microblogs.

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1157–1162, 2019. https://doi.org/10.1007/978-3-030-02804-6_150

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2 Data Collection and Processing Data is the foundation of research, and high-quality data is guaranteed to produce the right results. This paper guarantees the quality of data through strict random sampling process, fast and accurate data acquisition and data screening. 2.1

Sample Selection

In order to ensure the timeliness and accuracy of the research, this article is based on the 2017 Sina Government Microblogs Report released by People’s Daily Public Opinion Monitoring Office. Then we randomly selected 20 microblog accounts from the national top 100 government agency microblogs, and selected all the microblogs released between March 1 and March 31, 2018 as research samples. 2.2

Data Collection

Microblog service providers are open-minded For microblog data. Twitter has provided a complete set of API for microblog app developers and researchers. Developers can use this API to access Twitter’s various data, including user information, user relationships, and user-published Tweets. After calling the open API interface of microblog, this paper obtains the detailed information of 5804 microblogs through the limitation of microblog account name and release time, including the release time, publishing terminal, microblog content, and number of retweets, comments, and mentions. 2.3

Data Preprocessing

The quality of the data directly affects the results of the analysis, and the screening and pre-processing of the data is particularly important. According to the integrity check and manual selection of the library itself, screening reasonable data, deleting duplicate entries., and finally obtaining 5736 microblog data based on 20 microblog accounts.

3 Microblog Influence Evaluation Method Based on Comprehensive Analysis of User Behavior Qi Chao et al. proposed a PageRank algorithm based on the distribution of behavior weight. Estimate user influence by using retweets, comments, and mentions as user behavioral factors that measure user influence. Adding the weights of these three behavioral factors to obtain the formula for calculating the influence, but taking into account that the contribution of these three user behaviors to the microblog information propagation is inevitably different, so we use a, b, k to represent the contribution of retweet, comment, and mention. Expressed as a formula:
Q vi ; vj ¼ aRji þ bCji þ kLji

ð1Þ

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Rji represents the retweet weight, Cji represents the comment weight, and Lji represents the mention weight. Qi Chao et al. combined the PageRank algorithm to statistically analyze all the acquired data in the network, and obtained a = 4/7, b = 2/7, and k = 1/7 after calculation. The behaviors from the parameter values that reflect the contribution to the user’s propagation impact are, in turn, retweet, comment, and mention. These three values can be used to calculate the influence of subsequent microblog propagation.

4 Research on the Influencing Factors of Government Microblogs’ Communication Effect 4.1

Based on Release Time

The release time of microblog is related to the time users browse to the content. This paper divides a day into nine characteristic periods to study the impact of microblog release time on the behavior of fans. As shown in Fig. 1, most microblogs are released from 9 a.m. to 11 p.m., 9 p.m. to 11 p.m. We can draw the conclusion that the number of microblogs posted by bloggers tends to be roughly the same as the impact of microblog, so choosing to post microblogs in the morning and evening can achieve better results. We believe that the release of micro-blog’s time points has a significant impact on microblog’s communication effect.

Fig. 1. Microblog’s release time and average impact line chart

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Based on the Length of Microblog

Under normal circumstances, the more microblogs, the richer the content, the more information is expressed. Therefore, we believe that different lengths of microblog may affect the reader’s forwarding behavior. In this paper, the length of microblog is divided into 20 characters. After eliminating the singular value, it is found that the number of microblogs with a length of 20 words or less is the highest, accounting for 45% of the total, followed by microblogs with 21–40 words, accounting for 21% of the total. As the length of the microblog increases, the number decreases. From Fig. 2 we can see that the influence of microblog is basically increased with the increase of the length of microblog. The author believes that microblog, which has a small number of words, can express less information and less useful information. When the number of words is gradually increased, more and more information is expressed, and useful information is also increased. Therefore, we believe that the length of microblog significantly affects the dissemination effect of government microblog through the amount of information it expresses.

Fig. 2. Microblog’s length and average impact column chart

4.3

Based on Microblog Original

The originality of Weibo content may affect the behavior of Weibo audiences. From the perspective of originality, Weibo data can be divided into two categories: original and forward. Due to the original logo in Weibo metadata, this paper uses JAVA programming language to write programs to identify the originality of Weibo, and through the validity test, the 5736 data obtained are imported into SPSS for statistical analysis. The results are shown in Table 1. Table 1. Original difference statistics Original Original Non original Total

N Average influence Standard error Standard deviation 3755 126.25 21.261 2210.392 1981 15.79 0.437 30.248 5736 98.32 14.734 1840.335

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It can be clearly seen from Table 1 that the proportion of original Weibo is about twice that of non-original Weibo, and the original average forwarding number is significantly higher than non-original. This paper has the following conclusions: Within the scope of error tolerance, there is a significant difference between the original microblog and the forwarding microblog. The audience obviously favors the original microblog. The originality of the government microblog is a significant factor affecting the microblogs’ communication effect. 4.4

Based on Microblog Topic

According to the different topics of microblog, we divide microblog into eight types: government notice, service information, event promotion, life encyclopedia, news, interaction, online office, and other. In this paper, 500 random microblogs are extracted from the sample and classified by random extraction. It can be seen from Table 2 that the influence of different categories of microblog is different. The topics with the best communication effect are service information and event promotion. These two topics have much greater influence than other 6 microblog. We believe that the microblog topic significantly affects the spread of microblog.

Table 2. Original difference statistics Topic N Government notice 98 Service information 79 Event promotion 35 Life encyclopedia 42 News 114 Interaction 57 Online office 34 Other 41

Average influence 147 491 523 259 165 275 115 158

5 Conclusions and Recommendations After analyzing the data obtained, the following conclusions can be drawn: Firstly, the release time and length of microblog are the significant factors affecting the effect of microblog dissemination. We should grasp the active time period of the audience and control the length of microblog so as to achieve twice the result with half the effort. Secondly, The originality of microblog has a significant impact on the spread of microblog, and the original microblog is more popular with users.. Third, microblog’s topic category and microblog text content are significant factors affecting the effectiveness of government microblog communication. To enhance the practicability of microblog content, it can be released with pictures and videos if necessary. However, whether the results of this study are applicable to all government microblogs requires more sample data and deeper analysis.

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Based on the above research, we have the following suggestions for the construction of microblog: First, accurately positioning, giving full play to the strengths and creating the characteristic microblog plate. The publication of government microblogs should be closely related to the functional scope of the department, using people’s livelihood as a guide. For example, public security microblogs can focus on popular science and rumors, traffic police microblogs should focus on real-time road conditions and daily travel recommendations for the masses. Second, controlling the time and the length. Publishing microblogs will help you get more attention by choosing the leisure time in the morning and evening. And we should control the length of microblog, and publish as much useful information as possible to the masses when editing microblog. Last but not least, closely matching hotspots. A pair of eyes that are good at finding hot spots are the qualities that every government microblog operator should have. In addition, government microblog should abandon the idea of “official standard”, communicate with the masses in vivid and humorous language, listen to the voice of the people. Only by fully mobilizing the enthusiasm of the people to participate, can the dissemination effect of government microblog be improved to a greater extent.

References 1. Qi, C., Chen, H., Yu, H.: Method of evaluating micro-blog users’ influence based on comprehensive analysis of user behavior. Appl. Res. Comput. 31(7), 2004–2007 (2014) 2. Huang, Y., Zeng, R.: Analysis of the influencing factors of the officials’ micro-blogs based on opinion leaders cases. J. Intell. 9, 135–140 (2014) 3. Guo, H., Yu-Liang, L.U., Wang, Y., et al.: Measuring user influence of a microblog based on information diffusion. J. Shandong Univ. 47(5), 78–83 (2012) 4. Lian, J., Liu, Y., Zhang, Z.J., et al.: Analysis of user’s weight in microblog network based on user influence and active degree. J. Electron. Sci. Technol. 10(4), 368–377 (2012) 5. Riquelme, F., González-Cantergiani, P.: Measuring user influence on twitter: a survey. Inf. Process. Manag. 52(5), 949–975 (2016)

Author Index

B Bao, Shuxian, 904 Bao, Xingchuan, 137 Bin, Zeng, 159 Bisong, Liu, 424 C Cai, Jia Xiao, 216 Cao, Wenting, 1017 Cao, Xiaoming, 498 Chen, Fuli, 593, 642 Chen, Haihong, 241 Chen, Hui Ying, 216 Chen, Lifen, 454 Chen, Miaofang, 375, 936 Chen, Qiaohong, 397 Chen, Shu, 572 Chen, Si, 312 Chen, Xiangyu, 805 Chen, Xuezhong, 566 Chen, Zengmao, 862, 870, 879 Cheng, Kang, 1064 Cheng, Miaoting, 498 Cheng, Min, 1088 Cheng, Yi, 1064 Cheng, Yueming, 1064 Chong, Song-Kong, 836 Chuang, Li-Yeh, 109 Ci, Huiyue, 1130 D Deng, Mengwei, 404 Deng, Xia, 904 Diao, Ming, 862 Ding, Huafeng, 523 Ding, Jiafeng, 30

Dingyu, Jiang, 270 Dong, Hailing, 177 Dong, Maoqi, 81 Dong, Qingquan, 1053 Dou, Jianzhong, 103 Duan, Shihong, 95, 489, 847 Duan, Tingting, 611, 1080 E Ergashev, Botir, 661 F Fan, Rong, 177 Fan, Rui, 593, 633, 642, 714, 994 Fei, Feng, 985 Fei, Liu, 760, 768 Feng, Liang, 194 Feng, Tao, 375, 936 Feng, Xiaolei, 958 Feng, Yongzhao, 633 Feng, Youqian, 966 Fu, Chongguang, 81 Fu, Lianlian, 404 G Gao, Changchun, 40, 47 Gao, Sheng, 994 Gao, Yang, 789 Gong, Chibing, 382 Gong, Fei, 30 Gong, Jing, 805 Gu, Mengge, 1064 Guanglei, Li, 270 Guo, Baoxian, 558 Guo, Bifeng, 593, 633, 642, 714, 994

© Springer Nature Switzerland AG 2019 F. Xhafa et al. (Eds.): IISA 2018, AISC 885, pp. 1163–1167, 2019. https://doi.org/10.1007/978-3-030-02804-6

1164 Guo, Hongchen, 202 Guo, Mei, 745 Guo, Yidong, 917 H Han, Lei, 40, 47 Han, Yulong, 823 Hao, Chen, 974 Hao, Wang, 270 Hao-Yuan, Pang, 760, 768 Hayrutdinov, Saidjahon, 661 He, Fangfang, 699 He, Jie, 95, 468, 489, 847 He, Peng, 691 He, Weitao, 593, 994 He, Xiaomei, 917 Hong, Zeng, 270 Honggang, Han, 974 Hongtu, Cai, 650 Hongying, Jao, 954 Hou, Mengshu, 14 Hou, Yibin, 21 Hou, Yingkun, 115 Hou, Zhansheng, 142 Hu, Hong-Yu, 344 Hu, Jun, 375, 936 Hu, Xiaoping, 1064 Huang, Renzhong, 1031 Huang, Ruiping, 356 Huang, Ting, 691 Huang, Xueling, 137 Huang, Yunmin, 966 Hwang, Min-Shiang, 836, 842 J Jia, Fang, 958 Jia, Hui, 856 Jia, Lina, 3 Jia, Peng, 945 Jia, Yubo, 397 Jian, Ming, 661 Jie, Sun, 1117 Jing, Yuan, 722 Jinghua, Wen, 985 K Kan, Jiang, 424 Kang, Houliang, 283, 328, 1025 Kim, Jaeho, 186 Kun, Liang, 760 L Lai, Hongyi, 103 Lan, Shi-bin, 945

Author Index Lei, Che, 88 Li, Changkai, 417 Li, Dong, 736 Li, Dongsong, 47 Li, Guang, 928 Li, Guo, 369 Li, Haizhen, 194 Li, He-Ping, 1149 Li, Huaxin, 506 Li, Jian, 47, 81 Li, Jing, 369 Li, Junming, 14 Li, Kunshan, 1096 Li, Meiyu, 601 Li, Meng, 1113 Li, Qunshan, 103 Li, Renjie, 558 Li, Ruan, 424 Li, Shiming, 870, 879 Li, Shuxia, 797 Li, Xian, 823 Li, Xinmei, 30 Li, Xuepo, 88 Li, Xun, 706 Li, Yan, 249, 256 Li, Yang, 1096 Li, Yibo, 446 Li, Yipeng, 668, 1031 Li, Zhifeng, 30 Li, Zhiqiang, 202 Li, Zihao, 714 Lian, Li-ting, 291 Liang, Xiao, 30 Liang, Xingzhu, 462 Lin, Yu’e, 462 Lin, Yu, 904 Lin, Yu-Da, 109 Lin, Ziyu, 103 Liu, Baoqin, 440 Liu, Ding, 123 Liu, Gang, 1124 Liu, Huimin, 432 Liu, Jing, 30 Liu, Jingfang, 523 Liu, Kaida, 123 Liu, Lan, 1088 Liu, Penghui, 633 Liu, Qiubin, 642, 714, 994 Liu, Wen, 177 Liu, Wenbo, 1157 Liu, Xia, 958 Liu, Xiaohui, 682 Liu, Yanzhong, 489 Liu, Yaqi, 668

Author Index Lou, Jing, 789 Lu, Jinsheng, 797 Lu, Liu, 974 Lu, Xiaoxu, 123 Lu, Yufeng, 917 Luo, Bingbing, 782 Luo, Changshou, 566, 682, 1104 Luo, Da-Xiong, 572 Luo, Jiangyue, 1017 Luo, Jun, 476, 482 Luo, Qin, 30 Luo, Shenzeng, 103 Luo, Yunrong, 1002 Lv, Jiaguo, 3 Lv, Shiliang, 40 Lv, Shouguo, 40 Lv, Xiaoliang, 194 M Ma, Fuyu, 593, 633 Ma, Junbang, 202 Mao, Kun, 40 Mei, Zhang, 159, 985 Meigen, Huang, 351 Meng, Hengling, 699, 706 Meng, Qiaoling, 752 Mi, Shuo, 432 Miao, Fengjuan, 123 Miao, Jianqun, 404 Miao, Liu, 88 Miaohua, Liu, 954 Min, Duan, 227, 234 Min, Jiang, 650 Mo, Jun, 1002 Mou, Xiaodan, 432 N Ning, Xiaoyan, 870 O Olvera, Alejandro, 334 P Pakdeetrakulwong, Udsanee, 148 Pan, Jinxiao, 506 Pan, Yue, 862 Pang, Changqing, 432 Park, Jung Kyu, 186 Peng, Guo-Hua, 312 Peng, Hui, 476, 482 Peng, Lin, 137, 142, 264 Peng, Liu, 227, 234

1165 Q Qi, Xinshe, 356, 369 Qi, Yue, 95, 468, 489 Qiang, Li, 227, 234 Qiu, Lu, 194 Qu, Han, 446 R Rajapov, Azamat, 661 Ran, Qingpeng, 306 Ran, Zhu, 277 Ren, Shan, 699, 706 S Shan, Hongbo, 797 Shang, Shuyuan, 627 Shao, Guangting, 47, 81 Shi, Jiahao, 823 Shi, Yu, 177 Shidong, Fang, 650 Shuai, Fu, 63 Shun, Sufen, 566 Si, Jiacan, 847 Song, Yan, 722 Sun, Mengge, 578 Sun, Qi, 397 Sun, Qiaoyun, 830 Sun, Ruibin, 432 Sun, Shuguang, 620 Sun, Sufen, 682, 1104 Sun, Zhiguo, 870 Sun, Zhizhou, 47, 81 Sun, Zhuangwen, 586 T Tan, Lujun, 88 Tang, Qiuling, 823 Tang, Xinfa, 586 Tang, Xuemei, 627 Tao, Bairui, 123 Tian, Jianwei, 241 Tian, Kechao, 81 Tie, Guo, 974 Tong, Zhe, 797 V van Heuven, Vincent J., 454 W Wan, Fangpeng, 879 Wan, Ming, 722 Wang, Anshan, 40

1166 Wang, Chengjin, 462 Wang, Chunli, 194 Wang, Cong, 768 Wang, Di, 668 Wang, Gang, 264 Wang, Hai, 789 Wang, He, 142, 264, 775 Wang, Hongyan, 298, 454 Wang, Jian, 523 Wang, Jin, 21 Wang, Lei, 397 Wang, Lisong, 375, 936 Wang, Min, 830 Wang, Minghui, 578 Wang, Na, 356, 369 Wang, Pu, 550 Wang, Qun, 815 Wang, Rujuan, 321 Wang, Suhua, 321 Wang, Xiao-Ling, 137 Wang, Xifu, 601 Wang, Xin, 356, 369 Wang, Yali, 966 Wang, Yi, 782 Wang, Yun-he, 945 Wang, Zhaowei, 722 Wang, Zhenwei, 1143 Wang, Zhi-feng, 572 Wei, Chia-Hui, 842 Wei, Qingfeng, 566, 1104 Wen, Liu, 227, 234 Wenhao, Cui, 351 Wu, Binghui, 1080 Wu, Enna, 642, 714 Wu, Haoran, 137 Wu, Jian, 404 Wu, Qianjun, 1053 Wu, Xiaojuan, 1017 Wu, Yao-wu, 945 X Xhafa, Fatos, 334 Xia, Lei, 1072 Xia, Liu, 424 Xia, Yujuan, 241 Xiang-Zhen, Li, 760, 768 Xiao, Min, 745 Xie, Si-Lian, 1149 Xie, Wenjian, 958 Xie, Xiaomin, 417 Xie, Yaohua, 14 Xiuchao, Song, 954 Xu, Cheng, 95, 468, 847 Xu, Deng, 55

Author Index Xu, Guoxiang, 468 Xu, Min, 137, 142, 264 Xu, Pingping, 167 Xu, Qingzheng, 369 Xu, Song, 572 Xu, Wei, 540 Xu, Xiao-min, 572 Xu, Ying, 558 Xu, Yuting, 298, 454 Xu, Zhiqiang, 363 Xu, Zhishuang, 904 Xue, Huifeng, 736 Xue, Xiya, 498 Xue, Yixuan, 797

Y Yan, Kai, 14 Yan, Yijuan, 789 Yan, Zhiwei, 432 Yang, Baohua, 55 Yang, Chao, 103 Yang, Cheng-Hong, 109 Yang, Chenghui, 517 Yang, Cheng-Ying, 836, 842 Yang, Fan, 1046 Yang, Guoqing, 40, 47, 81 Yang, Jun, 550 Yang, Liang, 1113 Yang, Libo, 131 Yang, Liu, 974 Yang, Ming, 177 Yang, Mingjie, 775 Yang, Ming-ming, 291 Yang, Xi, 1117 Yang, Xusheng, 103 Yang, Yan, 167 Yang, Yanming, 1038 Yang, Yimin, 782 Yang, Yuting, 283, 328, 1025 Yang, Zhihan, 578 Yanrong, Wang, 227, 234 Yao, Cui, 468 Ye, Jun-min, 572 Ye-Shen, He, 760, 768 Yin, Junluo, 847 Yin, Zhonghai, 966 Ying, Wang, 210 Yi-Ying, Zhang, 760, 768 Yu, Gang, 1113 Yu, Hai, 142 Yu, Hongliu, 752 Yu, Jun, 566, 1053 Yu, Xiang-sen, 945

Author Index Yu, Xiaobin, 40 Yuan, Yuan, 601 Yue, Dai, 227, 234 Yue, Zhou, 1149 Yundong, Song, 974 Yuwen, Liu, 650 Yuxiao, Zhou, 974 Z Zeng, Yong, 417 Zhai, Ruifang, 476, 482 Zhang, Baoying, 782 Zhang, Changhe, 782, 789 Zhang, Chenghao, 887, 895 Zhang, Chengmao, 1137 Zhang, Chuanyou, 47, 81 Zhang, Chunyu, 887, 895 Zhang, Dongliang, 1002 Zhang, Fan, 417 Zhang, Honghui, 74, 533 Zhang, Hui, 752 Zhang, Manrui, 1157 Zhang, Meng, 3 Zhang, Qiusheng, 730 Zhang, Ruihua, 540 Zhang, Shuang, 523 Zhang, Shuguang, 830, 856 Zhang, Shujing, 47 Zhang, Tongyang, 917 Zhang, Wanli, 74 Zhang, Wei, 3, 699, 706 Zhang, Wenyu, 736 Zhang, Xi, 601 Zhang, Xiaodong, 264 Zhang, Xiaotong, 95 Zhang, Xiaoyan, 1002

1167 Zhang, Xingyun, 730 Zhang, Xinrui, 249 Zhang, Yan, 736 Zhang, Yao, 815 Zhang, Yichao, 177 Zhang, Yong, 1031 Zhang, Yongjian, 1130 Zhang, Yu, 830 Zhang, Yuming, 550 Zhang, Zhongshui, 722 Zhang, Zhuoni, 256 Zhao, Jianming, 722 Zhao, Jiaxiang, 540 Zhao, Xin, 699, 706 Zhao, Xue, 676 Zheng, Lin, 945 Zheng, Yaming, 682 Zhengdong, Liu, 210 Zhilong, Zhang, 985 Zhiyong, Li, 650 Zhou, Bo, 856 Zhou, Cheng, 966 Zhou, Liying, 1104 Zhou, Wen-ming, 945 Zhou, Xinmiao, 620 Zhou, Xuanwei, 1010 Zhu, Chengzhi, 264 Zhu, Guangxin, 1053 Zhu, Hao, 14 Zhu, Hongwei, 856 Zhu, Huayuan, 1038 Zhu, Jun, 137 Zhu, Liang, 775 Zhu, Liu, 760, 768 Zhu, Shan, 498 Zhu, Xingxiong, 558