Signal and Information Processing, Networking and Computers: Proceedings of the 5th International Conference on Signal and Information Processing, Networking and Computers (ICSINC) [1st ed.] 978-981-13-7122-6;978-981-13-7123-3

Table of contents :
Front Matter ....Pages i-xiv
Front Matter ....Pages 1-1
LP-HMM: Location Preference-Based Hidden Markov Model (Jianhua Huang, Feixia Wu, Weiqiang Meng, Jian Yao)....Pages 3-12
Design and Performance Evaluation of On-Board Computer Network Protocol Stack Based on Spacecraft Operating System (Lei Qiao, Bo Liu, Hongjin Liu, Hua Yang, Jian Xu, Shufen Liu)....Pages 13-21
Overview of Face Recognition Methods (Lingfeng Fang, Meixia Fu, Songlin Sun, Qianhan Ran)....Pages 22-31
HAR-Net: Fusing Deep Representation and Hand-Crafted Features for Human Activity Recognition (Mingtao Dong, Jindong Han, Yuan He, Xiaojun Jing)....Pages 32-40
Private-Preserving Analysis of Network Traffic (Yang Tang, Yan Ma)....Pages 41-48
Design of Fault Diagnosis System for Balise Cable Based on Machine Learning (Xiaoyi Cui, Jingyang Lv)....Pages 49-53
Design and Implementation of ARQ Mechanism in High-Speed Data Acquisition System (Xiao Li, Jingyang Lv)....Pages 54-60
Application of Hilbert Huang Transform in Car Signal Demodulation (Hongrui Xie, Jingyang Lv)....Pages 61-68
Human Action Recognition: A Survey (Meixia Fu, Na Chen, Zhongjie Huang, Kaili Ni, Yuhao Liu, Songlin Sun et al.)....Pages 69-77
Genetic Algorithm for Base Station ON/OFF Optimization with Fast Coverage Estimation and Probability Scaling for Green Communications (Yebing Ren, Wei Liu, Jiangbo Dong, Haobin Wang, Yaxi Liu, Huangfu Wei)....Pages 78-88
Front Matter ....Pages 89-89
Inter-layer Link Design for Resource-Constrained Double-Layered Satellite Network (Hongcheng Yan, Rui Zhang, Yahang Zhang, Luming Li, Jiaxiang Niu)....Pages 91-99
Safety Design for Customer Furniture Instrument in Satellite (Tian Tan, Chunping Zeng, Xiaodong Jia, Jian Shi, Guoqiang Jiang)....Pages 100-104
Dynamic Path Planning Algorithm Based on an Optimization Model (Jingjing Zhang, Hongning Hu, Yuting Wan)....Pages 105-114
Research on Autonomous Health Management and Reconstruction Technology of Satellite (Liuqing Yang, Xiaojuan Li, Chengyu Feng, Zhelei Sun, Tao Zhang)....Pages 115-123
The Design and Implementation of Digital Satellite Simulator (Dong Han, Wen-gao Lu, Lei Song, Shao-po Zhang, Ru Gao)....Pages 124-130
Research on Target Recognition Technology of Satellite Remote Sensing Image Based on Neural Network (Qiang Zhang, Xiaonan Wang, Hexiang Tian, Yanan You, Peng Kong)....Pages 131-138
Research and Implementation of Automatic Test Technology for Power Modules in Aerospace (Haibo Li, Qing Chen, Yan Li, Chenlei Cao)....Pages 139-145
Design and Implementation of User-Oriented On-Board Mission Management System for Remote Sensing Satellite (Li Pan, Yong Lei, Yu Jiang, Wenping Wang, Yiming Liu)....Pages 146-153
Extraction of Salient Region Based on Visual Perception (Yongchang Li, Pengluo Lu, Cheng Cheng, Jianing Hao, Li Liu, Jun Zhu)....Pages 154-163
Thermal Design to Meet Stringent Temperature Gradient/Stability Requirements of Space Camera’s Tube (Yifan Li, Yelong Tong, Tengfei Sun, Lei Yu)....Pages 164-172
Application Design of Virtual Assembly Simulation Technology for Installing Cables on Biaxial Drive Mechanism (Chunsheng Yang, Yufeng Huang, Yi Lu, Feng Xue, Zhenyue Ren, Guoyu Liu)....Pages 173-179
Data Analysis and Research Based on Satellite Micro-vibration Disturbance Test (Yang Gao, Qiang Wang, Fei-hu Liu, Lu Cao, Wei Cheng)....Pages 180-187
Spacecraft Automation Test Procedure and System Design (Yongcong He, Feng Yang, Liang Ren, Chao Cheng)....Pages 188-194
Analysis of Wave Propagation in Functionally Graded Material Annular Sector Plates Using Curved-Boundary Legendre Spectral Finite Elements (Teng Wang)....Pages 195-203
Research on Design of Quality Management Module in Spacecraft Assembly MES (Qiang Wang)....Pages 204-211
The Design and Implementation of Secure Cloud Desktop System (Huifang Pan, Yi Yuan, Wenlong Song, Zhou An)....Pages 212-218
Research on Satellite Power Subsystem Anomaly Detection Technology Based on Health Baseline (Lei Zhang, Zhidong Li, Bo Sun, Shuai Zhang)....Pages 219-226
Lifetime Prediction Method of Components Based on Failure Physics (Li Liu, Zhimin Ding, Nan Fang, Chao Duan, Nan Li, Qianqian Lv et al.)....Pages 227-237
Front Matter ....Pages 239-239
Fault Tolerant Method for Spacecraft Bus Based on Virtual Memory (Ning Zhao, Wei E.)....Pages 241-248
The Design of Interactive Framework for Space-Exploration Robotic Systems (Wei Shi, Shengyi Jin, Yang Zhang, Xiangjin Deng, Yanhong Zheng, Meng Yao et al.)....Pages 249-259
Research and Design of Hierarchical FDIR in Spacecraft (Xiaodong Jia, Chunping Zeng, Yufu Cui)....Pages 260-267
Research on Spacecraft Network Protocol Based on Space Packet (Wei E., Zhengwen He, Ning Zhao)....Pages 268-275
Research on Instrument Requirements and Configuration for High Resolution Infrared Observations (Qianying Wang, Fan Mo, Quan Jing)....Pages 276-283
A Method for Solving Generalized Implicit Factorization Problem (Zhelei Sun, Tianwei Zhang, Xiaoxia Zheng, Liuqing Yang, Liqiang Peng)....Pages 284-290
OCV-Ah Integration SOC Estimation of Space Li-Ion Battery (Dawei Fu, Lin Hu, Xiaojun Han, Shijie Chen, Zhong Ren, Hongyu Yang)....Pages 291-299
Analysis and Experimental Study on Influence Factors of Spacecraft Power Cable Temperature (Bingxin Zhao, Lequn Wu, Chenhua Zhang, Shijie Chen, Yi Yang)....Pages 300-309
Spacecraft System Autonomous Health Management Design (Yong Lei, Quanyou Qu, Deyin Liang, Yilan Mao, Xi Chen)....Pages 310-316
Design and Simulation Verification of Ground Charge Equipment for Li-Ion Battery Pack (Lin Hu, Dawei Fu, Hongyu Yang, Shuo Feng, Jianbo Du, Jinchen Zhao et al.)....Pages 317-325
Motion Control of Robot Mobile Platform Based on Indoor Positioning System (Zhiguang Jiang, Lijian Zhang)....Pages 326-335
A Process Method and Simulation Analysis of Spacecraft Wing Root Cable Fixing (Kai Xu, Lijian Zhang, Hao Li)....Pages 336-342
Study on 3D Cable Network Design Method (Yi Yuan, Wei Yu, Xiaoyi Ru, Zhou An, Qi Miao, Xuhua Hu et al.)....Pages 343-351
A Similarity Approach for Thermal Distortion Measurement of Large Spacecraft Structure (Hongtao Gao, Haitao Shi, Xiaofeng Zhang, Lu Ren)....Pages 352-359
Design Technology of Accelerated Life Test Based on the Load Spectrum Compilation and D-efficiency (Jixia Xia, Xiaokai Huang, Gang Sun, Fangyong Li)....Pages 360-370
Realization of Interconnecting Application of Non-secret-related Network and Secret-Related Network Based on Unidirectional Optical Shutter (Qi Miao, Xiaoyi Ru, Jiang Bian, Zhou An)....Pages 371-379
Application of Ion Beam Etching Technology in Spacecraft Encoder Lithography (Suran Qin, Na Zhao, Ronghui Jiao, Chunying Zhu, Jiang Liu, Jianmin Shi et al.)....Pages 380-390
Validation Technology in Super Power Supply System Design of Telecommunication Satellite (Ronghui Jiao, Suran Qin, Lili Yuan, Ding Song, Lei Yun, Jianwu Zhao)....Pages 391-399
Research on Application Method of 3D Digital Simulation Technology in Spacecraft Assembly (Boyin Zhang, Qiang Wang, Xingyan Wang, Yin Liang)....Pages 400-408
Front Matter ....Pages 409-409
Network Traffic Prediction Based on LSTM Networks with Genetic Algorithm (Juan Chen, Huanlai Xing, Hai Yang, Lexi Xu)....Pages 411-419
On Multicast-Oriented Virtual Network Function Placement: A Modified Genetic Algorithm (Xinhan Wang, Huanlai Xing, Hai Yang)....Pages 420-428
PM2.5 Concentration Forecast Based on Hierarchical Sparse Representation (Rui Zhao, Bingjian Lu, Zhenyu Lu, Hengde Zhang, Tianming Zhan)....Pages 429-436
Intelligent User Profile Prediction in Radio Access Network (Yaxing Qiu, Xidong Wang, Fengjun Wang, Sen Bian)....Pages 437-445
Realization of the National QPF Master Blender: A Big Data Approach (Jian Tang, Kan Dai, Zhiping Zong, Yong Cao, Couhua Liu, Song Gao et al.)....Pages 446-454
Artificial Intelligence Research on Visibility Forecast (Chao Xie, Xuekuan Ma)....Pages 455-461
Multi-model Ensemble Forecast System for Surface-Layer PM2.5 Concentration in China (Tianhang Zhang, Hengde Zhang, Bihui Zhang, Xiaoqin Rao, Linchang An, Mengyao Lv et al.)....Pages 462-470
Research on Natural Language Processing and Aspose Technology in the Automatic Generation of Ocean Weather Public Report (Xinping Bai, Zhongliang Lv, Hui Wang)....Pages 471-478
4G Video Service Stalling Perception Evaluation and Optimization Based on Big Data (Jinsong Yin, Heng Wei, Hui Zhu)....Pages 479-486
Application of the Bayesian Processor of Ensemble to the Combination and Calibration of Ensemble Forecasts (Yi Wang, Xiaomei Zhang, Zoltan Toth)....Pages 487-494
Initial Analysis of the Cell Selection Progress in SA of 5G NR (Zetao Xu, Yang Zhang, Ao Shen, Bao Guo, Yuehua Han, Yi Liu)....Pages 495-504
NB-IoT Network and Service Collaborative Optimization (Pengcheng Liu, Bao Guo, Yang Zhang, Yuehua Han, Yi Liu, Guozhi Wang)....Pages 505-515
A Gender and Age Prediction Algorithm Using Big Data Analytic Based on Mobile APPs Information (Jie Gao, Tao Zhang, Jian Guan, Lexi Xu, Xinzhou Cheng)....Pages 516-524
Medium-Extended-Range Weather Forecast Based on Big Data Application (Yong Li, Wei Huang, Zhengguang Hu, Huafeng Qin, Menglei Xu)....Pages 525-533
Application Research of Big Data in Heavy Rainfall Forecast Model in Meiyu Season (Shan Yin, Jie Ma, Ronghua Jin, Ningfang Zhou)....Pages 534-541
Predictability of an Extreme Rainfall Event in North China (Jie Ma, Shan Yin, Lijun Jin, Ronghua Jin)....Pages 542-550
Research on Visibility Forecast Based on LSTM Neural Network (Yuliang Dai, Zhenyu Lu, Hengde Zhang, Tianming Zhan)....Pages 551-558
Application of Artificial Intelligence on the Image Identification of Icing Weather Phenomena (Xiaoyu Huang, Chengzhi Ye, Ronghui Cai, Yao Zhang, Lianye Liu, Chenghao Fu)....Pages 559-569
The Realization Path of Network Security Technology Under Big Data and Cloud Computing (Nan Kang, Xuesong Zhang, Xinzhou Cheng, Bingyi Fang, Hong Jiang)....Pages 570-577
Rate Control in HEVC: A Survey (Jiaqi Zou, Bingyi Li)....Pages 578-583
Screen Content Coding: A Survey (Bingyi Li, Jiaqi Zou)....Pages 584-592
Intelligent Fitness Trainer System Based on Human Pose Estimation (Jiaqi Zou, Bingyi Li, Luyao Wang, Yue Li, Xiangyuan Li, Rongjia Lei et al.)....Pages 593-599
The Overview of Multi-person Pose Estimation Method (Bingyi Li, Jiaqi Zou, Luyao Wang, Xiangyuan Li, Yue Li, Rongjia Lei et al.)....Pages 600-607
A Network Information Data Protection Scheme Based on Superposition Encryption (Liu Zhe)....Pages 608-615
Back Matter ....Pages 617-620

Citation preview

Lecture Notes in Electrical Engineering 550

Songlin Sun Meixia Fu Lexi Xu Editors

Signal and Information Processing, Networking and Computers Proceedings of the 5th International Conference on Signal and Information Processing, Networking and Computers (ICSINC)

Lecture Notes in Electrical Engineering Volume 550

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Napoli, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, München, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science & Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Lab, Karlsruhe Institute for Technology, Karlsruhe, Baden-Württemberg, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, München, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martin, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Lab, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Kyoto, Japan Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Baden-Württemberg, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Junjie James Zhang, Charlotte, NC, USA

The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering - quickly, informally and in high quality. While original research reported in proceedings and monographs has traditionally formed the core of LNEE, we also encourage authors to submit books devoted to supporting student education and professional training in the various fields and applications areas of electrical engineering. The series cover classical and emerging topics concerning:

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Songlin Sun Meixia Fu Lexi Xu •

•

Editors

Signal and Information Processing, Networking and Computers Proceedings of the 5th International Conference on Signal and Information Processing, Networking and Computers (ICSINC)

123

Editors Songlin Sun Beijing University of Posts and Telecommunications Beijing, China

Meixia Fu Beijing University of Posts and Telecommunications Beijing, China

Lexi Xu China Unicom Beijing, China

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-13-7122-6 ISBN 978-981-13-7123-3 (eBook) https://doi.org/10.1007/978-981-13-7123-3 Library of Congress Control Number: 2019936553 © Springer Nature Singapore Pte Ltd. 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, expressed 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The 5th International Conference on Signal and Information Processing, Network and Computers (ICSINC 2018 Fall) provided a forum for researchers, engineers, and industry experts to discuss recent development, new ideas, and breakthrough in signal and information processing schemes, computer theory, space technologies, big data, and so on. We were honored to have the keynote speakers invited to present their outstanding achievements and understanding on the following topics: Space Technology, Accurate Medical Diagnosis and Treatment Guided by Augmented Reality, Publishing with Springer. ICSINC 2018 Fall received 152 papers submitted by authors, 72 papers were accepted, and finally, 71 papers were included in the final conference proceedings. The accepted papers were presented and discussed in 6 regular technical session and 2 workshops. On behalf of the ICSINC 2018 committee, we would like to express our sincere appreciation to the TPC members and reviewers for their tremendous efforts. Especially, we appreciate all the sponsors for their generous support and advice, including Springer, Beijing University of Posts and Telecommunications, China Unicom, and HuaCeXinTong. Finally, we would also like to thank all the authors and participants for their excellent work and cooperation. Songlin Sun Yue Wang ICSINC 2018 General Co-chairs

v

Organization

Committee Members International Steering Committee Songlin Sun Takeo Fujii Ju Liu Chenwei Wang

Beijing University of Posts and Telecommunications, China The University of Electro-Communications, Japan Shandong University, China DOCOMO Innovations (DoCoMo USA Labs), USA

General Co-chairs Songlin Sun Yue Wang

Beijing University of Posts and Telecommunications, China China Academy of Space Technology, China

Technical Program Committee Chairs Xinzhou Cheng

China Unicom Network Technology Research Institute, China

General Secretary Meixia Fu

Beijing University of Posts and Telecommunications, China

vii

viii

Sponsors Springer

Beijing University of Posts and Telecommunications

China Unicom

HuaCeXinTong

Organization

Contents

Computer Theory and Algorithms Optimization LP-HMM: Location Preference-Based Hidden Markov Model . . . . . . . . Jianhua Huang, Feixia Wu, Weiqiang Meng, and Jian Yao Design and Performance Evaluation of On-Board Computer Network Protocol Stack Based on Spacecraft Operating System . . . . . . Lei Qiao, Bo Liu, Hongjin Liu, Hua Yang, Jian Xu, and Shufen Liu Overview of Face Recognition Methods . . . . . . . . . . . . . . . . . . . . . . . . . Lingfeng Fang, Meixia Fu, Songlin Sun, and Qianhan Ran HAR-Net: Fusing Deep Representation and Hand-Crafted Features for Human Activity Recognition . . . . . . . . . . . . . . . . . . . . . . . Mingtao Dong, Jindong Han, Yuan He, and Xiaojun Jing Private-Preserving Analysis of Network Traffic . . . . . . . . . . . . . . . . . . . Yang Tang and Yan Ma

3

13 22

32 41

Design of Fault Diagnosis System for Balise Cable Based on Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoyi Cui and Jingyang Lv

49

Design and Implementation of ARQ Mechanism in High-Speed Data Acquisition System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao Li and Jingyang Lv

54

Application of Hilbert Huang Transform in Car Signal Demodulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongrui Xie and Jingyang Lv

61

Human Action Recognition: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . Meixia Fu, Na Chen, Zhongjie Huang, Kaili Ni, Yuhao Liu, Songlin Sun, and Xiaomei Ma

69

ix

x

Contents

Genetic Algorithm for Base Station ON/OFF Optimization with Fast Coverage Estimation and Probability Scaling for Green Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yebing Ren, Wei Liu, Jiangbo Dong, Haobin Wang, Yaxi Liu, and Huangfu Wei

78

Satellites and Remote Sensing Inter-layer Link Design for Resource-Constrained Double-Layered Satellite Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongcheng Yan, Rui Zhang, Yahang Zhang, Luming Li, and Jiaxiang Niu

91

Safety Design for Customer Furniture Instrument in Satellite . . . . . . . . 100 Tian Tan, Chunping Zeng, Xiaodong Jia, Jian Shi, and Guoqiang Jiang Dynamic Path Planning Algorithm Based on an Optimization Model . . . 105 Jingjing Zhang, Hongning Hu, and Yuting Wan Research on Autonomous Health Management and Reconstruction Technology of Satellite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Liuqing Yang, Xiaojuan Li, Chengyu Feng, Zhelei Sun, and Tao Zhang The Design and Implementation of Digital Satellite Simulator . . . . . . . . 124 Dong Han, Wen-gao Lu, Lei Song, Shao-po Zhang, and Ru Gao Research on Target Recognition Technology of Satellite Remote Sensing Image Based on Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 131 Qiang Zhang, Xiaonan Wang, Hexiang Tian, Yanan You, and Peng Kong Research and Implementation of Automatic Test Technology for Power Modules in Aerospace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Haibo Li, Qing Chen, Yan Li, and Chenlei Cao Design and Implementation of User-Oriented On-Board Mission Management System for Remote Sensing Satellite . . . . . . . . . . . . . . . . . 146 Li Pan, Yong Lei, Yu Jiang, Wenping Wang, and Yiming Liu Extraction of Salient Region Based on Visual Perception . . . . . . . . . . . . 154 Yongchang Li, Pengluo Lu, Cheng Cheng, Jianing Hao, Li Liu, and Jun Zhu Thermal Design to Meet Stringent Temperature Gradient/Stability Requirements of Space Camera’s Tube . . . . . . . . . . . . . . . . . . . . . . . . . 164 Yifan Li, Yelong Tong, Tengfei Sun, and Lei Yu Application Design of Virtual Assembly Simulation Technology for Installing Cables on Biaxial Drive Mechanism . . . . . . . . . . . . . . . . . 173 Chunsheng Yang, Yufeng Huang, Yi Lu, Feng Xue, Zhenyue Ren, and Guoyu Liu

Contents

xi

Data Analysis and Research Based on Satellite Micro-vibration Disturbance Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Yang Gao, Qiang Wang, Fei-hu Liu, Lu Cao, and Wei Cheng Spacecraft Automation Test Procedure and System Design . . . . . . . . . . 188 Yongcong He, Feng Yang, Liang Ren, and Chao Cheng Analysis of Wave Propagation in Functionally Graded Material Annular Sector Plates Using Curved-Boundary Legendre Spectral Finite Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Teng Wang Research on Design of Quality Management Module in Spacecraft Assembly MES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 Qiang Wang The Design and Implementation of Secure Cloud Desktop System . . . . . 212 Huifang Pan, Yi Yuan, Wenlong Song, and Zhou An Research on Satellite Power Subsystem Anomaly Detection Technology Based on Health Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Lei Zhang, Zhidong Li, Bo Sun, and Shuai Zhang Lifetime Prediction Method of Components Based on Failure Physics . . . 227 Li Liu, Zhimin Ding, Nan Fang, Chao Duan, Nan Li, Qianqian Lv, and Miao Zhang Spacecraft Technology and Application Fault Tolerant Method for Spacecraft Bus Based on Virtual Memory . . . 241 Ning Zhao and Wei E. The Design of Interactive Framework for Space-Exploration Robotic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Wei Shi, Shengyi Jin, Yang Zhang, Xiangjin Deng, Yanhong Zheng, Meng Yao, and Zhihui Zhao Research and Design of Hierarchical FDIR in Spacecraft . . . . . . . . . . . 260 Xiaodong Jia, Chunping Zeng, and Yufu Cui Research on Spacecraft Network Protocol Based on Space Packet . . . . . 268 Wei E., Zhengwen He, and Ning Zhao Research on Instrument Requirements and Configuration for High Resolution Infrared Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Qianying Wang, Fan Mo, and Quan Jing A Method for Solving Generalized Implicit Factorization Problem . . . . 284 Zhelei Sun, Tianwei Zhang, Xiaoxia Zheng, Liuqing Yang, and Liqiang Peng

xii

Contents

OCV-Ah Integration SOC Estimation of Space Li-Ion Battery . . . . . . . 291 Dawei Fu, Lin Hu, Xiaojun Han, Shijie Chen, Zhong Ren, and Hongyu Yang Analysis and Experimental Study on Influence Factors of Spacecraft Power Cable Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 Bingxin Zhao, Lequn Wu, Chenhua Zhang, Shijie Chen, and Yi Yang Spacecraft System Autonomous Health Management Design . . . . . . . . . 310 Yong Lei, Quanyou Qu, Deyin Liang, Yilan Mao, and Xi Chen Design and Simulation Verification of Ground Charge Equipment for Li-Ion Battery Pack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Lin Hu, Dawei Fu, Hongyu Yang, Shuo Feng, Jianbo Du, Jinchen Zhao, and Chengzhi Lu Motion Control of Robot Mobile Platform Based on Indoor Positioning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 Zhiguang Jiang and Lijian Zhang A Process Method and Simulation Analysis of Spacecraft Wing Root Cable Fixing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Kai Xu, Lijian Zhang, and Hao Li Study on 3D Cable Network Design Method . . . . . . . . . . . . . . . . . . . . . 343 Yi Yuan, Wei Yu, Xiaoyi Ru, Zhou An, Qi Miao, Xuhua Hu, and Zhenpeng Ding A Similarity Approach for Thermal Distortion Measurement of Large Spacecraft Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Hongtao Gao, Haitao Shi, Xiaofeng Zhang, and Lu Ren Design Technology of Accelerated Life Test Based on the Load Spectrum Compilation and D-efficiency . . . . . . . . . . . . . . . . . . . . . . . . . 360 Jixia Xia, Xiaokai Huang, Gang Sun, and Fangyong Li Realization of Interconnecting Application of Non-secret-related Network and Secret-Related Network Based on Unidirectional Optical Shutter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Qi Miao, Xiaoyi Ru, Jiang Bian, and Zhou An Application of Ion Beam Etching Technology in Spacecraft Encoder Lithography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 Suran Qin, Na Zhao, Ronghui Jiao, Chunying Zhu, Jiang Liu, Jianmin Shi, and Hanchao Fan Validation Technology in Super Power Supply System Design of Telecommunication Satellite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Ronghui Jiao, Suran Qin, Lili Yuan, Ding Song, Lei Yun, and Jianwu Zhao

Contents

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Research on Application Method of 3D Digital Simulation Technology in Spacecraft Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Boyin Zhang, Qiang Wang, Xingyan Wang, and Yin Liang Big Data Workshop Network Traffic Prediction Based on LSTM Networks with Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Juan Chen, Huanlai Xing, Hai Yang, and Lexi Xu On Multicast-Oriented Virtual Network Function Placement: A Modified Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420 Xinhan Wang, Huanlai Xing, and Hai Yang PM2.5 Concentration Forecast Based on Hierarchical Sparse Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Rui Zhao, Bingjian Lu, Zhenyu Lu, Hengde Zhang, and Tianming Zhan Intelligent User Profile Prediction in Radio Access Network . . . . . . . . . 437 Yaxing Qiu, Xidong Wang, Fengjun Wang, and Sen Bian Realization of the National QPF Master Blender: A Big Data Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 Jian Tang, Kan Dai, Zhiping Zong, Yong Cao, Couhua Liu, Song Gao, and Chao Yu Artificial Intelligence Research on Visibility Forecast . . . . . . . . . . . . . . . 455 Chao Xie and Xuekuan Ma Multi-model Ensemble Forecast System for Surface-Layer PM2.5 Concentration in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Tianhang Zhang, Hengde Zhang, Bihui Zhang, Xiaoqin Rao, Linchang An, Mengyao Lv, and Ran Xu Research on Natural Language Processing and Aspose Technology in the Automatic Generation of Ocean Weather Public Report . . . . . . . 471 Xinping Bai, Zhongliang Lv, and Hui Wang 4G Video Service Stalling Perception Evaluation and Optimization Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 Jinsong Yin, Heng Wei, and Hui Zhu Application of the Bayesian Processor of Ensemble to the Combination and Calibration of Ensemble Forecasts . . . . . . . . . . 487 Yi Wang, Xiaomei Zhang, and Zoltan Toth Initial Analysis of the Cell Selection Progress in SA of 5G NR . . . . . . . 495 Zetao Xu, Yang Zhang, Ao Shen, Bao Guo, Yuehua Han, and Yi Liu

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NB-IoT Network and Service Collaborative Optimization . . . . . . . . . . . 505 Pengcheng Liu, Bao Guo, Yang Zhang, Yuehua Han, Yi Liu, and Guozhi Wang A Gender and Age Prediction Algorithm Using Big Data Analytic Based on Mobile APPs Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516 Jie Gao, Tao Zhang, Jian Guan, Lexi Xu, and Xinzhou Cheng Medium-Extended-Range Weather Forecast Based on Big Data Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525 Yong Li, Wei Huang, Zhengguang Hu, Huafeng Qin, and Menglei Xu Application Research of Big Data in Heavy Rainfall Forecast Model in Meiyu Season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534 Shan Yin, Jie Ma, Ronghua Jin, and Ningfang Zhou Predictability of an Extreme Rainfall Event in North China . . . . . . . . . 542 Jie Ma, Shan Yin, Lijun Jin, and Ronghua Jin Research on Visibility Forecast Based on LSTM Neural Network . . . . . 551 Yuliang Dai, Zhenyu Lu, Hengde Zhang, and Tianming Zhan Application of Artificial Intelligence on the Image Identification of Icing Weather Phenomena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559 Xiaoyu Huang, Chengzhi Ye, Ronghui Cai, Yao Zhang, Lianye Liu, and Chenghao Fu The Realization Path of Network Security Technology Under Big Data and Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 570 Nan Kang, Xuesong Zhang, Xinzhou Cheng, Bingyi Fang, and Hong Jiang Rate Control in HEVC: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 Jiaqi Zou and Bingyi Li Screen Content Coding: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584 Bingyi Li and Jiaqi Zou Intelligent Fitness Trainer System Based on Human Pose Estimation . . . 593 Jiaqi Zou, Bingyi Li, Luyao Wang, Yue Li, Xiangyuan Li, Rongjia Lei, and Songlin Sun The Overview of Multi-person Pose Estimation Method . . . . . . . . . . . . 600 Bingyi Li, Jiaqi Zou, Luyao Wang, Xiangyuan Li, Yue Li, Rongjia Lei, and Songlin Sun A Network Information Data Protection Scheme Based on Superposition Encryption . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 Liu Zhe Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617

Computer Theory and Algorithms Optimization

LP-HMM: Location Preference-Based Hidden Markov Model Jianhua Huang1(&), Feixia Wu1, Weiqiang Meng1, and Jian Yao2 1

East China University of Science and Technology, Shanghai 200237, China [email protected] 2 China United Network Communications Limited, Shanghai Branch, No. 1033 Changning Road, Changning District, 200050 Shanghai, China

Abstract. Lots of mobility data have been generated with the emergence of smart devices and location-based services. The prediction of user mobility has become a key factor driving the rapid development of many location applications. Location prediction has attracted more and more attention in various fields, and many location prediction algorithms have been proposed. The data currently used for researches has many problems such as data noise and redundancy. Many researches directly used raw data and did not consider spatiotemporal characteristics of historical data enough, which leads to low prediction accuracy. This paper proposes a point-of-interest discovering algorithm, which fully considers spatiotemporal characteristics of data. By combining the location preference of users for location with the Hidden Markov Model (HMM), we propose LP-HMM (Location Preference-based Hidden Markov Model), a location prediction model based on location preference and HMM. The proposed model is compared with other location prediction models driven by the massive and real mobile dataset Geolife. The experiment results show that the prediction accuracy of the proposed model can achieve 6.4% and 7% higher than Gaussian Mixture Model (GMM) and traditional HMM respectively. Keywords: Hidden Markov Model
Location preference
Location prediction

1 Introduction With the rapid development of society informatization and mobile communication technology, human communication behaviors have been changed greatly and there is a large amount of mobility data that can be used to analyze various aspects of human behavior. Many services based on location require a precise location of users, so predicting user behaviors becomes a key factor for a wide range of applications based on geographic information, like accurate advertising, early warning systems [1]. Existing studies have found that it is very important to extract location information from mobile phone GPS data for studying human mobility patterns [2, 3]. GPS devices with less energy consumption collect location information passively, which has the characteristics of wider coverage and finer time granularity [4]. Compared with CDR data and smart traffic card data, GPS data has the characteristics of dense distribution and can reflect user mobility accurately. CDR data records the ID of the connected © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 3–12, 2019. https://doi.org/10.1007/978-981-13-7123-3_1

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cellular tower only when a mobile phone event occurs, resulting in the data to be sparse in time and space. The coverage of smart traffic card data is small and the data can be applied in specific scenarios merely. The shortcomings in smart traffic card data limit its application in the research of moving object mobility patterns. Studies of human mobility patterns show that the human moving trajectories have high temporal and spatial regularity, so the potential visited location can be predicted by human historical trajectory data. In order to exploit the common regularity of human movement and build the prediction model of potential visited location, many scholars have done some researches on human mobility pattern and location prediction in recent years. The common location prediction algorithms include Bayesian Model, Neural Network, Markov Model, Hidden Markov Model and Pattern Matching algorithm. Hidden Markov Model (HMM) can reflect the random process of user movement well and is suitable for short historical sequences. This paper proposes a prediction algorithm based on HMM and moving object location preference which considers time characteristics of historical trajectory data. We use a spatiotemporal threshold algorithm to transform trajectory data into a series of user stay points and a POI Discovering algorithm to solve the problem of low accuracy caused by long historical trajectory sequence.

2 Related Work 2.1

Points of Interest (POI)

Researchers have proposed many algorithms for extracting user stay points and points of interest [5, 6]. In [7], the algorithm for extracting stay points only consider the spatial factor, resulting in the travel sequence extracted from trajectory data containing many useless points. Literature [8] proposes a stay points extraction algorithm based on time and space, which can be better on extracting stay points and reflect user mobility more accurately. In [9], the k-means cluster algorithm is used to extract points of interest, but the algorithm needs to know the number of clusters in advance. In view of the shortcomings of the k-means cluster algorithm, in [10], the DBSCAN cluster algorithm is used to original trajectories. The original trajectories have a large amount of data which is disorderly. The efficiency of clustering is very low when the data is not preprocessed. Literature [11] uses the DBSCAN algorithm to cluster the user trajectory sequences based on the extraction of stay points and obtain trajectory sequences that are both concise and can truly reflect the user’s moving location. 2.2

Location Prediction Algorithms

There are many prediction models of user location such as linear regression models, neural network models, probability models, frequent trajectory models. The linear regression models, which assume that the trajectories of a moving object follow the rule of linear movement, are not suitable for complex real datasets [12]. With the development of artificial intelligence and neural network, neural network-based location prediction algorithms [13, 14] are also continuously applied to predict potential location. The training process of neural network-based algorithms requires a large amount of data. Probabilistic models can well describe the stochastic process of moving objects. Therefore, a large number of probabilistic models are proposed to predict

LP-HMM: Location Preference-Based Hidden Markov Model

5

user’s prediction location. Literature [15] proposes the model of personal mobility data by using Markov model. The paper compares the efficiency and accuracy on different orders of Markov model. In [16], a hybrid Markov model that combines the user’s location preference is proposed, the paper also consider to use the moving object data with similar trajectories for prediction when the user’s prediction sequence never appears in historical trajectory data. In [17], two prediction models based on HMM are proposed. One is the “temporal and spatial prediction” model which is used to predict regular moving trajectories and the other is the “next location” prediction model which is suitable for limited trajectory points.

3 User Mobility Modeling Based on HMM This section gives the methods and steps for modeling user mobility based on HMM for addressing the problem of datasets and the low accuracy of prediction models. 3.1

Data Description

A trajectory is generated when a user moves from a location (loc1) to another location (loc2). The trajectory is composed of a series of time-stamped latitude and longitude data represented by Traj ¼ l0 ; l1 ; . . .; lk , li ¼ ðxi ; yi ; ti Þ ði ¼ 0; 1; . . .; kÞ. (xi, yi) corresponds to the latitude and longitude coordinates of trajectory point i. The location device starts to record the user’s locations at the time stamp t. 3.2

Extracting Stay Points

After visualizing the original trajectory dataset of the moving object, it is found that trajectories are not discrete but very dense, because the moving object tends to stay in a certain place for a period of time. The GPS device can record a large amount of redundant data when the moving object stays at a location. This paper proposes a spatiotemporal threshold stay point extraction algorithm to solve the problem. The key of the spatiotemporal threshold stay point extraction algorithm is that the spatiotemporal property of stay points M (xi, yi, ti) and N (xi+1, yi+1, ti+1) satisfies some conditions, where (xi, yi) indicate the latitude and longitude coordinates, and ti is timestamp. When the trajectory points M and N satisfy the two conditions of Eqs. (1) and (2), M and N are considered to belong to the same stay area. The location of stay points is represented by the average latitude and longitude ðx; yÞ of all trajectory points in this area, where Dr in Eq. (1) represents the distance threshold and Tr in Eq. (2) represents the time threshold. qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðxi þ 1  xi Þ2 þ ðyi þ 1  yi Þ2  Dr

ð1Þ

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðti þ 1  ti Þ2  Tr

ð2Þ

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Extracting Points of Interest

Stay points do not reflect the locations of interest to users. Points of Interest (POI) are usually acquired based on stay points where the number of the stay points exceeds certain frequency threshold, so the distribution density of points of interest is higher than the density of other areas. This paper uses the DBSCAN algorithm to extract users’ POI. The algorithm description for extracting users’ POI is shown in Algorithm 1. First, the parameter radius Eps and the minimum number of stay points Minpts of the algorithm are set, and the sequences of stay points of each user are used as an input of the algorithm. Each user’s POI is extracted by clustering stay points based on DBSCAN. DBSCAN uses the Haversine formula as the stay points distance metric formula. The outputs of the algorithm are the clusters to which all stay points belong. The POI discovering algorithm uses the cluster results of the DBSCAN algorithm to identify the limited points of interest of users, when continuous stay points belong to the same cluster, we convert them to a point of interest which is used to users’ final sequence of points of interest.

LP-HMM: Location Preference-Based Hidden Markov Model

3.4

7

Prediction Model Based on HMM

The Markov model can be used to describe a user’s movement process since the user’s moving trajectories are composed of a series of sequential location points. The Hidden Markov Model (HMM) is developed based on the Markov model. HMM can solve the problem with hidden variables compared with the Markov model. It is suitable for the spatiotemporal sequences generated by the Markov process with unobservable states. Therefore, this paper uses HMM for location prediction. Based on the historical trajectory data, HMM firstly uses the Baum-Welch algorithm to calculate the HMM parameter k, where k is the set of {A, B, p} which are the transmission probability matrix, the transition probability matrix and the initial probability matrix of the model. According to the iterative training, the transition probability is obtained between two different locations. The probability of a given POI sequence visited any location can be obtained according to Eq. (3). In a word, according to the given the first k–1 locations where the user has visited, the corresponding predicted result is the one with the highest probability of the locations. PðO; SjkÞ ¼ ps1 bs1 ðo1 Þ

Ym i¼2

asi 1 ; si bsi ðoi Þ

ð3Þ

In Eq. (3), psi indicates the initial probability of hidden state si, bsi ðoi Þ indicates transmission probability from hidden state si to output state oi, and asi1 ;si indicates the transition probability from hidden state si–1 to hidden state si. 3.5

Modeling Personal Mobility Preferences Based on Temporal Characteristics

The frequency of user access to a location at different moments is different. For example, it is more likely to leave home in the morning to go to the company, while it is less likely to leave home at night to go to the company. This paper analyzes the user different probabilities between different visited locations based on the historical trajectory data. POIA ! POIB represents the moving process from the current point of interest A to the next point of interest B. Nt ðPOIA ! POIB Þ is the number of trajectories that the user moves from point A to point B at time t, Nt ðPOIA Þ indicates the number of trajectories where the user starts at point A at time t. We define a time-based personal mobility preference algorithm, in which mobility preference probability Ppref ðPOIA ! POIB Þ is shown in Eq. (4). Ppref ðPOIA ! POIB Þ ¼

3.6

Nt ðPOIA ! POIB Þ Nt ðPOIA Þ

ð4Þ

Modeling Based on HMM and Location Preference

The traditional HMM only considers the spatial continuity of a user’s historical trajectory data, but does not consider the difference in the transition probability between the trajectory points of the user in different contexts at different moments. Therefore,

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this paper proposes Location Preference-based Hidden Markov Model (LP-HMM), the prediction algorithm which combines the traditional HMM model and time-based location preference model. The probability of the LP-HMM algorithm is calculated by Eq. (5). PðPOIA ! POIB Þ ¼ b1  PðO; SjkÞ þ b2  Ppref

ð5Þ

Where parameters b1 and b2 are the weights of the two generation probabilities, b1 þ b2 ¼ 1, PðO; SjkÞ represents the probability of generating visited sequence, and Ppref represents the time-based location preference probability.

4 Experiment 4.1

Dataset

This paper uses the public GPS dataset Geolife, which is composed of 182 users’ GPS trajectory data collected by Microsoft Research Asia from 2008 to 2012. In the Geolife dataset, a GPS trajectory is represented by a series of time-stamped points, each of which contains latitude and longitude coordinates and timestamp. The complete dataset contains 18,670 trajectories, making it an ideal choice for validating location prediction methods. It can be seen from Fig. 1(a) that the distribution of user trajectory number is very uneven, and the number of users with more than 100,000 trajectory points is the most. Therefore, the dataset can be used to analyze the user’s mobility. We need to select the users we need from all the users. In Fig. 1(b), it can be seen that the number of users with a daily average of more than 2000 trajectory points is the most.

The trajectory of user per day 60 50 40 users

the number of trajectory point(10 3 )

The distribution of user trajectory point >100 90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 1-10 0-1

30 20 10 0 20

The number of trajectory per day(101)

(b) The trajectory of user per day

Fig. 1. The distribution of Geolife dataset

LP-HMM: Location Preference-Based Hidden Markov Model

4.2

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Data Preprocessing

Data preprocessing includes detecting stay points and extracting points of interest based on users’ stay points. Firstly, we define a stay area as the area where a user stays within a radius of 200 m for more than 15 min. In this paper, the mean values of the latitude and longitude of all the trajectory points in a stay area is used to indicate the location of stay points. Using the proposed stay point algorithm, it can be seen that the number of trajectory points after extracting stay points is significantly less than the number of the original trajectory points, the distribution of the user stay points is more uniform, which improves the clustering efficiency of the clustering algorithm. After extracting stay points from the original trajectories, the POI discovering algorithm is used to extract users’ points of interest, and then the clustering results of users are analyzed by using different Eps and Minpts. The Eps and Minpts values of this experiment represent the clustering radius and the minimum number of points in a certain cluster respectively. Figure 2a shows the effect of different Minpts values (3, 4, 5) on the clustering results under the condition where parameter Eps is 300 m. Figure 2b shows the effect of different Eps values (100,200,300) on the clustering results under the condition that Minpts is 4. We find that the clustering effect is not the same under different Minpts and Eps. According to the different parameter clustering, we compare the distribution of user trajectories. In order to satisfy the experiment demand, we choose the condition where the Eps is 200 m and the Minpts is 4. Under the specific parameter condition, in the experiment we choose the users whose number of trajectories is more than 80. Finally, we select the trajectory data of 16 users from the dataset to verify whether the model is more effective. Table 1 gives the specific situation of top 20 users’ trajectory distribution.

100_4

200_4

300_4 The number of users

The number of users

60 50 40 30 20

300_3

70

70

300_4

300_5

60 50 40 30 20 10

10

0

0 1

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The number of cluster labels

(a) The result in different Eps and same Minpts

0

1

2

3

4

5

6

7

8

9 10 >10

The number of cluster labels

(b) The result in different Minpts and same Eps

Fig. 2. The cluster distribution of user trajectories in different Eps and Minpts

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J. Huang et al. Table 1. The distribution of top 20 users’ trajectory User 000 014 030 084 144

4.3

Numbers 99 96 183 73 72

User 002 017 039 085 153

Numbers 102 188 143 116 393

User 003 022 041 126 163

Numbers 212 103 121 78 126

User 004 025 068 128 167

Numbers 241 77 214 174 145

Location Prediction

Before the experiment, the dataset of 16 objects was divided into two separate parts. One part is used as training set for training model parameters and the other is used as test set for verifying the validity of the model. By removing the last location in each verification trajectory, the experiment generates a prediction of the next location to be accessed, and then compares the next location predicted by the model with the actual location accessed by the given user. In this paper, the accuracy rate is used as a criterion to measure the quality of model. It refers to the ratio of the prediction results that are consistent with the actual location to the total prediction samples. The calculation method is shown in Eq. (6), where TP denotes the number of predicted locations that are consistent with the actual locations, TP + FP indicates the total number of predictions. Accuracy Rate ¼

TP TP þ FP

ð6Þ

A multi-fold cross-validation prediction experiment is carried out on the users and the experiment results are compared with the traditional HMM and GMM. The location sequences are used in different prediction models. It can be seen from Fig. 3 that the prediction accuracy of the hidden Markov model considering time factor is higher than the traditional HMM and GMM. The factors that determine the location prediction includes the space and time context. The proposed LP-HMM model considers the time and space characteristics of the location information at the same time, while the HMM and GMM only consider the conversion probability between the spatial locations, which is equivalent to only utilizing the spatial characteristics of historical information. LP-HMM improves the accuracy of prediction compared with HMM and GMM. When the number of cluster labels exceeds a certain value, the accuracy of model prediction decreases slightly, and GMM shows the superiority of prediction when the trajectory sequences are longer. By analyzing the accuracy of the selected 20 users, it was found that the increased accuracy predicted by the LP-HMM model can achieve 6.4% and 7%, respectively, compared to GMM and HMM.

LP-HMM: Location Preference-Based Hidden Markov Model

HMM

GMM

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0.8

Accuracy

0.78 0.76 0.74 0.72 0.7 0.68 0.66 1

2

3

4

5

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7

8 9 10 11 12 13 14 15 16 User

Fig. 3. The comparison of different models

5 Conclusion In this paper, we propose a method to discover POI sequences of moving objects using GPS data. We use the POI sequences to build a model to predict the potential visited locations of users, aiming at improving the prediction accuracy based on the HMM prediction algorithm by considering the spatiotemporal characteristics of real datasets. We represent a spatiotemporal threshold stay point extraction algorithm to extract stay points. The algorithm mainly processes GPS points according to two thresholds: time and distance. A POI discovering algorithm based on DBSCAN is proposed to obtain the user’s POI and the sequence data which is favorable to the prediction model. Finally, a prediction model LP-HMM combining HMM with temporal location preference is proposed based on HMM. Under the driving of the Geolife dataset, the proposed model is compared with HMM and GMM to verify the validity of the model. The results show that the prediction accuracy of the model is higher than that of HMM and GMM.

References 1. Sungjun, L., Junseok, L., Jonghun, P.: Next place prediction based on spatiotemporal pattern mining of mobile device logs. Sensors 16(2), 145–163 (2016) 2. Lin, Y., Huang, P.: Prefetching for mobile web album. Wirel. Commun. Mob. Comput. 16 (1), 18–28 (2016) 3. Yang, J., Qiao, Y., Zhang, X.: Characterizing user behavior in mobile internet. IEEE Trans. Emerg. Top. Comput. 3(1), 95–106 (2015) 4. Qiao, Y., Cheng, Y., Yang, J., et al.: A mobility analytical framework for big mobile data in densely populated area. IEEE Trans. Veh. Technol. 66(2), 1443–1455 (2017) 5. Wu, R., Luo, G., Yang, Q., Shao, J.: Individual moving preference and social interaction for location prediction. IEEE Access 6(1), 10675–10687 (2018) 6. Lee, W.C., Ye, M.: Location-based social networks. In: Alhajj, R., Rokne, J. (eds.) Encyclopedia of Social Network Analysis and Mining. Springer, Heidelberg (2014)

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7. Yi, X.: Research of Tourist Traffic Flow Characteristic Based on Phone Signaling Data. Southeast University, Nanjing (2017) 8. Jiang, S., Ferreira, J., Gonzalez, M.C.: Activity-based human mobility patterns inferred from mobile phone data: a case study of Singapore. IEEE Trans. Big Data 3(2), 208–2193 (2017) 9. Hu, Y., Zhu, X., Ma, G.: Location prediction model based on k-means algorithm. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds.) Advances in Computer and Computational Sciences, vol. 554, no. 1, pp. 681–687 (2018) 10. Kavak, H., Vernon-Bido, D., Padilla, J.J.: Fine-scale prediction of people’s home location using social media footprints. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, pp. 183–189. Springer, Heidelberg (2018) 11. Ikanovic, E.L., Mollgaard, A.: An alternative approach to the limits of predictability in human mobility. EPJ Data Sci. 6(1), 6–12 (2017) 12. Wei, Z.: Research on Key Technologies of Moving Object Location Prediction. Nanjing University of Aeronautics and Astronautics (2009) 13. Chen, N.C., Xie, W., Welsch, R.E., et al.: Comprehensive predictions of tourists’ next visit location based on call detail records using machine learning and deep learning methods. In: IEEE International Congress on Big Data. IEEE, Piscataway (2017) 14. Wu, F., Fu, K., Wang, Y., et al.: A spatial-temporal-semantic neural network algorithm for position prediction on moving objects. Algorithms 10(2), 99–110 (2017) 15. Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: Next place prediction using mobility markov chains. In: Proceedings of the First Workshop on Measurement, Privacy and Mobility. ACM (2012) 16. Qiao, W., Si, Z., Zhang, Y., et al.: A hybrid Markov-based model for human mobility prediction. Neurocomputing 7(1), 278–290 (2017) 17. Lv, Q., Qiao, Y., Ansari, N., et al.: Big data driven hidden Markov model based individual mobility prediction at points of interest. IEEE Trans. Veh. Technol. 66(6), 5204–5216 (2017)

Design and Performance Evaluation of On-Board Computer Network Protocol Stack Based on Spacecraft Operating System Lei Qiao, Bo Liu(&), Hongjin Liu, Hua Yang, Jian Xu, and Shufen Liu Beijing Institute of Control Engineering, Beijing 100190, China [email protected]

Abstract. The spacecraft network protocol is the critical platform for the future spacecraft cooperative mission. Especially at the level of operating system, it has to support the network function to provide agile and reliable data transmission. This paper proposes a new network protocol stack architecture based on the Berkeley BSD’s TCP/IP network protocol stack of a practical spacecraft operating system. A thin glue layer is inserted between the operating system and the protocol, and the performance testing results running on one real on-board SPARC hardware platform shows that the highest IP layer throughout of 24.7Mbps can be obtained. This architecture has been used in some practical spacecraft of China which has been proved to have high value of engineering. Keywords: Spacecraft Performance test


Network protocol
Operating system


1 Introduction At present, most of the computers in the on-orbit spacecraft are interconnected directly by buses [1]; however, network applications communicating through protocol has just come out in the field of China’s spacecraft [2, 3] such as the third phase manned craft, the space station and the third phase lunar exploration. In these projects, the connections between various computer systems are getting closer, and tasks to be conducted are becoming more complicated. Thus traditional communication methods using buses to connect various computers are no longer up to the standard for applications to work well because of the low latency and transmission rate. Therefore, it is imperative to utilize the network to realize the communication between various computer systems [4, 5] to improve the performance. Communication between the internal spacecraft computer systems have two main types of data packets [6, 7]. One is the control data packets for transmitting control commands between various modules, the other one is the ordinary data packets. On the spacecraft, control packets are composed of a large number of small size packets. Control packets are mainly control signals transmitting to another module which requires high real-time data transmission, but the data should also be kept accurate and integrated during transmission. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 13–21, 2019. https://doi.org/10.1007/978-981-13-7123-3_2

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Furthermore, traditional communication among computers on the spacecraft depends on the direct data transmission based on bus drivers such as the 1553B bus drivers by reading or writing data without operating system support. This mode cannot only guarantee the reliability of transmission but also provide a good programming interface for applications. Designing a dedicated specific network protocol for the spacecraft is very necessary.

2 The Challenges of the Spacecraft Network Protocol Stack The TCP/IP protocol [8, 9] software is the base of the network which is conceptually divided into 4 layers. When implementing a protocol stack of an actual spacecraft system, the details of the specific hardware interface, OS features and interfaces, buffer management, protocols, and error handling must be taken into account and therefore the structure of protocol software is more complicated than the conceptual structure. Specifically, to implement the TCP/IP protocol stack on a spacecraft computer [10, 11], especially on a practical operating system SpaceOS [12], in addition to the logic of each protocol of the protocol stack itself, the following aspects need to be taken into account: 2.1

Interface

Three interfaces should be covered. As for the lower level interface, in order to support all kinds of communication devices, the protocol stack should communicate with the END driver [13] through the MUX [14] layer which constructs a unified END driver model. For the middle level interface, the management of network tasks, a semaphore must provide mutual exclusion and synchronization, coupled with switch interrupt protection of critical data as well as timed interrupts, the allocation and release of the timers and the protocol stack’s memory pool must be realized as well. As for the high level interface, the application must use the socket to communicate with the protocol stack. 2.2

The Implementation of the Protocol Stack

The three-level buffer management structure of mBlk-clBlk-Cluster [15, 16] must be applied and some software timer mechanism should be provided by using the time wheel algorithm to guarantee the real time performance. The interface abstracted by applying data structures such as PROTOSW which includes the function pointers should be utilized among protocols instead of calling the function’s name directly. 2.3

The Testing, Demonstration and Verification

The architecture should provide a good interface for high level applications such as FTP, TFTP, Telnet, Http (and Web Server). Also YAFFS (Yet Another Flash File System) [17, 18], flash driver and other modules outside the protocol stack can use the internal interfaces well.

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The Quality of Protocol

The interfaces of OS, applications, hardware driver and protocol are decoupled as much as possible. The code should be tailored by using function pointers or applying macros to control the initialization function. The lookup algorithms by using independent network processing tasks, buffer queues and an efficient route should be applied to improve performance. For the purpose of improving the reliability, the heartbeat method should be used to monitor network tasks or reducing task status.

3 The New Design of Modular and Hierarchical Architecture of Protocol Stack 3.1

Architecture Design

Regarding the four challenges above, the new architecture design of the TCP/IP protocol stack on SpaceOS is shown in Fig. 1: In this architecture, the logic of the TCP/IP protocol stack is the core of the whole process, and the core of TCP/IP is IP. The application layer utilizes the protocol stack through the Socket layer, and the protocol stack communicates with the driver layer via the MUX layer. The entire protocol stack uses a single network task which is called netTask to handle the reception and processing of all data packets.

Fig. 1. The new architecture of the spacecraft protocol stack

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The specific process is as follows: Algorithm: Searching the longest match string 1. The netTask and the driver communicate via the ring work queue and semaphore; 2. According to the type field in the MAC header (the link layer) in the data packets, netTask decides whether to put the packet into the ARP queue or the IP input queue; 3. The IP layer decides whether to call ICMP to process ICMP subtype, IGMP, or send it to the UDP or TCP queue in line with the type field in the IP header, and determines the destination of the packet according to the destination IP address (send to the upper layer for processing, forwarding, discarding, and multicasting); 4. UDP (TCP) decides whether to send the packets to the corresponding Socket queue through the port number in the UDP (TCP) header. 5. If an application layer task is waiting for receiving in the corresponding Socket queue, the semaphore can be released, and then the application task can receive the data through the Socket queue.

The function call is applied in the entire process, which occurs in the context of application layer tasks. In addition to the implementation of the protocol stack, there are four tasks contained in the application layer: TFTP, FTP, Telnet, and HTTP to implement the four protocols and the application logic. The interface between the partial protocol and the SpaceOS is decoupled via a unified interface file which we call it the glue layer. The code in the protocol part only uses the functions in the file and is isolated from SpaceOS. Application layer programs all need to use the file system without exception. Here in this architecture the YAFFS is used, which is a POSIX-compliant filesystem designed for use with NAND and NOR flash. It is efficient, robust and also has low resource use on the host system. There is a thin layer of file system interface between YAFFS and the application, which also supports the control of the protocol stack. 3.2

Operating System Support

From the architecture above we can see that the OS need to provide some mechanisms to support the protocol stack including: network task management, semaphore mutual exclusion and synchronization, switch interrupt protection of critical data, timed interrupt, as well as the allocation and release of protocol stack memory pool. Furthermore, when running a web application on OS, a message queue, time management, some C library functions, and many others are needed to be provided. The main design idea is modular: to implement a single interface file, this file calls the SpaceOS function interface, all new functions only depend on this interface file, but not allow direct calls to SpaceOS functions, and minimize the call to SpaceOS functions. In this way, the relative independence of the new function and SpaceOS is guaranteed, which can be easily achieved when modifying new functions, modifying SpaceOS functions or cutting functions. 3.3

Modular Glue Layer Interface

The interfaces file above can be regarded as a thin glue layer which includes interface functions of task management, interrupt management, message queue management, memory management, time management, as well as timer interrupting callback (Fig. 2).

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New functions of protocol GLUE Layer Function call Task Management

Interrupt Management

Time Management

Task Scheduling

Message Queue Management

Timer Interrupt Memory Management

Function call

SpaceOS functions

Timer interrupt callback, initialization

Fig. 2. OS glue layer interface

4 Performance Testing Results The purpose of the protocol performance test is to verify whether the software system can achieve the performance index proposed by the user, find the performance bottleneck in the software system. 4.1

Test Scenario

The basic test scenario is as shown in Fig. 3. The target board is based on the BM3803 [19] processor which is a SPARC [20] architecture used in the China’s spacecraft and the main CPU frequency is set as 66 MHz. The SpaceOS operating system is used on the target board to run the network protocol.

Target Board Running SpaceOS+Protocol Stack+Application, 2 Ethernet cards

Switch

Test Host1 Windows OS + Test Cases Sending Port

Test Host2 Windows OS + Test Cases Receiving Port

Fig. 3. Test scenario

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The Testing Results

The following is the throughput data (unit BPS) measured at different levels of the protocol stack at different frame lengths with each change of CACHE enabled, CPU frequency, and instruction running in RAM/FLASH. 1. The impact of CACHE (Table 1) Table 1. DCACHE + ICACHE on (66 MHz, in RAM, IO read: 66666650, IO write: 86956500) Packet size MAC DRV IP UDP TCP 64 19065000 19065000 4112050 3679200 5825400 164 29960450 29960450 8924350 8224400 11038050 264 32264850 34953600 12336550 11336300 13530400 364 38133950 34956100 14981200 13531400 14464600 464 38132350 41945600 16778200 15535400 14980550 564 38146900 41961600 18244150 16139050 15541300

2. The impact of frequency (Table 2) Table 2. D + I CACHE on (66 MHz, in RAM, IO read: 66666650, write: 6956500) Packet size MAC DRV IP UDP TCP 64 19065000 19065000 4112050 3679200 5825400 164 29960450 29960450 8924350 8224400 11038050 264 32264850 34953600 12336550 11336300 13530400 364 38133950 34956100 14981200 13531400 14464600 464 38132350 41945600 16778200 15535400 14980550 564 38146900 41961600 18244150 16139050 15541300

3. The impact of instructions running in RAM/FLASH (Table 3) Table 3. DCACHE off + ICACHE off (66 MHz, in RAM, IO read: 47619000, write: 26666650) Packet size MAC DRV IP 64 6355000 5518800 934100 164 10230400 8388900 1889350 264 11984050 9320950 2496650 364 13108550 9987450 2913000 464 13530800 10230600 3251550 564 13987200 10490400 3556050

UDP 828900 1684500 2231050 2621700 2933250 3178900

TCP 1379700 2140000 2526750 2815250 2974850 3018800

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The Testing Conclusion

Given that the CPU frequency is 66 M, and the program runs in RAM, the relationship between the forwarding rate (IP routing) and the unidirectional transmission rate in different CACHE states is shown in Table 4 (the average frame length is about 1000 bytes). Table 4. The comparison of retransmission and one-way transmission. The CACHE status D-CACHE ON D-CACHE ON D-CACHE OFF D-CACHE OFF

ON, I-CACHE

Forwarding rate/ (Kb/s) 9165

Unidirectional transmission rate/ (Kb/s) 20982

OFF, I-CACHE

5942

11656

ON, I-CACHE

2979

5380

OFF, I-CACHE

1992

4282

We can conclude that: (1) The forwarding rate is approximately half of the rate of one-way transmission; (2) CACHE has a relatively bigger impact on the performance of the protocol stack. And the ICACHE has the greatest impact, which almost doubles that by the DCACHE. (3) The main frequency is positively proportional to the performance level; basically when the main frequency is doubled, the throughput rate will be doubled as well; (4) For the current target board, the most concerned bottleneck affecting the performance of protocol lies in the network card IO. (5) The throughput of the program running in RAM is twice as much of the rate when running in FLASH. (6) The throughput of long frame is significantly higher than that of short frame. Due to the preamble, the frame interval, and the total number of frames processed, the smaller the frame, the lower the throughput; (7) The rate of the IP layer is not much different from that of the UDP layer, and the TCP layer is much different. (8) Some other important index is as follows: – Throughput: the D + ICACHE are both turned on, and the program runs in RAM, then the highest throughput is: IP layer 24.7 Mb/s, UDP layer 22.1 Mb/s, and TCP layer 16.8 Mb/s (Table 5). – Frame loss rate: the frame loss rate is less than 0.5% (the average is about 0.2%–0.4%) in the critical area of the protocol stack’s throughput.

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5 Conclusion Taking into account the characteristics of large transmission volume, small data packet and high real-time requirements in the current control data packet, this paper proposed a new network protocol architecture that optimizes and updates the BSD protocol and integrates it with the frequently used spacecraft SpaceOS, so as to meet the decent realtime control required by the data packet of complex network spacecraft, thereby realizing the data transmission on the basis of operating system. Noticeably, the function and performance test result has been given on a practical computer target board, and positive results have been achieved which proves that it can satisfy the requirements of spacecraft network applications. Acknowledgment. This work is supported in part by grants from the National Natural Science Foundation of China (NSFC) under Grant Nos. 61632005 and 61502031.

References 1. Parkes, S.M., Armbruster, P.: SpaceWire: a spacecraft onboard network for real-time communications. In: IEEE NPSS Real Time Conference, pp. 6–10. IEEE (2015) 2. Tan, C., Gu, Y.: Space Data Systems. China Science and Technology Press, Beijing (2004) 3. Yang, M., Guo, S., Sun, Z.: Spacecraft on-board computer control technology applications. Aerosp. Control 23(2), 69–73 (2005) 4. Wolfgang, E., Ines, L., Reinhardt, W., et al.: Optical fibre grating strain sensor network for X-38 spacecraft health monitoring. In: Proceedings of SPIE the International Society for Optical Engineering (2000) 5. Alena, R., Nakamura, Y., Faber, N., et al.: Heterogeneous spacecraft networks: wireless network technology assessment. In: IEEE Aerospace Conference, pp. 1–13. IEEE (2014) 6. Kitts, C.A.: A global spacecraft control network for spacecraft autonomy research. In: Proceedings of Spaceops the Fourth International Symposium on Space Mission Operations & Ground Data Systems, vol. 394, no. 394, pp. 16–20 (1996) 7. LAWRENCEB. TCP/IP Volume Detailed 1: Protocol. Machinery Industry Press, Beijing (2017) 8. Tanenbaum, A.S.: Computer Networks, 4th edn. Tsinghua University Press, Beijing (2004) 9. Rose, M.: Structure and identification of management information for TCP/IP-based internets. RFC 105(2), 97–110 (1990) 10. Partridge, C., Shepard, T.J.: TCP/IP performance over satellite links. IEEE Network 11(5), 44–49 (1997)

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11. Ghani, N., Dixit, S.: TCP/IP Enhancements for Satellite Networks. IEEE Press, Piscataway (1999) 12. Qiao, L., Yang, M., Gu, B., Yang, H., Liu, B.: An embedded operating system design for the lunar exploration rover. In: International Conference on Secure Software Integration & Reliability Improvement Companion, pp. 160–165. IEEE (2011) 13. Kou, Y., Chen, H., Duan, X., et al.: Development and realization of END network driver design in VxWorks systems. Comput. Measur. Control (2009) 14. Liang, H.: Methods study binding protocol based on VxWorks network system MUX layer. Ind. Control Comput. (2012) 15. Ma, Q., Steenkiste, P.: Supporting dynamic inter-class resource sharing: a multi-class QoS routing algorithm. In: INFOCOM 1999. Eighteenth Joint Conference of the IEEE Computer and Communications Societies. Proceedings, vol. 2, pp. 649–660. IEEE (1999) 16. Wang, J.-G.: Based on VxWorks Embedded Real-Time System Design. Tsinghua University Press, Beijing (2005) 17. Son, I., Kim, Y., Baek, S., Choi, J.: Improving the reliability and performance of the YAFFS flash file system. IEICE Trans. Inf. Syst. 94(12), 2528–2532 (2011) 18. Kim, S., Cho, Y.: The Design and Implementation of Flash Cryptographic File System Based on YAFFS. In: International Conference on Information Science and Security, pp. 62– 65. IEEE (2008) 19. Wei, L.I.: On-board Computer standard design and realization based on BM3803. Spacecr. Eng. (2012) 20. SPARC International, Weaver, D.L., Gremond, T.: The SPARC architecture manual-version 9. In: SPARC Architecture Manual Version (1994)

Overview of Face Recognition Methods Lingfeng Fang1,2,3(&), Meixia Fu1,2,3, Songlin Sun1,2,3, and Qianhan Ran4 1 National Engineering Laboratory for Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing, China [email protected] 2 Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China 3 School of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing, China 4 Beijing Leimo New Media Culture and Communication Co., Ltd., Beijing, China

Abstract. With the continuous development of information technology, the demand for security and safety is gradually improving. For the consideration of security, face recognition has been studied for many decades. With the development of information technology, face recognition is widely used in our daily life, especially in security systems, information security, human-computer interaction. Researches are committed to improving the recognition accuracy and response speed of the face recognition system. The state-of-art of face recognition has been significantly improved by the appearance of deep learning. Although these systems perform well on large amounts of web collected facial data, the performance and accuracy are still limited when they are applied in actual scenarios. There is still a long way to go to improve the recognition accuracy of face recognition system in real scenarios. This paper gives a comprehensive description of a series of face recognition methods. In this paper, we introduce the definition and development of face recognition, and also indicate main challenges in this domain. Furthermore, some classical popular methods in the development of face recognition technology are described in detail. Finally, the application of face recognition technology will be introduced. Keywords: Face recognition Deep learning


ASM
AAM
PCA
Eigen face


1 Introduction Facial recognition is a Biometric Artificial Intelligence based application that can uniquely identify a person by analyzing patterns based on the person’s facial textures and shape. A facial recognition system is a technology capable of identifying or verifying a person from a video source [1]. Face recognition is a successful application of image analysis and understanding, which is applied in many aspects, such as business,

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 22–31, 2019. https://doi.org/10.1007/978-981-13-7123-3_3

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security, crime detection and so on. Therefore, face recognition technology is getting more and more attention and has become a hot research field. Face recognition is a routine task for human, but establishing a similar computer system and constantly improving the accuracy is an ongoing research. The research on face recognition can be traced back to 1950s. Up to now, researches have made fruitful achievements in the research of computer-based face recognition methods. Especially with the emergence of deep learning, face recognition technology has achieved a rapid development. However, face recognition technology still faces many challenges, and it is limited by the application environment. These limitations not only come from facial expression, posture, position and size changes, as well as the effects of occlusion and makeup, but also from the lighting, background changes. Face recognition technology is also affected by data size and data distribution. Face recognition techniques can basically be classified into two categories: feature based methods and holistic methods. In feature based methods, some geometric features like position and width of eyes, noses, and the thickness of eyebrow are extracted to represent a face. On the contrary, in holistic approach, the whole face is taken as an input to perform face recognition task. This paper will introduce some classical popular face recognition methods in detail.

2 General Challenges Although the emergence of deep learning has greatly promoted the development of face recognition technology, face recognition technology still faces many challenges. There is still a long way to go to improve the accuracy of the face recognition system in the actual scenarios. 2.1

Illumination

Illumination is a long-standing problem in computer vision field, especially in face recognition field. The shadow produced by illumination will enhance or weaken the original facial features. If the illumination tends to vary, even the same individual is captured by the same sensor with the same pose and expression, face recognition system may make mistake to verify them to be different people. 2.2

Pose

Similar to the illumination problem, the pose problem is also a technical difficulty needed to be solved in current face recognition research. Face recognition systems are highly sensitive to pose variations. The pose problem involves facial changes caused by head rotation in a three-dimensional vertical coordinate system. Deep rotation perpendicular to two directions of the image plane may cause partial loss of facial information.

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Occlusion

Occlusion is a serious problem for face images acquisition in a non-cooperative situation. This problem is more obvious in the face images captured by video surveillance. The monitoring object often wear glasses, hats and other ornaments, making the captured face images incomplete, thus affecting the subsequent feature extraction and recognition, and even lead to the failure of face recognition algorithm. 2.4

Age Variation

Age variation is a major problem in face recognition. Facial appearance changes with age, especially in adolescents, which can lead to a decline in recognition. And it is impossible to collect facial images of an individual at his different age to train the system. 2.5

Data

Data is crucial to the performance of face recognition system. Data distribution and data size do influence the performance of face recognition system. A research in [2] shows that large amounts of data can improve the face recognition system’ performance. The experimental results show that the performance of the system improves as the amounts of data accumulates. However, too much data will have a long tail effect, and this effect is caused by data imbalance, which means that some individuals have most facial image instances, but some only have a little. When we continuously increase the amount of data, the performance of the system may not increase or even be damaged. In other words, adding the individuals with only a few facial image instances do not help to improve the recognition performance and may harm the system’s performance.

3 Literature Review The labeled faces in the wild home (LFW) dataset is intended to test the face recognition systems’ performance and accuracy under an unconstrained environment. More than 13000 images of faces are collected from web. Each face has been labeled with the name of its owner. 1680 of the face images owner have two or more distinct image instances in the dataset. It has become a standard dataset to evaluate the performance of face recognition system. Other databases like LFPW (labeled face parts in the wild) contains 3000 face images collected from web. The obtained images are detected by face detection method and the positions of face in the images are obtained. Three groups of 35 face feature points which are marked manually are also given. The WDRef database contains 99773 images of 2995 individuals, and more than 2000 individuals have over 15 image instances in this database. In order to push the accuracy limit on these datasets, large amounts of works have been made. In this section, we will introduce some classic face recognition methods which are widely used.

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3.1

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Active Shape Model (ASM)

Active shape models (ASM), proposed by Cootes and Taylor, have been shown to be an effective tool to understand the configuration of the face images, as well as the medical images [3]. ASM is an algorithm based on Point Distribution Model (PDM). ASM is a method of building and using flexible models of image structures whose shape can vary [4]. The principle of ASM is that the structure of images can be represented by a series of points. These points can be points indicating the edge, the points representing internal structure, or even external points. This means that ASM can be used to extract the feature points of objects, and these feature points can be used as a form of representing the characteristics of objects. ASM algorithm needs to calibrate the training set manually, getting the shape model by training, and then realize the matching of specific objects by matching the key points. ASM algorithm includes two parts: training and searching [5]. The ASM training process consists of two parts: the establishment of shape models and the construction of local features for each feature point. Establishing shape model consists of the following five steps: collecting N training samples; manually recording K key feature points for each training sample; constructing shape vectors for training set; normalizing the shape; applying the principle components analysis (PCA) to the aligned shape vectors. ASM algorithm is suitable for representing typical shapes and typical shape changes. At present, it is a very mature algorithm. But its application scope is limited. If it is simply used for feature points location, its performance (positioning accuracy and efficiency) is completely acceptable. 3.2

Active Appearance Model (AAM)

Active appearance model (AAM), proposed by Cootes, Edwards and Taylor [6], is a feature point extraction method which is widely used in the pattern recognition field. AAM algorithm is an extension of ASM algorithm that uses all the information covering the target area rather than just the information near the boundary. The AAM algorithm mainly consists of two stages: model establishment phase and model matching phase. In the process of building face model based on AAM, not only local feature information but also global shape and texture information are taken into account. A face hybrid model, which is the final corresponding AAM model, is established by analyzing face shape and texture features statistically. In the process of image matching, in order to calibrate facial features quickly and accurately, an image matching fitting method is adopted to locate the feature points of the tested facial object. This method can be summarized as the process of “matching, comparison, adjustment and re-matching, re-comparison”. AAM model not only establishes the statistical shape model reflecting the shape change, but also establishes the global gray model reflecting the global texture change to make full use of the global texture information. The shape model and the gray model are combined to establish the appearance model. The obtained apparent model can accurately generate the target image with shape and texture changes by removing the correlation between shape and texture.

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The main idea of AAM search method is based on the hypothesis that the difference between the texture of the model and the region surrounded by the shape changes linearly with the change of the parameters. The hypothesis is reasonable only in a certain range of deviation, and its coverage is strongly dependent on the training set. Therefore, the robustness of AAM algorithm is limited when it is used to search. 3.3

Principle Component Analysis

Principal component analysis (PCA) is a method used to analyze data in multivariate statistical analysis. PCA transforms the original data into a representation in which each dimension is linearly independent by linear transformation. PCA is widely used to extract the main feature components of the data and reduce the dimension of highdimensional data. The most famous application of PCA method should be feature extraction and dimension reduction in face recognition. For instance, if a face recognition system takes a 200 * 200 face image as its input, and the system only extracts the gray value of this image as the original feature, then this feature will reach 40000 dimensions, which will bring great difficulty to the later classifier. Therefore, in this case, we must reduce the dimension of the feature. Dimension reduction, of course, means loss of information, but data itself has some relevance. That is to say, if one variable is relevant to another variable, which means that one of them is a redundancy, then we can remove one variable to reduce dimension. In this way can we reduce the loss of information while reducing the dimension. The basis of PCA algorithm is Karhunen-Loeve method. PCA uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components [7]. Face recognition system can obtain the Eigen values, Eigen vectors and Euclidian distance by applying the PCA algorithm. After comparing the Euclidian distance with the Euclidian distance of each image in the database, face recognition system can declare whether this person present in database or not. 3.4

Eigen Face

Eigen face, proposed by Turk and Pentland [8], is one of the most popular approaches in face recognition field. Eigen face algorithm is based on principal component analysis algorithm. The basic idea of feature-based face recognition technology is to find the basic element of face image distribution from the statistical point of view, that is, the eigenvector of covariance matrix of face image sample set, so as to approximate the representation of face images. These feature vectors are called Eigen faces. Eigen faces reflect the information contained in the face set and the structural relationship of the faces. M Eigen faces construct an M dimensional space, and this space is called “face space”. The projection distance of the test face image in the face space is calculated, and if this distance is less than the threshold, face recognition system can declare and match the unique individual in the database who own this face image. Principle component analysis is successfully applied in Eigen face method to perform dimensionality reduction task. In the experiment, the database contained 2500

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images from 16 individuals. This system achieved approximately 96% correct classification averaged over lighting variation, 85% correct averaged orientation variation, and 64% correct averaged over size variation [8]. The disadvantages of Eigen face methods are that the Eigen face is sensitive to lightening conditions and position of head, and it is time consuming to find the eigenvalues and eigenvectors. 3.5

Deep Learning Methods

With the development of computer science and the emergence of deep learning technique, performance of face recognition system improves rapidly. Recently, deep convolutional neural network (CNN) based methods on face recognition problem are outperforming traditional ones with hand-crafted features and classifies [9]. Deep learning is a cognitive learning that simulates the human visual perceptual nervous system and can obtain more powerful high-level features. There are commonly two steps in face recognition methods based on deep learning. First, deep convolutional neural network is used to extract relatively higher dimension feature vectors. Then the PCA or metric-learning method is performed to reduce the dimension of the feature vectors extracted by CNN, and in this way, more efficient and more discriminative low dimension feature vectors are obtained. And these vectors are used as the representation of the faces of different identities. Normally, L2 distance is used to represent the similarity of different faces. Through training, the appropriate threshold is determined to distinguish whether different face images come from the same identity. That is to say, when the L2 distance is greater than the threshold, the algorithm will judge that these different face images come from different people, and when the L2 distance is less than the threshold, the algorithm will judge that these different face images come from the same individual. The advantage of deep learning method in face recognition is that it can acquire a kind of expression for invisible rules of face image by learning, avoiding complex feature extraction, and is beneficial for hardware implementation. Deep learning method takes advantage of computing system composed of graphics processor unit (GPU) to perform big data analysis task. Under the framework of deep learning, learning algorithm can directly learn discriminant facial features from original face images. Supported by a large number of face data, face recognition methods based on deep learning have far exceeded human beings in speed and accuracy. Deep learning model helps to promote the development of artificial intelligence, and may even surpass human intelligence level in the future. But the algorithm is hard to explain. Because of the massive neurons and data, long operation time, and some parameters need to be adjusted manually in the training process, so the suitable range is limited to a small face database. In 2012, Huang, Lee and Learned-Miller first applied deep learning method to face recognition task in LFW database. They used the unsupervised feature learning method and achieve 87% accuracy [10]. In recent years, many international projects have successfully applied deep learning method to face recognition, such as: DeepFace [11], DeepID [12], FaceNet [13], etc. The above algorithms are all based on massive training data, allowing the deep learning algorithm to learn face features when keeping the illumination, expression and angle unchanged. Among the above algorithms, FaceNet

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[13] achieved the highest accuracy, which has reached 99.63% on the LFW database, and this accuracy even exceeded the ability of human eye. Table 1. Comparisons between some face recognition methods based on deep learning Method

IDL ensemble model IDL single model FaceNet [13] DeepID3 [14] Face++ [2] Facebook [15] Learning from Scratch [16] HighDimLBP [17]

Performance on tasks Verification Open-set Pair-wise Rank- DIR (%) Identification (%) @ (%) @ Accuracy (%) 1 (%) @ FAR = 1% FAR = 0.1% Rank = 1, FAR = 0.1% 99.77 98.03 95.8 99.41 92.09 99.68 99.63 99.53 99.50 98.37 97.73

97.60 NA 96.0 NA 82.5 NA

94.12 NA 81.40 NA 61.9 NA

99.11 NA NA NA NA 80.26

89.08 NA NA NA NA 28.90

95.17

NA

NA

41.66

18.07

Table 1 shows the comparisons between some face recognition methods based on deep learning. In this table, there are three evaluation protocols containing five tasks. The first protocol was proposed by Huang et al. [18] and it was used to test the recognition accuracy of 6000 face images pairs. The second protocol was proposed by Best-Rowden et al. [19] and contained two tasks: a closed-set identification task and an open-set identification task. The third protocol was proposed by Liao et al. [20] and contained two tasks: a verification task and an open-set identification task. This paper will introduce two face recognition systems based on deep learning. Face++. The Face++ (Megvii Face Recognition System) was proposed by Zhou, Cao and Yin [2]. The training set consists of 5 million face images collected from web. Four regions of face images are used for feature extraction. The neural network consists of 10 layers and the last layer is a softmax layer. PCA algorithm is used to reduce the dimension of the feature vectors extracted from the previous layers. And L2 distance is used to represent the similarity of different face images. After training, this system is tested on LFW, and it achieves 99.50% accuracy. Then a new test set called Chinese ID (CHID) is collected offline and specializes on Chinese people. Applying CHID test set to test this system in real scenarios, the true positive rate is only 66% when keeping the false positive rate to 10 5 , and this performance does not meet the requirement of real application. After further research, it is found that data is crucial to the performance of the system. Face recognition system faces three main challenges. First, unbalanced data do impact the performance of the system. The data of the identities with only a few face image instances do not work in a simple multi-class classification framework. Secondly, in real test scenarios, the false positive rate of this system is quite low, but the true positive rate

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is only 66%, and this performance does not meet the requirement. That is to say, very low false positive rate can also influence the system’s performance. Finally, there are some cross factors, such as occlusion, pose and age variation will affect the system’s performance. These problems needed to be further investigated. Two-Stage Approach From Baidu. Baidu has proposed a two-stage approach which combines a deep convolutional neural network on multi-patch and deep metric learning [9]. The same deep CNN structures are used to extract facial features centered on different landmarks on face region. And the features extracted by each network is simply concatenated together to form a high dimension feature. Since the high dimensional feature is not efficient and consumes a lot of resources, so the next step is to reduce the dimension of the feature. In this research, a metric learning supervised by a triplet loss is used and the number of dimension is reduced to 128. The goal of metric learning with triplet loss is to increase the L2 distance of different individuals and shorten the L2 distance of the same individuals. After training, this system achieving 99.77% pair-wise verification accuracy on LFW. In this research, the effect of data and multi-patch to the system’s performance are investigated. The evaluation protocol is proposed by Huang et al. [18], that is to test the accuracy of 6000 face pairs from LFW dataset. Table 2 shows the pair-wise error rate with different amount of training data, and Table 3 shows the pair-wise error rate with different number of patches. It is obvious that with the increase of the amount of training data, the error rate of the system decreases, and with the increase of the number of patches, the error rate of the system decreases, but somehow saturates after seven patches.

4 Application Compared with other identification methods, face recognition has the advantages of direct, friendly, convenient and robust. Its application field is gradually expending to every field of daily life. On the one hand, it obviously improves work efficiency, on the other hand, it is also secure and reliable. Its application prospect is very broad. 4.1

Payments

Face recognition has already been used in stores and ATMs, and customers can pay by camera. That is to say, after the face recognition system confirms customers’ identities, payment will success. This technology will be applied to online payment. Chinese ecommerce firm Alibaba is planning to apply the face recognition system to payment made over the Internet. 4.2

Access and Security

Instead of using passwords, mobile phone and many other mobile terminals can be accessed via owners’ facial features. Apple, Samsung and Xiaomi Corp, have all installed face recognition system in their products. And this is friendlier and more convenient compare to the traditional access method, which requires users to input the

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L. Fang et al. Table 2. Pair-wise error rate with different amount of data Identities 1.5K 9K 18K

Faces 150K 450K 1.2M

Error rate 3.1% 1.35% 0.87%

Table 3. Pair-wise error rate with different number of patches Number of patch Error rate 1 0.87% 4 0.55% 7 0.32% 9 0.35%

password. Innovative face recognition system could be especially useful for a company or organization. Organizations can also use face recognition system to record attendance records. 4.3

Criminal Identification

Surveillance video plays a key role in case detection and criminal identification. Video surveillance can be used to determine the suspect’s trajectory and even find his position, which is beneficial to the detection of the case. If the monitoring system realize the face recognition function, polices can directly lock and capture the suspects. Cameras equipped with artificial intelligence have been widely used in the field of surveillance.

5 Conclusion This paper introduces the definition and development of face recognition. General challenges faced by face recognition system is discussed. And then we give description to some classical popular face recognition methods in detail. At last, we indicate some application fields of face recognition system. In short, even though the emergence of deep learning greatly improves the performance of face recognition systems, this field still needs to be further studied in order to improve the performance of face recognition system in actual scenarios.

References 1. Wikipedia https://en.wikipedia.org/wiki/Facial_recognition_system. Accessed 18 October 2018 2. Zhou, E., Cao, Z., Yin, Q.: Naive-deep face recognition: touching the limit of LFW benchmark or not? arXiv preprint arXiv:1501.04690 (2015)

Overview of Face Recognition Methods

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3. Wang, W., Shan, S., Gao, W., Cao, B., Yin, B.: An improved active shape model for face alignment. In: 2002 Proceedings of IEEE International Conference on Multimodal Interfaces, pp. 523–528. IEEE (2002) 4. Cootes, T.F., Taylor, C.J., Cooper, D.H., et al.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995) 5. CSDN. https://blog.csdn.net/CCmilan/article/details/9424387. Accessed 8 October 2018 6. Edwards, G.J., Cootes, T.F., Taylor, C.J.: Face recognition using active appearance models. In: Computer Vision — ECCV 1998. Springer, Berlin, Heidelberg, pp. 581–595 (1998) 7. Joshi, A.G, Deshpande, A.S.: Review of face recognition techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5, 71–75 (2015) 8. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1991, pp. 586–591. IEEE (1991) 9. Liu, J., et al.: Targeting Ultimate Accuracy: Face Recognition via Deep Embedding (2015) 10. Huang, G.B., Lee, H., Learned-Miller, E.: Learning hierarchical representations for face verification with convolutional deep belief networks. In: Computer Vision and Pattern Recognition, vol. 157, pp. 2518–2525. IEEE (2012) 11. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708. IEEE (2014) 12. Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE (2014) 13. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering, pp. 815–823 (2015) 14. Sun, Y., Liang, D., Wang, X., et al.: DeepID3: Face Recognition with Very Deep Neural Networks. Computer Science (2015) 15. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Web-scale training for face identification. In: Computer Vision and Pattern Recognition, pp. 2746–2754. IEEE (2015) 16. Yi, D., Lei, Z., Liao, S., et al.: Learning face representation from scratch. In: Computer Science (2014) 17. Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3025–3032. IEEE (2013) 18. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07– 49, University of Massachusetts, Amherst, October 2007 19. Best-Rowden, L., Han, H., Otto, C., et al.: Unconstrained face recognition: identifying a person of interest from a media collection. IEEE Trans. Inf. Forensics Secur. 9(12), 2144– 2157 (2014) 20. Liao, S., Lei, Z., Yi, D., et al.: A benchmark study of large-scale unconstrained face recognition. In: IEEE International Joint Conference on Biometrics, pp. 1–8. IEEE (2014)

HAR-Net: Fusing Deep Representation and Hand-Crafted Features for Human Activity Recognition Mingtao Dong1, Jindong Han2(&), Yuan He2, and Xiaojun Jing2 1

2

The Second High School Attached to Beijing Normal University, Beijing, China Beijing University of Posts and Telecommunications, Beijing, China [email protected]

Abstract. Wearable computing and context awareness are the focuses of study in the field of artificial intelligence recently. One of the most appealing as well as challenging applications is the Human Activity Recognition (HAR) utilizing smart phones. Conventional HAR based on Support Vector Machine relies on manually extracted features. This approach is time and energy consuming in prediction due to the partial view toward which features to be extracted by human. With the rise of deep learning, artificial intelligence has been making progress toward being a mature technology. This paper proposes a new approach based on deep learning called HAR-Net to address the HAR issue. The study used the data collected by gyroscopes and acceleration sensors in android smart phones. The HAR-Net fusing the hand-crafted features and high-level features extracted from convolutional neural network to make prediction. The performance of the proposed method was proved to be higher than the original MC-SVM approach. The experimental results on the UCI dataset demonstrate that fusing the two kinds of features can make up for the shortage of traditional feature engineering and deep learning techniques. Keywords: Human Activity Recognition
Inception convolutional neural network
Hand-crafted features

1 Introduction With the advent of big data era, it has been an urge for humans to find valuable information. Among all types of applications addressing this issue, Human Activity Recognition (HAR), which is applied frequently to the fields of human-computer interaction, identification technology, and medical security, has been the one that is commonly utilized. Currently, studies related to HAR is based on several approaches: video-based HAR and wearable sensor-based HAR. The former approach of HAR is based on the analysis and recognition videos captured by camera from moving human bodies. This approach is mainly employed by the field of security monitoring, being especially useful when monitoring anomalous behavior of the elderly and children. Despite the multiple applications and benefits of video-based HAR, there are challenges that may hinder the development of the technology. Due to the demand for © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 32–40, 2019. https://doi.org/10.1007/978-981-13-7123-3_4

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storage resources and the limitations related to the deployment of cameras, the monitoring of users’ whole-day activity state is impossible. This restriction causes the considerable difficulty of popularization for the video-based approach. Wearable sensor-based HAR, as an area of intensive interest, on the other hand, can be achieved using wearable acceleration sensor, gyroscope, and heart rate sensor. The potential of this approach is enabled by the portability and low cost of wearable sensors. As an intelligent aided technology, sensor-based HAR has been employed into commercialized devices as fitness tracker and fall detector. More socially valuable applications, like providing dementia patients with Memory Prosthesis and proposing suggestions for daily exercise which require a comprehensive understanding about past and present human activity, are expected to be generalized. Whereas the traditional approach of wearable-based HAR can be challenging to generalize as the result of the requirement for specially designed sensors. In the 1950s, there were researchers related to sensor-based HAR whose progress was retarded due to the immaturity of recognition algorithm and unaffordable huge sized sensors. Decades of following advancement in microelectronics and computer motivated the invention of MEMS that permitted the micromotion and integration of sensors. Sensors now have attributes of high computational power, low cost, and tiny size. Meanwhile, the popularization of smart phone enables people to enjoy an intelligent platform that incorporates amusement, communication, and work, it counteracts the toughness faced by wearable-based HAR. With the rise of artificial intelligence and wearable computing, plenty of sensors have been integrated into smart phones. HAR based on smart phone built-in sensors has thus become research hotspots. The portability and the ease to develop software on smart phone makes real-time supervision of human body activity and generalization of smart phone-based HAR applications feasible. Real-time supervision quantifies human activity, raises humans’ self-awareness of health conditions, and offers reasonable fitness tips, all of which make it a promising market with high value. The structure of the remaining section is shown as subsequence: related work is summarized in Sect. 2. Details of HAR-Net are illustrated in Sect. 3. Our model will be evaluated in Sect. 4. Conclusions are made in Sect. 5.

2 Related Work With the hardware development, sensor-based HAR has been commercially applied to medical system and security. Information collected from sensors is a time series. Different effective approaches to process time series have been proposed to act as the foundation of HAR. The majority of focus in HAR study covers daily life, sport, socialization, and health. Layering classification is achieved by precepting group behavior using multiple sensors, while children’s socializing behaviors are successfully identified through physiological sensors [1, 2]. Through using acceleration sensor, researchers have achieved Parkinson patients’ recognition and detection of Down’s Syndrome patients [3, 4]. Researchers in Stanford have utilized multi-model sensors system to detect whether the elder is falling down [5]. Wearable sensors are also applied to the recognition of newborns’ disorder related to cerebral stroke [6]. Sensorbased HAR can be optimistic prospect when used to support vulnerable groups.

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Researchers have done study using an efficient Support Vector Machine algorithm to compensate for computational loss [7]. In recent years, deep learning is rising because of the big data. While conventional machine learning approach required researchers to manually extract features, deep learning can accomplish end to end learning, using original data as input without manual feature extraction. Deep neural network can provide powerful feature representation by complex non-linear transformation. The mostly used networks are deep convolutional neural networks and LSTM for video-based HAR. Researchers have shown the high competence of LSTM to classify activity appear in video by analyzing Sports-1 M and UCF-101 data set [8]. Utilizing three wearable sensor data set to assess the performance on deep learning framework, researchers are able to determine the effectiveness of deep learning on extracting features [9]. Additionally, conventional machine learning approach has been compared to sensor-based deep learning approach for human activity recognition [10]. When we use image and signals as input, convolutional neural networks perform better than multilayer neural networks. A study has been done by researchers with two dimensional images that are synthesized with 9 types of sensor data, processed by Discrete Fourier Transformation (DFT), and recognized with convolutional neural network [11]. Researchers have also adopted several convolutional neural networks trained in parallel followed by max pooling, concatenated the activations, and fed them into the fully connected layer. Convolutional neural networks can be advantageous when applied to HAR because of the local dependency and scale invariance [12].

3 Methodology For the wearable sensor-based human activity recognition task, we proposed a network architecture called HAR-Net. The structure is illustrated in Fig. 1. The input of the network will have a dimension of (1, 128, 9) with 9 channels, then will be processed by the separative convolutional layer. This means that the 9 channels of the input will be convoluted respectively by the same filters. For each channel of the input, it will be processed in parallel by filters with size (1, 1), (5, 1), (9, 1), (13, 1), (17, 1), (21, 1), (25, 1), (29, 1), and (33, 1) respectively, each followed by a max pooling layer with size (11, 1). The pattern of convolutional layer followed by a max pooling layer described above will be repeated for four times consecutively. Hand-crafted features, including time and frequency domain statistics, will be concatenated with the flattened output from the convolutional layers. Then input into the first fully connected layer with 2048 neurons with relu activation function. The output of the first fully connected layer will be input into the second layer with 64 neurons and tanh activation function. Softmax is applied to the output of the second fully connected layer to classify different kinds of activities. Adam optimizer with default parameters [13] was applied to the HAR-Net.

HAR-Net: Fusing Deep Representation and Hand-Crafted Features

35

Fig. 1. HAR-Net architecture.

The structure of the Inception Block is shown in Fig. 2. Figure 3 shows the illustration of Separable Convolution. Separative convolution is chosen because of its advantage of preventing the interference between different channels of input.

Fig. 2. Inception block with separable convolution.

Fig. 3. Separable convolution

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4 Experimental Results 4.1

HAR Dataset Description

The data adopted in this paper is a public domain dataset from UCI. The subject of the experiment used to collect the data set has chosen to be a group of 30 people with age between 19 to 48 years old. Each person was asked to carry out the six specified Activities: walking downstairs, walking upstairs, sitting, laying, walking, and standing. The data in the process was collected using build-in accelerometer and gyroscope in smart phone at a constant sampling frequency of 50 Hz. 4.2

Results on UCI Dataset

In order to evaluate the performance of the method we proposed, we compare out method with the following two baselines: MC-SVM [14]: it was proposed by researchers from Universit`a degli Studi di Genova. This approach adopted the One-Versus-All (OVA) approach in multiclass SVM and used 561 manually extracted features as input. The HAR-Net without Hand-crafted features: it was a modified version of the proposed HAR-Net model. The hand-crafted features were removed. And we fine-tune the previous model make it more suitable for this task.

Table 1. Comparison among different methods on UCI dataset Approach MC-SVM HAR-Net with hand-crafted features HAR-Net without hand-crafted features

Prediction Accuracy 96.0% 96.9% 93.5%

Table 1 shows the results of our model and other baselines on UCI dataset. According to the study proposing the MC-SVM approach, among the 2,947 test set samples, the overall accuracy of prediction can reach 96.0%. Despite the high accuracy reached, there are potential improvements for the approach. According to the HAR-Net proposed in this paper, convolutional neural network can automatically select the most effective features for higher classification accuracy through utilizing loss function and back propagation. Through applying different filters to the signal, the deep neural network is able to detect similar features in signal waveform and generate distinctive feature maps. When the network gets deeper, non-linearity of the neural network increases. More abstract features are extracted by the neural network adaptively through the automatic learning process. The advantage of the features extracted through deep learning is the decrease of the amount of human’s subjective judgement about which features to use. The HAR-Net combined the Hand-crafted features with the learnt features by the deep neural network to provide more comprehensive perspectives when making predictions. Regarding the prediction accuracy for the HAR-Net, the

HAR-Net: Fusing Deep Representation and Hand-Crafted Features

37

overall prediction accuracy reached 96.9% and outperformed the MC-SVM model by 0.9%, demonstrating the effectiveness of the HAR-Net. Meanwhile, we compare the HAR-Net with the network that didn’t use the handcrafted features. The network without manually extracted statistics features may fail to capture the extra effective representation and the performance is worse than the HARNet. This can further prove the importance of adding the auxiliary feature engineering. Table 2. Confusion matrix of HAR-Net Predicted class Actual class Walking Walking upstairs Walking downstairs Sitting Standing Laying

Walking Walking upstairs 490 0 14 454

Walking downstairs 6 3

Sitting Standing Laying 0 0

0 0

0 0

4

9

407

0

0

0

0 0 0

3 0 0

0 0 0

445 10 0

43 522 0

0 0 537

The confusion matrix of the HAR-Net is shown above in Table 2. Despite the overall high accuracy, confusions happened when the model was used to predict activities. From the confusion matrix above, a pattern of mild confusion between walking, walking upstairs, and walking downstairs appears. A more serious confusion between standing and sitting is discovered due to the large number of sitting that were misidentified as standing. The confusion between those activities were caused by the similarity in the signal’ frequency and amplitude.

Fig. 4. The HAR-Net without separable convolution

4.3

Separable Convolution Versus Conventional Convolution

A set of comparative experiments were done to illustrate the choice of Separable Convolution instead of default convolutional layer. The Inception Block for default convolution approach is shown below in Fig. 4.

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For conventional convolutional layer, the output channel size is equal to the number of filters applied. For each filter applied, the convolutions of the all the channels are added together to generate a single channel output. The prediction accuracy comparison is shown in Table 3.

Table 3. Comparison among the separable conv-based HAR-Net and conventional conv-based HAR-Net Approach Prediction accuracy HAR-Net based on conventional convolution 95.2% HAR-Net based on separable convolution 96.9%

As shown in Table 3, the HAR-Net based on separable convolution outperformed the convention convolution-based model by 1.7%. This indicate the advantage of separable convolution over conventional convolution approach in processing 1dimensional time series signal. The approach of separable convolution reduces the interference between data from different channels (axis). It retains the features extracted from separate channels apart to provide more valid features available for automatic learning in the deep neural network. The confusion matrix of model with conventional convolution is shown in Table 4 below. Table 4. Confusion matrix of the model using conventional convolution Predicted class Actual class Walking Walking upstairs walking downstairs Sitting Standing Laying

Walking Walking upstairs 481 0 1 454

Walking downstairs 15 16

Sitting Standing Laying 0 0

0 0

0 0

3

5

412

0

0

0

0 1 0

2 0 1

0 0 0

429 37 0

57 494 0

3 0 536

From Table 4, serious confusions appeared when distinguishing sitting and standing. The model leveraging the separable convolution performed better when distinguishing sitting and standing as well as walking and walking downstairs.

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5 Conclusions In the study, a new approach utilizing Inception Block for convolutional neural network with hand-crafted features was proposed to address Human Activity Recognition. The HAR-Net for convolutional neural network can be an appealing option for realizing HAR in health care industry to take care of the vulnerable groups. Due to the advantages of convolutional neural network’s local dependency and scale invariance [12], it can be a potential application in the field of HAR. The HAR-Net outcompetes the traditional MC-SVM approach because of the higher prediction accuracy resulted from the extra self-adaptive features extracted by deep learning in the model. The study confirmed that the combination of deep learning techniques and traditional feature engineering can outcompete MC-SVM approach using the same public data set. The result suggests an implication about the smartphone’s effect on recognizing human activity. Future work will include testing with the Residual Network (Resnet) and Recurrent Network to realize HAR utilizing more datasets and reduce the degree of confusion between distinctive activities especially sitting and standing.

References 1. Hernandez, J., Riobo, I., Rozga, A., Abowd, G.D., Picard, R.W.: Using electrodermal activity to recognize ease of engagement in children during social interactions. In: ACM International Joint Conference on Pervasive & Ubiquitous Computing, vol. 48, pp. 307–317. ACM, Geneva (2014) 2. Kjærgaard, M.B., Wirz, M., Roggen, D.: Detecting pedestrian flocks by fusion of multimodal sensors in mobile phones. In: ACM Conference on Ubiquitous Computing, pp. 240– 249. ACM, Geneva (2012) 3. Chengqing, Z.: Statistical Natural Language Processing. Tsinghua University Press, Beijing (2008) 4. Xinqing, S.: A Brief Treatise on Computational Electromagnetics. Press of University of Science and Technology of China, Beijing (2004) 5. Murata, S., Suzuki, M., Fujinami, K.: A wearable projector-based gait assistance system and its application for elderly people. In: ACM International Joint Conference on Pervasive & Ubiquitous Computing, pp. 143–152. ACM, Geneva (2013) 6. Fan, M., Gravem, D., Dan, M.C., Patterson, D.J.: Augmenting gesture recognition with Erlang-Cox models to identify neurological disorders in premature babies. In: International Conference on Ubiquitous Computing, pp. 411–420. ACM Geneva (2012) 7. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Energy efficient smartphonebased activity recognition using fixed-point arithmetic. J. Univ. Comput. Sci. 19(9), 1295– 1314 (2013) 8. Ng, Y.H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: deep networks for video classification. Comput. Vis. Pattern Recogn. 16(4), 4694–4702 (2015) 9. Plötz, T., Hammerla, N.Y., Olivier, P.: Feature learning for activity recognition in ubiquitous computing. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), pp. 1729–1734. IJCAI, Barcelona, 16–22 (2011)

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10. Jiang, W., Yin, Z.: Human activity recognition using wearable sensors by deep convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1307–1310. ACM, Geneva (2015) 11. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995) 12. Zeng, M., Le, T.N., Yu, B., Mengshoel, O.J., Zhu, J., Wu, P.: Convolutional neural networks for human activity recognition using mobile sensors. In: International Conference on Mobile Computing, Applications and Services, pp. 197–205. IEEE, New York (2015) 13. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980. http://arxiv.org/abs/1412.6980 (2014) 14. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A Public domain dataset for human activity recognition using smartphones. In: 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 24–26. ESANN, Bruges (2013)

Private-Preserving Analysis of Network Traffic Yang Tang and Yan Ma(&) Network Information Center, Beijing University of Posts and Telecommunications, Beijing, China {tangyang1,mayan}@bupt.edu.cn

Abstract. Network traffic analysis has important applications in many fields, such as the quality of service provisioning, network monitoring, network optimization, and so on. We can get regional network situation by analyze all parties’ data, however, the cooperation among multiple parties suffers an extra drag from privacy and regulation issues. In this work, we apply secure multiparty computation methods to analyze more than three parties’ traffic data without revealing the secrets themselves. We present a clustering method based on IP network prefix and apply it to the traffic. Through the results of clustering, we can get subnets with the most considerable traffic in the network, and make relevant network settings for these subnets. Keywords: Secure multi-party computation Unsupervised machine learning
Clustering


Netstream


1 Introduction It is necessary and useful to analyze network traffic, we can take measures to avoid network congestion problems and reveal potential anomalies by the result of analysis. But network providers usually know less about the real running status of the network. There are much research on analysing network traffic. Cluster network traffic with different methods is key technique of most research [1, 2]. Shadi and others opt netflow to analyze data, and they cluster network traffic with hierarchical cluster algorithm, eventually, they traverse a hierarchical cluster tree and get information about the status of a network [3], their work is similar to us, but they don’t consider the hierarchy of IP itself. Jeffrey and others apply unsupervised clustering algorithms to cluster network traffic, then they compare the result with the result using AutoClass cluster method and they find DBSCAN method has lower accuracy but produces better clusters [4]. Most of these studies focus on traffic of specific Internet Service Provider (ISP), however, there are usually more than three network service providers in a region by economic reasons. Our research pay attention to coordinate network providers’ network data for analysis. There is an obstacle to get a panoramic view of regional networks, as several Internet Service Providers (ISPs) make up the whole network, each of them prefers not to share their private data with other parties for commercial purposes, but they may want to use data from all parties to miming for more valuable information. We introduce secure multi-party computation (SMC) [5] into the field of data mining in network traffic. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 41–48, 2019. https://doi.org/10.1007/978-981-13-7123-3_5

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Administrative regulations prohibit Internet Service Providers (ISPs) from collecting sensitive privacy network traffic data. Some protocols, such as Net-flow/NetStream/IPFIX, can aggregate data as flows which include packets’ basic and statics information without keep payloads of packets. Some of those protocols can also aggregate several raw communications as a session, and the characteristics make flow records complicates to be a replacement of raw network traffic to analysis [6]. We opt netstream flow data as original data and use our hierarchical cluster method to get a cluster tree that contains all network traffic, by traversing the cluster tree, we can get clusters with significant traffic in different hierarchies, which are friendly to network operators. This paper is organized as follows, we introduce preliminary knowledge in Sect. 2, which includes the definition and adversary model of Secure Multi-party Computation (SMC) and hierarchical cluster algorithm. We explicit describe the scenario and introduce in detail how to apply secure multi-party computation to netstream dataset and cluster network traffic in Sect. 3, finally we conclude our work in Sect. 4. There is a brief acknowledgement in last section.

2 Preliminaries In this section, we introduce preliminaries and notations used later. 2.1

Definitions

Certain parties are about to jointly compute a function f, each side of n participants (P1, …, Pn) has its private input data, denoted as (v1, …, vn), all parties provide their input data and jointly compute f (v1, …, vn), finally all sides get the correct result. Assume that some adversary or participants try attack computing process f and except for other parties’ privacy data. Secure multi-party computation (SMC) is a set of protocols and algorithms to make sure that all participants can get correct result through jointly compute f and keep vi secret [5]. 2.2

Adversary Model

An adversarial attacker controls a subset of the parties and wishes to get other sharer’s private data. The parties under the control of the adversary are called corrupted parties. There are two primary types of adversary model, which mainly decided by actions that corrupted parties are allowed to take [7]. Semi-honest Adversaries: In this model, corrupted parties follow the protocol execution procedure correctly. However, the adversary can snoop the internal state through corrupted parties during the computation, the attacker attempts to use information to learn other’s private knowledge. Malicious Adversaries: In this adversarial model, corrupted parties arbitrarily deviate from the protocol during the execution process and put its own plan into execution, according to the adversary’s instructions.

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Our research based on the semi-honest adversary model, which means corrupt parties always follow the agreement of our protocol and try their best to derive other sides’ private information.

3 Privacy-Preserving Data Aggregation In this section, we consider the scenario that each Internet Service provider (ISP) has its own netstream data, we can mine for more accuracy and meaningful results from the overall dataset. We apply secure multi-party computation to do jointly computation without leaking each parties’ privacy. We select netstream data as origin data. A netstream flow can be expressed as a quintuple (source IP, source port, destination IP, destination port, volume). Raw network packets are aggregate with pieces of tetrad (source IP, source port, destination IP, destination port), packets that has the same tetrad are regarded as same network flow during a period. Each flow has a volume attribute, which is defined as the sum bytes of all packets that were aggregated in a flow. The problem can be described as getting a union set of all service providers’ destination IPs from netstream data and get the sum volume of each destination IP through all sharers, and how to cluster them. We first discuss how to union destination IPs then calculate volume corresponding with IPs, and continued to show how we cluster them as a clustering tree. 3.1

Downscale IP Number in Privacy

For the sake of getting all participants’ destination IPs and their corresponding volume, we first calculate the union set of all parties, it helps to shrink the scale of computation in the next step. Secure union sets method can get the union set without reveal each item’s source [8], and we apply the method to all parties and get the union set of all participants’ destination IPs set without revealing each IP’s derivation. We keep an IP’s source secret by encrypting and decrypting them, methods based on commutative encryption is suiting for this job. Commutative encryption method firstly generates a pair of keys (a encryption key and a decryption key) for each party, and those keys can use to encrypt and decrypt origin IPs. The encryption key and the decryption key meet the following regulations: Assume that there are n participants, each one has an encryption key, all keys can be denoted as K1, K2, …, Kn 2 K. Marking the set of all destination IPs as M, and for any permutation i,j:
 EKi1 ð. . .EKin ðIPÞ. . .Þ ¼ EKj1 . . .EKjn ðIPÞ. . .

ð1Þ

1 2k

 Pr EKi1 ð. . .EKin ðIP1 Þ. . .Þ ¼ EKj1 . . .EKjn ðIP2 Þ. . . \e

ð2Þ

8IP1 ; IP2 2 M and IP1 6¼ IP2 ; for gievn k; e\

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Pohlig-Hellman encryption [9] satisfies the two equations above, which means that each party encrypts an IP address in turn with its own secret key, and the encrypted data can be decrypted by all parties using their private keys in any order, after all parties decrypt the encrypted data, we can get origin secret IP. The encrypt example shown in Fig. 1.

Fig. 1. Example of how to encrypt all parts’ destination IPs and then communicate to get every parts’ IPs which are encrypted by every part

Through commutative encryption, we can obtain the union set of IPs owned by all participants without revealing an IP’s source. However, it does reveal the number of IPs that commonly exist in all parts, it can be reveal because encrypted IPs duplicate. However, in our scenario, this leak does not affect the overall confidentiality. 3.2

Processing IPs’ Volume Anonymously

We obtain a union set of all participants’ destination IPs, next we calculate the sum volume of each IP in the set. Each participant are welling to calculate the sum of volume without revealing its own relations between IPs and volume. There are some methods to do secure add between multi parties, such as add some noise to origin data [10], Shamir secret share [11, 12]. But we acquire a method that can provide precious data and has low complexity. We apply secure add algorithm based on confusing data and it is adapted to our requirement, each IP’s volume P lays range from 0 to the max, then we calculate the sum of volume, denoted as ni1 Vi , Vi presents volume that corresponding with the ith IP. A secure add example shows in Fig. 2. Part A with an IPa select a random value r and then add its own volume to r, part A pass IPa and value that confusing with random value r to next part B, the next part can get nothing about the previous part because the value and previous private volume lay in the same range, the part B do the same thing as part A, the technological process can be shown in Fig. 2, finally, part A get the sum values.

Fig. 2. Example of how to get the sum volume of each party’s volume, which is corresponding with an IP in unioned IPs set

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The secure add process of all IPs in the overall set can be done with parallel. Hence the method can reduce the time of calculating and quite suit for calculate big scale data. 3.3

Traffic Clustering

This section describes how we use IPs and their corresponding volume to cluster and analyze. We present a clustering algorithm to aggregate netstream flows into an IP traffic tree. By traversing the tree from top to the bottom, we can get characteristics of netstream flows, and it can be useful references for network operators when they apply some policies to networks. IP hierarchy which means that when routing an IP to the destination address, an IP address is divided into a network portion and a host portion, and routers handle different network portions hierarchically. And we cluster traffic with consideration of IP hierarchy. We define a data structure for clustering IP traffic as a tree, called tree_node, which includes an IP address with network prefix, its corresponding volume, a link to its parent node and links to its children nodes, the algorithm is described in Algorithm 1.

Fig. 3. Clusters that have different network prefix length

We first create a set of tree_node and denote as tree_nodes, we circularly perform merge procedure, and decrease prefix limit, for example, at first, we merge all nodes that possibly be merged as a node which network prefix length is 31. We also sort tree_nodes every time during the loop to keep ordered clusters. In MERGE_NODES function, we ignore those nodes that have a parent link, which means, those nodes have been merged. We find the first node that can be merged by tentatively and greedily attempt, and then try to merge the current node with that node. If merge result does not exceed the limit of IP network prefix length, we merge the two nodes officially, we try to merge another node to the new merged node next time, if we got the same merge result, we add those nodes to the new merged node. It may generate a duplicate merged node in merging process, we set a hashtable to filter those nodes, if there exists a merged node in the hashtable, we merge nodes to a same merged node, and finally, we change relations between new merged node and nodes that to be merged. The merge algorithm can be intuitional understand in Fig. 3, each tree_node has an IP with network prefix and corresponding volume, then we use the hierarchical method to merge nodes as a clustering tree.

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Analysis of IP Traffic Tree

The network traffic can divide as ingress traffic and outgoing traffic. We can determine ingress or outgoing traffic by judging the destination IP, If the destination IP belongs to internal nodes, the traffic can be classed as ingress traffic, otherwise, outgoing traffic. We collect netstream data from a backbone router at an Internet Data Center (IDC) during an hour, and finally, we got 12062 pieces of valid netstream data, which can be divided into ingress and outgoing traffic. There are 7561 pieces of ingress traffic records and the other part is outgoing traffic. We use the ingress traffic data as the data source and split it into three parts by the time, for simulating three participants. We apply our method on the data of the three parties to get a set of destination IP addresses, and then we apply secure add method to get the volumes corresponding to these IP, respectively.

Fig. 4. Cluster number in different network prefix

We construct tree_nodes from IPs and their corresponding volume, we use these tree_nodes to form a clustering tree by algorithm 1. For more detail, each node of the tree has an IP with network prefix and its corresponding volume, the volume of a node is the sum value of all its children’s volume. We count number of nodes which have different IP network prefix length vary from /0 to /31, clusters are normally distributed, which means that most of the user’s traffic can be covered by the network. Figure 4 shows there are most clusters in network prefix of /24, and end users’ traffic of Internet Service Providers (ISPs) or enterprises will be routed to the backbone router where we gather those data, and will finally send to service providers, we can find the status of the service provider’s services through our clustering method. To get more detailed information, we traverse the clustering tree to get the top 10 clusters with the most significant volume, and we can see 10 subnetworks with most massive traffic, in our example, we analyze the clustering tree nodes of the network with /24 network prefix. We can get the top 10 clusters with the highest traffic, as you can see from the Table 1, according to the information from the table, network administrators can specify reasonable network policies to ensure the optimal use of the network, it avoids looking for reports from the vast and complex raw network traffic.

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47

Table 1. IP network prefix & volume IP network Volume 45.115.217.0/24 222235 183.202.247.0/24 60906 120.239.106.0/24 45611 117.136.52.0/24 41625 183.200.7.0/24 37788 117.157.56.0/24 34679 113.57.183.0/24 32379 111.37.51.0/24 31353 120.239.108.0/24 29813 121.22.243.0/24 27487

4 Conclusion We take secure multi-party computation methods to analyze network traffic, we use secure multi-party union and add methods to get the sum of all sides’ volume without leaking their private data. Then we put our clustering method to the data for getting a

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clustering tree, the analysis of the tree can get conclusions that help network operators a lot. In this paper, we validate our method by using a real Internet Data Center’s traffic data, and our clustering method fits the data well. We believe that our work would help network operators deepening the understanding of the overall network situation and thus they can take more effective measures to ensure the rational use of networks and avoid attacks. Acknowledgement. The work in this paper is supported by the Joint Funds of National Natural Science Foundation of China and Xinjiang (Project U1603261).

References 1. Snort. https://www.snort.org. Accessed 19 Oct 2018 2. Shafiq, M., Yu, X., Laghari, A.A., Yao, L., Karn, N.K., Abdessamia, F.: Network traffic classification techniques and comparative analysis using machine learning algorithms. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pp. 2451–2455. IEEE (2016) 3. Shadi, K., Natarajan, P., Dovrolis, C.: Hierarchical ip flow clustering. ACM SIGCOMM Comput. Commun. Rev. 47(5), 48–53 (2017) 4. Erman, J., Arlitt, M., Mahanti, A.: Traffic classification using clustering algorithms. In: Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, pp. 281–286. ACM (2006) 5. Lindell, Y.: Secure multiparty computation for privacy preserving data mining. In: Encyclopedia of Data Warehousing and Mining, pp. 1005–1009. IGI Global (2005) 6. Technical white paper for net stream. http://efile.huawei.com/tr/marketing/material/global/ products/enterprise_network/ce_switches/hw_353223. Accessed 19 Oct 2018 7. Rokach, L., Maimon, O.: Clustering methods. In: Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer (2005) 8. Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.Y.: Tools for privacy preserving distributed data mining. ACM SIGKDD Explor. Newsl. 4(2), 28–34 (2002) 9. Ly, J.T.: A serial version of the pohlig-hellman algorithm for computing discret logarithms. Appl. Algebra Eng. Commun. Comput. 4(1), 77–80 (1993) 10. Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Annual International Cryptology Conference, pp. 420–432. Springer (1991) 11. Koga, H.: A simple secret sharing scheme using a key and its security against substitution of shares. In: 2010 IEEE International Symposium on Information Theory Proceedings (ISIT), pp. 2483–2487. IEEE (2010) 12. Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)

Design of Fault Diagnosis System for Balise Cable Based on Machine Learning Xiaoyi Cui(&) and Jingyang Lv(&) Beijing University of Posts and Telecommunications, 10 Xitucheng Rd, Haidian, Beijing 100876, China [email protected]

Abstract. Based on the research of the lineside electronic unit in the high-speed rail system, the design of the fault diagnosis system based on machine learningbased of balise cable is completed. For the cable fault characteristics, the impedance method is used to analyze the impedance of the cable and the magnitude and phase of the current is used for actual fault diagnosis. A FPGA-centric platform is designed to calculate the magnitude and phase of the current generated in cable in real time. Analysis by actual current data, the feasibility of the program has been verified. For the diagnosis of fault characteristics, the way of the classifier is adopted: Logistic Regression, and support vector machine (SVM). This paper briefly introduces the implementation principles of the two models. The training set and test set are constructed from the actual collected data. The two models are trained and tested respectively. According to the accuracy of the test results and the complexity of the model, the model suitable for fault diagnosis with FPGA is selected. Keywords: SVM


Logistic Regression
Impedance
Fault diagnosis

1 Background With the rapid development of China’s high-speed rail industry, the efficiency and security of high-speed rail communication systems are becoming more and more important. The lineside electronic unit is a pivotal part of the high-speed rail communication system. It is responsible for transmitting the train control information of the train control center to each balise, thus realizing the vehicle-to-ground communication and ensuring the normal operation of the train. The cable is an indispensable communication link between the lineside electronic and the balise, so it is essential to monitor and diagnose the real-time fault of the cable. At the same time, with the rise of artificial intelligence, there are more reliable analytical methods and theoretical models for fault data. More and more machine learning is applied to the field of fault diagnosis, which greatly reduces the difficulty of the theory and improves the reliability and extensiveness of fault diagnosis [1]. The combination of fault feature analysis and machine learning drives the development of fault diagnosis.

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 49–53, 2019. https://doi.org/10.1007/978-981-13-7123-3_6

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2 Analysis of Cable Fault Characteristics 2.1

Type of Cable Fault

Two common cable faults are high impedance faults and low impedance faults. Highresistance fault means resistance of the cable is within the required specification range, but the operating voltage cannot be transmitted to the terminal. While the lowresistance fault refers to the drop of the insulation resistance at the fault point, which is much smaller than the characteristic impedance of the cable [2]. 2.2

Impedance Method

A commonly used fault detection method at home and abroad is the impedance method. In a circuit, the impedance describes not only the relative magnitude of voltage and current, but also its phase. It is defined as the ratio of the total voltage of the circuit to the total current [3]: Z¼

U I

ð1Þ

According to the TSI standard, the C6 signal, output of the lineside electronic unit is a constant voltage source, which means the current is negatively correlated with the impedance, and the magnitude and phase of the current can reflect the impedance state of the current link. Collecting current signals and calculate the magnitude and phase by using the hardware diagram on the left, the impedance map is shown as the right figure (Fig. 1).

Fig. 1. Hardware block diagram and impedance map

From the above distribution map, when the cable is in a different State, the impedance is distributed in different intervals and linearly separable. Amplitude and phase can be used as features of fault diagnosis, and these two features are trained to establish a classification model. In this paper, Logistic Regression, and SVM are used for training, and the performance of the two classifiers is compared and analyzed.

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3 Fault Classifier Design The classification algorithm is a kind of supervised learning. It obtains optimal parameters by minimizing the loss function [6]. Logistic Regression, and SVM are two typical classification algorithms. 3.1

Logistic Regression

Logistic Regression, uses probability to class the input data by sigmoid function which maps the distance from the sample point to the classification boundary as a probability. The classification model is defined as follows [7]: hðXÞ ¼ gðf ðXÞÞ ¼

1 1 þ e wT X þ b

ð2Þ

When the sample point is above the classification boundary, the function output is greater than 0.5, and vice versa. Logarithmic loss function is used as loss function [5]: CostðhðxÞ; yÞ¼  y logðhðxÞÞ  ð1  yÞ logð1  hðxÞÞ

ð3Þ

The process of solving the Logistic Regression, model is the optimization process of the following objective function: min h

3.2

N 1X Cos tðhðxi Þ; yi Þ N i¼1

ð4Þ

Support Vector Machine

SVM is a classifier with maximum interval. The classification model of the support vector machine is defined as follows [5]: f ðxÞ ¼ sgnðwT x þ bÞ

ð5Þ

When the input data is above the classification boundary, the classifier output is positive, otherwise it is negative. By introducing Lagrange Factor and SMO algorithm, the following functions are optimized to get the classifier parameters [5]: N X 1 min jjxjj2 þ C ni ; yi ðxT xi þ bÞ  1  ni ; ni  0; i ¼ 1; 2; . . .; N 2 i¼1

3.3

ð6Þ

Multi-label Classification

In this system, the data is divided into three categories, short-circuit data, open-circuit data and normal data. One-to-one manner can be applied to solve multi-label

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classification. Any two types of data to build a classifier, and a total of three classifiers are needed. When predicting, vote according to the prediction result of each classifier, the label with the highest number of votes is the prediction result of the input sample. 3.4

Training Process and Performance Evaluation

Large number of impedance data is collected in the real operation. 70% of the samples were randomly selected as the training data set, and the remaining 30% were used as test data sets. The classifier selects Logistic Regression, and support vector machine to training and comparison. Which model is used for actual fault diagnosis, which is determined by the model’s prediction accuracy and complexity. The platform conditions used in the training and test results are shown in the following Tables 1, 2 and 3: Table 1. Training platform conditions Item CPU Memory size Python version Number of training sets Number of test sets

Version Intel Core i7-6700HQ @ 2.6 GHz 16 GB 3.6 1834880 787200

Table 2. Predictive accuracy Fault state High resistance Normal Low resistance Total

Sample numbers LR SVM 182040 100% 100% 431730 99% 99% 173430 100% 100% 787200 100% 100%

Table 3. Complexity assessment Item Training time Prediction time Parameter number

LR 46.98 s 0.07 s 9

SVM 2387.00 s 7.006 s 9

From the perspective of Prediction precision, Logistic Regression, and SVM have high precision, and meet the accuracy requirements of the system. For linearly separable data sets, the model can solve the classification boundary with the same kind parameters. During the training process, the SVM needs to find the support vector, which takes longer than the Logistic Regression, and the space occupied during the

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training process also multiplies. Logistic Regression, training takes a very short time and is not prone to over-fitting. It is more suitable for training and forecasting of large amounts of data. Therefore, the system uses Logistic Regression, to classify fault types.

4 Conclusion In this paper, the transponder transmission system in the high-speed rail system is taken as the research background. The C6 signal is used flexibly, and the fault characteristics of the balise cable are extracted by simple and efficient impedance method. The classification algorithm is used for training and prediction, and the balise cable fault diagnosis system is completed. At present, the system has been widely used in highspeed rail systems and has achieved good results. However, due to the actual application, the operating environment, cable characteristics, board characteristics and other aspects are more complicated, the accumulation of data is not comprehensive enough, and compatibility needs to be improved.

References 1. Kawai, T., Takinami, N., Chino, T.: A new approach to cable fault location using fiber optic technology. IEEE Trans. Power Delivery 10(10), 85–91 (1995) 2. Wang, L.: Design and implementation of transponder fault cable detection system. Beijing University of Posts and Telecommunications (2017) 3. Shen, Y., Liu, C.: Circuit Analysis. People’s Posts and Telecommunications Press, Beijing (2004) 4. Harrington, P.: Machine Learning in Reality. People’s Posts and Telecommunications Press, Beijing (2017) 5. Introduction to Support Vector Machines [EB/OL], July. https://blog.csdn.net/v_july_v/ article/details/7624837 6. Li, H.: Statistical Learning Method. Tsinghua University Press, Beijing (2017) 7. Zhou, Z.: Machine Learning. Tsinghua University Press, Beijing (2017)

Design and Implementation of ARQ Mechanism in High-Speed Data Acquisition System Xiao Li(&) and Jingyang Lv(&) School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China [email protected]

Abstract. The speed of high-speed railway in China is constantly increasing, and the demand for safety and reliability becomes more and more apparent. Therefore, it is especially important to collect and transmit high-speed data accurately, so high-speed data acquisition system emerged. Aiming at the problem of transmission packet loss in high-speed data acquisition system, a retransmission protocol is proposed for Automatic Repeat Request (ARQ) communication, which is implemented on field-programmable gate array (FPGA). This design compares several common retransmission protocols to select the optimal scheme. And the feasibility of the high-speed rail transponder system is analyzed. On the basis of that, this design takes full advantage of the flexibility and reconfigurability of the FPGA, uses the hardware description language VerilogHDL, uses Quartus II 13.1 for synthesis and routing, and finally verifies on the Cyclone VE series 5CEFA4F23F. The design has the advantages of convenient application and upgrade, good portability and versatility while solving the problem of packet loss. Keywords: High-speed railways


Gigabit Ethernet
ARQ
FPGA

1 Introduction From the perspective of post-maintenance of high-speed rail transportation systems, the collection and transmission of high-speed data has far-reaching significance [1–3]. Gigabit Ethernet with fast transmission rate, good portability and good versatility is used to transfer data between the acquisition system and the host [4, 5]. Currently, the Analog to Digital Converter (ADC) sampling rate of the acquisition system is 63 MHz. To ensure timely data transfer, the transfer rate requirements are as high as possible. At present, the data upload rate is above 50 MB, which is almost impossible to achieve through the TCP protocol (Transmission Control Protocol). Therefore, the UDP transport protocol (User Datagram Protocol) is the only viable option. Then the packet loss problem that occurs during UDP transmission becomes an important issue that we urgently need to solve. In response, this paper proposes an ARQ strategy for retransmission and implements it in the system.

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 54–60, 2019. https://doi.org/10.1007/978-981-13-7123-3_7

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2 Retransmission Protocol Feasibility Analysis 2.1

Basic Retransmission Type Selection

There are three basic types of retransmission: stop and wait, go-back-N, and SR-ARQ. The main difference is that feedback information are handled differently [6]. Although the stop and wait ARQ and go-back-N ARQ have the advantages of stable and small buffer storage space, they also have the disadvantages of low channel utilization, low throughput and large influence on channel conditions. In contrast, SRARQ provides the highest throughput efficiency at the expense of complex receive control mechanisms and large buffer capacity. Although the control logic is relatively complex, the current acquisition system resource utilization rate is enough. In order to meet higher performance, we choose SRARQ as the retransmission method of high-speed data acquisition system. 2.2

The Feasibility of Choosing Retransmission

Analyze the actual working environment of high-speed data acquisition system. The main monitoring target is FSK (Frequency Shift Keying) signal of the balise in invehicle communication. The two balise are separated by 5 m, and the single balise has a range of 0.5 m (Fig. 1).

t1

4.5M

Fig. 1. Acquisition system application environment

T represents the time which the train can transmit data between the two balise, which is, the maximum allowable processing time for a single transmission; t1 is the maximum processing time required for the acquisition system to communicate a balise data with the host computer. In normal operation, it is necessary to guarantee: t1  T

ð1Þ

The ADC data needs to be into a FIFO first, so it is later about 1:77  105 s when the train just passed the balise. Suppose the train travels at a speed of V m=s ð14:29 km=h  V  350 km=hÞ. T¼

5 ðmÞ 5  1:77  105 ðsÞ  ðsÞ Vðm=sÞ V

ð2Þ

Further analysis of the time required for the T1: The sending end uses FPGA as the master, and the network card can send and receive data at the same time. So ignoring

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the time spent on the sending side, the time required for T1 only contains 2 parts: The time to send data as tsend , and the time to retransmit data as tresend . t1 ¼ tsend þ tresend

ð3Þ

The sampling rate of the ADC data in the acquisition system is 120.58 MB/s. Suppose the amount of data sent by FPGA be P, P¼

120:58 ðMB=sÞ  0:5 ðmÞ 60:29 ¼ ðMBÞ Vðm=sÞ V

ð4Þ

The Gigabit Ethernet NIC (Network Interface Card) has a maximum transmission and reception rate of 125 MB/s, which is currently 57.97 MB/s. The length of read data is 1026 Bytes and the period is 17700 ns, so the transmission rate is (Fig. 2): Vsend ¼

14Byte

20Byte

1026 ðByteÞ ¼ 57:97 ðMB/sÞ 17700 ðnsÞ

8Byte

2Byte

ð5Þ

1024Byte

Fig. 2. The format of the data packet sent by the acquisition system to the host computer

A packet of data uploaded by the FPGA to the PC (Personal Computer) contains 1068 bytes, of which 1024 bytes of FSK data. Further, by formula (4) and formula (5), 1068  P 60:29  1068 1:08 1024 tsend ¼ 1024 ¼ ¼ V Vsend V 57:97

ð6Þ

Since the packet loss rate is positively correlated with the number of packets P, it is assumed that the packet loss rate is 1068 Pf ðPÞ 1:08f ðPÞ tresend ¼ 1024 ¼ Vsend V

ð7Þ

Bringing Eqs. (6) and (7) into Eq. (3), t1 ¼ tsend þ tresend ¼

1:08ð1 þ f ðPÞÞ V

ð8Þ

Bringing Eq. (8) into Eq. (1), f ðPÞ  3:63

ð9Þ

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It can be seen that sending a packet of data, only when all of it is lost and retransmitted over 3 times still cannot be received, the time may not be enough to cause timeout, which is impossible in practice. Therefore, the retransmission method can theoretically solve the problem of packet loss in high-speed acquisition systems.

3 Software Design of Retransmission Protocol 3.1

Acquisition System and Host Computer Interaction

During the transmission process, the IP header sent by the acquisition system is continuous [7, 8]. After the PC accepts the data, it judges whether the id of the received packet is continuous. If it is not, the intermediate difference is recorded in the lost packet list. After the end packet is sent, the acquisition system enters a state of waiting for the retransmission request sent by the host computer. After receiving the endpacket, the host computer sends a request packet to the acquisition system. The data portion of the request packet is 1024 bytes, and the content is the id number of the lost data packet, ending with 0xffff. If the lost packet exceeds 511 packets during this transmission, then one retransmission request only transmits 511 packets of the request. The specific workflow is shown in Fig. 3. Above the dashed line is the acquisition system FPGA internal operation mechanism, and below is the operating mechanism in the host computer. This paper focuses on the acquisition system software design. The whole process use full-duplex parallel operation to improve data processing speed.

Fig. 3. Acquisition system and host computer interaction flow chart

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Modular Design

The retransmission design of the acquisition system is implemented by Altera’s Cyclone VE series 5CEFA4F23F, and the layout is performed on the Quartus II [9]. The implementation of the retransmission protocol mainly consists of two parts: the data analysis center and the data transmission module, as shown in Fig. 4 [10].

Fig. 4. Modular design of the acquisition system

The data analysis center reads the buffered data and sends it to the data transmission module. If the packet is lost, it receives the retransmission packet from the PC. First store the retransmission information to the local ram. After the normal data has been sent, read from the ram for parsing. Re-read data based on the parsed row and column address to resend data. The data transmission module completes the parameter configuration of the network card, receives the data sent by the data analysis center for framing, controls the transmission of the Gigabit Ethernet IP core to the host computer and parse retransmission data in real time and send to the data analysis center.

4 Performance Testing The theoretical simulation of the three basic retransmission methods is shown in Fig. 5. The packet loss rate is set to be 40% similar to the actual acquisition system packet loss rate. In Fig. 5, the horizontal axis represents different propagation delays, and the

Fig. 5. Three kinds of retransmission mode throughput comparison chart

Design and Implementation of ARQ Mechanism

59

vertical axis represents the normalized throughput efficiency. It can be seen that the SRARQ is 1.5 times more efficient than the other two retransmission methods. So the implementation of the SR-ARQ mode is in line with actual needs. The resource occupancy of the software is shown in Fig. 6. The logic unit occupancy rate is 59%, and the storage unit utilization rate is 39%, which satisfies the design requirements.

Fig. 6. Resource utilization of high-speed acquisition system software

In order to further verify the feasibility of the design and test the performance, the acquisition system communicates with the host computer through the Gigabit Ethernet direct connection. 1000 independent experiments were performed on the data transmission, and the number of packages per group was 2,203. Count the number of packets lost during each transmission with or without retransmission, and plot the statistics according to the test results as shown in Fig. 7.

Fig. 7. A statistical comparison chart of the number of packets lost per transmission with or without retransmission

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In Fig. 7, the horizontal axis represents the serial number of the number of tests, and the vertical axis represents the number of lost packets of a complete transmission. When there is no retransmission, the maximum number of lost packets reaches about 900 packets, the maximum packet loss ratio is 40.8%, and the packet loss rate is 95%. If the retransmission is not performed, the data cannot be used normally. After the retransmission mechanism is increased, the maximum number of packet loss is 11 packets, the maximum packet loss ratio is 0.005%, and the packet loss rate is 0.012%, which is in line with the demand.

5 Conclusion This paper completes the design and implementation of SR-ARQ mechanism by optimizing FPGA software by analyzing the application scenarios and packet loss phenomena of the high-speed data acquisition system. Under the premise of timeliness and stability, the integrity of the data in the transmission process between the acquisition system and the host computer is guaranteed. Through many actual board tests, the practical significance of the retransmission strategy adopted was confirmed. In summary, the method has a high reference value for solving the data integrity problem existing in the modern high-speed railway data acquisition and transmission system.

References 1. Kai, L: Ethernet-based data acquisition system implemented on FPGA. Wuhan University of Science and Technology (2009) 2. Ning, Y., Guo, Z., Shen, S., Peng, B.: Design of data acquisition and storage system based on the FPGA. Procedia Eng. (2012) 3. Zhong, G.: Design of high speed data acquisition system based on FPGA. Nanjing University (2013) 4. Wu, C., Xu, J., Jiang, J.: Research and implementation of gigabit ether-net interface based on FPGA. Mod. Electron. Tech. 41(09), 1–5 (2018) 5. Shi, P.: Design of FPGA bidirectional data transmission system based on Gigabit Ethernet. Xidian University (2014) 6. Yang, J.: Research on ARQ Mechanism in MAC Layer of 802.16 m System. University of Electronic Science and Technology of China (2012) 7. Yi, Q.: Design and FPGA Implementation of Ten Gigabit Ethernet MAC Controller. IEEE Beijing Section, Global Union Academy of Science and Technology, Chongqing Global Union Academy of Science and Technology, Chongqing Geeks Education Technology Co., Ltd. Proceedings of 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC 2017). IEEE Beijing Section, Global Union Academy of Science and Technology, Chongqing Global Union Academy of Science and Technology, Chongqing Geeks Education Technology Co., Ltd:IEEE BEIJING SECTION (2017) 8. Xia, Y.: 10 Gigabit ethernet TCP frame data registration algorithm based on FPGA. In: Proceedings of 2016 2nd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2016) (2016) 9. Zhong, Z.: Top-down FPGA design. Electronic Products World (1998) 10. Zou, L., Tian, L.: Design and implementation of gigabit network data acquisition system based on FPGA. Digital Technol. Appl. 136 (2011)

Application of Hilbert Huang Transform in Car Signal Demodulation Hongrui Xie and Jingyang Lv(&) Beijing University of Posts and Telecommunications, Beijing 100876, China [email protected], [email protected]

Abstract. The car signal system is an important establishment to guarantee that a train operation safety. In recent years, with the rapid development of railway construction, the number of heavy haul trains and high-speed trains has increased rapidly. It makes the car signal system harder to demodulate the track circuit signal with higher interference and less response time. In order to ensure the safety of train operation, it is necessary to study a new demodulation algorithm for the car signal system. This paper takes widely used ZPW2000A track circuit as an example. We analyze the merits and demerits of its signal modulation mode, main interference sources and existing demodulation algorithms. After analyzing, we propose a new demodulation algorithm based on Hilbert Huang transform to solve these existing problems. Through the simulation results, the demodulation algorithm has outstand performances and a good application prospect in Car Signal system. Keywords: Car signal


Track circuit
HHT
Demodulation algorithm

1 Preface Railways are an important infrastructure of the country and an important support for the national economy. Railway signal equipment is an important part of the railway system, shouldering the heavy responsibility of ensuring the safe operation of the railway and improving the efficiency of train transportation [1, 2]. The Car Signal system is an important subsystem of the China Train Operation Control System (CTCS) [3–5]. It receives the ground driving commands sent by the track circuit through electromagnetic induction. After parsing the instruction, the car signal system will display the state of the front signal lamp, which can reduce the pressure of the driver’s look-out signal lamp. What’s more, it will sent the instruction to the on-board safety computer, which has the functions of automatic speed limit and emergency parking, so as to avoid accidents caused by human errors. Hilbert Huang Transform (HHT) is a signal processing method based on instantaneous frequency proposed by Huang et al. Compared with traditional signal processing methods, it can analyze non-stationary nonlinear signals, and is adaptive and suitable. Processing of mutational signals and other characteristics. China’s current track circuit system is numerous. This paper takes the ZPW2000 track circuit, which is widely used at present, as an example, and briefly analyzes its signal characteristics. Aiming at the problem of degraded quality degradation in the supporting locomotive © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 61–68, 2019. https://doi.org/10.1007/978-981-13-7123-3_8

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signal equipment, after analyzing the shortcomings of the existing demodulation algorithm, this paper proposes to use Hilbert Huang Transform as the demodulation algorithm and verify the performance of the algorithm through computer simulation. The simulation shows that the Hilbert Huang transform has the characteristics of high precision and strong anti-interference ability, and it can be used for demodulation of locomotive signals.

2 Car Signal System Analysis 2.1

Brief Introduction of Car Signal System

The car signal system reflects the status and operating conditions of the ground signal in front of the train, instructs the train to operate, and integrates with the CTCS to ensure the safety of the train and realize the functions of speed control and overspeed protection. The structure of the car signaling system shows as Fig. 1. The receiving coil induces the current signal transmitted by the track circuit in the rail through electromagnetic induction, and realizes the conversion from the track circuit current to the car signal voltage. The TCR Interface preprocesses the voltage signal to make it easier to demodulate. The car signal mainframe will demodulate the signal and control display information and communications with the ATP system.

ATP System

Car Signal System

Car Signal Mainframe Car Signal Power TCR Interface

Receiving coils 1

Receiving coils 2

Fig. 1. Brief introduction of Car Signal System

Application of Hilbert Huang Transform in Car Signal Demodulation

2.2

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Car Signal System Analysis

Signal Modulation Method The ZPW2000 orbit signal is a 2FSK modulated signal. In order to reduce the power frequency interference of 50 Hz, it selects higher frequency carriers, which are 1700 Hz, 2000 Hz, 2300 Hz and 2600 Hz respectively. It also selects 18 low frequency modulated signals at intervals of 1.1 Hz, increasing from 10.3 Hz to 29 Hz. The mathematical expression is FðtÞ ¼ A  cosðx0 t þ gðtÞÞ ( x1 t  T4 \t  T4
T  gð t Þ ¼ T 3T x1 4  t 4 \t  4

ð1Þ ð2Þ

Signal Interference Analysis The track circuit uses the rail as the transmission medium, and has the disadvantages of relatively large traction current and high interference. Various external disturbances are superimposed during transmission. These interferences are bound to affect the demodulation of the signal. Here is a brief analysis of several main interference sources [6, 7]. (1) Unbalanced current interference in railway In electrified orbits, the most serious disturbance is the interference caused by the unbalanced return of the electric traction current. In addition to the current of the track circuit in the rail, there is also the traction current of the electric locomotive. Ideally, the traction currents on the two rails are the same in the same direction, and the voltage generated by the electromagnetic induction can cancel each other out. However, in the actual circuit, the external environment is more complicated, and the leakage resistance of the rail to the ground is different, resulting in different traction currents in the two rails, thus generating interference voltage. (2) Neighboring line interference In the double track section, only the signal of the running road is received in ideal condition. But in fact, there are also problems of mutual interference between the two lines. The main reasons are mutual inductance between rails, earth and air leakage. The interference of adjacent lines may lead to the incorrect reception of information from adjacent track circuits. It might make car signal system display incorrect command information, resulting in potential accidents.

3 Demodulation Algorithm Analysis 3.1

Hilbert Huang Transform Principle

Aiming at some problems existing in existing algorithms, this paper proposes to use Hilbert Huang Transform in car signal demodulation. The algorithm is self-adaptive. It can analyze signals in both frequency and time domains. It can get instantaneous

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frequency and other information of signals. It is suitable for non-stationary and nonlinear signals. Hilbert Huang Transform can be divided into three parts: Hilbert transform, empirical mode decomposition and Hilbert spectrum [8–10]. Hilbert Transform The Hilbert transform is defined as follows: the original signal is x(t), and the Hilbert transform is ^xðtÞ 1 ^xðtÞ ¼ p

Z

1

xð t Þ 1 ds ¼ xðtÞ  pt 1 t  s

ð3Þ

Therefore, the Hilbert transform can be considered as the result of a filter whose unit response is hðtÞ ¼ pt1 . That is, the Hilbert transform has a positive frequency hysteresis of 90°, and a negative frequency of 90°. The analytic signal of x(t) can be defined as zðtÞ ¼ xðtÞ þ j  ^xðtÞ ¼ aðtÞejuðtÞ

ð4Þ

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 aðtÞ ¼ ðxðtÞÞ2 þ ð^xðtÞÞ2

ð5Þ

uðt) ¼ arctan

^xðtÞ xðtÞ

ð6Þ

As we know, in nonlinear and non-stable signal processing, instantaneous frequency is the most intuitive phenomenon. In order to get the instantaneous frequency with physical meaning, there are certain constraints on the signal. These constraints also provide a basis for decomposing signals, and empirical mode decomposition is one of them. Empirical Mode Decomposition (EMD) Empirical mode decomposition (EMD) is the first step of Hilbert-Huang transform, and it is a powerful tool for analyzing non-linear and non-stationary signals. N.E. Huang et al. proposed the concept of intrinsic mode function (IMF), believing that any signal can be composed of IMF. The function that satisfies the following two conditions is called IMF. (1) The difference between the number of zeros and the number of points in the whole signal is no more than one. (2) In the whole signal, the average value of the envelope formed by the local maximum and the local minimum of the signal is zero or close to zero. The main idea of EMD decomposition is to decompose the signal into a finite number of IMFs satisfying certain conditions. This process is the core of HHT transformation. The EMD decomposition process is as follows:

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(a) For the original signal xðtÞ, find all the local maximum points and all the local minimum points. The cubic spline interpolation method is used to fit the maximum points and the minimum points, and the upper and lower envelopes are obtained. (b) The average value of the upper and lower envelopes is calculated as m1(t), make h1(t) = x(t) – m1(t). (c) If h1(t) satisfies the IMF condition, we get c1(t) = h1(t); if not, we take h1(t) as a new signal, repeat steps (a), (b). Get the average of the upper and lower envelopes as m11(t), make h11(t) = h1(t) – m11(t), and continue to judge whether h11(t) satisfies the IMF condition. If not, we continue to repeat steps (a), (b) until screening. Select the k times to get h1k(t) satisfying the condition or satisfying the termination condition, and get c1(t) = h1k(t). (d) Based on the first IMF component c1(t), the residual component r1(t) = x(t) – c1(t) is obtained. When rn(t) satisfies the stopping condition, the decomposition ends. At this time, the signal has been decomposed into several IMFs components and one rn(t) remainder. Through these steps, we can get xðtÞ ¼

Xn j¼1

Cj ðtÞ þ rn ðtÞ

ð2Þ

Each IMF component contains information of different frequencies, which are arranged from high to low with different frequencies. The residual component rn(t) is usually a constant or monotone function, which indicates the long-term trend of the signal. The termination conditions in step (c) are generally limited by the maximum number of iterations besides the two judgment conditions of IMF to prevent the dead cycle in decomposition.

4 Demodulation Algorithm Simulation In this paper will introduce the concrete steps of demodulation algorithm and validates it by MATLAB simulation. (1) Simulate to generate 2CPFSK signal Based on the theoretical analysis of the second chapter, the 2CPFSK signal is generated by the method of integral, and the sampling rate of analog signal is 16384 Hz. (2) Signal preprocessing and EMD decomposition First, the signal is preprocessed through a strap-pass filter with a pass band of 1500 Hz to 2800 Hz. This is mainly to filter out 50 Hz frequency interference and its high harmonic to extract the effective spectrum of car signal. Next, decomposes

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signal by the EMD. By analyzing the IMF components obtained by EMD decomposition, it’s obvious that the IMF1 and IMF2 contain the almost all the features of the original signal. So in the next, only analyzes and processes the IMF1 and IMF2. In the vast majority of cases, the information contained in the IMF1 almost matches the original signal, shown as Fig. 2(a). (3) Calculate the instantaneous frequency First, calculate the analytic signal of the IMF1. After that, calculate the instantaneous frequency of the IMF1, shown as Fig. 2(b). It’s easy to see a certain mutation at the beginning and end of the instantaneous frequency. Therefore, before further processing, it is necessary to delete some of the data at the beginning and end. At last, check whether the mean of instantaneous frequency is near the following frequency points of 1700 Hz, 2000 Hz, 2300 Hz or 2600 Hz. If not, it means that the IMF1 is not valid and should be discarded. Besides, repeat these steps for the IMF2 component. If the average instantaneous frequency of the IMF2 still doesn’t match these frequency points, this EMD decomposition is failed and need to be done again. (4) Calculate carrier frequency The approximate carrier frequency is obtained in the previous step, in order to distinguish carrier type between type 1 and type 2, precise frequency values are required. The specific steps are as follows: (a) Using the mean value obtained in the third step, the instantaneous frequency is divided into the upper frequency and the lower frequency. (b) Calculate the mean value of the data in a certain range of the upper frequency, and discard the data outside the range, and do the same for the lower frequency. Calculate the average of the mean value of upper and lower frequency to get the accurate carrier frequency. (5) Calculate modulation frequency The modulation frequency can’t be obtained directly from the instantaneous frequency graph and requires further calculation. (a) Normalize the instantaneous frequency, by setting the instantaneous frequencies above the average to “1”, below the average to “0”. (b) The mean filtering of normalized instantaneous frequency is carried out to eliminate the individual jump points. And normalize again to get a square wave, shown as Fig. 2(c). (c) Find all the jump points between 0 and 1. Calculate the length of each two jump points and get the average of each length. Get the modulation frequency from the average length and the sample rate. Taking the signal of carrier frequency 2600 Hz as an example, the demodulated information is shown in the Table 1. It can be seen that the demodulation result of HHT algorithm is very accurate, which further shows that HHT algorithm is suitable for the demodulation of ZPW2000A track circuit.

Application of Hilbert Huang Transform in Car Signal Demodulation

Fig. 2. (a) EMD results, (b) instantaneous frequency, (c) Normalization processing

Table 1. Demodulation results Input Modulation frequency (Hz) 10.3 13.6 16.9 20.2 23.5 26.8 29

Demodulation result Center frequency (Hz) 2600.0016 2600.0005 2599.9989 2600.0038 2600.0016 2599.9955 2599.9970

Error (Hz) 0.0016 0.0005 0.0011 0.0038 0.0016 0.0045 0.0030

Modulation frequency (Hz) 10.2954 13.5963 16.8881 20.2980 23.5024 26.8187 29.0061

Error (Hz) 0.0046 0.0027 0.0119 0.0980 0.0024 0.0187 0.0061

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5 Peroration This paper introduces the application of HHT algorithm in car signal demodulation. Taking ZPW2000A track circuit signal as an example, the algorithm is simulated and verified. This paper summarizes the structure of car signal and the characteristics of track circuit, introduces the principle and implementation of HHT algorithm in detail. Through the MATLAB simulation, it has proved that the HHT demodulation algorithm can greatly improve the demodulation quality of car signal and ensure the safety of train operation.

References 1. Wang, R.: Foundation of Railway Signal Operation. Railway Publishing House, Beijing (2008) 2. Chao, Q.L.: Track Circuit and Electrification. Railway Publishing House, Beijing (2008) 3. China Railway Signal History Editorial Committee: Electrified Railway Signal Equipment. Railway Publishing House, China (2016) 4. Dong, Y.: Interval signal control and Train Operation Control System. Railway Publishing House, Beijing (2008) 5. Liu, X., Zhang, L., Liu, S.: Research and implementation of ZPW2000 shift frequency signal detection technology. Instrument technology and sensor, pp. 82–85, December 2014 6. Gai, Q., Zhang, H., Xu, X.: Adaptive frequency multiresolution analysis of Hibert Huang transform. Electron. J. 33(3), 563–566 (2005) 7. Zhang, J.: Adjacent line interference of type ZPW2000 track circuit. Railway Technol. Innov. 1, 53–55 (2009) 8. Qiong, G.: Application analysis of ZPW2000A track circuit for passenger dedicated line. Railway Commun. Signal Eng. 6, 85–87 (2012) 9. Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. 454, 903–995 (1998) 10. Huang, N.E., et al.: A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc. R. Soc. Lond. 459, 2317–2345 (2003)

Human Action Recognition: A Survey Meixia Fu1,2,3(&), Na Chen1,2,3, Zhongjie Huang1,2,3, Kaili Ni1,2,3, Yuhao Liu1,2,3, Songlin Sun1,2,3, and Xiaomei Ma4 1 National Engineering Laboratory for Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing, China [email protected] 2 Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China 3 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China 4 China United Network Communications Group Co., Ltd., Beijing, China

Abstract. In this paper, we provide a comprehensive survey in human action recognition and prediction, which has always been a universal and critical area in computer vision. Human action recognition is the first step for a machine to understand and percept the nature, which is small part in machine perception. Human action prediction is the higher layer than human action recognition that is small part in machine cognition, which would give the machine the ability of imagination and reasoning. Here, we only discuss human action recognition from two methodologies that is based on presentations and deep learning, separately. Then, 4 public datasets of human action recognition are descripted closely. Some challenges in dataset are also proposed because of the significance to the development of computer vision. Meanwhile, we compare and summarize recent-published research achievements under deep learning. In the end, we conclude about mentioned methods and future challenges to work on for computer vision. Keywords: Human action recognition
Computer vision
Machine perception
Human action prediction
Machine cognition Deep learning




1 Introduction Recently, computer vision has been a popular branch in artificial intelligence, which gives a critical influence on human-machine interactions, intelligent vision surveillance, automated driving and other applications based on vision. Most of the information is obtained through human vision as well as machine. Human action recognition is inescapable for a machine to understand the human behavior in computer vision. The definition of human action has many versions in psychology, sociology and philosophy. In computer vision, human action aims at the external performance in daily life for humans [1–3], which means that machine could understand what human do

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 69–77, 2019. https://doi.org/10.1007/978-981-13-7123-3_9

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Action representation Video Sequences

Processing

Action Recognition

Feature Extraction

Model

Action Labels

(a) Action Recognition Video Sequences

Model

Action Labels

(b) Fig. 1. The two architecture of human action recognition system. (a) shows a methodology based on presentations, which always extract the feature firstly and then recognize the actions. (b) presents another methodology, which completes future extraction and action recognition simultaneously in one model.

through itself way. According to the complexity of actions [3], human activity is di vided broadly into four levels, including gesture action, interaction and group activity. The research in this field is more close to real-sceneries, which is in the stage of actions and interactions. In this work, two methodologies based on presentations and deep learning are presented, as shown in Fig. 1(a) and (b). First, many previous-distinctive work are adopted in Sect. 2. Then we descript 4 public datasets in detail in Sect. 3. Besides, we compare the mentioned methods based on these public datasets. At last, we summary the previous work and propose new challenges for future work in this field in conclusion.

2 Previous Work Several techniques for human action recognition have been reported, the method based on presentations [4–6], deep learning [7–10]. We introduce these two methodologies separately. Figure 2 shows the development of human action recognition over time. 2.1

Methods Based on Presentations

Before 2010, most methods based on presentations like Fig. 1(a) consist of handcrafted feature extraction and action recognition. In [4], a binary motion-energy image and a motion-history image were considered as the feature of image sequences. Temporal template was utilized to evaluate the similarity between the stored modes and the current models. [5] proposed the interest point in spatial-temporal domain as the

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Human action Signal recognition

Deep Learning

Representations

Skeleton

Image MEI MHI + Template Matching [4]

2005 2007

CHSMM [11]

Space-time shape [13] LBP+HMM [12]

Two-stream [8]

CNN+LSTM [15]

TDD+FV [9]

2007 TSN [16]

2008 key frame + ST [17]

2013

2014

2015

time

3-D space-time volume [7]

3D CNNs [7]

time

2005

ROI + TM [5]

Survey [14]

2001

2018

2014

2018

2015

2018

Skeleton + BRNN [20]

Skeleton + LSTM [21]

ST-GCN [22]

RL +skeleton [23]

2016 2016

2010 Video Coding [18] ATW CNN [19]

2018 2018

Fig. 2. The development of human action recognition over time

input of matching. [6] detected all locations through 3-D space-time volume and matched with similar dynamic behaviors. In [11, 12], Hidden Markov Models (HMMs) were applied for the variations for sequences in an observation. [13] applied a 2D shapes to space-time action recognition. [2] provided a detailed overview of previous work in human action recognition. 2.2

Methods Based on Deep Learning

After 2010, many methods deep learning-based have been presented. A large of structures are similar to Fig. 1(b). According to the different input, we distribute two groups, image and skeleton as shown in Fig. 2. For image, the input of the proposed methods is the original frames or deep images. In [7], an automated human action recognition system was proposed, which developed a novel 3D convolutional neutral networks (CNNs) as shown in Fig. 3. In [8], Karen proposed two-stream convolutional networks according to “two-streams hypothesis”. One stream was used to depict scenes and objects in single frame and another stream was applied to extract motion information across frames. Each stream was using a deep ConvNet for action recognition shown in Fig. 4. [10, 14] showed long-term recurrent convolutional networks to recognize video sequences. [9] combined the hand-crafted features for spatiotemporal normalization and deep-learned features for channel normalization. [15] adopted multi-two stream networks for K segments in the same video.

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Fig. 3. The structure of 3D CNNs for human action recognition [7].

[16] presented a key volume mining framework to decrease calculation and complexity for human action recognition. [17] applied the compressed video theory to action recognition to decrease the redundant information among frames. [18] presented an attention-based temporal weighted (ATW) CNN to process spatial RGB images for object recognition, optical flow images for motion information and optical warped flow images for motion information in three streams, separately.

Fig. 4. The structure of two-stream ConvNet for human action recognition [8].

For skeleton, the input of the proposed model is skeleton information. In [19], a hierarchical recurrent neural network (RNN) was proposed to recognize actions. This paper divided the whole skeleton into five parts fed to five subnets. With the layer increasing, the features from subnets were fused to higher payers. In [20], geometric relation modeling among skeletons was applied to RNN-based action recognition. [21] presented a spatial-temporal graph convolution (ST-GCN) structure on skeleton sequences, as shown in Fig. 5. [22] showed a deep progressive reinforcement learning (RL) method skeleton-based for selecting key frames form original video sequences and a graph-based CNN to deal with key frames for human action recognition.

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Fig. 5. The structure of spatial-temporal graph convolution [21].

3 Datasets Description With the development of computer vision, the understanding on images and videos has attracted more attention from researchers. The most important part in this field is datasets, which has tremendous effect on the action recognition performance for realworld application scenarios. There are 4 datasets that are used widely in this field will be introduced in the following. 3.1

KTH

KTH was published by Christian Schuldt in 2004 [23], which has 6 human action classes, as shown in Fig. 6. In this dataset, each class consists of 400 clips approximately.

walking

jogging

running

boxing

hand waving

Fig. 6. Six examples of human actions in KTH [23].

hand clapping

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HMDB51

HMDB51 was published by Kuehne in 2011 [24], which has 51 action classes with 7000 annotated clips. Each class includes 101 clips at least. Some examples of HMDB51 are shown in Fig. 7. All the videos are from different movies and YouTube videos.

hand-waving

diving

drinking

running

sword fighting

kicking

Fig. 7. Six realistic examples of 51 classes human actions in HMDB51 [24].

3.3

UCF101

UCF101 was published by Khurram in 2012 [25]. It is a large dataset including 101 human action classes, some examples are shown in Fig. 8. The time span is over 27 h and each class has about 130 clips. All of the videos are from YouTube, which is more challenging and diverse than the existing datasets [23, 24].

Fig. 8. Nine frames of 101 classes human actions in UCF101 [25].

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Kinetics-600

Kinectics-400 was published by Will Kay in 2017 [26], which is used in ActivityNet from 2017. Kinectics-400 is a sub-set of Kinectics-600 generated from YouTube videos, which includes 600 human action classes with at least 600 video clips for each class. Six examples of 600 classes human actions are showed in Fig. 9. This dataset is about consist of 500,000 video clips and each clip lasts around 10 s. Up to now, Kinectics-600 is the largest dataset, which not only includes single class but also human-human interactions.

headbanging

shaking hands

robot dancing

stretching leg

tickling

salsa dancing

Fig. 9. Six examples of 600 classes human actions in Kinects-600 [26].

4 The Comparison Among Proposed Methods We present a comparison among proposed methods on KTH, HMDB51, UCF101 and Kinetics-600, as shown in Table 1. From architecture, there are two mainstream framework, 3D CNNs [7, 10] and two-stream network [8, 15, 18]. From input, there are two categories, images [14] and skeleton [21]. From complexity, [16] adopted selecting key frames and [17] applied video coding to decrease calculation and increase recognition speed. We achieve ATW CNN obtained the highest accuracy based on HMDB51 and UCF101. ATW CNN shared the advantage of two-stream network and multi-input information, which proposed three streams and employed an attention model for human action recognition.

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Model 3D CNNs LTC-CNN Two-stream TSN TDD + FV ATW CNN CNN + LSTM Key frame + ST Video coding ST-GCN

KTH 90.2%

HMDB51 UCF101 Kinetics-600

67.2% 59.4% 69.4% 96.65% 65.9% 70.5% 90.76% 99.94%

92.7% 88.0% 94.2% 91.5% 94.6%

30.7%

5 Conclusion In this paper, we presented a comprehensive survey for human action recognition. All proposed methods have achieved great performance on public datasets. Considering the practical sceneries, there are many challenges that needs the researchers to be dealt with in the future. For example, all of 4 datasets have one label for each video. However, there are not one class for single person in real-life, like running with listening, talking with shaking hands. And there are also not one class for several persons in one video, like one is drinking and others are talking. We would create new dataset according to real-life condition. Acknowledgment. This work is supported by National Natural Science Foundation of China (Project61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.

References 1. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Underst. 81(3), 231–268 (2001) 2. Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010) 3. Vishwakarma, S.: A survey on activity recognition and behavior understanding in video surveillance. Vis. Comput. 29(10), 983–1009 (2013) 4. Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Computer Society (2001) 5. Laptev, I., Lindeberg, T.: On space-time interest points. Int. J. Comput. Vision 64(2–3), 107–123 (2005) 6. Shechtman, E., Irani, M.: Space-time behavior based correlation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 405–412. IEEE Computer Society (2005)

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7. Xu, W., Yang, M., et al.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013) 8. Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: International Conference on Neural Information Processing Systems, pp. 568– 576. MIT Press (2014) 9. Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors. In: Computer Vision and Pattern Recognition, pp. 4305–4314. IEEE (2015) 10. Varol, G., Laptev, I., Schmid, C.: Long-term temporal convolutions for action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1510–1517 (2018) 11. Natarajan, P., Nevatia, R.: Coupled hidden semi Markov models for activity recognition. In: IEEE Workshop on Motion and Video Computing, p. 10. IEEE Computer Society (2007) 12. Kellokumpu, V., Zhao, G., Pietikäinen, M.: Human activity recognition using a dynamic texture based method. In: British Machine Vision Conference, Leeds. DBLP, September 2008 13. Gorelick, L., Blank, M., Shechtman, E., et al.: Actions as space-time shapes. IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2247–2253 (2007) 14. Donahue, J., Hendricks, L.A., Rohrbach, M., et al.: Long-term recurrent convolutional networks for visual recognition and description. IEEE Trans. Pattern Anal. Mach. Intell. 39 (4), 677–691 (2014) 15. Wang, L., Xiong, Y., Wang, Z., et al.: Temporal segment networks: towards good practices for deep action recognition. In: European Conference on Computer Vision, pp. 20–36. Springer, Cham (2016) 16. Zhu, W., Hu, J., Sun, G., et al.: A key volume mining deep framework for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1991– 1999. IEEE Computer Society (2016) 17. Wu, C.Y., Zaheer, M., Hu, H., et al.: Compressed video action recognition. In: Computer Vision and Pattern Recognition. IEEE (2018) 18. Zang, J., Wang, L., Liu, Z., et al.: Attention-based temporal weighted convolutional neural network for action recognition. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 97–108. Springer, Cham (2018) 19. Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: Computer Vision and Pattern Recognition, pp. 1110–1118. IEEE (2015) 20. Zhang, S., Xiao, J., Liu, X., et al.: Fusing geometric features for skeleton-based action recognition using multilayer LSTM networks. IEEE Trans. Multimed. 20, 2330–2343 (2018) 21. Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeletonbased action recognition (2018) 22. Tang, Y., Tian, Y., Lu, J., et al.: Deep progressive reinforcement learning for skeleton-based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5323–5332 (2018) 23. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 32–36. IEEE (2004) 24. Kuehne, H., Jhuang, H., Garrote, E., et al.: HMDB: a large video database for human motion recognition. In: IEEE International Conference on Computer Vision (ICCV), pp. 2556– 2563. IEEE (2011) 25. Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012) 26. Kay, W., Carreira, J., Simonyan, K., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)

Genetic Algorithm for Base Station ON/OFF Optimization with Fast Coverage Estimation and Probability Scaling for Green Communications Yebing Ren1,2(&), Wei Liu1, Jiangbo Dong1, Haobin Wang2,3, Yaxi Liu3, and Huangfu Wei2,3 1

China Mobile Group Design Institute Co., Ltd., Beijing 100083, China [email protected] 2 Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, Beijing 100083, China 3 School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 10083, China

Abstract. Minimizing the power consumption while maximizing the quality of service has become a mainstream problem in green communications. The existing approaches of network configurations often ignore the computation complexity caused by the large number of user terminals. Our contributions mainly lie in two folds. We formulate an optimization problem to maximize the user terminal coverage ratio with a given number of activated base stations. We propose a novel genetic algorithm to optimize the ON/OFF status of base stations with fast coverage estimation, in which the scaling and selection operators are carefully designed to take the probability distribution of the estimated coverage ratio into account. Experiments have been conducted to prove the proposed algorithm for the network configuration for green communication. Keywords: Green communications Genetic algorithm
Probability


Base station ON/OFF strategy


1 Introduction Green communication is a new paradigm which reduces environmental pollution and power consumption for cellular networks [1–3]. A large portion of the network operator’s operating expenses is used to pay for the electricity [4]. In all the energy consumption, about 60%–80% of the power consumption is the executed in the base stations [5]. Thus, turning on or off the base stations, i.e., adjusting the pattern of activated base stations, is one of the most important techniques to save power consumption of the mobile networks [6]. In order to meet the various demands of users anytime and anywhere, ensuring the quality of user service has become the focus of network operators. Coverage is one of the most important performance indicators for assessing the quality of cellular network services [7].

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 78–88, 2019. https://doi.org/10.1007/978-981-13-7123-3_10

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Various methods of base station ON/OFF strategies have been proposed in green communications. Oh et al. [8] proposed an dynamic base station ON/OFF strategy algorithm to reduce the power consumption in wireless cellular networks. The procedure is to turn off the base station which has the least effect on the network by considering the additional influence of the adjacent base stations. Zhou et al. [9] solved the concerned base station energy saving problem in green communications and presented an algorithm to dynamically turn off certain base stations when the network traffic is low. Son et al. [10] designed a theoretical framework to save the energy consumption of base station, which consisted of the dynamic BS ON/OFF strategy and the user association problem. The optimization also emphasized the total cost minimization but neglecting the quality of service. Bousia et al. [11] developed a switch base station ON/OFF algorithm green communications, which adequately considered the information of the distance between the neighbor user equipment and their associated base station. The ON/OFF algorithm mainly lied on reducing the power consumption of the communication networks by minimizing the power utilization. Bousia et al. [12] presented a dynamic base station switch ON/OFF strategy to solve the problem of the inefficient use of the base station power as an improvement of the previous work. Kim et al. [13] formulated an energy minimization problem to balance the power consumption and the revenue of the cellular networks. They also proposed an algorithm for both base station ON/OFF strategy and user terminal association in heterogeneous networks. Oh et al. [14] researched a dynamic base station ON/OFF strategy system to reduce the power consumption of base station considering the temporal characteristic of the user terminals. The existing approaches of network configurations are mainly ignore the user terminal number and the quality of service. In this paper, we propose a novel probability aware genetic algorithm for base station ON/OFF strategy in green communications. Our contributions mainly lie in two folds. We introduce an optimization problem to maximize the user terminal coverage ratio with a given number of ON state base stations. Our contributions mainly lie in two folds. We formulate an optimization problem to maximize the user terminal coverage ratio with a given number of activated base station. We propose a novel genetic algorithm to optimize the ON/OFF strategy with fast coverage estimation, in which the scaling and selection operators are carefully designed to take the probability distribution of the estimated coverage ratio into account. Experiments have been conducted to prove the proposed algorithm for the network configuration for green communication. The rest of the paper is organized as follows. Section 2 formulates the problem models and Sect. 3 proposes the novel probability aware genetic algorithm. Section 4 shows the experiments and discusses the results. Finally Sect. 5 concludes this paper.

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2 System Model and Problem Formulation This section introduces the network scenario and formulates the optimization problem. 2.1

Network Scenario

Assume that there are n base stations distributed in the predefined Region of Interest (ROI). Let the base station set be B = {B1, B2, …, Bn}. Each base station has two states, on state and off state. Denote the ON/OFF state SBi of BS Bi, 1  i  n, as
SBi ¼

1 0

if Bi is on state; if Bi is off state:

ð1Þ

We can turn ON/OFF the base stations artificially according to the variations of the users’ locations. The ON/OFF state strategy can be expressed as G ¼ ½SB1 ; SB2 ; . . .; SBn :

ð2Þ

Note that the strategy G is an n-length vector in which the elements are the states of the base stations. An ON/OFF strategy represents a set of the activated base stations’. There are m user terminals distributed in region R. Let the user terminal set be U = {U1, U2,. .., Um}. The illustration of the network deployment is shown in Fig. 1.

Fig. 1. Illustration of the network scenario.

To simplify the coverage problem, we assume that if user terminal Uj is in the range of a circle whose center is base station Bi with a predefined radius RadiusBase, the user terminal Uj is covered by base station Bi. Covered user terminals have good communication performance and satisfied quality of service, and vise versa. The illustrations of the covered user terminal and the uncovered user terminal are demonstrated in Fig. 2.

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Fig. 2. Illustrations of the covered user terminal and the uncovered user terminal.

Then the coverage ratio of region R can be calculated by C¼

  U ^ jU j

ð3Þ

^ is the set of covered user terminals, and |X| is the cardinality of set X. where U 2.2

Problem Formulation

Our goal is to maximize the region coverage ratio, that is, the number of the covered user terminals, with limited number of on state base stations. Thus the optimization problem can be described as max C ðSB1 ; . . .; SBn Þ SB1 ; . . .; SBn s:t:

n X

ð4Þ

SB1 ¼ ^n:

i¼1

where ^n is the maximum number of the on state base stations.

3 Genetic Algorithm with Fast Coverage Estimation 3.1

Gene, Individual and Population

The gene in base station ON/OFF strategy problem refers to the ON/OFF state SBi of the BS Bi. An individual refers to one ON/OFF state strategy G. Population is a set of ON/OFF state strategies. Denote the population set as Population ¼ fG1 ; G2 ; . . .; Gk g

ð5Þ

where k is the population size, that is, the number of the state strategies in a population.

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Let the individual Gl be n o Gl ¼ SlB1 ; SlB2 ; . . .; SlBn

ð6Þ

Now the optimization goal is to find the optimal individual Gl to maximize the region coverage ratio with a given number of the on state base stations. 3.2

Fast Coverage Estimation

The optimization goal is to maximize the coverage ratio C. A naive coverage calculation method is by counting in (3), which sums the above-threshold terminals among all the terminals. However, the number of the user terminals is usually large. Thus if we check the coverage status of all the terminals, the computation load will be large even in our simplified coverage model, and even larger to calculate the signal strength and link quality for all the user terminals with other complicated models. Thus, we can only use a batch of sampled user terminals to fast estimate the coverage in a stochastic manner. We randomly select M user terminals, Ui0 , 1  i  M, without repetition to do the coverage calculation. The estimated coverage can be given by M  X ^  ^ ðGl Þ ¼ M ¼ 1 I Ui0 is covered in Gl C M M i¼1

ð7Þ

^ is the number of the covered user terminals among the sampled user termiwhere M nals? Note that the coverage here is not the accurate coverage, but an roughly estimated one which only calculated by M user terminals. Assume that the coverage status of any user terminal, say Uk obeys the Bernoulli distribution with the covered probability P(Uk is covered) ¼ C. Here C is the accurate coverage ratio and is treated as a parameter to be estimated. Denote the probability density function of Bernoulli distribution for user terminal Uk as
f ðxÞ ¼ Cx ð1  CÞ1x ¼

C B is covered; 1  C otherwise:

ð8Þ

 Thus, the mean value and the variance for the stochastic variable I Ui0 is covered in Gl Þ is given by 1   X xi f ðxÞ ¼ 0 þ C ¼ C; E I Ui0 is covered in Gl ¼

ð9Þ

i¼0 1   X   ^ ^ C ðxi  CÞ2 f ðxÞ ¼ 1  C Var IðUi0 is covered in Gl Þ ¼ i¼0

ð10Þ

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^ l Þ, which is the summation of M 0-1 distributions, The estimated coverage CðG ^ l Þ  B(M,C). The mean value and the variance obeys the binomial distribution CðG ^ l Þ is given by for the stochastic variable CðG M X   ^ ðGl Þ ¼ 1 C ¼ C; E C M i¼1

ð11Þ

M X   C ð1  C Þ ^ ðGl Þ ¼ 1 : Var C C ð1  C Þ ¼ 2 M i¼1 M

ð12Þ

Note the variance of the estimated coverage ratio is reduced if the sample number M is increased. 3.3

Scaling and Selection Operations

The coverage by fast estimation is a stochastic variable which obeys a distribution with   ^ 1C ^ =M. Now that the coverage ratio itself is the mean value C and the variance C not accurate, we must take the probability into account if it is selected as the replacement of the object function to reduce the computation complexity. ^ is The illustration of the probability density function of binomial distribution C shown in Fig. 3.

Fig. 3. Illustrations of the covered user terminal and the uncovered user terminal.

In order to calculate the fitness function, we first select a scaling baseline constant THP. Now we check the probability Fitl that the estimation value is greater than or equal to THP. MTH XP   ^ ðGl Þ  THP ¼ 1  Fitl ¼ P C i¼0



M Ci ð1  C ÞMi i

ð13Þ

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The larger the fitness function is, the better the individual is. Selection operation is an important way to generate new population by selecting the good performance one into next generation. Denote the selection probability for individual Gl as Fitl ProSel l ¼ Pk n¼1 Fitl

ð14Þ

Sel Sel Note that 0  ProSel 1  Pro2  . . .  Prok ¼ 1. We generate a random decimal x range in [0,1]. If 0  n  ProSel 1 , we add the individual I into the next generation set. If Sel ProSel \n  Pro , we add the individual Gl into the next generation set. Repeat the l1 l above-mentioned steps until the number of the new generation set is k. If the constant THP is large, the scaled fitness of the individual Gl is very small and is not seldom to be selected as the new generation. If the THP constant is small enough, i.e. near zero, the scaled fitness values of all the individuals are almost equal and all the individuals have the identical probability to be selected in the next generation. Usually, we set the constant THP to the minimal coverage ratio in the population.

3.4

Crossover and Mutation Operations

Crossover is a biological operation to generate new individuals with the parents’ genes. We pair the individuals according to the order of selection. For each individual pair, we randomly generate an integer x range in [2, n − 1] and split the two individuals into two parts at x-position, respectively. Let the crossover probability be ProCro , we randomly generate a decimal x range in [0,1], if x  ProCro , change the two parts of the individual pair. The illustration of crossover operation is shown in Fig. 4.

Fig. 4. Crossover operation of the individual pair (Im1, Im2) in subregion C1

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Fig. 5. Mutation operation of the individual Im in subregion C1.

Mutation is an important operator for genetic diversity. Denote the mutation probability as ProMut . We randomly select k ProMut individuals to do the mutation operation. For each selected individual, randomly choose one gene SBi to convert the state. If SBi = 1, we make SBi = 0 and randomly covert one off state SBi = 1, and vise versa. The illustration of mutation operation is shown in Fig. 5.

4 Experiments and Discussions Experiments are executed to verify the accuracy and efficiency of Algorithm 1. We uniformly select 10000 user terminals in a 25 km 25 km region. We select 600 base stations in R and there are 213 base stations are distributed as ideal honeycomb shape. The maximum number of the ON state base station is 213. The settings of parameters in our simulation experiments are listed in Table 1. Table 1. Settings of simulation parameters Parameter n ^n m k RadiusBase ProCro ProMut THp

Value 600 213 10000 60 1000 m 0.9 0.1 ^ Mean value of C

The original base station location map and original user terminal coverage map are illustrated in Fig. 6. The blue dots are the on state base stations in Fig. 6(a). We randomly generate an ON/OFF base station strategy and the on state base stations are disperse in region R. The red region is the covered region in Fig. 6(b).

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Fig. 6. The base station location and the user terminal coverage map in the initial state.

The base station location map and user terminal coverage map after the optimization of the probability aware genetic algorithm are illustrated in Fig. 6. We can see from Fig. 7(a) that the on state base station distribute more even. More even distribution makes larger coverage ratio. Thus, the algorithm is valid in the base station ON/OFF strategy. We can see from Fig. 7(b) that the user terminal coverage area is larger than the initial one. Therefore, the algorithm is accurate to maximize the number of the user terminal, that is, the user terminal coverage, whose quality of service meets the required threshold in green communications.

Fig. 7. The base station location map and the user terminal coverage map of the optimization of the probability aware genetic algorithm after 50 iterations.

The relationship of the coverage ratio and the computation load, i.e., the product of iterations and samples, of genetic algorithm is illustrated in Fig. 8. If the We can see from Fig. 8 that the convergence rate of the proposed genetic algorithm is fast even with a small sample number M. In the same time span, we can run 50 iterations for M = 100 but only 10 iterations for M = 500. Note that if M = m, i.e., we sample all the user terminals, our algorithm degenerates into a naive one, which still in its first iteration in the same time span. Therefore, compared to the naive one without fast coverage estimation, the proposed algorithm converges faster and is more efficient.

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Fig. 8. The relationship between the maximum coverage and the iterations of probability aware genetic algorithm.

5 Conclusion Minimizing the power consumption while maximizing the quality of service has become a mainstream problem in green communications. The existing approaches of network configurations are mainly ignore the user terminal number and the quality of service. Our contributions mainly lie in two folds. We introduce an optimization problem to maximize the user terminal coverage ratio with limited on state base station number. We not only consider the power consumption by limiting the on state base station number, but also concern the quality of service by maximizing the user terminal number. Moreover, we propose a novel probability aware genetic algorithm to optimize the ON/OFF strategy. The coverage fitness function is converted into a probability one with less user terminals. Experiments have been conducted to prove the proposed algorithm for the network configuration. Acknowledgement. This research is funded by the Joint Foundation of MoE (Ministry of Education) and China Mobile Group (No. MCM20160103).

References 1. Mahapatra, R., Nijsure, Y., Kaddoum, G., Hassan, N.U., Yuen, C.: Energy efficiency tradeoff mechanism towards wireless green communication: a survey. IEEE Commun. Surv. Tutorials 18(1), 686–705 (2016) 2. Abrol, A., Jha, R.K.: Power optimization in 5G networks: a step towards GrEEn communication. IEEE Access 4, 1355–1374 (2016) 3. Gandotra, P., Jha, R.K., Jain, S.: Green communication in next generation cellular networks: a survey. IEEE Access 5, 11727–11758 (2017) 4. Luo, C., Guo, S., Guo, S., Yang, L.T., Min, G., Xie, X.: Green communication in energy renewable wireless mesh networks: routing, rate control, and power allocation. IEEE Trans. Parallel Distrib. Syst. 25(12), 3211–3220 (2014)

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5. Son, K., Kim, H., Yi, Y., Krishnamachari, B., Wu, J., Rangan, S., Zhang, H.: Toward energy–efficient operation of base stations in cellular wireless networks. In: Green Communications: Theoretical Fundamentals, Algorithms, and Applications (2012) 6. Wu, Q., Li, G.Y., Chen, W., Ng, D.W.K., Schober, R.: An overview of sustainable green 5G networks. IEEE Wirel. Commun. 24(4), 72–80 (2017) 7. Hasan, Z., Boostanimehr, H., Bhargava, V.K.: Green cellular networks: a survey, some research issues and challenges. IEEE Commun. Surv. Tutor. 13(4), 524–540 (2011) 8. Oh, E., Son, K., Krishnamachari, B.: Dynamic base station switching–on/off strategies for green cellular networks. IEEE Trans. Wirel. Commun. 12(5), 2126–2136 (2013) 9. Zhou, S., Gong, J., Yang, Z., Niu, Z., Yang, P.: Green mobile access network with dynamic base station energy saving. In: ACM MobiCom, vol. 9, no. 262, pp. 10–12 (2009) 10. Son, K., Kim, H., Yi, Y., Krishnamachari, B.: Base station operation and user association mechanisms for energy-delay tradeoffs in green cellular networks. IEEE J. Sel. Areas Commun. 29(8), 1525–1536 (2011) 11. Bousia, A., Antonopoulos, A., Alonso, L., Verikoukis, C.: “Green” distance-aware base station sleeping algorithm in LTE– Advanced. In: IEEE International Conference on Communications (ICC), pp. 1347–1351 (2012) 12. Bousia, A., Kartsakli, E., Alonso, L., Verikoukis, C.: Dynamic energy efficient distanceaware base station switch on/off scheme for LTE– Advanced. In: Global Communications Conference (GLOBECOM), pp. 1532–1537. IEEE (2012) 13. Kim, S., Choi, S., Lee, B.G.: A joint algorithm for base station operation and user association in heterogeneous networks. IEEE Commun. Lett. 17(8), 1552–1555 (2013) 14. Oh, E., Krishnamachari, B.: Energy savings through dynamic base station switching in cellular wireless access networks. In: GLOBECOM, vol. 2010, pp. 1–5 (2010)

Satellites and Remote Sensing

Inter-layer Link Design for Resource-Constrained Double-Layered Satellite Network Hongcheng Yan1,2(&), Rui Zhang1, Yahang Zhang1, Luming Li1, and Jiaxiang Niu1

2

1 Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China Science and Technology on Communication Networks Laboratory, Shijiazhuang 050081, China [email protected]

Abstract. Due to the relative motion of low-orbit satellites and high-orbit satellites, the inter-layer links (ILL) of double-layered satellite networks (DLSN) need to be dynamically switched, thus causing the satellite network topology changing dynamically. Traditional inter-layer link establishment strategy is loworbit-centric which assumes that the number of ILLs equipped on high-orbit satellite is not limited. However, actual DLSNs are generally resourceconstrained, such as weight and power. Thus the number of ILLs can be equipped on one satellite is limited. Therefore, traditional ILL establishment strategy is hard to apply in practice. In this paper, we first propose an ILL establishment strategy which is high-orbit-centric. We assume that high-orbit satellites have only one ILL and use this as a precondition to establish ILL. Then, the system snapshot performances of two ILL establishment strategies under two typical DLSNs constellation configurations, which are LEO/MEO and IGSO/MEO, are simulated and analyzed. Finally, since ILL establishment strategy which is high-orbit-centric can make the ILLs of some low-orbit satellites idle for some time, we point out that further research should be carried out about how to effectively use low-orbit satellite ILLs. Keywords: Inter-layer link design
Double-layered satellite networks Higher-orbit-centric
Simulation analysis




1 Introduction Since double-layered satellite networks (DLSN) can integrate the strong points of both low-orbit satellites and high-orbit satellites, it has attracted much attention in the research field [1]. Due to the relative motion of low-orbit satellites and high-orbit satellites in the DLSN, they cannot be always visible. Therefore, the inter-layer links (ILL) of the DLSN need to be dynamically switched, thus causing satellite network topology changing dynamically. Since the inter-satellite link within a layer can generally be fixed by selecting an appropriate constellation configuration [2], the dynamical topology of DLSN is mainly attributed to the continuous switching of the ILLs. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 91–99, 2019. https://doi.org/10.1007/978-981-13-7123-3_11

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Currently, the academic community has carried out some research work on the inter-layer topological dynamics of DLSN, and mainly uses some optimal strategies to establish ILL, such as the shortest distance, the longest connection time, and the maximum resource utilization. Currently, the most popular ILL establishment strategy is based on the longest connection time strategy, that is, low-orbit satellites will choose a high-orbit satellite which has the longest predictable connection time to establish ILL [3]. Each time the ILL changes, a new topology snapshot will be generated. Since this strategy does not consider snapshot optimization issues, it will generate a large amount of snapshots and the durations of snapshots are mostly short. Zhou et al. [4] and Long et al. [5] proposed a snapshot optimization method based on snapshots merging, which can greatly reduce the snapshots amount, while at the same time, increase the snapshots duration. Wu et al. [6] propose a concentrated link establishment strategy for ILLs of LEO/MEO satellite networks. The authors force the ILLs to be reestablished at the same time to reduce the topological dynamics. For example, for concentrated time strategy, each LEO satellite will independently find the MEO satellite that can provide the longest coverage time to establish ILL, and the next actual reconstruction time of all LEO ILLs is the minimum of all LEO theoretical reconstruction time. Because the reconstruction of the laser inter-satellite link takes a long time, the centralized ILL reconstruction strategy of DLSN will cause the MEO layer and the LEO layer to be isolated during the link reconstruction. Therefore, Li et al. [7] proposed a two-step synchronous handover scheme for laser ILLs. The authors divide the ILLs into two groups which perform ILL switching alternately. On the basis of ensuring the connectivity of satellites in two layers, the topological dynamics of the inter-layer can be reduced. Shi et al. [8] propose a traffic aware ILL selection method by considering both the flow situation of the associated nodes and the connection time of the associated nodes and obtain balanced flow distribution among high-orbit satellites. The above ILL establishment strategy is low-orbit-centric and assumes that the number of ILLs of high-orbit satellite is not limited. However, actual DLSNs are generally resource-constrained, such as weight and power. Thus the number of ILLs can be equipped on one satellite is limited. Therefore, traditional ILL establishment strategy is hard to apply in practice [9]. In this paper, we first propose an ILL establishment strategy which is high-orbitcentric. We assume that high-orbit satellites have only one ILL and use this as a precondition to establish ILLs. Then, the system snapshot performances of two ILL establishment strategies under two typical DLSNs constellation configurations are simulated and analyzed. Finally, further research trends are pointed out.

2 ILL Establishment Strategy Which Is High-Orbit-Centric The typical architecture of DLSN is shown in Fig. 1. Since the inter-satellite links (ISL) in the same layer can be fixed by choosing an appropriate constellation configuration [2], the topology of low-orbit satellite constellation or high-orbit satellite constellation alone can be regarded as constant. However, due to the relative motion of high-orbit satellites and low-orbit satellites, the ILLs have to be dynamically switched.

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Then, it can be summarized that the topological dynamics of DLSN are mainly attributed to the dynamic switching of the ILLs.

Fig. 1. Architecture of double-layered satellite networks (DLSN)

The traditional ILL establishment strategy is generally based on the longest coverage time strategy [3], where, the low-orbit satellite will choose a high-orbit satellite with longest predicted connection time in all visible high-orbit satellites until the highorbit satellite can no longer be visible. Then, the low-orbit satellite will continue to use the same longest coverage time strategy to select the next high-orbit satellite to establish ILL. The traditional ILL establishment strategy is low-orbit-centric, that is, all low-orbit satellites can establish ILLs with high-orbit satellites. Since the high-orbit satellites is generally less than low-orbit satellites, high-orbit satellites generally need to establish multiple ILLs. Taking the LEO/MEO constellation selected in [9] for example, the MEO needs to establish 10 ILLs at most, 2 ILLs at least, 6 ILLs on average. This poses very high requirements for the MEO satellite platform, since it should be equipped with many ILLs. Therefore, traditional ILL establishment strategy is hard to apply in practice. We propose an ILL establishment strategy which is high-orbit-centric based on the premise that each high-orbit satellite can only establish one ILL. The ILL establishment strategy which is high-orbit-centric is described as follows: the high-orbit satellite will choose a low-orbit satellite with longest predicted visible time in all visible low-orbit satellites until the low-orbit satellite can no longer cover itself. Then, the high-orbit satellite will continue to use the same longest visible time strategy to select the next low-orbit satellite to set up ILL. The ILL establishment strategy which is high-orbit-centric can guarantee that each high-orbit satellite only establishes one ILL, and because there are generally fewer high-orbit satellites than low-orbit satellites, it cannot guarantee that all low-orbit satellites can establish ILL.

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3 Simulation Analysis This section analyzes the ILL topology through simulation. The simulation time is 24 h and we choose the simulation step as 1 s. The simulation analysis is focusing on two typical DLSNs, one is LEO/MEO and the other is IGSO/MEO. For each type of DLSNs, traditional ILL establishment strategy which is low-orbit-centric and the proposed ILL establishment strategy which is high-orbit-centric are used to establish ILL. The snapshots are all merged using the method proposed by Zhou-Long. 3.1

LEO/MEO

The constellation configuration of the LEO/MEO DLSN selected is shown in Table 1, in which LEO adopts the Celesti constellation and MEO adopts the ICO constellation. Table 1. Constellation parameters of LEO/MEO DLSN Constellation parameter LEO MEO Inclination (°) 48 45 Altitude (km) 1400 10390 Number of planes 7 2 Number of satellites per plane 9 5 Inter-spacing of planes (°) 51.43 180 Inter-spacing of satellites in one planes (°) 40 72 Phase factor 5 0

Figure 2 shows the system snapshot sequence generated by two ILL establishment strategies. Figure 2(a) shows the simulation result of traditional ILL establishment strategy, and Fig. 2(b) shows the simulation result of proposed ILL establishment strategy. From Fig. 2, it can be seen that the system snapshot generated by the proposed

Fig. 2. System snapshot sequence generated by two ILL establishment strategies (a) low-orbitcentric, (b) high-orbit-centric

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strategy is less than traditional strategy. The system snapshot duration generated by the proposed strategy is also larger than that of the traditional strategy. This is due to the reason that since there are fewer ILLs generated by the proposed strategy, the inter-layer topology may be less dynamic which is demonstrated by fewer number and larger duration of system snapshot. Table 2. Statistical characteristics of system snapshots Snapshots characteristics Total number Shortest duration/s Longest duration/s Average duration/s

Traditional strategy Proposed strategy 226 176 46 30 731 2289 378.6593 485

The number and duration of system snapshots generated by two ILL establishment strategies are shown in Table 2. The system snapshot generated by the proposed strategy has a 22% reduction in number compared to the traditional strategy. In terms of time, the shortest snapshot duration is reduced, the longest snapshot duration is increased to 3.1 times and the average snapshot duration increased by 28%.

Fig. 3. The LEO satellites to which MEO satellite ILLs is connected

The visualization of inter-layer topology of LEO/MEO DLSN is shown in Fig. 3 which shows the LEO satellites to which MEO satellite ILLs is connected. The horizontal axis represents time and the vertical axis represents the MEO satellite number. The “x” symbol indicates that the MEO satellite has an ILL switch at that moment. The number in the upper right corner of the “x” symbol indicates the LEO satellite number to which the MEO satellite is switched at that moment.

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The visualization from another perspective of inter-layer topology of LEO/MEO DLSN is shown in Fig. 4 which shows the MEO satellites to which LEO satellite ILLs is connected. The horizontal axis represents time and the vertical axis represents the LEO satellite numbers. The “x” symbol indicates that the LEO satellite had an ILL switch at that time. The number in the upper right corner of the “x” symbol indicates the MEO satellite number to which the LEO satellite is switched at that moment. If there is no number in the upper left corner of the “x” symbol, then the ILL of the LEO satellite will be idle after this time, i.e. no MEO satellite will establish an ILL with it. The solid line between the “x” symbols indicates that the LEO satellite established an ILL with a certain MEO satellite during that time period, and the dotted line indicates that the ILL of LEO satellite is idle during that time period.

Fig. 4. The MEO satellites to which LEO satellite ILLs is connected

From Fig. 4, it can be seen that the proposed ILL establishment strategy will make the ILLs of LEO satellite idle for certain periods of time, i.e., it cannot establish an ILL with the MEO. Moreover, the ILL of some LEO satellites are always in an idle state, i.e., these LEO satellites can never establish an ILL with MEO satellites. 3.2

IGSO/MEO

The constellation configuration of the IGSO/MEO DLSN selected is shown in Table 3, in which MEO adopts the walker 24/3/1 constellation and IGSO adopts a constellation with the same satellite sub-point track. Figure 5 shows the system snapshot sequence generated by two ILL establishment strategies. Figure 5(a) shows the simulation result of traditional ILL establishment strategy, and Fig. 5(b) shows the simulation result of proposed ILL establishment strategy. Similar to the LEO/MEO constellation, it can also be seen from Fig. 6 that the system snapshot generated by the proposed strategy is less and longer than that of traditional strategy.

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Table 3. Constellation parameters of IGSO/MEO DLSN Constellation parameter MEO IGSO Inclination (°) 55 55 Altitude (km) 21500 35768 Number of planes 3 3 Number of satellites per plane 8 1

Fig. 5. System snapshot sequence generated by two ILL establishment strategies (a) low-orbitcentric, (b) high-orbit-centric

Fig. 6. The MEO satellites to which IGSO satellite ILLs is connected

The number and duration of system snapshots generated by two ILL establishment strategies are shown in Table 4. The system snapshot generated by the proposed strategy has a 29% reduction in number compared to the traditional strategy. In terms of time, the shortest snapshot duration is increased by 112 times, the longest snapshot is increased to 4 times and the average snapshot duration increased by 33%.

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The visualization of inter-layer topology of IGSO/MEO DLSN is shown in Fig. 6 which shows the MEO satellites to which IGSO satellite ILLs is connected. The visualization from another perspective of inter-layer topology of IGSO/MEO DLSN is shown in Fig. 7 which shows the IGSO satellites to which MEO satellite ILLs is connected. From Fig. 7, it can be seen that the proposed ILL establishment strategy will make the ILL of MEO satellite idle for certain periods of time, i.e., it cannot establish an ILL with the IGSO. This feature is similar to LEO/MEO constellation. However, in IGSO/MEO constellation, all MEO will establish ILLs with IGSO which is different from LEO/MEO constellation.

Fig. 7. The IGSO satellites to which MEO satellite ILLs is connected

3.3

Discussion

From the above simulation results we can see that since the number of high-orbit satellites is generally smaller than that of low-orbit satellites, low-orbit satellites will not establish ILL with high-orbit satellites at certain time intervals. Moreover, some low-orbit satellites can never establish ILLs with high-orbit satellites.

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Therefore, in the future, we need to study how to make more efficient use of ILLs of low-orbit satellite. For example, we can make all low-orbit satellites alternately establish ILLs with high-orbit satellites to prevent some low-orbit satellites ILLs from being idle; we can also make the idle low-orbit satellites ILLs establish intra-layer links and increase the topology connectivity of low-orbit layer; or, we can just make some loworbit satellites equipped with less ILLs to saving satellite construction cost, and so on.

4 Conclusions In this paper, we propose an ILL establishment strategy which is high-orbit-centric. We assume that high-orbit satellites have only one ILL and use this as a precondition to establish ILLs. Then, the system snapshot performances of two ILL establishment strategies under two typical DLSNs constellation configurations are simulated and analyzed. Finally, further research interests are discussed.

References 1. Qi, X., Ma, J., Wu, D., Liu, L., Hu, S.: A survey of routing techniques for satellite networks. J. Commun. Inf. Networks 1, 66–85 (2016) 2. Werner, M., Frings, J., Wauquiez, F., Maral, G.: Topological design, routing and capacity dimensioning for ISL networks in broadband LEO satellite systems. Int. J. Satell. Commun. Netw. 19, 499–527 (2001) 3. Chen, C., Eylem E., Akyildiz, I.F.: Satellite grouping and routing protocol for LEO/MEO satellite IP networks. In: Proceedings of the 5th ACM international workshop on Wireless mobile multimedia, pp. 109–116. ACM (2002) 4. Zhou, Y., Sun, F., Zhang, B.: A novel QoS routing protocol for LEO and MEO satellite networks. Int. J. Satell. Commun. Network. 25, 603–617 (2007) 5. Long, F., Xiong, N., Vasilakos, A.V., Yang, L.T., Sun, F.: A sustainable heuristic QoS routing algorithm for pervasive multi-layered satellite wireless networks. Wirel. Netw. 16, 1657–1673 (2010) 6. Wu, T., Wu, S.: Performance analysis of the inter-layer inter-satellite link establishment strategies in two-tier LEO/MEO satellite networks. J. Electron. Inf. Technol. 30, 67–71 (2008) 7. Li, Y., Wu, J., Zhao, S., Deng, B., Miu, X.: A two-step synchronous handover scheme of optical inter-orbit links in LEO and MEO satellite network. Acta Electronica Sinica 45, 762– 768 (2017) 8. Shi, W., Gao, D., Zhou, H., Xu, Q., Foh, C.H.: Traffic aware inter-layer contact selection for multi-layer satellite terrestrial network. In: GLOBECOM 2017, IEEE Global Communications Conference, pp. 1–7 (2017) 9. Yan, H., Guo, J., Wang, X., Zhang, Y., Sun, Y.: Topology analysis of inter-layer links for LEO/MEO double-layered satellite networks. In: International Conference on Space Information Network, pp. 145–158. Springer, Singapore (2018)

Safety Design for Customer Furniture Instrument in Satellite Tian Tan(&), Chunping Zeng, Xiaodong Jia, Jian Shi, and Guoqiang Jiang DFH Satellite Co. Ltd., Beijing, People’s Republic of China [email protected]

Abstract. More and More piggy-back units are required in recently spacecraft mission due to the willingness of space industry development of customer. The risk caused by such kind unit shall be considered in all projects, and normally the units which are mounted on board are required to ensure the safety of satellite. However, the risks shall be considered at system level. These risks are analyzed at system level, and some measures are taken in a typical project which include the power supply safety, communication safety and system safe mode design. All these methods are validated in ground test. The satellite was launched in 2018 and the performance can meet the requirement. The safety design to CFI is successful and all the measures can be a reference to other similar mission. Keywords: Safety design


Power supply
CAN bus
Safe mode

1 Introduction The spacecraft design related with its safety issue is a key element during the whole process which had indicated in the national standard program [1] and also issued in the earlier spacecraft design [2, 3]. As aerospace technology keeps developing during past years, the usage of satellite is very important for many countries and companies; however, the satellite design and manufacture process became more attractive for those end users. In fact, the satellite design and manufacture process is quite comprehensive so that will cause a huge risk when they start from a spacecraft development directly. A general way is to develop a unit of spacecraft to gather experience at the initial stage before they start the whole satellite design and manufacture. The piggy-back unit requirement from customer is more and more frequency in spacecraft development mission. The risk shall be recognized and the measures shall be taken since design phase to eliminate the fault in mission level, even in case of piggy-back unit abnormal. A lot of research have been done and most of them are focused on how to design this kind new equipment, [4] especially in energy interface design [5, 6] and signal interface design [7]. A customer furnished instrument (CFI) mission is received from customer in some project. The power safety, communication safety and safe mode design are designed at spacecraft level in this paper. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 100–104, 2019. https://doi.org/10.1007/978-981-13-7123-3_12

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2 Design Method 2.1

Description to CFI

The CFI unit which is used for data process on board was designed, developed inhouse, tested by customer and supplied to the satellite contractor in line with the contract. The design, material, manufacture and process can be validated in orbit through this unit. It was required to provide necessary resource which include power, communication and so on. The core requirement of design is safety of the satellite and no operation of CFI will cause any degradation in the performance of the satellite. 2.2

Piggy-Back Design

The CFI shall not be included in the main mission of satellite in accordance with piggyback design rules. In general, separation design and safe mode design is the most important design method as follow. Power supply separation: to ensure the power supply of this unit can be controlled by satellite and the power can be shut down in case of abnormal occurred in CFI. Communication separation: to ensure the communication with this unit will not interfere the communication of other on-board units, even in case of abnormal occurred in CFI. Safe mode design: to ensure the status of CFI is under monitoring at satellite level and this unit can be separation with satellite in case of abnormal occurred in CFI. Besides, the restriction of CFI usage is necessary to ensure the main mission will not be interfered in the lifetime. The CFI is not allowed to be powered on when the main mission is performing.

3 Power Supply Design The CFI unit is separated in 2nd payload section of power distribution design, and the power on and off are controlled by power distribution unit (PDU) directly. There is no other unit is included in this 2nd payload power section. In normal case, the power on and off are controlled by tele-command through on-board computer (OBC). However, the power distribution unit will monitor the current and it can also be powered off by power distribution unit directly. A fuse is designed in power distribution unit, and it will ensure the shortcut fault will not affect the other part of satellite. At the same time, another fuse is required inside of the CFI as a redundancy design. The permitted current of the fuse inside the unit is a little lower than the outside one. The CFI unit is powered by 30 V power bus and the power interface is shown in Fig. 1. Two fuses in parallel are designed for overcurrent protection. The rated power consumption of CFI is 4 W and the rated current is 0.135 A. The working current of chosen fuse can meet the requirement of derating design. The 30 V power bus is powered by power control unit and the maximum current is 13 A for this circuit. In case of the shortcut occurred in CFI, the instantaneous shortcut current will be 22 A when the other units are working in normal case. The fuse can be fused in this case and the abnormal will not affect the other unit of satellite.

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nd Current Current of of 22nd Payload Payload SecƟon SecƟon

CFI CFI Main Main Power Power Supply Supply ++

30V 30V Power Power Bus Bus ++

CFI CFI backup backup Power Power Supply Supply ++ nd Voltage Voltage of of 22nd Payload Payload SecƟon SecƟon

30V 30V Power Power Bus Bus --

CFI CFI Main Main Power Power Supply Supply -CFI CFI backup backup Power Power Supply Supply --

Fig. 1. CFI unit power interface with satellite

4 Communication Design The OBC provides dual redundant CAN 2.0 bus interface link with the CFI-EnD unit for sending and receiving data streams. CFI unit connect to a separated payload CAN bus and only this unit is connected with satellite through this CAN bus. 4 OC commands to power on/off CFI-EnD unit are designed for this unit (Fig. 2).

OBC

CAN Bus A Payload CAN Bus CAN Bus B

Fig. 2. CFI communication interface with satellite

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Following rules are mandatory for CFI. (1) CFI CAN bus Interface shall be DB25 connector, there are 2 matched resistance (120 X) in CFI B board. One is for CAN A, the other is for CAN B. (2) CFI CAN bus chip shall be appointed by system designer. (3) CFI shall output respond data after receiving indirect TC or data block. (4) CFI shall not send TM data to CAN bus until receive OBC collect sequence. (5) CAN bus baud rata is 307.2 kbps ± 0.5%. (6) OBC collect CFI TM each designed period. (7) OBC communicates with CFI on CAN bus A or CAN bus B, and can switch the bus used automatically.

5 Grounding Requirements 5.1

Grounding Criteria

The CFI unit chassis shall be bonded at multi-point to the structure. All metallic parts of the unit shall be electrically connected. The requirements for bonding resistance are the following: (1) The bonding resistance between CFI unit and structure shall be less than 10 mX. (2) The bonding resistance between any two points of CFI unit chassis shall be less than 5 mX (Using 500 mA or 1A to test). (3) The DC resistance between harness connector and CFI unit chassis shall be less than 5 mX (Using 500 mA or 1A to test). 5.2

Primary Power Bus Grounding

The primary power bus shall have return lines. The return lines are connected to PSS. The power return lines are insulated from CFI unit chassis and the secondary power returns. The insulating resistance shall be higher than 1 MX. 5.3

Secondary Power Bus Grounding

The return lines of DC/DC converters are connected to PSS. The secondary power return lines are insulated from CFI unit chassis and the primary power returns. The insulating resistance shall be higher than 1 MX. 5.4

Signal Return Grounding

Signal interfaces between units shall provide appropriate insulation to avoid grounding loops. The return line of telemetry and tele-command signals shall refer to the ground at the users end.

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6 Safe Mode Design A CFI current safe mode and a CFI communication safe mode are designed in system level. The healthy status is monitored and the safe mode will be implemented automatically by on board computer (OBC) through the telemetry. The PDU will monitor the current of 2nd power section also. The power supply subsystem is most important part in the safety design, so this status is monitored by both PDU and OBC. The current will be collected every second by PDU and a threshold value (4 A) is designed. Once the current is exceeded, the telecommand of power off CFI will be send by PDU directly. This safe mode is tested during PDU acceptance test. It is also tested in satellite level. As to the communication safe mode, the OBC will communicate with the CFI each second to collect the telemetries. The safe mode will be trigged when 20 times continuous communication abnormal occurred. The CFI will be powered off by OBC automatically. This safe mode is tested during the interface test between CFI and OBC. It is also tested in satellite level.

7 Conclusion Based on the CFI embarked requirement, the power supply design, communication design, grounding design and satellite safe mode design are considered in satellite development. All these methods are validated in ground test. The satellite was launched in 2018 and the performance can meet the requirement. The safety design to CFI is successful and all the measures can be a reference to other similar mission.

References 1. Guide to satellite reliability design. QJ 2172A-2005 2. Tang, B.: Satellite safety design. Spacecr. Eng. 1–5 (1994) 3. Jin, L., Wu, D.: Satellite system reliability and safety review. Spacecr. Environ. Eng. 500–504 (2010) 4. Du, Z., Zhen, G., Dong, X.: Safety and reliability design of ignition controller. Comput. Measur. Control (2013) 5. Yu, L., Ren, X.: Design of sapcecraft power system. Chin. J. Power Sources (2013) 6. Yu, L., Ren, X.: Study on DC/DC structure analysis for spacecraft. Electron. Product Reliab. Environ. Test. (2010) 7. Chen, X., Wang, Y.: The analysis and conceiving for the grounding architecture of satellite. Aerosp. Control (2011)

Dynamic Path Planning Algorithm Based on an Optimization Model Jingjing Zhang1(&), Hongning Hu2, and Yuting Wan1 1

Warship Command and Fire Control Teaching and Research Section, College of Ordnance Engineering, Naval University of Engineering, Wuhan 430032, Hubei, China [email protected] 2 Unit 91053, Beijing 100070, China

Abstract. Unmanned surface vessels (USVs) have been extensively employed in the past few decades. Traditional path planning algorithm assumes that obstacles remain stationary, and the USV dynamic constraints is not taken into account. In this paper, a path planning algorithm that considers time dimension and the dynamic performance of a USV is proposed. The algorithm abstracts the kinematics constraints and obstacle distance constraints into nonlinear constraints and abstracts the path planning problem into a nonlinear optimization model. The nonlinear model is approximated to a least squares model to improve the speed of solution. The experimental results show that this algorithm is reasonable and advantageous. Keywords: Path planning Graph optimization


USV
Nonlinear optimization
Least squares


1 Preface The study of unmanned surface vessels (USVs) began during World War II [1]. USVs have to function in more complex sea conditions [2]. This causes application of autonomous navigation which contain generate obstacle maps and obtain path points via path planning. The common path planning for USV are as follows: After constructing map, a graph search is often performed to search the optimal trajectory. A* [3] is a heuristic optimal search algorithm that employs a heuristic function to guide the nodes close to the target point to obtain a preferential expansion opportunity. In [4], A first-responder path planning under uncertain moving obstacles based on algorithm A* is proposed. Zuo proposed a new hierarchical path planning method for navigation in complex environments [5]. This method has all the future path information known after the planner execution and before the vehicle movement [6]. However, the number of nodes is excessive and exponentially increases in the dimension. The Morphin algorithm [7] is a path planning method that is based on limited environmental information. This method is typically employed to understand only local environmental information and global goals. Considering the dynamic performance of the algorithm, algorithm based on the optimization model is gradually applied to motion planning, with the development of optimization theory in recent © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 105–114, 2019. https://doi.org/10.1007/978-981-13-7123-3_13

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years [8, 9]. Besides, potential field method is a prevalent method due to its small computational load; however, it may be trapped in the local optimum [10]. Enhanced discrete particle swarm optimization path planning is proposed for vision-based surface inspection [11]. Adaptive algorithm was proposed to estimate the scalar field over a region of interest [12]. Devaurs developed sampling-based approaches combining RRT⁄ and T-RRT [13]. The algorithm is fast, guarantees the optimum but may not be solved. Two problems need to be solved for planning in a marine task: (1) without considering obstacle’s moving; (2) without considering USV dynamic constraints which cause path fitting effect is poor. They are not both considered meanwhile in planning before. For this reason, this paper proposes a path planning algorithm that incorporates time dimension and considers the kinematics constraints of an USV. The algorithm abstracts the path planning into a nonlinear optimization model and outputs the sequence data of the planned position and speed of USV.

2 Path Planning Process Based on the distance between the USV and an obstacle, a nonlinear optimization model is established with some limits, such as speed and acceleration limitation, distance from obstacle, finally obtain the optimized pose sequence. 2.1

Obstacle Indication

O denote obstacle set. Ok;t denote the k-th obstacle at the discrete time t. O ¼ fOk;t jk ¼ 1; 2; . . .m; t ¼ 1; 2; . . .ng

ð1Þ

Ok;t ¼ ½xk þ vk t sin hk ; yk þ vk t cos hk ; dt T

ð2Þ

Ok;t including the position ½xk þ vk t sin hk ; yk þ vk t cos hk  (obstacle position ðx; yÞ, velocity ðvÞ, angle ðhÞ at 0 time) and the expansion distance dk;t . The accuracy is lower after a period of time, the dynamic obstacle may appear in a larger range. The 0 time and the t time space obstacle is shown in Fig. 1(a),(b).

Fig. 1. (a) 0 time position, (b) t time position

Fig. 2. Position-time latitude obstacles

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As the obstacle may move with time, this paper establishes the xyt space to represent the position of the obstacle as shown in Fig. 2. The horizontal plane is xy(position), and the vertical axis is t(time). Stationary obstacle is the cylinder which is perpendicular to the plane. Dynamic obstacles position shifts at certain velocity, as an inclined cylinder. For obstacles with lower certainty, the inflated circle increases with time. The cylinder’s upper base is larger than the lower base. Setting Optimization Variables. The location at the discrete time t is st ¼ ½xt ; yt  2 R2

ð3Þ

The position sequence composed of dots is S ¼ fst j t ¼ 1; 2; . . .; ng. Setting Optimization Model. The core purpose of optimization is to keep the end of path sequence as close to the end as possible. So, the optimization model is V ðsÞ ¼ min s

n X

½ðxt  xe Þ2 þ ðyt  ye Þ2 

ð4Þ

t¼m

ðxe ; ye Þ is the end. The Euclidean distance between the USV and the end which is calculated from time m to the final time n is calculated to minimize. Define the time interval of adjacent values in USV and obstacle position sequence is Dt. The conditions to be satisfied are (1) The starting point of the USV is s1 ¼ ss , which is known. (2) b 2 C is the yaw angle of the USV in the map coordinate system, x 1  xt . bt ¼ arctan ytt þ þ 1  yt (3) The distance between the USV and the k-th obstacle at the position st minus the expansion distance of the k-th obstacle at time t needs to be greater than 0.: DðOk;t ; st Þ ¼ distðOk;t ; st Þ  dk;t  0

ð5Þ

(4) The speed falls within a certain range. vlt is the forward speed at time t, vlmin ; vlmax are the limit, correspondingly; vrt is the steering speed at time t, vrmin ; vrmax are the limit, correspondingly. vlmin  vlt ðst þ 1 ; st ; DtÞ  vlmax

ð6Þ

vrmin  vrt ðbt þ 1 ; bt ; DtÞ  vrmax

ð7Þ

vlt ¼ distðst þ 1 ; st Þ=Dt

ð8Þ

vrt ¼ ðbt þ 1 bt Þ=Dt

ð9Þ

(5) Similarly, the acceleration falls within a certain range. alt is forward acceleration at time t; almin ; almax are the limit, correspondingly. art is the angular acceleration at time t; armin ; armax are the limit, correspondingly.

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almin  alt ðst1 ; st ; st þ 1 ; DtÞ  almax

ð10Þ

armin  art ðbt1 ; bt ; bt þ 1 ; DtÞ  armax

ð11Þ

alt ¼ ðdistðst1 ; st Þ=Dt  distðst ; st þ 1 Þ=DtÞ=Dt

ð12Þ

art ¼ ððbt bt1 Þ=Dt  ðbt þ 1  bt Þ=DtÞ=Dt

ð13Þ

(6) Due to the existence of obstacles, the model is a typical nonlinear non-convex optimization model (The objective function is nonlinear, and the feasible region is the position coordinate definition domain, not connected region), and its local optimal point is related to the number of obstacles. KKT condition explain that the dimension of the H matrix to be solved in the process of solving the Lagrange function is equivalent to the dimension of the independent variable of the original optimization. Thus, direct solving of this type of model is difficult. To improve efficiency, the original problem is approximate to a least squares optimization problem to solve the inequality constrained problem. The method employs the first derivative to approximate the H matrix. The sparsity of the matrix is utilized to improve the efficiency of the solution. For example, the inequality constraints can be approximate to a least squares penalty function.  2 DðOk;t ; st Þ ¼ distðOk;t ; st Þ  dk;t  0 ! r0 minf0; DðOk;t ; st Þg2

ð14Þ

r0 denotes the penalty factor, which represents the weight relative to other penalty items. The greater r0 , the more focused is the objective to satisfy this items. For convenience, the penalty factor is omitted in the latter formula. The objective function to be optimized is 8 k¼m;t¼n n   P P > 2 2 > > V ð s Þ ¼ ½ðx x Þ þ ðy y Þ  þ jjmin 0; DðOk;t ; st Þ jj22 þ Fvp þ Fap > t e t e > > t¼p k¼0;t¼0 > > > tP ¼n tP ¼n > > 2 > Fvp ¼ jjminf0; vlmin  vlt gjj2 þ jjminf0; vlmax  vlt gjj22 > > > t¼0 t¼0 > < tP ¼n tP ¼n þ jjminf0; vrmin  vrt gjj22 þ jjminf0; vrmax - vrt gjj22 > > t¼0 t¼0 > > t¼n t¼n > P P > 2 > jjminf0; almin  alt gjj2 þ jjminf0; almax  alt gjj22 > > Fap ¼ > t¼0 t¼0 > > > tP ¼n tP ¼n > 2 > > þ jjminf0; armin  art gjj2 þ jjminf0; armax - art gjj22 : t¼0

t¼0

ð15Þ

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The final optimized parameter vector is s ¼ arg min FðsÞ S

2.2

ð16Þ

Model Solution

Gauss Newton (GN) method does not require the Hessian matrix and involves fewer computations. The Levenberg-Marquart (LM) method is a GN method in a certain trust region. In this paper, the Gauss-Newton method and Levenberg-Marquart method are employed to obtain the path sequence point S. The specific process is detailed as follows. From Sect. 2.1, we obtain a series of initial values ^

S ¼ fst jt ¼ 1; 2; . . .; ng

ð17Þ

the sum of the formula is a series of least squares models: VðsÞ ¼

n X

½ðxt - xe Þ2 þ ðyt - ye Þ2  þ

t¼p

¼

n X

Fvp ¼

ðxt - xe Þ2 þ

n X

t¼n X

jjminf0; vrmin  vrt gjj22 þ

t¼n X

jjminf0; vrmax - vrt gjj22

t¼0

jjminf0; almin  alt gjj22 þ

þ

jjminf0; vlmax  vlt gjj22

t¼0

t¼0

t¼0 t¼n X

  jjmin 0; DðOk;t ; st Þ jj22 þ Fvp þ Fap

k¼0;t¼0

jjminf0; vlmin  vlt gjj22 þ

þ Fap ¼

k¼m;t¼n X

ðyt - ye Þ2 þ

t¼p

t¼0 t¼n X

t¼n X

  jjmin 0; DðOk;t ; st Þ jj22 þ Fvp þ Fap

k¼0;t¼0

t¼p t¼n X

k¼m;t¼n X

t¼n X

jjminf0; almax  alt gjj22

t¼0 t¼n X

jjminf0; armin  art gjj22 þ

t¼0

jjminf0; armax - art gjj22

t¼0

ð18Þ For the convenience of subsequent derivation, every least-squares item is recorded as e2t;j ðsÞ; thus, the optimization target is VðsÞ ¼

t;j X

e2t;j ðsÞ

ð19Þ

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where j is the j summation of VðsÞ. The first-order Taylor expansion of et;j ðsÞ at ^

S ¼ fst jt ¼ 1; 2; . . .; ng is performed: ^

^

^

et;j ðs þ DsÞ  et;j ðs Þ þ Jt;j Ds S ¼ fst jt ¼ 1; 2; . . .; ng

ð20Þ

^

where Jt;j is the Jacobian matrix of et;j ðsÞ at, and e2t;j ðs þ DsÞ is ^

^

^

e2t;j ðs þ DsÞ ¼ et;j ðs þ DsÞT et;j ðs þ DsÞ ^

^

 ðet;j ðs Þ þ Jt;j DsÞT ðet;j ðs Þ þ Jt;j DsÞ ^

^

^

T Jt;j Ds ¼ et;j ðs ÞT et;j ðs Þ þ 2 et;j ðs ÞT Jt;j Ds þ DsT Jt;j |fflfflfflfflfflffl{zfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl} |ffl{zffl} ct;j

bt;j

ð21Þ

Hi;j

¼ ct;j þ 2bt;j Ds þ Ds Hi;j Ds T

^

The approximate value of Vðs þ DsÞ is obtained as follow. ^

Vðs þ DsÞ ¼

t¼n;j¼2 X

^

e2t;j ðs þ DsÞ þ

t¼p;j¼1



t¼n;j¼2 X

t¼n;j¼9 X

^

e2t;j ðs þ DsÞ

t¼0;j¼0

ct;j þ 2bt;j Ds þ DsT Hi;j Ds þ

t¼p;j¼1

t¼n;j¼9 X

ct;j þ 2bt;j Ds þ DsT Hi;j Ds

t¼0;j¼0

¼ c þ 2bDs þ Ds HDs T

ð22Þ P P P ^ where c ¼ ct;j , b ¼ bt;j , and H ¼ Ht;j . Thus, the derivative of Vðs þ DsÞ is 0, and we can obtain the following linear equation: HDs ¼ b

ð23Þ ^

To obtain the Ds that corresponds to the minimum Vðs þ DsÞ, the minimum optimal solution of approximate V (s) is obtained: s ¼ s þ Ds ^

ð24Þ

The Gauss-Newton algorithm updates the H matrix and the b matrix in each iteration. The iteration variation Ds is obtained from Eq. (21) and the optimal solution s is updated from Eq. (22). In each iteration, the optimal solution s is considered to be the ^

initial solution S ¼ fst jt ¼ 1; 2; . . .; ng in the last iteration until the given termination condition is satisfied.

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The algorithm flow is shown in Table 1. Table 1. Gauss Newton algorithm flow Gauss Newton algorithm pseudo code ^

1. Given initial value S ¼ fst jt ¼ 1; 2; . . .; ng 2. For the next k-th iteration, we obtain the current H matrix and the b matrix. 3. Solve incremental equations HDs ¼ b 4. When the Ds is sufficiently small to stop the computation, or s ¼ s þ Ds , return to step 2 ^

In the Levenberg-Marquart algorithm which can effectively reduce the problem that the simple Gauss-Newton method may not converge [14], the damping coefficient is added to place Ds within a certain range, which prevents Ds from being too large to cause inaccurate approximate results and affects the convergence effect. Unlike Eq. (20), the Levenberg-Marquart algorithm solves a linear equation with a damping coefficient. ðH þ kIÞDs ¼ b

ð25Þ

where k is a damping coefficient. The larger k, the smaller Ds . This is very useful for controlling step size. The idea of the Levenberg-Marquart algorithm is to dynamically control the damping factor. In each iteration, we compare the difference between the approximate model and the actual function. If the difference is small, we reduce k. Otherwise, we increase k to re-solve until Ds is sufficiently small. The initial value of the damping coefficient k is closely related to the state of the coefficient matrix H [14, 15], which can be expressed as k ¼ s
maxfðHÞii g; i ¼ 0; . . .; n  1

2.3

ð26Þ

Result Optimization and Computation Speed and Acceleration

The previously described optimization models obtain a series of position points S. The point at the end of the sequence sn may be near the end (not arrive) or clustered at the end. If clustered, delete extra point. If not arrive, extend time scale. Then recalculate path. Until sn at the end point. According to the adjacent positions, the velocity and acceleration values at each point can be calculated.

3 Experiment 3.1

Simulation Experiment

The g2o [16] library is used to optimize the operation in experiment. Comparison Test 1. Set Dt ¼ 2, vlmin ¼ 0, vlmax ¼ 4, vrmin ¼ 4, vrmax ¼ 4, almin ¼ 0, almax ¼ 2, armin ¼ 1, armax ¼ 1, and dk;t ¼ 20. The origin coordinates of the

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ship represent the origin of the world coordinate system (0, 0), and the destination coordinates are (0,230). Specify the Y axis direction b ¼ 0. Three obstacles   Dk ¼ ½xk ; yk ; vk ; hk ; dk;t T are inserted in the clockwise direction: D1 ¼ ½50; 230; 3:5; 215; 20T , D2 ¼ ½0; 110; 0; 0; 20T , D3 ¼ ½30; 60; 3; 255; 20T , and dk ¼ 15(k = 1,2,3). This paper’s algorithm is shown in Fig. 3. While A* algorithm is shown in Fig. 4. The blue point is the output locus which start from left and end from right. Circles are obstacles. Arrow indicates the obstacles direction and speed. The A* algorithm assumes that the obstacle is stationary; thus, the resulting path will coincide with the obstacle. The algorithm proposed in this paper considers the location of the obstacle at multiple times and the kinematics constraints of the USV. Thus, a better degree of fitting is achieved. Comparison Test 2. The Timed-elastic-bands algorithm [8] is shown in Fig. 5, while the effect of our algorithm in the same environment is shown in Fig. 7. The Timed-elastic-bands algorithm gets the path points by solving the convex optimization model, but does not consider the time axis. All obstacles are assumed to be static. Obviously, as the obstacle moving, the shortest path don’t need avoid the current position of the obstacle, because while the USV starts from the starting point and reaches the initial position of the obstacle, the obstacle has already moved away. The algorithm proposed in this paper can get shorter path.

Fig. 3. Schematic of experimental results

Fig. 5. Effect of timed-elastic-bands algorithm

Fig. 4. Effect of A* algorithm

Fig. 6. Effect of Morphin algorithm

Comparison Test 3. Path planning based on Morphin [7] is shown in Fig. 6, while the effect of our algorithm in the same environment is shown in Fig. 7. The environment is same as above test. Morphin algorithm can quickly calculate the path, because its model is simple, the computational amount is very small, and it can satisfy the real-time (100 Hz can be achieved in actual navigation). However, the dynamic nature of

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Fig. 7. (a) Effect of this paper’s algorithm (flat) (b, c) this paper’s algorithm (space) (Green line: path. Start: left. End: right. Arrow direction & length: obstacles direction & speed)

obstacles is also not taken into account, and the planned path cannot be dynamic, so the path accept from Morphin algorithm is longer than this paper’s algorithm. On the other side, The path obtained by Morphin algorithm doesn’t connect start point and end point. This may result in a path that appears to be circumnavigated on a larger scale. 3.2

Real Experiment

To test the actual effect of this paper’s algorithm, the Zhoushan Islands in Zhejiang Province was chosen as the experimental site. Average speed of the obstacles and USV was 4 knots and 2 knots. To highlight the experimental results and enhance the experimental safety, the expansion distance of the obstacle ship is 400 m. The expansion distance of the static obstacles is 200 m. As shown in Fig. 8, the blue line moves in a safe direction after bypassing a sunken ship (static obstacle) to prevent the future position of a dynamic ship.

Fig. 8. Real experiment (requires 0.2 s). Blue line: planned path; Hollow triangle: dynamic obstacle

4 Conclusion Compared with the three algorithms, we find that traditional path planning algorithm assumes that all obstacles are stationary. In Marine applications, USVs and dynamic obstacles usually have large inertia, that meanings changing their state is slow and difficult. If only the current position of the obstacle is considered for path planning, the dynamic obstacle may move to the planned path at the next moment, A collision may occur. Or there is a shorter path because of obstacle’s moving away but not find. On the other hand, planning should consider the USV dynamic constraints, such as the speed and acceleration limit. Otherwise, USVs may not fit the trajectory. The experimental results show that this algorithm which considers time dimension and USV dynamic constraints has more advantages.

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References 1. Motwani, A.: A survey of uninhabited surface vehicles. Marine and Industrial Dynamic Analysis, School of Marine Science and Engineering, Plymouth University (2012) 2. Mooney, D.: Metric path planning. robotics notes (2009). http://clipper.ship.edu/djmoon/ robotics/robotics-notes.html 3. Yoo, S.-J., Park, J.-H., Kim, S.-H., Shrestha, A.: Flying path optimization in UAV-assisted IoT sensor networks. ICT Exp. 2(3), 140–144 (2016) 4. Wang, Z., Zlatanova, S., Oosterom, P.V.: Path planning for first responders in the presence of moving obstacles with uncertain boundaries. IEEE Trans. Intell. Transp. 99, 1–11 (2017) 5. Zuo, L., Guo, Q., Xu, X., Fu, H.: A hierarchical path planning approach based on a* and least-squares policy iteration for mobile USVs. Neurocomputing 170(C), 257–266 (2015) 6. Shum, A., Morris, K., Khajepour, A.: Direction-dependent optimal path planning for autonomous vehicles. USV. Autonom. Syst. 70, 202–214 (2015) 7. Simmons, R., Henriksen, L., Chrisman, L.: Obstacle avoidance and safeguarding for a lunar rover. Proc AIAA Forum Adv. Develop. Space Robot. (1996) 8. Rösmann, C., et al.: Integrated online trajectory planning and optimization in distinctive topologies. USVics and Autonomous Systems (2016) 9. Rösmann, C., et al.: Timed-elastic-bands for time optimal point-to-point nonlinear model predictive control. European Control Conference (ECC), pp. 3352–3357 (2015) 10. Wu, B., Wen, Y., Huang, Y., Zhu, M.: Research of Unmanned Surface Vessel (USV) path planning algorithm based on ArcGIS. In: Proceedings of ICTIS 2013, pp. 2125–2134. ASCE (2013) 11. Phung, M.D., Cong, H.Q., Dinh, T.H., Ha, Q.: Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection. Autom. Constr. 81, 25–33 (2017) 12. Cui, R., Li, Y., Yan, W.: Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT*. IEEE TSMC. 46(7), 993–1004 (2016) 13. Devaurs, D., Simon, T., Corts, J.: Optimal path planning in complex cost spaces with sampling-based algorithms. IEEE Trans. Autom. Sci. Eng. 13(2), 415–424 (2016) 14. Madsen, K., Nielsen, H.B., Tingleff, O.: Optimization with Constraints. 2nd Edn. (2004) 15. Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for Non-Linear Least Squares Problems. 2nd Edn. (2004) 16. Kummerle, R., Grisetti, G,, et al.: G2o: a general framework for graph optimization. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3607–3613 (2011)

Research on Autonomous Health Management and Reconstruction Technology of Satellite Liuqing Yang(&), Xiaojuan Li, Chengyu Feng, Zhelei Sun, and Tao Zhang Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China [email protected]

Abstract. In this paper, we analysis the development and proposed design principles of autonomous heath management. Firstly, we introduce the development of autonomous health management between domestic and Overseas. Then, we analyze the shortage of development and its impact. Finally, we propose a new design principle of autonomous health management, which laid the foundations for the future research. Keywords: Health management


Fault diagnosis
Fault prediction

1 Introduction Autonomous health management, as one of the important research directions in the field of satellite autonomous intelligent management technology, can provide comprehensive guarantee for the safe and stable operation of on-orbit satellites. Due to the harsh space environment, each component on the satellite cannot work stably for a long time. Therefore, in order to ensure the working order of various electronic devices and subsystems on the satellite, establishing a complete health management system is essential for on-orbit satellites. In this paper, we address the problem of autonomous health management and reconstruction technology of satellite. Firstly, we study the differences of satellite independent health management technologies at home and abroad. We then propose design principles of satellite autonomous health management technology, which laid the foundations for the future research.

2 Overseas Research Status of Autonomous Health Management To improve the operational safety of on-orbit satellites, international aerospace organizations and research institutions have invested a large amount of human, financial and material resources to conduct research on the safe operation management, fault diagnosis and early warning system development of on-orbit satellites. In the 1980s, foreign countries conducted comprehensive researches on the testing, monitoring and © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 115–123, 2019. https://doi.org/10.1007/978-981-13-7123-3_14

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fault diagnosis of complex systems, which attracted great attention from the aviation and spaceflight field including National Aeronautics and Space Administration (NASA), Jet Propulsion Laboratory (JPL), Ames Research Center, DLR and European Space Agency (ESA), etc. Meanwhile, early applications mainly include various types of satellites, autonomous spacecraft and landers, such as Deep Space series, the Phoenix lander, etc. As a result, Europe and America are far ahead in the field of spacecraft management and fault diagnosis. Generally, the development of foreign spacecraft autonomous health management technology can be divided into five stages: reliability analysis, fault diagnostics and prognostics, integrated diagnostics and system monitoring, integrated system fault prediction and health management. Its origins can date back to the birth of reliability theory, environmental testing and system testing, and quality methods in the 1950s and 1960s. In the 1970s, methods for diagnosing sources and causes of failures emerged, and the development of prediction and health management technologies was beginning to emerge. Integrated diagnostics technique became popular in the field of space and aeronautics in U.S. in 1980s. Inspired by this, NASA proposed the concept of flight vehicle health monitoring on the 1990s, and ultimately proposed the concept of Integrated Systems Health Management (ISHM), which can support the high performance requirements of the new generation of Reusable Launch Vehicles (RLVs), in 2000s. One of typical ISHM method is Complex System Integrated Health Management (CSIHM). CSIHM has entered the engineering practice stage and has been fully applied and verified in projects such as X-33, X34 and X-37 in present. Among these projects, many organization and companies, such as NASA, Lockheed Martin, proposed two specific techniques: Integrated Vehicle Health Management (IVHM) system, and Prognostic and Health Management (PHM) system [1–4]. Meanwhile, with the development of IVHM, many diagnostic reasoning tools have emerged, including Livingstone, BEAM and SHINE, etc. Compared with U.S., Europe has also conducted in-depth research on spacecraft fault diagnosis technology, and developed practical fault diagnosis systems. Many scientists have researched and developed a variety of knowledge-based fault management and fault diagnosis systems for different tasks, such as environmental control and life support system (ECLSSD) for Columbus module. Russia also has advanced technology in fault diagnosis technology. Boris Katorgin et al. developed the health monitoring and life prediction system for cryogenic liquid-propellant rocket engines. Georgy Vasilchenko et al. developed track real-time monitoring system that provides visualization information for astronauts of Buran spacecraft. In conclusion, the development of ISHM is still in its infancy, and a system with full functionality of ISHM does not exist yet.

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3 Domestic Research Status of Autonomous Health Management The self-management of the existing spacecraft in China is mainly based on the data management system that can only take some emergency measures for the failure, which is a safety-oriented design. Common ones include autonomous energy management, autonomous thermal control, and measurement and control component management. The method adopted is to collect the security status information of the entire satellite system by using the data management subsystem or the service subsystem. According to the preset control strategy, it will controls and manages the key equipment that affects the safety of the whole satellite system. The specific method is to design a safety threshold for some key telemetry parameters. The software analyzes the telemetry parameters collected by the software and compares them with the corresponding thresholds. If the value exceeds the threshold, the fault is considered to occur, and the predetermined safety instruction sequence is executed autonomously, such as close the payload. By analyzing the requirements of autonomous health management of satellites such as the moth series satellites, the Mars Explorer Yinghuo-1 and the small satellite Trial 5, we summarizes the principles of satellite safety mode design in China: (1) The system carries out safety mode management independently. (2) Detect and identify faults as quickly as possible, and take necessary measures to isolate the faults in order to prevent the expansion of the scope. (3) To partly carry out reconfiguration operation of the system to ensure the continuous operation of the task. Generally, each model mainly carries out the design of security mode control strategy aiming at energy conservation, including the central computer security mode, attitude security mode, power security mode, BAPTA blocking and switching safety mode, bus communication abnormal security mode and so on. Specific examples are shown as follow: (1) Central computer security mode: satellite due to loss of programmable area instructions when the central computer is reset or cut off. The working state of the load is unknown. If the short-term payload equipment can’t shut down after starting, and the short-term work becomes long-term work, it will inevitably affect the safety of the whole satellite power supply. (2) State Safety Mode: if the satellite’s attitude is abnormal, or the satellite can’t imagine normally, or the satellite can’t face the sun normally, the satellite may lose energy, so it is necessary to shut down the payload equipment to keep the satellite in a stable low-power working state. (3) Power Safety Mode: when the power supply system is extremely difficult, it will affect the normal operation of the satellite, and be incapable of working. So it is necessary to remove unnecessary loads as much as possible, so that the whole satellite is in the lowest power consumption mode which can ensure the normal TT&C of the satellite.

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Although the above measures have been verified on-orbit, it is sure that all fault diagnosis and treatment are based on a small number of measured parameters monitoring and threshold judgment. Moreover, the fault type is also known in advance. Thus, a fault diagnosis method with comprehensiveness and depth are yet to be solved. In addition, in order to improve the on-orbit operation management ability of spacecraft, the on-orbit fault diagnosis method of spacecraft is studied. And the fault diagnosis system is established to improve the ability of diagnosis and location when the system fails and improve the efficiency of operation management.

4 Comparison Between Domestic and Overseas Through the analysis of the above investigation and comparison of the domestic and foreign research, and application of autonomous health management technology, it is not difficult to find that there is a big gap between China and foreign countries. The domestic gap is mainly reflected in the following aspects: (1) The research work started late, the theoretical basis is weak and engineering experience is few. (2) The existing satellite health management methods mainly rely on the ground system, which is not conducive to timely and effective handing of unexpected problems and emergency faults in orbit, and it’s liable to cause the expansion of on-orbit faults, or even the risk of the failure of the entire satellite. (3) Ground is usually monitored by the traditional way of edge measurement and alarm. Fault diagnosis relies on manual work. Satellite long tube has many items of manual monitoring, so it is impossible to carry out on-orbit fault early warning and fast fault diagnosis. Generally speaking, the rescue level after the failure does not really involve autonomous health management and fault prediction. The existing satellite fault diagnosis expert system is basically in the ground test type, and there are few practical applications; even if there are engineering applications, the real-time effect of autonomous fault diagnosis and processing can’t be achieved.

5 Requirement Analysis of Autonomous Health The health management of satellite can be divided into two parts. The first part focuses on the health management of the design, testing and verification process. Its purpose is to ensure that the delivered products meets the technical requirements and works properly. Therefore, we need to find out the problems and hidden dangers in the system through testing and verification. The second part focuses on the health management of on-orbit satellites. By analyzing the telemetric data and observed data of on-orbit satellites, we can not only evaluate the health status of the system, but also discover and even predict fault of the system [5, 6].

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Based on the concept above, autonomous health management and system reconstruction is divided into several levels: Status Detection: Through effective technical methods, various parameters closely related to the working state of each component of the measured object are detected. Then, a multi-parameters model of the operating state of the system is established. On this basis, it is possible to further analyze the development and trend of the working state of each component based on the detected data, and make valuable judgments to diagnose the fault that has occurred, and even predict the impending fault. Fault Diagnosis: By analyzing the characteristic signals reflecting the state of the system and the information recorded in the system log under a specific working environment, we can identify whether the system is in working order, or whether the system structure is degraded or deteriorated. Furthermore, we can isolate and locate the fault based on the relationship between system failure and actual reflection. Failure Prediction: By pre-detecting the state of the system, or detecting the state when the system completes the task, we can get performance indicators that reflect the normal operation of the system. Then, we can provide an early warning to the system based on the existing records. Health Assessment and Generates Decision: Because it is impossible to complete equipment replacement and maintenance for on-orbit satellites, health assessment method for on-orbit satellite systems is completely different from the classical method. Thus, we need to conduct a comprehensive analysis based on the existing data, and use a variety of methods to evaluate the health of the system, and finally give reasons and suggestions for equipment or sub-system with faults or troubles. Moreover, we can estimate the life of the equipment to provide reference for task decision. In conclusion, the autonomous health management and system reconstruction means of satellite is a method apply in the period of design, verification and on-orbit operation. It can accurately detect the fault information and identify the cause, location, type and degree of the fault. Then, we can take corresponding measures to isolate, reconstruct and recover the fault [7]. By this way, the risk of system failure will be reduced. Even the satellite is working properly, and the reliability of the satellite is improved. Furthermore, by analyzing the system logs, sensor information and environmental information, we can predict faults of satellites and accurately assess the health of the satellites so that we can detect system performance degradation and faults in time. Finally, we can provide assistance for management works of on-orbit satellites, and even extend the working life of satellites. In fact, the autonomous health management and system reconstruction means are committed to solve the following problems: (1) Fault detection and diagnosis: It can detect the faults of satellites in real-time, and quickly diagnose and locate the faults. (2) Real-time fault self-management: According to the specific faults that being detected and diagnosed, evaluating its severity and development trend, and give timely countermeasures for the fault. Moreover, completing the system reconstruction autonomously according to the available resources and task

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requirements, and completing the fault recovery or downgrade operation to ensure the normal operation of the satellite. (3) Fault prediction: Provide early warning of hidden faults and deploy the appropriate measures, system restructuring strategy to mitigate and avoid serious the occurrence of faults to ensure reliable operation of the satellite satellites. (4) Health management: By conduct a comprehensive analysis of the state of the onorbit satellites periodically, we can master the health of all aspects of the satellites and then provide suggestions for the operating of on-orbit satellites, and improve the overall availability of satellites. At present, most domestic organizations still use traditional method to management satellites, and each state data needs to be transmitted to the ground station through the downlink. Then staffs confirm whether the satellite operation normally or not based on the data. When an error occurs, a detailed instruction sequence will be layout and transmitted to the satellite. However, satellites need to fix the fault autonomously without the need for ground monitoring and control in the feature. Thus, the ability to directly process various state data by satellite and formulate corresponding instruction sequences for various faults is what we need to do. The satellite electronic system is responsible for the key functions of satellite platforms and payloads such as remote control, telemetry, energy management and thermal management, and is the core of the satellite. The characteristics of high-risk and highcost of satellites let the system must be entirely reliable. Especially for integrated electronic control and other key subsystems, it must satisfy requirements of high reliability. Meanwhile, not only many new technologies are adopted in the system, but also high integration level of software and hardware present a challenge for us. Therefore, the system needs to have the characters of Failure Detection Isolation and system Reconfiguration (FDIR) to ensure the safety and reliability of the satellite system. Currently, most satellites have the requirement of long-life design. Especially, the design life of small satellites has increased to 5–8 years. In addition to hardware improvements, autonomous health management and fault diagnosis techniques are two key factors to ensuring the life of satellite. It ensures that the satellite will not be scrapped in the event of fault, and even can be repaired by software or minimized losses at least. Nowadays, system-level fault diagnosis technologies for satellite remain at levels with telemetry parameter monitoring and threshold judgement. The system completes the operation according to predefined instructions. Therefore, the current fault diagnosis technology has a small diagnostic range and relies on existing fault cases, and can’t do anything for the diagnosis of unknown faults. In order to solve this problem, we must further improve the autonomous health management and fault diagnosis techniques. With the increase of new types of satellites, the existing management mode and fault handling methods cannot be used for on-orbit failure warning and rapid fault diagnosis. The processing time of satellite anomalies is too long, which may cause faults to expand and even cause catastrophic consequences. Therefore, carry out independent health management on-orbit satellites operating conditions, and on-orbit satellites security assessment, fault warning, diagnosis and treatment technology, has a very urgent military aerospace demand.

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Therefore, there are a variety of methods to meet the above needs. On one hand, we can establish an automatic judgment system for in-obit satellite status, quickly and intuitively display the working status of the on-orbit satellite, detect hidden dangers in the early stage of fault indication. On the other hand, we can establish the system which can quickly locate and provide correct fault countermeasures for satellite design and maintenance personnel when the fault occurs. Both of them are important methods to ensure the healthy and reliable operation and extend the life of on-orbit satellites. In addition, the development of military satellites in the future will gradually be transformed from satellites used for research to satellites with equipment. In this case, requirements of rapid satellite development, rapid equipment and reduced development costs, will demand better results on the comprehensive support links for satellite development. From the existing satellite development process we can find that, on the one hand, due to the satellite product design and testing are completely separated into independent links, resulting in complex ground testing, system fault location, and troubleshooting, the entire testing process takes up a lot of satellite development time. On the other hand, stand-alone test, sub-system test and whole-satellite test are independent. However, none of the test support equipment is uniform, and the models cannot be universal. At the same time, the product has no self-test capability and completely relies on external test equipment, resulting in huge cost of test equipment. For the above problems, the development requirements of satellite autonomous health management technologies mainly include the following aspects: (1) The requirements of task continuity and success: In order to meet the application requirements, the satellite needs to ensure the timeliness, accuracy and continuity of the system work firstly. By this way, the satellite will have the abilities of timely discover faults, realize redundancy management, and improve the reliability and success of the mission. Secondly, the satellite could detect concealed faults, and then notify the upper-level system in time to take measures to avoid occurrence of them and ultimately improve the reliability and security of the mission. Thirdly, the satellite can record information when the state of system has changed. Then it should analyze and predict the fault trend, and remind the ground station to take preventive measures. Finally, through system fault detection, isolation and autonomy, the satellite will have the ability to build and enhance the continuity of the task. (2) The requirements of reliability and security: Due to the increased complexity and design life of the satellite, the environmental effects of the space will cause degradation of the performance parameters of the satellite-related components, resulting in abnormal and faults in the satellite. If anomalies and faults for on-orbit satellite cannot be detected in time, or corresponding control measures are taken, it will bring serious consequences to safety of the satellite, and even invalidate the entire satellite. (3) Timeliness requirements for troubleshooting: With the increase of new types of satellites, the existing management mode and fault handling methods cannot be used for on-orbit failure warning and rapid fault diagnosis. The processing time of satellite anomalies is too long, which may cause faults to expand and even cause catastrophic consequences.

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(4) Further improvement of fault prediction capability: Through fault prediction and health management technologies, the focus of satellite operation management will shift forward, and give a warning of potential damage timely. Through the analysis of satellite itself, or through the control of the ground station, hidden troubles for the satellite can be avoided timely. Further, it can even avoid unrecoverable damage that will affect system reliability. (5) The requirements of usability evaluation for the system: Satellite users need to have a comprehensive of the health and life of satellites when establish project or performance evaluation. By this way, users can acquire an effective assessment of the availability of satellite systems. This is also one of the research priorities of failure prediction and health management. (6) Improve test efficiency and reduce test costs: By analysis the test design technology of the system and improving the test design level of the system, the real-time detection method during system operation can quickly find faults, locate, improve test efficiency, reduce system test time, and reduce external test equipment and manpower requirements. Reducing costs will help the future development of military satellites gradually transform from satellites of research to satellites with equipment.

6 Conclusion Autonomous health management capability is one of the capabilities that satellites need to focus on development and improvement in the future. It is an important guarantee for the safe and stable operation of on-orbit satellites. This paper investigates the current research of autonomous health management technologies in the aerospace field at home and abroad, and analyzes the gaps at home and abroad. Aiming at the needs of the future satellite intelligent autonomous, we analysis the task requirements of health management in the two stages of satellite development and satellite operation in detail. By this way, we finally provide a guideline for the in-depth study of satellite intelligent self-management.

References 1. Chang, Q., Yuan, S.: Overview of integrated vehicle health management (IVHM) technology and development. Syst. Eng. Electron. 31(11), 2652–2657 (2009) 2. Datta, K., Jize, N., et al.: An IVHM systems analysis & optimization process. In: Aerospace Conference, pp. 3706–3716 (2004) 3. Shi, W., Sun, Y., Wang, Z., et al.: A study of PHM system and its fault forecasting model. Fire Control Command Control 34(1), 29–35 (2009) 4. Zeng, S., Michael, G., Wu, J., et al.: Status and perspectives of prognostics and health management technologies. Acta Aeronautica ET Astronautica Sinica 26(5), 626–632 (2005)

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5. Gordon, B., Patterson, A.A.: Review of system health state determination methods. In: First Space Exploration Conference: Continuing the Voyage of Discovery, pp. 1–13 (2005) 6. Ke, L., Kaiwen, D., Rui, D., et al.: Autonomous on-orbit health management architecture and key technologies for manned spacecrafts. Manned Spaceflight 20(2), 116–121 (2014) 7. Felke, T., Gadden, G., Miller, D., et al.: Architectures for integrated vehicle health management. In: IEEE Aerospace Conference 2010, Atlanta, Georgia, pp. 20–22 (2010)

The Design and Implementation of Digital Satellite Simulator Dong Han1(&), Wen-gao Lu1, Lei Song2, Shao-po Zhang1, and Ru Gao1 1

2

DFH Satellite Co. Ltd., Beijing 100094, China [email protected] Shandong Institute of Space Electronic Technology, Yantai 264003, China

Abstract. Digital Satellite Simulator (DSS) is a system which can realize high precision simulation of each subsystem on board, verify and drill the flight process in orbit. It can generate the original Telemetry (TM) frames according the frame format of the real satellite, execute and respond the Telecommand (TC) correctly, provide the real TM, TC, and external data interface for the TT&C ground test system. DSS can be used to verify the correctness of the TT&C ground test system hardware function, verify the correctness of TC sequence which is used for mission planning in orbit, verify the correctness of attitude in orbit and orbital maneuver, verify the authenticity and reliability of the software operating environment on board, provide a real training and operating platform for the satellite designers and operators, this platform can help users to deal with the FDIR in orbit much more better in the exercise. Function, structure, and realization method of DSS are introduced in this document from the angle of software engineering. Generality and mass production requirement has been considered in this design, the product development mode has been formed. This DSS has been applied in many projects such as VRSS-1, VRSS-2, PRSS-1 and so on. Some suggestions are also proposed for the DSS follow-up technological development. Keywords: Digital satellite simulator
Virtual testing Simulation and verification
Onboard software




1 Foreword With the development of the spacecraft, more and more spacecraft development company have begun to pay attention to the application and technology development of satellite simulator. The subsystem lever simulation platform could be built by the simulation of units. The system lever simulation could be built by the simulation of subsystems. Unit equipments could be connected to subsystem lever simulation platform for subsystem lever design and function testing, subsystem equipments could be connected to system lever platform for system lever design and function testing in the future. In this way, product design defects can be exposed as early as possible in the product development stage, it can also reduce the development cost of product re-upgrade caused by functional abnormality after product delivery [1] and provide a © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 124–130, 2019. https://doi.org/10.1007/978-981-13-7123-3_15

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more realistic environment for the product joint testing. Compared with the conditions for the expensive spacecraft equipment joint testing, the satellite simulator will greatly reduce the cost of product development, improve the efficiency and shorten the development cycle [2]. The satellite simulator can also realize the simulation and verification of in-orbit flight mission, failure mode and effects analysis, as well as the functional verification of ground control system, providing a more authentic training platform for in-orbit operation [3].

2 System Design 2.1

System Architecture

The satellite simulator is made up of all digital simulation system and simulated environment platform. All digital simulation system includes TT&C (Tracking Telemetry & Command Subsystem), OBDH (On-Board Data Handling Subsystem), PSS (Power Supply Subsystem), AOCS (Attitude and Orbit Control Subsystem), DTS (Data Transmission Subsystem), and PLD (Payload Subsystem). TT&C subsystem is composed of Transponder and GPS lower computer. OBDH subsystem is composed of TU (Telecommand Unit) lower computer, OBC (On-Board computer) upper computer, TCU (Thermal Control Unit) lower computer, PTU (Payload Thermal Unit) lower computer. PSS subsystem is composed of PCU (Power Control Unit) lower computer, PDU (Power Distribution Unit) lower computer. AOCS subsystem is composed of AOCC (Attitude and Orbit Control computer) lower computer, ALTU (Actuator Local Terminal Unit) lower computer. DTS is composed of DTS lower computer. PLD is composed of Camera lower computer. Simulated environment platform include monitoring console, 3D tool, track maintenance tool and simulate model database. Monitoring console as the core, is responsible for configuring simulation task, and loading corresponding simulation software and onboard flight procedures, meanwhile it is also responsible for setting simulation scenarios, simulation process control and simulation data display. In addition, monitoring console is the bridge of data interaction between the DSS and SCC, achieve the input of remote control commands and telemetry data, ensure correct satellite control, transmit the test data and the test results to the SCC through the communication network. 3D tool can display the working status of the satellite in orbit dynamically. Capable of showing the relative position and trajectory of the satellite relative to the earth and sun from the perspective of satellite, earth and sun. Track maintenance tool provide the function of orbit prediction, simulate the information of position and velocity of satellite flight, calculate the information of celestial position, ground station tracking and measurement which related to the satellite operation scene. Simulate model database is composed of simulation information database and realtime database. Simulation information database is used to store simulation scenarios, simulation instruction, TM data, simulation process data and model information. Realtime database is used to store TM frame, TM package, TM value [4–6].

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Digital satellite simulator architecture is shown in Fig. 1

All Digital Simulation System

TT&C GPS Lower computer

OBDH TU Lower computer

Transponder

AOCS

PSS PCU Lower computer

TCU Lower computer

OBC Upper computer

PDU Lower computer

AOCC Lower computer

ALTU Lower computer

PTU Lower computer

DTS

PLD

DTS Lower computer

Camera Lower computer

CAN Bus ( Protocol)

LAN ( Protocol ) Simulated Environment Platform

GCS SCC

Monitoring Console

3D Tool

Track Maintenance Tool

Simulate Model Database

Fig. 1. Digital satellite simulator system architecture

2.2

Hardware Composition

The satellite simulator hardware consist of a high-performance workstation. All digital simulation system, monitoring console, track maintenance tool, 3D tool, simulate model database perform on the workstation centrally. The subsystem lower computers in all digital simulation system use network link communication according to satellite CAN bus protocol [7]. Other software uses network link communication according to satellite simulator ICD (Interface Control Document) protocol. The communication protocol between the satellite simulator and GCS (Ground Control System) follows real satellite and GCS communication protocol [8, 9].

3 Functional Design and Implementation 3.1

All Digital Simulation System

(1) OBDH subsystem main Functions in satellite simulation are as follows: • To receive commands and data block from ground stations and distribute them to the satellite subsystems. • To collect the satellite telemetry data and send them to ground stations. • To manage the on-board time and broadcast it. • To provide the capability of receive and distribute the on-board commands and time-tagged commands.

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To provide the capability of thermal control. To store and restore important data between OBDH and AOCS. To manage the communication of the other sub-system via CAN bus. To monitor the important parameter of satellite and manage the safe mode of satellite.

(2) TT&C subsystem main Functions in satellite simulation are as follows: • To transmit the downlink telemetry frame and other data to the ground station. • To receive the telecommand from the ground station and transmit to OBDH subsystem. • To compute position and orbit determination messages based on track maintenance tool, and then broadcast them on LAN via CAN protocol. (3) PSS subsystem main Functions in satellite simulation are as follows: • To simulate solar energy convert into electrical energy during sunlight period. • To simulate energy storage in the battery during sunlight period. • To simulate electrical energy provide to the loads continuously during all mission lifetime, using the battery when the SA output power is not enough (4) AOCS subsystem main Functions in satellite simulation are as follows: • To eliminate initial deviation after the satellite’s separation from the launcher, and eliminate attitude disturbance from solar array deployment; eliminate attitude disturbance from data transmission antenna deployment. • To perform Earth acquisition, and establish coarse Earth pointing attitude. • To drive solar array for sun pointing. • To satisfy performance requirements of attitude control in normal mode. • To perform crab angle control automatically by the satellite. • To perform global attitude acquisition and emergency sun pointing. • To perform initial orbit acquisition and long-term orbit maintenance, and satisfy requirements of nominal orbit maintenance. • To perform roll-axis maneuver, pitch-axis maneuver and yaw-axis maneuver • To calculate the angle of rotation for data transmission antenna. • To calculate the solar elevation angle of the ground imaging point. (5) DTS subsystem main Functions in satellite simulation are as follows: • To simulate the DTS four working mode, include Real time mode, Quasi-realtime, Record mode, and Replay mode. (6) PLD subsystem main Functions in satellite simulation are as follows: • To simulate the PMC (Panchromatic & Multi-spectrum Camera) imaging mode. • To simulate the thermal control of the camera.

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Monitoring Console

Monitoring Console consist of three parts: network communication layer, business logic layer and UI presentation layer. Network communication layer is responsible for the interaction with simulation information database, real-time database, and other related software; business logic layer is responsible for data read, retrieval, caching, and controlling with other related software, data uplink and downlink; UI presentation layer, as a visible layer for terminal users, is responsible for receiving of users’ operation, pushing data needed by users, setting friendly user experience as design target. Monitoring Console is designed in modularization, functionalization and generalization, including login management, scenario management, multi-satellite simulation management, telemetry data management, telecommand management, simulation fault management, command script management, key parameters analysis management, satellite control center management and execution log management modules. 3.3

Orbit Maintenance Tool

Functions of the Orbit Maintenance Tool software include: (1) Orbit Forecasting Based on the primary orbital elements, the software can calculate and get the realtime simulation data including UTC time, the position and velocity of the satellite presented in J2000, the position and velocity in WGS84, the real-time orbital elements, the longitude and latitude of the sub satellite point, and the orbit altitude. (2) Ground Tracking Calculation The Orbit Maintenance Tool software should provide interface for the ground tracking calculation which produces the command signal for the designated ground station about the angle of altitude, azimuth angle, the distance and the range conversion rate based on the input data of time and orbit position. The input information of the orbit position includes the position vectors in both J2000 and WGS84. Input information for the ground station includes the longitude, latitude and the altitude of the ground station and constraints on the antenna elevation angle. (3) Solar Azimuth Calculation The Orbit Maintenance Tool software can calculate the position and angular vectors and the distance between the sun and the earth presented in J2000 and spacecraft body fixed coordinate based on the UTC time, and at the same time justify whether the spacecraft is in the shadow of the earth based on the orbit data and solar ephemeris which will be transmitted to the power supply and distribution subsystem and the attitude-orbit control subsystem for the calculation of solar battery output power and the sun sensors.

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(4) Orbit Maneuver Planning The Orbit Maintenance Tool software can plan and calculate the orbit maneuver parameters including the expected orbit transfer velocity increment, the expected orbit transfer altitude, the orbit maneuver point and the fuel consumption based on the input UTC time and orbit data. Orbital maintenance tool contains 5 computing modules, including Orbit Propagator Module, Orbit Control Module, Ground Station Visibility Forecast Module, Human-Computer Interaction Module, and Data Management Module. 3.4

3D Monitoring

3D monitoring displays various working modes and the flight attitude of the satellite in 3D mode. Functions of the 3D monitoring software include: (1) To support online and offline mode. (2) To support satellite perspective, earth perspective, sun perspective. To show the relative position of the sun, earth and satellite. (3) To display orbit track and the subastral points. (4) To display working modes of the satellite. (5) To display orbit maneuver of the satellite. (6) To display satellite and ground station transmission.

4 The Key Technology The key technologies for satellite simulate include: (1) The onboard software runs and verifies under the ground virtual environment. AOCS FM software runs in VTest (virtual test) environment, including virtual CPU, virtual memory, target machine and peripheral environment. VTest can meet the requirements of embedded development and debugging, without physical equipment and realize the FM software development and verification in virtual environment. (2) The onboard software is recompiled on the ground development environment. OBC CPU (80C86) software is recompiled and run on a workstation. (3) To realize AOCS high-precision orbit control and high precision attitude dynamics simulation. (4) To realize thermal high-precision simulation. To design the thermal mode of the satellite via Thermal Desktop. Thermal model receives track information in real time to calculate the current temperature distribution [10]. (5) To realize saving and loading of simulation scenario at any time. (6) Design of satellite simulation model relational database (Fig. 2).

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Fig. 2. Digital satellite simulator

5 Conclusion Digital Satellite Simulator can realize high precision simulation of each subsystem, help users to conduct in-orbit operation and training. System testing and equipment acceptance test can be performed in the virtual simulation environment. This helps designers expose the design flaws on the onboard software as early as possible.

References 1. Zheng, A., Zhou, J., Ma, Y.: Development of Chang’E-1 operational simulator. J. Beijing Univ. Aeronaut. Astronaut. 39(1), 57–61 (2013) 2. Aghili, F., Namvar, M., Vukovich, G.: Satellite simulator with a hydraulic manipulator. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, pp. 3886–3892 (2006) 3. Yuan, X.: Key research programs of LEO communication Microsat Constellation. In: CAS Shanghai Institute of Microsystem and Information Technology (2002) 4. Chen, Q., Yu, S., Guan, H.: Design and implementation of satellite failure simulation system based on satellite simulator. J. Acad. Equip. Command Technol. 22(5), 63–67 (2011) 5. Yang, H., Zhang, F., Liu, J.: Design of digital simulator platform for satellite’s in orbit faults. J. Spacecr. Eng. 21(4), 21–25 (2012) 6. Wu, Z., Xiong, X., Song, Q.: Object-oriented Design of Satellite Simulators. J. Spacecr. TT&C Technol. 27(2), 13–16 (2008) 7. Li, X., Xu, X., Chen, J.: Design of generic infrastructure for telecommunication satellite simulators. J. Spacecr. TT&C Technol. 30(3), 1–5 (2011) 8. Cui, W., Yang, H., Yang, J.: Design and implementation of simulation test platform for satellite subsystems. Comput. Measur. Control 23(10), 3264–3266 (2015) 9. Zhao, G., Zheng, B., Liang, J.: Design of universal simulator for small satellite. J. Sci. Instrum. 30(6), 357–360 (2009) 10. He, Z., Xu, X., Zhao, Q.: Design of thermal control subsystem for flying satellite simulator. Journal of Spacecraft Engineering 20(1), 82–87 (2011)

Research on Target Recognition Technology of Satellite Remote Sensing Image Based on Neural Network Qiang Zhang1(&), Xiaonan Wang1, Hexiang Tian1, Yanan You2, and Peng Kong1 1

2

Beijing Institute of Spacecraft System Engineering, Beijing 100094, China [email protected] Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract. With the rapid development of satellite remote sensing technology, the resolution of satellite image is getting higher and higher, and more and more satellite data can be obtained on the ground. Traditional artificial image translation methods can not deal with massive data, and can not efficiently, quickly and accurately obtain the information of interested objects. In view of this problem, considering that the depth convolution neural network technology has achieved good results in the natural image target recognition, this paper uses the typical depth neural frame Faster R-CNN as the basic frame, and uses the image augmentation method to enhance the accuracy and generalization ability of the neural network model, and multi-resolution optical remote sensing image data to achieve automatic target recognition processing. The results show that the proposed method can translate images automatically and quickly, the recognition rate of ship and other targets is better than 75%. Keywords: Neural network


Remote sensing
Satellite
Target recognition

1 Introduction Remote Sensing technology is an important means for human beings to carry out scientific investigation of their living environment. In 1972, the United States launched its first earth resource satellite Landsat-1, since then, remote sensing technology has become a new and high technology developed by major powers in the world. Remote sensing satellites with multi-source sensors can obtain the spatial and physical information of various targets in the earth’s sphere, such as vegetation, lakes, oceans and atmosphere, and present them in the form of two-dimensional image. Geological information contained in remote sensing images can be used in land resources survey, urban development space monitoring, disaster assessment and early warning and other fields, which are closely related to the development of national economy. With the rapid development of remote sensing technology, the number of remote sensing satellites is increasing, the resolution of remote sensing satellite image is getting higher and higher, and the image coverage is getting wider and wider. Remote sensing images show the development trend of “big data”, single satellite daily data © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 131–138, 2019. https://doi.org/10.1007/978-981-13-7123-3_16

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increased at TB level, national data center archive data reaches PB level. Relying entirely on manual translation of images requires a lot of manpower and material resources, and the timeliness is not good, this can not meet the requirements of continuous upgrading applications. This brings urgent demand for automatic processing and intelligent application of massive remote sensing images. In recent years of research, deep learning method has achieved remarkable results in target recognition of natural images. Especially based on Deep Convolution Neural Network, the target feature can be automatically extracted and analyzed, and the target can be accurately identified. The remote sensing image translation framework constructed by convolution neural network can obtain the type information and location information of the target. Therefore, this depth learning translation method has a wide range of application prospects in the task of satellite remote sensing image translation [1, 2]. This kind of translation method can extract the features of the target independently and form an “end-to-end” remote sensing image processing system, which improves the intelligence of remote sensing image processing. It can not only save a lot of manpower in the complicated remote sensing image interpretation work, but also effectively improve the processing speed of optical remote sensing image, while ensuring the timeliness and accuracy of information acquisition. In this paper, a method of remote sensing image target recognition based on depth neural network is proposed. Typical frame Faster R-CNN is used as the basic frame, and the image augmentation is used to improve the accuracy and generalization ability of the neural network model. The proposed method is verified by ship recognition and playground recognition cases. The results show that the proposed method can be used in remote sensing image recognition. Under high speed and automatic processing, the accuracy of target recognition is better than 75%.

2 Convolution Neural Network Technology In recent years, with the rapid development of in-depth learning technology, it has been paid more and more attention on speech signal processing, image processing, pattern recognition and other fields. Its research value and application value have been fully affirmed by academia and industry. In deep learning, Convolutional Neural Network is a feed-forward neural network, its artificial neurons can respond to some of the sensory units within the converge, especially suitable for large-scale image processing. CNN includes input layer, convolutional layer, pooling layer, activation layer and other important network units. 2.1

Network Unit

Data Entry Layer The main task of this layer is to preprocess the original image data, which includes: • Mean Value: zero mean for every dimension of input data that is, drawing the sample center to the origin of the coordinate system.

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• Normalization: the variance is normalized to the same range, reducing the interference caused by differences in the range of values of each dimension. • PCA/Albefaction: using PCA to achieve dimensionality reduction; albefaction is normalization of amplitude on each characteristic axis of data. The effect diagram of mean value and normalization are shown in Fig. 1.

Fig. 1. The effect diagram of mean value and normalization

Convolution Layer Digital image is a two-dimensional discrete signal. Convolution operation for digital image is to use convolution kernel (convolution template) to slide on the image, multiply the pixel gray value to the image point with the corresponding convolution kernel value, then add all the multiplied values as the gray value to the pixel in the image corresponding to the middle pixel of the convolution kernel, and finally slide all the image process. Image convolution is shown in Fig. 2.

Fig. 2. Image convolution process

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Pooling Layer The Pooling layer follows the convolution layer, which is used to compress data and parameters and reduce over fitting. The pooling layer has the following functions: (1) Feature invariant; (2) Feature dimensionality reductional; (3) Avoiding over fitting. As pooling processing has the function of feature aggregation, the introduction of pooling layer can reduce the risk of network falling into over-fitting and ensure that neural network has strong generalization ability. The working instruction of pooling layer is shown in Fig. 3.

Fig. 3. Working sketch map of pooling layer

Activation Layer The role of the activation layer is to incorporate non-linear factors, because the linear model is not good at expressing practical problems. In convolution neural network, when convolution filtering is performed on an image, each pixel is assigned a weight, this assignment is linear. However, in practical applications, the target characteristics are not necessarily linearly separable. Therefore, the nonlinear model can be simulated better by introducing an activation layer into convolution neural network and utilizing the nonlinear factors produced by the activation layer. Commonly used activation functions should be nonlinear and continuous differentiable. In addition, they should have the following properties: • Larger unsaturated zone: when there is a saturated interval, if the system optimization enters the interval and the gradient approximates to zero, the learning of the network will stop. • Monotonicity: when the activation function is monotonic, the error function of the single-layer neural network is convex, which is beneficial to the optimization process. • Approximately linear at the origin: when the weight is initialized to a random number near zero, the gradient of the activation function is large in the approximate linear interval, which can speed up the iteration of the network. According to the above characteristics, the commonly used activation functions are: Sigmoid function, Tanh function, ReLU function and etc.

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Loss Function

The selection of loss functions also plays an important role in convolutional neural network. The representative loss functions are: squared error loss, cross entropy loss and etc. Squared error loss function is expressed as: C¼

 1 X yðxÞ  aL ð xÞ2 2n x

ð1Þ

Among them, C means cost function, n means total sample size, x means sample, y means actual value, a means input value. Cross entropy loss is expressed as: C¼

1X ½y ln a þ ð1  yÞ lnð1  aÞ n n

ð2Þ

The higher the value of the cost function, the faster the network weight is adjusted, and the training efficiency is much higher than that of the square error function. 2.3

Classical CNN

In the history of convolution neural network, there are many typical structures for classification. Among them, the most classical network structures are LeNet, AlexNet, VGGNet, GoogLeNet and ResNet [3, 4]. With the development of application requirement, CNN network is becoming deeper and wider. With the increase of network depth, DCNN network itself can better approximate the objective function with a greater degree of nonlinearity and get a better feature representation. In addition, according to the different tasks of image processing, DCNN presents the development trend of specialization. Therefore, some typical DCNN methods for specific tasks are produced, the more famous ones are: Faster R-CNN for target recognition, FCN for semantic segmentation, Inception mechanism for enhancing feature extraction capability.

3 Satellite Remote Sensing Image Target Recognition Remote sensing image automatic target recognition technology has great research value and application value [5, 6]. For example, in military applications, automatic identification of strategic objectives such as aircraft, ships and oil depots is one of the key link to improve military reconnaissance capability. On the civilian side, automatic identification and statistics of specific targets such as stadiums, supermarkets and bowling alleys will greatly improve the level of urban regulation.

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Recognition Method Based on Neural Network

The neural network technology is introduced into the task of remote sensing image target recognition. The typical frame Faster R-CNN is used as the basic frame. The remote sensing image pre-classified by the trained Scene Classification Network to obtain the clear image and foggy image. The obtained image is augmented and input to two parallel images respectively. Target recognition network is used to train and adjust the parameters of dual to obtain the target recognition information. 3.2

Recognition Process

A complete remote sensing image can be divided into typical scenes. Scene classification is helpful to quickly obtain the main semantic information contained in remote sensing images. Recognizing targets in the scene with stable spatial characteristics can ensure the accuracy of target recognition. DCNN can also be used to achieve target recognition after scene classification. In order to improve the accuracy of target recognition results and the generalization ability of the model, it is necessary to argument the satellite remote sensing image data set. In fact, the rotation and inversion of objects in satellite remote sensing images are in line with the reality, so the methods of horizontal inversion, vertical inversion and optical remote sensing image rotation 90°, 180°, 270° are adopted to argument the data. Meanwhile, the corresponding target frame coordinates should also be transformed accordingly. The width of the optical remote sensing image is W, and the height is H. Formulas (3) and (4) are horizontal transformation and vertical reversal coordinate transformation respectively. Set the rotation angle to a, the coordinate transformation of rotation is shown in formula (5). ðx; yÞ ! ðW  x; yÞ

ð3Þ

ðx; yÞ ! ðx; H  yÞ

ð4Þ

ðx; yÞ ! ðx  cos ay  sin a; x  sin a þ y  cos aÞ

ð5Þ

In addition, data argumentation can also be achieved by using salt-and-pepper noise and Gaussian noise to pollute remote sensing images. Artificial addition of salt-andpepper noise and Gaussian noise to optical remote sensing image can indicate the accuracy of the model for remote sensing image recognition after noise pollution. After the remote sensing image is blurred, a part of the details are lost, and the neural network is easier to extract the important information from the remote sensing image, ignoring the unimportant information. Using wavelet transform to get image pyramids to argument remote sensing images, can improve the accuracy and generalization of the model.

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4 Recognition Result Two cases of ship target recognition and motion field recognition are used to verify the performance of the proposed method based on neural network for remote sensing image target recognition. 4.1

Ship Target Recognition

Using 12650 optical remote sensing images as data set, the size is 10241024. Among them, three types of vessels have been marked: 8875 cargo ships(T1), 2398 liners (T2), 589 yachts (T3). In the satellite remote sensing image, the scale of vessel target changes greatly, which increases the difficulty of recognition. The data set is randomly divided into 4/9 training sets and the rest is divided into validation sets and test sets. The accuracy of recognition is shown in Table 1, the recognition results are shown in Fig. 4. Table 1. Statistical table of recognition accuracy Accuracy (T1) Accuracy (T2) Accuracy (T3) Average accuracy 86.00% 94.12% 45.96% 75.69%

Fig. 4. Ship Target recognition results

4.2

Playing Field Target Recognition

The stadium data set consists of 150 remote sensing images with the size of 1075923. After training, the recognition accuracy of the stadium is 0.88. Some samples of recognition result are shown as Fig. 5.

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Fig. 5. Result of playing field target recognition

5 Conclusion Aiming at the time-consuming and laborious problem of extracting interested objects from mass remote sensing images manually, an automatic remote sensing image target recognition method based on depth neural network is proposed. This method uses typical frame Faster R-CNN as the basic frame, and uses image argumentation to improve the accuracy and generalization ability of neural network model. The proposed method is validated by two cases of ship target recognition and moving field target recognition. The results show that the neural network technology can achieve a recognition rate better than 75%, while automating the processing. The proposed method can be used for automatic target recognition processing of massive satellite remote sensing image data.

References 1. Yu, D., Deng, L.: Deep learning and its applications to signal and information processing. Signal Process. Mag. 28(1), 145–154 (2011) 2. Lu, C., Tang, X.: Surpassing human-level face verification performance on LFW with GaussianFace. CoRR: abs/1404.3840 (2014) 3. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. Stateline: NIPS 2012, 1097–1105 (2012) 4. Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference oil Computer Vision and Pattern Recognition, pp. 580–587. IEEE Press, Piscataway (2014) 5. Chen, T.Y., Chen, J.Y., Zhao, H.P.: Ecognition-based ship detection on optical remote sensing images. Radio Eng. 43(11), 11–13 (2013). (in Chinese) 6. Wang, Y.Q., Ma, L., Tian, Y.: State-of-the–art of ship detection and recognition in optical remotely sensed imagery. Acta Automatica Sinica 37(9), 1029–1039 (2011). (in Chinese)

Research and Implementation of Automatic Test Technology for Power Modules in Aerospace Haibo Li(&), Qing Chen, Yan Li, and Chenlei Cao Beijing Satellite Manufacturing Factory, Beijing 100096, China [email protected]

Abstract. The increasing variety and quantity of power modules for aerospace causes difficulty and complication for their testing. This paper studies the testing technology, develop the testing software and design with hardware universal interface to realize an automatic testing technology with data collection and interpretation, waveform automatic storage, command automatic switch, output channel automatic switch. The technology not only increases the testing efficacy by more than 100%, but also the risk factors caused by manual operation during the test are eliminated and the reliability and safety of the test system are improved. The technology is suitable for testing all current power modules. Keywords: Automation


Software
Hardware
Interface
Test technology

1 Introduction In the field of aerospace, the aerospace power module is responsible for providing stable voltage and current for subsystems of aerospace equipment such as computer systems, navigation systems, and communication systems. As a basic component, it is used in a variety of aerospace projects and is directly connected to electrical equipment. Its quality and reliability are one of the basic conditions for ensuring the completion of space missions. According to statistics, 60 to 100 power modules are generally required on a common satellite. In recent years, with the in-depth development of manned spaceflight engineering and deep space exploration technology, the wide application of distributed power supply and the increase of payload, the types of power required by aircraft are more and more complicated, and the requirements for performance indicators of various aspects of power supply are also stricter. Therefore, the test of the electrical performance indicators of the power module is more arduous. Through the research of power module test technology, a fully automated one-button test technology is provided to replace the traditional manual operation mode, which can eliminate the wiring and measurement errors caused by human factors, improve test efficiency and save labor cost.

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2 System Design System plan diagram as shown below, the system is mainly composed of three parts: the instrument required for testing, the hardware interface and the test program. The test system uses the GPIB interface card to closely link several program-controlled test equipment with the industrial computer and realizes the operation and control of the instrument through the industrial computer. During the test, the tester connects the equipment and cables, operates the test program, starts the test project, the system will automatically execute the test, and automatically save the test results (see Fig. 1). Secondary power supply for test

Hardware equipments

Software test itmes

DC regulated power supply Device initialization Electronic load Equipment calibration

34922 Board 34921 Board

Power module

Function test Telemetry impedance Waveform storage

34938 Board Command power

Oscilloscope 3032A

Pull off and on/off test Input ripple surge

Oscilloscope 6014A

Data collector 34980A

Efficiency and stability test

Data interpretation

GPIB to USB

Output dynamic characters Output overcurrent & Overcurrent recovery

Industrial computer

Fig. 1. System design

Instruments used in the test system and instructions for use: DC power supply Agilent N5770A: The analog star bus provides an input bus for the module power supply. It has overcurrent and overvoltage protection functions. The voltage and power should meet the test requirements. The output ripple voltage peakto-peak (when purely resistive load) less than 50 mV; DC regulated power supply Agilent E3631A: provides command pulses for relays; Electronic load Agilent N3300A: used to simulate external load, should meet the output voltage and output current requirements of the module power supply; Digital Collector Agilent 34980A: Data acquisition and programming of the following boards: 34921 board: acquisition module power telemetry voltage and telemetry impedance; 34922 board: automatic switching function of output channel (multi-output module power supply measurement output when starting transient process, program control to replace each output ripple clip); 34938 board: command automatic switching function (module power supply product with relay switch machine command, program control command switch machine); Digital Storage Oscilloscope Agilent DSO-X 3032A: Input reflection ripple and surge waveforms used to acquire module power supplies;

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Digital storage oscilloscope Agilent DSO6014A: used to collect the output dynamic characteristics of the module power supply, over-current recovery waveform; GPIB interface card: used to communicate with each other between hardware devices; GPIB to USB bus interface card: used for communication connection between hardware and industrial computer, software sends control instructions of the instrument and reads device data through GPIB interface; Industrial computer

3 Industrial Computer 3.1

System Hardware Interface Design

The part connecting the power module and the test device is the hardware interface part, which is one of the important components of the test system. In order to improve the reliability and safety of the test system and achieve the universality of the system, the system designs the output interface. The telemetry and telemetry impedance test interface, the output channel automatic switching function interface and the command automatic switching function interface are all four parts. Currently, the power module can be used to produce this interface. 3.2

Output Interface

According to the characteristics of the Agilent N3300A electronic load 6-channel output, 6 universal cables are designed, and the plug is connected to the plug connected to the output of the power module. This saves the preparatory work during the debugging and the number of repeated disconnection and wiring during the debugging process. It saves a lot of time, improves efficiency, and effectively prevents the problem of polarity reversal during the wiring process of the module power supply output, and improves the reliability and safety of the test system. 3.3

Telemetry and Telemetry Impedance Test Interface

The telemetry and telemetry impedance test are used to test the voltage and interface impedance of the analog telemetry. It is realized by the 34921 board of the 34980 data collector. The collector can realize the analog acquisition and collect the telemetry voltage U1. And in the test of the telemetry impedance, an external 10 k resistor is required. In order to avoid manual replacement of the resistor, the Abus1 external interface of the 34980 data collector is used, the resistor is connected in parallel to the interface, and then the interface is connected to the COM terminal through a control program. When testing the telemetry of the corresponding road, the resistor is connected to the telemetry interface by the route of “Abus1—COM1—test end” and tests the voltage value U0, by calculating the formula R = (U1/U0−1)*10 to get the telemetry impedance value.

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Output Channel Automatic Switching Function Interface

The 34922 board in the 34980A is used to solve the problem of manually replacing the ripple clip on the output channel, saving test time and avoiding the problem of polarity reversal of the ripple clip caused by manual operation, improving efficiency and improving system reliability and safety. The specific implementation method is to set the channel 1-30 of the 34922 board as the switching channel, connect to the test end of the electronic load, and connect up to 5 electronic loads for a total of 30 channels, and place a bleeder resistor on the 35th channel, through the resistor. Formula with capacitance and time: s = RC, C = 4.710 −6F. When R selects 7 KX resistor, the capacitor bleeder time is 0.0329 s according to the formula. Then the channel switching waiting time is set by the test program, and the capacitor is discharged. The channel switching function is executed later. COM1 is connected to the positive and negative poles of the ripple clip connected to the oscilloscope probe for measurement and data acquisition. Note that the high and low potentials are distinguished when COM1 is connected. 3.5

Instruction Automatic Switching Function Interface

Using the 34922 board in the 34980A, the problem of the power on and off of the module power supply product containing the relay switch machine command during the test process is solved. The program control command switch machine avoids the risk of manual operation and improves the reliability of the test system. The specific implementation method is to set the 34938 board as shown in the above figure. By connecting the COM terminals of each channel together, and then connecting the external control signals, the channel 1 to the channel 20 are respectively connected to each control signal, and the channel 001-Channel 020 is controlled by the program. The on/off and on-off time of the relay can generate a pulse control signal with a time width of T (generally 100 ms) to realize the automatic transmission of the relay switch machine command. [3–5]. 3.6

System Control Software Design

The system control software uses Agilent VEE, a powerful graphical programming language from Agilent that features: management and control of instrumentation, data acquisition and processing, visual data display, etc. via GPIB serial devices and cards. The data acquisition board constitutes a data acquisition system. The realization of the aerospace power module automatic test system is essentially an automated test system of virtual software instrument technology. Its software part is its core part, which is the key part of realizing the measurement function, the summary, processing and storage of test data. 3.7

Protective Measures Taken by the System

Input Overvoltage Protection Design: It is the test program that initializes the output and protection value of the primary power supply and sets the overvoltage protection function of the power supply once the input of the electronic load is turned off.

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By setting the power supply overvoltage protection function, the subsequent control voltage can be limited to the voltage range allowed by the tested product, preventing the power supply output from overvoltage due to software programming errors and manual operation errors during the test. Input Overcurrent Protection Design: The positive and negative reverse connection of the power supply is the most likely mis-operation of the power supply product testing process, which is a low chromatographic quality problem. In order to prevent the reverse connection operation, it is necessary to design the power-on program. The power-on mode is to set the load to no load, set the input current to 0.2A (according to the actual no-load power consumption of the product), and then the input voltage starts from 0 V. The 1 V step is continuously energized, so that even if there is a reverse connection operation, the primary power supply will over-current protection when the input voltage is low, which will not cause damage to the product and improve the reliability of the test system. 3.8

Modular Programming Design of the System

VEE adopts modular programming mode, with clear and concise architecture, and its system function test is shown in Fig. 6. Test items include: dynamic performance test unit, stability test unit, overcurrent and overcurrent recovery test unit, telemetry voltage and impedance test unit, pull-off function test unit, input reflection ripple and surge test unit. The included test items basically cover all the electrical performance indicators of the power module test, and the test coverage is comprehensive. Combined with the hardware interface design, the program automatically adds the output channel automatic switching function and the instruction automatic switching function module, which realizes the one-button test without manual operation in the whole test process. For the multi-output module power supply and the stand-alone product, the pole Greatly improves test efficiency and improves the reliability and safety of the test system. 3.9

Automatic Storage of Waveforms and Data

The test data is stored in the form of an EXCEL form. The test waveform is stored in the file of the specified path in the form of a file, which is beneficial to the analysis and processing of the later data and improves the traceability of the product test [7]. 3.10

Automatic Interpretation of Data

After the electrical performance test, the measured data is stored in the EXCEL file. The system will automatically interpret whether the measured data meets the requirements, and directly interpret the technical indicators as “√”, and directly ignore the technical indicators as “” to avoid the interpretation error brought by human factors improves the reliability of the test system (see Fig. 2).

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7.8

8.0

≥80 ≤100 2

2 1.2 times 9.48V 12.75 ≤10 ≤20 ≤0.38 ≤0.38 ≤5 0

5

≤5

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Fig. 2. Automatic data interpretation

4 Application Analysis and Outlook Comparison with before and after using the test system (see Fig. 3)

Fig. 3. Comparison result

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5 Summary In summary, using the automatic test system to complete the test work can reduce the labor intensity and save the test time by more than 95%, which can effectively reduce the cost, improve the quality of the test, improve the profitability of the enterprise, and enhance the enterprise’s core competitiveness. At present, the system has been successfully applied to the testing of all kinds of module power products produced by our company, and the application prospect is broad. The follow-up will continue to transform and upgrade the test system, focusing on the comparison of the test data of different production stages of the same module power supply, which will form a database to make the application of the test system reach a higher level and give full play to the power of automation equipment and digital technology. To make greater contributions to the smooth development of spacecraft products and the development of space industry.

References 1. Beshr, H.M., et al.: Modelling of a residential solar standalone power system. In: Proceeding of the 1st International Nuclear and Renewable Energy Conference (2010) 2. Bhandari, R., Stadler, I.: Electrification using solar photovoltaic systems in Nepal. Appl. Energy J. 88, 458–465 (2011) 3. Dambrine, G., Cappy, A., Heliodore, F., Playez, E.: A new method for determining the FET small-signal equivalent circuit. IEEE Trans. Microw. Theory Tech. 36(7), 1151–1159 (1988) 4. Gu, W.B., Wang, C.Y., Liaw, B.Y.: Numerical modeling of coupled electrochemical and transport processes in lead-acid batteries. J. Electrochem. Soc. 144(6), 2053–2061 (1997) 5. Vandersmissen, R., Ruythooren, W., Das, J., Germain, M., Xiao, D., Schreurs, D.: Transfer from MHEMT to GaN HEMT technology: Devices and integration. In: Int. Compound Semicond. Manuf. Technol. Conf., pp. 237–240, April 2005 6. Lee, J.-H., Yoon, H.-S., Park, C.-S., Park, H.-M.: Ultra low noise characteristics of AlGaAs/InGaAs/GaAs pseudomorphic HEMT’s with wide head T-Shaped gate. IEEE Electron Device Lett. 16(6), 271–273 (1995) 7. Liu, W.: “FET high-frequency properties” in Fundamentals of III-V Devices: HBTs MESFETS and HFETs/HEMTs, pp. 422–429. Wiley, New York (1999)

Design and Implementation of User-Oriented On-Board Mission Management System for Remote Sensing Satellite Li Pan(&), Yong Lei, Yu Jiang, Wenping Wang, and Yiming Liu China Academy of Space Technology ISSE, Beijing, China [email protected]

Abstract. In order to improve the operation, efficiency and reduce the project cost of remote sensing satellites, this paper proposes a user-oriented hierarchical and distributed on-board mission management system (OMMS) design and implementation. Combined with user requirements for remote sensing satellite applications, the typical mission mode of the OMMS and the simple and flexible user interface are designed. The mission mode implementation adopts the method based on sub-mission sequence, mission mode construction rules and system configuration database to implement the OMMS software. The actual test shows that the system can greatly reduce the amount of mission instruction data upload by users, shorten the task preparation time and reduce the project cost. Keywords: User-oriented
Remote sensing satellite On-board mission management system




1 Preface In order to facilitate users, improve the efficiency of the usage of the remote sensing satellite, and save cost, the paper proposes a user-oriented design and implementation method of the OMMS. The mission management system can provide flexible and efficient control interfaces while shielding the internal design details of the satellites [1]. The missions can be achieved merely through transmitting mission related satellite parameters, which greatly improves the operation efficiency of the satellite.

2 User-Oriented Overall Design of the OMMS 2.1

Design Principle

The design principles of the user-oriented OMMS are as follows: (1) (2) (3) (4)

The Simplification of the operation. The Improvement of the using efficiency. System coordination management of payload mission. The use safety of the satellite.

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 146–153, 2019. https://doi.org/10.1007/978-981-13-7123-3_18

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Analysis of the Design Constraint

Figure 1 shows the main constraints which need to be considered for the design of the OMMS and their impact on the mission management system design. Constraint level Engineering system Satellite system

Sub-system

Constrain analysis

Spaceborne mission system design

User Demands Number of available station Measurement and control resources Satellite energy supply capacity Attitude maneuverability Antenna rotation capability Data storage capability System reconstruction capability Load operation timing Working time limit Other settings, etc.

Task system working mode Load data transmission strategy Task interface design Energy security strategy design Attitude maneuver preparation time Cutting station and capture strategy Storage and transmission strategy Task reconstruction design Load operation timing design Load security strategy System configuration scheme

Fig. 1. Analysis of the design constraint

2.3

Design of OMMS Mode

The modes of OMMS are designed based on the user demands, the project system configuration, and the system metrics of the satellite. This section discusses the typical modes of the OMMS. Also, the application scenarios of each mode are described in Table 1 [2]. Table 1. Design of OMMS mode Mode Real-time imaging mode Record mode

Mode MS_RT

Record and playback mode

MS_RCPL

Playback mode

MS_PL

2.4

MS_RC

Application scenarios Imaging needs to be done in real-time, within the visible range of the ground station Support to acquire images outside the visible range of the ground station When the payload data rate is greater than the data transmission channel data rate, Record and playback are performed. The time consuming of transmitting data will be longer than that of imaging It is used to playback the payload data that was recorded in the DDR

OMMS Architecture

The OMMS adopts a layered and distributed architecture design. It is divided into four levels. The top layer is the user interface, the second one is the satellite system task, the third one is the sub-mission, and the lowest one is the execution, as shown in the Fig. 2.

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Fig. 2. OMMS architecture

The satellite system mission layer is the core of the mission management system. It is responsible for constructing the sub-mission for the lower subsystems, according to the user demands and the system configuration database. Each subsystem may execute their mission separately accordingly. The safety monitoring modules is responsible for judging the rationality of user mission demands and monitoring the execution of each level to ensure satellite security during mission execution. The system configuration database is used to store the system optional configuration, such as the working status and mode of each subsystem.

3 Design of the User Mission Operation Interface The traditional satellites provide the sub-sequence of subsystem interface or execution layer interface to users [3], however the OMMS provide the system mission layer operation interface, as shown in Fig. 2. This section will put forward the operation interface for each mode shown in Table 1. 3.1

Real Time Imaging Mode Interface

MS_RT {Mode, Imaging start time and stop time, Payload parameters, attitude angle, station}. 3.2

Record Mode Interface

MS_RC {Mode, Imaging start and stop time, Payload parameters, Attitude angles, Record parameters}. 3.3

Record and Playback Mode Interface

MS_RCPL {Mode, Imaging start time and stop time, transmission stop time, Payload parameters, attitude maneuver angles, station, record and playback parameters}.

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Playback Mode Interface

MS_PL {Mode, transmission start and stop time, playback parameters, station ID}.

4 System Mission Mode Construction Design 4.1

Sub-mission Sequence Design

Each subsystem sub-mission sequence needs to be constructed according to its working mode, operation timing requirements, configuration requirements and so on. According to the typical work mode of remote sensing satellites, sub-mission types can be generally divided into attitude control sub-mission, data transmission and processing submission, and antennas control sub-mission, data record and playback sub-mission, payload control sub-mission, etc. The composition of the typical sub-mission of the OMMS is shown in Fig. 3.

Fig. 3. Sub-mission sequence composition

Each sub-mission consists of different subsequences, and the subsequences are composed of specific device operation instructions. Different subsequence combinations can implement different sub-mission. 4.2

System Mission Mode Construction

The mission mode construction is the core of the OMMS. The mission mode construction mainly completes the sub-mission sequence construction and parameter configuration according to the user’s mission requirement, system configuration, mission mode construction rule [4], etc. The mission mode construction process is shown in Fig. 4.

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L. Pan et al. User's mission requirement User task legiƟmacy judgment Mission mode construcƟon rule

Mission and sub-mission sequences construcƟon

System configuraƟon

Sub-mission sequences legiƟmacy judgment

System Security monitoring module

Sub-mission sequences distribuƟon and execuƟon

Fig. 4. The mission mode construction process

4.3

System Mission Mode Construction Rule Design

The system mission mode construction rule is the basis of mission mode construction and sub-mission sequence construction. The main information includes: the relationship between mission mode and sub-mission/sub-sequence, the timing relationship between sub-sequences, the sub-sequence parameter transfer/configuration strategy, etc. The main rule information is shown in Table 2.

Table 2. System mission mode construction rule Mode ID MS_RT

MS_RC

MS_RCPL

MS_PL

Correlated sub-seq. AOCS-AM, DTS-ON, DTS-RT, DTS-CH, ANT-SON, ANT-TRC, CM-ON, CM-SET, DTS-OFF, ANT-SOFF, CM-OFF AOCS-AM, DTS-ON, DTS-RC, CM-ON, CM-SET, DDR-ON, DDR-RC, DDR-OFF, DTS-OFF, CM-OFF AOCS-AM, DTS-ON, DTS-RC, DTS-PL, DTS-CH, ANT-SON, ANT-TRC, CM-ON, CM-SET, DDR-ON, DDR-RC, DDR-PL, DDR-OFF, DTS-OFF, ANTSOFF, CM-OFF DTS-ON, DTS-PL, DTS-CH, ANT-SON, ANT-TRC, DDR-ON, DDR-PL, DDR-OFF, DTS-OFF, ANT-SOFF

Rules (1) Each subsequence execution time consuming (2) Timing relationship between subsequences (3) Sub-sequences parallel rule (4) Time-optimal rule

Para. Transfer user parameter and autocomputation

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System Configuration Design

The system configuration database is used for the storage system optional configuration and is mainly used for the mission sub-sequence construction process, as shown in Fig. 4. It includes information such as the main redundant selection status and working mode selection of the sub-system. Construct a subsequence by reading the system configuration database to select the main or redundant, the working mode and other specific instructions of each subsystem. 4.5

System Safety Monitoring Design

Safety monitoring is mainly used for mission mode and sub-mission sequence construction, mission execution and other processes, as shown in Fig. 4. With the following monitoring features: (1) Check the conformity and feasibility of the timing rules; (2) Check the legitimacy and safety of all the sub-sequences to be executed; (3) Check the correctness of the actual execution of the sub-sequence.

5 Software Design and Implementation 5.1

Software Function and Process Design

The system mission management software is implemented in a satellite system management software framework using a separate mission management process. The satellite system management software usually includes processes such as mission management, instruction receiving and verification, real-time instruction distribution, time-tagged instruction distribution, and telemetry [5]. The mission management process interacts with other processes in the form of message communication. 5.2

Mission Modes Software Implementation Results

Based on the satellite system mission mode, sub-sequence, mission mode construction rules, system configuration and mission management process design, Table 3 shows the software implementation results for all the mission modes.

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6 Conclusion This paper is based on the mission construction rules, system configuration database and sub-sequence design to implement the remote sensing satellite OMMS. Compared with the traditional mission operation mode, the actual test data indicates that the mission instruction data uploaded by the user is reduced to less than 5% of the traditional mode. The time for the user to build the ground control system and the system development workload are also greatly reduced, greatly improving the satellite’s use efficiency. The implementation of the OMMS lays solid foundation for its subsequent development to the intelligent networked direction. The subsequent OMMS will implement on-board autonomous mission planning, constellation mission planning and constellation mission assignment, based on the functions of self-feedback mission planning for autonomous image processing on the satellite will provide users with a more flexible, powerful and intelligent OMMS, which will further enhance the mission capability of the remote sensing satellite and reduce the project costs.

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References 1. Bonnet, J., Gleizesy, M-P., Kaddoumy, E., Rainjonneau, S., Flandin, G.: Multi-satellite mission planning using a self-adaptive multi-agent system. In: 2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems, pp. 1949–3673. IEEE (2015) 2. Pan, Y., Chi, Z.M., Rao, Q.L., Sun, K.P., Liu, Y.N.: Modeling and simulation of mission planning problem for remote sensing satellite imaging. In: MATEC Web of Conferences, vol. 179, p. 03024 (2018) 3. Lic. De Elia, Estefanía: Master in Emergency Early Warning and Response Space Applications. Septiembre (2010) 4. Donati, A.: Infusion of innovative technologies for mission operations. Acta Astronaut. 67, 1132–1137 (2010) 5. European Cooperation for Space Standardization (ECSS): Space project Management, Project planning and implementation. ECSS-M-ST-10C Rev. 1, 6 March 2000

Extraction of Salient Region Based on Visual Perception Yongchang Li1, Pengluo Lu2(&), Cheng Cheng1, Jianing Hao1, Li Liu1, and Jun Zhu1 1

DFH Satellite Co., Ltd., Beijing 100094, China 2 Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China [email protected]

Abstract. In order to better analysis and understand digital images, a salient region extraction algorithm based on visual perception is proposed. First, a multi-scale difference of gaussian filter is used to the image, which simulates the center-peripheral response of the human visual nerve cell; Then, we use the pulse cosine transform to extract the edge information of the image, simulate the side inhibition process of the nerve cell, and obtain the image feature maps at different scales. Finally, the threshold segmentation and regional expansion of the feature graph are used to construct the focal window in the region with the most prominent image features. The experiment was performed on 500 images of the salient object database provided by Microsoft Research Asia (MSRA) using the proposed method, take b2 ¼ 0:3, the value of F-measure is as high as 0.816. Results show that the method can be effectively to extract salient area of the image with different content, different target location and size, and has the location and size on the adaptability. Keywords: Space technology  Data analyzing  Visual perception Salient region  Pulse cosine transform  Feature extraction



1 Introduction The physiological structure of human visual perception system is very complex. We perceive changes in our environment and receive vast amounts of information from the outside world, but our brains cannot process and analyze it at the same time. Therefore, the human eye actively senses the object’s morphological characteristics, abandons the useless background information, and automatically extracts the visual regions that the human eye is interested in for detailed observation, analysis, memory, recognition and learning and other advanced cortical processing. This process is called visual attention. In 1980, Marr [1] proposed Computational Theory of Vision for the first time. In the same year, Treisman and Gelade proposed a visual attention model based on feature integration theory [2]. In 1985, the literature [3] proposed the winner take all neural network about attention focus transfer. On this basis, Itti et al. proposed the most famous Itti visual attention calculation model [4], which is a bottom-up visual attention model based on spatial domain. Since then, many researchers have made improvements © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 154–163, 2019. https://doi.org/10.1007/978-981-13-7123-3_19

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on this basis. However, the drawback is that the space-based visual attention model focuses too much on the location information of the focus, ignores the integrity of the imaging subject, and the computational structure is usually complex. In order to simplify the computational structure and enhance the integrity of the target, a visual attention model based on transform domain is proposed. In 2007, literature [5] put forward Spectral Residual (SR) significant detection method based on Fourier transform for the first time by using frequency domain information of natural image scale invariance characteristics. Later, the literature [6] proposed the Phase spectrum Quaternion Fourier Transform (PQFT) model. In 2009, Achanta et al. [7] proposed IG algorithm based on frequency tuning, which has stronger detection ability than SR algorithm, but the algorithm overemphasizes image edge contour and texture, which is not conducive to the next step of processing. Although these methods can show good detection ability in experiments, they do not have biological theoretical support. From the perspective of biological vision, a visual attention model based on transformation domain is put forward in this paper to extract the salient region of images. The image characteristic graph in multi-scale is obtained by using gaussian pyramid decomposition, difference of gaussian (DoG) filtering and PCT transformation. Through the optimal adaptive segmentation threshold, the regular focus window is effectively constructed. Meanwhile, experiments were carried out on the image library of significant objects provided by Microsoft research Asia (MSRA). Experiments show that this method can adapt to the change of the position and size of the subject and overcome the interference of background.

2 Establishment of Visual Perception Model 2.1

Multi-scale Difference of Gaussian Filter

In the human visual nervous system, there is a kind of cells whose receptive field has central – peripheral antagonism, which means that the response of sensory cells in the center of the receptive field is stimulated, while response of the cells around is suppressed. This characteristic has the nature of band-pass filtering, and can be simulated through central - peripheral difference operation, which is manifested as DoG filter in the spatial domain. In order to give consideration to both the overall size and the edge information of the image target, we propose a multi-scale sampling strategy on the basis of Itti and Koch’s research before conducting the DoG filtering on the image, and adopt the typical gaussian pyramid model to conduct multi-scale and multi-resolution analysis of the image. The image I ðx; yÞ can be decomposed into a series of sub-images with different scales and resolutions by gaussian pyramid decomposition. The formula is as follows. Lðx; y; rÞ ¼ Gðx; y; rÞ  I ðx; yÞ

ð1Þ

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Where, Gðx; y; rÞ stands for the scaled variable gaussian function, as defined below. r is the scale coordinate, and  represents the convolution operation. Gðx; y; rÞ ¼

1 ðx2 þ y2 Þ=2r2 e 2pr2

ð2Þ

The commonly used 5x5 gaussian filtering template is as follows. 2

1 4 6 6 4 16 24 6 1  6 6 24 36 w¼ 256 6 4 4 16 24 1 4 6

4 16 24 16 4

3 1 47 7 67 7 45 1

ð3Þ

The original image is taken as the 0-level image. First, the image is processed by gaussian smoothing with a low-pass filter, and then the image is sampled to obtain the image of the next level. Three sub-images are selected as input images for the next step. Then, the sub-image is convolved with the gaussian function with different standard deviations. gðx; yÞ ¼ Gr ðx; yÞ  I ðx; yÞ

ð4Þ

The gaussian function Gr ðx; yÞ is a normal distribution function, defined as Gr ðx; yÞ ¼

1 ðx2 þ y2 Þ=2r2 e 2pr2

ð5Þ

Two images in the adjacent gaussian scale space are performed by centralperipheral difference operation, namely the subtraction operation. Feature detection of the image can be carried out on a certain scale to obtain DoG response value image. g1 ðx; yÞ  g2 ðx; yÞ ¼ Gr1  I ðx; yÞ  Gr2  I ðx; yÞ ¼ ðGr1  Gr2 Þ  I ðx; yÞ

ð6Þ

Where, DoG can be expressed as DoG ¼ Gr1  Gr2

  1 1 ðx2 þ y2 Þ=2r21 1 ðx2 þ y2 Þ=2r22 ¼ e  2e 2p r21 r2

ð7Þ

In the above equation, r1 [ r2 . When the standard deviation of the two gaussian functions r1 =r2 ¼ 1:6, DoG filter is widely used for edge detection. Its performance is similar to that of gauss Laplace filter, but the calculation quantity is far lower than that of gauss Laplace transform. And when the ratio of the two approaches 5, it approximates the response of the ganglion cells in the retina, simulating the central - peripheral antagonistic properties of the human eye.

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Discrete Pulse Cosine Transform and Feature Graph Extraction

The discrete cosine transform is a special case of Fourier transform, both of which reflect the characteristics of the image frequency variation distribution. Compared with the Fourier transform, the coefficients of the discrete cosine transform (DCT) are real numbers, and the calculation speed is faster, so it is more advantageous. The formula of two-dimensional DCT transformation is M 1 X N 1 X 1 ð2x þ 1Þup ð2y þ 1Þvp Cðu; vÞ ¼ pffiffiffiffiffiffiffiffi aðuÞaðvÞ cos f ðx; yÞ cos 2M 2N MN x¼0 y¼0

ð8Þ

In the formula, the value range of u is u ¼ 0; 1; 2;    ; M  1, the value range of v is v ¼ 0; 1; 2;    ; N  1, the formula of að xÞ is defined as below.  að x Þ ¼

p1ffiffiffi 2

x¼0 1xM  1

ð9Þ

The formation of visual saliency is mainly due to the lateral inhibition between similar neurons in the V1 region of the primary visual cortex [10]. The so-called lateral inhibition is that when a neuron detects the same visual characteristics as the neighboring neurons, the excitability of the neuron will inhibit the excitability of the neighboring neurons and reduce the frequency of its pulse distribution. Instead, the frequency of the pulses is increased. Therefore, the frequency of distribution of corresponding neurons is higher at positions where are more prominent of the object, such as the edges and contour. Lateral inhibition helps the eye separate objects from the background. The DCT coefficient can reflect the statistical characteristics of similar visual features in the visual space. The higher the coefficient, the higher the probability of the occurrence of the visual features corresponding to the frequency and direction in the space. Discrete pulse cosine transformation (PCT) extracts the symbolic information of DCT coefficient and sets the amplitude uniformly to 1. By this operation, the visual features with high probability are suppressed and that with low probability are improved. From the perspective of visual neurons, this process simulates the lateral inhibition of similar neurons in the V1 region. For a given image I, the calculation process of PCT is P ¼ signðdct2ðIÞÞ

ð10Þ

F ¼ absðidct2ðPÞÞ

ð11Þ

Where, signðdct2ðÞÞ represents the two-dimensional DCT transformation of the image and takes the symbol, while absðidct2ðÞÞ represents the two-dimensional DCT inverse transformation and takes the absolute value. In order to further clarify the nature of PCT transformation, two grayscale images were given, as shown in Fig. 1(a). The characteristics of coefficient distribution of the image after two-dimensional DCT transformation were as shown in Fig. 1(b). Meanwhile, PCT transformation was performed according to formula (1). As can be seen

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from Fig. 1(b), the two-dimensional DCT transformation of natural images has the characteristic of “energy concentration”. The low-frequency information is mainly concentrated in the upper left corner of the DCT coefficient matrix, while the highfrequency information corresponding to the image edge information of the space domain is concentrated in the lower right corner of the coefficient matrix, with the coefficient amplitude close to 0. PCT transformation only retains the symbol of DCT coefficient, namely the phase information, and sets the amplitude to the uniform value. Through this operation, the low-frequency components of the image are suppressed and the high-frequency components are amplified, which is helpful to extract the edge information of the image spatial domain, as shown in Fig. 1(c).

(a) Original image

(b) DCT coefficient distribution

(c) PCT transformation

Fig. 1. Frequency domain transformation

2.3

Extraction of Salient Map

After multi-scale DoG filtering and PCT transformation, we can get the feature graph of the source image at each scale. The response position and degree of each feature graph may vary. We need to integrate the feature graphs of each channel in order to obtain the final salient graph. To highlight salient regions, we conduct a weighted fusion of the graphs. Firstly, the size of the characteristic graph under each scale was unified by sampling, and then calculated according to the formula below. P Si Fi ðx; yÞ F 0 ðx; yÞ ¼ i P ð12Þ Si i

Extraction of Salient Region Based on Visual Perception

Si ¼

m1 X n1 X

Fi ðx; yÞ

159

ð13Þ

x¼0 y¼0

Where, Fi ðx; yÞ is the feature graph to be fused, and i is the number of feature graphs. In order to distinguish the target subject from the background and extract the salient area, filtering and binarization should be performed on the fused feature map. Adaptive threshold method is adopted. The filtering formula and threshold calculation formula are as follows. Fsaliency ðx; yÞ ¼ G  F 0 ðx; yÞ2 T¼

m1 X n1 1:5 X Fsaliency ðx; yÞ m  n x¼0 y¼0

ð14Þ ð15Þ

Where, G represents the two-dimensional gaussian function. In order to linearly enhance the contrast of the characteristic graph, a square operation is applied to F 0 ðx; yÞ. Fsaliency is the final salient graph, m and n are the transverse and longitudinal pixels of Fsaliency respectively. When the pixel value of the final salient graph is above the threshold T, the value of the pixel is set to 1, otherwise 0. 0 Fsaliency ðx; yÞ

2.4

 ¼

1 Fsaliency ðx; yÞ [ T 0 others

ð16Þ

Build the Regular Focus Window

After binarization, the target area has a distinct dominant boundary, and we can obtain a regular window through a variety of ways, such as boundary extension method, diagonal neighborhood method. Considering the complexity of calculation, in this paper, Minimum Bounding Rectangle (MBR) [11] method is used in this paper. The MBR constructs a rectangle that contains the given target region and is tangent to the target contour, with each side of the rectangle parallel to the horizontal or vertical axis. The minimum external rectangle can be constructed by searching the maximum and minimum vertical and horizontal coordinates of the target region in the coordinate axis. Sometimes, there are one or more target regions in the image, and the target regions are usually disconnected. This is due to the fact that sometimes there is small target interference in the image or there is a high contrast area in the background area. Thus, the feature weight of the corresponding characteristic graph F of each target region is calculated. The calculation formula is shown in formula (17). The region W with the maximum feature weight is selected as the only focus window.

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WIx ¼

1 X F ði; jÞ NIx ði;jÞ2I

ð17Þ

x

W ¼ maxfWIx g

x ¼ 0; 1; 2   

ð18Þ

Where, NIx is the amount of pixels contained in the target region Ix ðx ¼ 0; 1; 2. . .Þ, and F ði; jÞ is the pixel value of the corresponding characteristic graph.

3 Experimental Results and Analysis Through the above steps, the interested region of human eyes can be extracted as the only focus window. Taking an actual image as an example, the image processing results of each calculation stage of the visual attention model are illustrated, as shown in Fig. 2. The pixel number of the source image is 512768. After the gaussian pyramid decomposition, three sub-images of different scales are selected for DoG filtering, with the size of 256384, 128192 and 6496 respectively. Then, PCT transformation was performed for the sub-images after multi-scale gaussian difference filtering, and the larger two images were sampled to the size of 6496, and weighted fusion was performed. Finally, gaussian low-pass filtering and adaptive threshold binarization are performed on the fused feature map, and then sampled to the size of the source image. The unique regular rectangular window of the image is constructed by using MBR method and feature weight method. The window size constructed in this example is 247 by 399.

Fig. 2. The calculation process of multi-scale PCT model

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To test and verify the extraction effect of salient regions, the experiments are conducted on the salient object image library provided by Microsoft research Asia (MSRA). The gallery contains 5000 images, including people, scenes, etc., whose salient regions were manually marked by nine volunteers according to their subjective intentions. Here are some of the results. Figure 3(a) is the original image, Fig. 3(b) is the extracted feature map, Fig. 3(c) is obtained after binarization, Fig. 3(d) is the regular window constructed by MBR method, and Fig. 3(e) is the salient object manually marked. From the experimental results, it can be seen that the focusing window algorithm proposed can effectively separate the subject and background regions, extract the regions that human eyes are interested in. The focusing window extracted automatically (as shown in Fig. 3(d)), basically contains the salient target manually marked (as shown in Fig. 3(e)). In order to evaluate the focus window construction method proposed in this paper effectively and objectively, regional precision and recall are adopted, which are respectively defined as Precision ¼ ðS \ AÞ=S Recall ¼ ðS \ AÞ=A

Fig. 3. Extraction of salient region

ð19Þ

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Where, S is the salient region automatically extracted, and A is the salient region manually marked in the reference set, \ shows the intersection. The precision reflects the proportion of the correct salient region in the salient region automatically extracted, while the recall reflects how much of the salient area manually marked is detected by the algorithm. There is a certain mutual restriction relationship between the two, expressed by F-measure parameters, and the calculation formula is as follows.  F-measure ¼

 1 þ b2 Precision  Recall b2  Precision þ Recall

ð20Þ

In order to focus on precision, we chose b2 ¼ 0:3. 500 images of different image features and target locations in MSRA image library were selected for testing, and the precision, recall and F-measure were calculated respectively. The average value was calculated and compared with that of the classic Itti method, as shown in Fig. 4.

Fig. 4. Comparison between the proposed and Itti method

It can be seen from the above figure that the method proposed is superior to the classic Itti method in terms of the precision, recall and F-measure. When b2 ¼ 0:3, the value of F-measure of proposed method can reach up to 0.816, while that of Itti is lower than 0.6. Moreover, the Itti algorithm needs to extract color, brightness and directional features at different scales, which requires a large amount of calculation. However, this algorithm only needs grayscale information and the computing time is greatly reduced.

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4 Conclusion A salient region extraction algorithm based on visual perception is proposed in this paper. Firstly, multi-scale DoG filtering is used to detect the features of the source image at different scales, and then PCT transform is further used to extract the features. In this way, the salient graph is generated through weighted fusion of the obtained feature graphs at different scales, and binarized by adaptive threshold. Finally, the unique focus window is determined by MBR and feature weight method. A lot of experiments have been carried out on the salient object image database provided by MSRA. The experiments show that this method can effectively establish the focus window for images with different characteristics and target locations, and the precision and recall are high. Acknowledgment. This work was supported by the Youth Foundation of High-resolution Program (No. GFZX04061502).

References 1. Marr, D.: Visual information processing: the structure and creation of visual representations. Philos. Trans. R. Soc. Lond. 290(1038), 199–218 (1980) 2. Treisman, A.M., Gelade, G.: A feature integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980) 3. Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985) 4. Itti, L., Koch, C., Niebur. E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Comput. Soc. (1998) 5. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR 2007, pp. 1–8. IEEE (2007) 6. Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2009) 7. Achanta, R., Hemami, S., Estrada, F., et al.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 1597–1604. IEEE (2009) 8. Yu, Y., Wang, B., Zhang, L.: Pulse discrete cosine transform for saliency-based visual attention. In: International Conference on Development and Learning, pp. 1–6. IEEE (2009) 9. Zhang, X.: Computational models and applications of the retinal color vision. University of Electronic Science and technology of China. (2017) 10. Li, S.: Research on visual perception based spatial gamut mapping. Tianjin University (2016) 11. Xiong, W., Xu, Y., Cui, Y., et al.: Geometric feature extraction of ship in high-resolution synthetic aperture radar images. Acta Photonica Sinica 47(1), 49–58 (2018)

Thermal Design to Meet Stringent Temperature Gradient/Stability Requirements of Space Camera’s Tube Yifan Li, Yelong Tong(&), Tengfei Sun, and Lei Yu Beijing Institute of Spacecraft System Engineering, Beijing, China [email protected]

Abstract. A space camera is mounted on a remote sensing satellite in a lowearth orbit. The thermal environment of the space camera’s tube is analyzed and the formula of external heat fluxes for tube is given subsequently. The factors affecting the temperature stability and gradient of tube are obtained. The tube is cold biased, and active thermal control system is used to meet requirements. System design based on multi-level insulation and graded heating is proposed. Passive thermal control strategies, such as enhanced thermal insulation design and optimum design of the baffle length, are used to reduce the sensitivity of space camera’s tube to the external thermal environment. An active thermal control system is taken to solve the temperature control problem for multi-zone coupling with each other. In-orbit analysis of the last 3 years shows that, the temperature of tube is in the range of 20 °C ± 1.5 °C. The axial and circumferential temperature differences are less than 1 °C. The temperature fluctuation is less than 0.33 °C/3 month. Periodic average heating power for tube is 16.7 W. The validity of thermal design for tube is proved. Keywords: Space camera
Tube
Thermal design Thermal balance test
On-orbit performance


Thermal baffle


1 Introduction and Thermal Requirements A space camera is mounted on a remote sensing satellite in a low-earth orbit. The satellite is a 3-axis stabilized satellite, operating in a 10:30 am Sun synchronous orbit, at an altitude of 500 km and an orbital inclination of 97.2°. The satellite is to have a 5 year design life. Orbital imaging maneuvers include ±45° rolls and ±45° pitch, with the definition of these maneuvers in reference to the satellite coordinates system. The camera is exposed to cold space with an opening structure in the front, which adopts coaxial three-mirror optical system. The supporting structure between the primary mirror and the secondary mirror is main scope tube. The secondary mirror is in the front of the tube and back to cold space. The focal plane assembly is mounted at the rear of the camera [1] (Fig. 1).

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 164–172, 2019. https://doi.org/10.1007/978-981-13-7123-3_20

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Fig. 1. Structure of the space camera

In addition to temperature range requirements, the camera has to fulfill precision thermal requirements. This is valid for spatial temperature gradients and temporal temperature fluctuation to guarantee full optical performance. The main thermal control requirements for the main scope tube are: (1) 20 ± 1.5 °C absolute temperature to ensure the optical quality (2) no more than 1 °C spatial temperature gradient for 5 years (3) no more than 0.3 °C temporal temperature gradient for imaging This paper presents thermal design considerations to meet these stringent thermal requirements.

2 Thermal Environment External heat flux in the inner surface of space camera’s tube is obtained through theoretical analysis, which includes direct solar heat flux, albedo heat flux and earth infrared heat flux [2, 3]. The orbit beta angle (b), angle between sunlight and orbital plane, is from 17.2° to 27°. a, argument of apogee, is from 115° to 245°. The depth of solar radiation in the tube, H, is calculated with the following formula [4]. H ¼ D= tan U cos U ¼ maxð0; cosbcosa)

ð1Þ

Where D is the camera aperture. When a = 17.2° and b = 115°, H is maximum. Hmax ¼ 0:44D:

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Q2, albedo heat flux reaching on the inner surface of space camera’s tube, is calculated as follow. 

RE Q2 ¼ S
q
maxð0; cos b cos aÞ
RE þ h

2
X12

ð2Þ

where X12 is the angle factor between inner surface of tube and the virtual surface of the light inlet. q is albedo factor. S, the intensity of sunlight reaching Earth, varies approximately 3.5%, depending on Earth’s distance from the sun. At summer solstice, the value of S is equal to 1322 W/m2. At winter solstice, the value of S is equal to 1414 W/m2. RE is Earth’s radius. h is distance from orbital plane to the surface of Earth. h1 is depth from camera entrance (Fig. 2).

light inlet h1=0.4D h1=0.6D h1=0.8D h1=D

330 300

albedo heat flux Q2/ W/m2

270 240 210 180 150 120 90 60 30 0

0

50

100

150

200

250

300

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α/ °

Fig. 2. Albedo heat flux at different depths of tube

Q3, Earth infrared heat flux flow on the inner surface of space camera’s tube, is calculated as follow [5].  Q3 ¼ S
ð1  qÞ


RE RE þ h

2
X12

ð3Þ

The variations in +Z Earth-emitted IR are much less severe than the variations in albedo. Q3 is depend on X12. The depth from camera entrance is increases and Earthemitted IR decreases rapidly (Table 1).

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Table 1. Earth infrared heat flux at different depths of tube Depth from camera Earth infrared heat Remarks entrance flux Q3/(W/m2) 0 212.8 Virtual surface of the light inlet 0.4D 46.6 0.6D 30.1 0.8D 19.7 D 13.2

The baffle is attached to tube at 24 points by screws and heat insulation pads are installed on each point. However, heat conduction between the baffle and tube is considerable. The tube views space (3K). The upper part of the tube has more heat dissipation to space than the lower part. The orbital heat flux absorbed by tube in the circumferential and axial directions is not uniform. At the same time, albedo heat flux will fluctuate periodically. It is a challenge to meet the stringent thermal requirements.

3 High Stability Temperature Control The tube is cold biased, and active thermal control system is used to meet the thermal requirements. System design based on multi-level insulation and graded heating is proposed. Passive thermal control strategies, such as enhanced thermal insulation design and optimum design of the baffle length, are used to reduce the sensitivity of space camera’s tube to the external thermal environment. An active thermal control system is taken to solve the temperature control problem for multi-zone coupling with each other. 3.1

Optimum Thermal Design of the Baffle

The Albedo flux and Earth infrared heat flux are the external heat fluxes that affect tube’s temperature fluctuations. The orbital heat flux of tube is related to the length of baffle and attitude maneuver. (1) The length of baffle should be large than 0.44 times the camera aperture to avoid direct sunlight. (2) Earth infrared heat flux received inside the tube is relatively stable. However, Earth albedo is periodic fluctuations, which is the main reason to cause tube’s temperature fluctuations. It can be reduced by increasing the length of baffle. If the baffle length is greater than 0.8 times the camera aperture, the peak albedo heat flux will be reduced to 30 W/m2. (3) Heat flux of Earth infrared and albedo will decrease due to attitude maneuver. Due to envelope limitation of the launch vehicle, the length of baffle can reach to 0.9 m, 0.75 times the camera aperture.

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The external surfaces of baffle are covered with MLI. The baffle is thermally insulated from tube by layered polyimide gaskets to increase contact thermal resistance (Fig. 3). Four heater loops are mounted on the baffle near the 100 mm of tube. The temperature set point for baffle’s heaters should be designed so that its peak temperature is lower than the temperature set point for tube’s heaters in order to avoid heat transfer from baffle to tube. Therefore, heater controller set points for heaters are set to 12 °C. The heating power is 10 W for one loop.

Fig. 3. Enhanced design for insulation

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Enhanced Thermal Insulation

The thermal coupling between the baffle and tube is reduced through measures such as reducing the contact area, layered polyimide gaskets and installing polyimide gasket between the baffle and screw. Some special thermal control measures are adopted to decrease the variation of tube’s orbital heating. (1) The inner thermal baffle is set to thermal isolation tube from cold space. (2) MLI is mounted between tube and thermal baffle, which not only decrease the temperature fluctuations of tube, but also reduce the heating power. (3) All the external surfaces of tube are covered double MIL by adding special thin ribs, which can enhance the thermal insulation with outside thermal environment (Fig. 4).

Fig. 4. Enhanced design for radiation insulation

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Heater Control Approach

Due to the required optical accuracy, amongst these are the heater control loops for the tube. The temperature gradients are strongly dependent on the division of heater zones. Optimizing the division of heater zones will ensure the above requirement met. The tube is therefore provided with 32 feedback-controlled heater loops. Electronic proportional heater controllers that have a tolerance of ±0.1 °C or better will be used. They are modeled as thermostats that open at temperature set point plus 0.3 °C and close at temperature set point minus 0.3 °C. The schematic of the heater control approach is given in Fig. 5 [6]. The temperatures are measured by MF61 thermistors. According to MF61, the tolerance of temperature measurement is ±0.1 °C from 0 °C to 30 °C. The set point of heater controller is 19.5 °C, which can be adjustable in flight.

Proportional heater controller

Td

Heater output

u

OBJECT

Tm

Temperature measurement

Fig. 5. Active heater control loop diagram

The required heater time is calculated by the control algorithm. The control period applied is 10 s. The resolution is better than 1 s.

4 Thermal Analysis and Validation 4.1

Thermal Analysis and Testing

Thermal Desktop is used to establish the thermal analysis model. The cold case and hot case are solved and the temperature variations of tube is shown in the Table 2. The temperature predictions of the baffle adjacent to the tube are in the 11.2 °C to 19.8 °C range.

Table 2. Analysis results and test results of tube Item

Analysis results (°C) Hot case Cold case Temperature range 19.2–20.6 19.3–20.2 Temperature variation during imaging 0.19 0.16 Circumferential temperature gradient 0.82 0.71 Axial temperature gradient 0.78 0.65

Test results (°C) Hot case Cold case 19.29–20.12 19.30–19.81 0.08 0.06 0.57 0.41 0.43 0.29

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Fig. 6. Test temperature curve of tube in hot case

A thermal balance test was undertaken to validate the thermal design and to provide measured data for improving the thermal model. Thermal test results are shown in Table 2 and Fig. 6. The thermal balance tests show that: (1) The temperature of tube is 19.29–20.12 °C, both their radial and axial temperature gradient are less than 0.57 °C, their temperature fluctuation during imaging are less than 0.08 °C. (2) The periodic average heating power for tube is 18.2 W in cold case test. (3) The analysis results are close to the thermal balance tests, indicating that thermal analysis model is correct. The thermal control system can meet the stringent thermal requirements.

Fig. 7. Temperature curve of tube for first 3 months in orbit

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On-Orbit Performance

The in-flight results are presented in this section (Figs. 7 and 8) and where possible compared with predictions, ordered according to the following characteristics: 1. At first 3 month in orbit, the temperature of baffle is 11.7–16.5 °C. The temperature of tube is 19.43–19.84 °C. Their axial temperature gradients are less than 0.21 °C. Their circumferential temperature gradients are less than 0.24 °C. Their temperature fluctuations are less than 0.22 °C/3 month. Periodic average heating power for tube is 16.7 W. 2. After 3 years in orbit the degradation, the temperature variation of tube is significantly lower that predicted. Therefore, the design can meet the stringent thermal requirements for its entire lifetime. The radial temperature gradients are less than 0.26 °C. The circumferential temperature gradients are less than 0.35 °C. The temperature fluctuations are less than 0.33 °C/3 month.

Fig. 8. Temperature variation of tube for first 3 years in orbit

5 Conclusions The thermal environment of the space camera’s tube is analyzed and the formula of orbital heat fluxes for tube is given subsequently. The tube is cold biased, and active thermal control system is used to meet the thermal requirements. Enhanced thermal insulation design and optimum design of the baffle length, are used to reduce the sensitivity of space camera’s tube to the external thermal environment. An active thermal control system is taken to solve the temperature control problem for multi-zone coupling with each other. In-orbit analysis of the last 3 years shows that, the axial and circumferential temperature gradient of tube are less than 0.5 °C. The temperature fluctuation are less than 0.33 °C/3 month. Periodic average heating power for tube is 16.7 W.

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References 1. Wang, X., Guo, C., Hu, Y.: Design and verification for front mirror-body structure of on-axis three mirror anastigmatic space camera. Acta Photonica Sinica (2011) 2. Gilmore, D.G.: Spacecraft Thermal Control Handbook. The Aerospace Press, California (2002) 3. Hou, Z., Hu, J.: Foundation and Application of Spacecraft Thermal Control Technology. China Technology Press, Beijing (2007) 4. Zhang, R.: Satellite Orbit Attitude Dynamics and Control. Astronautic Press, Beijing (1998) 5. Ning, X., Zhang, J., et al.: Extreme external heat flux analytical model for inclined-orbit hexahedral satellite. J. Astronaut. (2008) 6. Tong, Y., Li, G., Geng, L.: A review on precise temperature control technology for spacecraft. Spacecraft Recovery Remote Sens. (2016)

Application Design of Virtual Assembly Simulation Technology for Installing Cables on Biaxial Drive Mechanism Chunsheng Yang1(&), Yufeng Huang2, Yi Lu1, Feng Xue1, Zhenyue Ren1, and Guoyu Liu1 1

2

Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China [email protected] Institute of Telecommunication Satellite, Beijing 100094, China

Abstract. Based on virtual assembly simulation technique for installing cables on biaxial drive mechanism, this paper describes the process of building flexible virtual assembly environment and simulating installation of cables. First of all, the basic element of cable-laying is simulated, such as wires, thermistors, heat plates, etc. Then the process of cable laying is simulated and analyzed by step level in detail. The laying path of each wire is carefully planned in the complex space surface of biaxial drive mechanism. The feasibility of the path is validated in view of overall laying course. The length of each wire can be determined. So it can get the specific process parameters. The project application results indicate that the virtual simulation technique can optimize the process of the spacecraft assembly and integration design, avoid faults by mistake, extend the process expression technique, insure the reliability of irreversible operations, and enhance the level of process design. Keywords: Virtual assembly simulation Assembly process design


Biaxial drive mechanism


1 Introduction With the development of technology, virtual assembly has been widely used in spacecraft assembly integration. It has achieved application results in three dimensional space interference analysis, ergonomics analysis, assembly mode selection, mechanical grounding supporting equipment (MGSE) design, site adaptability etc. [1]. It has guided the specific realization of process design, process planning and MGSE design. Virtual simulation technology is the combination of computer technology and virtual reality technology. Virtual environment and virtual products are constructed to simulate and control the real entities. Virtual simulation technology is used to verify the correctness and rationality of the whole spacecraft and component-level assembly process and assembly routes [2]. Digital visualization technology is used to test the assembly sequence and assembly route, evaluate the product whether can be assembled, optimize the process design, and further improve the design quality and extend the process expression technique [3]. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 173–179, 2019. https://doi.org/10.1007/978-981-13-7123-3_21

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At present, virtual assembly technology has gradually played an irreplaceable role in the assembly process design with research of deep space exploration engineering, manned spaceflight engineering, navigation project, and DFH-5 platform trussed satellites. It not only helps to solve the project bottleneck technology research, but also helps to significantly improve the level of process planning [4]. However, for the flexible cable process, virtual simulation technology is subject to the factors such as complex material constraints, large variations in material properties, meticulous operation steps, and more coupling with thermal control and assembly welding. In this paper, the application of virtual assembly simulation technology in flexible cable assembly is studied based on the cable laying of biaxial drive mechanism of the two-generation data relay satellite.

2 Analysis of Cable Laying Process in Biaxial Drive Mechanism 2.1

Characteristics of Biaxial Drive Mechanism

The biaxial driving mechanism of the two-generation data relay satellite is used for attitude control of the large umbrella shaped antenna outside the star, so that it accurately points to the user satellite and ensures the relay communication between the ground station and the user satellite. The biaxial driving mechanism has two directions of rotation function (azimuth and pitch). By adjusting the two degrees of freedom, the direction determination and continuous transformation of the umbrella antenna on orbit are realized. The driving mechanism is composed of two rotating mechanisms, elevation axis and azimuth axis, as well as high-frequency cabin connecting bracket, twoaxis connecting bracket and expansion-arm connecting bracket. The cables between the elevation axis and the expansion arm support bracket are fixed cables, which do not move during the whole lifecycle. The cables between the azimuth axis and the elevation axis are movable cables, which have the active range from the compression state to the maximum expansion position, and the free cable movement does not rub with other parts of the satellite. The structure of the device is shown in Fig. 1.

Fig. 1. Structure diagram of biaxial drive mechanism

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Analysis of Cable Laying Process

Due to the characteristics of long-term reciprocating large angle motion of two degrees of freedom, design of installing cables of the driving mechanism are much more complicated. The rotation of the axis should not be affected. After many times of rotation, the cables should not be dragged, pressed, and scraped together to ensure that the signal transmission performance work well. At the same time, the driving mechanism is installed outside the satellite, which makes the thermal control measures of the device more complicated. It involves heating plate, platinum resistor, thermistor, dehumidifying heating plate, thermal control sleeve etc., while the process is irreversible. Meanwhile, it is also necessary to ensure that the additional cables at the above thermal control are reserved for a certain length of abundance, so as to ensure the high cables have sufficient tensile distance (including thermal expansion and cold shrinkage allowance) and tolerable bending degree when the biaxial driving mechanism reciprocates. Additionally, the cable laying in the complex narrow interspace of threedimensional is also a difficult problem for design expression. Conventional twodimensional drawings are difficult to accurately express information with poor readability. We need to use multimedia tools to complete the visual output of design information, intuitively express the contents of each step of drive mechanism thermal control implementation, specify the implementation location and the results of requirements. These characteristics bring great difficulty to process design, and the above harsh conditions can’t be verified by conventional physical simulation test. Virtual assembly simulation technology can solve this problem that process design can’t be verified.

3 Simulation of Cable Laying Process in Biaxial Drive Mechanism 3.1

Build Simulation Analysis Environment

Primarily, we should obtain product resource model to build simulation analysis environment. The product resource model is the most basic part of the whole system, which is usually provided by the designer or the design process itself. Its correctness will directly affect the results of the simulation research. The model provided by the designer must be checked according to design documents to avoid simulation errors. For complex products, technicians can newly design models and simplify the models, but they should meet the key parameters of simulation. In the research of this project, the product model of biaxial drive mechanism, the model of thermal control sleeve, the model of moving cables, the model of thermal control lead and others are involved. Through the acquisition of the above model, the simulation analysis unit is created. Then, all the units are combined and laid out to achieve integration, and the component-level construction is completed. Thereafter, the model needs to be pretreated, mainly by analyzing the process parameters of the unit to modify the adaptability of the components. The technique research route is shown in Fig. 2.

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Fig. 2. Implementation path of simulation analysis environment construction

In this project, the emphasis is laid on laying and routing of thermal control cables. Therefore, the diameter, minimum turning radius and elastic performance of cables should be clear. Meanwhile, for additional heating plates, thermistors, platinum resistors and so on, it is necessary to model matching and revising and positioning with the size specified in the design documents, so as to complete the building of the simulation analysis environment. 3.2

Simulation of Cable Laying Process

According to the layout of thermal control components, the cable laying is divided into three stages, and virtual assembly simulation is carried out according to different stages. The 3 stages of the division are: (1) uniaxial state: including pasting heating plate, platinum resistance, etc. (2) before pasting sleeve and after two axis combining: including assembling the two axis, pasting thermistor on the surface of axis, installing wires on the sleeve, pre installing grounding wires for sleeve, etc. (3) the biaxial state adheres the sleeve: including the dehumidification cable wires, welding plugs, installing cables through brackets, along sleeve flanging and surface of axis, welding heating loop and reserved cables, all cable dispensing etc. During the above three stages, according to the principle of from one part to the whole, from inside to outside, from single wire to whole cables, from the beginning position of the paste to the cable end locating outside of the device, we can analysis the layout of the thermal control components with its wire routes step by step. Taking the azimuth axis wire routes as an example, the simulation analysis process is shown in Fig. 3.

Application Design of Virtual Assembly Simulation Technology

1) the layout of the thermistor wires through

2) the layout of backup-heater the wire

the bracket to the cable beam

through the bracket to the cable bundle

3) the layout of main-heater wires through

4) the inner heater loop of the sleeve is con-

the bracket to the flange of the sleeve

nected in series

5) the sleeve is connected in series with the

6) the layout of heating circuit wires after

external heater circuit

connection in series

7) the layout of platinum resistance wires is

8) the layout of dehumidifier wires through the

through the biaxial bracket

biaxial bracket

9) welding plugs for dehumidification wires

10) stick the thermistor outside of the cable

near the bracket

bundle and layout relevant wires

Fig. 3. The simulation and analysis process of layout cables on azimuth axis

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4 Study on Implementing Analysis and Layout Optimization of Cable Laying In the process of virtually simulate cable installing, we should pay attention to cable layout, cable lines fixed mode and interference feasibility analysis, operational feasibility analysis, cable routes optimization, etc. [5, 6]. Taking the feasibility analysis of the cable through hole in bracket as an example, we premise the diameter of the single thermal control wire to be 0.8 mm, then we can learn that no more than 19 wires can go through the same hole together. However, there are 15 wires are designed to go through the hole. We layout wires carefully, then we can analysis that the minimum distance between wires are less than 0.5 mm. Considering the requirements of process protection in actual implementation process, the thermal wires will also be wrapped with many layers of 3M tape for protection, which will increase the diameter of the wire. Therefore, it is necessary to optimize the layout of wire by separating the harness to different holes or changing the path. The simulation analysis is shown in Fig. 4.

Fig. 4. The analysis and study of the feasibility of cable laying

In addition, through the video animation formed by virtual assembly simulation, both the contents of each step of the thermal implementation of the biaxial driving mechanism, and the specific implementation location with the demand results can be intuitively expressed. With the help of multimedia, the video provides great convenience to operators, ensures the irreversible operation with safe and correctness, improves product reliability and improves assembly efficiency [7].

5 Concluding Remarks In this paper, the flexible cable laying of two-axis drive mechanism is studied. Set up simulation environment should be according to the design requirements to layout cables and plan the routes, simulate and analyze the wire installing process. In the course of the study, we can identify the problems which may appear during design phase or operating phase. We can optimize the cable layout effectively to ensure the accuracy and enforceability of the process design. So it can effectively reduce the

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failure rate of assembly defects and products, reduce the risk of product development, and ensure the quality of product assembly. The successful application of virtual assembly simulation in the cable laying of biaxial drive mechanism of the twogeneration data relay satellite can provide reference and guidance for the assembly process design for other spacecraft.

References 1. Yang, Z., Li, L., et al.: 3D digital assembly process for rocket body cabin. Aerosp. Mater. Technol. 4, 85–91 (2016) 2. Yuan, C.: Assembly process simulation of rocket components and on-site visualization technology application. Xi’an University of Electronic Science and Technology (2015) 3. Feng, W., Zhang, Y., et al.: The virtual simulation technology in assembly process design of china’s lunar exploration project II. Spacecraft Environ. Eng. 6, 326–331 (2014) 4. Zhou, R., Zhang, H., et al.: Virtual assembly process simulation of Bogie based on DELMIA. Mach. Electron. 2, 47–50 (2015) 5. Shen, Z., Zheng, H., et al.: Application of digital assembly simulation technology for complicated vehicle based on DELMIA. Missile Space Launch Technol. 6, 42–45 (2015) 6. Ritchie, J.M., Dewar, R.G., Simmons, J.E.L.: The generation and practical use of plans for manual assembly using immersive virtual reality. In: Proceedings of the Institution of Mechanical Engineers (Part B), vol. 213, pp. 461–474 (1999) 7. Li, T., Han, F., et al.: Virtual assembly simulation of catheter based on DELMIA. Mech. Manuf. 5, 74–77 (2014)

Data Analysis and Research Based on Satellite Micro-vibration Disturbance Test Yang Gao1(&), Qiang Wang2, Fei-hu Liu3, Lu Cao3, and Wei Cheng3 1

Beijing Institute of Spacecraft System Engineering, Beijing 100094, China [email protected] 2 Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China 3 School of Aeronautical Science and Engineering, Beihang University, Beijing 100191, China

Abstract. The micro-vibration generated by the moving parts is always present, and has a great influence on the satellite. It is very necessary to study the influence of the micro-vibration and take the vibration isolation measure. This paper introduces a micro-vibration test scheme of control moment gyroscope (CMG) on a satellite, and analyzes the measured data from two aspects: background noise and disturbance characteristics. In order to suppress the influence of the vibration of the control moment gyroscope (CMG) on a whole star, we design a type of spring isolator. To verify the effectiveness of the isolator, two installation states are designed, namely rigid installation state and isolator installation state. We test and analyze the disturbing force produced by the CMG under the conditions of time domain and frequency domain. The test results indicate that the vibration isolation effect is obvious. The vibration isolation effect of CMG base frequency can reach over 90%. Keywords: CMG


Micro-vibration
Vibration isolation
Data analysis

1 Introduction With the increasing resolution of remote sensing satellites, the requirements of satellite jitter and attitude stability are becoming more and more important. The main influencing factors include external force interference, attitude maneuver and microvibration of satellite moving parts (such as momentum wheel, CMG etc.) [1, 2]. The micro-vibration generated by these moving parts is always present, and has a great influence on the satellite. Therefore, the suppression of micro-vibration becomes a necessary measure to ensure the image quality [3–5]. If the disturbance characteristics of the disturbance source are known in the development stage of the product components, the corresponding isolation and vibration reduction measures can be taken in the structural design stage [6–10]. During the working process of a certain type of CMG, the disturbance characteristics of the CMG and the effectiveness of vibration isolation scheme are still unclear. Therefore,the corresponding micro-vibration test and analysis evaluation are required. This paper introduces the test scheme of a certain type of CMG micro-vibration test, © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 180–187, 2019. https://doi.org/10.1007/978-981-13-7123-3_22

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analyzes the test results of rigid installation state and isolator installation state and evaluates the effectiveness of vibration isolation scheme.

2 Test Scheme 2.1

The Micro-vibration Force Measuring Platform

This experiment is carried out by the micro-vibration force measuring platform developed by Beihang University. The platform is mainly composed of eight unidirectional piezoelectric force sensors, sensor top plate, sensor bottom plate and mounting base. It can measure the micro-vibration load produced by the moving parts. The physical diagram of the platform is shown in Fig. 1.

Fig. 1. The physical diagram of the platform

The platform can measure six components of vibration force (three axial forces, two bending moments and one torque). The main performance indexes are shown in Table 1. Table 1. The main performance indexes of the platform Project Index Carrying capacity  200 kg Force resolution 1 mN Torque resolution 1 mN
m First order natural frequency of test system 1096 Hz Frequency measurement range 0.5 Hz–2 kHz

When the disturbance source is installed and working on the platform, there will be eight voltage signal outputs. The calibration process is needed to determine the

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conversion relationship between eight voltage signals and six-component forces, so that the eight voltage signals obtained by the test can be used to inversely calculate the six-component forces acting on the platform. The schematic diagram of calibration is shown in Fig. 2.

Fig. 2. The schematic diagram of calibration

F0 ðxÞ616 ¼ C616 FðxÞ1616

ð1Þ

In the above formula, C is used to make the force system of hammering force (F) equivalent to the force system at the geometric center point O(F0 Þ. UðxÞ816 is the output voltage of the platform; WðxÞ68 is the calibration matrix. Then there are the following formulas: WðxÞ68 UðxÞ816 ¼ C616 FðxÞ1616

ð2Þ

  WðxÞ68 UðxÞ816 UH ðxÞ168 ¼ C616 FðxÞ1616 UH ðxÞ168

ð3Þ

 1 WðxÞ68 ¼ C616 FðxÞ1616 UH ðxÞ816 UðxÞ816 UH ðxÞ816

ð4Þ

If the output voltage is UðxÞ81 , the six-component forces produced by the measured disturbance source are as follows: FðxÞ61 ¼ WðxÞ68 UðxÞ81

ð5Þ

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Test Condition

This test mainly tests the micro-vibration characteristics of CMG and the effect of vibration isolation on the micro-vibration suppression of CMG. Therefore, it is necessary to test the micro-vibration characteristics of CMG under the condition of rigid installation and isolator installation, and then make a comparison. In addition, the schematic diagram of the test under the condition of isolator installation is shown in Fig. 3.

Fig. 3. The schematic diagram of the test under the condition of isolator installation

Under the working condition of CMG, the speed of the high-speed shaft is stable at 7000 rpm, and the low-speed frame rotates around the low-speed shaft at a certain speed. Therefore, we should carry out the test when the low-speed frame is at different positions (0°–330°, every 30°, for a total of 12 positions). After the stabilization, the micro-vibration test is performed. The test conditions are shown in Table 2.

Table 2. The test conditions (The high-speed shaft: 7000 rpm; The low-speed frame: 12 different positions.) Number of test conditions Description of test conditions I CMG is rigidly mounted on the force measuring platform II CMG is mounted on the force measuring platform via isolators

3 Data Analysis 3.1

The Data of Background Noise

After setting up the test system, LMS and the control equipment of CMG boot up. At this moment, the high-speed rotor part is not activated. The data collected by LMS is considered as the background noise of the system. Figure 4 shows the background noise diagram of Fx in time domain. From Fig. 4, the amplitude of background noise of Fx is within 0.35N, and the root mean square value in time domain is 0.14N. The root mean square values of the residual force and torque are all below 0.14.

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Fig. 4. The background noise diagram of Fx in time domain

3.2

Analysis of Disturbance Characteristics

(1) Rigid installation state When the low-speed frame is located at the 0° position, the time-domain amplitude increases from 0.35N to 8N, which increases by two orders of magnitude; the timedomain rms value increases to 4.7N. Figure 5 shows the diagram of Fx in frequency domain (the low-speed frame is at 0° position; condition I). Compared with the background noise data, the peak value is increased at 116.9 Hz, 233.8 Hz and 350.7 Hz. The analysis shows that the above three frequency values are all multiplied by 116.9 Hz. Because the high-speed rotor speed of ia at 7000 rpm, the calculated fundamental frequency value is consistent with 116.9 Hz.

Fig. 5. The diagram of Fx in frequency domain (the low-speed frame is at 0° position; condition I)

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(2) Isolator installation state When the low-speed frame is located at the 0° position, the time-domain amplitude increases from 0.35N to 2.5N; the time-domain rms value increases to 0.9. Figure 6 shows the diagram of Fx in frequency domain (the low-speed frame is at 0° position; condition II). Compared with the background noise data, the peak value increases at 15.75 Hz, 116.9 Hz, 233.8 Hz and 350.7 Hz, the latter three frequency values are consistent with the frequency values of the condition I, and the new frequency value 15.75 Hz is related to the frequency of the isolator.

Fig. 6. The diagram of Fx in frequency domain (the low-speed frame is at 0° position; condition II)

(3) Comparison between condition I and condition II When the low-speed frame is at the 0° position, the latter is about 70% lower than the former in the time domain. The amplitude corresponding to the fundamental frequency and the multiplied frequency is 94%, 87% and 80% lower respectively in frequency domain. In the two installation states, we obtain the micro-vibration data when the lowspeed frame is at 12 different positions. And finally plot the waterfall diagram in frequency domain. Figure 7 shows the waterfall diagram of Fx in frequency domain. Compared with condition I, condition II has some special and obvious peak in the range of 0–50 Hz, which is related to the isolators. The peak value decreases in the whole frequency band, and the peak value of the fundamental frequency decreases most obviously. The mean values of the root mean square values (between 0–400 Hz; in the range of 0°–330°) of the six component forces (Fx, Fy, Fz, Mx, My and Mz) are shown in Table 3; the mean values of the peak values at the fundamental frequency (Fx, Fy, Fz, Mx, My and Mz; in the range of 0°–330°) are shown in Table 4. Therefore, the vibration isolation effect of isolator is good.

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Fig. 7. The waterfall diagram of Fx in frequency domain. Table 3. The mean values of the root mean square values (Band: 0–400 Hz; Position: 0°–330°) Conditions Fx (N) Fy (N) Fz (N) Mx (Nm) My (Nm) Mz (Nm) Condition I 6.06 11.95 1.13 0.80 0.44 4.81 Condition II 1.11 1.11 0.66 0.45 0.12 0.62

Table 4. The mean values of the peak values at the fundamental frequency (Position: 0°–330°) Conditions Fx (N) Fy (N) Fz (N) Mx (Nm) My (Nm) Mz (Nm) Condition I 4.08 11.60 0.76 0.22 0.41 4.67 Condition II 0.09 0.06 0.03 0.02 0.01 0.12

4 Conclusion The laboratory carries out the micro-vibration test of a CMG in two installation states: rigid installation and isolator installation. Then we obtain the data of background noise and the data in CMG working status. (1) Compared with the data of background noise, the data in CMG working status is increased by at least one order of magnitude. Thus the test result is considered valid. (2) Comparing the data of two installation states: the rigid installation state and the isolator installation state, there is a significant decline in both the root mean square value in time domain and the peak value in frequency domain, especially the peak value at the fundamental frequency decreases most obviously. Therefore, it is considered that the vibration isolation effect of the isolator is obvious, and the vibration isolation effect at fundamental frequency is the best.

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References 1. Wu, Y., Xie, Y.-c., Yan, T.-f., Fang, G.-q., Jiao, A.C.: Micro-vibration Testing Technology for Space Station Control Moment Gyroscope. Equip. Environ. Eng. 94–99 (2018) 2. Zhang, Z.-h., Yang, L., Pang, S.-w.: High-precision mechanical environment analysis of micro-vibration of spacecraft. Spacecraft Environ. Eng. 528–534 (2009) 3. Wang, G.-y., Zhou, D.-q., Zhao, Y.: Data analysis of on-orbit micro-vibration measurement by remote sensing satellite. Astronaut. J. 261–267 (2015). Author, F., Author, S., Author, T.: Book title. 2nd edn. Publisher, Location (1999) 4. Lambert, S.G., Casey, W.L.: Laser Communications in Space. Artech House, Boston (1995) 5. KATZMAN, M.: Laser Satellite Communications, p. 250. Prentice-Hall Inc., Englewood Cliffs (1987) 6. Wu, D.-y., Li, G., Lu, M.: Noise measurement and analysis of 1000Nms control moment gyroscope. Space Control Technol. Appl. 31–55 (2012) 7. Zhou, D.-q., Cao, R., Zhao, Y.: Measurement and analysis of micro-vibration in orbit by remote sensing satellites. Spacecraft Environ. Eng. 627–630 (2013) 8. Zhou, J.-p.: General conception of space station project in China. Manned Spaceflight 1–10 (2013) 9. Wang, T.-m., Wang, H., Li, H.-y.: Space station common orbit vehicle deployment for supply mission. Manned Space 583–596 (2017) 10. François, D., Mark, W., Stephen, A.: New facility for micro-vibration measurements ESA reaction wheel characterisation facility. In: Proceedings of the 12th European Conference on Space Structures, Materials & Environmental Testing, Noordwijk (2012)

Spacecraft Automation Test Procedure and System Design Yongcong He(&), Feng Yang, Liang Ren, and Chao Cheng Institute of Manned Space System Engineering, China Academy of Space Technology, Beijing, China [email protected]

Abstract. In view of the fact that the existing manned spacecraft electrical measurement process cannot be streamlined and automated, it consumes a considerable amount of human resources and time costs, the manned spacecraft automated test program and system were designed. Through the analysis and design of the format, function and processing flow of the automated test program, the basic data resources for spacecraft electrical survey are provided, and the automatic test system is designed to realize the automation and process of the spacecraft electrical test cycle. It is suitable for different series spacecraft electrical survey, effectively reduce the manual operation of the testers and the number of personnel, improve the test efficiency, meet the high-intensity ground test, and efficiently complete the high-density manned spacecraft launch mission. Keywords: Spacecraft Automation test




Electrical test




Automation test procedure




1 Introduction Spacecraft electrical testing refers to a comprehensive inspection of the spacecraft’s functions, performance, interfaces, etc. under power supply. Spacecraft electrical testing is one of the most important aspects of several spacecraft development activities [1, 2]. As the spacecraft model development task is further intensified, there are mass production and high-density launch missions for spacecraft, the traditional test mode dominated by manual sending instructions can no longer meet the requirements. The spacecraft electrical testing process must be streamlined and automated. Improve the efficiency of electric testing, reduce costs, solve the contradiction between manpower and material resources, and meet the needs of spacecraft high-density launch missions. Spacecraft automation testing is the main development direction and trend of spacecraft testing technology, That is, using the computer to automatically complete the control command transmission and the downlink data monitoring according to the predetermined program is an inevitable requirement and an effective means for improving the test efficiency, ensuring the test quality and safety, shortening the test period, and streamlining the test team. Spacecraft automation testing has made great progress abroad. Boeing has a worldclass advanced satellite assembly test factory equipped with a complete automation test © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 188–194, 2019. https://doi.org/10.1007/978-981-13-7123-3_23

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system to form an efficient running test procedure. Thales Alenia Space has implemented fully automated testing of payloads. China’s aerospace technology department introduced the idea of automation testing to improve the automation and intelligence of test systems. Compared with foreign countries, the domestic research on spacecraft automation test is still in its infancy, mainly summarizing the characteristics of spacecraft automation test language [3–8], a valid test executable digital resource has not yet been formed. In order to improve the research capability of China’s spacecraft and catch up with international advanced testing technology, it is extremely urgent to develop spacecraft automation testing.

2 Automation Test Language Design The test procedure is composed of a single piece of text and organized by time. Each field is separated by a tab, and each row represents a specific operation. Each operation may include multiple sub-operations according to different opcodes, and each suboperation may include multiple operation parameters. The definition of the test language begins with a step number and ends with a specific field. By designing the format and function of the automated test language, the spacecraft automation testing can be streamlined and fully cycled. Table 1 shows the automation test procedure format (Tables 2 and 3).

Table 1. Automation test procedure format Step number The serial znumber of the operation

Time type Time type

Time value The execution time of the operation

Opcode

Subaction 1 Defines the content The specific and process of content of subsequent operations. Different the first subopcodes have different number of operation sub-operations

Subaction … Terminator 2 – The specific content of the second suboperation

Table 2. Examples of automation test procedure Step number 1 2 3

Time type 0 1 2

Time value 0 3.5 12

Opcode Subaction 1 2 3 1

K1 (DAHE.A005 >5) WAIT(100)

4

3

2.5

1

JUMPTO PROCEDURE3

Subaction 2

K1 IF(DAHE.C060 > 0.5) GOTO 90 ELSE GOTO 4

Terminator – – –

–

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Opcode Subaction name 0 Judging telemetry parameters

1 2 3 4 5

6

…

Subaction description Determine the telemetry parameters and perform subsequent operations after satisfying the judgment conditions Testing program control Control testing program start, pause, resume, delay, stop, jump, link, etc. Send instructions Send instructions Determine the telemetry parameters and Send the instruction after satisfying the send instructions judgment conditions Send instructions and determine Determine the specified telemetry telemetry parameters parameter after sending the instruction Determine telemetry parameters, send First determine the specified telemetry instructions, and determine telemetry parameters, send the instruction after parameters satisfying the judgment condition, and then judge the specified telemetry parameters Determine the specified telemetry Judging telemetry parameters, test program control parameters, control the test program start, pause, resume, delay, stop, jump, link, etc. after satisfying the judgment condition … …

Step number: The serial number of the operation of the line is generally incremented in order from the beginning of 1. Time type: 0 means execution in absolute time (relative to the first step), 1 means execution in relative time (relative to the previous step), Time value: The time of execution of the operation, absolute time, relative time are expressed in two ways, one is “number” and the unit is second; one is “day: hour: minute: second”, such as “00: 01:03:14” is equivalent to “3794 s”. Opcode: The content and process of subsequent sub-operations are defined. Different opcodes have different number of sub-operations and can be a combination of multiple sub-operations. Subaction: In general, sub-operations can be divided into judging telemetry parameters, test program control, sending instructions, and combinations thereof. Judging telemetry parameters support: 1. The bitwise operation “bitwise AND”, “bitwise OR” is supported in the conditional judgment; 2. The conditional judgment supports the comparison operators , =, ==, *=, which means less than, greater than, no greater than, no less than, equal, not equal; 3. The conditional judgment supports the numerical operators +, –, *, /, ^, and %, which respectively represent addition, subtraction, multiplication, division, exponential power, and modulo;

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4. Support for logical operations and, or, not, in conditional judgment; 5. In the conditional judgment, the displacement operation is supported to shift to the left and to the right; 6. The following common mathematical functions are supported in conditional judgment (Table 4): Table 4. Mathematical functions supported by parameter parsing language Keyword abs cosh cos exp floor log10 log sinh sin sqrt tanh tan …

Meaning Absolute value Hyperbolic cosine function Cosine function E-based x-th power The largest integer not greater than x Logarithm of base 10 Natural logarithm of a number Hyperbolic sine function Sinusoidal function Open square function Hyperbolic tangent function Tangent function …

Test procedure control operation contents can indicate start (EX), pause (HOLD), delay execution (WAIT), stop (QUIT), inter-procedure jump (JUMPTO), in-procedure jump (GOTO), program link (LINKTO), Conditional judgment (IF…ELSE), etc. (Table 5).

Table 5. Test procedure control operation classification Logical type Conditional judgment

Meaning Conditional judgment based on telemetry parameter values (IF… ELSE) Jump directly to other Jump to the other steps of this test procedure. Consists of steps “keyword” + “step number”, specifying the step to jump to the specified step number of this test procedure (GOTO) Jump directly to other test Jump to other test procedures, consisting of “keyword + other test procedure procedure name”, specify jump to a specific test procedure (JUMPTO) Link into other test Link into other test procedures, consisting of “keyword + test procedures project name”, specify link specific test project to this test project; link and jump are different, jump will not actively return to this test project, when the linked test project is executed, the link returns to the test project to continue execution (LINKTO)

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3 Automation Testing System Design The spacecraft automation testing business process is shown in Fig. 1. It realizes the automation of the three stages of test preparation, test execution and test evaluation, and realizes the automation of the whole test process [9].

Fig. 1. Spacecraft automated test business process

Test preparation: Through the modular design idea, the test project design is carried out according to the test outline, the test project is organized, and the test rules and test procedures are automatically generated. Test execution: The test procedure generated during the test preparation phase is input, the test procedure is executed, and the unattended self-monitoring is performed in real time. When an abnormality occurs, the program jumps autonomously according to the program design. Test evaluation: The spacecraft and subsystem are used as the structure to evaluate the implementation of each test procedure and the execution of the test procedure, and the detailed test evaluation report is automatically generated according to the test rules. The tester’s phased test coverage and test intensity analysis are shown in schematic form. The automation test system consists of a control terminal, an execution terminal, a database, and a display terminal. The automation test system consists of a control terminal, an execution terminal, a database, and a display terminal. The execution end of the automation test system extracts the corresponding test resources from the automation test database, the operator operates the control terminal, controls the execution terminal to run the automation test program, and the execution terminal broadcasts the test process information to the local area network, and the database records the test process information to provide a number for the test evaluation. The tester monitors the test process through the display terminal. The automation test system and other components of the spacecraft integrated test system work together to complete the automation test of the spacecraft, as shown in Fig. 2.

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Fig. 2. Schematic diagram of automated test system deployment

4 Application and Effectiveness The automation test procedure and system are applied to the electrical test of a certain type of spacecraft, covering electrical performance test, mechanical test, thermal test, launch field test, etc. The test program execution accuracy is 100%, and the command is sent correctly and on time. After the test procedure is applied, the instructions are not manually sent, the test task is programmed, the test implementation is standardized, the manual operation is reduced by about 60%, the test time is saved by about 30%, and the accuracy of the test operation is improved. According to statistics, in the same series of models, 201 of the 225 test procedures can be directly inherited, and the proportion of use is close to 90%, which can effectively reduce the number of test personnel and achieve better application results (Table 6).

Table 6. Application statistic Test procedure type Number of test procedures Inheritable quantity Power up and power off the device 54 54 Subsystem check and match 132 117 Flight mission simulation test 12 8 Mechanical test and thermal test 27 22 Total 225 201

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5 Conclusion The automation test procedures and applications provide the basic data resources for the spacecraft electrical test, so that the spacecraft test is no longer an independent individual and discrete instructions, with process and test procedures control capabilities, which can effectively reduce the manual operation of testers and manual intervention. At the same time, integrate test resources, reduce unnecessary duplication of work, effectively reduce testers, adapt to high-intensity testing, and efficiently complete high-density spacecraft launch missions.

References 1. Wang, Q.: Eletrical Test Technology of Spacecraft. Chinese S&T Press, Beijing (2007) 2. Li, X., Wei, C., Zhang, W., Xia, Q., Li, T.: On-orbit maintainability design system for manned spacecraft. J. Syst. Eng. Electron. 38(1), 84–89 (2016) 3. Britton, K.J., Schaible, D.M.: Spacecraft Testing Programs: Adding Value to the Systems Engineering Process, Kennedy Space Center, Langley Research Center, 29 March 2011 4. Ma, S., Yu, D.: Automated Test Language and system for Spacecraft Test. National Defence Industry Press, Beijing (2011) 5. Zhu, W., Wang, J., Li, D.: Modeling for spacecraft automated test and design of automated test language. J. Electron. Meas. Instrum. 118–121 (2009) 6. Sun, B., Ma, S., Yu, D.: Spacecraft automatic test and spacecraft test language. J. Beijing Univ. Aeronaut. Astronaut. 35(11), 1375–1378 (2009) 7. Dang, J., Yu, J., Zhou, J.: Automatic Measurement Technology of Integrated Test System for FY-2 Meteorological Satellite, Aerospace Shanghai, pp. 72–77 (2005) 8. Zhu, W., Wang, J., Li, D.: Design of spacecraft automated test software based on directed graph modeling. Comput. Eng. Des. 31(8), 1702–1705 (2010) 9. He, Y., Pan, S., Li, H., et al.: Design and application of automatic test system for manned spacecraft. Comput. Meas. Control 23(10), 3258–3263 (2015)

Analysis of Wave Propagation in Functionally Graded Material Annular Sector Plates Using Curved-Boundary Legendre Spectral Finite Elements Teng Wang(&) Beijing Institute of Spacecraft System Engineering, Beijing 100094, China [email protected]

Abstract. Functionally graded materials (FGMs) are a kind of composite materials, where its properties change along spatial coordinates. Study on wave propagation in the FGMs is for the detection of damage. A time-domain 2D curved edge spectral finite element method (SFEM) is introduced in this paper to model wave propagation in complex FGM structures. According to the classic finite method, the shape functions of SFEM uses the Lagrange interpolation polynomials at Gauss-Lobatto-Legendre (GLL) points. GLL quadrature rules are used to calculate the element matrix, which brings the advantage of diagonal mass matrix. In addition to beyond that, the spatial variation of material properties inside the element is under considerations and the quadratic Lagrange polynomial as interpolation functions can represent the curved boundary of structure. The efficiency of the introduced SFEM model simulating lamb wave propagation in FGM rectangle plates is illustrated and verified by analytical data. The wave responses in a FGM ring are solved by straight edge SFEs and curved edge SFEs respectively to prove the efficiency of the curved edge element. Finally, wave propagation in the FGM ring is studied through the vibration mode, time domain response and phase velocity. Also, the effects of excitation frequency and FGM parameters are investigated. The results demonstrate that the developed curved edge SFEs and SFEM model can offer an efficient and realistic simulation for wave propagation in two-dimensional FGM structures with curved edge. Keywords: Wave propagation
FGM
Spectral finite element
Curved edge

1 Introduction Functionally graded materials (FGMs) are increasingly used in the fields of aerospace, civil and automotive engineering. Generally, FGMs are made of two kinds of materials to satisfy specify structural performance demands [1]. Compared to traditional composite, the significant property of FGMs is that the spatial coordinate dependent material properties can overcome the interfacial problem typical for mostly layered composite structures. However, the FGM structures are often subjected to corrosion, impact or fatigue under severe environment condition. The damage detection of those © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 195–203, 2019. https://doi.org/10.1007/978-981-13-7123-3_24

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structures is critical for maintaining their status. Recently, the guided-wave based damage detection has been is proposed as a very potential way to identify and location damage in structures [2]. Understanding the wave propagation behavior of wave is very important for building an effective damage detection strategy. In the past decades, dynamics analysis of FGM structures becomes necessary and significant. A number of investigations have been done to study the wave propagation behavior in the FGM structures by analytic methods and numerical methods. Numerical methods are proposed to analyze the wave propagation in FGM structures with complex geometries recently. Among many numerical methods, the finite element method (FEM) and the spectral finite element method (SFEM) are two popular tools. To analyze wave propagation in FGM structures with numerical methods, material simplification has to be done to consider the continuous varying of material properties [3]. However, the studies in [4] show that only the continuous material model can give more accurate and detailed results for FGM structures. Simulations of wave propagation in FGM structures with finite element method can be found in [4–6]. Exact numerical simulation of wave propagation in structures always costs a lot of time and memory space. The spectral finite element method has attracted high interest in recent years, which can improve computational efficiency and reduce computation time. The time-domain spectral finite element (SEM) is proposed by Patera in the 1984 [7]. A Chebyshev spectral plate element for wave propagation in isotropic structures is developed in [8]. Curved structures are widely used in Engineering. To the author’s best knowing, wave propagation in curved component has not been paid much attention. In this paper, elastic wave propagation in a curved FGM waveguide is investigated. To achieve more accurate geometry approximation, a curved-edge time-domain spectral element is developed. A quadratic Lagrange function is to conduct the geometry shape transform while higher orders Lagrange function is to interpolate the displacement field. The continuous material model is involved to model FGMs.

2 Motion Equation Wave propagation in three-dimensional solids is a dynamic problem that satisfies the elastic dynamic equation, including equilibrium equation, geometrical equation and physical equation. Based on the strain or stress condition, three-dimensional problem can be simplified to the two-dimensional problem. Following the traditional Galerkin procedure, the motion equation can be described by € þ C Q_ þ KQ ¼ F; MQ

ð1Þ

Like the classic FEM, the spectral finite element method follows the similar procedure. Then the establishment of the two-dimensional spectral finite element method is presented in detail.

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3 Spectral Finite Element Formulations 3.1

Multiple Waveguides Structure

A multiple waveguides structure with straight-boundary and curved-boundary parts is shown in Fig. 1. The curved-boundary part is a sector with an angle of h, internal radius Ri and outer radius Ro. the straight-boundary part is a rectangle with a dimension of L  (Ro – Ri). This structure is established in a global coordinate system and a denotes the angle relative to the x positive direction. This two-dimensional model is under the hypothesis of plane strain. The toneburst line excitation is loaded on the left side, as shown in Fig. 1. The wave response is measured on the outside edge. Parameters of Ro and a decide the specific location of measuring point.

Fig. 1. Structure with straight-boundary and curved-boundary parts

The whole structure is made of functionally graded material. Outside and inside phases are pure Ceramic and Chrome. Properties between them change along the thickness. Establish a one-dimensional system d along the thick direction. Property values f ðdÞ of the middle material, including elastic modulus, Poisson ratio and density can be determined by the volume fraction. The volume fraction is a power law function with exponent v. f ðdÞ ¼ fi þ ðfo  fi Þ  gðdÞ

3.2

ð2Þ

Shape Transform

Notice that there are curved-boundary elements in this structure. The classic spectral finite element is established in natural coordinate system (n, η). To complete the analysis in the global coordinate system, coordinate transformation has to be involved here. Geometry of each element can be determined through node location and interpolation function. A quadric Lagrange function is implemented in this paper for the accurate description of curved-boundary elements. The grids of nine-node 2D element can be generated by Ansys ICEM CFD 15.0 and exported in a Nastran format.

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Then element geometry can be expressed through interpolation form below x¼

n X

u k xk ;

y¼

k¼1

n X

uk yk

ð3Þ

k¼1

where uk ðk ¼ 1; 2. . .9Þ denotes Lagrange function. 3.3

Nodes Distribution and Displacement Field Interpolation

The Legendre spectral element is estimated in local coordinate. Different from the traditional high order finite element method (FEM), nodes of the Legendre spectral element are uniformly distributed in a square shown in Fig. 2. Coordinates of these nodes are determined through the roots of Gauss-Lobatto-Legendre polynomial.

Fig. 2. Shape transform

3.4

Continuous FGM Property Model

Considering the spatial variation of the FGM property, elastic modulus, density and Poisson ratio are continuous functions of the coordinates. Then a continuous material model inside the spectral finite element is introduced to model the FGM structures accurately. In the continuous material model, the properties of each point are calculated through the continuous function of coordinates. According to the elastic mechanics and finite element method, density and elastic matrix of 2D plane strain problem are expressed as  le ðn; gÞ ¼ 2

qðn; gÞ 0 1vðn;gÞ 12vðn;gÞ

6 v D ðn; gÞ ¼ 6 4 12vðn;gÞ e

0

 0 ; qðn; gÞ vðn;gÞ 12vðn;gÞ 1vðn;gÞ 12vðn;gÞ

0

0

3

7 Eðn; gÞ 07 5 1 þ vðn; gÞ : 1 2

ð4Þ

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199

Mass and Stiffness Matrices

With the interpolating displacement field and geometry in the element area, based on the Hamilton variational principle, the element mass matrix, stiffness matrix and equivalent node force are expressed in an integrating form. Z Me ¼ Z Ke ¼ Z F ¼

Xe

Xe

e

N e ðx; yÞT le ðx; yÞN e ðx; yÞdXe  Be ðx; yÞT De ðx; yÞBe ðx; yÞdXe 

i¼1 n X i¼1

T

N ðx; yÞ PdXe  e

Xe

n X

n X

xi

n X

i¼1

xi xi

n X j¼1 n X

xj N e ðni ; gj ÞT lek ðx; gÞN e ðni ; gj Þ det½J e ðni ; gj Þ xj Be ðni ; gj ÞT Dek ðx; gÞBe ðni ; gj Þ det½J e ðni ; gj Þ

j¼1

xj N ðni ; gj ÞT P det½J e ðni ; gj Þ e

j¼1

ð5Þ The quadrature characteristic of the Legendre polynomial results in the diagonal matrix of mass. The mass matrix and stiffness matrix of element can be integrated to the global mass and stiffness to solve the dynamics equation. Explicit-format numeric method is generally used to solve the wave dynamic equation. Central difference method is implemented in this paper.

4 Numerical Validation To validate the efficiency of the proposed element, an FGM plate excited by a timeload is investigated, shown in Fig. 3. In this paper, the FGM plate excited is simplified to 2D plane strain problem. The continuous material model in Legendre spectral finite element is implemented to solve the 2D wave propagation problem. The results are validated by the traditional FEM, operating in ABAQUS. In FEM analysis, the structure is divided into many layers and in each layer, material properties are regarded as constant value, which lies on the average of each layer.

Fig. 3. Two-dimensional model of FGM plate

The 2D model is shown in Fig. 3 with the thickness of 0.02 m and a finite length of 3 m. The material property is changing linearly in the thick direction where grads parameter. Phases of top and bottom are Aluminum and Zirconia where elastic moduli are 70 GPa, 151 GPa and densities are 2700 kg/m3, 3000 kg/m3 and Passion ratio is

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the 0.3 constantly. The point impact is 5 periods sinusoid with the central frequency of 50 K modulated by the Hanning window function. The elements are 2  80 and there are 7  7 nodes distributed in each element for SFEM. The elements are 16  1200 and element type is 4-node bilinear plane strain element. To catch the wave responses accurately, an time increment of 1  10–8 s is needed. Displacements responses of measuring points located at x = 1.5 m, y = 0.02 m are recorded. As shown in Fig. 4, a very good agreement with FEM solution is observed. The results demonstrate that the introduced continuous material model for FGM is validated.

Fig. 4. Displacements of X, Y on top surface at x = 1.5 m

5 Results and Discussion The wave propagation in FGM multiple waveguides structure subjected to longitudinal plane load is then investigated. In the structure, Ri = 1 m, Ro = 1.02 m, L = 0.48 m, h = 90°. A Hanning-windowed three-cycle sinusoidal toneburst is used as excitation signal. Wave field, time domain responses of a measuring point and phase velocity dispersion are all presented. The measuring point is located at the outside of the structure away from the load along the boundary. 5.1

Straight Element and Curved Element

To analyze wave propagation in simple FGM plate structure with straight boundary, the linear Langrage function with four corner nodes is enough. For the complex curved edge structures, high order Langrage function is necessary. In this paper, second order Langrage function with four corner nodes, four nodes on the middle edge and the central node is implemented to compare to the straight elements. The structure is meshed by 3  16 elements. The integration increment is 2  10−8 s. Figure 5 gives the displacement contours at t = 280 µs obtained by the curved edge elements and the straight edge elements, respectively. The difference in geometry shows that the quadratic Lagrange function has the full ability to describe the quadratic boundary accurately. The numerical model made by straight elements only has broken lines to describe the profile of the structure. There is small obvious difference in wave field between curved elements and straight elements. Time-domain responses of different measuring points are farther investigated.

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Fig. 5. Displacement field at t ¼ 280 ls. (a, b) Displacement of X, (c, d) Displacement of Y; (a, c) Solved by curved element and (b, d) solved by straight element.

The results show that using curved edge elements is very necessary in wave propagation analysis of the FGM structures with curved boundaries. 5.2

Wave Responses in the FGM Multiple Waveguides with Changing Grads Parameters

As described in Sect. 3.1, power low is used here to define the spatial variation of the FGM material property. And the power v is used to denote the changing rate of material property. In this section, the effects of v on wave propagation in the FGM multiple waveguides are investigated. Figure 6 gives the responses of the measuring point with varied grads parameters. Note that amplitude and phase offset are different because of the power variation. As the power increases, amplitude of the wave packets descends and phase offset increases. Thus time domain displacements responses have the ability to recognize the value of the power. It should be pointed out that the material property is uniform as v = 0. There is only one wave pocket in time-domain response, presenting S0 vibration mode.

Fig. 6. Responses of the measuring point at a = 90°, Ro = 1.02 m, with varied grads parameters.

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Effects on dispersion are investigated through the phase velocity. Figure 7 shows the phase velocity of S0 mode oscillation and A0 mode oscillation. The dispersion results demonstrate that with the increase of the material grads parameters, amplitude of the phase velocity decreases. The level of the dispersion with varied powers does not change obviously.

Fig. 7. (a) Phase velocity of S0 mode oscillation and (b) phase velocity of A0 mode oscillation.

6 Conclusion In order to investigate the wave propagation in the functionally graded material multiple waveguides with complex boundary, a curved-edge Legendre spectral element involved a continuous material model, is introduced. In the new element, quartic Lagrange function is implemented to complete the coordinate transform from the real structure element to standard element. Considering continuously spatial changing of the material property inside the element, determine the property value in the integral node through the volume fraction function of the coordinates for accurate modeling of FGM. Then numerical analysis on a finite FGM plate with a point impact validates the continuous material model, compared to the FEM results. And wave propagation in the multiple guides is studied. Wave field, time-domain response and dispersion of phase velocity are presented. Comparison between curved SFE and straight SFE is done. Effects of difference material grads parameters on wave propagation behavior are investigated. It is validated that the quartic Lagrange function for shape transform and the continuous material model inside the Legendre spectral finite element is very necessary and efficient when analyzing wave propagation in FGM multiple waveguides structure. Results show that the FGM multiple guides structure loaded an impact holds two different vibration mode S0 and A0. A0 mode oscillation disperses obviously while S0 mode oscillation does not. A further study on material grads parameter show that as the power v increases, amplitude of wave response increases and phase velocity descends. It is suggested that the stability of S0 mode oscillation is preferred to be chosen for damage detection based on guided wave. The future work can be extended to the crack monitoring in FGM structures or material characterization.

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References 1. Markworth, A.J., Ramesh, K.S., Parks Jr., W.P.: Modelling studies applied to functionally graded materials. J. Mater. Sci. 30(9), 2183–2193 (1995) 2. Birman, V., Byrd, L.W.: Modeling and analysis of functionally graded materials and structures. Appl. Mech. Rev. 60(5), 195–216 (22 pages) (2007). https://doi.org/10.1115/1. 2777164 3. Chen, W.Q., Wang, H.M., Bao, R.H.: On calculating dispersion curves of waves in a functionally graded elastic plate. Compos. Struct. 81(2), 233–242 (2007) 4. Berezovski, A., Engelbrecht, J., Maugin, G.A.: Numerical simulation of two-dimensional wave propagation in functionally graded materials. Eur. J. Mech. A. Solids 22(2), 257–265 (2003) 5. Cho, J.R., Ha, D.Y.: Averaging and finite-element discretization approaches in the numerical analysis of functionally graded materials. Mater. Sci. Eng. A 302(2), 187–196 (2001) 6. Patera, A.T.: A spectral element method for fluid dynamics: laminar flow in a channel expansion. J. Comput. Phys. 54(3), 468–488 (1984) 7. Żak, A.: A novel formulation of a spectral plate element for wave propagation in isotropic structures. Finite Elem. Anal. Des. 45(10), 650–658 (2009) 8. Dauksher, W., Emery, A.F.: Accuracy in modeling the acoustic wave equation with Chebyshev spectral finite elements. Finite Elem. Anal. Des. 26(2), 115–128 (1997)

Research on Design of Quality Management Module in Spacecraft Assembly MES Qiang Wang(&) Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China [email protected]

Abstract. Based on the research and development of spacecraft assembly, this thesis starts from “how to improve the quality management level of spacecraft assembly work under the rapid growth of spacecraft business in China”, and referring to the design and development experience of quality management system in enterprise informatization practice, and combined with the practical experience of spacecraft assembly development and management, and puts forward suggestions for improving the design of quality management module in MES of spacecraft assembly- “To build an integrated quality management environment” and “To build Quality-Knowledge-Base as a support platform”, and preliminarily considers the quality management mode for improving spacecraft assembly work under the condition of informationization. Keywords: Spacecraft assembly


MES
Quality management

1 Introduction With the rapid growth of China’s aerospace business, a large number of major Aerospace missions with high-quality and high-efficiency support have put forward new requirements and challenges to the development of spacecraft AIT projects. However, it is difficult to meet the requirements for core competency and leapfrog development of spacecraft AIT projects only by expanding the scale of scientific research and production and increasing the cost of scientific research investment. Therefore, it is necessary to continuously deepen and improve the model of spacecraft AIT project to meet the needs of development, while systematically plan to seek breakthroughs, practice and explore new models, fundamentally enhance basic capabilities, and promote the sound and rapid development of the aerospace industry. For the high-risk aerospace industry itself, the improvement of quality management capability is particularly prominent and urgent. In recent years, information technology has been integrated into the quality management of aerospace enterprises, and has become an important technical way to upgrade and optimize the quality management capability [1]. Based on the analysis and elaboration of the characteristics of spacecraft assembly, this thesis puts forward some suggestions for improving the design of quality management module in spacecraft assembly MES (Manufacturing Execution System). This thesis mainly studies the design idea of quality management function in spacecraft assembly MES, so it does not discuss the specific overall MES framework and information technology. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 204–211, 2019. https://doi.org/10.1007/978-981-13-7123-3_25

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2 Development Features of Spacecraft Assembly The assembly work of spacecraft model is the terminal part of spacecraft development. (1) Spacecraft assembly has the characteristics of small batch. Most of the platform spacecrafts are manufactured at the same time in a small number. The engineering experience of previous models has strong reference significance for spacecrafts in research. (2) Spacecraft assembly work requires a tight schedule and high-quality requirements. Spacecraft development is mostly a national key project, which is of great significance. Once the progress delays and quality problems occur as the terminal link, the launch and use of spacecraft will be directly affected. (3) Under the management system of the combination of R&D and production in spacecraft assembly, it has strong dynamic characteristics in the process of assembly implementation, including demand change, design change, process route, resource conflict, single product change and other factors. (4) Spacecraft assembly involves many fields and units. There may be no administrative subordinate relationship between units. Cooperative working environment is complex, and conflict coordination and problem handling is complex.

3 The Design of Quality Management Module in MES 3.1

To Build an Integrated Quality Management Environment

The development and production process of spacecraft is complex system engineering. It needs the research units from many fields and the research personnel from different majors. Even in various units and personnel within the aerospace inside, there are different understandings of quality management mode, habits and even quality management. At the same time, the sharing of quality knowledge in spacecraft assembly design and implementation is not enough. The objective reason is that in the process of multi-unit technical coordination, problem handling and management coordination, the problem is particularly prominent in the stage of spacecraft assembly. The increase of collaboration scope leads to the increase of working interface, which leads to the increase of spacecraft assembly. The implementation quality synergy efficiency is not high. All these cause it difficult to solve the problem in the process of multi-unit technical coordination, problem handling and management coordination, and result in poor quality coordination efficiency in spacecraft assembly. Therefore, it is necessary to build an integrated quality management environment based on the information systems of spacecraft assembly design units and assembly implementation units (see Fig. 1) to support spacecraft assembly with high efficiency and quality.

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Fig. 1. Integrated quality management environment.

To Build a Cooperative Environment for Spacecraft Assembly Spacecraft assembly is based on the combination of R&D and production management system. There is a need for collaboration between design units and implementation units. Therefore, the integrated quality management environment should first achieve a cooperative working environment for spacecraft assembly design and implementation (see Fig. 1) [6]. At a technical level, it is not only to achieve the coordinated processing of technical problems and technical coordination in the same system and environment, but also to construct the quality information transmission channels of spacecraft assembly design system and MES, to realize the interaction of design information and implementation information [3]. Design information can support technical problem processing and coordinated technical solution, which can be used to guide the implementation, and the implementation information can be used to improve the design. At the same time, the system can set up the state information of the design phase. The state information of the design phase can be compared with that of the spacecraft assembly phase, and the correctness of the state of the assembly can be checked automatically (see Fig. 1). In terms of management, it is necessary to establish an integrated process management model based on workflow and related business with cooperating units, and then to realize the business Coordination of spacecraft assembly quality management, such as operation on board, processing of related procedures of status confirmation on board, so as to ensure the standardization of spacecraft assembly quality management (see Fig. 1). To Strengthen the Main Environment of Quality Management The integrated quality management environment extends the boundaries of upstream and downstream cooperative units for spacecraft development and provides information interface (see Fig. 1). The information interface mainly transfers the quality

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knowledge of the shared spacecraft assembly to the development and cooperation units, strengthens the main environment of spacecraft assembly quality management, transmits the quality management requirements, rules and workflow in specific problems, gradually eliminates differences in understanding quality management with collaborative units, strengthens the identity of the collaboration units for the quality control of the spacecraft assembly, creates the foundation for multi-unit collaboration and efficient deployment of spacecraft assembly, and avoids the difficulty in handling and technical coordination caused by the difference of quality management perception, and time delay, which will affect the progress and quality of spacecraft assembly. 3.2

To Build Quality-Knowledge-Base as a Support Platform

The Quality-Knowledge-Base support platform can realize the accumulation and inheritance of knowledge and experience in the process of spacecraft assembly implementation, and effectively support the spacecraft assembly work. For example, managers hope that risks and their influencing factors in the same or similar work in the past can be paid attention to before the implementation of a certain assembly work, and relevant control measures and implementation experience can be used for reference in the implementation process. At the same time, the quality requirements and technical specifications related to operation can be transmitted, reminded and implemented in place, so as to prevent the occurrence of repetitive problems. Therefore, it is necessary to build a multi-dimensional quality-knowledge-base support platform in MES and a spacecraft assembly support mode based on the support platform. (see Fig. 2).

Fig. 2. Quality-Knowledge-Base structure

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In addition, discovering and forming quality knowledge from quality information is an important way to discover and improve quality knowledge. However, in the database design of MES, Quality-Knowledge-Base and quality information base are designed separately. There is no Quality-Knowledge-Base design without them. If Quality-Knowledge-Base and quality information base are not considered as a whole, this isolated design leads to the inefficiency of finding new knowledge or knowledge from quality information [7]. Quality-Knowledge-Base Structure The Quality-Knowledge-Base structure should be divided into three parts: index library, quality knowledge information base and solution Library (Fig. 3). According to user’s input, index library extracts key features by means of language understanding and analysis technology, and retrieves them in the index database [2, 4]. Typicalproblem users can directly access solutions from the solution database. To atypical problems, the system locates the key features on the specific quality knowledge database. Information base mainly stores basic information such as quality problems, engineering experience, quality requirements and technical specifications. Users can automatically or manually form solutions by extracting relevant quality knowledge information from index base [5]. Solution Library mainly stores solutions of typical operation, typical technical problems and typical management problems.

Fig. 3. Quality-Knowledge-Base structure

Quality-Knowledge-Base Establishment Quality-Knowledge-Base is an evolving dynamic system. The acquisition of new knowledge needs to be completed in many times of spacecraft assembly practice. The existing engineering experience, quality control measures, technical specifications and quality requirements need to be verified, evaluated and improved through many times of spacecraft assembly practice. Therefore, quality information is the source and carrier

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of Quality-Knowledge-Base for spacecraft assembly. Based on this, MES database design should consider the organization and expression of quality information base and Quality-Knowledge-Base. The presentation of quality knowledge should be easy to guide the assembly work of spacecraft. The structure design of quality information base should consider the knowledge discovery technology which is easy to use, so as to discover quality knowledge and continuously improve and enrich quality knowledge. To Build up Support Mode of Spacecraft Assembly Based on Quality-Knowledge-Base In MES, the correlation functions of assembly process and Quality-Knowledge-Base is designed. In the planning and compiling stage of process regulations, guided by technicians and quality managers, and with assembly process as the core, a structured relationship between Quality-Knowledge-Base and assembly process is constructed to ensure that before the implementation, the staff of each position can learn the process requirements and contents, and draw lessons from their own implementation experience, understand the past quality problems and identify risks and hidden dangers ahead of time, and implement control measures to reduce the probability of occurrence of quality problems. Spacecraft assembly mode based on multi-dimensional information support of Quality-Knowledge-Base is formed to achieve accurate monitoring and warning of information technology.

4 Expected Results and Prospects The integrated quality management environment constructs a cooperative working environment for spacecraft assembly design and implementation. The two-way sharing of design and implementation of information frees from technology coordination, problem handling and design collaboration from space constraints. It is not necessary to switch between various discrete documents and multiple software manually and easier and more efficient to work cooperatively. And the support platform of QualityKnowledge-Base has realized the support mode of the spacecraft assembly based on the platform. In this mode, quality knowledge is continuously acquired, shared, applied and improved, and the working will achieve higher-quality stability, the repetitive quality problems will be greatly reduced in the future. The technology level will also be steadily improved with the continuous improvement of Quality-Knowledge-Base. At the same time, the sharing of integrated Quality-Knowledge-Base makes the knowledge and understanding of spacecraft assembly quality management of multi-unit and multi-position more convergent, the coordination complexity of specific management issues will be reduced. Based on the above design, the spacecraft assembly quality management capability, work efficiency, reliability and process level will be greatly improved, which will further promote the core competence of spacecraft development (see Fig. 4).

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Fig. 4. Integrated quality environment etc. anticipation effect

A special quality management system and a set of system oriented to the whole process of model development should be established. On this basis, a unified quality management process technology interface and management interface should be established to eliminate the differences in understanding quality management modes, habits and even quality management among different units, and to establish quality management adapted to multi-enterprise environment. The mechanism of coordination is used to achieve quality information interaction and development collaboration in all aspects of spacecraft development.

5 Conclusion Aiming at the characteristics of spacecraft total quality management, this thesis studies the design of quality management module in spacecraft assembly MES. The spacecraft assembly design system, the integrated quality management environment taking MES as the cores, quality-knowledge-base support platform, as well as the design of quality management platform for tooling are emphatically discussed. These designs can promote the improvement of quality management capability and quality management mode.

References 1. Tang, X., Duan, G., Du, F.: Implementing Technologies of Quality Information System in Manufacturing Enterprises. National Defense Industry Press, Beijing (2009) 2. Feng, X., Zhou, L., et al.: Study of supporting platform in bi-base based integrated quality management system. Eng. J. Wuhan Univ. 39(2), 67–69 (2006) 3. Han, X., Li, M., et al.: Design and key technologies of spacecraft assembly MES. Aerosp. Manuf. Technol 2015(4), 55–59 (2015) 4. Baujut, J.F., Laureillard, P.: A co-operation framework for product-process integration in engineering design. Des. Stud. 23(6), 497–513 (2002) 5. Yuan, F., Xu, D., et al.: Reserach and realization of the quality management system based on case reasoning. Sci. Technol. Eng. 8(9), 2502–2506 (2008)

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6. Shanbao, Y., Wei, H.: The construction and implement of quality management system which faced to the manufacture process of aerospace product. Aerosp. Manuf. Technol. 2012(6), 19– 23 (2012) 7. Bingru, Y.: Knowledge Discovery Theory Based on Internal Mechanism and Its Application. Electronic Industry Press, Beijing (2009)

The Design and Implementation of Secure Cloud Desktop System Huifang Pan(&), Yi Yuan, Wenlong Song, and Zhou An Beijing Institute of Spacecraft System Engineering, No. 104, Youyi Road, Haidian District, Beijing, China [email protected]

Abstract. PCs with Windows XP have been widely used in enterprises. Confidential data is stored on the single hard disk of the PC. There are some disadvantages in this way, such as Poor data reliability, Low data security and so on. So, it is urgent to innovate and change the traditional PC usage model. With mainstream virtualization technology and customized security design to build a secure cloud desktop, it shall meet users’ needs and enhance the reliability and security of the data. Keywords: Secure cloud desktop


Reliability
Virtualization platform

1 Background PCs with Windows XP have been widely used in enterprises, and confidential data generated by users during the work is stored on the single hard disk of the PC. Thus the following problems occur: (1) Poor data reliability: Most staff may have access to confidential information and a large number of confidential documents are stored locally on the PC. Once key business data is lost due to physical or logical damage of the hard disk, it may cause incalculable loss. Moreover, there are uncertainties in the maintenance time and the, which seriously affects the user’s work. (2) Low data security: It is required for the system with confidential information to prevent external illegal intrusion and illegal data stealing by internal personnel. Although the PC implements multiple security protection methods such as host monitoring and three-in-one, a large amount of confidential data is scattered and stored in the user’s local PC. There is still chance that users maliciously destroy security protection or physically dismantle the local hard disk. (3) High-cost maintenance: Owing to different PC models and configuration, the user terminal administrator needs to completely install, configure, and manage the operation system, application software, security protection program, and patch update after testing and verifying deployment of multiple PC configurations, which is labor-consuming. Meanwhile, it may further increase the support cost since technical personnel often need to go to the filed to solve the problem due to the low degree of standardization. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 212–218, 2019. https://doi.org/10.1007/978-981-13-7123-3_26

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(4) Limited access: Since the data is stored locally on the PC, the users need to need to temporarily collect, sort, and copy it when they discuss plans in the conference room, develop the design in the designing room, or compare the data in the laboratory. The data may be lost or incomplete during transmission, which makes it difficult for users to work outside of office. Based on the above problems, it is urgent to innovate and change the traditional PC usage model. With mainstream virtualization technology [1] and customized security design to build a secure cloud desktop [2], it shall realize centralized storage of user data and unified release management of desktop application, to meet the needs of users’ daily office use, effectively control the output of user data, enhance the security of the user’s desktop system, and improve operational management and maintenance efficiency.

2 Cloud Desktop Design 2.1

Design Principles

The design principles are as follows. (1) Safety: it shall meet the requirements of security and confidentiality of military enterprises, in line with relevant national regulations; (2) Practicality: it shall deeply understand and analyze user requirement combined with current problems to design a secure cloud desktop system [3]; (3) Reliability: it shall have a comprehensive redundancy design to meet the requirements of uninterrupted operation, which will not affect the operation of the entire system in the event of failure of important node equipment; (4) Easy maintenance: it shall simplify system maintenance procedures and provide a more rational design and deployment method; (5) Scalability: it shall reserve sufficient expansion space and information interface for future business development to facilitate future expansion and adjustment; (6) Stability: it shall ensure existing application systems to move smoothly and safely to the new architecture platform, to maintain daily use and smooth transition. 2.2

Overall Architecture Design

The secure cloud desktop system consists of a virtualization platform, network side, and a terminal. The virtualization platform deploys hardware devices and desktop virtualization software in the data center, in charge of virtual desktop creation, scheduling, access control, and management. The network device and the security device in network side provide a secure and reliable link for the data access between the terminal and the virtualization platform [4]. After the PC or the thin client terminal is deployed by the terminal, it is coordinated with the virtual desktop platform to provide users with Virtual desktop image to meet the needs of daily office work and professional design (Fig. 1).

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Fig. 1. Overall architecture design of secure cloud desktop system

On the virtualization platform: the underlying server cluster and storage devices form the physical layer of hardware to provide a secure and stable computing, network, and storage environment for the upper layer. By adopting the leading virtualization technology as the underlying platform for the virtual desktop, it enables a single physical server to be run by multiple virtual desktops to form a virtual desktop resource pool. After virtual desktop management and access server clusters are deployed, it can be uniformly managed with terminal session access and desktop state, to effectively solve the tight coupling problem among operation system application, and hardware. All data is stored on a centralized storage device, and data is accessed through the FC switch between the server and the storage. On the Internet, switches, firewalls, and link transmission protection devices are deployed to provide a secure and reliable data transmission link for data transmission between the terminal and the virtualization platform [5]. It can be modified through either an existing PC or a customized thin client terminal. PC can be modified by replacing the hard disk with an electronic disk. It shall deploy a simplified embedded operating system, install a USB Key driver, add a domain, install a lock screen program, etc.; The thin client terminal performs safe reinforced cutting based on customized operating system to provide secure and stable operating environment for users.

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Security Design

From the prospective of physical security, operational security, information security and confidentiality, security and confidentiality management, it provides comprehensive security protection [7] for cloud desktop system. Physical Security The operation environment of the host machine in virtualization platform, blade workstation, and centralized storage device is stored in the data center computer room. It adopts physical protection methods such as the national secret access control system and video surveillance system, equipped with UPS power supply. With a strict management system and computer room inspection system, it ensures that all equipment is physically and safely controllable. Operational Safety The storage system is used to implement centralized storage of virtual desktop data and platform management data. To ensure the security of virtual desktop images and templates, a unified backup and recovery policy is implemented to support recovery through templates, snapshots, and migration modes, which ensures the integrity of virtual machine image and user data. Information Security In terms of information security and confidentiality, a series of security measures are taken. Identity authentication adopts “domain user” and “USB Key” authentication, to ensure the legal identity of users in the desktop virtualization environment. The secure cloud desktop system performs security auditing by recording logging of user virtual desktop access behaviors, including login, startup, shutdown, restart, maintenance, status query, etc.; Auditing Administrator operations includes virtualized resource management, update maintenance, storage resource allocation, data backup and recovery operations. Computer virus protection shall be conducted by deploying anti-virus software on the virtual desktop, and selecting an anti-virus version optimized for the virtualized environment to prevent the virtual machine from simultaneously scanning for disk viruses and upgrading the anti-virus storm caused by the client side. User data protection shall be realized by encrypting and protecting with encryption technology in the centralized storage device. The user data shall be effectively read and written after the identity of the user is recognized through identity authentication. Regarding transmission network security, the transmission network between the user terminal and the back-end virtual desktop, uses the USB Key certificate to establish an encrypted network transmission channel, to ensure data encryption and integrity protection between the terminal and the virtual desktop, and effectively prevent data information from being intercepted or analyzed. In terms of storage environment security, the terminal does not store user data in the desktop virtualization environment. All data is stored in centralized storage. Commands and image information are transmitted between the terminal and the virtual desktop, so that the storage environment shall be secured.

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Security and Confidentiality Management The virtual desktop security level identifier is used to process the information with confidentiality level of the virtual desktop according to the virtual desktop. The secret level identifier is bound to the virtual desktop and cannot be modified without authorization. About virtual desktop security management [6], it shall formulate strict management standard of virtual desktop usage. Virtual desktop creation, configuration modification, and abolition shall be approved. Virtual desktop migration shall be realized through the virtual machine isolation technology to achieve separation between different virtual machines on the same host. Only authenticated user terminal can access their own virtual desktop resources (hardware, software, and data). When an abnormality occurs on a host, the virtual machines run on the host are allowed to migrate in the same security domain to ensure high availability of the virtual machine. Terminal Security From the prospective of the convenience of management and the security of usage, the terminal in the secure cloud desktop adopts software and hardware design, and multiple security protection mechanisms to ensure its security. The security zone of the terminal device is separately divided by devices such as firewalls and switches. The data of the confidential information system is encrypted and saved to the back-end centralized storage without data in the terminal. 2.4

System Reliability Design

Storage Reliability Design High-reliability devices for storage mainly include: Raid-DP protocol for disk arrays. Physical damage of two hard disks at any time will not cause data loss. Disk arrays adopts hot standby of array dual-heads. In the event of failure of any controller, the other controller shall automatically take over all of the work; meanwhile, multiple copies of the user data shall be saved in the disk array. Network Reliability Design The virtualized platform devices, such as the host and the blade workstation, are connected to multiple access switches through multiple NICs, and then connected to the aggregation switch in the equipment room. The aggregation switch of the building where the user is located and the aggregation switch in the equipment room use duallink connections, to ensure the reliability of the Ethernet network. In terms of storage, it connects different fiber switches through two HBAs. The centralized storage adopts the multi-port redundancy architecture and the storage multi-path technology to ensure the reliability of the storage network link.

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Reliability Design of Virtualization Platform By establishing a resource pool, it shall realize the failure migration in the virtual desktop, to ensure online migration of virtual desktops from one host to another, and simultaneously achieving high-availability functions of the host. When an abnormality occurs on one host, the virtual desktop will automatically launch from another host. Virtual Desktop Service Reliability Design The virtual desktop platform separates the virtual desktop image from the user data by using the pooled desktop. Even if a virtual desktop image is abnormal, after the user relogin the system, the available desktop can be used to load personal data in the virtual desktop pool to get the available virtual desktop services immediately.

3 Application Effect Analysis 3.1

Reliability Analysis

During the construction of a secure cloud desktop system, all hardware is redundantly deployed including switches, network links, servers, storage devices, etc., to avoid system unavailability caused by hardware failures; all application components of cloud desktops adopts dual-system hot standby mode to eliminate single point of failure and avoid service interruption caused by failure of a component. It also uses technologies such as hot migration of virtual machine and dynamic resource adjustment to ensure load balance of the background resources and the stability of the system. Specifically, the original PC hard disk failure will result in user data loss. While the secure cloud desktop system saves the data on the back-end storage device and periodically backs up the data, thereby it ensures high reliability of user data; the user cannot work normally during repairing, but the secure cloud desktop system provides a redundant desktop resource pool. Each time the user starts up, an available desktop is taken from the resource pool, thereby improving the high reliability of the system. 3.2

Security Analysis

Traditionally data is scattered on each PC, with a risk of data being destroyed and stolen. In the construction of secure cloud desktops, all aspects of security protection measures have been developed in terms of physical security, operational security, information security and confidentiality, security and confidentiality management, etc., to achieve secure centralized storage and management of data. For example, through the security authorization policy, each user can only access his or her own directory; through the peripheral management policy, the download and copy of data is prohibited, and the security of the data is effectively improved. With the application mode of the secure cloud desktop, the user can only see the program image running in the background in the front end, and there is no data in the front end, thereby fundamentally ensuring the security of the client-side system and data, and effectively preventing the front-end user from illegally stealing and maliciously destroying confidential data.

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Maintenance Efficiency Analysis

In terms of traditional PCs, users need to go through a series of actions such as PC collect, operation system installation, confidential computer access, application software installation, security protection program installation, system patch upgrade, etc. The entire installation process takes about one day. After adopting the secure cloud desktop, the system has developed a standardized operation and maintenance management process at the beginning of the construction, including the standardized configuration of the front-end thin client and the monitoring and management of the back-end cloud desktop, so that the entire installation process takes only half an hour, which greatly improves the efficiency of operation and maintenance management. In addition, in the case of system upgrades, patch updates, etc., the administrator only needs to perform unified operations in the background once, so that all users can use it, thereby reducing the maintenance complexity. Meanwhile, according to the role of the user, the administrator can control the flow according to given privilege, configure the access policy flexibly and dynamically in the background, adjust the application and data range accessible by the user in time, and improve the fine configuration capability of the access control, which enhances the security of the system and applications.

4 Summary In summary, with the “demonstration project of confidential information system based on cloud computing technology”, a secure cloud desktop system is constructed, which realizes centralized storage of user data and unified release management of desktop applications, enhances centralized management and control of user desktops, and improves the reliability of the confidential information system and the security of the data. It has achieved good results with the significance of demonstration and promotion.

References 1. Wang, L.Z., Laszewski, G., Younge, A., et al.: Cloud computing: a perspective study. New Gener. Comput. 28(2), 137–146 (2010) 2. Yang, C.: Big Data and cloud computing: innovation opportunities and challenges. Int. J. Digit. Earth 1, 13–53 (2016) 3. Netto, M., Calheiros, R., Rodrigues, E., et al.: HPC cloud for scientific and business applications: taxonomy, vision, and research challenges. ACM Comput. Surv. 51(1), 8:1–8:29 (2018) 4. Luis, M.V., Luis, R.M., Juan, C., et al.: A break in the clouds: towards a cloud definition. ACM SIGCOMM Comput. Commun. Rev. 39(1), 50–55 (2009) 5. Chris, R.: A break in the cloud: the reality of cloud computing. In: EABR & TLC Conferences Proceedings, pp. 1–5 (2009) 6. Von Laszewski, G., et al.: Comparison of multiple cloud frameworks. In: IEEE 5th International Conference on Cloud Computing (CLOUD) (2012) 7. Kostantos, K., et al.: OPEN-source IaaS fit for purpose: a comparison between OpenNebula and OpenStack. Int. J. Electron. Bus. Manage. 11, 3 (2013)

Research on Satellite Power Subsystem Anomaly Detection Technology Based on Health Baseline Lei Zhang(&), Zhidong Li, Bo Sun, and Shuai Zhang Institute of Spacecraft System Engineering, 104 Youyi Road, Beijing, China [email protected]

Abstract. There is a huge amount of data in the satellite power subsystem, and the information and knowledge related to the operating status of the power subsystem in these data is an important for the abnormal state detection of the power subsystem. In order to make data mining of satellite power subsystem and set the reference for its anomaly detection, a satellite power subsystem anomaly detection technology based on the health baseline is proposed. According to the core operating conditions of the satellite power subsystem, health baselines are constructed respectively, and the effectiveness of the proposed method is verified by the telemetry data of the satellite power subsystem. With robustness, the test results show that anomaly detection of the onboard power subsystem can be achieved based on the constructed health baseline. Keywords: Satellite


Power subsystem
Health baseline

1 Introduction The satellite power sub-system is the satellite subsystem used for the whole star power supply and distribution and power connection between equipment. The health status of the satellite power subsystem largely determines whether the satellite can operate normally. All studies on the anomaly detection of satellite power subsystem are of great significance. In the internal data stream of the satellite power subsystem, there are massive multisource heterogeneous data such as design data, on-orbit telemetry data, historical telemetry data and ground test data. The information and knowledge about the health status of the power subsystem in these massive data is an important basis for the health management of the power subsystem. On the one hand, because a variety of telemetry parameters can reflect the health status of the same subsystem and equipment from different angles; On the other hand, due to the complex relationship between the power subsystem and the real-time telemetry parameters of the equipment, the evolution of its operating state can be reflected by the association of multiple parameters. The mutual penetration and cross-correlation pose new challenges for satellite operation state feature extraction and anomaly detection. For abnormal detection of power systems or electromechanical systems, in previous studies, thresholds were often set for individual parameters as the state of health baseline © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 219–226, 2019. https://doi.org/10.1007/978-981-13-7123-3_27

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judgment parameters to determine the state of the system. However, more and more scholars have begun to pay attention to the coupling relationships between internal parameters of the system, and to propose the standard of system anomaly detection. Wang et al. [1] introduced a spatial anomaly detection method based on attribute-related quantitative values. The attribute correlation value is calculated by quantitative analysis, and the spatial statistic is searched by the attribute correlation matrix and the R-tree dynamic index structure to realize spatial anomaly detection. Reference [2] proposed a DoS attack detection system based on multivariate correlation analysis [2]. The system used multivariate correlation analysis (MCA) to extract geometric correlation between network traffic characteristics, and uses anomaly-based detection principle for attack identification. Literature 3 proposed a method called Potential Correlation-Based Anomaly Detection (LCAD), which used a probability distribution model called a potential correlation probability model to correlate potentials between multiple correlated heterogeneous monitoring data series [3]. Sexual modeling to identify anomalies helps to detect anomalies in the working state of the device based on the correlation between massive data. Zhang et al. [4] built a healthy baseline by mining the correlation of internal characteristics of vibration signals, and successfully classified the inner ring fault, outer loop fault and roller fault of the bearing. It can be seen that under the condition of massive data from satellite telemetry, mining the associations and rules in the data, using the associations and rules to construct the health baseline, it is feasible and effective for the satellite abnormal state detection. In this paper, the satellite power subsystem is taken as the research object, and the satellite health baseline under different working conditions is proposed to detect the operating states of satellite. This paper was structured as follows: The second section elaborates the main research context and structure of this paper, and briefly introduces the main steps of constructing a healthy baseline; the third section introduces the health baseline construction process based on two core operating conditions of satellite power subsystem. And through the real telemetry data of the satellite power subsystem to analyze and evaluate its effect; the fourth section draws conclusions.

2 Methodology This paper mainly analyzes the satellite power subsystem design data and various types of telemetry data to find out the core operating conditions of the power subsystem. Based on this, two types of health baselines are constructed to realize the anomaly detection of the satellite power subsystem as shown in Fig. 1. Specific steps are as follows: Firstly, according to the satellite design information and data feature analysis, the satellite operating conditions are divided and screened, that is, the sub-basin constant current charging condition and the sub-basin discharge condition.

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Baseline build Massive status monitoring and test verification data INPUT

Statistical distribution method Expert knowledge Rule based ……

Linear health baseline Nonlinear health baseline OUTPUT

Core working condition analysis Satellite power subsystem telemetry

Fig. 1. Method flow chart

Secondly, through further condition analysis and data feature extraction, we obtained two types of health baselines, linear health baseline and nonlinear health baseline. Based on the health baselines, we can determine the state of the satellite.

3 Case Study In this paper, we take all telemetry of the power subsystem as filter objects, mining their internal relationships and building a healthy baseline, and based on the statistics of all data fluctuations, then get the fluctuation range of the constructed health baseline. Then we combine the health baseline with the fluctuation threshold to detect the status of the power subsystem. Under the two core conditions of the power subsystem, two types of health baselines, namely the linear health baseline and the nonlinear health baseline, were constructed. 3.1

Sub-basin Constant Current Charging Condition

3.1.1 Binary Linear Health Baseline Through statistical analysis, it is found that under the constant current charging condition of the satellite, recharging current A and recharging current B show a linear correlation, and the correlation equation is obtained by the algorithm as its health baseline, in order to further improve the fitness baseline fitness. The upper and lower limits of the normal fluctuation range are obtained by using a large number of satellite telemetry data. The health baseline construction is shown in Fig. 2. The details of the constructed baseline and parameters are as follows (Table 2): • Health Baseline style: y_pre = a  x + b, • Health Baseline Threshold: y_upper = a_upper  x + b_upper & y_lower = a_lower  x + b_lower. All the parameters involved in the health baseline are shown in Table 1. In fact, x and y represent telemetry names, and a, b, a_upper and b_upper represent model fitting parameters, respectively.

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Fig. 2. Health baseline between recharging current A and B under working condition 1 Table 1. Recharging current A&B health baseline parameter value list Health baseline parameters Test parameter value x Recharging current B y Recharging current A a 0.969 b 0.362 a_upper 0.975 b_upper 0.420 a_lower 0.962 b_lower 0.085

Health baseline and threshold \ y_pre = 0.969  x + 0.362 y_upper = 0.975  x + 0.420 y_lower = 0.962  x + 0.085

3.1.2 Binary Nonlinear Health Baseline In addition to the linear health baseline, there are nonlinear associated parameters in the satellite power subsystem. Within the parameters of Battery-A, there is a nonlinear relationship between the voltage and the charging current, so we construct its health baseline and obtain a quantitative equation for its fluctuation range by statistically normal data. Its health baseline and fluctuation range are shown in Fig. 3. The details of the constructed baseline and parameters are as follows (Table 3): R • Health Baseline: y pre ¼ a x  dt þ yð1Þ • Health Baseline Threshold: y_upper = y_pre + b_upper; y_lower = y_pre + b_lower

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Table 2. Battery A: Voltage vs. recharging current health baseline parameter value list Health baseline parameters Test parameter value x Recharging current y Battery A voltage a 0.0000389 y(1) Initial charging current b_upper 0.5 b_lower –0.5

Health baseline and threshold \ R y ¼ 0:0000389 x  dt þ yð1Þ y_upper = y_pre + 0.5 y_lower = y_pre – 0.5

Fig. 3. Health baseline between Battery A: Voltage vs. recharging Current under working condition 1

3.2

Sub-basin Discharge Condition

3.2.1 Binary Linear Health Baseline Different from the charging conditions, under the discharge condition, the number of parameters of the power subsystem and the data of each parameter will change, so it is necessary to re-excavate its internal relationship and build a healthy baseline under this condition. There is a linear relationship between Battery Pack Discharge Current Backup: A vs. B. Using the method described above, build its health baseline and get its threshold range, as shown in Fig. 4. The details of the constructed baseline and parameters are as follows (Table 4): • Health Baseline: y_pre = a  x + b • Health Baseline Threshold: y_upper = a_upper  x + b_upper; y_lower = a_lower  x + b_lower

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Table 3. Health baseline between battery pack discharge current backup: A vs. B parameter value list Health baseline parameters x y a b a_upper b_upper a_lower b_lower

Test parameter value Battery pack A discharge current backup Battery pack B discharge current backup 0.952 1.570 0.967 1.8045 0.937 1.2025

Health baseline and threshold \

y_pre = 0.952  x + 1.570 y_upper = 0.967  x + 1.8045 y_lower = 0.937  x + 1.2025

Fig. 4. Health baseline between battery discharge current A&B under working condition 2

3.2.2 Binary Nonlinear Health Baseline Consistent with the working conditions, there are also nonlinear correlations in the parameters of the operating conditions. Data analysis found that there is a nonlinear relationship between Battery A: Voltage vs. Discharge Current, and its health baseline and statistical threshold are shown in Fig. 5. The details of the constructed baseline and parameters are as follows: R • Health Baseline: y pre ¼ a x  dt þ yð1Þ • Health Baseline Threshold: y_upper = y_pre + b_upper; y_lower = y_pre + b_lower

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Table 4. Health baseline test parameter value. Health baseline parameters Test parameter value x Discharge current y Battery A voltage a 0.0000356 y(1) Initial discharge current b_upper 0.3 b_lower –0.3

Health baseline and threshold \ R y ¼ 0:0000356 x  dt þ yð1Þ y_upper = y_pre + 0.3 y_lower = y_pre – 0.3

Fig. 5. Health baseline between battery discharge current and voltage under working condition 2

4 Conclusion This paper proposes a new anomaly detection method based on health baseline construction for satellite power subsystem. By obtaining the different characteristics of the telemetry data under the core conditions of the satellite power subsystem, and mining the relationship between them, two types of health baselines based on the sub-basin constant current charging condition and the sub-basin discharge condition are established, that is, the linear health baseline and nonlinear health baseline. The test results show that the method is feasible and effective for realizing the anomaly detection of the spaceborne power subsystem.

References 1. Wang, Z., Chen, H.: Research on spatial outlier detection based on quantitative value of attributive correlation. Comput. Eng. 32, 37–39 (2006) 2. Tan, Z., Aruna, J., He, X., et al.: A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE Trans. Parallel Distrib. Syst. 25, 447–456 (2014)

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3. Ding, J., Liu, Y., Zhang, L., et al.: An anomaly detection approach for multiple monitoring data series based on latent correlation probabilistic model. Appl. Intell. 44, 340–361 (2016) 4. Zhang, T., Lu, C., Tao, L., Li, K.: Rolling bearing fault diagnosis based on health baseline method. In: Vibroengineering Procedia. 28th International Conference on Vibroengineering, vol. 14, pp. 141–145 (2017) 5. Pang, J., Liu, D., Liao, H., et al.: Anomaly detection based on data stream monitoring and prediction with improved Gaussian process regression algorithm. In: International Conference on Prognostics & Health Management (2015) 6. Lee, H., Byington, C., Watson, M.: PHM system enhancement through noise reduction and feature normalization. In: Aerospace Conference (2010) 7. Xiong, L., Ma, H.D., Fang, H.Z., et al.: Anomaly detection of spacecraft based on least squares support vector machine. In: Prognostics & System Health Management Conference (2011)

Lifetime Prediction Method of Components Based on Failure Physics Li Liu, Zhimin Ding(&), Nan Fang, Chao Duan, Nan Li, Qianqian Lv, and Miao Zhang China Aerospace Components Engineering Center, Beijing 10094, China [email protected]

Abstract. With more and more extensive application of microelectronic devices, the production cost and performance requirements of semiconductor devices will be replaced by the reliability requirements. The reliability test must be carried out in a bid to analyze and evaluate the reliability of device and predict its life from failure mechanism. Under the normal work conditions, in order to obtain the reliability data of devices faster, it is usual to adopt accelerated life test. Besides, the efforts will be made to analyze the corresponding device failure models in accordance with its failure mechanism and adopt various life prediction models and methods. The reliability of the semiconductor device directly affects the working life of the device. After the accelerated test, people can establish all kinds of lifetime forecasting models and methods by analyzing the failure mechanism of semiconductor devices. This paper minutely summarizes the evolution process of the methods that are used to forecast the life of semiconductor devices and based on failure physics, and then, these methods will be introduced in detail with their corresponding reliability problems. There are five models for accelerated test showed at the end of the paper. Keywords: Life prediction Model
Method


Failure mechanism
Evolution process


1 Evolution of PoF Approach Affecting Device Life Prediction In 1952, Shockley proposed the junction gate field-effect transistor theory, in the early 1960s when mass production of MOS transistor began, it was found that the charge related to the thermal oxidation structure of silicon had serious influence on device reliability [1]. People started the research on the charge in oxide layer. Since then, people have used PoF approaches to predict device life, including high-heat treatment, defect and corrosion, and studied its effect on device electromigration and service life from the perspective of impurity. In the age of transistor, people already did a raft of studies on planar state. Around the 1970s, with the emergency of planar device (MOS), people fully studied Si/SiO2 system. In the 1970s, leakage current aroused the attention and there were oceans of studies on soft error of packaging materials. With the rapid development and expansion of nuclear technology in the Cold War, the studies on radiation resistance of © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 227–237, 2019. https://doi.org/10.1007/978-981-13-7123-3_28

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semiconductor devices were abundant and mature because the devices were mostly applied for national defense and military purposes. In the early 1970s, the researchers studied the failure mechanisms including water vapor, mechanical vibration, superpower operation and high temperature reverse bias. In the mid-1970s, the research on thermoelectric effect became mature and the deterioration of the interface between metals resulted in the research on metal materials. Investigations into metallization issues became extremely in-depth, including low ohmic contact, good bounding and adhesion and stability of SiO2, such as Al electromigration, failure mechanism of AuAl bounding, aluminum film rupture at the step of the oxide layer and Al corrosion. People began looking for new metal materials as well. In addition, the effects of failure caused by electric stress on service life of devices were also studied around the mid1970s, including flashover short-circuit, transient failure caused by surge current, solder thermal fatigue caused by intermittent action and overload loss caused by switching power dissipation. Second breakdown was already studied at that time but it was still a theoretically unresolved issue. Meanwhile, some progress was made in the study on gate breakdown caused by electrostatic discharge damage of MOS integrated circuit. From the late 1970s to the early 1980s, Very Large Scale Integration (VLSI) was researched and developed. As the influence of hot carrier on the device attracted people’s attention, the research on the influence of hot carrier injection (HCI) on device life was started. At the same time, there also emerged some studies on Drain Induction Barrier Lower (DIBL). In the early 1990s, study of DIBL became mature with much relevant literature published, which continued into this century. Negative bias temperature instability (NBTI) was first discovered by Miura [2], Goetzberger [3] and others in 1966. However, there was not much research on NBTI and its effect on device reliability and life in the early stage. In the late 1980s, research on NBTI was carried out to a certain extent, the integration degree of integrated circuits was getting higher and people paid more attention to research on NBTI. After the 1990s, a large quantity of research literature on NBTI was published successively, especially after entering the period of micro- and nano- scales. As early as the 1980s, gate oxide breakdown attracted people’s attention, and the time dependent dielectric breakdown (TDDB) ramp voltage histogram was used to speculate on the device’s failure rate. In 1981, Arnold Berman of IBM proposed that the previous models for inferring reliability failures through laboratory life test were essentially incorrect as they failed to consider the effects of temperature on TDDB and the distribution of failure time [4]. Since then, a lot of improvement research has been done on TDDB. In the mid and late 1970s, it was discovered that the increase of the gate voltage VG of MOS devices could cause a rise in leakage current among the poles [5]. In the 1980s, researchers began to study the impact of gated-induced drain leakage (GIDL) on device life and how to reduce GIDL. Today, with the continuous improvement of the integration degree and the decreasing thickness of the gate oxide, the research of TDDB and GIDL has become more and more important and cannot be ignored. On this basis, researchers often need to study its effects in depth when predicting device life.

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Currently, the mainstream life prediction methods based on physics-of-failure include hot carrier injection (HCI) [6], negative bias temperature instability (NBTI) [7], time dependent dielectric breakdown (TDDB), off-state characteristics and others.

2 Life Prediction Based on Various Physics-of-Failure 2.1

Electromigration

Electromigration is the process of transporting aluminum ions along a stream of electrons in an energized aluminum strip at high temperature and high current density. It is the most important failure mechanism in the electrode system of semiconductor devices and integrated circuits (IC). The accelerated life test is affected by its electromigration. For long metal wires, the aluminum alloy strip is usually connected by the pad without preventing metallization. The overall failure life can be controlled by nucleation, and the current density N = 2 is generally adopted. Secondly, since the butterfly-type NIST electromigration test structure with simple pad connection is not suitable for the existing multi-layer metal system, very good results of electromigration can be obtained compared with the channel feedback test structure. Thirdly, the channel feedback test structure must be carefully designed to avoid resistance saturation and storage effect, as they can lead to incorrect median life. The equation [8] describing the electromigration using the Black model to determine the life is as follows: TF ¼ A0 ðJ  Jcrit ÞN exp½Ea =kT Where A0 is a constant, N is the current density factor, Ea is the activation energy, k is the Boltzmann constant (8.62  10−5 eV/K), T is the absolute temperature, and the applied current density J must be greater than critical current density Jcrit, so as to form the failure. The current density and the length of the wire could cause a change in N. Considering the change in N, an approximation of N can be obtained according to the following formula: N  dðInTF Þ=dðInJÞ Hypothesis: There is a very long aluminum-bronze metal tube with particle size larger than line width; the mobile environment is the chip environment (80 °C) inside the portable computer; the office environment is the chip environment (50 °C) inside the case; the current density in mobile environment and office environment is 2.5 mA/cm2 and 2.0 mA/cm2 respectively; and J>>Jcrit, Ea = 0.8 eV, and N = 2. On this basis, applying the above life model can get the ratio of life value in office environment to that in mobile environment, i.e., AF: AF ¼ ðJOffice =JMobile ÞN exp½ðEa =kÞð1=TOffice  1=TMobile Þ

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The value of AF (about 18) is calculated after inputting the aforesaid data. It can thus be seen that the life value in low-temperature, low-current density office environment is 18 times larger than in high-temperature, high-current density mobile environment. Specifically, a 1.6-times difference in current density and an 11.5-times difference in temperature could finally translate into an 18-times increase in overall life. 2.2

Hot Carrier Injection

Hot carrier injection (HCI), which usually occurs at interfaces and inside the oxide, is a phenomenon where carriers with sufficient energy are injected to the gate oxide layer. In the accelerated life test, when a hot carrier is injected, for devices longer than 0.25 micron, the N-channel peak substrate current and P-channel peak gate current can accurately simulate the degradation of the transistor in which the hot carrier is injected. For the P-channel devices shorter than 0.25 micron, under the influence of hot carrier, drive current tends to decrease just like NMOS. Besides, the off-state leakage current will significantly increase, especially when driven by huge current starting from the P channel. The physical property associated with hot carrier injection will be changed at the length of 0.25 micron or even below, which would worsen the stress situation. In fact, accurate voltage model is more practical than substrate current and gate current. The testing structure generally selected for HCI assessment can be directly used for products and for direct current working status. Therefore, the calculated value of life can be seen as an indicator for process comparison; when Vcc is lower than 2.5 V, substrate current related to temperature is likely to obtain activation energy. In fact, the Eyring model [8] should usually be adopted for channel-phase devices: TF ¼ BIN expðEa =kTÞ Where, B refers to an arbitrary value (coefficient related to dopant distribution and lateral wall partitioning number, etc.), N can be set at 2–4 (typical value being 3), and Ea = 0.1 eV–0.2 eV. For N-channel devices, I means the peak value of substrate current Isub, while for P-channel devices, I means the peak value of gate current Igate. A rule of thumb applicable to the ratio of the substrate current to voltage of P-channel devices is that when the source-drain voltage is 0.5 V higher, the peak value of substrate current will double. A comparison between the office environment and accelerated testing environment can help calculate the acceleration coefficient of failure of N-channel devices caused by HCI. If the chip temperature under office environment is 50 °C, the substrate current is 1 lA; while the chip temperature under acceleration environment is –40 °C, the substrate current is 10 lA, and N = 3, Ea = –0.15 eV, the life rate under the two types of environment, i.e., AF is: AF ¼ ðIoffice =Iaccel ÞN exp½ðEa =kÞð1=Toffice  1=Taccel Þ AF is calculated to be 8,000. It can be shown that a shift from accelerated test environment to office environment will bring an 8,000-times increase in the life value, including a 1,000-times increase from substrate current and an 8-times increase from temperature.

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TDDB

In principle, the TDDB process is divided into two stages: The first stage involves breakdown accumulation. The characteristic of this stage is that under the influence of electric stress, new-generated traps (charge) are accumulated inside the oxide layer and at the Si-SiO2 interface. This results in the electric-field modulation effect inside the oxide layer. When local electric field or current hits the critical value, the second stage, i.e., rapid breakdown, begins. In this stage, the process of positive feedback between electricity and heat leads to gate oxide breakdown. Given that the oxide breakdown is due to the hole being trapped and accumulated at local traps of the oxide layer, the trapped hole flow can be expressed as [1]: Qp / JðEOX ÞaðEOX Þt Where, J(EOX) refers to the F-N current density, which is directly proportional to e−B/EOX, a is the ionizing collision induced hole generation coefficient
 / EOX / eH=EOX , B, being approximately 240 MV/cm, is a constant related to the electron effective mass and cathode interface barrier, H is approximately 80 MV/cm, and t is the duration. When Qp reaches the critical value, breakdown occurs: tBD / eðB þ EÞ=EOX /eG=EOX ; G ¼ B þ H There are two common TDDB failure models used for predicting life: E model and 1/E model. The E model assumes that the oxide burn-in and breakdown is a thermodynamic process during which breakdown occurs possibly because Si-O bond is destroyed due to the mutual interaction between dipoles under thermal stress and applied electric field. In E model, there exists a linear relationship between the electric field under Eoxcondition and time, with the expression [9] as follows: tBD ¼ t0 expðcEox Þ In the equation, tBD refers to the life of gate oxide layerunder the TDDB stress condition, s0 means the time of intrinsic breakdown, c is the parameter of electric field proportion, with unit of MV/cm. All these variables are related to temperature process, their expressions being as follows respectively: 

 Ea t0 ¼ A exp ; KT

c ¼ bþ

c T

Where, Eox is the electric field of oxide layer, Ea is thermal activation energy, K refers to the Boltzmann constant, T represents the absolute temperature, and both b and c are process-related constants. E model is usually used for evaluating the life of gate oxide layer when the SiO2 dielectric is relatively thick and the electric field is relatively low.

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1/E model (hole-induced injection model) mainly relates to the F-N tunneling current. If the breakdown process is deemed to have resulted from gate-through current, 1/E model is generally used to predict the gate oxide life [9]: tBD ¼ t0 expðG=Eox Þ Where, tBD refers to the life of oxide layer under the TDDB stress condition, t0 represents the time of intrinsic breakdown, which, like G, is a constant related to temperature process, as shown in the following expressions: 

  Eb 1 1  t0 ¼ A exp  ; Kb T 300



  d 1 1  G ¼ G0 1 þ Kb T 300

Under the same temperature and different electric field stresses, A and G are constants. In the logarithmic coordinate, there is a linear relationship between the average breakdown time (TDDB life) and the reciprocal of applied electric field. 1/E model is generally used for life evaluation when the SiO2 dielectric is relatively thin and the electric field is relatively high. 2.4

NBTI

NBTI effect is found in PMOS devices. When the device grid is under negative bias, the device’s saturation drain current Idsat and transconductance Gm is continually decreased while the absolute value of threshold voltage is continually increased. An increase in the bias action time on the grid will be accompanied by continuous degradation of the device’s electrical parameters. There are mainly two models used to explain the degradation mechanism in NBTI: The first is the reaction-diffusion (R-D) model: When the vertical electric field is applied, the generated hole and Si-H bond react to isolate the hydrogen atom and leave one charge, then the hydrogen atom keeps away from the siliconoxide interface through diffusion. Given its very slow speed to diffuse in the oxide layer, hydrogen thus becomes the bottleneck for the NBTI degradation speed; The second is the charge trapping and de-trapping model. When the electric field is applied, the charge at the Si/SiO2 interface is trapped by defects in the oxide layer, which brings more fixed oxide charges. When the electric field is removed, some trapped charges can be divorced from the oxide layer. Severe recovery or passivation effect exists in NBTI effect. The longer the stress continues, the more severe the irreversible damage to the gate oxide will be. The charge trapping and de-trapping model can well explain the rapid recovery effect of NBTI within a short period of time, while the R-D model can explain the phenomenon of NBTI recovery effect existing for a long period of time after the stress condition is removed. At present, we are inclined to believe that the two mechanisms play a joint role in NBTI degradation. In the testing, the two mechanisms are manifested by the exponential relationship between electrical property degradation and time. Through a theoretical calculation of the speed of diffusion in the oxide layer for the hydrogen atom, molecule and ion, relationship between the device’s electrical property degradation and time is reflected

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as a power exponent ranging from 0.165 to 0.25 in the R-D model and less than 0.165 in the charge trapping and de-trapping model. The two models are applicable to different scopes of time after the electric field condition is removed, that is, a time lag between when the electric field stress is removed and when the electrical property parameters begin to be measured, is closely related to the performance of the tested device. With the adoption of new testing means and improvement of test device performance, the time lag is greatly reduced. New researches have generally found a relatively small power exponent ranging from 0.08 to 0.14. Researchers have put forward testing methods like fast NBTI and on-the-fly NBTI to reduce the NBTI recovery effect [10]. Under the old testing methods, a relatively long time delay based on the recovery effect result showed a smaller value in electrical property degradation compared with that of rapid testing. But this does mean that the impact of NBTI was underestimated. The reason is, as study suggests, with a reduction in the electric field stress, the failure time difference calculated under different testing speeds is smaller accordingly. As a result, the electric-field acceleration factor varies by testing speed. By deducing the device life from the result calculated under a relatively slow testing speed, the actual service life of the device is sometimes underestimated. 2.5

Thermoelectric Effect

When a transistor is under operation, junction temperature (active area) rises due to conversion of consumed power to heat. The relationship between working junction temperature Tj and device life t is as follows [1]: ln t ¼ A þ B=Tj Where, A and B are constants. Therefore, determining the working junction temperature is an important constant in the reliability design considered for the device use. In general, for silicon devices, the maximum allowable junction temperature is 150 °C– 175 °C in case of metallic packaging, and 125 °C–150 °C in case of plastic packaging. Heat is transferred through three means, including radiation, convection and conduction. The heat generated in the active area of the semiconductor chip is transferred to the adjacent areas through heat conduction, making temperatures in different chip areas unevenly distributed. In other words, a temperature field is generated. The temperature field distribution of the chip can be calculated by solving the heat conduction equation under certain boundary conditions, which is helpful for the reliability of heat design. Generally speaking, heat Q transmitted from section A is directly proportional to the temperature gradient in this direction: Q / OT
A ¼ kðTÞOT
A ¼ DT=RT In the equation, k(T) refers to temperature-related thermal conductivity of material. Assume that k(T) is a scalar which is irrelevant to direction and is of the same property in each direction. OT represents the temperature gradient, “-” indicates heat transmission from high temperature to low temperature. DT is the temperature difference,

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and RT represents thermal resistance, which equals to the proportion of temperature difference to heat flow between any two points and forms resistance on the path of heat transmission. Heat is in direct proportion to power consumption, so the equation can be rewritten as follows:
 Pc ¼ Tj  Tc =RTjc ¼ DT=RTjc In the equation, RTjc represents the thermal resistance between the collector junction and case of the transistor. Tj and Tc refer to the junction and case temperature respectively. Pc is the dissipated power of the transistor. Based on this, thermal resistance can be defined as the junction temperature rise caused by power consumption per unit of transistor (unit:°C/W). In fact, driven by switch and impulse voltage, the temperature in the active area of the transistor will go through some relaxation time (thermal time constant s) before gradually reaching the steady state when the temperature field and thermal resistance will become stable. Before that, all of them constitute a function showing exponential change over time. Tj  Tc ¼ DT ¼ Pc RT ½1  expð1  t=sÞ ¼ Pc RTS RTS is called transient thermal resistance, and s is related to thermal resistance and thermal capacity of the material. 2.6

GIDL (off-State Characteristics)

In the circuit, when a device is under the off state or waiting state, GIDL current plays a dominant role in leakage current inducing static power consumption. When the gatedrain voltage at the gate-drain overlap area VDG is very huge, the electrons in the silicon surrounding the overlap area witness band-band tunneling between the valence band and conduction band. Then GIDL tunneling current is formed. As the gate oxide layer becomes thinner and thinner, the GIDL tunneling current sharply increases. When the GIDL tunneling effect occurs, hole electron pairs will be generated at the LDD overlap region close to the Si-SiO2 interface. With electrons collected by the drain, most holes flow to the substrate, and the remaining ones speed up in the horizontal electric field along the channel direction. Not being hit, the latter gain sufficient energy from the electric field to cross the interface barrier, and finally change the direction via elastic scattering before being injected into the gate oxide layer. Q0, the total number of hole generated following the GIDL tunneling, is related to the strength generated by the GIDL effect. The hole injection in the process of GIDL stress can be exhibited by a quantitative model [11]: QT / Q0 pðLÞ ¼ Q0 expðLÞ L ¼ UB =ðqkH EM Þ

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Where, p(L) means the probability that the hole crosses the Si-SiO2 potential barrier uB without being hit after a long enough distance L. The hole and total number of holes injected into the oxide layer are in direct proportion to p(L). kH is the free path of holes and QT is the charge injected into the oxide layer because of band-band tunneling. EM is the maximum horizontal electric field. ID ¼ A  ES  expðB=ES Þ ES ¼ ðVDG  1:2Þ=3TOX R ∵Q0 = ID dt , where ID is the GIDL tunneling current. R ∴QT/ Q0 p(L) = ID dtexp(UB =ðqkH EM ÞÞ The one-dimensional GIDL tunneling current model is as follows: ID ¼ A  ES  expðB=ES Þ ES ¼ ðVDG  1:2Þ=3TOX In the equation, A is a constant, B amounts to 21.3 MV/cm, ES refers to the electric field at the interface of the drain-gate overlap region, 1.2 is the minimum band curvature that can occur with the tunneling at the vertical interface, and 3 is the dielectric constant ratio of SiO2 to Si. In addition, there are also some factors which have to be taken into account for their huge impact on predicting the device life, such as corrosion, electrostatic discharge damage, radiation damage, soft errors and packaging.

3 Acceleration Model A product in use is vulnerable to complex environmental stresses. For instance, the product will be influenced by temperature, electricity, humidity and other stresses simultaneously. It is exactly also the combined effect of these stresses that has an impact on the product life. For this reason, introducing the acceleration model of combined stress into the acceleration test can help simulate the actual environment conditions more accurately. Further, data obtained from the acceleration test can be used to predict the product life under normal use. The following is an introduction to five acceleration models [12]. 3.1

Arrhenius Model

Arrhenius model applies to test projects for temperature acceleration, with the formula as follows: AFðTÞ ¼ exp½Ea=k ð1=TUSE1=TSTRESSÞ In the formula, AF(T) is the temperature acceleration factor, Ea is the activation energy (eV), an empirical value ranging from 0.2 to 1.2 eV depending on the failure mechanism, k is the Boltzmann constant (8.617  10-5 eV/K), TUSE is the environmental temperature in actual use, and TSTRESS is the test temperature.

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3.2

Eyring Model

Eyring model, an extension of the Arrhenius model, is used for test projects with simultaneous voltage and temperature acceleration, with the formula as follows: AF ðT; VÞ ¼ exp[BðVSTRESS VUSE Þexp½Ea=kð1=TUSE 1=TSTRESS Þ In the formula, AF(T,V) is the temperature and voltage acceleration factor; VUSE is the voltage in actual use; VSTRESS is the voltage applied during test; coefficient B, which varies by failure mechanism, has a default value of 1. Other parameters are the same as those of the Arrhenius model. 3.3

Peck Model

Peck model is also seen as an extension of the Arrhenius model, and is applicable to test projects with temperature and humidity acceleration, with the formula as follows: AF ðT; rhÞ ¼ ðrhstress =rhuse Þn exp½Ea=kð1=TUSE 1=TSTRESS Þ In the formula, AF(T, rh) is the temperature and humidity acceleration factor; rhuse is the humidity in actual use; rhstress is the test humidity; coefficient n is generally set at 3. 3.4

Coffin-Manson Model

Coffin-Manson model applies to acceleration test projects with changes in temperature, the model formula shown as below: AF ðDTÞ ¼ ðDTSTRESS =DTUSE ÞC In the formula, AF(DT) is the acceleration factor due to changes in temperature; DTSTRESS is temperature changes during test; DTUSE is temperature changes in actual use; C is the material characteristics coefficient whose value ranges from 1 to 9 (1–3 for ductile metal; 3–5 for hard metal; 6–9 for brittle fracture). In the event that detailed information cannot be obtained, C is usually 4 at a conservative estimate. 3.5

Multi-stress Combined Acceleration Model

When a device is exposed to temperature, voltage and humidity stresses simultaneously in the working environment, suppose there is no mutual interaction between stresses, the acceleration model under combined multi-stress conditions can be seen as follows: AF ðT; V; rhÞ ¼ ðrhstress =rhuse Þn exp½BðVSTRESS VUSE Þexp½Ea=kð1=TUSE 1=TSTRESS Þ AF(T, V, rh) is the temperature, voltage and humidity acceleration factor. Other parameters are the same as above.

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4 Conclusion This paper takes the PoF reliability analysis (TDDB, HCI, NBTI and off-state characteristics are among the current mainstream methods) of semiconductor devices as the starting point. The paper further offers a detailed summary of PoF-based methods to predict the device life and their development history, in an effort to provide relatively systematic reading materials for studying the device failure analysis and life prediction. Furthermore, the paper gives a brief introduction to several acceleration models needed and optional when predicting the device life as a supplement to the life prediction methods.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

12.

Shi, B., Jia, X., et al.: Reliability of microelectronic devices (1999) Miura, Y., Matukura, Y.: Jpn. J. Appl. Phys. 5(180) (1966) Goetzberger, A., Nigh, H.E.: Proc. IEEE 54, 1454 (1966) Berman, A.: Reliability physics symposium. In: 19th Annual Digital Object Identifier, 362997 (1981) Ji, X.: Photoelectric coupling – a probe into simulating the switching type with MOSfield effect transistor. Appl. Electron. Tech. 5 (1979) Yao, L.: Reliability Physics. Electronic Industry Press, Beijing (2004) Campbell, S.A.: The science and engineering of microelectronic fabrication. In: Zeng, Y., Yan, L., et al. (2nd edn). Electronic Industry Press, Beijing (2003) Zhao, X., Wu, J., et al.: A study on semiconductor device life model based on failure mechanism. Electron. Compon. Dev. Appl. 9(12), 69–71 (2007) Wang, X.: TDDB study of thin gate oxide layer. Micronanoelectron. Technol. 6, 12–20 (2002) Hicks, J., Bergstrom, D., et al.: 45 nm transistor reliability. Intel Technol. J. 12(2), 131–144 (2008) Jian, C., et al.: The enhancement of gate-induced-drain-leakage (GIDL) current in shortchannel SOI MOSFET and its application in measuring lateral bipolar current gain b. IEEE EDL 13(11), 572–574 (1992) Cai, L.: Calculation of semiconductor device life. J. Hangzhou Dianzi Univ. 32(5), 312–314 (2012)

Spacecraft Technology and Application

Fault Tolerant Method for Spacecraft Bus Based on Virtual Memory Ning Zhao(&) and Wei E. Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing, China [email protected]

Abstract. In this paper, a fault-tolerant method of spacecraft bus chip based on virtual memory is proposed. This method applies the address mapping method of virtual memory technology. By means of self-detection and page management, it realizes the dynamic allocation and management of internal RAM pages in bus chip and achieves the purpose of fault tolerance. In detail, each message area in bus chip is called page. The page index table is defined to store the page state and give mapping for physical memory. CPU refreshes the page state in page index table by self-detection. When sending bus messages, CPU applies pages according to the page index table instead of writing physical memory directly. This method can recover the bus chip damage of onboard spacecraft. Keywords: Virtual memory


Spacecraft bus
Tolerant method

1 Introduction In recent years, the development of spacecraft has turned to the direction of intellectualization, networking and integration. Autonomous health and task planning have become the hotspots of spacecraft development. These functions are emerging with the wide application of computers in spacecraft, such as bus technology. As an important means of communication between onboard computers, 1553B bus is the key part of spacecraft function realization. If the 1553B bus chip fails, it may cause partial or overall bus communication function failure of the spacecraft, seriously affecting the service and safety of the entire spacecraft. 1553B bus was first used in the field of aviation in 1980’s, and gradually became the control bus widely used in aerospace area. The most famous bus controller chip is the BU61580 which is designed by DDC. It is advanced communication engine between onboard computer. High level reliability is needed because of its importance. In the process of spacecraft design, the following methods are adopted to improve the reliability of spacecraft: (1) Multi-level redundant backup strategy. Spacecraft equipment usually adopts double or even three backups to avoid the failure of a mission caused by the failure of a certain device. Such as some function can be realized by both onboard computer and ground control computer.

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(2) The selection of high-level chip. Special design in onboard chip can avoid the damage caused by space particles. With the application of software on spacecraft, it is possible to adjust the configuration of system by modifying onboard software. In this paper, a fault-tolerant method of spacecraft bus chip based on virtual memory is introduced to avoid RAM damage in the bus chip, which is the same principle.

2 Principle 2.1

1553B Bus Chip Principle

BU61580 is typical bus chip applied in spacecraft. BU61580 has internal RAM (usually 4K bytes), which is used to define the details of bus message sending. In Table 1, “Message block” is the physical address for storing communication data. The other area is the control area, which determines the bus message sending format and other options. When BU61580 works in BC mode, the internal RAM is defined as follows [1]: Table 1. Internal RAM defined TYPICAL NON-ENHANCED BC MEMORY MAP (shown for 4 K RAM, ENHANCED mode) ADDRESS(HEX) DESCRIPTION 0000-00FF Stack A 0100 Stack Point A (fixed location) 0101 Message Count A (fixed location) 0102 Initial Stack Point A (Auto-Frame Repeat Mode)1 0103 Initial Message Count A (Auto-Frame Repeat Mode)1 0104 Stack Point B (fixed location) 0105 Message Count B (fixed location) 0106 Initial Stack Point B (Auto-Frame Repeat Mode)1 0107 Initial Message Count B (Auto-Frame Repeat Mode)1 0108-012D Message Block 0 012E-0153 Message Block 1 …… …… …… …… 0ED6-0EFB Message Block 93 0EFC-0EFF Not Used 0F00-0FFF Stack B

When BU61580 works in BC mode, the process of sending messages is as follows: Firstly, it initializes the chip setting, such as working mode, stack pointer, message count, etc. The physical RAM address used is in the range of 0x0000*0x0107.

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Secondly, it writes message control words into stack area. The physical RAM address is 0x0000*0x00FF for A area and 0x0F00*0x0FFF for B area. To send a message, the data is written into the message block according to the address specified in the message control word, ranging from 0x108-0xEFB. Then the message count is changed by modifying value of message count A or message count B. Finally, after sending, it sets START register in order to start transmission the message, and judges the message sending status by reading the value of special address in stack. When sending bus message periodically, repeat the above steps. In this procedure, the chip RAM is utilized as shown in Fig. 1 below. Message sending flow Initial BU61580 Write subaddr control data to STACK Write message data to Block

Change Message Count

Sending

Check send status

BU61580 Physical memory address REG and RAM(0x0000-0x107)

RAM(0x00000xFF)OR(0xF00-0x0FFF)

RAM(0x101)OR (0x105)

RAM(0x108-0xEFB)

Message receiving flow Initial BU61580 Write subaddr control data to STACK Change Message Count

Sending

Check send status

Read Message data

Fig. 1. RAM utility while message sending

As shown in Table 1, there are 93 message blocks which store message data. However, due to the limitation of stack size, BU61580 is able to send 64 messages at most in one frame. 2.2

Virtual Memory Principle

Virtual memory is a memory management technology in computer science. CPU uses continuous RAM address and does not care how physical memory is arranged through this technology. Actually, physical RAM is split into segments which are not continuous, and even some segments are temporarily located in external disk. Memory management unit, which can be realized by operation system or special hardware, performs the virtual memory function. Data exchange between physical memory and virtual memory is carried out in the form of pages [2].

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The special hardware designed for virtual memory is called memory management unit (MMU). It performs efficient address mapping and translation. Due to MMU, discontinuous and different types of physical addresses appear to be continuous and single type. The address space model of real-time system can be divided into four kinds: (1) plat address space, which is original physical address; (2) equivalent single address space (SAE), which maps physical address to virtual address directly; (3) single address space (SAS), which maps physical address to virtual address point to point but maybe physical address is discontinuous while virtual address is not; (4) multiple address space (MAS), which maps physical address space to different processes and uses them separately [3]. 2.3

Fault Tolerance Principle for Spacecraft Bus Chip

The fault-tolerant principle for spacecraft bus chip refers to the idea of virtual memory address mapping. It provides mapping from internal RAM of bus chip to virtual RAM for CPU using. The mapping transforms when part of internal RAM is damaged. Fault isolation is achieved through this method. Applying the fault-tolerant method to spacecraft requires two special situations to be considered [4]: (1) There is invisible time in every track for some spacecrafts due to the spherical earth. Invisible time could be more than a dozen hours. Bus chip needs to independently identify the damaged location of RAM and dynamically adjust address mapping. (2) Weight and power consumption are strictly limited because of emission ability. MMU can’t be widely applied to onboard computer, so the sketch of virtual memory should be as simple as possible in realization.

3 Fault Tolerance Method for Spacecraft Bus There are two parts in the fault-tolerant method for spacecraft bus: self-detection and memory management. 3.1

Self-detection Strategy

The self-detection strategy needs to consider the opportunity, content and method. Opportunity: The central computer on orbit, as the core of satellite data processing, is a real-time multi-task system. It acts as BC unit in the bus network and the machine-time is rather tense. Therefore, bus RAM self-detection should be carried out during the minimum priority task, when other data processing tasks have been finished in a cycle. Content: As shown in Table 1, there is fixed location area in bus chip, whose mapping can’t be transformed. Message block area is located at 0x108-0xEFB physical address. It occupies a large amount of internal RAM space in bus chip, but in practice, a small

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amount of the area is utilized. Mapping of the message block can be adjusted when parts are damaged. Therefore, the content of self-detection is the message block area. Method: In order to detect RAM fault as soon as possible, self-detection method is designed simply. CPU writes 0x55555555 to a physical address, reads and verifies, then writes 0xaaaaaaaa, repeats the above steps. If the validations are correct, it is considered that the address is working normally. It also can be confirmed by the bus monitor [5]. 3.2

RAM Management Strategy

Referring to the virtual memory management principle of general operating system, the fault-tolerant method of spacecraft bus chip defines data structure named page to index the physical bus chip address. The method achieves the purpose of substituting damaged RAM address by transferring content of page [6]. Each message area of BU61580 is 32 words (16 bits), which is the maximum capacity of a single message, therefore each page index is designed to identify a status of 32-word size physical address block. The page index could be changed by the memory management module according to the result of self-detection mentioned above. Normally, when the onboard computer initiates communication through 1553B, the communication content is divided into 32 word-sized fragments. CPU applies for memory allocation application to the memory management module. The memory management module returns the address list of the message area of this application according to the status of the page table and the size of the application space, that is, the address mapping result. Then the memory management module locks the message block to prevent conflict. Finally, when communication is completed, the message block is released and the page table is updated. When part of spacecraft bus RAM is damaged, CPU locates the location and scope of RAM errors by regular self-detection, and then updates the status in index page table. When the communication is initialized, the memory management module returns the address mapping result except the damaged pages. Thus, the communication between onboard computers can be completed normally even if parts of bus chip are damaged (Fig. 2).

Applicati on Sending or Receiveing message

Melloc and Allocate Message block1 Message block2

Page Status Table

Page (Physical Address)

Page Status Table1

Physical Address1

Page Status Table2

Physical Address2

Page Status Table3

Physical Address3

Page Status Table4

Physical Address4

Page Status Table5

Physical Address5

Page Status Table6

Physical Address6

Page Status Table...

Physical Address

Page Status Table n

Physical Address n

Fig. 2. Fault-tolerant method of bus chip based on virtual memory

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4 Verification The fault-tolerant method for spacecraft bus chip based on virtual memory is applied on a MEO satellite. The MEO space environment is harsh and the chip is greatly affected by space radiation. The normal traffic of bus communication is 0.119 Mbps, which includes: important data storage, telemetry parameters, bus terminal status polling, and instructions. Similar data is sent in the same frame, in which the routine telemetry parameters and bus polling messages need to be sent once every 500 ms to 4 slave computers. In addition, the telemetry acquisition parameters need to be strong real-time, so other messages can be interrupted. The fault-tolerant method of bus chip RAM based on virtual memory is used to manage message blocks. After the satellite enters orbit, all pages (physical address of message block) are in good self-detection status at the beginning of its life. When the remote telemetry messages apply for addresses, there may be some pages occupied. The memory management module assigns a page table for each message, as shown in Table 2. Table 2. Original RAM mapping NO Function 1. Important data recover 2. Telemetry parameters 3. Bus terminal status polling 4. Important data storage 5. Instructions

Communication size 1.25K (41 message blocks) max 320 (10 message blocks) *4 1 (1 message block) *4

Page index 0*40

Physical address 0x108*0x627

10 indexes among 0*50 0

320 words among 0x108*0x768 0x108*0x127

1.25K (41 message 0*40 blocks) 6 (1 message block) *4 0

0x108*0x627 0x108*0x127

The satellite has a breakdown of bus chip RAM after 36 months of satellite launch. Some address reading and writing exceptions of 0x400*0x7FF in RAM area are found. Write 0x5555 to each 16bit and read it out, then write 0xaaaa to each 16bit and read it out. The results are shown in Fig. 3.

Fig. 3. RAM self-detection result of bus chip

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It can be determined in Fig. 3 that the 0x400-0x7FF area of the bus chip has been damaged. After the bus RAM fault tolerance strategy based on virtual memory detects the fault, the status of pages 23 (physical address 0x3E8*0x407) *55(physical address 0x7E8*0x807) is set to be unavailable. The memory management module assigns a page table for each message, as shown in Table 3. Table 3. Fault-tolerant RAM mapping NO Function

Page index

Physical address

1.

0*22, 56*73

0x108*0x3C8, 0x808*0xA47 320 words among 0x108*0x3C8, 0x808*0xB87 0x108*0x127

2.

3. 4. 5.

Communication size Important data 1.25K (41 message recover blocks) Telemetry max 320 (10 parameters message blocks) *4

Bus terminal status polling Important data storage Instructions

10 indexes among 0*22, 56*83

1 (1 message 0 block) *4 1.25K (41 message 0*22, 56*73 blocks) 6 (1 message 0 block) *4

0x108*0x627 0x108*0x127

As can be seen from Table 3, the fault-tolerant method of bus chip RAM based on virtual memory can skip the area where reading and writing exceptions occur, and provide reliable memory pages for CPU, so as to achieve the purpose of fault tolerance of bus chip.

5 Conclusion In this paper, a fault-tolerant method of on-orbit satellite bus chip based on virtual memory is proposed. Without increasing hardware devices, the local fault of RAM can be avoided autonomously on orbit by means of self-detection and page management of the bus chip message physical address, and the reliability of satellite can be improved. This method can be extended to other satellite equipments and ground systems to improve the fault tolerance of the whole system.

References 1. ACE/Mini-ACE Series BC/RT/MT Advanced Communication Engine Integrated 1553 Terminal User Guide. DDC User Guide (1999) 2. Ling, Z., Zhang, L.: An implementation of the virtual memory management mechanism of the embedded system. Sci. Technol. Eng. 10(27) (2010) 3. Xu, R.: Virtual memory techniques for real-time systems. Comput. Eng. Sci. 28(11), 116–118 (2006)

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4. Sun, D., Wu, W., Zheng, L., Zhao, M., Li, B.: Design of a dynamic memory manager for embedded real-time systems. J. Chin. Comput. Syst. 35(5) (2014) 5. Wang, X.: Design of multi-platform 1553B bus simulator. Comput. Measur. Control 20(2) (2012) 6. Li, G.: Research of the Virtual Memory Management Technology on ARM. Beijing Jiaotong University, Mater thesis (2013)

The Design of Interactive Framework for Space-Exploration Robotic Systems Wei Shi(&), Shengyi Jin, Yang Zhang, Xiangjin Deng, Yanhong Zheng, Meng Yao, and Zhihui Zhao Beijing Institute of Spacecraft System Engineering, Beijing 100092, China [email protected]

Abstract. The deep space-exploration spacecraft or robot need to perform missions in complex and harsh environments and far away from the earth. Restricted by large communication delay and low-bandwidth, the operator on the earth can’t interact frequently with spacecraft or robot. For the reasons, the system design of spacecraft is required to powerfully autonomous and reliable. This paper is based on the application of the sampling robot for extraterrestrial planets, described the design of interactive framework for task-level Command control of robotic systems, establish a standard planning operators(POs) sets that can cover the operating space basically, and shows how to improve system autonomy through interactive planning and learning. Under this framework designation, with a small amount of task-level command and state feedback telemetering between the operator on the earth and the spacecraft, it can meet the mission. Keywords: Interactive-framework
Planning operators (POs) Space-exploration robotic systems
Task-level command




1 Introduction Space is fundamentally one of the most challenging domains that humans have ever tried to explore. For exoplanet soil/rock sampling tasks, restricted by incomplete initial environment information, uncertain execution duration and large communication delay [1], and so on. For this reasons, space- exploration spacecraft or robot is required to be powerfully autonomous and reliable. However, it is limited by the current computing and storage level of computers on spacecraft, the development of artificial intelligence technology, and the high reliability and safety requirements of spacecraft missions, making spacecraft have powerfully autonomous is a great challenge. Lightweight, low volume, simple and convenient application need to be considered. This study, we propose a design of interactive framework for application of space- exploration robotic systems.

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2 Related Work With the continuous development of space action, the field of exploration is expanding. Space-exploration spacecraft have also evolved from simplicity to complexity, from a single mission to a variety of tasks, from relying mainly on manual operation to gradually autonomous development. “Surveyor” (1966–198), “Luna16/20” (1970– 1972)”, “Luna24” (1976), “Viking” (1976), “Sojourner” (1997), “Hayabusal” (2003), “Mars express” (2003), “Rosetta” (2004), “Curiosity” (2011), “Jade Rabbit” and so on. These successful space exploration actions represent a significant trend. In order to realize the goal of space exploration spacecraft/robot with autonomous and reliability, a complete system framework is necessary. Base on the design of framework, through experiments and knowledge learning, the intelligent level of spacecraft is gradually optimized and improved to meet the application requirements of various missions. The US Air Force Research Laboratory (AFRL) Space Vehicles Directorate, the team of Orbit Logic, PnP Innovations, and Emergent Space are developing an Autonomous Planning System (APS) framework. The core of APS is specialized Autonomous Planning Agents (SAPAs) and Master Autonomous Planning Agent (MAPA), SAPAs generate plans, and the plans are integrated by [2]. Many artificial intelligence techniques have been used to learn and reason in a way that humans describe the world [4, 5]. In the described method, a set of POs is generated using a specific use case developed by the expert system, and these operators are then optimized using an improved PRODIGY planner [6] and the PO is optimized using a modification of the version space method named OBSERVER. In [7], a TRIAL plan and learning strategy is proposed. It learns and optimizes POs when generating plans and executing plans. Until the system deadlocks, external teachers are called to participate in controlling the completion of tasks. In [8], a system architecture design solution named EXPO was proposed. This system uses PRODIGY as a benchmarking program to improve knowledge in multiple domains. The characteristics of the space exploration mission determine that the robot system has a high rate of undefined operations in an unknown environment, and it is extremely important to be able to react and process quickly when encountering undefined operations. Therefore, we place greater emphasis on the online learning capabilities provided by the architecture. Teacher intervention occurs during the execution of the task, providing only the operational instructions that need to be performed, and returning the control of the task to the system after the operation. Our strategy for action guidance is similar to other types of tele-operation technology [6]. The robot system provides a set of functions (actions) in advance, and the teacher (served by the ground operator) simply indicates which functions (actions) to perform in a given situation. After instructing actions, the robot is allowed to perform actions and generate state transitions for learning [9]. In particular, the teacher does not supervise the behavior and does not provide clues as to the relevant attributes of the world that should appear in the successful execution of the task [10]. This greatly reduces the number of humancomputer interactions, and it should be noted that, depending on the complexity of the application, indicating all relevant properties of the world in every possible situation

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faced by the robot may be complicated. To alleviate this burden, we propose a method of automatically learning world-related attributes that should exist to allow POs to perform successfully [11].

3 The Design of Interactive Framework The framework design described in this paper provides two different modes of operation: the ground development mode and the task execution mode. The ground development model is mainly used for training and improving system intelligence, and the task execution mode is used for deep space exploration of spacecraft on-orbit task execution. The core of the two model frameworks is the planning module and the learning module. The planning module searches, generates, and optimizes the sequence of motion controls needed to achieve the mission objectives based on established execution rules and constraints. The learning module is used to interact with the operator to create, edit, and repair these execution rules and constraints. Schematic of the Interactive framework is shown in Fig. 1. The framework includes planning module (PM), learning module (LM), Teaching module (TM), Operation basic set module(OBM), Control rule set module (CRM), Emergency response module (ERM), Control execution interface module (EIM), execution module (EM), environment Perception and modeling module (EA&M). The space-exploration spacecraft system, the application examples corresponding to two different operation modes in the framework are shown in Fig. 2.

Fig. 1. Schema of interactive framework

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(a) the ground development mode

(b) the ground development mode

Fig. 2. Schematic of two mode

The ground development mode is used in the development stage before the mission is executed. The model is shown in Fig. 2(a). The system consists of Substitute spacecraft, Substitute DSN (Deep Space Network) and GSS (Ground support system). The EM and the environment perception part of EA&M functions are provided by Substitute spacecraft, and the EIM function is provided by Substitute DSN. The PM, LM, TM, OBM, CRM, ERM and the environment modeling part of EA&M functions are all included in the GSS. Initially, the OBM consists of a limited number of metaoperations that described in accordance with the prescribed the declarative instruction. The manual operator interacts with the LM of the system by the EIM to form the original CRM. The PM is based on this CRM, receive task-command and planning to generate an initial operation sequence, through the EIM controls the EM sequentially complete the state transition specified by the operation sequence. Meanwhile the EA&M continuously monitors and establishes or updates the environmental state model, and feeds back the latest environmental state model to the PM, which executes according to the feedback status update or control operation sequence. If the planning module finds that the feedback status is undefined or does not find a solution in the CRM, then the ERM is activated. The operator interacts with the LM of the system to complete the CRM and exit the ERM. The PM continues to complete the control execution of the planning and operation sequence until the task-command requirements are met. The mission execution mode is shown in Fig. 2(b). Unlike the ground development model, the system consists of spacecraft, DSN, and GSS in mission execution mode. In addition to EM and the environment perception part of EA&M functions, the spacecraft is also loaded with PM, LM, TM, ERM and optimized OBM and CRM. The environment modeling part of EA&M which need large computational complexity and large storage requirements,is retained in the GSS. The GSS also includes a complete ground development mode framework to synchronize Substitute spacecraft with the mission spacecraft state.

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When the system is running, the task commands received by the PM may be task commands such as “detect and collect samples at target A”, requiring the system to understand these task commands and translate them into instructions that control the EM. The declarative description of the states of the world that currently exists is need to be provided. These descriptions need to be unambiguous and can be programmed to generate control sequences for controlling EM without confusion. Based on this, the planner attempts to find a sequence of actions that converts the current state to a state that satisfies the target of the task command specification. 3.1

The Declarative Description of Actions and States

In this section, we introduce the declarative description of actions and states in this study. Here we define a finite states space Š and a finite actions space Â. State s2Š and s ¼ fd1 ; d2 . . . . . .dm g; m 2 N. di is a subdivision unit that describes a state and a predicate is map the properties. ‘s’ is regard as a state that can be described by multiple logic predicates. A action a2Â may take arguments representing the objects to which the action is applied. In the planning, the planner is given a description of the initial state, ‘s_ini’ and the target state description ‘Des’. Using these elements, the planner searches for sequences of actions that allow the changes form ‘sini ’ reach to the goal ‘Des’. This search is performed using a set of POs, ‘ ’, each of which encodes the expected changes after performing an action. A PO is represented as
 pi ¼ sp ; a; e; P

ð1Þ

Where ‘sp’ is the states of precondition requirements, ‘a’ is the action, ‘e’ is the expected results, and ‘P’ is a probability estimate, which indicates the probability of reaching the ‘e’ when ‘sp’ is observed and action ‘a’ is executed.    P ¼ Pr esp ; a ð2Þ A PO encodes the changes that should be observed when an action ‘a’ is executed. The ‘sp’ contains the initial values of the state descriptors changed by the action ‘a’, while the ‘e’ contains their final values. The action ‘a’ describes the action that needs to be executed. For example, a ‘PO’ for an action of move the robot end actuator from ‘A’ position to ‘B’ position by ‘default’ mode and description of ‘velocity’ may have the following parts: sp ¼ fPoweronðrobotÞ; Ishold ðrobotÞ; AtpositionðAÞg; a ¼ Moveðrobot:end actuator; 'default'; velocityÞ; e ¼ fAtpositionðBÞg

ð3Þ

Where PoweronðrobotÞ is a predicate that takes a value ‘true’ when ‘robot’ is power on, Ishold ðrobotÞ is a predicate that takes a value ‘true’ when robot is no move and hold on at the current position, AtpositionðAÞ is a predicate that takes a value ‘true’ when the current position of the robot end actuator is at ‘A’. Where Moveðrobot:end actuator; 'default'; velocityÞ is a predicate that move the robot end

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actuator with ‘default’ mode and ‘velocity’. And AtpositionðBÞ is a predicate with a value true when the robot end actuator at ‘B’ position. In this case, the state descriptor that changes with the action is AtpositionðAÞ. However, for this change to occur, it is also necessary that robot is hold on, which is a fact specified by the descriptor Ishold ðrobotÞ at remains unchanged by the action. The PO provides a concise description of the action state and execution results. Of all the descriptors associated with completing the task, the particular PO only considers those descriptors associated with its effect, and other descriptors may be ignored or ignored. For instance, if the PO in (3) is used in a task that also requires other actions, e.g. the current position is not exactly at the ‘A’ position, a private PO, pk ¼ fsk ; ak ; ek ; Pk g, is need be completed sk ¼ fPoweronðMAÞ; IsholdðMAÞ; notðAtpositionðAÞÞg ak ¼ MoveðMA:end teminalÞ; ek ¼ fAtpositionðAÞg

ð4Þ

This is because the a private PO, ‘pk’, not relevant to ‘move the robot end actuator from ‘A’ position to ‘B’ position’. In general, the number of descriptors used in the state representation is much larger than the number of descriptors used in a particular PO [16]. In the proposed framework, not all of the POs are available for planning and some will only be handled by the learning method. We make this distinction when needed. The training instances for learning comprise state transitions of the form: pt ¼ ð S t ; a t ; S t þ 1 Þ

ð5Þ

Where t is the time step, st is the state before executing the action, at is the executed action, and st þ 1 is the state after executing the action. A positive instance pt;iþ for the PO pi ¼ fsi ; ai ; ei ; Pi g is defined as that where all of the predicates in the precondition are observed in the state before the execution of the action, the action coincides with the action of the state transition, and the predicates in the effect are observed in the state after the execution of the action: pt;iþ ¼ fsi St ; ai ¼ at ; ei St þ 1 g

ð6Þ

By contrast, a negative instance p t;i , represents a state transition where the PO was applied but the expected changes did not occur. p t;i ¼ fsi  St ; ai ¼ at ; ei 62 St þ 1 g

ð7Þ

We say that an operator pi covers an instance when si  St ; ai ¼ at . 3.2

The Algorithm Description

The main algorithmic description of the interactive framework is presented in Algorithm 1. Note that the initial set of POs could be the empty set. The predefined operational description has been included in the OBM is defined as Ppredef , the

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description of the operation that was learned has been included in the CRM is defined as Plearned . The main algorithm includes two key steps algorithm, and . FindPlan Algorithm can use binary tree search algorithm, here we focus on the Learned algorithm.

4 Learning and Planning Operators Learning and planning are the most important components of the interactive framework. The following sections focus on these two aspects. 4.1

PO Evaluation

How to evaluating each PO to determine its probability in Eq. (2) is a very important issue. In our application, the evaluation of each PO is strictly limited to the current state and the risk in the implementation of the PO. we divide the PO in the task into two categories.

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 The first category, pdef ¼ sdef ; adef ; edef ; P , pdef can be verified by a large number of tests, And when sdef 2 St ¼ et1 , Calculate P according to the formula P¼

nþ n þ þ n

ð8Þ

Where n þ is the number of positive experience instances covered by the PO, n is the number of negative instancesn covered. o
 þ ; a; e; P , p2N ¼ st ; a; edef ; P , where p1N repreThe second category, p1N ¼ sdef sents a positive experience instances state, by executing action a, reaching a expected results. p2N represents an inverse operation of p1N by executing action a to reach a positive experience instances state from an arbitrary state, So the probability P of p1N þ and p2N , generally using the distance between e and sdef , st and edef as the estimation evaluation method, at the same time to ensure that the robot movement safety type will be constrained according to the configuration of the robot and the recognition environment model, according to the configuration of the robots assuming a function: ( FðX; Yt Þ ¼

0; 8xi 2 X; 8yj 2 Yt ; D;

    minxi 2X;yj 2Yt y2j  x2i  [ C other

ð9Þ

Where X describes the robot’s own configuration description and Yt describes the configuration of the moving part on the robot in the current state. When the function is 0, the moving part of the robot is described in the current state. No contact is made with the body structure. In addition, we also need to define the function: ( GðE; Yt Þ ¼

    0; 8ei 2 E; 8yj 2 Yt ; minxi 2X;yj 2Yt y2j  e2i  [ C D; other

ð10Þ

Where E describes the reconstruction description of the task environment, and when the function is 0, the moving part of the robot is not generated in the current state and the task execution environment contact. Here, P can be expressed without consider the measurement error: 0     P ¼ Pr esp ; a; FðX; YÞ; GðE; YÞ ¼

Z

tþ1 t

@ 1 E 2

2 1 þ 2 y2 t sdef 2

A dt  FðX; YÞ  GðE; YÞ ð11Þ

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In our application, the second category PO is verified once, the probability PN of OP pN is can be temporarily looked as the first category PO. If pN is positive expen þ þ 1. When the environment changed, calculate PN rience instances, n þ according to the formula: P ¼ PrðejGðE; YÞÞ ¼ PN  4.2

Yt þ 1 t

GðE; Yt Þdt

ð12Þ

PO Generation

The states processing rules contained in CRM are limited and cannot completely cover or cope with complex and variable state transitions in real tasks. PM search generation POs that reflect the best state transition is a typical optimal path planning problem. Finding the optimal path in a large number of states space requires a lot of computational time investment. In order to improve system reliability and reduce time investment, ERM is started to generate new POs in two cases. (1) When planning, it is found that there is undefined actions definition, and the corresponding operation rules need to be added in the CRM. (2) When the execution of a PO has an unexpected effect. Generation from an Action Instruction Undefined actions may prevent the generation of plans, thereby triggering an action instruction request to ERM be started. After executing the instructed action, ainst , a new For descriptors this PO, first, the PO is generated to fill the Plearned , and changed descriptors are extracted from the observed state transition, sc ¼ fsi 2 St jsi 62 St þ 1 g ec ¼ fsi 2 St þ 1 jsi 62 St g

ð13Þ

Where sc and ec are the sets of the changed descriptors, respectively, St is the state before and St þ 1 is the state after executing the action. Then, a new PO, pinst , is created by using sc , ainst and ec as the precondition, action, and effect parts, respectively. The new PO becomes available immediately for planning. However, the new PO only considers the changed descriptors in its precondition, so some unchanged causative descriptors may be missing. In this case, the execution of the newly generated PO may have an unexpected effect. Generation from Unexpected Effects Without considering the system failure, the PO extracted and executed from the set of operations defined by OBM and CRM has an unexpected effect, indicating that there is a constraint defect in the initial state definition of the execution of the PO. When the system state does not satisfy the real execution condition of the PO, the PM plans and starts the execution of the PO according to the state definition of its. The refinement process starts by bringing together all the POs that code the same changes an the failing PO set, pexe , which have been accumulated in the PO set: , where aexe and eexe are the action and effect parts of pexe , respectively. In this case, first try to find a candidate OP, pc ¼ feexe ; ac ; ec 2 st g,

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if such a candidate pc can be found, then add pc to the CRM. Since eexe is an unexpected state, such a candidate OP cannot be found which is a high probability event to bring the system control into a transition state. The transition state is generally selected from the safe parking state, which is a limited state set Sinterim . And the safe  parking state selection strategy is handled by the proximity principle sk ¼ minsi 2Sinterim e2exe  s2i  ,The problem turns into finding a PO set : ð14Þ Learner Algorithm

description as follow:

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5 Conclusion We integrated the artificial intelligence technology into space-exploration robotic systems design effectively. In this study, our main goal is to design a framework of robotic systems that can be commonly applied in the field of space exploration, which make the robot system can autonomously plan, made decisions, and complete the tasks required by the command after receiving task-level commands. In this way, we propose to use simple task-level commands to trigger task-related operations, while simple fast learning can be used to generate POs from observed state transitions to quickly increase robot autonomy.

References 1. Colby, M., Yliniemi, L., Tumer, K.: Autonomous multiagent space exploration with highlevel human feedback. J. Aerosp. Inf. Syst. 13(8) (2016) 2. Ella, H., Josh, N., Marc, S., Doug, G., Tim, E., Ken, C.: Onboard Autonomous Planning System, SpaceOps Conferences 5–9 May 2014, Pasadena, CA SpaceOps 2014 Conference (2014) 3. Truszkowski, W.F., Hinchey, M.G., Rash, J.L., Rouff, C.A.: Autonomous and autonomic systems: a paradigm for future space exploration missions. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 36(3), 279–291 (2006). https://doi.org/10.1109/tsmcc.2006.871600 4. Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009) 5. Veloso, M., Bala, J., Boloedorn, E., Bratko, I., Cestnik, B., Cheng, J., De Jong, K., Dzeroski, S., Fisher, D., Fahlman, S., et al.: The NONK’s problems: a performance comparison of different learning algorithms, Technical report CMU-C5-91-197. Carnegie-Mellon University (1991) 6. Veloso, M., Carbonell, J., Perez, A., Borrajo, D., Fink, E., Blythe, J.: Integrating planning and learning: the prodigy architecture. J. Exp. Theor. Artif. Intell. 7(1), 81–120 (1995) 7. Benson, S.: Inductive learning of reactive action models. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 47–54. Morgan Kaufmann (1995) 8. Gil, Y.: Learing by experimentation: incremental refinement of incomplete planning domains. In: Proceedings of the Eleventh International Conference on Machine Leaning (1994) 9. Rybski, P., Yoon, K., Stolarz, J., Veloso, M.: Interactive robot task training through dialog and demonstration. In: 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 49–56. IEEE (2007) 10. Nicolescu, M., Mataric, M.: Natural methods for robot task learning: instructive demonstrations, generalization and practice. In: Proceedings of the Second International Joint Conference on Autonoumous Agents and Multiagent Systems, pp. 241–248. ACM (2003) 11. Polushin, I.G., Dashkovskiy, S.N.: A small gain framework for networked cooperative teleoperation. In: Proceedings of the 8th IFAC Symposium on Nonlinear Control Systems, pp. 90–95. Bologna, Italy (2010) 12. Agostini, A., Torras, C., Worgotter, F.: Efficient interactive decision-making framework for robotic applications. J. Artif. Intell. 247, 187–212 (2017)

Research and Design of Hierarchical FDIR in Spacecraft Xiaodong Jia(&), Chunping Zeng, and Yufu Cui DFH Satellite Co., Ltd., Beijing 100094, China [email protected]

Abstract. A method for designing hierarchical fault detection, isolation and recovery (FDIR) is proposed in this paper for the limited visible period of inorbit spacecraft. The hierarchical FDIR design uses the highly integrated in-orbit autonomous operation of CAST2000 platform satellite including centralized management in system level, autonomous control in subsystem level and redundancy design in unit level to optimize satellite FDIR design and structure. The design is widely used in CAST2000 platform satellite and verified in system level performance test and in-orbit operation. The verification shows hierarchical FDIR could reduce adverse effects of in-orbit failure and support the satellite stable operation. Keywords: Hierarchical fault detection
Isolation and recovery (FDIR) In-orbit autonomous operation
Safety mechanisms




1 Introductions Advanced autonomous management functions have been widely used in spacecraft system level design. The classification standard of spacecraft autonomous management based on different mission requirements in the ECSS standard is as follows: – Autonomous execution of routine tasks – Autonomic treatment of failures – Autonomous processing of task data Deep space exploration projects of ESA, such as Mars Express and Rosetta missions, have put forward the requirements of highly autonomous management on spacecraft and achieved remarkable results [1]. For earth observation satellites, autonomous management ability is also an important aspect of satellite design in order to reduce operational costs and improve security disposal capacity. As the main small satellite platform of China, CAST2000 platform mainly runs in the sun synchronous orbit [2], which is the most commonly, used orbit design for earth observation. The orbit altitude is about 500 km to 700 km and about 15 cycles per day, so as to realize the high coverage of earth observation. On the other hands, the impact is the visible time of ground control station is less and the satellite runs outside more time. So, satellites in SSO need the capability of carrying out various missions independently. In particular, the satellite needs to be able to detect the fault in real time, locate the fault and reconstruct it to ensure the safe and reliable operation of the satellite, © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 260–267, 2019. https://doi.org/10.1007/978-981-13-7123-3_31

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at least keep satellite safe until it enters visible area again, so that the ground control system can intervene and handle the failure. Fault detection, isolation and recovery (FDIR) to ensure the safe and reliable operation of spacecraft has become an indispensable content of spacecraft. The concept of hierarchical FDIR was proposed by R. Gessner of EADS-Astrium [3], which was extracted and summarized from the experience of multiple projects of ESA. In China’s satellite design, satellite safe mode has become an important method, but the concept of systematic and hierarchical FDIR design has not yet been formed. Based on the project experience of remote sensing satellite of CAST2000 platform, this paper elaborates the design concept of hierarchical FDIR of satellite. Through the centralized management at system level, autonomous disposal at subsystem level and redundant design of equipment level, it optimizes the parameters of software and hardware design of satellite FDIR and realizes the FDIR architecture at satellite system level.

2 Overview of Fault Detection, Isolation, and Recovery (FDIR) FDIR is an important function of spacecraft autonomous management, which is realized from the system level to each sub-system, so as to achieve a higher level of autonomous management. FDIR means fault detection, isolation and recovery. In common design, in order to ensure the reliability of the in-orbit work of the satellite, engineers consider both the redundancy design and the safety of the satellite operation. The design of improving the in-orbit viability and reliability of the satellite could reduce the impact of the fault on the performance of satellite and guarantee the service life of the satellite. The safety design is based on the reliability design, aiming at the failure which can not be eliminated, ensuring the failure diffusion and avoiding the impact on the performance of the satellite. The design of FDIR is the main aspect of satellite safety design, which is mainly aimed at building a work mode of autonomous fault processing by the satellite during the in-orbit operation. 2.1

Fault Detection and Isolation

The main part of fault detection is the on-board computer (satellite housekeeping computer and sub-computers), which is used for fault judgment based on the telemetry range of pre-set fault [4, 5]. The main part of fault isolation is also the on-board computer. The diagnosis algorithm and isolation measures are all stored in the on-board computer. In order to identify the satellite fault accurately and on time, the on-board computer software collects data and carries out comparison periodically. At the same time, according to the pre-designed strategy, the fault is isolated through the independent operation of satellite housekeeping computers and sub-computers to ensure the safety of satellite.

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Fault Recovery

When autonomously identifies fault, the satellite will deal with the fault according to the pre-designed fault isolation and recovery strategy. Generally speaking, when the failure affecting the satellite at system level occurs, the on-board computer will switch on backup of hardware according to the relevant criterion algorithm and restore the preset related parameters. When the failure affect in lower level (subsystem or unit level), the method is introducing backup data by software or switching to backup hardware in unit level or subsystem level to ensure the normal operation of the system through corresponding error correction. The overall goal is to ensure the reliable operation of the satellite by not reducing the system performance after fault recovery. 2.3

Fault Classification

The basis of FDIR and the fault it detects should be the result of FMEA (Failure Mode and Effects Analysis). The unit/subsystem level FDIR solution can be obtained from the unit/subsystem level FMEA, and the system level FDIR solution can be obtained from the system level FMEA result. The SSO satellite has the characteristics of battery discharge in each cycle and can’t enter the visible arc in multiple cycles. Therefore, various in-orbit faults are classified as follows according to the damage grade of different faults to satellite, Classification of in-orbit faults is as follows: • Level I: When the equipment relating to satellite energy failure, will directly affect the satellite energy safe and in-orbit survival ability, this kind of fault is defined as level I. • Level II: When a satellite from normal mode to abnormal operating mode, lead to payload cannot work normally, this kind of fault is defined as level II. • Level III: Because some equipment fails in-orbit, only cause local temporary loss of function, but does not affect the safe of the entire satellite, this kind of fault is defined as level III.

3 FDIR Design Process The article [3] shows the design flow of ESA. The Fig. 1 shows the design flow of CAST2000 satellite. This figure based on the ESA design flow and mixed the typical CAST develop phase. The main flow is similar. Our design flow focuses on project development instead of study or science experiment. Also for project, we emphasis heritage, so the study phase is simplified to mission analysis phase. In this phase, we should identify a basis heritage project and technical status changes. For the second phase, instead of ESA definition phase, we define design phase including preliminary design and critical design. The develop and test phase is for the FDIR implementation and test in different levels. In orbit phase, we have same FDIR activity as ESA. Mission analysis phase: according to the mission requirements, the requirements of autonomous satellite processing and the preliminary FDIR requirements at the system level are proposed.

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Fig. 1. Hierarchical FDIR develop flowchart

Design phase: complete the development requirements of FDIR, analysis of FDIR and detailed design of FDIR implementation through FMEA, available software and hardware resources. Development and testing phase: complete software and hardware implementation of FDIR, finish the FDIR test at sub-system testing and system level. The develop flow chart of hierarchical FDIR is as follows: In whole process, the first step is making the FDIR development requirement which includes mission/system level constrains, mission definition, failure categories and FMEA (Failure mode and effect analysis). The most important work is FMEA. The method is identifying the object of FDIR by subsystem/unit level FMEA and making the design requirements to software/hardware. The tools used in FDIR are basically from ECSS, including classification of system failure categories, definition of probability of occurrence and FMEA methods. From ECSS [6], the classification of system failure categories and definition of probability of occurrence are shown as following. Following failure categories are defined based on the effects of the failure on mission Likely hood of failures are categorized into five levels of probability from extremely unlikely to very likely. The level of probability is determined considering the mode of failure, components involved and operating environment (Tables 1 and 2). Table 1. Severity category of system level analysis Failure category Severity Failure effect I Catastrophic Loss of the spacecraft, or seriously damaged II Critical Loss of one or more major functions of the satellite III Major Minor degradation of a subsystem function IV Negligible No effect

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Definition Very likely Medium likely Likely Unlikely Extremely unlikely

The second step is FDIR analysis, which including system failure handling strategy, subsystem/unit level failure list based on FMEA, design of system and subsystem/unit interface, software design. The main work is close-loop the FMEA result by FDIR analysis. The following table shows the example of unit level FMEA and its FDIR requirement to system level (Table 3). Table 3. FMEA result S. Unit/item Failure No. mode

Failure causes

Failure effect Criticality level/occurrence possibility

Observable symptoms

Preventive/compensatory measures and remarks

1

Component failure

Redundancy loss

Communication failure between unit and OBC Ground TT&C would could receive all TMs packet except this unit

OBC FDIR Automatic Operation OBC will Switch from unit to redundant unit Tele-command Operation TC can be used to switch to redundant unit

Unit

Unit function failure OR Unit DC/DC converter failure

No III/D

The FDIR analysis is to ensure the compensatory measures could be implementing by different level. The redundant design should be done by unit hardware and relative interface (TM, TC and power). The communication failure criterion should be designed in OBC software and OBC has the strategy to switch from unit to redundant unit. All of these requirements should be delivered to different designers. This is the basic flow of hierarchical FDIR.

4 Hierarchical FDIR Design 4.1

Typical FDIR Levels

Hierarchical FDIR design in satellite is from top to bottom with four layers [3], the break down is shown in Fig. 2. This figure shows the actual project design for hierarchical FDIR application. • Level 4 (Top) is satellite safe mode layer which is controlled OBC (onboard computer). The satellite enters the safe mode, and at the same time, the system reconstruction is realized, that is, each subsystem is switched to the corresponding mode.

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• Level 3 is system handling layer, that is, when subsystem fails and being judged by the onboard computer, the fault subsystem is disposed. So that the subsystem is restored to normal without affecting the whole satellite mission. • Level 2 is subsystem handling layer, that is, when fault occurs under subsystem level and the subsystem (sub-computer) independently judges, the fault processing is completed within the subsystem, without affecting the subsystem functions. • Level 1 (Bottom) is unit handling layer, that is, when unit (software/hardware) autonomous judge fault occurring, and the fault processing is completed within the unit, so that the unit can recovery without affecting the unit function.

Fig. 2. FDIR hierarchical breakdown

Each FDIR layer has specific definition of I/O interface. The input interface is the alarm symbol or specific TMs of lower layer. The output interface has two kinds: one is sending the alarm symbol or specific TMs upper layer, the other one is sending telecommand to lower layer (switch off payload or switch to redundant unit, etc.). 4.2

Typical FDIR Measures

Redundant Design. FDIR function at unit level is commonly used as redundant design. Hot redundant is the main measure at unit level. the advantage of hot redundant is not affecting the unit function. Cold redundant is the measure which needs the external inferior, like upper layer FDIR. Typical redundant design is (Table 4): Table 4. Redundancy design of Power supply subsystem Unit/Module DC/DC Battery Shunting regulator Sub-computer EED

Redundant design Hot redundant Hot redundant Hot redundant Cold redundant Hot redundant

FDIR Unit/Module level Unit/Module level Unit/Module level System level Unit/Module level

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One advantage of cold redundant is the saving of power energy. So in CAST2000 platform, all sub-computers are designed as cold redundant and the FDIR design ensures OBC could switch sub-computers from main to redundant. The strategy is OBC could detect CAN bus communication with each sub-computer, when the communication failure accumulate to a set threshold. Compared to cold redundant, hot redundant design could save TMs and TCs resources and be more intelligent inside unit. Unit intelligent design is better for higher levels which could release more space to management high level FDIR. Safe Mode. For the serious failure which could affect the satellite safety, like battery discharge voltage abnormal, attitude abnormal, satellite must automatically handle it as soon as possible. The basic rule is make sure the satellite has enough power energy, stable attitude, maintain the link between satellite and ground control station (Fig. 3).

Fig. 3. Flow chart of typical safety mechanism

Health Status Monitor. OBC is the core unit of FDIR. In order to detect fault as soon as possible, the OBC could monitor the system and subsystem by check the “health status symbol” of each subsystem. The health status monitor is designed as hierarchical design: lower level generates health status symbol to upper layer. OBC is on the top layer and monitor the whole satellite health status (Table 5). Table 5. Design of health symbol in safety mechanism Subsystem1

Subsystem2

……

Parameter1 Health status symbol1 Health status symbol2 ……

Parameter2 Enable/disable

Enable/disable

……

Parameter3 Fault trigger counters Fault trigger counters ……

Parameter4 Relative recovery commands sending counters Relative recovery commands sending counters ……

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5 Conclusions and Prospect This paper gives an example of hierarchical FDIR application and improvement on small satellite. The hierarchical FDIR improves FDIR design by close loop the FMEA result. The structure of “from top to bottom” ensures the FDIR covers all compensatory measures from FMEA. The four layers FDIR structure uses on board resources effectively. The next step of hierarchical FDIR is to analysis fault and makes the recovery strategy by artificial intelligence.

References 1. Rabenau, E. et al.: Mars express mission planning – expanding the flight box in flight. In: SpaceOps2010 Conference, Huntsville, USA, 25–30 April 2010 2. Yang, W.: A control model for satellite orbit maintenance. Chin. Space Sci. Technol. 1, 11–15 (2001) 3. Gessner, R., et al.: Hierarchical FDIR Concepts in S/C Systems [EB/OL]. http://arc.aiaa.org, https://doi.org/10.2514/6.2004-433-249 4. Lang, L.E., et al.: Research and design of FDIR techniques for satellite avionics. Comput. Eng. Des. 35, pp. 2607–2611 (2014) 5. Jiang, L., et al.: Fault Detection, isolation and recovery design for micro-satellites. In: 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, Harbin, China, 21–23 July 2016 6. Schmidt, M., et al.: The ECSS Standard on Space Segment Operability. SpaceOps2004, Montreal, Canada (2004)

Research on Spacecraft Network Protocol Based on Space Packet Wei E.(&), Zhengwen He, and Ning Zhao Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing, China [email protected]

Abstract. This paper proposed a research on spacecraft and intra-spacecraft network protocol based on space packet protocol, and application efficiency improvement are studied. This paper analyzes the architecture, the characteristics and the addressing mechanism of space packet protocol. Based on standard space-borne interface service and space internet protocol, it discusses the method of interaction between space packet protocol and on-board subnet, space subnet and application support layer. It puts forward the extended definition of space packet format, constructs the application scenario of space packet protocol in satellite networks, promotes the standardization of inter-satellite, satelliteearth, intra-satellite protocols, and promotes the integration of resources between different fields. Keywords: Spacecraft network protocol Application support layer


Space packet protocol


1 Introduction With the wide application of space technology and the diversification of spacecraft functions, space communication has evolved from spacecraft-ground to inter-satellite links. The information exchange relationship within and between the spacecraft is increasingly complicated. The unified information network service requires the multispacecraft, multi-application process in spacecraft, and multi-user task coordination. It requires integrated network communication among devices to provide reliable operation and management for the integrated network [1]. For this reason, CCSDS has extracted the concepts of sub-package telemetry (TM), sub-package remote control (TC) and advanced on-orbit system package (AOS). The latest research results in this area is standard space packet protocol (SPP), it solved the limitation that packet utilization standard (PUS). The PUS service is too closely related to TM and TC, only focused on satellite-earth link interaction [2]. The SPP can enable space missions to efficiently transmit space application data with different types of characteristics in a network including satellite-ground and inter-satellite links, and can realize interconnection and intercommunication between spacecraft and spacecraft under CCSDS standards. This paper studies the application of space packet protocol, explores and proposes data exchange strategies among satellite, earth, inter-satellite and intra-satellite information © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 268–275, 2019. https://doi.org/10.1007/978-981-13-7123-3_32

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nodes, which is helpful to promote the unification of protocol and information sharing, promote the integration of resources in various fields, and improve the space application capability and efficiency.

2 The Concept of Space Packet Protocol 2.1

Architecture

In the traditional spacecraft on-board data network, space packet protocol is located at the network layer. The subnet protocol only includes the on-board subnet protocol, which uses packet service, memory access service, equipment discovery service, etc. to realize SOIS data service, uses the convergence sublayer to realize the mapping between data link and data service, and uses 1553B, space wire and other on-board subnet protocols to realize the data link layer and physical layer. With the joining of intra-satellite links, the subnet protocol is extended to UDP/IP and other internet protocols. As the carrier of asynchronous message transmission and other services in the application support layer, the space packet protocol can realize the subscription and distribution of messages in the space internet and on-board equipment networks. The interaction process of space packets in the network is shown in Fig. 1. The path from the source to one or more destination through the subnet is called the logical data path (LDP) [8]. The path id, including the source, the terminal and one or more subnets, authenticates each LDP uniquely. When the application data passes through the LDP subnet, the protocol provided by the subnet layer is used. The accessor carries the subnet when the path is inside the spacecraft and accesses the space subnet when the path passes through the space internet. The protocols used in the on-board subnetwork and the space subnetwork are independent of each other, and the logical data paths between the subnetworks can be configured and implemented through the management system to map the relationships between the protocols.

Fig. 1. Space packet interaction process

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Protocol Characteristics

The protocol data unit used by the space packet protocol is a space packet. Except that the packet header is mandatory, including the packet version number, the packet identification domain, the packet sequence control domain, and the packet data length. The user application process rather than the lower subnet transmission mechanism completely determines the contents of the space packet secondary header and the data domain. However, if different users define different package contents, when sharing information among users, they must learn the package formats of other users and provide interactive support operation methods. 2.3

Addressing Mechanism

The space packet main header only contains the application process identifier (APID) as the path identifier, and only the 16 - bit APID can be used as the address identifier for data routing within the spacecraft. However, when transmitting in the space subnet, the limitation of the value range of the APID will be highlighted. Therefore, the space package protocol establishes an APID naming domain for each spacecraft. Every APID is unique only in one spacecraft and can be defined repeatedly among the different spacecraft. The lower subnet protocol determines the value of the APID naming domain. If the IP protocol is used as the protocol of the space subnet, the IP address can be used as the APID naming domain. If the AOS link protocol is used, the main channel identifier consisting of the transmission frame version number and the spacecraft identifier can be used as the APID naming domain. APID is used as the addressing mark of the LAN in the satellite, while the APID naming domain expands the address range of the user application process in the integrated network. Because space packets are transmitted in one direction, APID can subscribe, publish, and identify application messages between on-board devices, between different spacecraft, and between the ground and spacecraft. However, the subnet protocol is not necessarily unidirectional, so when the user application process initiates the message transmission between subnets, the destination address must be mapped according to the configuration information.

3 Space Packet Protocol Application Method Due to the development process, PCM remote control system and CCSDS standard remote control space link protocol [9] are mainly used in the uplink of spacecraft spaceto-ground links, AOS [10] is mainly used in the spacecraf-ground to inter-satellite links, and the adjacent space link protocol is being preliminarily applied. The internal communication bus of the spacecraft also presents diversified development trends, including 1553B bus and CAN bus at low speed, 1394 bus, SpaceWire bus and Ethernet bus at high speed. When different spacecrafts interoperate or share information, space packet protocol, as a protocol data unit with the smallest granularity, has the advantages of good compatibility with various protocols, small resource overhead and flexible customization, and can assume the responsibility of generalization of interfaces

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between spacecraft in various fields. The following analyzes and illustrates the application methods of space packet protocol from the perspective of compatibility between different layers of space packet protocol and network architecture. 3.1

Interaction with Onboard Subnets

The on-board subnet provides a set of entities defined by SOIS for supporting the network layer’s space packet protocol and the application support layer, including the data service sublayer, the convergence sublayer, the data link layer and the physical layer. The packet service of the data service sub-layer can provide the transmission function of the space packet through the aggregation sub-layer. The aggregation sublayer completes the analysis and conversion of user process identification APID to the specific address of the data link in the space packet, and carries out the communication between the sub-network nodes through the data link layer and the physical layer. When designing the information-flow of spacecraft, the scope of APID should be divided according to the structural characteristics of spacecraft in different fields. When the spacecraft routes space packets, it can map to different terminal addresses according to the value of APID, thus realizing the correspondence between APID and application process in the terminal. The main head of a space packet is used to distinguish between a space packet and a remote control packet based on a packet type. However, with the development of intelligent spacecraft, the space packet type in satellite is no longer limited to remote sensing and control, and various types of payload data or user data appeared. The logical path direction, data domain format, and the processing method of different packet types are quite different. Therefore, it is necessary to add a packet type extension field to represent different data types together with the packet type field in the main header. There are two implementations for the packet type extension field, one is to occupy the high position of the APID, and the other is to define it in the secondary header. It is more reasonable to define the packet type extension domain in the secondary header. In order to comply with the agreement of the advanced on-orbit data system, the packet type is expanded as 4 bits high and the packet type as 1 bit low, forming a packet type field of 5 bits. The reserved value of ‘00000’ represents the TM packet, while ‘00001’ represents the TC packet, providing (25–1) extended identifiers for other data types in the spacecraft for identifying remote sensing image data, autonomous navigation data, asynchronous message management data, etc. The definition of the main header of the transmitted space packet in the on-board subnet is shown in the following Fig. 2.

Fig. 2. Packet header format

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The space packet protocol uses APID to perform simple network topology routing in the satellite. In the space internet scene, the space packet protocol uses the naming domain in the space subnet protocol to address complex networks. No matter using IPoC or DTN technology, when the source system of spacecraft A initiates message transmission to the terminal system of spacecraft B, it is necessary to specify not only the application process identification and data type of the terminal system, but also the address of the terminal system in the network. For the source system, the address may be generated by the registration and subscription managers in the asynchronous messaging service, or it may be due to customized heartbeat data or collaboration data conventions between spacecraft constellations. However, when spacecraft nodes of various TC and TM systems are interconnected with ground nodes, the mapping between named domains becomes extremely difficult. The PCM TC system, TM link protocol and AOS TM protocol adopted in low-speed satellite-ground link do not contain destination address identification. Increasing IP and other network layer protocol fields under the limited satellite-ground link channel rate will result in increased encapsulation overhead. In order to connect the low-speed link with the high-speed link, the method of storing a copy of the named domain in the secondary header of the space packet can be used. Each space packet stores the network destination address in the secondary header. When the link without the network layer and the link with the network layer carry out packet relay, the named domain in the secondary header is used for routing and mapping. When the link with the network layer carries out packet relay, the named domain of the network layer is directly used for routing. For the selection of the named domain, in order to realize forward compatibility, the communication link of the traditional measurement and control system uses SCID as the named domain, and the high-speed communication link uses IP address as the named domain. When relaying between the low-speed link and the high-speed link, the control center constructs and manages the mapping relationship between SCID and IP address. However, in the era of space internet, the 8-bit spacecraft identification SCID is far from meeting the needs of network addressing. On the premise of not changing the SCID field definition, the 4-bit spacecraft type extension field can be added to the secondary pilot to solve the dilemma. It should be pointed out that when the spacecraft uses the time-triggered Ethernet bus, each device of the spacecraft will have an independent IP address, and each device will form a sub-network on the device, while each device will form a space subnetwork, and each device will become a network node in the space internet. The data domain of the space packet encapsulated in the IP packet carries out message transmission inside and between the spacecrafts. If the space packet is transmitted inside the spacecraft, the source address and the destination address in the packet sub-header are filled into the satellite, and the mapping between the APID and the IP address realizes the intra-satellite routing. The secondary leader after adding the network layer addressing copy is shown in Fig. 3. The contents of the packet sub-header are specified by the source end user of each path id and notified to the end user through asynchronous message service.

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Fig. 3. Package sub-header format

3.3

Interaction with the Application Support Layer

In the application support layer, space packets can be used as service carriers regardless of command and data acquisition service, time access service, file and packet storage service, message transmission service, and device enumeration service. Command and data acquisition service uses TC space package and TM space package to register the analog quantity, temperature quantity and subset of command transmission of the equipment with APID. The application program only needs to access the data domain of the space package in device data pool service according to the configuration information, and does not need to care about the physical location information of the device. The on-board network layer automatically initiates the routing of space packets in the on-board subnetwork and the space subnetwork, and converts the on-board subnetwork into communication with the destination terminal. The file and package storage service enables users to access storage areas across devices or spacecraft according to LDP paths. When the spacecraft adds equipment, the equipment enumeration service allocates the global virtual equipment identification to provide mapping between the virtual equipment identification and the APID. When the equipment is revoked, the relevant application process identification in the equipment is deleted at the same time. The asynchronous message service (AMS) provides a complete set of registration and management mechanism for the transmission of various types of space packets in space- ground integrated network. When an application process node wants to join the network, it first obtains the registrar’s address in the spacecraft from the configuration server and registers the APID and the APID named domain with the registrar. The registrar updates its member information and informs the configuration server, registrars of other spacecraft in the inter-satellite network, and other application process nodes of the spacecraft of the access address of the application process. Other application process nodes notify their own access information to the new node. At this point, the new application process can learn the addresses of any devices in the on-board subnet and the inter-satellite subnet, thus completing the invitation, transmission, group sending, subscription and publishing operations based on APID and its named domain addressing.

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4 Verification The global navigation system is composed of dozens of satellites, which often have inter-satellite communication links, and can measure and control all satellites through limited ground stations. For remote sensing satellites, a limited number of ground stations means a limited observation time, because remote sensing satellites generally do not need to form constellations. The spacecraft network protocol based on space package proposed in this paper can enable remote sensing satellites, navigation satellites and ground stations to support each other at the network layer of the spacecraft network, thus enabling all kinds of satellites to cooperate with each other and give play to their respective advantages (Fig. 4).

Fig. 4. Space packet protocol application

When the ground user initiates the TC operation of the spacecraft, any satellite node can be selected to inject the TC space package according to the visibility. The earthvisible satellite node extracts the space packet APID naming domain, selects the link network to route to the destination node according to the space internet network layer protocol, the destination node identifies the space packet APID, searches for corresponding equipment through an addressing mechanism, transmits the equipment-borne subnet to the terminal system, and executes the instruction unit in the space packet by the application process in the terminal system. When the remote sensing satellite and navigation satellite work together, the spacecraft and gateway learn the APID naming domain of service providers and service requestors through asynchronous message transmission services to form address

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routing tables in configuration servers, registrants, and nodes. At the same time, service demanders need to bind the auxiliary guide of the space package and the analysis rules of the data domain generated by the service provider’s application process.

5 Summary and Suggestions The network protocol based on space packet proposed in this paper will inevitably become the basis of interactive support in future space-based networks due to its small field overhead, strong scalability, good compatibility with link protocols at various stages, and taking into account the characteristics of low-speed and high-speed link performance. Due to the flexible application of the space packet protocol, there are differences in the current use methods in various models. This paper starts with the application methods of the space packet protocol, makes clear the characteristics and the addressing mechanism of the space packet protocol, and studies the interaction methods between the space packet protocol and the carrier subnet, the space subnet and the application support layer. It is suggested that in future spacecraft design in various fields, the space packet protocol should be adopted to construct the integration network interoperability model of space and ground to achieve the goal of resource integration and interactive support among different types of spacecraft.

References 1. Zhao, H.: Building an intelligent and easy way for spacecraft with integrated electronic technology. Spacecraft Eng. 24(6), 1–6 (2015) 2. Merri, M., Cooper, S.: What has CCSDS SM&C to do with ECSS PUS? In: SpaceOps 2010 Conference (2010) 3. Zhang, Q., Guo, J., et al.: Space data system. China science and technology publishing house, p. 118, July 2016 4. He, X.W., Zhu, J., et al.: Application method of spaceborne standard interface service in spacecraft. Spacecraft Eng. 24(6), 52–58 (2015) 5. CCSDS713.0-B-1 Space Communication Protocol Specification (SCPS)-network protocol (SCPS-NP). Washington DC: CCSDS (1999) 6. CCSDS 702.1-B-1. IP over CCSDS Space Links. Washington D.C.: CCSDS (2012) 7. Scott, K.,Burleigh, S.: Bundle protocol specification [EB/OL], 15 November 2015. http:// tools.ietf.org/html/rfc5050 8. CCSDS 133.0-B-1 SPACE PACKET PROTOCOL. Washington D.C.: CCSDS (2003) 9. CCSDS 232.0-B-3 TC space data link protocol. Washington D.C.: CCSDS (2015) 10. CCSDS 732.0-B-3 AOS space data link protocol. Washington D.C.: CCSDS (2015)

Research on Instrument Requirements and Configuration for High Resolution Infrared Observations Qianying Wang(&), Fan Mo, and Quan Jing Beijing Institute of Spacecraft System Engineering, Beijing, China [email protected]

Abstract. High resolution infrared remote sensing can realize all-day, noncontact, high-precision temperature measurement such as the atmosphere, water and other objects, which is an important means of environmental monitoring and resource detection. But High-resolution infrared data are relatively scarce data sources, especially thermal infrared. This paper introduces the research on instrument requirements and configuration for high resolution infrared observations. By using large aperture and integrated optical system combined with linear push broom, high resolution multi-spectral imaging can be realized which covers multiple detection channels from visible light, short wave infrared, midwave infrared and long wave infrared spectral bands, and remote sensing data with a panchromatic resolution better than 1 m and an infrared resolution better than 5 m can be obtained, which can serve environmental protection, mineral exploration, and urban remote sensing after ground processing. Through the research in this paper, it provides a reference for the design and engineering of high resolution infrared payload. Keywords: High resolution


Thermal infrared
Payload configuration

1 Introduction High-resolution mid-wave and long wave infrared data are relatively scarce data sources. In the field of terrestrial observation in China, most of the payloads are limited in some applications such as environmental protection, mineral exploration, and urban remote sensing due to the lack of infrared spectrum, especially the long-wave infrared spectrum. The spatial resolution of ASTER short-wave and long-wave infrared developed by Japan is 30 m and 90 m respectively, which is one of the sources of our infrared data, but the payload has been invalidated. In this paper, the concept of highresolution from visible light to thermal infrared multi-spectral integrated camera payload is proposed from the perspective of Instrument Characteristics.

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 276–283, 2019. https://doi.org/10.1007/978-981-13-7123-3_33

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2 Instrument Requirements This chapter analyzes the basic spatial resolution, swath, spectral bands and accuracy requirements from the application in environmental protection, mineral exploration, urban remote sensing and other fields. 2.1

Spatial Resolution

In the aspect of environmental protection [1], the infrared spectrum, especially the midwave and long wave spectrum segment, has a spatial resolution of 5 m, and can monitor cooling tower of large power plant, most of the hot pollution drainage and warm drainage, and urban black and odorous water. For example, most of the cooling tower tops are 15 m in diameter and need 3 pixels to distinguish. Most black and odorous water have a width of 15 m. It requires 3 pixels to distinguish. The diameter of the Smokestack is 5 m, which need panchromatic image to support identification. In the aspect of mineral exploration, the basic geological remote sensing survey and the 1:50000 scale geological mapping require a spatial resolution of 6 m. In terms of urban environmental monitoring, targets such as buildings and urban facilities are small and require high resolution. When the resolution is 10 m, the thermal structure of roads, small gardens, buildings and other facilities can be distinguished. When the resolution is lower, a single pixel is fused with too much detail information, which cannot meet the refined application of urban remote sensing. In summary, the infrared resolution of 5 m can meet the needs of most applications. At the same time, some applications require a high-resolution panchromatic image that is better than 1 m. 2.2

Swath

The imaging swath is mainly designed to meet the needs of fast revisiting and global coverage. For high-resolution payloads, the width is limited by the detector, and it is generally difficult to achieve a large swath. Among the international mainstream of the same type of payload, the MTI which has the same resolution has a swath of only 12 km, and the Worldview-3 with a shortwave resolution of 3.7 m is only 13.1 km wide. Among the low and medium resolution satellites, Landsat-8 has a swath of 185 km, but its corresponding short-wavelength and long-wavelength infrared spectrum resolution is only 30 m/100 m. ASTER has a swath of only 30 km when the short-wavelength infrared spectrum resolution is 30 m and the long-wavelength infrared spectrum resolution is 90 m. For urban black and odorous water, straw burning, the warm water discharge in nuclear power plant, evaluation of urban heat island effect and other applications, since the target scale is relatively small, the 30 km imaging swath capability can meet the requirements, which can cover monitoring targets and sensitive areas of the surrounding environment. However, for large inland water bodies such as Taihu Lake, large-scale monitoring areas such as nature reserves, they require large-scale observations superior to 100 km.

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2.3

Spectral Bands

It is necessary to adopt visible and near infrared spectrum to realize aerosol optical thickness monitoring, use mid-wave infrared spectrum to realize monitoring of fire points such as straw burning, use thermal infrared spectrum to realize normal temperature target and high temperature gas monitoring. In the aspect of water environment monitoring, it is necessary to adopt the visible and near infrared spectrum to realize the monitoring of black and odorous water and the water bloom monitoring, and the thermal infrared spectrum to realize the thermal pollution of water bodies and the monitoring of nighttime sewage discharge [2]. In the aspect of ecological environment monitoring, it is necessary to adopt visible and near infrared spectrum to realize vegetation monitoring, use short-wave infrared spectrum to realize atmospheric correction and vegetation/soil moisture monitoring, use thermal infrared band to realize nature reserve monitoring and drought monitoring. In mineral exploration, it is necessary to use short-wave infrared and long-wave infrared spectrum to realize the characteristic spectrum monitoring of minerals. In the aspect of urban environmental monitoring, the visible and near infrared spectrum is used to realize land cover classification and sewage treatment facility identification. The long wave infrared spectrum is used to monitor land surface temperature, water temperature and urban heat island effect. 2.4

Accuracy

The distribution of surface temperature is an important research object in the fields of environmental monitoring, urban heat island, vegetation ecology, etc. It is also an important parameter in remote sensing models such as surface flux, soil moisture content and crop estimation. According to the research of application requirements, the accuracy of surface temperature inversion is better than 1K.

3 Spectral Bands Configuration 3.1

From Visible Spectrum to Mid-Wave Infrared Spectrum

For the visible light to medium-wave infrared spectrum, the atmospheric transmittance curve is shown below (see Fig. 1). It can be seen from the figure that the ground observation spectrum is mainly concentrated in three spectral segments of 300 nm– 1300 nm, 1300 nm–2500 nm and 3500 nm–5000 nm. The wavelength range from 0.3 lm to 1.3 lm includes all visible light bands, partial ultraviolet bands and some near-infrared bands. The wavelength range from 1.3 lm to 2.5 lm belongs to shortwave infrared. The atmospheric transmittance between the wavelength range from 1.55 lm to 1.75 lm is high, and it is mainly used for remote sensing during the daytime. The wavelength range from 3.5 lm to 5 lm belongs to the mid-wave infrared, and the atmospheric transmittance is 0.6–0.7. This band mainly includes the reflection and emission spectrum of the ground object, and the detectable temperature range is relatively wide, which can be used to detect high temperature targets such as fire and high temperature pollution sources.

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Fig. 1. Atmospheric spectral transmittance curve from visible to mid-wave infrared.

In the visible/near-infrared spectrum configuration of the payload, some relatively common spectral bands which include one panchromatic spectral band and four multispectral bands, which is 0.450–0.900 lm, 0.520–0.590 lm, 0.630–0.690 lm, 0.770– 0.890 lm. The short-wave infrared spectrum is mainly used for the identification of iron oxides and hydroxides, minerals which contain Al-OH, and minerals which contain Mg-OH. The spectral range is referred to the SWIR5*SWIR8 in WorldView-3, which is 2.145–2.185 lm, 2.185–2.225 lm, 2.235–2.285 lm, 2.295–2.365 lm. The mid-wave infrared spectrum is mainly used to monitor the surface high temperature fire point target. Generally, the temperature of the forest fire is as high as 300–800 °C, the peak wavelength of the forest fire is generally 3–5 lm. Finally, one spectral band is arranged which is 3.5 lm–4.1 lm. 3.2

Thermal Infrared Spectrum

In order to obtain surface radiation information, the band configuration in thermal infrared spectrum focuses on two aspects: atmospheric absorption and surface emissivity. For atmospheric absorption, the thermal infrared band should be in the band of the highest atmospheric transmittance because the atmospheric effect is the lowest in this case, and the atmospheric water vapor is the main component of atmospheric absorption. The figure below (see Fig. 2) shows the atmospheric permeability of the mid-latitude summer standard atmosphere with an atmospheric water vapor content of 2 g/cm2. The transmittance values vary with changes in atmospheric water content, but the shape of the spectra remains similar, so the analysis described below is also valid for other atmospheric profile information. Based on this spectrum, two different atmospheric windows can be identified: one is a window of 8 to 9.4 lm and the other is approximately 10 to 12.5 lm. Therefore, the thermal infrared band for surface temperature inversion should be located in these two atmospheric windows [3]. In addition, the following figure also shows that the highest atmospheric transmittance is between 10.5 and 11 lm, so a thermal infrared band should be set up in this area only considering atmospheric transmittance information. Because the thermal infrared signal is not only affected by atmospheric absorption but also by the surface emissivity, the best spectral range for obtaining accurate surface

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Fig. 2. Mid-latitude summer atmospheric transmittance of atmospheric water content of 2 g/cm2

temperature should have the highest emissivity for most natural surfaces. The emissivity value between 8 and 10 lm is relatively low, and the volatility is also higher than the value of the 10 to 13 lm interval [4]. In summary, the atmospheric window of the main infrared observation in the thermal infrared band is mainly 8–9.4 lm and 10–12.5 lm. These two windows are the most concentrated bands of thermal radiation energy at room temperature. The detection information mainly reflects the emissivity and temperature characteristics of the ground objects. Among them, the window has a high atmospheric transmittance and a higher surface emissivity in the 10–12.5 lm window. Long-wave infrared data needs to be inverted to obtain the surface temperature and emissivity information required for the application. The accuracy of different inversion methods is different. There are three commonly used surface temperature inversion methods. One is single-channel algorithm (SC) which only contains one thermal infrared channel, the other is dual channel algorithm or split window algorithm (SW) which requires two thermal infrared channels, the third is temperature and emissivity separation algorithm (TES) which requires multiple thermal infrared bands, at least three or four thermal infrared bands. Through the configuration of multiple thermal infrared spectral bands, the temperature emissivity separation algorithm can be used to simultaneously invert the surface temperature and emissivity. For example, the TES algorithm is proposed for ASTER data. The ASTER sensor has five thermal infrared spectral bands, so we can learn from ASTER’s thermal infrared spectrum configuration and add two variations to improve the ASTER configuration [5]. First of all, The spectrum around 8.3 lm is not considered because of the atmospheric effects. Second, The spectral segment located at 11.3 lm is placed with 12 lm, and the spectral segment of 10.6 lm is combined to use the split window algorithm, since the 13 and 14 spectral bands of ASTER are too close to fit the split window algorithm. In summary, the thermal infrared is configured with four spectral bands which are 8.475–8.825 lm, 8.925–9.275 lm, 10.3–11.3 lm, and 11.5–12.5 lm.

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Atmospheric Correction Spectrum

In the thermal infrared remote sensing surface temperature inversion, water vapor is an important input parameter, which affects the inversion accuracy of surface temperature. Among them, the inversion of atmospheric water vapor by the absorption of water vapor in the 0.94 lm band has high precision and is widely used in inversion of atmospheric water vapor in satellite remote sensing. By using atmospheric radiation transport model MODTRAN to simulate the change of atmospheric water vapor transmission rate with wavelength in mid-latitude summer, we found that the transmittances in the range of 0.84–0.88 lm, 1.00–1.07 lm and 1.22–1.26 lm are close to 1, which is the transmission band of atmospheric water vapor, and 0.89–0.99 lm is the atmospheric water vapor absorption band. Taking MODIS data as an example, band 2 (0.84–0.885 lm) and band 5 (1.23–1.25 lm) are atmospheric window channels, band 17 (0.890–0.920 lm), band18 (0.931– 0.941 lm) and band 19(0.915–0.965 lm) are absorbs channels for the atmosphere. Combining the current international mainstream infrared multispectral payload configuration and water vapor transmission rate curve, four spectral bands of 0.845– 0.885 lm, 0.89–0.92 lm, 0.931–0.941 lm and 0.915–0.965 lm were selected for the near-infrared spectrum of water vapor inversion, two short-wavelength infrared spectral bands of 1.36–1.39 lm and 1.56–1.66 lm are selected for detection and identification of thin clouds, thick clouds and snow. 3.4

Spectral Bands Configuration Results

In summary, the payload is configured with one mid-wave and four long wave infrared spectrum segments, multiple near-infrared and short-wave infrared observation bands, and a high-resolution visible light full-length segment. The following table gives a summary of all spectral bands (Table 1). Table 1. The summary of all spectral bands. Spectral bands VNIR SWIR MWIR TIR Atmospheric correction

Value (lm) 0.450–0.900, 0.630–0.690, 2.145–2.185, 3.5–4.1 8.475–8.825, 0.845–0.885, 1.56–1.66

0.450–0.520, 0.520–0.590, 0.770–0.890 2.185–2.225, 2.235–2.285, 2.295–2.365 8.925–9.275, 10.3–11.3, 11.5–12.5 0.89–0.92, 0.931–0.941, 0.915–0.965, 1.36–1.39,

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4 Payload Configuration Analysis 4.1

Imaging Mode Selection

In terms of the technical implementation of the current infrared camera, it mainly includes two methods of scanning and pushbroom [6]. If the orbital height of the satellite is 500 km, the scanning imaging system has a short dwell time and cannot meet the signal-to-noise ratio requirement when the spatial resolution is 5 m. It is necessary to use pushbroom imaging to obtain a longer integration time. From the requirement of the resolution and swath of the camera payload, it can be seen that 6000–8000 pixel detectors are needed (see Fig. 3). In addition, a larger 100 km range of observations needs to be implemented in conjunction with the agile working mode of the satellite. Since the detectors of this scale are available, the camera payload chooses pushbroom means in terms of system design and performance.

Scanning

Application requirements

Instrument requirements

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5m IR resolution 30km swath 1K temperature inversion accuracy

6000~8000 pixels 0.05K NETD

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Smaller scale detector Easy to achieve large field of view Short cell dwell time, low signal to noise ratio Satellite attitude/stability is affected

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Pushbroom Higher signal to noise ratio Relatively simple system / high reliability High geometric accuracy of image data Small impact on satellite platforms Long line array infrared detector is needed

Fig. 3. Imaging mode selection

4.2

Realization Method Selection

In order to achieve high-resolution infrared multi-spectral observation, an optical lens larger than 1 m is required. If the optical lens of visible light full-color and multispectral remote sensors are separately configured, the entire payload scale will be difficult to control. At the same time, an infrared lens with an optical lens larger than 1 m can collect enough energy to balance the needs of visible light full-color and multispectral remote sensing. At the same time, using a lens larger than 1 m in the infrared spectrum can collect enough energy to balance the needs of visible panchromatic and multi-spectral remote sensing. To this end, the integrated optical system design is adopted, and various types of detection tasks share the front optical system, and the rear optical system and the focal plane are modularized and independent of each other (see Fig. 4). Through such design ideas, the comprehensive observation task is achievable.

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Multiple optical system Application requirements 5m IR resolution 0.8m Pan resolution Multi-spectral 1K temperature inversion accuracy

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Instrument requirements

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Relatively simple system Large size payload Great influence on satellite platform

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Relatively complicated system High geometric accuracy Small influence on satellite platform

Multi-channel Multiple focal plane

Integrated optical system

Fig. 4. Realization method selection

5 Conclusion In this paper, the concept of high-resolution visible light to thermal infrared multispectral integrated camera payload is proposed. From the spatial resolution, the infrared multi-spectral has a resolution better than 5 m, and the panchromatic has a resolution better than 1 m. From the spectral configuration, it covers multiple detection channels from visible, short-wave, medium-wave to long-wave. Simultaneous phase observation of different spectral segments is realized on a remote sensor, which improves the information content and accuracy of the data.

References 1. Sobrino, J.A., Del Frate, F., Drusch, M., Jiménez-Muñoz, J.C., Manunta, P., Regan, A.: Review of thermal infrared applications and requirements for future high-resolution sensors. IEEE Trans. Geosci. Remote Sens. 54(5), 2963–2972 (2016) 2. Hook, S.J.: NASA 2014 The Hyperspectral Infrared Imager (HyspIRI) – Science Impact of Deploying Instruments on Separate Platforms, California (2014) 3. Sobrino, J.A., Jiménez-Muñoz, J.C.: Minimum configuration of thermal infrared bands for land surface temperature and emissivity estimation in the context of potential future missions. Remote Sens. Environ. 148, 158–167 (2014) 4. Lagouarde, J.-P., Bach, M., Sobrino, J.A., Boulet, G.: The MISTIGRI ther-mal infrared project: scientific objectives and mission specifications. Int. J. Remote Sens. 34(9–10), 3437– 3466 (2013) 5. Ramsey, M.S., Realmuto, V.J., Hulley, G.C., Hook, S.J.: HyspIRI Thermal Infrared (TIR) Band Study Report, California (2012) 6. Johnson, W.R., Hook, S.J., Foote, M.: Infrared instrument support for HyspIRI-TIR. Proc. SPIE 8511(23), 02 (2012)

A Method for Solving Generalized Implicit Factorization Problem Zhelei Sun1(&), Tianwei Zhang1, Xiaoxia Zheng1, Liuqing Yang1, and Liqiang Peng2,3 1

Beijing Institute of Spacecraft System Engineering, Beijing 100 094, China [email protected] 2 State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100 093, China 3 Data Assurance and Communication Security Research Center, Chinese Academy of Sciences, Beijing 100 093, China

Abstract. The problem of factoring RSA moduli with the implicit hint was firstly proposed by May and Ritzenhofen at PKC’09 where unknown prime factors of several RSA moduli shared some number of least significant bits (LSBs), and was later considered by Faugère et al. where some most significant bits (MSBs) were shared between the primes. Recently, Nitaj and Ariffin proposed a generalization of the implicit factorization problem. Let N1 ¼ p1 q1 and N2 ¼ p2 q2 be two distinct RSA moduli, Nitaj and Ariffin showed that when a1 p1 and a2 p2 share enough bits, N1 ; N2 can be factored in polynomial time, where a1 and a2 are some unknown positive integers. They also extended their work to the case of k ð  3Þ moduli. In this paper, we revisit Nitaj-Ariffin’s work and transform the problem into solving small roots of a modular equation. Then by utilizing Coppersmith’s method, for the case of two moduli we improve NitajAriffin’s result when the unknowns a1 ; a2 are relatively small, and our result is always better than Nitaj-Ariffin’s result for the case of k ð  3Þ moduli. Keywords: RSA scheme Coppersmith’s method

 Implicit factorization problem 

1 Introduction RSA [13] is one of the most widely deployed public-key cryptosystem. Its security relies on the difficulty of factoring large composite integer. A brief description on the key generation algorithm of the RSA scheme is given as follows: Key Generation of RSA: Let N ¼ pq be an RSA modulus, where p and q are primes of the same bitlength. Randomly choose an integer e such that gcdðe; uðNÞÞ ¼ 1, where uðNÞ ¼ ðp  1Þðq  1Þ and calculate d such that ed  1ðmod uðNÞÞ by the Extended Euclidean Algorithm. The public keys are N and e, and the private key are p, q and d. Although the problem of constructing efficient algorithm to factor N ¼ pq has been studied for decades, there are still no polynomial time algorithms known except for quantum algorithm. And it is believed as a mathematical hard problem in computational © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 284–290, 2019. https://doi.org/10.1007/978-981-13-7123-3_34

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number theory. However, the factorization may become feasible in polynomial time under several special cases, such as small decryption exponent attacks [1, 15]. In 2009, May and Ritzenhofen [10] proposed the problem of factoring RSA moduli with the implicit hint and showed that if the unknown prime factors of several RSA moduli shared enough number of LSBs, the moduli can be factored in polynomial time. Later, Faugère et al. [4] extended the problem to the case of unknown prime factors shared MSBs. In 2011, Sarkar and Maitra [14] related both May-Ritzenhofen’s work and Faugère et al.’s work to computing approximate common divisors problem and slightly improved previous results. Since then, the methods for solving implicit factorization problem has been well studied and deeply understood by many researches [7, 12]. Recently, Nitaj and Ariffin [11] proposed a generalization of implicit factorization problem. Suppose that Ni ¼ pi qi are k distinct n-bit RSA moduli with an-bit qi where a 2 ð0; 1Þ: If there exist kð  2Þ unknown positive integers ai satisfy that ai pi share tn LSBs or MSBs where t 2 ð0; 1Þ and ai  2bn where b 2 ð0; 1Þ for i = 1, …, k. Then Nitaj and Ariffin showed that Ni can be factored in polynomial time if t  2a þ 2b þ 1; for k ¼ 2; 2 k k or t [ k1 a þ k1 b; for k  3:

ð1Þ

Note that, here we ignore the small constants of Nitaj-Ariffin’s result. In this paper, we revisit Nitaj-Ariffin’s work and utilize Coppersmith’s method to propose the new result of generalized implicit factorization problem. By the comparison, for the case of two moduli, i.e. k ¼ 2, our result is better than Nitaj-Ariffin’s result when the unknowns a1 ; a2 are relatively small, and our result is always better than Nitaj-Ariffin’s result for the case of k  3 moduli. We organize our paper as follows. In Sect. 2, we introduce the lattice-based Coppersmith’s method which can solve small roots of modular equations and the background of lattice. In Sect. 3, we propose our method, and Sect. 4 is the conclusion.

2 Preliminaries In 1996, Coppersmith [2, 3] successfully applied the L3 lattice basis reduction algorithm to find small roots of modular equations, typically called Coppersmith’s method. To describe the sketch of Coppersmith’s method, we first give a brief review on the definition of lattices. Let L be a lattice which is spanned by k linearly independent vectors v1 ; . . .; vk 2 Zn . Namely, lattice L is composed by all integer linear combinations, c1 v1 þ . . . þ ck vk , of v1 ; . . .; vk , where c1 ; . . .; ck 2 Z. Then the set of vectors v1 ; . . .; vk is called a lattice of L and K is the lattice dimension of L. Moreover, there will be infinite lattice bases for any lattice L whose dimension is greater than 1. In 1982, A.K., Lenstra, H.W. and Lovász, L. introduced the famous L3 lattice basis reduction algorithm to find a lattice basis with good properties in polynomial time.

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Lemma 1. (L3 , [6, 9]) Let L be a lattice of dimension k. Applying the k algorithm to the basis of L, the output reduced basis vectors v1 ; . . .; vk satisfy that kðkiÞ

1

kvi k  24ðk þ 1iÞ detðLÞk þ 1i ;

for any 1  i  k:

ð2Þ

We also state a useful lemma from Howgrave-Graham [5] which gives a sufficient condition to transform a modular equation into an integer equation. We define the norm P of a polynomial gðx1 ; . . .; xn Þ ¼ ði1 ;...;in Þ ai1 ;...;in xi11 . . .xinn as kgðx1 ; . . .; xn Þk ¼ ð

X

1 a2 Þ2 : ði1 ;...;in Þ i1 ;...;in

ð3Þ

Lemma 2. (Howgrave-Graham, [5]) Let gðx1 ; . . .; xn Þ 2 Z ½x1 ; . . .; xn  be an integer polynomial with at most k monomials and m be a positive integer. Let p; X1 ; . . .; Xn be positive integers. Suppose that gð xe1 ; . . .; xen Þ  0 ðmod pm Þ for j xe1 j  X1 ; . . .; j xen j  Xn ; and pm kgðx1 X1 ; . . .; xn Xn Þk\ pffiffiffi : k

ð4Þ

Then gð xe1 ; . . .; xen Þ ¼ 0 holds over the integers. Then based on the above two lemmas, we give a brief sketch of Coppersmith’s method. For a modular equation f ðx1 ; . . .; xn Þ  0 modulo p, we want to solve the desired roots ð xe1 ; . . .; xen Þ. Firstly, construct k polynomials hi ðx1 ; . . .; xn Þ which have the same roots ð xe1 ; . . .; xen Þ modulo pm , where i ¼ 1; . . .; k and k should be larger than n. Then construct a lattice basis whose row vectors correspond to the coefficients of the selected polynomials hi ðx1 X1 ; . . .; xn Xn Þ, where j xe1 j  X1 ; . . .; j xen j  Xn . Suppose that by applying L3 algorithm to the lattice basis, one can obtain n polynomials he1 ðx1 ; . . .; xn Þ; . . .; hen ðx1 ; . . .; xn Þ corresponding to the first n reduced basis vectors whose norms are sufficiently small enough to satisfy Howgrave-Graham’s Lemma. Once the conditions are satisfied, one can find the roots xe1 ; . . .; xen from the polynomials he1 ðx1 ; . . .; xn Þ; . . .; hen ðx1 ; . . .; xn Þ, if he1 ðx1 ; . . .; xn Þ ¼ 0; . . .; hen ðx1 ; . . .; xn Þ ¼ 0. Note that, to satisfy the conditions in Howgrave-Graham’s Lemma, based on Lemma 1 one have     kðk1Þ 1 e     h1 ðx1 X1 ; . . .; xn Xn Þ  . . .   hen ðx1 X1 ; . . .; xn Xn Þ  24ðk þ 1nÞ detðLÞk þ 1n :

ð5Þ

Moreover, since the obtained polynomials he1 ðx1 ; . . .; xn Þ; . . .; hen ðx1 ; . . .; xn Þ are some integer combinations of the polynomials hi ðx1 ; . . .; xn Þ which are used to construct lattice, he1 ðx1 ; . . .; xn Þ; . . .; hen ðx1 ; . . .; xn Þ have the same roots ( xe1 ; . . .; xen ) modulo pm . Then if the norm of he1 ðx1 ; . . .; xn Þ; . . .; hen ðx1 ; . . .; xn Þ satisfy the second condition kðk1Þ

p ffiffi of Lemma 2, namely if 24ðk þ 1nÞ detðLÞk þ 1n \ p we have that hold over the integers. k e e Here we ignore h1 ðx1 ; . . .; xn Þ ¼ 0; . . .; hn ðx1 ; . . .; xn Þ ¼ 0 small terms and only 1

m

simply check whether detðLÞ\pmk does hold or not. Then based on the following

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heuristic assumption, we can solve the roots xe1 ; . . .; xen from the polynomials h1 ð xe1 ; . . .; xen Þ ¼ 0; . . .; hn ð xe1 ; . . .; xen Þ ¼ 0. Assumption 1. The polynomials he1 ðx1 ; . . .; xn Þ; . . .; hen ðx1 ; . . .; xn Þ derived from L3 output vectors are algebraically independent. Then the common roots of these polynomials can be efficiently computed by using techniques like calculation of the resultants or finding a Gröbner basis.

3 Our Method for Generalized the Implicit Factorization Problem In this section, we revisit the generalization of the implicit factorization problem proposed by Nitaj and Ariffin [11] and propose our improved analysis by utilizing Coppersmith’s method. Theorem 1. Let Ni ¼ pi qi be k distinct n-bit RSA moduli with an-bit qi where a 2 ð0; 1Þ. Suppose that there exist k unknown positive integers ai satisfy that ai pi share tn LSBs where t 2 ð0; 1Þ and ai  2bn where b 2 ð0; 1Þ for i ¼ 1; . . .; k. Then under Assumption 1, Ni can be factored in polynomial time if   1 k2 b: t [ kð1  aÞ 1  ð1  aÞk1 þ k1

ð6Þ

Proof: Let ai pi ¼ p þ 2tn ~pi . Then moduli Ni can be represented as a1 N1 ¼ q1 ðp þ 2tn ~p1 Þ; . . .. . . ak Nk ¼ qk ðp þ 2tn ~ pk Þ:

ð7Þ

Furthermore, we can get following modular equations N11 N2 aq1  aaa12q2  0 N11 Nk aq1  aaa1kqk  0

mod 2tn ; . . .. . . mod 2tn ;

ð8Þ

Q where a ¼ kj¼1 aj . Then we can firstly construct a k-dimensional lattice L1 which is generated by the row vectors of following matrix 0

1 B0 B. @ .. 0

N11 N2 2tn .. . 0

1 . . . N11 Nk ... 0 C .. C .. . A . ... 2tn

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  Since Eqs. (1) hold, the vector v ¼ aq1 ; aaa12q2 ; . . .; aaa1kqk 2 L1 . According to Gaussian heuristic, the length of the shortest non-zero vector of L1 is rffiffiffiffiffiffiffiffi ðk1Þtn 1 k detðL1 Þk ffi 2 k : 2pe

ð9Þ ðk1Þtn

Then with a good possibility, the vector v is the shortest vector when kvk  2 k , k k2 a þ k1 b. By applying L3 reduction algorithm to the lattice L1 , the namely t  k1   vector v ¼ aq1 ; aaa12q2 ; . . .; aaa1kqk can be found out, which means one can obtain some unknown multiples of q1 ; . . .; qk . Then one can easily factor Ni by computing greatest k k2 a þ k1 b, the reduced basis ðk1 ; . . .; kk Þ common divisors. However, when t\ k1 doesn’t contain vector v. For this case, we can represent the vector v into the form with a linear combination of reduced basis. Namely, there exist integers x1 ; . . .; xk such that v ¼ x1 k1 þ . . . þ xk kk . Moreover, we can obtain following modular equations x1 l11 þ x2 l21 þ . . . þ xk lk1 ¼ aq1  0 mod q1 ; . . .. . . x1 l1k þ x2 l2k þ . . . þ xk lkk ¼ aaa1kqk  0 mod qk ;

ð10Þ

where ki ¼ ðli1 ; li2 ; . . .; lik Þ for i ¼ 1; . . .; k. Based on the Gaussian heuristic, the length of ki and the size of the entries lij can be 1

ðk1Þtn

roughly estimated as detðL1 Þk ¼ 2 k , hence the solution of above equations can be tðk1Þ bounded as jx j ffi aa1 qi ffi 2ða þ kb k Þn : i

lij ai

Then using the Chinese Remainder Theorem, from the above equations we can obtain the following homogeneous modular equation a1 x1 þ a2 x2 þ . . . þ ak xk  0 mod q1 q2 . . .qk ;

ð11Þ

where ai is an integer satisfying ai  lij mod Nj for 1  j  k and can be calculated from lij and Nj . To solve the above homogeneous linear modular equation, we utilize the following theorem introduced by Lu et al. [8]. Theorem 2. LetN be a sufficiently large composite integer (of unknown factorization) with a divisor p p  N b . Furthermore, let f ðx1 ; . . .; xn Þ 2 Z ½x1 ; . . .; xn  be a homogeneous linear polynomial in nðn  2Þ variables. Under Assumption 1 we can find all the solutions ðy1 ; . . .; yn Þ of the equation f ðx1 ; . . .; xn Þ  0 (mod p) with gcdðy1 ; . . .; yn Þ ¼ 1, and jyi j  N ci for i ¼ 1; . . .; n if   n 1 n1  nð1  bÞ 1  ð1  bÞn1 : c  1  ð1  bÞ i i¼1

Xn

ð12Þ

The running time of the algorithm is polynomial in logN but exponential in n.

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For the homogeneous linear equation in k variables modulo q1 . . .qk ffi ðN1 . . .Nk Þa , by Theorem 2 with the variables xi \ðN1 . . .Nk Þd ffi 2kdn , for i ¼ 1; . . .; k, we can solve the variables when   k 1 kd  1  ð1  aÞk1 kð1  aÞ 1  ð1  aÞk1 ;

ð13Þ

namely, a þ kb 

  k 1 t ð k  1Þ  1  ð1  aÞk1 kð1  aÞ 1  ð1  aÞk1 : k

ð14Þ

Then we can obtain that when t

  k 1 k  a þ kb  1 þ ð1  aÞk1 þ kð1  aÞ 1  ð1  aÞk1 k1   1 k2 ¼ k ð1  aÞ 1  ð1  aÞk1 þ b: k1

ð15Þ

we can find the vector v, then we can factor Ni for i ¼ 1; . . .; k. This concludes the proof of our Theorem 1. Our method also can be extended to the case of the multiples of primes share MSBs and we can obtain the same bound. As it is shown, for the case of two moduli, i.e. k ¼ 2, our result is better than Nitaj-Ariffin’s result when the unknowns a1 ; a2 are relatively small, and our result is always better than Nitaj-Ariffin’s result for the case of kð  3Þ moduli.

4 Conclusion In this paper, we revisit the problem of generalized implicit factorization problem proposed by Nitaj and Ariffin, and then transform the problem into solving small roots of a modular equation. By utilizing Coppersmith’s method, we show that the result of Nitaj-Ariffin’s bound can be further improved.

References 1. Boneh, D., Durfee, G.: Cryptanalysis of RSA with private key d less than N0.292. IEEE Trans. Inf. Theory 46(4), 1339–1349 (2000) 2. Coppersmith, D.: Finding a small root of a univariate modular equation. In: EUROCRYPT 1996, pp. 155–165 (1996) 3. Coppersmith, D.: Finding a small root of a bivariate integer equation factoring with high bits known. In: EUROCRYPT 1996, pp. 178–189 (1996) 4. Faugère, J.-C., Mariner, R., Renault, G.: Implicit factoring with shared most significant and middle bits. In: PKC 2010, pp. 70–87 (2010)

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5. Howgrave-Graham, N.: Finding small roots of univariate modular equations revisited. In: Cryptography and Coding 1997, pp. 131–142 (1997) 6. Lenstra, A.K., Lenstra, H.W., Lovász, L.: Factoring polynomials with rational coefficients. Math. Ann. 261(4), 515–534 (1982) 7. Lu, Y., Peng, L., Zhang, R., Hu, L., Lin, D.: Towards optimal bounds for implicit factorization problem. In: SAC 2015, pp. 462–476 (2015) 8. Lu, Y., Zhang, R., Peng, L., Lin, D.: Solving linear equations modulo unknown divisors: Revisited. In: ASIACRYPT 2015, Part I, pp. 189–213 (2015) 9. May, A.: New RSA vulnerabilities using lattice reduction methods. Ph.D. thesis, University of Paderborn (2003). http://ubdata.uni-paderborn.de/ediss/17/2003/may/disserta.pdf 10. May, A., Ritzenhofen, M.: Implicit factoring: on polynomial time factoring given only an implicit hint. In: PKC 2009, pp. 1–14 (2009) 11. Nitaj, A., Ariffin, M.: Implicit factorization of unbalanced RSA moduli. J. Appl. Math. Comput. 48(1–2), 349–363 (2015) 12. Peng, L., Hu, L., Xu, J., Huang, Z., Xie, Y.: Further improvement of factoring RSA moduli with implicit hint. In: AFRICACRYPT 2014, pp. 165–177 (2014) 13. Rivest, R.L., Shamir, A., Adleman, L.M.: A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2), 120–126 (1978) 14. Sarkar, S., Maitra, S.: Approximate integer common divisor problem relates to implicit factorization. IEEE Trans. Inf. Theory 57(6), 4002–4013 (2011) 15. Wiener, M.J.: Cryptanalysis of short RSA secret exponents. IEEE Trans. Inf. Theory 36(3), 553–558 (1990)

OCV-Ah Integration SOC Estimation of Space Li-Ion Battery Dawei Fu(&), Lin Hu, Xiaojun Han, Shijie Chen, Zhong Ren, and Hongyu Yang Beijing Institute of Spacecraft System Engineering, Beijing, China [email protected]

Abstract. The state of charge of battery is a key, basic parameter of the battery management, which represents the current capacity of the battery and is a health criterion for the consistency of each cell. The accurate estimation of SOC will provide effective technical support for extending battery life and enable the battery to give full performance in the best state. In this paper, an optimized OCV-Ah integration method is proposed. It can eliminate the influence of internal resistance on the estimation error and provide an online estimation, which is suitable for space Li-ion battery. Compare to the experimental value, the estimation accuracy of calculated SOC is better than 4%. This method has been applied to the analysis of a space battery, and the fault cell is identified with the performance difference between the cells. Keywords: OCV


Ah integration
SOC estimation
Space
Li-ion battery

1 Introduction Li-ion battery is characterized by higher energy ratio, smaller thermal effect, lower selfdischarge rate and higher cell voltage. However, it has the disadvantages of bad overcharge and over-discharge resistance. Because of the gradually diverging of the state of charge (SOC) of each cell, it will lead to over-charge or over-discharge. In order to improve the life of Li-ion battery, an effective management is needed. SOC is a key, basic parameter of the battery management, which represents the current capacity of the battery. In addition, it is a health criterion for the consistency of each cell, which will determine the battery management strategy. The accurate estimation of SOC will provide effective technical support for extending battery life and enable the battery to give full performance in the best state. At present, the SOC estimation method of space Li-ion battery is generally Ampere-hour (Ah) integration method, which is simple with poor accuracy and low reference value. In this paper, the principle and application range of Ah integration method, open circuit voltage (OCV) method are introduced. And an optimized OCV method combined with Ah integration method is proposed. It can eliminate the influence of internal resistance on the estimation error with estimation accuracy better than 4%.

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 291–299, 2019. https://doi.org/10.1007/978-981-13-7123-3_35

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2 The Estimation of SOC 2.1

State of Charge

SOC is the state of charge of the battery, which represents the current capacity [1]. It is defined as below, where Qt is current capacity, and Q is the nominal capacity. SOC ¼

Qt Q

ð1Þ

For Li-ion battery, the relation between OCV and SOC isn’t linear with a plat region as shown in Fig. 1 from 20%–80% SOC. But in a certain range of voltage, there is a good one-one mapping relation [2].

4.1

OCV

3.9 3.7 3.5 3.3 3.1 0%

20%

40%

60%

80%

100%

SOC

Fig. 1. OCV-SOC curve

2.2

The Estimation of SOC of Li-ion Battery

SOC is a key, basic parameter of the battery, but it can’t be measured directly which is usually estimated by other parameters such as voltage and current. The first step of the estimation is modeling the battery, such as equivalent circuit model, electrochemistry model and neural network model. For the equivalent circuit model [3], the electrical properties of the battery in the circuit are utilized. For electrochemistry model [4], the complicated chemical reaction is analyzed. With enough training data, neural network [5] can be used to build up Li-ion battery model, too. The second step is the estimation of SOC based on battery model. There are several methods such as Ah integration method [6], OCV method [7], Kalman-Filtering method [8] and neural network method [9]. Ah integration method, which is widely used, is simple and applicable to online estimation, but the estimation error caused by initial integration value, time accumulation and charging efficiency is hard to be removed [10]. OCV method is suitable to the battery with obvious voltage change caused by the SOC change. The biggest drawback is that the precision can only be guaranteed after standing of enough time without charging and discharging. Therefore, it’s not suitable for the online estimation.

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But after enough standing, it will estimate the SOC accurately, providing initial value or reference for error correction of other methods.

3 Battery Model The relation between battery performances with each factor should be described in the battery model. Good model generally can describe the above relation accurately with parameters easy-to-get and lower order for implementation. 3.1

R-Int Model

R-int model is simple but effective battery model as shown in Fig. 2(a). Uoc is the open circuit voltage. R0 is the internal resistor. Uout is the output voltage. And I is the charging current or discharging current, which is negative for charging and positive for discharging.

R0

Rp

I

I

R0

UOC

Cp

Uout

(a) R-in Model

UOC

Uout

(b) Thevenin Model

Fig. 2. The equivalent circuit of R-in model and Thevenin model

So, the output voltage of battery is Uout ¼ UOC  IR0

ð2Þ

This model can describe the constant current mode of batteries. But the internal polarization and self-discharging of the battery are not involved in this model. 3.2

Thevenin Model

Thevenin model is proposed based on Thevenin’s theorem. There is a R-C network in this model as shown in Fig. 2(b). R0 is the internal resistor which will simulate the voltage drop of the output property. CP is the polarization capacitor, and RP is the polarization resistor, simulating the gradual change of output voltage. This model can explain the output voltage difference between no-load mode and output mode. The polarization of battery can also be explained.

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4 OCV-Ah Integration Method In this chapter, an optimized OCV method combined with Ah integration method (OCV-Ah Integration Method) is proposed, which can correct the accumulated error of Ah integration method and give an online estimation result. 4.1

OVC vs. SOC

The corresponding relation between output voltage and SOC of a space Li-ion battery is shown in Fig. 3 with charging mode, discharging mode and static mode. The charging current and discharging current are both 15A. Because of the long time standing, there are only 14 points of test result for the static mode in the figure and Table 1.

Fig. 3. Corresponding relation between output voltage and SOC

Table 1. Corresponding relation between OCV and SOC No. 1 2 3 4 5 6 7

Voltage (V) SOC No. 3.178 0.00% 8 3.707 7.92% 9 3.739 15.83% 10 3.781 23.78% 11 3.805 31.66% 12 3.813 39.58% 13 3.828 47.49% 14

Voltage (V) SOC 3.850 55.41% 3.878 63.33% 3.925 71.24% 3.972 79.16% 4.017 87.07% 4.075 94.99% 4.109 100.00%

Due to the low data volume, the static data can only provide an unsmooth curve as shown above with bad accuracy. The charging and discharging curves are smoother with more testing data. However, there is a large deviation to the static curve. It means that all these three curves can’t estimate the SOC accurately.

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Considering the R-int battery model in the Sect. 3, during the charging or discharging of battery, the relation between output voltage and OCV is Uout ¼ UOC  IR0

ð3Þ

In the charging mode where I = –15A, the output voltage is Uout1 ¼ UOC þ 15R0

ð4Þ

In the discharging mode where I = 15A, the output voltage is Uout2 ¼ UOC  15R0

ð5Þ

Uout1 þ Uout2 2

ð6Þ

So, the OCV is UOC ¼

The OCV-SOC curve is the average of charging and discharging curve with the same current. And, it is between the two curves in Fig. 3 in accordance with Eq. (6). 4.2

Fitting of OCV-SOC Curve

The output-voltage vs. SOC curves of charging and discharging mode can be fitted firstly. Then, according to Eq. (6), the average of these two curves is the OCV-SOC curve. The two curves in Fig. 3 can be fitted with 9-order polynomial. When the Li-ion battery is charged constantly from 0% to 100% with 15A current, the relation between the output voltage and SOC is Uout1 ¼ p1 x9 þ p2 x8 þ p3 x7 þ p4 x6 þ p5 x5 þ p6 x4 þ p7 x3 þ p8 x2 þ p9 x þ p10

ð7Þ

where p1 = 1247.74, p2 = –5977.63, p3 = 12218.22, p4 = –13882.28, p5 = 9570.00, p6 = –4100.79, p7 = 1077.63, p8 = –165.91, p9 = 13.99, p10 = 3.27, as shown in Fig. 4(a). When the Li-ion battery is discharged constantly from 100% to 0% with 15A current, the relation between the output voltage and SOC is Uout2 ¼ p1 x9 þ p2 x8 þ p3 x7 þ p4 x6 þ p5 x5 þ p6 x4 þ p7 x3 þ p8 x2 þ p9 x þ p10

ð8Þ

where p1 = 1398.64, p2 = –6814.82, p3 = 14118.08, p4 = –16210.84, p5 = 11274.44, p6 = –4875.28, p7 = 1295.88, p8 = –202.12, p9 = 17.04, p10 = 3.06, as shown in Fig. 4(b).

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(a) Charging

(b) Discharging

Fig. 4. Fitting result of voltage-SOC curve

According to Eq. (6), the OCV-SOC curve of this type space Li-ion battery is Uout ¼ p1 x9 þ p2 x8 þ p3 x7 þ p4 x6 þ p5 x5 þ p6 x4 þ p7 x3 þ p8 x2 þ p9 x þ p10

ð9Þ

where p1 = 1323.19, p2 = –6396.23, p3 = 13168.15, p4 = –15046.56, p5 = 10422.22, p6 = –4488.03, p7 = 1186.76, p8 = –184.01, p9 = 15.51, p10 = 3.17. So, the OCV-SOC curve of the space Li-ion battery is shown in Fig. 5.

Fig. 5. The OCV-SOC curve of the space Li-ion

4.3

Validation of the OCV-SOC Equation

According to the Eq. (9), the calculated SOC of the 14 points of open circuit voltage of Table 1 is shown in Fig. 6 in contrast to the experimental results. The solid line is the curve of Eq. (9) and the dashed line is the experimental data. The good consistency of calculated and experimental SOC is clear.

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Fig. 6. The calculated and experimental OCV-SOC curve

In consequence, Eq. (9) is a good expression for the OCV-SOC curve of the space Li-ion battery. And it will provide accurate estimation of SOC. For a spacecraft using Li-ion battery as the energy storage device, there are nine cells in series. The cell voltage of the battery on June 11 and July 5 are listed in Table 2. In this duration, the battery is only discharged by the measurement circuit. The corresponding SOC of the cell voltage in Table 2 is calculated according to the Eq. (9) and shown in Table 3. From the table, it is concluded that the decrease of SOC of cell 7 is faster than the others after twenty-three days’ standing. That means, the selfdischarge rate of cell 7 is larger than the others with poorer performance. Table 2. The cell voltage of the Li-ion battery Cell No. 1 2 3 4 5 6 7 8 9 June 11 3.9543 3.9550 3.9534 3.9543 3.9522 3.9522 3.9509 3.9536 3.9540 July 5 3.9330 3.9343 3.9326 3.9349 3.9300 3.9295 3.9106 3.9318 3.9335

Table 3. The SOC of each cell Cell No. 1 2 3 4 5 6 7 8 9 June 11 78.47% 78.58% 78.32% 78.47% 78.13% 78.13% 77.92% 78.35% 78.42% July 5 74.86% 75.10% 74.80% 75.21% 74.32% 74.23% 70.45% 74.65% 74.95%

The charge holding performance of each cell is listed in Table 4. The charge holding rate of cell 7 is 90.41%, but all the others’ are larger than 95% after twentythree days’ standing. So the fault cell is cell 7 with lower charge holding rate. Table 4. The charge holding performance of each cell Cell No.

1

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Compare to the static mode, there is a voltage step for the voltage of battery in charging or discharging mode, caused by the internal resistance as shown by Eq. (2). But in the above section, the OCV-SOC equation provides accurately estimation of battery in static mode. So, the estimation result of OCV-SOC equation can be used as initial integration value for Ah integration method. Equation (1) can be modified as Z I SOC ¼ SOC0 þ g
dt ð10Þ Q0 where SOC0 is initial state of charge, I is the charging current, Q is nominal capacity and η is the charging efficiency. For the Li-ion battery, the online estimation of charging mode and discharging can be obtained using Eq. (10) where SOC0 is estimated by the OCV-SOC equation. For the Li-ion battery in the above section, the initial voltage and SOC before charging, the voltage and SOC (Experimental and Calculated) after charging of 3Ah is shown in Table 5. The results show that the largest deviation is 3.84% between the calculated and experimental value, with accurate estimation.

Table 5. The experimental and calculated SOC No. Initial Voltage (V) SOC 1 2 3 4 5 6 7 8 9 10 11 12 13

3.178 3.707 3.739 3.781 3.805 3.813 3.828 3.850 3.878 3.925 3.972 4.017 4.075

0 7.92% 15.83% 23.78% 31.66% 39.58% 47.49% 55.41% 63.33% 71.24% 79.16% 87.07% 94.99%

Charged Estimation Error Voltage (V) SOC Experimental Calculated 3.707 7.92% 10.08% 2.16% 3.739 15.83% 17.99% 2.16% 3.781 23.78% 26.12% 2.34% 3.805 31.66% 35.00% 3.34% 3.813 39.58% 43.42% 3.84% 3.828 47.49% 49.74% 2.25% 3.850 55.41% 57.55% 2.14% 3.878 63.33% 64.07% 0.74% 3.925 71.24% 70.91% 0.33% 3.972 79.16% 81.29% 2.13% 4.017 87.07% 89.08% 2.01% 4.075 94.99% 95.67% 0.68% 4.109 100.00% 100.63% 0.63%

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5 Conclusion At present, the SOC estimation method of space Li-ion battery is generally Ah integration method with poor accuracy. In this paper, an optimized OCV-Ah integration method is proposed. It can eliminate the influence of internal resistance on the estimation error and is suitable for space Li-ion battery. Compare to the experimental value, the estimation accuracy of calculated SOC is better than 4%. This method has been applied to the analysis of a space battery, and the fault cell is identified with the performance difference between the cells.

References 1. Zhang, J., Lee, J.: A review on prognostics and health monitoring of Li-ion battery. J. Power Sources 6007–6014 (2011) 2. Broussely, M., Perelle, M., McDowell, J., et al.: Lithium ion: the next generation of long life batteries-characteristics, life predictions and integration into telecommunication systems. In: Proceedings of IEEE International Telecommunications Energy, V01.2, pp. 194–201 (2000) 3. Gomez, J., Nelson, R., Kalu, E.E., et al.: Equivalent circuit model parameters of a highpower Li-ion battery: thermal and state of charge effects. J. Power Sources 4826–4831 (2011) 4. Santhanagopalan, S., White, R.E.: State of charge estimation using an unscented filter for high power lithium ion cells. Int. J. Energy Res. 152–163 (2010) 5. Hu, Y., Yurkovich, S.: Battery cell state-of-charge estimation using linear parameter varying system techniques. J. Power Sources 198, 338–350 (2012) 6. Ng, K.S., Moo, C.S., Chen, Y.E.: Enhanced coulomb counting method for estimating state of charge and state of health of lithium-ion batteries. Appl. Energy 1506–1511(2009) 7. Lee, S.J., Kim, J.H., Lee, J.M., et al.: The state and parameter estimation of a li-ion battery using a new OCV-SOC concept. In: 2007 IEEE Power Electronics Specialists Conference, vol. 1–6, pp. 2799–2803. IEEE, Electron Devices Soc & Reliability Group, New York (2007) 8. Di Domenico, D., Fiengo, G., Stefanopoulou, A., et al.: Lithium-ion battery state of charge estimation with a Kalman filter based on an electrochemical model. In: 2008 IEEE International Conference on Control Applications, vol. 1, 2, pp. 425–430 (2008) 9. Lee, Y.S., Wang, W.Y., Kuo, T.Y.: Soft computing for battery state-of-charge (BSOC) estimation in battery string systems. IEEE Trans. Electron. 55(1), 229–239 (2008) 10. Bakkera, S., Maat, K., van Weeb, B.: Stakeholder’s interests, expectations, and strategies regarding the development and implementation of electric vehicles: The case of the Netherlands. Transp. Res. Part A Policy Pract. 66, 52–64 (2014)

Analysis and Experimental Study on Influence Factors of Spacecraft Power Cable Temperature Bingxin Zhao1(&), Lequn Wu1, Chenhua Zhang2, Shijie Chen1, and Yi Yang1 1

Beijing Institute of Spacecraft System Engineering, Beijing 100094, China [email protected] 2 Beijing Satellite Factory, Beijing 100094, China

Abstract. The design of the power cables is related to the safety of the satellite power supply. High temperature of the power cables may lead to short circuit, cable transmission performance degradation, cable damage or other risks. The temperature of the power cables is difficult to get accurate result by theoretical calculation and simulation analysis. In this paper, cable temperature rise tests under different constraints are carried out, and the influence of the factors on cable temperature changes is analyzed. Based on the effect of various factors on cable temperature rise, the design methods and treatment measures of the power cables in practical application are summarized. The results can provide a reference to the design of the spacecraft power cables. Keywords: Spacecraft
Power cable
Temperature rise
Influence factor
Test

1 Introduction The spacecraft cable network is responsible for the transmission of energy and signals between the various devices, mainly has power cables, signal cables and high frequency cables. The main power cables have higher load current and higher temperature rise. Once a short circuit occurs, the cable temperature rises more severely or even burns, which is related to the safety of the satellite. The ambient temperature of the spacecraft is generally good, but there are also working conditions with high ambient temperature reaching 50–55 °C and the load power is larger. Under the severe working conditions, the reliability of the cable is put forward higher. To ensure reliable transmission, the ability to carry a certain load is required and the temperature rise also needs to meet the derating requirements. The main power cables bundle of the spacecraft is generally thick, and limited by the space layout and the through-hole, it is impossible to achieve separate lashing and laying. When laying cables, power cables, signal cables, buses are often partially processed together. The temperature of complex cables has many influencing factors, and it is difficult to accurately estimate the temperature of the real cable bundle of the spacecraft by establishing a simulation model. On spacecraft, the temperature measurement is usually performed by the thermistor. Due to the limited number and © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 300–309, 2019. https://doi.org/10.1007/978-981-13-7123-3_36

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location of the measuring points, the monitored cable temperature may not be the highest temperature, and there is a hidden danger. In addition, according to the development process of the spacecraft, the monitoring of the cable temperature is only in the late stage of development, and if the temperature is too high, the cost of the change is large. Therefore, it is necessary to study the cable temperature. Cable temperature measurement points selection, cable temperature rise factors, the relationship between temperature and wire selection, and cable laying design need to be solved. For the cable temperature rise problem, there are many researches [1–4] on the high-voltage transmission power cables in atmospheric environment, but there are few studies on the cable temperature rise of spacecraft. Due to lack of convection in a vacuum environment and the effective heat conduction path on spacecraft, it relies on radiation to dissipate heat, and the cable heat dissipation problem is more severe. In this paper, temperature rise tests of single cable and multi-cables are carried out, the actual use on the spacecraft is simulated, the influencing factors of the cable temperature rise are analyzed, and the influence degree of each influencing factor on the cable temperature rise is given. This paper proposes constructive suggestions and guidance for the design of the cables.

2 Cable Temperature Rise Tests 2.1

The Plan of the Cable Temperature Rise Tests

The test cables are suspended in a test box placed in a vacuum rank. The cables have no contact with the test box. The connection diagram of the test system is shown in Fig. 1. The tests only radiate heat, regardless of conduction heat dissipation. The simulation tests are more severe than the actual conditions. The rated temperature of the selected wire is 200 °C, and the cable temperature specified in the standard should not exceed 120 °C. The single wire is folded and lashed repeatedly to simulate a single cable with multi-wires [5]. The lashing points are spaced 300 mm apart. The cable tying of a single cable test is shown in Fig. 2. The multi-cables are bundled as shown in Fig. 3.

Vacuum tank Test box Temperature Measuring Equipment

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Fig. 3. The multi-cables lashing diagram (W01, W02, W03, W04, W05)

The temperatures of the cables are measured by thermocouples, which are set at the beginning (marked as -01), middle (marked as -02) and end (marked as -03) of the single cable. There are two temperature measurement points for each temperature measurement position, namely inner temperature named as W01N-03 and outer temperature named as W01Y-03 by taking the end position of W01 cable as an example. The inner temperature of the multi-cables at a specific location is defined as W01N-A by taking the inner temperature of W01 at position A as an example. The bonding points of the inner temperature and the outer temperature are required to be set at the same section of the cable temperature measurement position. The inner temperature measurement point is placed at the center of the cable, and the outer temperature measurement point is placed on the outer surface of the cable, as shown in Fig. 2. The cables are tested under vacuum environment, at ambient temperatures of 25 °C, 45 °C, and 55 °C, respectively. The current of the cable is loaded according to a certain Table 1. Test cable design Test Case 1 2 3

Cable Number W07 W06 W09 W01 W02 W03 W04 W05

Line type 0124-22 0124-20 0124-20 0114-26 0124-22 0124-26 0124-22 0124-20

Number of wires in the lashing 40 40 40 80 24 16 16 48

Remark Loading Loading; Different ways of tying Without load Loading Loading Loading Loading

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timing, and the temperature data is recorded every minute. The design of test cables is shown in Table 1. 2.2

Test 1: The Temperature Rise Test of a Single Cable at Different Ambient Temperatures

The single cable: The test cable is W07 and the current is loaded according to the timing shown in Fig. 4.

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Test 2: The Temperature Rise Test of Single Cables with Two Tying Methods at Different Ambient Temperatures

The single cables: The test cables are W07 and W09. The difference between the two test cables is only the way of tying. W07 is the whole cable tied together. W09 is tied by grouping, that is, four twisted pair wires are tied together. The loading current is as follows (Fig. 5): TEST2: W06/W09 Current

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Test 3: The Temperature Rise Test of Combined Lashing Multi-cables at Different Ambient Temperatures

The cables of Test 1 and Test 2 are all single laying. In actually, the cables in spacecraft are lashing together, and the cable temperature rise problem is more complicated. The number of wires at the lashing point increases, and the temperature rise of each cable affects each other, so the heat dissipation conditions are more severe. According to the actual situation of the spacecraft, design cables named W01 to W05. The tying method of the cables is shown in Fig. 4. W01 cable is signal cable without load. The cables named W02 to W05 are loaded according to the timing shown in Fig. 6. TEST3: W02/W03/W04/W05 Current

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3 Test Results and Analysis 3.1

Test 1 Results and Analysis

When the ambient temperature is 25 °C, the temperature rise of W07 cable is shown in Fig. 7. The temperature measured at W07N-02 point is the highest, that is, the temperature in the middle of the cable is slightly higher than the temperature at the beginning or the end of the cable, and the highest temperature in the middle of the cable is at the center of the cable. According to the figure, the most important factor affecting cable temperature is loading current. Generally, when the current becomes larger, the cable temperature will rise rapidly, and the cable temperature rise rate will slow down after about ten minutes. After the cable is loaded with a constant current for one hour, the temperature rise of the cable is basically stable. Certainly, current loading time is also one of the factors affecting the temperature rise of cable. At different ambient temperatures, the temperature of W07N-02 point as a function of time is shown in Fig. 8. Ambient temperature is also one of the main reasons that affect cable temperature rise. When ambient temperature rises, the temperature when the cable temperature stabilizes will also increase, but the increase in the cable temperature rising is less than ambient temperature increment. At 25 °C ambient temperature, the maximum temperature of W07 cable is 126.171 °C; At 45 °C ambient temperature, the maximum temperature of W07 cable is 137.92 °C; At 55 °C ambient temperature, the maximum temperature of W07 cable is 144.25 °C. The maximum temperature of the cable consisting of 40 wires of AWG22 exceeds 120 °C when the current value is

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2.32 A, that is the deducted current value of the cable, however, when the current is 2.05 A, the maximum temperature when the cable is stabilized is less than 120 °C. 3.2

Test 2 Results and Analysis

The temperature rise curve of the inner temperature of W06 cable and W09 cable with the load current is shown in Fig. 9 under different temperature conditions. When the rated current is 3.28A, the temperature of W09 cable is 8–9 °C lower than the temperature of W06 cable when the cable temperature is stable. It shows that the group tying method is better than the whole bundle tying method. It can be seen that the tying method of power cables has a certain influence on the temperature rise. When the number of wires in high-power cables is large, the group tying method should be selected.

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At 25 °C ambient temperature, the maximum temperature of W06 cable is 74.064 °C; At 45 °C ambient temperature, the maximum temperature of W06 cable is 89.309 °C; At 55 °C ambient temperature, the maximum temperature of W06 cable is 97.106 °C. The maximum temperature of the cable consisting of 40 wires of AWG20 does not exceed 120 °C when the current value is 3.28 A, that is the deducted current value of the cable. Compared with W06 cable, W07 cable has the same number of wires, length, lashing point, and temperature measurement points, only the selected line type and loading current are different. At the same ambient temperature, W07 cable at the maximum derating current (2.32 A) has a much higher temperature rise than W06 cable at the maximum derating current (3.28 A). When designing a high-power cable, the load current is close to the derating current and the number of wires is large, it is recommended to select a thicker wire. The choice of wires is also the direct cause of temperature rise. 3.3

Test 3 Results and Analysis

The inner temperature curve at the different lashing points of W02 cable is shown in Fig. 10 at an ambient temperature of 25 °C. The temperature at the lashing point F is the highest, and the number of wires at the position F is the highest, which is not conducive to heat dissipation. Compared with position A, due to the heat conduction between the wires, position A is close to the end of the whole bundle cable, and the heat dissipation area is larger than the area of the middle. Therefore, the temperature of position A is slightly lower than the temperature of position F, but still higher than other lashing position temperature. W02N-A/B/E/F/G Cable Temperatures (Ambient Temperature 25 100

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Fig. 10. W02 cable temperature as a function of time

The temperature rise curves of cables named W01 to W05 at the F lashing position are shown in Figs. 11, 12 and 13 at different ambient temperatures. W01 cable is not energized and is bundled with other cables. The temperature of W01 cable will also change with the temperature of surrounding cables. The highest temperature position

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of W01 is W01N-F point. At 25 °C ambient temperature, the maximum temperature of W01 cable is 77.15 °C; At 45 °C ambient temperature, the maximum temperature of W01 cable is 92.58 °C; At 55 °C ambient temperature, the maximum temperature of W01 cable is 99.96 °C. The temperature of signal cables will also be affected by power cables bundled with. Power cables should be lashed separately from other signal cables to avoid the influence of temperature rise on signal transmission. Among the whole bundle of cables, W05 cable has the highest temperature. At 25 °C ambient temperature, the maximum temperature of W05 cable is 112.62 °C; At 45 °C ambient temperature, the maximum temperature of W05 cable is 125.15 °C; At 55 °C ambient temperature, the maximum temperature of W05 cable is 131.85 °C. W05 cable (2.7 m length, 48 wires, 3.32 A current load) has a longer length and a slightly larger number of wires than W06 cable (0.8 m length, 40 wires, 3.28A current load). In addition, W05 is a cable lashed together with multiple cables and W06 cable is a single cable. But the temperature of W05 cable is about 34 °C–39 °C higher than the temperature of W06 cable. In summary, the temperature of the cable is the result of multiple parameters. The influencing factors are ambient temperature, line type, number of wires, position of the lashing point, working current, working time, etc. In the cable design process, in addition to meeting the current derating requirements specified in the standard, the selection of the power cable channel is particularly important, and that is one of the key factors affecting the maximum temperature of the cable. The laying path should be set as much as possible, and a high-power cable should be laid separately to ensure that the cable has good heat dissipation conditions.

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4 Conclusion Through the test and analysis of this paper, the following conclusions can be drawn: (1) For the case that power cables are concentrated and the load is large, temperature monitoring needs to be set and the position should be specified. The location of the temperature measurement should be selected as close as possible to the middle position of the cable and the lashing position where the number of wires is the highest, and paste it at the centerline of the cable. (2) Power cables should be laid as close as possible to the deck to facilitate heat dissipation. (3) When signal cables are bundled with power cables, the temperature of signal cables increase with the temperature of power cables, and finally approaches the external temperature of power cables. Power cables should be laid separately and separated from other cables, which is beneficial to the heat dissipation of power cables, avoiding the temperature influence on other cables, and reducing the influence range in the case of power cables failure. (4) The high-power cables can be tied by grouping to facilitate heat dissipation and avoid local overheating, which can reduce the cable temperature. (5) Considering the temperature rise in the case of complex cables lashed together, the high-power concentrated cable should be further derated on the basis of the specified derating to ensure that the cable temperature rise does not exceed the requirements. (6) In the early stage of spacecraft design process, high-power laying path and sufficient access should be reserved when planning the overall layout and cable path. Other methods can be considered to solve local overheating and insufficient access problems, such as high-voltage bus, buried cable or wireless transmission.

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References 1. Blums, J., Budahs, M., et al.: MV Cable Temperature Characteristics in Dependence of load Current, Ambient Environment and Temperature. Determination of the Critical Environment (2016) 2. Du, B.: Research on Temperature Field and Ampacity of Power Cables. Zhengzhou University, Zhengzhou (2016) 3. Lu, Z., Zhang, H., Ao, M., et al.: Research of thermal time constant and steady criterion used in the power cables ampacity test. J. Northeast Dianli Univ. 36(5), 25–31 (2016) 4. He, J., Jiao, Y., Ye, D., et al.: Simulation and computation of temperature field and ampacity of conduit cable laying in different ways. Electr. Meas. Instrum. 53(3), 99–104 (2016) 5. Zhang, P., Wu, L., Jiang, D., Wu, L., Zhang, Y.: The temperature rise model of spacecraft cables in vacuum environment. Spacecr. Environ. Eng. 30(3), 235–239 (2013) 6. Geng, L., Peng, F., Wang, T., et al.: Analysis of the impact of high-power electric cable heating on satellite thermal design. Spacecr. Environ. Eng. 31(1), 83–87 (2014) 7. Kong, W., Wang, B., Lao, S., Ai, Y.: Study on fire initiation of wire insulations on board the satellite. Chin. J. SpaceSci. 28(1), 28–32 (2008)

Spacecraft System Autonomous Health Management Design Yong Lei(&), Quanyou Qu, Deyin Liang, Yilan Mao, and Xi Chen China Academy of Space Technology ISSE, Beijing, China [email protected] Abstract. In order to ensure the stable operation of spacecraft in orbit, improve continuous working capacity and reduce ground management pressure, this paper proposes a hierarchical and distributed spacecraft health management system architecture design, and proposes a healthy data flow design scheme, health data generation and processing methods, health data sharing mechanisms and health data scheduling methods. For the system health management requirements, the paper put forwards some system health management strategies such as spacecraft system monitoring, payload mission safety, power supply safety, safety mode, system recovery and system reconfiguration, etc. Keywords: Health management architecture Health management strategy


Health data management


1 Preface With the development of aerospace technology and applications, users’ requirements for spacecraft have been upgraded from the survival to continuous execution of missions. The spacecraft mission has the characteristics of high cost and complex system composition. There is limited ground monitoring and control time, ground fault diagnosis and disposal are not always timely. At the same time, with the increasing number of spacecraft operating in orbit, the pressure to monitor and manage spacecraft is growing. In order to improve the spacecraft’s continuous service capability, improve the timeliness of fault handling, and reduce the pressure on ground management, it is necessary to study and improve the spacecraft autonomous health management capabilities [1]. Spacecraft health describes the ability of spacecraft systems, subsystems, and components to perform their design functions. The spacecraft health management can be defined as a management activity directly related to its health status, that is, it can independently monitor and diagnose its own health status, in the event of an anomaly, it can be handled autonomously and return to normal work mode or minimize the risk of safety and the impact on the mission. Health management uses monitoring and diagnosis as the main means and it is the decision-making process and execution process with perception as the core. This paper carries out the research and design work of spacecraft system autonomous health management, and presents a hierarchical and distributed spacecraft health management system architecture design. Based on this, the spacecraft health data management mechanism and system health management strategies have been researched and designed. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 310–316, 2019. https://doi.org/10.1007/978-981-13-7123-3_37

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2 Health Management Architecture Design Depending on the complexity of the spacecraft system, the autonomous health management architecture can be divided into centralized, distributed, and hierarchical and distributed types. The core of the centralized architecture is a central management controller or processor that collects and processes all health information. This type is suitable for simple spacecraft. The distributed architecture consists of tasks such as status monitoring, fault detection, and isolation processing independently. The advantage is that system integration and testing are easy. The disadvantage is the inability to perform data fusion between subsystems, the reliability of fault diagnosis and detection is lower. Considering the advantages and disadvantages of centralized and distributed architecture, and the spacecraft system usually consists of multiple sub-systems and products, its functions, composition, working modes, etc. are more complex, thus providing a hierarchical and distributed autonomous health management architecture design that combines the advantages of centralized and distributed [2], as shown in Fig. 1. The architecture is divided into system, subsystem and component layers, it include main functions such as health data management at all layers, fault perception, data fusion diagnosis, and fault handling based on knowledge database.

Health data management System layer

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Fig. 1. Hierarchical and distributed health management architecture

The system layer mainly completes the following functions: acquiring, storing, and processing the health data, and adopting different strategies to interact with the ground system according to the critical degree of health status; exchanging health information with each subsystem, in the event of a fault, according to system fault-tolerant resources and Knowledge model, perform system-layer and subsystem-layer fault handling and reconfiguration [3]; integrate health data and information from different subsystems to eliminate data inconsistency, identify and isolate faults, and obtain more reliable subsystem health status.

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The subsystem layer mainly completes the following functions: obtaining subsystem, component-layer health data and information, and performing fusion processing, performing subsystem-layer, component-layer fault handling and reconfiguration according to subsystem-layer fault detection, diagnosis, and knowledge model; exchanging the subsystem health data and fault handling information with the system layer. The component layer will complete the following functions: according to the selftest information or internal sensor information of the component, the fault diagnosis of the component is performed and the fault management strategy is executed according to the knowledge model; exchanging the component health information and fault handling information with the subsystem layer.

3 System Health Data Management Design 3.1

Health Data Flow Design

The spacecraft health data is generated and processed by each component, subsystem and system layer. The low-layer data is shared with the upper layer for data fusion diagnosis, and the high-layer shares the fusion results, system health information, and fault diagnosis to the lower layers. Figure 2 shows the Health data flow. The system management unit undertakes system health management tasks, and implements functions such as health data collection, processing, sharing and scheduling. The health data storage unit is used to store all health data, fault diagnosis information, system configuration status, etc. and can assist the system management unit in data statistics, fusion, query, playback, etc. Data sharing and storing

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Health data management functions can be divided into three parts: health data generation and processing, health data sharing, and health data organization scheduling. Health Data Generation and Processing. System layer commonly used health data generation and processing methods include health status synthesis, statistical analysis, Point extraction, mutation monitoring, event reporting, and system working status records, as shown in Table 1.

Table 1. System level common health data generation and processing method Method Health status synthesis

Description Synthesize and analyze the health status of each subsystem, perform fusion diagnosis processing, and obtain system health status summary information, including system working mode, system event report index, energy/attitude/load/communication/propulsion and resource margin evaluation Statistical According to the specific period (one track, one analysis day, one month, load working period, etc.), count the maximum/minimum value and average value of key parameters affecting the health status of the system Point For the key parameters of continuous change, it sampled can also be sampled and stored according to the period, and generated a curve according to the period (one track, one day, one month, etc.) Mutation Performs system mutation monitoring and monitoring important telemetry parameters, including 0-1 mutation, excessive variability of transitions, and out of range Event Corresponding event reports are generated for reporting faults and abnormal events monitored at the system level, and record all relevant data Real-time record system latest working status, System system configuration status, and reliable storage working through multiple methods status records

Application Generate the health status of the spacecraft for the ground to quickly and accurately understand the health status of the spacecraft

Used to analyze and diagnose long-term characteristics of key parameters

Draw curves for key parameters in a specific period

Used to detect a sudden change or an excessive range of changes Used for further analysis, location and disposal of faults on the ground Record the critical state of the system and use it for system recovery in case of fault

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Health Data Sharing. The purpose of spacecraft health data sharing is to synchronize system state, improve the accuracy, robustness and efficiency of health management. Spacecraft system layer health data sharing is mainly through the following ways. Publish the whole system parameters, health status, system configuration status, etc. to the sub-systems, and synchronize the current status of the system. Correlate the current working parameters between the relevant subsystems for verification diagnosis. To ensure that all kinds of data required for fault detection are comprehensive and accurate, and improve detection accuracy. Store the critical state of the system in different storage units or sub-systems. When a sub-system fails, the working parameters before the failure can be restored to maintain the continuity of the system as much as possible; the health data storage unit can also perform fusion analysis on health data to achieve accurate fault diagnosis and location. Health Data Scheduling. Health data scheduling includes spacecraft internal scheduling and scheduling between the spacecraft and ground. The principle of data scheduling is to transmit critical health data in a timely and on-demand manner. The spacecraft internal scheduling adopts a periodic and burst-compatible scheduling strategy. The periodic scheduling strategy is used for periodic messages, and the burst-compatible strategy is adopted for the changing messages, which can ensure that information is delivered in a timely manner and save communication bandwidth resources. In addition, all health data is stored in the health data storage unit, so that all data can be queried when needed. AOS-based channel and packet two-level scheduling strategy is adopted between the spacecraft and ground. The channel is used to distinguish different types of data, the channel scheduling uses a policy triggered by an instruction or an abnormal event. The packet scheduling strategy adopts a two-dimensional dynamic scheduling method based on the shortest transmission period and the importance of packet.

4 System Layer Health Management Strategy The spacecraft system layer health management strategy mainly includes: system monitoring strategy, payload mission safety strategy, power supply safety strategy, transfer to safety mode strategy, system recovery strategy, system reconfiguration strategy, etc., and the implementation of each health management strategy depends on lots of basic technologies, including fault detection, diagnosis, data fusion, software development, PUS [4] (Telemetry and telecommand packet Utilization) anomaly event handling mechanisms. The composition of the spacecraft system layer health management strategy is shown in Fig. 3.

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Fig. 3. The spacecraft system layer health management strategy

Table 2 gives a description of the system layer health management strategies.

Table 2. System layer health management strategies Strategy System monitoring

Payload mission safety

Power supply safety

Safety mode

System recovery

System reconfiguration

Strategy description It is used to monitor the status of the intelligent control unit of each subsystem. When the abnormality is detected, the system management unit takes corresponding recovery procedure for each intelligent control unit, such as reconfiguration, reset, switch main/backup, etc. Check the validity of the user mission command, the verification includes whether the task conflict, whether the system capability, on-board storage, energy and other resources are sufficient to complete the mission; During the execution of the load task, monitor the payload and the platform status. When the abnormality is found, execute the payload forced shutdown to ensure satellite safety Monitoring of key parameters such as current, subsystem current, battery discharge depth and charging current under various working modes of the spacecraft, when the abnormality is found, the payload is turned off, the relevant control parameters are adjusted or the equipment is switched to ensure the energy safety of the spacecraft In the event that the attitude or the power supply are abnormal, if the spacecraft cannot be returned to normal, the spacecraft can be transferred to the safety mode for ground processing. The safety treatment corresponding to the safety mode usually include stopping all the payload missions, shifting to the minimum energy mode, emergency or the stop control mode, and maintains the minimum working state The latest critical parameters are stored in the health data storage unit and different subsystems. When the intelligent control unit reset, switched or other failure mode needs to be recovered to the previous working mode, the subsystems or health data storage units can be read to quickly recovery the working mode before the fault In the case of equipment failure, working mode error and other abnormal conditions, system reconfiguration are required to restore the system status. Reconfiguration method include: power off and power on, software reboot, Transfer the task between different devices or subsystems [5], software maintenance, component switch, etc.

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5 Conclusion According to the spacecraft health management objectives and requirements, this paper proposes a hierarchical and distributed health management design, from the aspects of health management architecture, health data management and health management strategy. The hierarchical and distributed architecture has the advantages of simple and efficient implementation, sufficient data fusion and sharing, and accurate fault diagnosis. The health data management is based on the system management unit and the health data storage unit. The health data flow design scheme, health data generation and processing method, health data sharing mechanism and scheduling method are put forward. Finally, for the health management application, the health management strategies such as system monitoring, payload mission safety, power supply safety, safety mode; system recovery and system reconfiguration are designed. The system layer health management scheme proposed in the paper is reasonable in architecture, complete in health data management, effective in strategy, and can adapt to the health management needs of complex spacecraft. It can be used as a reference for spacecraft system layer health management research, system design, and spacecraft ground control system design.

References 1. Tipaldi, M., Bruenjes, B.: Spacecraft Health Monitoring and Management Systems (2014). 978-1-4799-2069-3/14 ©IEEE 2. Kolcio, K., Breger, L., Zetocha, P.: Model-Based Fault Management for Spacecraft Autonomy (2014). 978-1-4799-1622-1114 ©IEEE 3. Figueroa, F., Walker, M.G.: Integrated System Health Management (ISHM) and Autonomy, 8–12 January 2018, Kissimmee, Florida (2018) 4. ECSS. Ground systems and operations - Telemetry and telecommand packet utilization, ECSS-E-70-41A (2003) 5. Fayyaz, M.: Survey and future directions of fault-tolerant distributed computing on board spacecraft. Adv. Space Res. 58, 2352–2375 (2016)

Design and Simulation Verification of Ground Charge Equipment for Li-Ion Battery Pack Lin Hu(&), Dawei Fu, Hongyu Yang, Shuo Feng, Jianbo Du, Jinchen Zhao, and Chengzhi Lu China Academy of Space Technology, Beijing, China [email protected]

Abstract. Li-ion battery packs are aerospace energy storage components that have been used and have good prospects. Because it is not overcharge resistant but needs to use them in series, the complex requirements of charging management have become an important factor restricting their development. It designed a li-ion battery pack ground charging device from the satellite interface to meet the demand in this paper, this device used CC-CV charge method, equalization charging for cells, and has completed simulation verification, laid a good foundation for subsequent charging equipment. Keywords: Ground charge equipment
CC-CV charge method Equalization charging
Design and simulation verification




1 Introduction Compared with Ni-Cd and Ni-MH batteries, li-ion battery has higher operating voltage without memory effect, smaller volume, lighter weight, higher energy ratio, higher discharge current, longer life, lower self-discharge rate, and wider available temperature range and so on. Therefore, li-ion battery is the first choice when we need the higher performance, reliability, security electrical energy storage device [1]. The charging process of the li-ion battery is completely different from the other’s, as the performance of li-ions decreases, the imbalance between battery cells becomes more serious after repeated charge and discharge cycles, the li-ion battery is not overcharge resistant, overcharging will seriously affect the cycle life of the li-ion battery. The charge termination voltage is generally controlled within 1% accuracy. Thus, in order to meet the needs of routine testing, it is necessary to use a charging equalization circuit to manage the charging process of the battery pack, it should design special charging device for li-ion battery packs. It designed a more efficient equalization charging ground equipment for li-ion battery packs in this paper, which charges from the interface of the spacecraft with the existing charging power supply.

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2 Systematic Review 2.1

Charging Mode

The development of charging mode starts with constant current charging but this method is not easy to control. Constant voltage charging method, keeps the charging voltage constant during the charging process, has some adaptability, and is closer to the optimal charging curve than constant current charging method. However, the initial charging current is too large, which has a great impact on the life of the battery, and even causes the battery damage, so it is also hardly used. Compared with the two methods, the stage charging is more reasonable, especially the two-stage charging method which combines constant current and constant voltage. This method charges constant current to the rated voltage and uses constant voltage charging to complete the remaining charge [2]. Through the combination of the previous two charging methods, the battery safety is better guaranteed. Although the charging control circuit is more complex, because of its short charging time and high charging safety, it plays a dominant role in the charging of li-ion batteries. The charging device designed in this paper adopts this charging mode. 2.2

Equalizing Charge Type and Comparison

Equalizing charge management is needed in the charging process, which has become the most difficult problem, and is also a hot topic in battery charging research. Among them, battery optimization, battery screening, optimize battery usage, negative balance and other ways to solve the problem of battery balance is too negative, cannot fundamentally ensure battery equalization in use. And the control circuit of energy transfer equalization is too complex, the loss of energy of storage elements and switches makes its advantages more obvious in the application of high capacity battery packs over 100 Ah. By comparing the voltage of single cell with the reference voltage, the shunt resistance equalization method uses bypass resistance to shunt the charge current to the higher voltage cell [3]. Among them, the average voltage equalization method which compares the average battery voltage with the single cell voltage to decide shunt or not has good effect with more simple structure. Considering various factors, this design selects the equalization mode of average voltage shunt resistance.

3 The Design and Simulation Verification of the Equipment The design of the equipment mainly includes the design of the system (including the whole system, constant current charging loop, constant voltage charging loop), related modules design (including equalization module, system stability, overvoltage protection module, etc.) and the simulation of the system. The setup of system and simulation of the circuit use Oracd Pspice.

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Design of the System

The system topology is shown in Fig. 1. According to the previous description, the system design of the circuit uses the CC (constant current)-CV (constant voltage) charge method. The circuit consists of two loops: a constant voltage charging loop and a constant current charging loop.

Fig. 1. System topology

In view of the current common and the increasing demand for battery capacity with the development of aerospace, the 70 Ah battery pack is selected as the charging target. In the constant current charging process, the charging current is selected to be 10 A, which improves the charging efficiency and safety; in the constant voltage charging, the charging current is selected to be 0.7 A. The single cell voltage range is set to 2.7 V to 4.2 V. For the common 30 V spacecraft bus voltage, 7 cells are connected in series, the voltage of the whole pack is from 18.9 V to 29.4 V. Agilent 6670 series power supply is used as the charging device.

Fig. 2. Constant current charging loop

Fig. 3. Constant voltage charging loop

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As the system topology shown in Fig. 1, the feedback of the current loop is obtained by collecting the voltage across the current sampling resistor. The feedback of the voltage loop is the output voltage division. gain ¼

DVEA R6 ¼ DVSENSE R5

Figure 2 shows the constant current charging loop. Through the two resistors R5 and R6 on the periphery of the amplifier, when performing the gain calculation of small signals, it is known The system selects 7.5 times gain to ensure the accuracy of charging current within 3%. It is proved by simulation that the system is stable and can meet the charging current accuracy. If the sampling resistor is considered at the same time, the loop gain is 3. The design of the constant voltage charging loop is shown in Fig. 3, using the output voltage divider for feedback. The gain of the loop is the same as the gain calculation of the constant current charging. The error amplifier gain is 40 when the sampling gain is not considered, and the gain obtained by considering the sampling gain is 5.39. 3.2

Designs of Related Modules and the Others

Designs of the Equalization Module Each single cell is connected in parallel with an equalization module to achieve balanced charge management. The composition of the entire circuit shown in Fig. 4. The negative end of the battery which comes from the constant current charging sampling resistor is non-zero.

Fig. 4. The equalization module

Fig. 5. The equalization module for single cell

There is a series relationship between the battery cells. During the charging process, the positive and negative voltage of each battery cell are floating. Each equalization module is shown in Fig. 5: each single cell is connected in parallel with a shunt resistor R, and the output of this current is controlled by switch; each module samples and calculates the total voltage of the entire pack and the cell, it compares the average voltage of the entire battery with the cell, so that, during the

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charging process, relatively smaller charging current is achieved for the higher voltage battery and larger for the lower ones, so the entire pack is matched. According to Vcomp þ  Vcomp ¼

  m 1 ðVcell þ  Vcell Þ  ðVBAT þ  VBAT Þ mþ1 m

ð1Þ

the circuit shown in Fig. 5: When m (the number of battery segments) is 7, the amplification factor of the single cell voltage compared with the average of the whole pack is 7/8 = 0.875. Design of System Stability Since the system has two control loops, the common control method which compensates the error amplifier will not be applicable for switching mode power supply. The main reason is that the compensation between the two control loops of voltage and current will affect each other, Considering the small signal model of the control signal of BUCK circuit, it can be known by calculation that: when the inductance and the capacitance value are increased, so that small pole exists in the circuit, the system can be stabilized. The dynamic small-signal model of the BUCK circuit is established. The calculation results show that the two poles are 3.4*104 and 2.5*107 respectively. The abovementioned pole calculation conditions are satisfied, and the conclusion is correct. The system gain itself is small and can be stabilized before 3.4*104. The ratio of the pole frequency 3.4*104 to the system switching frequency is less than 1/10, which ensures that the system is not affected by the switching frequency. Design of Indicators and Parameter Calculation System parameters: The switching frequency is selected to be 1 MHz, the input voltage is 40 V, and the variation range is between from 35 V to 55 V. Constant current charging: charging current is 10 A; charging current ripple during charging is less than 1%; during stage of voltage rise during charging, charging current error does not exceed 3%; Constant voltage charging: voltage ripple is less than 2%; charging current is 700 mA; The inductor current ripple depends on the inductor, switching frequency, duty cycle, etc., according to the relationship between inductor current and voltage, it can be known that: 1 1 2Iripple ¼ ðVin  VoutÞD L f

ð2Þ

D ¼ Vout=Vin.

ð3Þ

among them,

Comprehensive consideration of system stability and other factors, choose L to be 100 uH. The capacitor is selected to 10 uF level to ensure voltage ripple.

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Design of Over Voltage Protection To prevent overcharging of the battery, there is overvoltage protection in the circuit: When the output voltage exceeds the rated value, the system turns off the switch and stops charging. The overvoltage protection module compares the voltage sampled with two reference, and completes the hysteresis through logic judgment. 3.3

Design of Interface and Safety Reliability

The charging device and the satellite are connected with a satellite interface. The connection mode is as shown in Fig. 6: The positive and negative terminals of each cell in the whole pack are led to the interface which is connected to the device with the special cable during charging or is protected in the rest of the time. In terms of safety reliability design, overvoltage protection is added to the system to prevent overcharging of the whole battery.

Fig. 6. Schematic diagram of connection with satellite

3.4

System Simulation Results

In the simulation, when the system simulation starts, the system is not working, the output voltage is low and the output current is extremely small. When establishing the state through negative feedback, the charging current is gradually increased to the set 10 A rated charging current, the simulation result is shown in Fig. 7. During this process, the current increase is linear and the system is stable.

Fig. 7. Charging establishment process

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Fig. 8. Charging current ripple

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Fig. 9. Current ripple on the inductor

Simulate the charging current ripple at some selected part of the simulation process. According to Fig. 8, the maximum charging current of the sample is 10.02 A, the minimum is 9.98 A, and the ripple is less than 0.02 A, which is much less than 1%. It meets the requirements of the indicator. Figure 9 reflects the ripple variation of the current flowing through the inductor, it can be seen that the ripple value is less than 1%. it can also meet the requirements of the indicator.

Fig. 10. Charging current with voltage

Fig. 11. Charging voltage

current with

input

During the charging process, as the battery voltage gradually increases, the charging current decreases accordingly. However, under the regulation of the system’s negative feedback, the charging current is relatively stable. The simulation results in Fig. 10 show that when the battery voltage increases from 18.9 V to 29.4 V, the ripple decreases and the charging current decreases accordingly. During the whole change, the DC component of the charging current decreases from 10.25 A to 9.92 A. The change is within 3%. When charging li-ion battery through the interface of the satellite, an existing ground power source can be used as the supply. The design of the system with a large input range can increase the universality of the system accordingly. This design selects 40 V as the input voltage selection, and the input voltage range can be selected from

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35 V–55 V. Figure 11 shows the simulation result. When the input voltage changes from 35 V to 55 V, the current is between 9.95 A and 10.10 A, and the variation range is within ±0.5%.

Fig. 12. Output voltage ripple of CV loop

Fig. 13. Inductor current of CV loop

Figure 12 shows the output voltage of the constant voltage loop. The maximum voltage is 29.989 V, and the minimum is 29.256 V, the average is 29.622 V. Considering that the voltage drop of the sampling resistor at the negative end of the battery pack is 0.28 V, it sets the output voltage as 29.68 V, the ripple is 0.367 V which is 1.2% compared with the rated voltage. Figure 13 shows the inductor current value, the maximum is 744.473 mA, the minimum is 670.244 mA, the average inductor current is 707.359 mA, the average inductor current design of the constant voltage loop is 700 mA, the error is 1%, and the ripple is 37.1 mA, it is 5%. Since the main control object of constant voltage charging is lower voltage ripple, the current performance is generally sacrificed, so the system is not as good as the constant current loop in terms of current characteristics.

Fig. 14. CC loop voltage at the SW point

Fig. 15. CV loop waveform at the SW point

Figure 14 shows the voltage at the SW point which is between the switch and the inductor in the constant current loop. Figure 15 shows the voltage at SW point in the constant voltage loop. The input voltage is PWM modulated by the switch, the maximum 36 V and the minimum –1 V SW voltage are obtained. The loss of the 36 V compared to the 40 V input voltage is resistance consumption of the resistance in the switch, and the negative 1 V voltage comes from the voltage drop of the freewheeling

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diode. According to this figure, it can be seen that the output current/voltage of the charging system is stable. According to the above simulation results, it can be seen that the current loop is stable and can reach various indexes.

4 Summary and Prospect Li-ion battery packs are aerospace energy storage components that have been used and have good prospects. Because it is not overcharge resistant but needs to use them in series, the complex requirements of charging management have become an important factor restricting their development. How to satisfy the requirement of equalization charge of li-ion batteries in spacecraft AIT stage has become one of the problems that restrict the upgrading and optimization of spacecraft energy storage devices. It designed a li-ion battery pack ground charging device from the satellite interface to meet the demand in this paper, this device used CC-CV charge method, equalization charging for cells, and has completed simulation verification, laid a good foundation for subsequent charging equipment.

References 1. Slimm, M., et al.: Lithium-ion batteries for space. Space Power 502(416), 477 (2002) 2. Pan, J.: Lithium battery intelligent management system. Doctoral dissertation, Zhejiang University (2004) 3. Bian, Yankai, et al.: Equilibrium control and design of Li-ion batteries. J. Northeast Electric Power Univ. 26(2), 69–72 (2006)

Motion Control of Robot Mobile Platform Based on Indoor Positioning System Zhiguang Jiang(&) and Lijian Zhang Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China [email protected]

Abstract. This paper studies and designs the electric omnidirectional mobile platform which based on the Mecanum wheel for the robot automation assembly. The host computer communicates with the indoor positioning system and the mobile platform to remotely track and control the mobile platform. At the same time, the visualization module can view the movement trajectory of the mobile platform, the state of the platform and the positional relationship with the obstacle in real time, and can perform basic motion control and path planning and execution operations on the mobile platform. By properly arranging the location of the base station, the global positioning of the 100-m plant can be achieved, and the positioning accuracy can reach 10 cm, which is better applied to the development of spacecraft and provides technical support for the realization of smart factories in the future. Keywords: Robot mobile platform
Mecanum wheel
Indoor positioning system
Visualized numerical control

1 Introduction With the maturity of robotic precision assembly technology, more and more spacecraft models use this technology to assemble large-weight equipment, and achieved good results. However, there are still some problems in the use process [1], in which the lack of automation of the mobile platform is the main problem affecting its ease of use. The existing robot mobile platform is purely mechanical and relies on manual pulling and moving, and has the following problems: (1) The transfer is time-consuming and labor-intensive, and the manual control has low control accuracy for the entire system position; (2) Insufficient steering ability, it is not convenient to adjust the direction of the robot within a limited station; (3) There are hidden dangers to people and products during the manual pulling process. The degree of automation of the manual pull-type mobile platform and the industrial robot does not match, which affects the application efficiency and effect of the entire system. It is necessary to introduce an automated electric mobile platform to further improve the transport adjustment capability of the robotic precision assembly system, making it better suited for spacecraft assembly. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 326–335, 2019. https://doi.org/10.1007/978-981-13-7123-3_39

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The omnidirectional mobile technology based on the Mecanum wheel structure has mature applications [2, 3], and the technology is applied to the robot precision assembly system, which can realize the automatic movement of the system’s zero turning radius and improve the use efficiency [4]. After adopting the electric omnidirectional mobile platform, the system can be more conveniently transported at different stations according to the application requirements, and the system orientation can be flexibly and quickly adjusted to improve the automation level and application efficiency of the entire system. The operator can precisely control the movement of the mobile platform through the control terminal, and improve the position control precision of the system. Compared with the manual pulling mode, the safety risk to the person and the product can be greatly reduced. In addition, after adopting the electric omnidirectional mobile platform, the robot system will have the capability of remote automatic control, combined with the indoor positioning system, which can realize high-precision remote closed-loop control in the use site. Intelligent factory is an important development trend of industrial production in the future. It realizes remote path planning and closed-loop control of mobile platform, which can provide technical support for the realization of smart factory in the future. This project has carried out research on remote visualization CNC software of robot mobile platform, aiming at realizing remote path planning and closed-loop control of mobile platform.

2 Overall Design 2.1

Functional Requirements

It can communicate with the indoor positioning system [5, 6] and the mobile platform through the host computer, and then remotely track and control the robot mobile platform. At the same time, the visualization module can view the movement trajectory of the mobile platform, the state of the platform (coordinates, speed, etc.) and the positional relationship with the obstacles in real time. It enables basic motion control and path planning and execution of mobile platforms. 2.2

Scheme Design

The main function of the robot mobile platform control software is to realize the remote control of the movement of the robot mobile platform between different stations in the plant and its real-time online condition monitoring by constructing a global positioning measurement field in the spacecraft assembly plant. The overall working principle of the system is shown in Fig. 1. The software uses the indoor positioning system as the position feedback means of the omnidirectional mobile platform, and drives the platform to reach the command position by sending identifiable speed commands (including speed gear position information, yaw angle information and yaw rate information) to the omnidirectional mobile platform. The system control module includes path planning and simulation functions to realize automatic and manual planning and simulation of non-interference

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paths [7, 8]. In addition, the system can provide related operation auxiliary interfaces such as compilation of NC program files, such as editing and saving of files, and data feedback of programs written.

Fig. 1. Schematic diagram of the system working principle

Fig. 2. Robot mobile platform remote visualization control software overall program framework

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The status monitoring functions carried by the system include: online display of real-time position information of the platform (obtained by the indoor positioning system), platform movement speed and angular velocity information, alarm information, and obstacles (in the vehicle body safety zone or alarm zone) information [9]. In addition, the system will also realize the feedback and monitoring of the stable support state information, and achieve the virtual support requirement of the platform by independent drive adjustment of four independent supports, the sub-module including the drive adjustment module and the status display module. The control software will realize the transmission of instruction information and status monitoring with the platform in a wireless manner, and achieve the expected application goal of remote real-time control and monitoring. The overall framework of the specific scheme is shown in Fig. 2.

3 Detailed Design Based on the above design, the basic interface of the developed control software is shown in Fig. 3. The independent mode and the modules can be switched to each other. The input and output of the control commands are visualized. The important state monitoring information (such as alarms) is displayed in real time in the fixed window position, and is not affected by the switching of each module.

Fig. 3. Preliminary framework of remote visualization CNC module for robot mobile platform

The data communication of the system module adopts the wireless mode, and the underlying driving communication with the omnidirectional mobile platform is based on the RS232/485 data protocol.

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3.1

Serial Communication

The host computer communicates with the robot mobile platform through the wireless device. The wireless device is connected to the computer through the R232 port, and the software implements serial communication, and transmits and receives data through the serial port. The wireless communication device performs wireless pairing with the robot mobile platform (Fig. 4).

Fig. 4. Wireless communication box

3.2

Indoor Positioning System

This project uses ultra-wideband carrierless communication technology to transmit data using non-sinusoidal narrow pulses of nanoseconds to picoseconds [10, 11]. It has a strong penetration precision and can be accurately positioned indoors and underground,

Fig. 5. Location information of the indoor positioning system output

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with a transmission distance of less than 10 m. The global positioning of the entire plant can be achieved by rationally arranging the location of the base station, and the positioning accuracy can reach 10 cm [12] (Fig. 5). 3.3

Mobile Platform Path Planning

In the software two-dimensional coordinate system, you can plan the path that the mobile platform will walk by clicking on the points in the coordinate system. The mobile platform has a moving forward direction, so the coordinate values and direction vectors established in the indoor positioning system are first read by two or more PCEs placed on the mobile platform, and the passing point coordinates in the planned path are selected at one time. Two PCEs are placed on the mobile platform to mark the mobile platform. A PCE is placed in the center of the platform, and the coordinates of the center point position of the mobile platform can be read in real time, and also serves as the starting point of the vector (point B), and the other PCE is placed at the forefront of the moving platform (point C). Then the vector BC (starting point -> platform front end point) direction indicates the current heading direction of the platform (Fig. 6).

Fig. 6. Specifies the motion path

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4 Software Introduction 4.1

Technology Implementation

(1) Communication function (1) The remote visualized numerical control module can perform wireless data communication with the vehicle-mounted PC, feedback the motion state of the mobile platform in real time, and send control commands, and the communication cycle does not exceed 100 ms; (2) The remote visualization CNC module can receive the indoor positioning system data in real time, and the positioning data update period does not exceed 100 ms. (2) Motion control (1) It can perform basic translation and steering motion control on the mobile platform, and has continuous motion and quantitative motion functions; (2) For the determined sequential path point, the closed-loop control function with continuous execution motion passing through the path point in turn, the offset of the straight line between the two points during the movement between the two path points does not exceed 200 mm; (3) It can control the execution of the platform legs and the automatic leveling action; (4) With remote emergency stop control. (3) Software visualization function (1) The software interface can display the working platform of the mobile platform in the form of a virtual two-dimensional plane, and can draw graphics in the virtual field by means of mouse drawing to indicate the boundary of the site, obstacles, etc. [13, 14]; (2) With a visual path planning function, the path point is picked up and recorded by clicking on the mouse in the virtual field; (3) It can graphically display the perception of obstacles by the obstacle avoidance device of the mobile platform; (4) The software interface has the necessary display, operation controls, operation boundaries, and is not prone to errors. 4.2

Instructions for Use

(1) Interface introduction Mobile platform control interface is shown in Fig. 7. • The upper part is the information display column; • The information can be set as yaw angle, angular velocity, and motion speed; • “Run” button: control the movement of the mobile platform according to the entered value; • Path Planning button: Plan the platform rotation angle and rotation direction according to the n coordinates selected by the operator in the plane coordinate system (the path consisting of n points will be presented in this coordinate system). The platform will eventually reach the final target point.

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Fig. 7. Motion Control Module Interface

(2) Operation steps: • Click on any continuous point in the coordinate system, these points form the path and display in the coordinate system; • Click the “Path Planning” button, the mobile platform will automatically follow the path (first determine the direction of rotation, then determine how big the angle needs to be rotated, and finally drive to the target point in a straight line after the rotation is completed); • “Stop” button: Stops immediately regardless of what manipulation the mobile platform is performing (Fig. 8).

Fig. 8. Mobile platform support module interface

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You can choose to set the mobile platform to “stable support adjustment”/”stable support release”. 4.3 • • • •

System Hardware Components Mobile platform Computer Indoor positioning system Mobile platform wireless communication equipment

5 Conclusion The wireless communication between the upper computer and the indoor positioning system and the robot mobile platform is realized, and the movement of the mobile platform is controlled through the visual interface operation, and the movement is performed according to the trajectory. By properly arranging the location of the base station, the global positioning of the 100-meter plant can be achieved, and the positioning accuracy can reach 10 cm.

References 1. Zhou, Y., Zhang, J., Le, Y., et al.: Application of mobile robot technology in aerospace manufacturing industry. Mech. Des. Manuf. Eng. (2018) 2. Zhu, Y.: Research on Design and Performance of Omnidirectional Vehicle Based on Mecanum Wheel. Yanshan University, Qinhuangdao (2016) 3. Chung, J.H., Yi, B.J., Kim, W.K., et al.: The dynamic modeling and analysis for an omnidirectional mobile robot with three caster wheels, vol. 03, pp.3091–3096 (1994) 4. Lu, W.: Structural design of omni-directional wheel moving mechanism. Mech. Electron. (2006) 5. 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, 1067–1080 (2007) 6. Lee, B.-G., Chung, W.-Y.: Multitarget three-dimensional indoor navigation on a PDA in a wireless sensor network. IEEE Sens. J. 11, 799–807 (2011) 7. Wang, Z., Guo, G.: Current status and prospects of mobile robot navigation technology. Robotics (2003) 8. Luo, M., Wu, H., Du, Y.: Research on mobile robot navigation based on human-computer interaction. J. Comput. Measur. Control, 1291–1293 (2013) 9. Lu, X., Zhang, G.: Research on navigation method of indoor service robot. Robotics (2003) 10. Deng, P., Fan, P.: Principle and application of wireless positioning in cellular system. Mobile Commun. (2000) 11. Lee, J.-Y., Scholtzr, A.: Ranging in a dense multipath environment using an UWB radio link. IEEE J. Sel. Areas Commun. 20, 1677–1683 (2002)

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12. Cao, S.: Research on Key Technologies of Ultra-Wideband Indoor Positioning System. Guilin University of Electronic Technology (2006) 13. Li, Z., Wang, H.: Design and implementation of remote human-computer interaction software platform for mobile robots. J. Comput. Measur. Control 121–125 (2017) 14. Pfeifer, R., Iida, F., Bongard, J.: New robotics: design principles for intelligent systems. Artif. Life 11(1/2), 99–120 (2005)

A Process Method and Simulation Analysis of Spacecraft Wing Root Cable Fixing Kai Xu1,2(&), Lijian Zhang1,2, and Hao Li1,2 1

Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China [email protected] 2 Beijing Engineering Research Center of the Intelligent Assembly Technology and Equipment for Aerospace Product, Beijing 100094, China

Abstract. In order to avoid the situation that the cable is damaged during the unfolding and folding process of solar wings, a spacecraft solar wing root cable tying process is designed. Through the simulation of the root-hinge cable laying process, it is concluded that setting the appropriate reserved length and cable tie point, improving the installation flexibility of the wire harness, and the wire harness can be prevented from being stressed after the connector is plugged, thereby improving the service life of the cable. Then through the simulation of the motion of the root cable, it is concluded that the interference of the root cable is not affected during the deployment of the solar wing, and the correctness of the static simulation is verified, which provides a theoretical basis for the satellite assembly operation. Keywords: Multiple deployment of solar wing
Root cable
Process method
Simulation analysis

1 Introduction The solar wing is the main energy supply device of the spacecraft. Due to the limitation of the vehicle, the solar wing folds and the gathers in the fairing during the launching stage. When the spacecraft is separated from the vehicle, it expands into a plane and locks. Therefore, the reliable deployment of the solar wing is the key to ensuring its normal on-orbit operation and accurate orientation of the sun [1–3]. Due to the uncontrollable expansion speed of the solar wing energy-free development agency, the active deployment mechanism has increasingly become a hot field in research of the solar wing mechanisms at home and abroad. However, at present, there are no optimized design schemes for the solar wing root cable lashing point and the solar wing root cable reserved length. The solar wing root cable tying and fixing method has not been finalized yet, and the solar wing unfolding and folding process exists the danger of squeezing, pulling, and damaging the cable. Therefore, this paper simulates and analyzes the cable tying of the root cable, and determines the reserved length of the cable, the way of walking, and the cable state after the solar wing is rotated [4–7].

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 336–342, 2019. https://doi.org/10.1007/978-981-13-7123-3_40

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2 The Multiple Deployment of Solar Wing 2.1

The Multiple Deployment of Solar Wing Structure and Motion Process

The multiple deployment of the solar wing consisting of a root hinge, a center plate, and a side plate, and the collapsed state is pressed the side wall of the satellite through the pressing point. The solar wing uses a root hinge in a series with the center plate, and the center plate is parallel with two side plates [8–11]. After the multiple deployment of solar wing pressing device is unlocked, the unfolding process is divided into two steps [12–14]. First, the upper and lower side panels and the center panel are simultaneously unfolded under the constraint of the side panel release device. Thereafter, the side panel release device releases the restraint on the upper and lower side panels, and the upper and lower side panels are unfolded, as shown in Fig. 1.

Fig. 1. Schematic diagram of the solar wing deployment process

2.2

The Multiple Deployment of the Solar Wing Root Hinge and Cable Design

In the course of research, the design model was first confirmed, and the distance between the lower plane of the root hinge inserting the plate and the center of the root hinge was confirmed. There are 4 connectors for the solar wing hinge design. They are: X07, X08, X09, X10. Among them, X07 is a rectangular connector, X08, X09 and X10 are circular connectors. Currently, the circular connector has Two tail covers, one is a straight tail cover, and the other is a tail cover. The X8 adopts a straight tail cover when designing, and the X9 and X10 adopt a curved tail cover. In order to avoid the X9 tail cover and the X10 interference, rotate the X9 tail cover, as shown in Fig. 2.

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Fig. 2. Schematic the diagram of the solar wing root cable routing

3 Simulation Analysis The length of each electrical connector lead in the design: the X07 lead length is about 350 mm, the X08 lead length is about 300 mm, the X09 lead length is about 210 mm, the X10 lead length is about 140 mm. Due to the design of the cable, the cable is considered as a rigid body, and the true state of the cable under the influence of its material parameters is not considered, and whether the minimum bending radius, the maximum tension and the like are reached. This simulation is based on the real material parameters of the cable for real-time simulation of the one-dimensional finite element. In order to meet the minimum bending radius requirement of the cable, the standard length of the cable needs to be bundled. After flexible processing, the parameters of the cable are input, the parameters are shown in Table 1. Table 1. Cable related parameters Related parameters Young’s module Material density Inner diameter Outer diameter Minimal bending radius Minimal bending radius Maximal tension force Maximal torsion moment

Value 2:76e þ 07 N=m2 1200 Kg=m3 0:000 mm 13:00 mm 26:000 mm 2 Factor 50 N 1 Nm

The length of the lead is the design length after conversion. The result of the standard length of the rectangular connector and the round connector tail, cable must be tied, as shown in Fig. 3. The blue segment is the minimum bending radius prompt, the red segment indicates interference with the rigid body structure.

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Fig. 3. Cable retention standard length, lashing segment results

3.1

Cable Laying Simulation

After the cable is flexibly processed, through the comprehensive simulation of the laying process of the root cable, the reserved length of the cable, the position of the lashing point, and the order of the wire harness installation are determined to improve the installation flexibility, thereby avoiding the stress or reaching the limit of cable insertion status. Cable Length and the Lashing Point Position Simulation After the flexible treatment of the cable, due to its material parameters, its natural routing method is different from the design time. The cable is designed as a rigid body without considering the influence of material parameters, thus causing the difference between the direction and mode of the design and the actual installation state. The difference from the actual installation state may cause the flexible cable to interfere with the rigid body, reach the minimum bending radius, and there are tensions, etc., and there are hidden dangers in the daily operation of the cable. After the flexible treatment, it can be seen that the cable interferes with the surrounding components, and the long-term use will cause wear on the cable. It is recommended to increase the lashing point, as shown in Fig. 4. And the X08 cable bundle reaches the minimum bending radius, there is a hidden danger in operation, and the reserved design length needs to be adjusted at the same time. It can be seen from the setting of the lashing point in Fig. 4. The original design length can not meet the cable routing requirements. It is necessary to adjust the cable length while setting the lashing point. This simulation follows the on-site assembly situation. The adjustment of the cable is adjusted in 1 mm increments, and the length of the cable is adjusted. Through preadjustment, the maximum tensile force and the disconnection state of the cable are eliminated, but there is still a case where the X08 cable reaches the minimum bending radius and interferes with surrounding components. The reserved length needs to be further adjusted.

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Fig. 4. Sets the lashing point (pink represents the maximum tension of the cable, red represents the pull-off state)

Through the adjustment of the reserved length to eliminate various minimum bending radius, maximum tensile force, interference and other unfavorable conditions, the adjusted cable length is the preliminary reserved length of the simulation result, in the future, the dynamic simulation process will be combined to further simulate the reserved length, as shown in Fig. 5.

Fig. 5. Cable status after simulation

After preliminary simulation, when the X07 lead length is 368 mm, the X08 lead length is 318 mm, the X09 lead length is 213 mm, and the X10 lead length is 145 mm can meet the working requirements. The position of the ligature point to the lower section is: the X07 lead length is 68 mm, the X08 lead length is 77 mm, the X09 lead length is 73 mm, the X10 lead length is 68 mm.

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Cable Installation Sequence Simulation Using the function of IC.IDO, physical simulation the installation sequence of the cable, while verifying the dynamic assembly process of the four joints together with the leads, the ergonomic analysis of the hand space of the assembler is also performed to confirm the space, ensure the installation of four connectors. Through the analysis and simulation of the installation process and hand ergonomics, the installation sequence is determined as the first step to install the X10 connector, the second step X09, the third step X08, and the last step X07. 3.2

Root Cable Motion Process Simulation

After static simulation (laying simulation) of the root cable, determining the reserved length, the position of the lashing point, and the installation sequence, the deployment process of the solar wing is simulated to investigate whether the root cable interferes or hooks during the deployment process, and further examine the results of the static simulation. By simulating the motion process of the stranded cable, it can be seen that the minimum clearance between the expansion limit position with the sun wing is 17.15 mm; the minimum distance from the remaining fixed components is 5.43 mm; the minimum distance of the cable from the bottom hinge is 13.21 mm; the minimum distance from the moving parts is 17.15 mm and 16.81 mm, as shown in Fig. 6.

(a) Minimum clearance between the extended limit position and the solar wing

(b) The minimum distance of the cable from the bottom hinge Fig. 6. Simulation of the movement process of the root cable

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It can be seen from Fig. 6 that in the whole process, there is no influence on the root cable, and there is no interference or hooking, so there is no stress.

4 Conclusion Through static and dynamic simulation analysis, the optimal design of the lashing point and the cable reserved length under the overall wiring state of the solar wing root cable is realized, avoiding the minimum bending radius, the maximum tension and the occurrence of interference, hooking and other undesirable phenomena, guarantee the safety of the spacecraft solar wing working state.

References 1. Fei, K.: Design and Performance Test of Repeated Folding and Locking Solar Wing Mechanism. Zhe jiang University of Technology, Hangzhou (2013) 2. Yang, J., Huang, H., Liu, Y., et al.: The evidential network for fault tree analysis with imprecise knowledge. Int. J. Turbo Jet Engines 29(2), 111–122 (2012) 3. Zhou, Z., Wu, Y., Wang, J., et al.: Development status and trend of the circular solar wing. Spacecr. Eng. 24(6), 116–122 (2015) 4. Zhang, L.: Design and Research of the Spacecraft Step-and-Expanding Solar Wing. Shanghai Jiaotong University, Shanghai (2012) 5. Li Entrusted: Tension design and analysis of the solar wing linkage device. Chin. Space Sci. Technol. 26(2), 52–57 (2006) 6. Eacret, D.L., White, S.: ST8 validation experiment: Ultraflex-175 solar array technology advance: deployment kinematics and deployed dynamics ground testing and model validation. In: AIAA 2010-1497. AIAA, Washington D.C. (2010) 7. Hua, D.: Design and Analysis of the Repetitive Locking and Release Mechanism of the Solar Wing. Harbin Institute of Technology, Harbin (2008) 8. Wang, X., Yan, H., Zhou, Z.: Development status and trend of two-dimensional multipleexpansion solar wing technology. In: Expandable Spatial Structure (2014) 9. Liu, Z., Wu, Y., Qi, H., et al.: Evaluation method for reliability of spacecraft solar wing expansion. Chin. Space Sci. Technol. 52–56 (2013) 10. Solar, J.: Dynamic Fault Tree Analysis of Solar Wing Drive Mechanism. The university of Electronic Science and Technology of China (2012) 11. Ren, S., Shang, H., Shang, H., Qi, H.: Simulation analysis of deployment dynamics of a twodimensional unfolded solar wing. Spacecr. Eng. 21(4), 32–36 (2012) 12. Murphy, D.M.: MegaFlex-the scaling potential of UltraFlex technology. In: AIAA 20121581. AIAA, Washington D.C. (2012) 13. Xinggao, Z., Fengxi, C., Tingwei, L., et al.: Research on vibration test specifications of solar wing drive mechanism based on modal analysis. Min. Mach. 38(5), 171–173 (2017) 14. Hu, M., Li, W., Chen, W., et al.: Motion simulation and functional experiment of repeated folding mechanism of sectoral solar wing. Chin. J. Space Sci. 36(1), 92–98 (2016)

Study on 3D Cable Network Design Method Yi Yuan(&), Wei Yu, Xiaoyi Ru, Zhou An, Qi Miao, Xuhua Hu, and Zhenpeng Ding Beijing Institute of Spacecraft System Engineering, Beijing 100094, China [email protected] Abstract. In order to solve the problems in the spacecraft 3D cable network design flow, this paper rebuilds the 3D cable design mode according to the spacecraft development flow, brings forward the improvement method in detail, describes the key points which was used, confirms the method by developing the software based on Pro/E. This paper as a reference can help to improve the 3D cable network design automation level. Keywords: Spacecraft cable network Second-development
Pro/E


3D design


1 Introduction 3D cable network design is an essential part in the 3D design of spacecraft. A common satellite has about 200–300 cables, 1500 branches and more than 20000 connections. It is the bottleneck of the 3D cable network design of spacecraft, as in the traditional pattern there is a huge and error-prone workload when the designers are adopting the cable module of Pro/E software for manual modelling. They have to figure out the spatial location extracting and accessing the electric connectors and execute selection through the mouse. In this paper, according to the 3D cable network development of a domestic general design institute of spacecraft and the secondary development of Pro-E software, the 3D design flow of cable network is optimized. The manual operation difficulties are automatically achieved through software to reduce the difficulties in the 3D cable network design and improve the design efficiency.

2 3D Cable Network Design of Spacecraft 2.1

Current 3D Cable Network Design

The 3D cable network design flow of a general space design unit is as shown in Fig. 1. First of all, to develop the IDS signature. IDS is the interface pattern of spacecraft development data originally created by the unit. As the only basis to carry out the development of spacecraft device, it stipulates the functional indexes such as the mechanical properties, electrical characteristics, thermal performances and telemetry parameters of the spacecraft device etc. The standalone unit starts developing the standalone device according to the DIS file and proposes the standalone device and the matching 3D model of IDS to the general unit. The general design adopts the © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 343–351, 2019. https://doi.org/10.1007/978-981-13-7123-3_41

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standalone device model to carry out the general configuration layout design and form the whole-spacecraft configuration layout model. The logic design of cable network forms the table of the cable branch connection relation according to the electric connector information and contact distribution relation in the IDS file. Based on the wholespacecraft configuration layout model, the general designer starts the 3D cable network design according to the connection directing in the table of cable branch connection relation to form the 3D cable network model finally [1].

Interface design

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3D cable

design activity bobbin design

network design path planning

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branch design

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table of cable branch connection relation

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Fig. 1. 3D cable network design flow of spacecraft

As shown in Fig. 1, data of standalone modelling, configuration layout and logic design of cable network are from IDS, which has guaranteed the unification of the data source during the design. However, as most operations of the 3D cable network design are excessively dependent on manual execution of the designers, the procedure are complicated and error-prone. It is rather difficult to search for the matching cable inlet and outlet among such tremendous information manually, especially there are dozens of slabs, hundreds of devices, thousands of electric connectors and interlaced subsystem models during the 3D design of whole-spacecraft cable network. Therefore, on the condition of maintaining the current flow, the design steps and outputs shall be regulated by achieving the procedural and repetitive operations automatically through software to improve the design efficiency and correctness of the design result. 2.2

Improvement of 3D Cable Network Design Method

There are the following problems in the current 3D cable network design: (1) The device model is nonstandard and the information are incomplete. The 3D models proposed by all subsystems and standalone development units have no unified requirements for modelling, a lot of key information are unattainable and missing.

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(2) The design link interface is unclear. The current 3D cable network design is based on the general configuration model. When the general configuration layout model is adjusted, the 3D cable network mode will change automatically, which is not helpful to clear the responsibility interface and control the technical state of model. (3) The key links are lower automatized. The cable network design is composed of several links i.e. analysis of cable branch file, installation of electric connector plug, electric connector designation, wire gauge design, routing path planning and 3D modelling etc., all of which will be manually completed by the designers. In the early stage of the cable network design, the 3D models are constructed by IDS and the standalone development units manually, hence lack the corresponding tools and software. Therefore, to realize the fast 3D cable network design, we must start from the following aspects: (1) Normalize the device. Realize the automatic modelling of device based on the IDS file and the unify the model patterns. Identify the key information (i.e. electric connector name) required by the 3D cable network design in the automatic modelling to facilitate the subsequent design and extraction of cables. (2) Clarify the working interface and connector. Construct the 3D design input model of whole-spacecraft cable network according to the whole-spacecraft configuration layout model. When the spacecraft configuration layout is changed, the input models can be updated only after the designers execute the change and confirmation. This can help the designers to control the technical design state. (3) Develop the specific tools according to the design platform. Realize the branch length analysis, electric connector designation, installation of electric connector plug, cable directing planning, wire gauge design, 3D modelling automation and simplify the design process.

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Fig. 2. Ideal development mode of 3D cable network

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Figure 2 is the ideal development mode of 3D cable network. Except for the design activities such as configuration layout, cable logic design and cable path planning that are inseparable from the manual intervention, other works can be automatically realized through the software tools. After making the information preparation for the 3D cable network design, the modelling process of 3D model is a black box that can lower the complexity of cable network design and reduce the design workload.

3 Implementation of the 3D Cable Network Design Based on Pro/E The fast design of 3D cable network contains four parts namely the device normalization modelling, construction of input interface model, analysis cable network connection relation and automatic modelling of cable network. Now the implementation method will be expounded in four aspects: 3.1

Device Normalization Modelling

The spacecraft device can be simplified into the combination of the device itself and electric connector. The device itself reflects the spatial contour of the device, the electric connector is the outlet and inlet of cable, so that the specific sign shall be set on the electric connector for programming identification. The device normalization modelling can be decomposed into automatic construction of device contour, automatic installation of electric connector and property setting of the electric connector interface. (1) Device modelling. The device contains the device box and installation lug. The device box is rectangular and the installation lug can be summarized as several patterns. Therefore, the device box and the installation lug can be respectively defined as the user-defined features. During the automatic modelling, the userdefined features of device box shall be inserted and the user-defined features of lug can be selected and inserted according to the description of the lug type in the IDS file to complete the automatic construction of the device box. (2) Electric connector installation. The electric connector library of spacecraft corresponding to the physical electric connector is built and maintained by specially. The geometric shape of the 3D electric connector models can be simplified properly, yet it must reflect the elements required for the cable network design, for instance the outline, straight length and so on. Each electric connector shall be equipped with specific coordinate system as the installation reference to the cable plug. During the device modelling, according to the electric connector type description in IDS file, the corresponding 3D electric connector models will be selected automatically. As each electric connector installed on the spacecraft has the unique sign during the development process, the congruent relationship between each 3D electric connector model installed on the device and the unique sign shall be established by setting the parameters and properties for the automatic identification in the 3D cable network design (Figs. 3 and 4).

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Fig. 3. Implementation method of device normalization modelling

Fig. 4. Normative device established automatically

3.2

Construction of Input Interface Model

Input interface model of the cable network design is based on the spacecraft configuration layout model and reflects the outline and installation location of the spacecraft device. The process is implemented through the contracting and enveloping functions of Pro/E; as the model input of the 3D cable network design, it contains the data (i.e. the installation coordinate system of the cable plug on the electric connector) required for the sequent design at the same time of contracting the geometric shape (Fig. 5).

Fig. 5. Example of 3D cable network input interface model

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Analysis on the Branch Connection Relation File of Cable Network

Connection relation file of cable network is in excel format containing three parts namely the wire gauge definition, connector definition and connection relation definition. The wire gauge defines the specification data of cable such as the diameter, turning radius and linear density etc.; the connector definition sets the plug types corresponding to all electric connectors; the connection relation definition describes the logic connection of cables including the harness, branch, start point, end point and corresponding wire gauge of the cables. The three parts have completely expressed the physical properties and connection properties of cables. Analysis on the connection relation file of cable network is to adopt the object-oriented method to structure the descriptive text into the information flow that can be identified by computer as the design input of the 3D cable network modelling (Fig. 6).

Fig. 6. Example of cable branch connection relation table

3.4

Automatic Cable Network Modelling

Automatic cable network modelling is implemented in line with the following steps. (1) According to the description of the plug type corresponding to each electric connector in the table of cable branch connection relation, plug corresponding to each electric connector shall be automatically invoked from the template library through the software development. The installation method is to superpose the installation coordinate system on the plug model with that on the electric connector to facilitate the software realization (Fig. 7).

Fig. 7. Example of cable plug installed fast

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(2) Pro/E takes the +Z axis in the coordinate system as the cable inlet. The coordinate system shall be designated under the Pro/E cable module, so that it can be identified in modelling. The cable design coordinate system shall be preset on the template of cable plug and make sure the +Z direction is outward. Also, the open interface Pro/Toolkit of Pro/E has provided a large number of cable network implementation functions. During the software implementation, all connector plugs in the current model can be traversed first; the cable installation coordinate system on each cable plug can be obtained through feature analysis; the batch setting implementing the cable installation coordinate system can be designated cyclically through software; each cable installation coordinate system is endowed with a unique internal sign by interpreting the whole-spacecraft unique sign of the electric connector corresponding to the cable plug; the cable installation coordinate system endowed with sign can be identified by Pro/E subsequently (Fig. 8).

Fig. 8. Example of cable installation interface

(3) Main cable path is realized through the routing network equipped on the Pro/E software. Its principle is to preplan a virtual path on the spacecraft model, the Pro/E will search the main path with the shortest distance from the directing automatically during implementing the cable network directing design. Under the circumstances of various cable networks and complicated directing, try the best to make sure several cables can share the common path, so that the cables can be bound and tied up. The main cable path design shall take the whole-spacecraft cable network layout into full consideration; whether the main path is reasonable has determined whether the cable directing is reasonable and whether the target of cable weight control can be realized [5, 7]. (4) Pro/E provides a large number of automatic realization interfaces of cable network; however, the realization process is rather complicated and the developers must have deep understanding on the realization mechanism of Pro/E cable network. To lower the development difficulty, Pro/E also provides the neutral file format (nwf) to define the wire gauge and connection logic of the cables in the neutral file. After Pro/E reads the neutral file, the wire gauge can be established automatically to connect the automatic identification of the start point and end point. The developers only have to make fewer encoding work to construct the 3D cable network modelling automatically [6] (Fig. 9).

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Fig. 9. Definition example of nwf file wire gauge, component and connector, connection relation

According to the neutral file, development of the fast design function of 3D cable network modelling can be simplified into obtaining the wire gauge and logic connection relation of cable network from the table of cable branch connection relation and writing it into the neutral file according to the canned format of the neutral file. As each wire coordinate system has been identified during designating the installation interface of cable network, when Pro/E reads the neutral files, it can identify the cable network design port on the model corresponding to the logic relation automatically. By adopting the canned 3D cable network construction function of Pro/E, the 3D cable network modelling can be generated as shown in the following figure [2–4].

4 Conclusion On the basis of summarizing up the 3D cable network design characteristics of spacecraft, the paper optimizes the design flow by putting forward the fast implementation method of 3D cable network design. It verifies some functions according to the secondary development of the Pro/E software and has a reference value and guiding significance to improve the quality and automation level of 3D cable network design.

References 1. Chen, Y.: An Introduction of Digital Design for Spacecraft, pp. 386–387. Science and Technology Press, Beijing, China (2010) 2. Wang, W.: The Example of Pro/E wildfire 4.0 Second Development, pp. 53–55. Tsinghua University Press, Beijing (2010) 3. Wu, L., Chen, B.: The Base of Pro/ENGINEER second development, p. 1. Publish House of Electronics Industry, Beijing (2006)

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4. Lin, L.: Second Development of Pro/E Wildfire 2.0 with Pro/Toolkit, pp. 529–530. Publish House of Electronics Industry, Beijing (2005) 5. Li, Y., Li, C., Liang, D., et al.: Pro/Engineer 4.0 Self-Study Manual, pp. 156–164. Publish House of Electronics Industry, Beijing (2010) 6. PTC Corporation. Pro/TOOLKIT Wildfire 2.0 APIWizard. PTC Corporation, Needhan (2011) 7. Heng, S.: Learn Pro/Engineer the Right Way, pp. 325–326. China Youth Publishing Group, Beijing (2007)

A Similarity Approach for Thermal Distortion Measurement of Large Spacecraft Structure Hongtao Gao(&), Haitao Shi, Xiaofeng Zhang, and Lu Ren ISSE of China Academy of Space Technology, Beijing, China [email protected]

Abstract. Thermal distortion is one of the key factors to satellite image geolocation accuracy. To evaluate the thermal stability of large spacecraft structure, a similarity approach of thermal distortion measurement on a downscaling model is presented. Similarity relations of thermal distortion between downscaling model and the full-size structure were derived, and a thermal distortion test system based on 3D-DIC (digital image correlation) was built and used to distortion measurement of several structures with dimensions from submeters to meters. Results showed that measurement on the downscaling model could achieve nearly same precision as that on full-size structure, and could reduce test complexity, cost and time. Keywords: Spacecraft structure


Thermal distortion
Similitude
DIC

1 Preface With the development of remote sensing satellite technology, image geolocation and resolution will be promoted further, which always requires large dimension and low thermal distortion. For example, a pointing error of 1 arcsecond in 500 km SSO orbit will cause 2.5 m horizontal error. Analysis is the primary tool now in structure thermal stability design [1, 2], and design result need to be validated by the thermal distortion test. In the 1990s, both NASA JPL and ESA did research on thermal distortion test methods [3–7]. In these methods, coordinate measurement machine [8] and theodolites [9] have relative low precision. Though laser interferometer has precision of sub-micro, it is vulnerable to environment disturbance. 3D-DIC (digital image correlation) methods has developed rapidly in recent years, and measurement precision can be submicros, but it is still difficult for large scale structure measurement because of influence by environment vibration, thermal control difficulty, high cost and long test time. This paper presents a method to evaluate the thermal stability of large-size structure by means of measuring its down-scaling model.

2 Similarity Relations of Thermal Distortion Similarity relations of thermal distortion are based on the three similitude theorems [10], and from that we deduce the similarity relations of thermal distortion between similar structures. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 352–359, 2019. https://doi.org/10.1007/978-981-13-7123-3_42

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When applied with thermal load, the thermoplastic equations of solid are [1]: 8 < C ¼ ður þ ruÞ=2 r
Tþf ¼ 0 ðIn XÞ ð1Þ : T ¼ 2lC þ ðkJ ðCÞ  3K0 aðh  h0 ÞÞI k ¼ Em=ð1 þ mÞð1  2mÞ; l ¼ E=2ð1 þ mÞ

K0 ¼ k þ 2l=3

ð2Þ

u; C; T are displacement vector, strain tensor, and stress tensor respectively. I is unit tensor, and J ðCÞ is the trace of C. k and l are Lame coefficients, and f is body force. a is thermal expansion coefficient, and E is modulus of elasticity. m is Poisson’s ratio, and K0 is equivalent modulus of compression. The boundary conditions on boundary @X of region X are: 8  On @u X

> > 0 > t2j ¼ gg1 a2 þ gg1 t2j ; 1  j  r2 ; < 2 2 .. > > . > > : t0 ¼ g1 a þ g1 t ; 1  j  r ; k kj g k g kj k

ð5Þ

k

rk is the failure data under the k stress. ai is the compensation from the test time s1 ; s2 ; . . .; si1 under the stress of (T1, V1), (T2, V2), …, (Ti-1, Vi-1) to the failure time ti1  ti2  . . .  tiri  si and a1= 0, ai ¼ s1i þ s2i þ . . . þ si1;i ¼

i1 X

ðgi sj =gj Þ

i ¼ 2; . . .; k:

j¼1

Hence, the maximum likelihood estimation of cumulative failure probability density function with n samples is,

  r tj ðB; DÞ m n! Y m m1 LðA; B; D; mÞ ¼ tj ðB; DÞ
exp  ðn  r Þ! j¼1 gm g1 1

 m nr tr ðB; DÞ
exp  g1

ð6Þ

The unknown parameters are A, B, D, m. At last, the prediction of life parameters is given. The Weibull distribution is rewritten to extreme value distribution. If d ¼ ln t, zi ¼ ðdi  li Þ=r, ni ¼ ðln si  li Þ=r, where li ¼ ln A þ Bxi þ Dyi , the maximum likelihood function of cumulative failure probability density function with n samples is, L¼

n X

fIi ½ ln r þ zi  expðzi Þ þ ð1  Ii Þ½ expðni Þg

ð7Þ

i¼1

Ii is the schematic function. si = 1 if failure time di is shorter than truncation time si , otherwise si = 0.

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According to the maximum likelihood theory, the covariance matrix of parameters A; B; D; r in the accelerated life model with Weibull distribution could be written as, 0 X

B ¼B @

  ^ Var A

  ^ B ^ Cov A; ^ Var B

  ^ D ^ Cov A;   ^ D ^ Cov B; ^ Var D

symmetrical

 1 ^ r ^ Cov A;   ^ r ^ C Cov B;  C ^ r ^ A Cov D; Var^ r

ð8Þ

The Information matrix F is both the inverse matrix of the covariance matrix of R (R = F−1) and the expectation of negative two order partial derivative matrix about likelihood function, so it could be written as, 0 F¼

n B X B @ i¼1

A11

symmetrical

A12 A22

A13 A23 A33

1 A14 A24 C C A34 A A44

ð9Þ

The analytic solution of life parameter could be obtained by calculation of negative two order partial derivatives of function L about relating parameter of A, B, D, m.
2  A11 ¼ E  @@AL2i ¼ f1  exp½ expðni Þg;

 @ 2 Li @ 2 Li ¼ xi A11 ; A13 ¼ E  @A@D ¼ yi A11 ; A12 ¼ E  @A@B
 R 2 ni @ Li ¼ 1 zi expð2zi Þ exp½ expðzi Þdzi þ ni expðni Þ exp½ expðni Þ; A14 ¼ E  @A@r
2 
 @ 2 Li ¼ xi yi A11 ; A22 ¼ E  @@BL2i ¼ x2i A11 ; A23 ¼ E  @B@D

2  2 @ Li ¼ xi A14 ; A33 ¼ E  @@DL2i ¼ y2i A11 ; A24 ¼ E  @B@r
 @ 2 Li ¼ yi A14 ; A34 ¼ E  @D@r
2  A44 ¼ E  @@rL2i R ni ¼ 1 ½zi expðzi Þ þ expðzi Þ  1zi expðzi Þ exp½ expðzi Þdzi þ ðni þ n2i Þ expðni Þ exp½ expðni Þ

4.2

D-efficiency Optimal Design for Accelerate Test Time

The covariance matrix is always used for solutions of relative parameters in model. Regarding to the reciprocal characteristics between the covariance matrix and the Fisher information matrix, it is considered that the two plans are equivalent in the information extraction if the Fisher information matrix determinants of the two plans are equal. In conclusion, if the determinant ratio of Fisher information matrix s ¼ jM j=jM0 j is bigger than a specified threshold where |M0| and |M| are the Fisher information matrix determinants of a definite accelerate life test plan and another plan to be defined

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respectively, the two plans are considered to be equal based D-efficiency. And that’s the definition of D-efficiency based equal Fisher information matrix determinants. Thereby, the mathematical model of D-efficiency of the total test time for temperature and vibration step-stress accelerated life test when the shortest test time is the constraint condition could be deduced as below, min s:t:

tðA; B; D; mÞ s

jM j jM0 j

k T1 Þ Ti ¼ T1 þ ði1ÞðT ; i ¼ 1; 2


k k1 ði1ÞðVk V1 Þ i ¼ 1; 2


k Vi ¼ V1 þ k1

Tk and Vk are the highest level of the test stress as design variables. And the Defficiency s is also the design variable and it’s set to 0.95 in this paper.

5 Residual Life Assessment of Signal Output Board 5.1

Step-Stress Test Plan Based on Operation Profile

The signal output board is a key product for rail transportation that ensures a safe output of the constant voltage of 24 V. 4 groups of constant-stress test for over 2000 h are needed if the conventional accelerated life test method is adopted and it is hard to accept. The parameters of the accelerated life model are A ¼ 4:5432; B ¼ 2:9876; D ¼ 0:2081; m ¼ 2:9753, and the Fisher information matrix determinant is M0 = 2.524  106. The current operation profile and time need to be compiled as the first stage in the residual life assessment for a step-stress accelerated life optimization with a shorter time. The operation temperature and vibration stress are T1 = 305 K and V1 = 0.539 g (in vertical direction) respectively. The maximum temperature and vibration stress detected through reliability enhancement test are T4 = 368 K and V4 = 2 g respectively. Setting s to 0.95 and conversing the Fisher information matrix determinant, the constraint condition for the optimized model are obtained. Then the mathematical model of D-efficiency with minimum test time is calculated by genetic algorithm.

Table 2. D-optimization design for step-stress test plan No. Temperature stress (°C) Vibration stress (g) 1 305 0.539 2 326 2 3 347 1.026 4 368 1.513 The Fisher information matrix determinant = 2.397  106

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The optimized test time decreased to 1080 h, 920 h shorter than the conventional plan with the same evaluation accuracy which is more time-saving. The detail of the optimized test plan is shown in Table 2. 5.2

Residual Life Assessment Model

20 signal output boards that had been operated for 2 years are tested according to the step-stress accelerated life test plan in Table 2. The life-reliability-acceleration model is identified through data analysis as below, ln tR ¼ 133:4688 þ

17475000 1 ln½lnð1=RÞ þ 0:4851 ln V þ Tk 0:0128

When the duty ratio was set to 2/3 which means the product is in work of 16 h each day, the average of residual life is calculated to be 15.62 years with confidence (c) of 0.7 and reliability (R) of 0.8.

6 Conclusion The design technology of accelerated life test based on the load spectrum compilation and D-efficiency is proposed in this paper, which accomplished the mutual support and verification of measured load, test optimization and residual life assessment. The technique ensures the effectiveness and economy of the accelerated life test. The main creative work is as blow. (1) The temperature and vibration load spectrum are measured for the signal output board in operation and it is compiled and equivalently conversed into test profile which is efficient for the accelerated life test design. (2) The nonlinear accumulate damage model of step stress is deduced and the test optimization method based on D-efficiency is defined for the Fisher information matrix determinant is equal. It established the theoretical foundation for the accelerated life test optimization design. (3) A residual life assessment case for the signal output board in rail transportation is given. It verified the feasibility and practicability of the load spectrum compilation, test optimization and residual life assessment.

References 1. Chen, X., Zhang, C., Wang, Y., et al.: The technology and application of accelerated life test. National Defense Industry Press (2013) 2. Chen, Q.: Research on the equivalent load spectrum method in accelerated validation of the fatigue life. Beihang University (2014) 3. Zhu, H., Yu, Z., J, L.: Calculation method of equivalent fatigue stress range based on CortenDolan accumulative damage rule. J. Highw. Transp. Res. Develop. (2010)

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4. Liao, H., Elsayed, E.A.: Equivalent accelerated life testing plans for log-location-scale distributions. Nav. Res. Logist. (NRL) 57, 472–488 (2010) 5. Ng, H.K.T., Balakrishnan, N., Chan, P.S.: Optimal sample size allocation for tests with multiple levels of stress with extreme value regression. Nav. Res. Logist. 54, 237–249 (2007) 6. Cai, C.: The development of automotive fatigue life test technique based on the real road load spectrum. Automotive Collaborative Innovation Center of Chongqing University (2015) 7. Zhao, C.: Study on preprocessing and feature extraction of high speed rail vibration data based on cloud computing. Southwest Jiaotong University (2013)

Realization of Interconnecting Application of Non-secret-related Network and Secret-Related Network Based on Unidirectional Optical Shutter Qi Miao(&), Xiaoyi Ru, Jiang Bian, and Zhou An Institute of Spacecraft System Engineering, CAST, Beijing 100094, China [email protected]

Abstract. The terminals, networks and systems involved in the daily work, scientific research and production of military enterprises are all defined as secretrelated. In accordance with the national requirement, physical isolation from the internet and internal non-secret-related network must be maintained. But at the same time, each enterprise also deploys a private network for testing, experimenting and processing, and the data exchange between the networks must be completed by manual transfer, which can be as far as one or two hours fast and as slow as half a day, and has been far from meeting the requirement of military enterprises’ scientific research tasks in the information age. In this paper, the utilization of unidirectional optical shutter is proposed to realize the data transmission between non-secret-related network and secret-related network. Furthermore, the feasibility is analyzed and the technical verification is completed. Keywords: Information security
Controlled interconnection
Optical shutter

1 Introduction 1.1

Current Situation

A military enterprise is responsible for satellite research and development, space technology development and application and other fields of work, and its secret-related office responsible for the daily office work (hereinafter referred to as the secret-related network) has been in accordance with national security requirements and completed the construction of various security protection. In addition, in order to effectively strengthen the test work of satellites and spacecraft, a test network for non-secret-related network for spacecraft (hereinafter referred to as test network) has been established, which is physically isolated from the secret-related network. A dedicated test device is deployed in the network so as to store the process data in the testing process. Since the special device cannot implement the security protection means according to the national security requirement, the test network is kept physically isolated from the secret-related network.

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 371–379, 2019. https://doi.org/10.1007/978-981-13-7123-3_44

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Transfer Manner

Data exchange between the two networks is often involved in the work. At present, it is realized by manual transfer. The actual operation is [5]: (1) The special security software is installed in the terminal of the test network, which can identify the corresponding “USB flash drive”, and the users will save the data needing to be imported into the secret-related network into the USB flash drive. (2) The operator connects the USB flash drive to the “intermediate machine”, which is in the state of not being connected to the network, and the data are burned onto after the checking and killing of the virus and Trojan. (3) A special terminal in the secret-related network is connected with a “CD-ROM”. After reading the contents of the CD-ROM, the information is sent to the user through a specific approving system. From the secret-related network to the test network, the flow direction of information is opposite, and the schematic diagram of the transfer operation is shown in Fig. 1.

Fig. 1. Transfer operation

The whole process of transfer work requires manual participation, and the operation is tedious and laborious, which leads to a large delay in data transfer, seriously affecting work efficiency.

2 Design Plan At present, the mainstream research direction is to realize one-way data transmission [1] through the “the controlled connection” of the network. The key equipment used is divided into two independent parts, connecting to two types of networks respectively. By using physical characteristics in the intermediate area, the data in the form of file are forwarded from one end of the network to the other end [2, 3] without the occurrence of direct connection between TCP/IP and its upper layer protocol. Since the importance of the secret-related network is higher than that of the test network, the impact of data transmission from the test network to the secret-related network is much lower than that of the reverse, as far as the data flow direction is

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concerned. Therefore, this paper mainly analyzes the data exchange from the test network to the secret-related network, which has been able to meet the transfer need of more than 80% of scientific research personnel, and achieve the stated object. 2.1

Overview of Optical Shutter

According to the market research, there are three kinds of products corresponding to the “key equipment” mentioned above: one-way light shutter, laser one-way transmission gatekeeper and automatic optical disk ferry system. The first two are essentially the same kind of products, all using the one-way transmission characteristics of light to achieve one-way, non-feedback transmission of data; automatic optical disk ferry system is to use a manipulator to simulate the operation of manual transfer, with the transmission rate being limited by the capacity of optical disk and the up-to-minute delay [10]. By comparing the inspection qualification of product and data transmission rate, and combining with the actual situation of the enterprise, the way based on one-way optical shutter is selected to carry out the project construction. System Architecture. The one-way optical shutter system consists of two parts: oneway isolation device and management control system. The one-way isolation device is special-purpose hardware, which connects the secret-related network and the test network respectively to restrict the one-way transmission of data. The architecture is shown in Fig. 2.

Fig. 2. Device architecture

Unidirectional Transmission. After changing the electrical signal into an optical signal, the external exchange host divides the light into 2 bundles [6–8] through a beam splitter. One is received by the intra-change host to complete the one-way transmission of data; the other returns to the external exchange host to check the validity and integrity of the transmitted data. The schematic diagram of data transmission is shown in Fig. 3. Through one-way optical shutter system, the data in test network can be transmitted one-way to the secret-related network.

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Fig. 3. Unidirectional transmission

2.2

Network Interconnection

Network interconnection is based on one-way optical shutter devices, but in order to ensure the regularity of data transmission and the formation of record, the data transfer and approving system [4, 9] need to be deployed in both types of networks. Data exchange area involves one-way optical shutter equipment, servers, firewalls, anti-virus walls, intrusion detection machines and supporting network equipment. The schematic diagram of the connection is shown in Fig. 4.

Fig. 4. Schematic diagram of network interconnection

2.3

Data Exchange Area Design

In order to ensure that the “interconnected” network will not bring in new security risks, and can effectively control the flow of data transmission, the security design of data exchange area will refer to the protection standard of secret-related network in line with the principle of strict control. IP Address Design. The secret-related network and the test network are divided according to the scope of optical shutter equipment, and one new IP address field is allocated respectively. On the one hand, it is easy to form the audit record by

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distinguishing it from the existing IP address field; on the other hand, it adds network control measures to the address field to strictly restrict data access. For the ACL reference under the VLAN interface, the following principles should be complied with: (1) Secret-related network segments in the data exchange area: Secret-related networks are only allowed to be accessed by the user departments and other departments are prohibited from accessing; (2) Test network segment in the data exchange area: only “user departments of the unit” within the test network are allowed to access, and “externally-connected areas (external units) of the test network” are prohibited to access. Firewall Design. In the data exchange area, two firewalls are configured as the logical boundaries of secret-related network and test network respectively. According to the minimum release principle of data, the source IP address, destination IP address and TCP/UDP port are restricted. They mainly are: (1) Restricting the access of the data sending server to the terminal IP address of the “user department of the unit” in the test network, being convenient for the submission of data transmission application and uploading of the data; (2) Restricting the “data sending server” solely to send the data to the interface of the test network of the optical shutter equipment. (3) Restricting the interface of optical shutter devices in the secret-related network can only send data to the data receiving server. (4) Restricting the access to the data receiving server only by the “user department” terminals in the secret-related network, being convenient for the examination and approval of application procedures and downloading of data. Anti-virus Wall Design. The anti-virus wall is connected to the link in a serial way, aiming at checking and killing Trojans, viruses and malicious attacks on the data entering the secret-related network. The feature library must be ensured to be updated at least once a week. Intrusion Detection Design. By configuring the port mirroring function on the secretrelated network interchanger, the traffic mirroring can be sent to the intrusion detection machine to detect and analyze the data so as to ensure that no malicious attack data are mingled. The feature library must be ensured to be updated at least once a week. 2.4

Internal Design of Test Network

Network Architecture. The application mode of the test network has its particularity. In the test process, the terminal of the task cooperative unit also needs to join up the network to obtain data. The current test network is a two-tiered architecture, where all terminals share the same address field, and there are no effective control measures for access isolation between the terminal of the unit and the terminal of external unit. In view of this, it is necessary to redesign the test network, by adding access control

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measures, standardizing terminal security policy settings, etc., which can reduce security risks from the source. The redesigned test network is shown in Fig. 5.

Fig. 5. Architecture of the test network

The network adopts the divisional design, which is divided into the terminal area of the unit and the external terminal area. The access control between the regions is completed through the firewall, restricting the server IP address which can be accessed by the external terminal area, and setting up the anti-virus wall on the critical link to avoid the external terminals’ carrying viruses. Safety Control Measures. Although the safety protection standard and requirement of the test network are not as high as those of the secret-related network, the working ideas are the same. The safety control within the network mainly consists of the following aspects: (1) Firewall: Increase the deployment of firewalls, in accordance with the principle of “traffic minimization” to control the flow of data within the network, specially focusing on the control of the data access from external terminal; (2) Antivirus: Antivirus system is deployed in the “terminal area of the unit”, which is C/S architecture. The computer needs to be installed with an anti-virus software client. Through the configuration strategy, the virus library of the system is kept up-to-date and the virus is checked and killed at least once a week. The anti-virus wall will be deployed in a serial way between the regions to avoid the virus carried by external terminals which will affect the operation of the terminal of the unit. (3) Network access control: a network admission control system will be deployed within the network to restrict the access, except for the MAC address of legitimate terminals to enter the network, and wipe out the illegal terminal access through the functions such as “MAC address whitelist”;

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(4) Inspection software: network security inspection system software will be installed in the computers and servers in the “terminal area of the unit”. The computer user records, system access behavior, connection records of USB interface, etc. are checked regularly by a specially-assigned person, and the problems found will be corrected in time. 2.5

Operation Process Design

In order to import the test network data into the transmission direction of secret-related network, the work process is as follows: (1) The applicant visits the “data sending server” in the test network, fills in the application procedures, and uploads the data; (2) Personnel with the authority of examination and approval (usually the head of the department) logs in to the “data sending server” to examine and approve the application procedures, and then the system automatically sends the documents to the “data receiving server” within the secret-related network. (3) The applicant logs on the “data receiving server” in the secret-related network to save the data and complete the one-way transmission.

3 Technical Identification In order to test the actual performance of the above design schemes, a company has carried out the small-scale verification. The local controlled interconnection is performed according to the topological link shown in Fig. 4. The key device is unidirectional optical shutter device developed by domestic enterprises. The uploading and downloading of data are accomplished through a dedicated transfer system. Compared with the traditional way, the intuitive improvement can be seen in Table 1. More detailed data records are available (running cycle is about 3 months) as follows: (1) Transmission rate: matching 1000 Mbps Ethernet and the data transmission of 1 GBps size needs about 12 s; (2) Transmission delay: the delay of data transmission from “data sending server” to “data receiving server” is second level. (3) System stability: there are totally more than 200 times of data transmission, single data size ranges from 10 MB to 15 GB, and one-time success rate of data transmission is about 95%; (4) Multi-person parallel operation: there are up to 30 people online simultaneously, conducting data uploading, data downloading and the operation of process approval respectively. The system has no obvious delay and functions well. (5) Security: No virus, malicious code and Trojans are found in the secret-related network, and no illegal data transmission event occurs.

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The actual realization of the network controlled interconnection conforms to the expectation. The unidirectional optical shutter equipment runs stably and the safety protection measures are in place, which proves that the interconnection design is feasible. Table 1. Correlation table of effect No. 1

2

3

Comparative item Elapsed time of data exchange Data exchange quantity Reliability of data exchange

4

Convenience of data exchange

5

Record of data exchange

Before

After

1–2 h

 5 Min

 700 MB (Limited to the capacity of single disk) Low The problem of loss of CD often takes place

Unlimited

Low involving the operations from many departments and operators Poor readability Involving multiple links, including manual operation record, system record, etc

High Even if a file loss takes place in transmission, it will be automatically retransmitted High Solely needs to be approved by the department director Strong readability The complete record can be checked from the transfer system

4 Conclusion Through the above analysis and technical verification, it can be seen that the controlled “interconnection” between the non-secret-related network based on one-way optical shutter and the secret-related network can ensure that the data can only enter the secretrelated network unidirectionally and without any feedback, so as to avoid the missing of secret-related information and the risk of leakage. The construction of security protection system in data exchange area and test network can ensure that the data entering the secret-related network does not contain virus and malicious code, and will not affect the stable operation of the secret-related network. In the process, supplemented by the formulation of management system, the management main body and responsibilities of all parties of “interconnection” are defined, which can reduce more than 90% of the “interconnection” risk. The interconnection mode discussed in this paper has the value to be expanded, which can be used as a reference for the construction of secret-related networks and other non-secret-related networks used internally. The controlled “interconnection” of the network, on the one hand, practices “intelligent manufacturing 2025” and makes bold innovations and changes; on the other hand, it effectively optimizes work process and improves the efficiency of research in military enterprises.

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References 1. Yan, M.-y., Wei, L.: The design of the one-way isolation net gap and its transmission reliability. Value Eng. 219–220 (2014) 2. Ao, L-q, Chen, Z.: Research on information resource sharing scheme based on network isolation technology. Softw. Guide 16(6), 163–167 (2017) 3. Zuo, Z.-y., Pu, X.-p.: Analysis and practice of non-confidential information exchange in military enterprises. Inf. Secur. Commun. Priv. 117–122 (2016) 4. Wang, Y.-j., Yang, J.-h., Guo, G.-t., Liu, Y-t.: Analysis and prospect of physical isolation technology for network security. Inf. Secur. Commun. Priv. 117–122 (2016) 5. Zhai, J.: Network Edge—Evolution of Thought of Safety Protection 23–24 (2013) 6. Zhang, X.-q.: Analysis of principle and function of unidirectional optical shutter. Netw. Secur. Technol. Appl. 99–100 (2016) 7. Wei, X.-z.: Study on Dual-network Safe Access on the Basis of Double Unidirectional Optical Shutter. Beijing University of Chemical Technology (2015) 8. Wan, Y.-l., Zhu, H.-j., Liu, H.-z., Zhang, K.-f.: Research on reliability of unidirectional transmission system based on optical shutter. Hacking Def. Res. 25–27 (2010) 9. Liu, B.: Design and Implementation of Multiple Networks Synthetic Integrated System. Northeastern University (2014) 10. Zhong, J., Li, Q.: Research on information security management based on network isolation technology. Sci. Educ. J. 158 (2016)

Application of Ion Beam Etching Technology in Spacecraft Encoder Lithography Suran Qin1(&), Na Zhao2, Ronghui Jiao3, Chunying Zhu2, Jiang Liu2, Jianmin Shi2, and Hanchao Fan2 1

Beijing Institute of Spacecraft System Engineering, Beijing 100194, China [email protected] 2 Beijing Institute of Control Engineering, Beijing 100194, China 3 China Academy of Space Technology, Beijing 100094, China

Abstract. The encoder is a core component of encoding sun sensor, the photolithography precision and quality has the serious impact on the measurement accuracy of sun sensor. Traditional encoder photolithography uses wet etching technology, and line edge has sawtooth and burr phenomenon, photolithography accuracy is low, can not meet the aerospace products high precision and high quality requirements. In this paper, iron beam etching technique is used to verify the feasibility of ion beam etching technology used in encoder, which solves the problem of edge sawtooth and burr, and the technical problems of low photolithography accuracy, which laid the foundation for the further development of products to higher precision. Keywords: Ion beam etching


Photolithography
Quality improvement

1 Introduction In recent years, Chinese spacecraft are developing to the direction of low cost, high precision and high reliability, and spaceflight products need to be miniaturized, high precision and high quality. The same is true for encoding sun sensors which are used as a general product of spacecraft [1]. The encoder is the core part of the encoding sun sensor. By coating and photolithography, 14 code channel patterns arranged according to certain rules are prepared, which are composed of more than 300 rectangular windows with different width of 0.07 mm–8 mm. The lithography accuracy and quality of the encoder seriously affect the measuring accuracy of the encoding sun sensor, and this error is random error, which can not be corrected by the way of error compensation. Therefore, the influence of this error must be reduced by improving the lithography accuracy [2]. The traditional lithography of the encoder adopts wet etching technology, and there are some phenomena such as sawtooth and burr on the edge of the graph. The lithography accuracy is low, which can not meet the requirements of high precision and high quality of aerospace products. This paper attempts to use ion beam etching technology to verify the feasibility of ion beam etching technology used in encoding sun sensor lithography, in order to solve the technical problems of irregular edges and low lithography accuracy of product lithography. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 380–390, 2019. https://doi.org/10.1007/978-981-13-7123-3_45

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2 Photolithography Technology for Spacecraft Encoder 2.1

Wet Lithography Technology of Encoder

Lithography is a kind of precision micro-nano machining technology, which is widely used in semiconductor industry. Encoder lithography adopts the traditional wet etching technology, by coating photoresist on the dielectric layer (chromium film layer) of the encoder substrate as the intermediate medium, the mask pattern is transferred to the photoresist by exposure and development. Then, through the hard film process, the photoresist is further hardened as a protective layer that does not need to be etched, and then the bare medium layer is etched by the etching link, and finally the excess photoresist that plays a protective role is removed through the degumming process. Present the desired lithography pattern [3]. Encoder lithography process schematic as shown in Fig. 1. Photoresist

Dielectric layer

Encoder substrate

Ultraviolet lamp

Photoresist layer

Preroasting

Coating photoresist

Development liquid

Mask pattern

Exposure

Development(positive photoresist)

Photoresist

Roasting

Dielectric layer Check Wet etching

Photoresist removing

Fig. 1. Schematic diagram of lithography process of encoder

2.2

Problems in Wet Lithography

In the specification of the lithography quality of the encoder, it is clearly required that the edge of the lithography pattern be observed under a microscope of 40 times, no sawtooth, no burr, and the lithography accuracy  3.0 lm. However, the traditional wet etching process soaking in the etching liquid will make the edge of the lithography appear sawtooth, burr and so on because the metal coating of the encoder is thicker and the etching time is long, so the quality of the lithography becomes worse, the lithography precision drops, and the product is not qualified. Figure 2 shows a picture of an unqualified product microscope with edge sawteeth and burrs.

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Edge sawtooth

Edge burr

Fig. 2. Unqualified products with edge sawtooth and many burrs in photolithography (X40)

In order to analyze the specific situation of the unqualified item, the unqualified encoder produced within one year was investigated statistically. There are 91 unqualified encoders participating in the statistical investigation, from the coating, lithography, flanging, cleaning of the encoder, surface shape and other aspects of the collection, formed an unqualified items questionnaire, see Table 1. According to the results of the survey, the arrangement is drawn, as shown in Fig. 3. Table 1. Encoder (chromium film) ion beam etching test scheme No. 1 2 3 4 5 6 7 8

Test content Selective ratio test Low energy and high beam etching test Medium energy and beam etching test High energy and low beam etching test Preparation of etched samples Edge quality check of lithography Lithography precision detection Etching effect analysis

Number of test pieces 1–2 1–2 1–2 1–2 8–10 4–5 4–5 4–5

It can be seen from the questionnaire of unqualified items and the Pareto diagram that: in the unqualified products produced within one year, the proportion of photolithography is 68.2%, in which the main item (42.9%) is the precision of lithography, and the minor item (25.3%) is that the edge of lithography is not up to standard. According to the principle of quality improvement, two main quality problems caused by the low pass rate of encoder can be solved by solving the problems of over-precision and unqualified edge of lithography.

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Pareto diagram for unqualified items of encoder

Fig. 3. Pareto diagram for unqualified items

3 Ion Beam Etching Technology 3.1

Ion Beam Etching Technology

Ion beam etching (IBE) is a kind of dry etching. It is bombarded by Ar+ ion beam with certain energy under vacuum condition, so that the surface atoms are sputtered and removed from the sample. Finally, the graphics that need to be processed are presented [4]. Because of its strong directivity, anisotropy and high resolution, ion beam etching has super fine machining ability and can achieve nanoscale precision. And also because of the beam density, energy, incident angle of ion beam, the rotation speed of the workpiece, sample temperature, gas flow rate, working pressure and other parameters can be accurately adjusted and controlled, therefore, it is easy to achieve the best process conditions, process flexibility and good reproducibility [5]. In this paper, we try to use ion beam etching instead of wet etching, so that the lithography can be observed neatly under 100 times microscope, without sawtooth or burr. At the same time, the lithography accuracy of the encoder is improved from 3 lm to 2 lm, and the quality and precision of the lithography are greatly improved. 3.2

Technical Proposal

(1) Train of thought of selecting technical scheme The ion beam etching of the encoder is made by the ion beam bombarding the coating layer of the encoder, so that the unnecessary coating layer is stripped out from the surface of the encoder, and the desired pattern is formed. As ion beam etching technology is pure physical sputtering, in principle any material will be etched, that is to say, the coating that needs to be etched will be etched, and the non-etched lithography layer will also be etched [6]. In order to verify the feasibility of ion beam etching for

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encoder lithography, it is necessary to determine the ratio of the thickness of film layer to the rate of etching layer thickness through selective ratio test, so as to ensure that the photoresist can protect the coating which does not need to be etched in the process of etching. After verifying the feasibility of ion beam etching, through a series of experiments, the key optimum technological parameters of ion beam etching are selected, and the experimental samples are prepared. Finally, the edge quality and lithography accuracy of the samples are analyzed and evaluated under microscope, and the application effect is confirmed. Technical scheme determination Based on the above ideas, the ion beam etching test of chromic film encoder is carried out, and the test scheme is shown in Table 1. (2) Technical Measures In this paper, IBE-150B standard ion beam etching machine is used to carry out ion beam etching test of chromic film encoder. The main performance index of ion beam etching machine is shown in Table 2. Table 2. Main performance indexes of IBE-150B standard ion beam etching machine Bed dimension Ion beam incident angle Etching rate Etching inhomogeneity Ar+ ion energy range Ion beam current density Effective ion beam diameter

U150 mm (maximum sheet diameter  4 in.) 0–90º arbitrary adjustment 10 nm–200 nm/min (relating to etching materials and processes)  ±5% 100–1000 eV, Continuous adjustable 0–1 mA/cm2, Continuous adjustable  U100 mm

The sample of ion beam etching of encoder is sent to the professional inspection mechanism for lithography accuracy detection, and the number of drawing figure is 124 per piece of encoder. Testing qualification and testing devices are as follows. • Testing qualification: • The uncertainty of calibration results is evaluated and expressed in accordance with the requirements of JJF1059 series standards. • Calibration of environmental conditions: • Temperature: (20±0.2) °C Humidity: (50±10)% RH • Calibration of metrology base (standard) devices (including reference materials)/main instruments (Table 3).

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Table 3. Calibration device information Name 2-D linear working reference device

Measuring range 300 mm  300 mm

Uncertainty/accuracy rating qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi UðXÞ ¼ 1042 þ ð0:16
xÞ2 þ ð0:28
yÞ2 nm qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi UðYÞ ¼ 1042 þ ð0:16
yÞ2 þ ð0:28
xÞ2 nm k = 2, x, y unit is mm

Where x stands for the horizontal ordinate and y for the ordinate.

4 Verification of Ion Beam Etching Technology 4.1

Determination of Ion Beam Etching Selection Ratio

Different materials have different etching rates under the same etching parameters. In the experiment of the ratio of chromium film layer to photoresist, two specimens were selected and the commonly used etching parameters, namely ion energy 250 V and ion beam current 100 mA, were used to carry out the experiment. The test results are shown in Table 4.

Table 4. Selection ratio test results No. Test article number 1 JM-001 2 JM-002

Film Thickness of thickness rubber layer before etching 350 nm 2900 nm 350 nm 2300 nm

Thickness of rubber layer after etching 2700 nm 2100 nm

Etching time 20´ 20´

Film etching Coating rate etching rate 17.5 nm/min 10 nm/min 17.5 nm/min 10 nm/min

The experimental results show that under this condition, the etching rate of chromium film is 17.5 nm/min, the etching rate of photoresist is 10 nm/min, the selection ratio of chromium film and photoresist is 1.75:1, that is to say, under the same conditions, the etching rate of chromium film is faster than that of photoresist by ion beam. This is also a need trend. After obtaining this data, we can draw a conclusion that when the thickness of chromium film is 350 nm under the condition of medium energy 250 V and medium plasma beam 100 mA, if the thickness of photoresist is more than 200 nm, the etching of chromium film can be guaranteed in theory. When chromium film is etched clean, photoresist can also play a masking effect. 4.2

Selection of Key Parameters for Ion Beam Etching

Under the condition that the incident angle of the surface bombarded by ion beam and the atomic structure and crystal direction of the processed material are relatively fixed, the etching rate is related to the ion energy on the surface of the coating. The higher the voltage is, the higher the beam current is. The larger the ion beam energy, the higher

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the etching rate, but the larger the voltage, the larger the ion energy will lead to the substrate damage. Therefore, it is necessary to select the appropriate voltage to maintain the appropriate etching rate. In addition, the ion beam density is also one of the main factors directly affecting the etching rate. Ion beam energy and beam current in ion beam etching are controlled [7]. The two key parameters of the effect, the higher the energy and the higher the beam current, the faster the etching rate of the chromium film is, but the greater the damage to the photoresist. In order to guarantee that the photoresist can not only play a better protective role, but also improve the etching efficiency, it is necessary to set different process parameters for process test [8]. According to the experience of ion beam etching parameter selection, three groups of experiments have been carried out, including low energy 200 V, high beam current 120 mA, medium energy 250 V, medium beam current 100 mA, high energy 500 V and low beam current 80 mA. The test parameters are shown in Table 5. The etching results of chromium film encoder are shown in Table 6. Table 5. Ion beam energy and beam parameters of chromium film encoder Sample number 009 010 011

Energy and beam

Parameter

Low energy, high beam Medium energy and beam High energy, low beam

200 V, 120 mA 250 V, 100 mA 500 V, 80 mA

Etching time 25 min

Chromium etching rate 14 nm/min

20 min

17.5 nm/min

30 min

11.7 nm/min

Under the Leica M2500 metallographic microscope (Fig. 4, left) and 200 times (Fig. 4, right), the black rectangular strip edge is the edge of the etched figure. As can be seen from Fig. 4, the chromium film layer in the window is etched cleanly, and the graphic edges are neat. No sawtooth, no burr, good etching effect. It can be seen from the above experimental conditions that the etching parameters of high energy 500 V and low beam current 80 mA have not been etched clean and do not meet the requirements of lithography index. Therefore, the test conditions are not suitable. The etching parameters of medium energy 250 V and medium beam current 100 mA are adopted, the graphic edges are neat, the sawtooth and burr are free, the resolution is high, the lithography index is satisfied, and the etching efficiency is the highest. So the medium energy 250 V and the medium beam 100 mA are chosen as the best parameters of ion beam etching. 4.3

Verification Results of Ion Beam Etching

4.3.1 Analysis of Edge Quality Effect of Ion Beam Etching Lithography According to the optimum technological parameters of ion beam energy 250 V and ion beam current 100 mA, the sample of encoder etched by ion beam was prepared in different stoves. The sample number was 10 pieces of Cr-1– Cr-10, respectively.

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Table 6. Results of chromium film code plate etching test Sample number 009

010

011

Visual inspection after etching Surface adhesive, film without damage Surface adhesive, film without damage Surface adhesive, film without damage

Microscopic examination of 40 times after etching Cr etching clean, etched lines neat edge, high resolution, edge steepness, no sawtooth or burr Cr etching clean, etched lines neat edge, high resolution, edge steepness, no sawtooth or burr Part of the window Cr is not completely etched clean, neat edges, no sawtooth and burr

Conclusion Qualified

Qualified

Unqualified

Fig. 4. 010 Chromium film encoder sample window edge etching effect (X200)

The ion beam energy and beam current used in the preparation process are the same and the operation process is the same. The edge effect of lithography pattern was observed under Leica M2500 metallographic microscope by random sampling of 1 Cr6 number disk out of 10 etched samples. It can be seen from the figure that the edge of lithography pattern, sawtooth and burr of wet etching sample (Fig. 5) is serious. The edge of the Ion beam etching of Cr-6 sample (Fig. 6) lithography is neat, no sawtooth, no burr, and the edge of the ion-beam etched lithography is improved obviously. 4.3.2 Precision Analysis of Ion Beam Etching Lithography In order to investigate the error distribution of lithography accuracy of 10 chromium film encoders, the lithography error data of the 10 code plates are statistically analyzed. The lithography error distribution of the coded Cr-7 encoder is shown in Fig. 7. In Fig. 7, the horizontal coordinates represent the lithography error in lm, the vertical coordinates represent the frequency, and the sampling number N is 124. It can be seen from Fig. 7 that the LSL = −2.0 lm and USL = 2.0 lm are limited under the lithography precision of the encoder, while the error distribution of the 124 points photolithography measured by the Cr-7 encoder is −0.60 lm–0.7 lm, the error distribution accords with the normal distribution, and the data is more concentrated, the standard deviation is 0.3245. The histogram results show that compared with the

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Fig. 5. Edge etching effect of wet etching encoder (X100)

Fig. 6. Ion-beam etching of Cr-6 codedisk graphics edge effect (X100)

Fig. 7. Lithography error distribution curve numbered as Cr-7 encoder (−0.6 lm–0.7 lm)

lithography index of  2.0 lm, the lithography accuracy of the Cr-7 encoder prepared by ion beam etching technology meets the requirements. The precision distance between the upper and lower specifications is sufficient margin. In order to observe the whole lithography error distribution of 10 encoders, the box diagram is drawn by using 124 lithography precision test data extracted from each encoder, as shown in Fig. 8. In the graph, the horizontal coordinate represents the encoder number, the vertical coordinate represents the error value, the unit is lm, the specification limit is LSL = −2.0 lm under the lithography precision, the upper specification limit is USL = 2.0 lm, and the black spot line in the diagram is the median line. It can be seen from the box diagram in Fig. 8 that the lithography errors of the 10 encoders are within the upper and lower specification limits, which meet the requirements of the lithography accuracy  2.0 lm. Where the maximum lithography

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error appears in the Cr-5 encoder, the maximum value is 1.3 lm, the maximum value is 35% margin from the upper specification limit, the minimum lithography error appears on the Cr-5 encoder, the minimum value is −1.4 lm, and there is a 30% margin at the minimum distance.

Fig. 8. 10 control charts of mean value of lithography error of encoder

5 Conclusion In this paper, a lithography method based on ion beam etching is designed according to the problems existing in the sun sensor encoder lithography of spacecraft. Through a series of experiments, the feasibility of ion beam etching applied to the encoder lithography of spacecraft solar sensor is verified. The application results show that the ion beam etching technique can ensure that the edge of the lithography of the encoder is neat, no sawtooth, no burr, and at the same time, the precision of encoder lithography has been improved from  3 lm to  2 lm. Ion beam etching technology has improved the quality and precision of the lithography pattern of the encoder of the solar sensor of spacecraft.

References 1. Zheng, K.: The development direction and prospect of solar sensor. Control Eng. pp. 6–9 (2004) 2. Tu, S.: Satellite Attitude Dynamics and Control. Aerospace Press, Beijing (2003) 3. Zhou, H., Yang, H.: Present situation and prospect of lithography and microfabrication technology. Micro/Nanoelectron. 49, 936–939 (2012) 4. Orloff, J., Utlaut, M., Swanson, L.W.: High Resolution Focused Ion Beam, Chapter 2, In: Physics of Liquid Metal Ion Sources. Kluwer Academic Publisher (2003) 5. Li, J.: Progress of dry etching technology in semiconductor device process. Micromachining 3, 43–48 (1993)

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6. Cui, Z. (ed.) Microsystem technology standardisation roadmap (2003). http://www.memstand. org/ 7. Li, H., Lu, Z., Liao, J., et al.: Study on the slope of step side wall during ion beam etching. Micromach. Technol. no. 2, pp. 28–32 (2000) 8. Liu, Z.: Optimization method of lithography process parameters. Semicond. Optoelectron. 22 (1), 52–53 (2001)

Validation Technology in Super Power Supply System Design of Telecommunication Satellite Ronghui Jiao1(&), Suran Qin2, Lili Yuan1, Ding Song1, Lei Yun1, and Jianwu Zhao1 1

2

China Academy of Space Technology, Beijing 100094, China [email protected] Beijing Institute of Spacecraft System Engineering, Beijing 100194, China

Abstract. It’s difficult at designing super power system of telecommunication satellite whose power achieves 20 KW at Present. The power system applying PCUNG and super capability Li-Battery can be work stably in the power processes of producing, transmitting, controlling and storing by design validation. In addition, design validation can ensure satisfiable output electrical characteristics of power system. In this paper, the all-digital, semi-physical and allphysical design verification techniques are used to test and verify the communication satellite power supply system in each stage, to check whether the technical specifications of the power supply system meet the requirements. The super power reliability growth test is carried out to investigate the operating reliability of the power supply system, which lays a solid foundation for the successful application of the super power communication satellite power supply systems. Keywords: Super power


Power system
Design validation
Imitation

1 Introduction The communication satellite power supply system is generally composed of solar cell array, power controller, battery group and distribution system. Solar cell array generates energy, and the power is adjusted by power controller to form a primary power supply bus. In addition, the power controller stores the excess energy generated by the solar cell array in the battery set for use when the satellite solar light is invisible or the output power of the solar cell array is insufficient. The distribution system is responsible for distributing the primary bus energy to each load, which not only ensures the low energy loss, but also ensures the safety of energy transmission [1]. The satellite power supply system is related to the lifeblood of the satellite, and the failure or instability of the power supply system is directly related to the success or failure of the satellite mission. For example, the INTERSAT19 satellite solar panel failure caused the satellite power loss of 50%, the satellite mission was seriously affected, the domestic satellite power supply system transmission path SADA fault, resulting in satellite mission failure. The statistical results of the faults of satellite power system in orbit at home and abroad in recent years show that the problems concerning © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 391–399, 2019. https://doi.org/10.1007/978-981-13-7123-3_46

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solar cell array, power supply regulation, battery group and distribution fault are relatively high, and the omnidirectional design of power supply system is carried out. It is urgent to improve the reliability and operation stability of satellite power system.

2 Verification Technology of Power Supply System Design The design and verification technology of communication satellite power supply system mainly includes digital simulation verification technology, semi-physical simulation verification technology and all-physical test verification technology. 2.1

Full Digital Simulation Verification Technology

In the field of full digital simulation and verification, at present, communication satellites are based on DFH-4 platform satellites, and many full digital simulation models of power supply system have been established. For example, single-junction and three-junction GaAs solar cell array simulation model, S3R shunt regulator simulation model, BCR charging regulator simulation model, BDR discharge regulator simulation model and battery group simulation model [2], based on the power system simulation model, Set up the whole digital simulation verification platform of super power supply system. The whole digital simulation and verification platform of super power supply system is shown in Fig. 1. The platform consists of power supply and distribution simulation analysis system, simulation planning and dispatching management system, simulation database and three dimensional demonstration system of satellite power flow. The power supply and distribution simulation analysis system is composed of solar cell array output model (including solar wing shading model), power controller module model, battery group model, power transmission model and load model. Based on the models It completes power system simulation analysis and key technical indicators verification. The simulation planning scheduling management system realizes the data scheduling and planning management of power system simulation [3]. The simulation process is as follows. The input of the system is satellite orbit and attitude data, satellite 3D geometry model, antenna and solar wing motion characteristics, etc. The simulation results of solar cell array occlusion and power output are given under different operating conditions of the satellite in orbit. The calculation results are input to the power controller model and the battery group model through the power transmission model. The power controller model is controlled by shunt regulation and charge/discharge regulation, and the satellite primary bus is formed. The power flow of the satellite is formed through the power transmission model to the load model. At the same time, the whole process is displayed in the three-dimensional demonstration system of the satellite power flow [4]. The model parameters of the power supply system are simulated under different operating conditions of the satellite in orbit to ensure that the model parameters meet the requirements of the device, and the primary bus characteristics of the power system meet the load requirements. The impedance characteristics of the power supply system can meet the requirements of stable operation of the system [5].

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Power supply and distribution simulation analysis system, Solar cell array occlusion software Simula tion planni ng and dispatc hing manag ement system

power supply and distrib ution

solar cell array occlusion

antenna and solar wing 3D geometry model satellite orbit and attitude data antenna and solar wing motion characteristics solar cell position in space

Other input

Solar cell array power predict

po w er tr an s m is si on m od el

S3Rs model

BDRs model

MEA model

BCRs model

PCU model Battary group model

po we r tra ns mi ssi on mo del

Lo ad mo del

three dimensi onal demons tration Simulate system data of satellite power flow

Simulation database Solar cell array occlusion data Solar cell array power predict data

Device simulate data Load simulate data

Simulate data

Fig. 1. Full digital simulation verification platform for super power supply system

2.2

Semi-physical Simulation Verification Technology

The schematic diagram of super power supply system semi-physical simulation platform is shown in Fig. 2, which includes the following four parts. (1) Verification Environment for High Power supply system By means of ground hardware equipment and software simulation, the super power supply verification environment is built up with full hardware, full simulation and half hardware and half simulation system verification environment. This environment is called HILS (Hardware-in-the-Loop Simulation) that is hardware in loop simulation. A real-time simulation system based on mathematical model is established by using hardware-in-loop simulation technology to replace the real power component equipment and satellite computer to realize the function verification of the key modules in the system design [6]. (2) Verification Environment for High Power Distribution system The verification environment of super power distribution system realizes the interface matching verification, performance index matching verification, equipment usage policy verification and power transmission characteristic verification between distribution equipment, initiator manager, low-frequency cable network and so on. (3) Verification Environment for Distributor The verification environment of distributors realizes the verification of high speed signal transmission of bus cable, solid state distributor, DC/DC and the fault isolation, detection, recovery and power converter performance.

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(4) DSPACE Semi-physical Simulation Verification Environment The DSPACE (digital Signal Processing And Control Engineering) real-time simulation development environment realizes the complete seamless connection with MATLAB/Simulink. The DSPACE real-time system has a hardware system with high speed computing power, a convenient code generation/download tool and a software environment for testing/debugging [7].

Fig. 2. Principle block diagram of super power supply system semi-physical simulation platform

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Based on DSPACE, the hardware-in-the-loop simulation environment of satellite power supply system can be established. The digital models of each device can be used to carry out fault test, boundary test and limit test. Therefore, the system fault handling measures can be verified and evaluated under the premise of ensuring the safety of the test equipment, and the design cycle of the power system can be greatly shortened and the development cost can be reduced. 2.3

Full Physical Test Technique

In the field of full physical simulation and combined test certificate, the construction and related test verification of communication satellite primary power supply test certificate system are carried out [8]. The whole physical simulation and test system is shown in Fig. 3 [9]. The system is based on the power controller and battery group. The peripheral equipments include the analog solar array output equipment, the primary bus test equipment and the development platform of energy management strategy. The main functions of the system are as follows [10]:

Solar array simulator

Battary group

BCRB

Mass power

Power for charge

Load for discharge

Computer

Power analyzer

Power control unit Twinkle load

Prima ry bus Bus load

Interface unit for battary group

Platform of energy management strategy

Impedance test system

Fig. 3. All-physical simulation and test system

• Test the correctness and matching of the interface between the devices of the power supply system, and test the power supply path; • A primary bus output characteristic test, including bus impedance, bus ripple and bus transient response test, etc. • Energy management strategy test, including battery charge and discharge management logic verification, various operating conditions switching threshold test and so on.

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3 Design Verification Results The simulation results of output power of solar cell array are as shown in Fig. 4. The bottom of the diagram shows the occlusion of the two solar arrays in the north and south, the gray is occluded, the white is unoccluded, and the occlusion rate curve of the upper right corner of the diagram can be seen. In the simulation period, the maximum power loss rate of solar cell array is about 25%. The middle diagram of the right side of the diagram shows the output curve of solar cell array current in the same simulation time period, which shows the dynamic characteristics of the output current of solar cell array in the case of occlusion. The bottom right corner of the diagram shows the typical values of the solar cell array voltage output histogram in this time period.

Fig. 4. Simulation results of solar cell array output power

The following figure shows the simulation results of impedance and dynamic characteristics of power supply system. Figure 5 shows the output impedance and load input impedance diagram simulated in S3R domain. The maximum output impedance of the power supply system is 25.4 m X, which does not intersect with the input impedance and the system is stable. Figure 6 shows the BDR output impedance and the maximum load input impedance diagram. The maximum output impedance of the power supply system is 38 m X, which is not intersected with the input impedance and the system is stable. Figure 7 shows the characteristics of output impedance and load input impedance in BCR domain. The maximum output impedance of power supply system is 26 m X, which is not intersected with load input impedance, and the system is stable.

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Fig. 5. Impedance simulation results in S3R domain

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Fig. 6. Impedance simulation results in BDR domain

Figure 8 is a dynamic characteristic diagram of power supply system. When the steady power of the load is 6915 W, the step power of the load is 2600 W, corresponding to the primary bus voltage step waveform, it can be seen that at 2600 W load step time, the primary bus overshoot is within 800 mV. The power system meets the load requirements.

Fig. 7. Impedance simulation results in BCR domain

Fig. 8. Dynamic characteristic diagram of primary bus

Table 1 is the test list of power supply system test system, including power system interface inspection [11], power system health inspection, power system function test and power system special test. The test of power system interface is to ensure the matching and safety of single-machine interface between power systems. Power system health check is to do low-power level of the power system functional inspection. The function test of power supply system is to carry out 1:1 function verification of power supply system according to orbit condition. The special test of power supply system is to carry out FDIR and reliability growth test of power system [12].

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NO. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Project category Power system interface check Power system health check

Power system function test

Special test of power system

16 17 18

Project name PCU interface check Battery group interface check Others interface checks PCU health check Battery group health check Others health check Busbar overvoltage protection function test S3R shunt regulation function test S3R shunt regulation function test BCR charging function test BDR discharge function test BDR discharge redundancy function test Battery charge function test Battery discharge function test Incoming and outgoing shadow switching function test Load step dynamic response test FDIR test of power supply system Power system reliability growth test

4 Conclusion In this paper, the design and verification system of super power supply system is designed, the full digital simulation verification platform and the all physical test certificate platform are built, and the practical application is carried out. The application results show that, the full digital simulation platform effectively verifies the system characteristics of the power supply system and ensures that the design of the power supply system meets the requirements of the satellite system. The whole physical test platform is used to test and verify the satellite power system products, and the reliability of the system is tested to ensure that the power supply system does not have any doubt. After that, the semi-physical simulation of the super-power supply system will be carried out, and the influence of the power supply system on the power supply system will be simulated under the condition of part of the device failure. To ensure the satellite power supply system single failure service continuity, double failure to ensure satellite safety.

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References 1. Li, G.-x.: Introduce to Spacecraft Power System Technology. China Aerospace Publishing House, Beijing (2008) 2. Zhang, Y., Jiang, D.-s., Liu, Z.: Research on Modeling and Simulation of Satellite Electrical Power System 3. Ma, X., Zhang, D., Xu, D.: S3R&S4R control strategy analysis and simulation. Manag. Control Technol. 26(6), 45–50 (2007) 4. Miao, D., Zhang, D.: Simulation of S4MPR application for spacecraft power supply system. 21(19), 6298–6302 (2009) 5. Zhao, G.-w., Han, F.-t.: MPPT technology for high orbit and high power satellite power system, design and research of power technology, 1002-087X 08-1675-04 (2016) 6. Ma, P.-b., Wu, J.-h., Ji, J., Xu, X.-l.: Software environment and application of dSPACE realtime simulation platform. J. Syst. Simul. 1004-731X 04-0667-04 (2004) 7. Zhnag, X., Guo, Y., Tong, L., Chen, H.: dSPACE real-time simulation and controller parameters tuning for three-phase PWM rectifier. Trans. Chin. Electrotech. Soc. 28, 219–224 +232 (2013) 8. Liu, L.: Faults simulation and diagnosis of satellite power system. Dissertation for the Master Degree in Engineering, Harbin Institute of Technology (2015) 9. Xiao, L.: Faults simulation and diagnosis of satellite primary electrical power system. Dissertation for the Master Degree in Engineering, Harbin Institute of Technology (2016) 10. Wang, H.: Spacecraft electrical test technology. Beijing Institute of Technology Press (2018) 11. Jiao, R., Chen, Y.: Design and realization of automatic system in satellite low frequency interface test. In: Proceedings of Spacecraft Extend Life and Reliability Study, pp. 397–406. China Aerospace Science and Technology Corporation, Beijing (2011) 12. Song, S., Pan, J.: Key ability of EPS in integration test. In: Proceedings of Technology Committee of Beijing Institute of Spacecraft System Engineering, pp. 23–26. Beijing Institute of Spacecraft System Engineering, Beijing (2009)

Research on Application Method of 3D Digital Simulation Technology in Spacecraft Assembly Boyin Zhang(&), Qiang Wang, Xingyan Wang, and Yin Liang Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China [email protected]

Abstract. The process method shall be considered completely and implemented systematically in assembly of spacecraft products due to complicated structure and high integration and coupling of mechanical, electric and heat characteristics. An application method based on 3D digital simulation technology in assembly of the spacecraft is proposed in this paper and some tests are implemented for verification. This method proposes a brand-new solution means for installation of complicated products in model development. This method features extensive promotion and application significance and can effectively improve assembly efficiency and quality of spacecrafts. Keywords: 3D digitalization


Spacecraft
Assembly application

1 Introduction Spacecraft products feature complicated structures and integrate mechanical, electric and heat characteristics on the star integration phase. To satisfy layout requirements of different payloads, generally a star structure is composed of multiple independent boxtype typical structures and different devices are deployed inside the star body. Different devices are connected as systems via complicated cable networks. The devices and space cabins are coated by different heat control materials on their surface to satisfy the operating temperature range of instrument. The process methods shall be completely considered and systematically implemented in case of assembly of spacecraft products due to high integration and coupling of mechanical, electric and heat characteristics. Design and virtual simulation of the digital assembly process are applied in fields such as 3D interactive process planning, virtual simulation of assembly sequence and assembly path and visual guidance document output. It plays an important role in optimal solution to manufacturing of complicated products such as spacecraft [1]. Design of the assembly process is throughout whole spacecraft assembly, which is main basis to guide tooling design and product assembly and is the key part to ensure assembly efficiency and quality of spacecraft products. Studying process design of digital assembly and assembly process simulation is significant for solving of defects in traditional assembly and improvement of assembly efficiency [2, 3]. With complicated load assembly of a spacecraft model as the object, this paper studies the process model and virtual simulation flow for assembly and gets assembly process design and simulation scheme of spacecraft under 3D virtual environment as © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 400–408, 2019. https://doi.org/10.1007/978-981-13-7123-3_47

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the foundation for full 3D digital design and manufacturing of spacecraft products, which provides references for assembly of spacecraft products and can promote implement of related industry work.

2 Requirement Analysis for Digital Assembly of Spacecraft 2.1

Overview of Spacecraft Assembly

Spacecraft assembly indicates to combine massive parts and components into complete products according to the assembly process plan and technical requirements. The whole assembly process includes process design, process planning, component assembly and assembly and is the most significant part in spacecraft production. The spacecraft assembly process design is the foundational work in spacecraft development and is the significant means to design products, ensure product quality, reduce consumption and improve productivity [4, 5]. Assembly and test of a spacecraft is the final phase in spacecraft development. Its assembly and test technology will directly affect general performance, function and reliability of the spacecraft and play a critical role in normal operation of a spacecraft in the orbit. Based on the statistics of the aerospace sector of Russia, the assembly workload is about 35% of total machining workload of a spacecraft. Assembly and test cycle is about 30% of total development cycle. 2.2

Main Features of Spacecraft Assembly

Assembly is implemented strictly according to the requirements in design documents, drawings and process documents and includes the following three features: 1. Extensive and diversified specialties involved and high comprehensiveness. Spacecraft assembly and test covers assembly of bench workers, electric installation workers and metalers, heat control manufacturing and assembly, pipeline manufacturing and installation, instrument, device installation precision test, airtight system leakage detection, quality characteristics test, electric performance test, dynamic environmental test and heat vacuum test, so it features diversified, crossed and frequent work procedures and long work cycle and depends on comprehensive application and coordination of diversified specialties and technologies. Some new processes and new technologies are continuously developed to adapt requirements of assembly and test technologies of different spacecraft models. 2. Multi-model and pilot production. With diversification and quick development of spacecraft and their application and long period and high investment of spacecraft development, a spacecraft features diversified models and pilot production, even single piece production. It is difficult to mechanically produce spacecrafts due to these production features, narrow space inside spacecraft, high-density installation of instrument, devices, cables and pipes, and bad openness, so massive work depends on manual operation. The operation skills, work responsibilities and work experiences of workers play a key role in quality and security of spacecraft assembly and test.

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3. High assembly workload and long period of spacecraft. Generally assembly workload of a mechanical product is about 20% of total assembly workload. The assembly process of a space is about 40%–50% product manufacturing cost and work hours and will consume massive manufacturing time and resources. 2.3

Design and Analysis of Existing Spacecraft Assembly Process

Now traditional process design system is still used for 2D assembly process design in the domestic assembly process design of a spacecraft. The following issues exist: 1. The assembly process planning method cannot guarantee uniform manufacturing information. Now generally the 3D product design model provided by a design department cannot cover complete assembly process information and cannot be throughout the whole manufacturing process in design, production and manufacturing of spacecraft. The 2D process guidance document is prepared as the production basis according to digital product design model in assembly. Such serial mode cannot effectively associate manufacturing process information with design information and reduce transmission and distribution efficiency of manufacturing information data. Ambiguity of design information and process information definition may lead to inconsistent data. Possible issues in production preparation cannot be discovered in advance and effectively solved, so it leads to an error in field production. 2. Assembly process design results are lack of effective validation and optimization method. Now the assembly process design mainly includes assembly process scheduling, assembly step management result distribution and association among assembly equipment, assembly environment and assembly process is not wholly considered. Before the assembly process document is distributed, assembly process cannot be simulated and validated effectively. Feasibility of process design, operability of assembly tooling design and openness of assembly space cannot be discovered and solved in time on the design phase, so it increases error rate at the production site. Based on above analysis, the digital product model shall be fully applied, the visual assembly process design mode is implemented, assembly process simulation technology is introduced, and complete assembly process data information management and transfer process are constructed to effectively improve assembly quality and increase assembly efficiency. 2.4

Requirements of Digital Assembly

Digital assembly process design indicates a system method in which digital models of the 3D products are introduced to digital process design environment via the data management system interface and the assembly process is planned via human-machine interaction to form guidance assembly process documents. Contents and features of the assembly process are analyzed under the digital environment and the technical system of visual assembly process design for assembly process is built by combining the existing assembly process design mode based on the digital product model [6].

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1. Assembly process design technologies. To completely transfer assembly process design information, with assembly process as the main body and with digital models of 3D products as the foundation under the digital assembly process design mode, assembly information such as product information, process information and tooling information in assembly are effectively associated to form single data set in product production and manufacturing and ensure effective transmission and distribution of product information based on the enterprise production capabilities, production step scheduling and product manufacturing resource configuration. 2. Assembly process simulation technology. The assembly process is simulated prior to assembly according to the assembly process plan. Assembly sequence and assembly path of parts are simulated in spacecraft assembly to check and verify feasibility of assembly process implementation plan, discover and feed back problems for correction. Simulation covers verification of production environment, manufacturing resources and process equipment and can optimize production line layout and assembly tooling design. 3. Output of assembly process design results. The 3D process documents generated in 3D visual assembly process design mode include the video, cartoons and 3D images to guide production. The network data transmission mode can facilitate timely feedback and correction of problems in the process design.

3 Digital Assembly Process Design Process of Spacecraft Assembly process design can prepare the process for spacecraft product assembly. Since the product design information is received, the assembly process plan is prepared according to the structural features of products, the assembly process model is created by combining the manufacturing resource information, assembly process design process of spacecraft is digitalized, the assembly task is scheduled and assembly process is simulated under the 3D environment, and the feasible assembly process execution documents can be obtained [7, 8]. With complicated load assembly of a spacecraft model as one example, digital applications of its assembly process are studied by using the design method for digital assembly process in this paper. 3.1

Analysis of Assembly Working Conditions and Division of Assembly Units

A load model covers the camera body, lens hood, star sensor, integrated structure, rear hood of camera, 4 camera heat pipe and corresponding fixing hoop. To satisfy the requirements of the process and assembly, the integrated camera structure combination is divided into several assembly units in installation. For the list of assembly units, refer to the Table 1. 1. After 3 star sensors are combined with the bracket, star-sensitive radiation plate and star-sensitive heat pipe under the star, they will be installed on the camera body;

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Product name Quantity Mass (kg) Remark Camera body 1 390 Camera rear cover 1 / Lens hood 1 75 Heat pipe 4 0.65 (single piece) Hoop 11 / Star sensor combination 3 7 (single piece)

2. The camera body and the lens hood are installed on the spacecraft floor via the integrated structure; 3. The camera radiation plate bracket is installed on the floor of the spacecraft; 4. The south and north radiation plate and 4 heat pipes (GFY07-4) of the camera have no direct installation interfaces with the whole star. The radiation plate is installed on the radiation plate bracket. 5. Two ends of the heat pipes are connected to the radiation plate via the hoop (4 hoops at each end). The middle part of the heat pipe is connected to the heat collector plate under the camera body via 3 hoops. After all heat pipes are installed in place, the gap in the contact area among heat pipes, radiation plate and heat collector plate is less than 0.2 mm. 3.2

Establishment of Assembly Environment

4 operators (A, B, C and D) are arranged according to the process features of heat pipes of camera/integrated structure combination under the start under the Delmia environment. The body model is 170 cm high. The operator A and B disassemble and assemble the bottom cover inside the unwheeling and install the hoop, shown as the Fig. 1. The operator C and D install the hoops outside the unwheeling and assist conveying of the bottom cover, shown as the Fig. 2.

Fig. 1. Diagram of operator A and B

Fig. 2. Diagram of operator C and D

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Assembly Process Analysis

Verify the installation flow of heat pipes of the camera/integrated structure combination under the star and analyze visibility and reachability of difficult parts under Delmia environment and record visibility and reachability analysis results of key parts. The following risky points shall be verified in simulation: (a) Turning space inside the unwheeling, implementability, hazardous points and risky points with possible interference with instrument during removal of camera bottom cover; (b) Implementability for bottom cover transfer under the unwheeling and possible hazardous points and risky points for collision with instrument; (c) Implementability for one operator to support heat pipes and hoops with a tray; (d) Operability for realizing coplane of “U-shaped” part of the heat pipe with the “heat pipe regulation and installation plate” tooling; (e) Implementability for two operators to operate at exchanged position inside the unwheeling during heat pipe regulation. 3.4

Outputs of Simulation Results

After assembly process simulation is completed, the reasonable assembly process plan is obtained. The process design results and analysis results are outputted via the data output options of the DELMIA platform to form the electronic document of assembly process guidance. The assembly simulation process is recorded via the screen recording to get the assembly cartoon and video for browsing and facilitate technology training and field assembly guidance. Optimization of Assembly Process 4 heat pipes are installed at the bottom of the camera and are drilled out via the notch on the wall of the integrated structure. After optimization based on simulation process, the process steps for heat pipe installation are described as follows: (a) Hoist the camera/integrated structure combination away from the unwheeling installation plane; Place 4 heat pipe on the surface of the unwheeling connection frame and fix it with the hoop temporarily; (b) Make the camera/integrated structure combination fall onto the unwheeling; (c) Most outside 2 hoops fixing the heat pipes of the heat collector plate at the bottom of the camera are placed on the tray (tooling) and then the operator supports and makes it contact the heat pipe; (d) Remove the hoops fixing the heat pipe on the connection face of the unwheeling; (e) Support the tray and hoop to the corresponding position of the heat collector plate at the bottom of the camera together with 4 heat pipe, shown as the Fig. 3; (f) An operator keeps supporting posture and another operator slightly relaxes the fastener at two ends of the hoop;

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Fig. 3. An operator supports tray and hoop together with 4 heat pipes

(g) The operator outside the unwheeling keeps “U-shaped” coplane by using the terminal tube hoop and fastener via the regulation plate on the side of the terminal tube (tooling). The operator inside the unwheeling regulates looseness of the fastener at the camera heat collector plate to assist the operator outside the unwheeling to regulate the “U-shaped” coplane of the heat pipe, shown as the Fig. 4.

Fig. 4. Fix heat pipe on the regulation plate on the side

(h) The operator inside the unwheeling completes all hoops and fasteners at the heat collector plate of the camera, fasten them by the regulated torque, and disassemble the regulation plate on the side; (i) Detect the gap in the contact part between the heat collector plate and the heat plate by using a feeler. The gap shall be less than 0.2 mm; (j) Disassemble 4 heat pipes and scrape and coat GD414 silicon rubber in the contact area of the heat pipe of the heat collector plate of the camera. (k) Install 4 heat pipes again according to the step (4)–(10); Wipe the overflowing GD414 silicon rubber by using the dust-free cloth dipped with the absolute ethyl alcohol; (l) Install the camera’s bottom cover again; Risk Analysis in Assembly It is slightly difficult for an operator to support about 3 g tray and heat pipe in the step (6) via Delmia environment analysis [9], but the maximal supporting weight of the operator is 8.942 kg at this time, which can be withstood. When the operator installs the fixing hoop of the heat pipe at the side regulation plate, the minimal distance from the integrated structure is 15.523 mm and the minimal distance from the heat pipe is 6.508 mm. Collision probability is very high. A safety officer shall be assigned for careful operation (Fig. 5).

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Fig. 5. Collision risk in operator

Human-Machine Effect Analysis in Assembly The operator color represents operation comfort in Delmia software, green indicates comfortable, yellow indicates better, orange indicates good and red indicates discomfortable. When the operator installs the fixing hoop of the heat pipe at the side regulation plate in the step (8), the waist, arm and hand muscles of the operator feel fatigue. If the operator keeps operation for a long period, it will feel discomfortable, so it shall break and regulate operation. For effect analysis diagram, refer to the Fig. 6.

Fig. 6. Operator comfort

4 Conclusions This paper describes application process of the digital 3D simulation technology in spacecraft assembly. The digital assembly simulation of a spacecraft can plan the assembly process under the spacecraft 3D reality by fully using 3D digital sample machine of the spacecraft and assembly resources, so the errors and defects in design, tooling design and process design can be discovered before the drawing is delivered to the plant or earlier in order to reduce redoing and decommissioning of parts, cost and shorten the development period, provide the visual field assembly guidance, and improve assembly quality and efficiency.

References 1. Jayaram, S., Jayaram, U., Kim, Y.J., et al.: Industry case studies in the use of immersive virtual assembly. Virtual Reality 11(4), 217–228 (2007) 2. Christiand, J.Y.: Assembly simulations in virtual environments with optimized haptic path and sequence. Original Res. Artic. Robot. Comput. Integr. Manuf. 27(2), 306–317 (2011) 3. Yin, Z.P., Ding, H., Xiong, Y.L.: A virtual prototyping approach to generation and evaluation of mechanical assembly sequences. Proc. Instn. Mech. Engrs. Part B(S0954-4054), (04): 87– 102 (2004)

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4. Selvaraj, P., Radhakrishnan, E., Adithan, M.: An integrated approach to design for manufacturing and assembly based on reduction of product development time and cost. Int. J. Adv. Manuf. Technol. 42, 13–29 (2009) 5. Curran, R., Gomis, G., Edgar, T., et al.: Integrated digital design for manufacture for reduced life cycle cost. Int. J. Prod. Econ. 109, 27–40 (2007) 6. Ning, R., Zheng, Y.: Research progress and development trend analysis of virtual assembly technology. Chin. Mech. Eng. (15), 1004–1398 (2015) 7. Zhang, L., Zhu, X., Liu, Z., et al.: Research on planning technologies of spacecraft product assembly process. Chin. Mech. Eng. (02), 265–268 (2014) 8. Han, W., Ma, P.: Research on design and simulation application of digital assembly process. Chin. Technol. Inf. (05), 127–129 (2015) 9. Bullinger, H.J., Richter, M., Seidel, K.A.: Virtual assembly planning. Hum. Factors Ergon. Manufact. 10(3), 331–341 (2000)

Big Data Workshop

Network Traffic Prediction Based on LSTM Networks with Genetic Algorithm Juan Chen1, Huanlai Xing1(&), Hai Yang2, and Lexi Xu3 1

2

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Southwest Jiaotong University, Chengdu 611756, China [email protected] 10th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China China Unicom Network Technology Research Institute, Beijing 100048, China

Abstract. Network traffic prediction based on massive data is a precondition of realizing congestion control and intelligent management. As network traffic time series data are time-varying and nonlinear, it is difficult for traditional time series prediction methods to build appropriate prediction models, which unfortunately leads to low prediction accuracy. Long short-term memory recurrent neural networks (LSTMs) have thus become an effective alternative for network traffic prediction, where parameter setting influences significantly on performance of a neural network. In this paper, a LSTMs method based on genetic algorithm (GA), GA-LSTMs, is proposed to predict network traffic. Firstly, LSTMs is used for extracting temporal traffic features. Secondly, GA is designed to identify suitable hyper-parameters for the LSTMs network. In the end, a GA-LSTMs network traffic prediction model is established. Experimental results show that compared with auto regressive integrated moving average (ARIMA) and pure LSTMs, the proposed GA-LSTMs achieves higher prediction accuracy with smaller prediction error and is able to describe the traffic features of complex changes. Keywords: Genetic algorithm
Long short-term memory recurrent neural networks
Network traffic prediction

1 Introduction Cloud computing has become a popular topic in the field of information computing resources. There are an estimated 50 billion connected devices worldwide by 2020 [1]. Interconnections among these devices would cause generation of massive data. These data must be stored and processed so that their profits can be explored and utilized. Many large data centers are built to provide different type of services. Network traffic prediction is a fundamental service that is needed to enable any traffic management operation, such as differentiating traffic pricing and treatment (e.g., policing and shaping) and security (e.g., firewall, filter and anomaly detection) [2]. Numerous models have been proposed to exploit the temporal and self-similar property of network traffic classification and prediction. Existing models, such as auto regressive integrated moving average (ARIMA) [3] and support vector machines [4], © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 411–419, 2019. https://doi.org/10.1007/978-981-13-7123-3_48

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are mainly linear models that cannot well describe high dimensional data and capture complex nonlinear relationships [5]. To cope with nonlinear modeling, neural networkbased regression algorithms are widely used since neural networks are capable of modeling highly nonlinear and complex structures [6]. However, it is reported that they are prone to over-fitting problems and cannot always achieve adequate performance in certain applications [7]. Recurrent neural network (RNN) is also advocated to predict or class the time series data. In this paper, we want to explore the potential of deep neural networks as feature extractors and learn long term temporal dependencies by LSTMs, a type of RNN [8]. Reference [9] applied the bilinear recurrent neural network (BLRNN) to the real world Ethernet network traffic data set and obtained decent results. However, the parameter setting was based on local information, which was not globally optimized. For random network traffic, structural parameters in LSTM model should be carefully set to achieve high prediction accuracy. Genetic algorithm (GA) is a class of evolutionary algorithms (EAs). GA is widely used for search and optimization problems, where structures of chromosome and evolutionary operations such as selection, crossover and mutation, are mimicked. In recent years, significant attention has been attracting by integrating GA with deep learning. More specifically, it has been utilized for more efficient selection of hyperparameters, e.g. kernel size, and network structures [10, 11]. In this paper, we apply GA-LSTMs to network traffic prediction, where GA is developed to optimize the hyper-parameters in the LSTM model. The GA-LSTMs model is mainly composed of two parts. One is the LSTMs model itself that serves as a mapping rule between input and output variables and also the core in fitness function. The other part is GA that optimizes the hyper-parameters in the LSTMs model. Experimental results demonstrate that GA-LSTMs outperforms ARIMA and LSTMs in terms of the prediction accuracy. The rest of this paper is organized as follows. Section 2 introduces the LSTMs model and briefly describes GA. Section 3 presents the GA-LSTMs method and the implementation process. Section 4 discusses the experiment design and results. Finally, the conclusion is given in Sect. 5

2 The Model 2.1

LSTM

In this section, we introduce deep learning-based structures used for network traffic forecasting. LSTMs are good at memorizing information for a long time. As the accuracy of the model may be affected by more previous information, LSTMs become an appropriate choice for employ. LSTMs have been demonstrated to be effective in language translation [12] and speech recognition [13]. Figure 1 shows a basic LSTMs architecture. The network model is composed of three stages: input, feature extraction (based on LSTMs), and regression stage (flatten layers). We use the window method in the input stage. The input window [Xt–n, Xt–n+1, …, Xt–2, Xt–1, Xt] is consecutive samples for regression, where each of them is

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normalized to fit in [0, 1]. The historical samples are forwarded to the LSTMs for temporal traffic feature extraction and then to flatten layers for single upcoming network traffic prediction.

Fig. 1. The GA-LSTMs architecture

2.2

GA

GA is a meta-heuristic that mimics the biological evolution according to the rule of nature, i.e. the survival of the fittest. We encode all hyper-parameters in the LSTMs model as a chromosome (also called individual/solution), where each hyper-parameter is a gene with predefined value range. A population consists of a number of chromosomes. The initial population is randomly generated. Each individual is evaluated by some performance metrics of the LSTMs model. Fitter individuals are more likely to survive than others in the population in the selection process. Then, crossover and mutation are performed to the selected individuals. Selection, crossover and mutation are repeated to evolve the population for promising values for the hyper-parameters in the LSTMs model until that a predefined number of generations are finished.

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3 Methodology In order to predict the future service traffic accurately, a LSTMs model based on GA is proposed, where the flowchart is illustrated in Fig. 2. LSTMs is for network traffic prediction while GA is utilized to tune the hyper-parameters in the LSTMs network. Besides, the LSTMs network is integrated into the fitness evaluation in GA.

Fig. 2. The flowchart of GA-LSTMs

The procedure of GA-LSTMs in Fig. 2 is expressed as follows: 1. Dataset Preprocessing. We divide the dataset into two subsets, one for training and the other for testing. 2. Population initialization. Set parameters of GA, including the maximum generation of evolution, the population size N, the crossover probability Pc, the mutation probability Pm, the number of genes in each individual Ngene, the value range of each gene. 3. Fitness evaluation. The LSTMs model associated with each individual is tested and the mean absolute error (or root mean square error) of the test dataset is assigned to this individual as its fitness value. 4. Execution of the evolutionary operations. Selection, mutation and crossover are performed to generate a new population. In addition, single-point crossover is used.

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5. Stopping condition judgement. If it is the predefined generation number, GA stops and output the best individual as the hyper-parameter setting for the LSTMs model and the associated test dataset is used for training it; otherwise, go to Step 3.

4 Experiment Design and Performance The GA-LSTMs prediction model is programmed by Python based on the TersorFlow deep learning framework and it runs on a Windows 10 workstation with Intel(R) Core (TM) i7-6700 3.4 GHz, 16G RAM, and GPU NVIDIA GeForce GTX 1070. 4.1

Dataset Description

For data analysis with deep learning, it is essential yet often challenging to obtain a high-quality dataset. The experiment dataset in this work is collected from real world network traffic traces of Yahoo! data centers locating at Dallas, USA. It approximately contains a one-day data (1383 data points) from 8:55 am, April 28th, 2008, to 7:57 am, April 29th, 2008. A data point represents a summation of the traffic flow values obtained in one minute. In our experiment, data is divided into two parts, i.e. dataset for training (80%) and that for testing (20%). 4.2

Hyper-parameters Settings

The value ranges of the hyper-parameters used in the LSTMs model is described below. The number of layers is randomly selected from 1 to 5. The number of LSTMs cells is each layer is randomly selected from [32, 64, 128, 256, 512, 1024]. The number of batch size is randomly selected from 1 to 64. The length of inputs is from 1 to 64. The dropout to avoid over-fitting and the learning rate are set to 0.5 and 0.0006, respectively. The four hyper-parameters above are encoded as an individual in GA, so the number of genes Ngene is set to 4 for each individual. In GA, the population size and the predefined number of generations are set to 500 and 35, respectively. The crossover and mutation probabilities, Pc and Pm, are set to 0.6 and 0.1, respectively. GA is utilized to find the optimal combination for hyper-parameters. After training the model for 100 times, we obtain the best combination of hyper-parameters. Table 1 shows the best hyper-parameter setting for GA-LSTMs and the default hyperparameters setting for LSTMs and ARIMA models. The batch sizes in GA-LSTMs are smaller than the default ones. As for the number of cells in each layer, GA-LSTMs is larger than the default ones. 4.3

Evaluation Criteria

The mean absolute error (MAE) and the root mean square error (RMSE) are used to evaluate the prediction accuracy. Equations (1) and (2) define the above errors.

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J. Chen et al. Table 1. Hyper-parameter setting in different models. Hyper-parameters Best (GA-LSTM) Default (LSTM) Default (ARIMA) Layers 1 2 8 Cells in each layer 32 10 16 Batch size 2 60 60 Length of inputs 24 20 50

1 Xn jyðtÞ  xðtÞj t¼1 n rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 Xn RMSE ¼ ð yð t Þ  xð t Þ Þ 2 t¼1 n MAE ¼

ð1Þ ð2Þ

Where y(t) and x(t) are the predicted and actual output values at time t, respectively, and n represents the number of predictions. 4.4

Results

The actual data points are verified by various models, including ARIMA, LSTMs and GA-LSTMs. The network traffic prediction results are shown in Fig. 3. In Fig. 3, we sample these data points every five minutes because the amount of data points are too many to be shown. Figure 4 is an extracted segment from Fig. 3.

Fig. 3. Comparison among different models

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Fig. 4. The partial comparison among different models

In Figs. 3 and 4, it is easily seen that GA-LSTMs has a higher prediction accuracy than models based on LSTMs and ARIMA. Besides, data points in the next 240 min are also forecasted in Fig. 5. The forecasting data points are almost the same with the actual traffic data, reflecting a promising performance of GA-LSTMs. In fact, compared with neural network, the GA-LSTMs model not only reduces the computational complexity, but also improves the generalization ability for the model.

Fig. 5. The network traffic prediction results obtained by the GA-LSTMs model

Table 2 shows the MAE and RMSE results of ARIMA, LSTMs and GA-LSTMs. Apparently, GA-LSTMs performs significantly better than the other two methods, indicating the superiority of GA-LSTMs for network traffic prediction.

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J. Chen et al. Table 2. Prediction errors of different models Model MAE RMSE ARIMA 1188005 1534919 LSTM 815774 1336008 GA-LSTM 117965 179655

5 Conclusion A long short-term memory method based on genetic algorithm is presented for network traffic prediction in this paper. The network traffic is forecasted by training the GALSTMs model. The original big data can be analyzed effectively by GA-LSTMs. Experimental results show that GA-LSTMs achieves much better prediction results than ARIMA and LSTM. Our future work will consider user perception aware applications and resource allocation schemes in cellular networks [14, 15].

References 1. Kumar, J., Goomer, R., Singh, A.K.: Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters. Procedia Comput. Sci. 125, 676–682 (2018) 2. Khater, N.A., Overill, R.E.: Network traffic classification techniques and challenges. In: International Conference on Digital Information Management, pp. 43–48 (2015) 3. Moayedi, H.Z., Masnadi-Shirazi, M.A.: Arima model for network traffic prediction and anomaly detection. In: International Symposium on Information Technology, pp. 1–6 (2008) 4. Nikravesh, A.Y., Ajila, S.A., Lung, C., Ding, W.: Mobile network traffic prediction using MLP, MLPWD, and SVM. In: International Congress on Big Data, pp. 402–409 (2016) 5. Leland, W.E., Taqqu, M.S., Willinger, W., Wilson, D.V.: On the self-similar nature of Ethernet traffic (extended version). IEEE ACM Trans. Netw. 2(1), 1–15 (1994) 6. Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991) 7. Vanli, N.D., Sayin, M.O., Delibalta, I., Kozat, S.S.: Sequential nonlinear learning for distributed multiagent systems via extreme learning machines. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 546–558 (2016) 8. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997) 9. Park, D.: Structure optimization of bilinear recurrent neural networks and its application to ethernet network traffic prediction. Inf. Sci. 237(13), 18–28 (2013) 10. Hossain, D., Capi, G.: Genetic algorithm based deep learning parameters tuning for robot object recognition and grasping. World Acad. Sci. Eng. Technol. Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng. 11(3), 629–633 (2017) 11. David, O.E., Greental, I.: Genetic algorithms for evolving deep neural networks. In: Genetic and Evolutionary Computation Conference, pp. 1451–1452 (2014) 12. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Neural Information Processing Systems, pp. 3104–3112 (2014)

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13. Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: International Conference on Machine Learning, pp. 1764–1772 (2014) 14. Xu, L., Luan, Y., Cheng, X., Xing, H., Liu, Y., Jiang, X., Chen, W., Chao, K.: Selfoptimised joint traffic offloading in heterogeneous cellular networks. In: 16th IEEE International Symposium on Communications and Information Technologies, pp. 263–267. IEEE Press, Qingdao (2016) 15. Xu, L., Cheng, X., et al.: Mobility load balancing aware radio resource allocation scheme for LTE-advanced cellular networks. In: 16th IEEE International Conference on Communication Technology, pp. 806–812. IEEE Press, Hangzhou (2015)

On Multicast-Oriented Virtual Network Function Placement: A Modified Genetic Algorithm Xinhan Wang1, Huanlai Xing1(&), and Hai Yang2 1

2

Southwest Jiaotong University, Chengdu 611756, People’s Republic of China [email protected] 10th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, People’s Republic of China

Abstract. Network function virtualization (NFV) is an emerging network paradigm that will ease the network reconfiguration and evolution for Network Service Providers (NSPs). In NFV, the virtual network function placement (VNFP) problem has become a hot topic. However, little research attention has been paid to multicast-oriented VNFP (MVNFP) problem. This paper studies the MVNFP problem and presents a two-step approach to address it. The first step constructs a multicast tree for a given multicast service request and the second one places VNFs onto the tree. In the first step, Dijkstra’s algorithm is adopted while in the second step, a modified genetic algorithm (mGA) with problem-specific chromosome encoding, crossover and mutation is proposed. Simulation results show that mGA performs better than a number of evolutionary algorithms with respect to the solution quality and convergence. Keywords: Genetic algorithm
Multicast Virtual network function placement


Network function virtualization


1 Introduction Network function virtualization (NFV) is an emerging network architecture that was introduced by the European Telecommunications Standards Institute (ETSI) in 2012 [1]. NFV decouples network functions (such as NAT, DNS, IDS, Proxy, etc.) from traditional network hardware by introducing a virtual resource layer that enables the deployment of functional network elements via virtual machines. Deploying virtual network functions (VNFs) on compute nodes not only improves the network flexibility but also greatly reduces the capital and operational expenses (CAPEX/OPEX). In NFV, a service function chain (SFC) is a set of ordered VNFs. A network service is provided by deploying a SFC onto the corresponding substrate network. In order to receive a network service, a data flow needs to be processed by all VNFs of a SFC in right order before it reaches the destination. VNF placement (also known as SFC mapping) has been regarded as one of the most challenging issues when considering the practical deployment of NFV. Unfortunately, this problem is NP-hard [2].

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Currently, the main research stream on VNF placement focuses on unicast communications. For example, Refs. [3, 4] studied the establishment and placement of VNFs. On the other hand, with the emergence of more and more multimedia applications, e.g. remote education and IPTV, multicast has become one of the key communications technologies. In NFV, network service should also be provided to multicast sessions. The multicast-oriented VNF placement (MVNFP) problem, however, has not attracted enough research attention. In [5], NFV-enabled multicast routing based on SDN was studied, where the branch and bound method was adopted. Then, Xu et al. [6] proposed the concept of pseudo-multicast tree. The same network functionality is only deployed once, which means any two VNFs in a multicast tree stand for two different network functions. Nevertheless, some data flows need to travel longer distance before reaching their destinations. Longer distance is more likely to lead to larger delay, which to a certain extent deteriorates the performance of NFV. Genetic algorithm (GA) has been successfully applied to solve the unicast-oriented VNFP problem, where resource allocation in cloud datacenter is considered [4]. Compared with traditional mathematical methods, GA is self-organizing, adaptive, selflearning, etc., especially suitable for tackling the NP-hard problems. The MVNFP problem has been proven to be NP-hard. This paper formulates a MVNFP problem with the average transmission delay in a multicast tree minimized and proposes a two-step approach to handle it. The first step adopts the Dijkstra’s algorithm to build a multicast tree once there is a multicast service request. The second one deploys the VNFs for the tree, where a modified GA (mGA) with problem-specific chromosome encoding, crossover and mutation is devised. Compared with population based incremental learning, ant colony optimization and particle swarm optimization, the proposed mGA achieves better solutions and convergence.

2 Multicast Virtual Network Function Placement (MVNFP) Problem A communications network is represented by a graph G ¼ ðV; EÞ, where V and E are
node and link sets, respectively. Let s and D ¼ d1 ; d2 ; . . .; djDj be the source node and the nodes, respectively, where jDj is the cardinality of D. Let
set of destination  F ¼ f1 ; f2 ; . . .; fjF j represent a set of ordered VNFs (i.e. a SFC request), where fi denotes the i-th VNF, i ¼ 1; 2; . . .; jF j. Then, we denote a multicast service request by MR ¼ fs; D; F g. Assume that all nodes in a network are NFV-enabled, which means any node is able to host one or more VNFs subject to its physical resource availability. In order to maximize the effectiveness of each VNF, we do not allow the deployment of two or more VNFs with the same functionality on a single node, which means any two VNFs on a node are different.

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Multicast Tree Construction

In a multicast tree, nodes hosting VNF(s) incur processing delays since data flows passing by them are processed by one or more VNFs on them. Nodes that do not host any VNF are responsible for forwarding data flows only. The processing delays incurred on them are trivial, compared with those on nodes with VNF(s) hosted. We thus simply ignore the processing delay on each forwarding node. Denote the propagation delay on link e 2 E by DelayðeÞ. Let Pathi be the path from source s to destination di ði ¼ 1; 2; . . .; jDjÞ in the multicast tree. Denote the propagation delay  along Pathi before   placing VNFs  by i ¼ Delaybfr ðPathi Þ. Let EPath

i and VPath be ei1 ; ei2 ; . . .; ei Ei ¼ vi1 ; vi2 ; . . .; vi V i j Path j j Path j  i   i  the link and node sets of Pathi , respectively. EPath  and VPath  are the number of links and that of nodes along Pathi , respectively. Then, Delaybfr ðPathi Þ is defined in Eq. (1).

Delaybfr ðPathi Þ ¼

X i e2EPath

DelayðeÞ;

i ¼ 1; 2; . . .; jDj

ð1Þ

Given a multicast service request MR, we use Dijkstra’s algorithm to find the multicast tree GT ¼ ðVT ; ET Þ with the average propagation delay minimized, where GT  G; VT  V, and ET  E. The objective is defined in Eq. (2). Minimize: 1 XjDj Delaybfr ðPathi Þ i¼1 jD j

2.2

ð2Þ

Multicast-Oriented VNF Placement
 After the multicast tree construction, F ¼ f1 ; f2 ; . . .; fjF j is to be placed to the resulted GT . Note that in a multicast tree there is a path from the source to a destination, i.e. Pathi originates from s and terminates at di ði ¼ 1; 2; . . .; jDjÞ. In order to ensure user experience, we expect to reduce the transmission delays along each path in the multicast tree. Hence, we propose to deploy F on each source-destination path, with the purpose of achieving smaller transmission delay. Let Udpl ðvÞ  F represent the set of VNFs hosted by node v. Let RAvailable ðvÞ be the compute resource available on node v and let RConsumed ðfk Þ denote the compute resource consumed by deploying VNF fk , where k ¼ 1; 2; . . .; jF j. Let DelayiPath ðfk Þ represent the processing delay caused by VNF fk on path Pathi , where i ¼ 1; 2; . . .; jDj and k ¼ 1; 2; . . .; jF j. As aforementioned, the VNF placement step deploys F on each source-destination path on GT , which means for an arbitrary path Pathi , the data flow originated from the source is processed by f1 ; f2 ; . . .; fjF j before arriving at destination di . The propagation delay on Pathi after F is deployed, Delayaft ðPathi Þ, is defined in Eq. (3). Note that the processing delays incurred on those nodes along Pathi that host one or more VNFs are not included in Delayaft ðPathi Þ.

On Multicast-Oriented Virtual Network Function Placement

Delayaft ðPathi Þ ¼

X

DelayðeÞ þ

Path e2Es!f i 1

X Path e2Ef !fi k kþ1

DelayðeÞ þ

X Pathi jF j !di

e2Ef

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DelayðeÞ ð3Þ

Pathi Pathi i i where Es!f ; EfPath ; EfPath  EPathi , i ¼ 1; 2; . . .; jDj and k ¼ 1; 2; . . .; jF j  1. Es!f 1 k !fk þ 1 1 jF j !di i represents the link set of the sub-path from s to the node hosting f1 on Pathi . EfPath k !fk þ 1 stands for the link set of the sub-path from the node hosting fk to the node hosting fk þ 1 i on Pathi . EfPath is the link set of the sub-path from the node hosting fjF j to destination jF j !di di on Pathi . In the VNF placement, this paper aims to find an appropriate SFC deployment solution for the multicast tree. The objective and constraints are shown in Eqs. (4)–(7). Minimize:

i XjF j 1 XjDj h i Delay ð Path Þ þ Delay ð f Þ aft i k Path i¼1 k¼1 jD j

ð4Þ

Subject to: [ i v2VPath

Udpl ðuÞ

\


 Udpl ðvÞ ¼ f1 ; f2 ; . . .; fjF j

Udpl ðvÞ ¼ £; 8u; v 2 Pathi ; u 6¼ v; i ¼ 1; 2; . . .; jDj

RAvailable ðvÞ 

X fk 2Udpl ðvÞ

RConsumed ðfk Þ; v 2 V

ð5Þ ð6Þ ð7Þ

Objective (4) is to minimize the average transmission delay of the multicast tree, where the propagation delay on links and the processing delay on nodes that host VNFs are both considered. Constraint (5) specifies that all VNFs in F are deployed on each source-destination path, e.g. Pathi . Constraint (6) explains that any two VNFs on the same source-destination path are different, which helps to improve the utilization of compute resource. Constraint (7) specifies that the compute resource consumed by deploying VNFs cannot exceed the available compute resource a node can offer prior to the VNF placement.

3 mGA for VNF Placement As mentioned in Sect. 2, Dijkstra’s algorithm is employed to build a multicast tree GT ¼ ðVT ; ET Þ with the average propagation delay minimized. Note that, in this step, the processing delays caused by nodes hosting VNFs are not taken into account. Then we deploy SFC of multicast service request MR, F, onto GT by a modified GA (mGA), where the average transmission delay is minimized with the processing delay incurred by VNFs considered. GA is a stochastic search evolutionary algorithm in the field of computational intelligence [7]. GA first generates a population of solutions (also called chromosomes).

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It then calculates the fitness value of each solution in the population via fitness evaluation. Later on, it selects solutions into a mating pool, where better solutions are selected with higher probability. After that, crossover and mutation are applied to the solutions in the mating pool to generate a new population. The selection, crossover and mutation operations are repeated to evolve the population until termination condition is met. A typical GA has the following steps [7]: 1. Initialization: Set the generation number s ¼ 0. Set the predefined maximum generation number to Ng and randomly generate Np individuals to form population PðsÞ. 2. Fitness evaluation: Calculate the fitness value of each individual in PðsÞ. 3. Selection: Repeatedly select an individual from the population until a mating pool with Np individuals is generated. 4. Crossover: The crossover operation is performed to PðsÞ with a crossover probability Pc . 5. Mutation: The mutation operation is performed to PðsÞ with a mutation probability Pm . The population PðsÞ goes through the selection, crossover, and mutation operations and becomes Pðs þ 1Þ. 6. Termination condition: If some termination criteria are satisfied, the evolution of population stops and the best individual obtained in the evolutionary search is output as the optimal solution. In mGA, we set the population size to Np and the maximum number of iterations to Ng . We use Eq. (4) as the fitness function. 3.1

Solution Encoding and Fitness Evaluation

As known, an appropriate individual encoding not only clarifies the relationship among decision variables of the problem, but also reduces the time complexity of the algorithm. According to the features of the MVNFP problem, we design a problem-specific individual encoding scheme. As aforementioned, SFC of a given multicast service request MR, F, is to be deployed on each source-destination path in the multicast tree. This paper proposes a two-dimensional solution encoding scheme, where an individual is a jDj  jF j matrix. The i-th solution Xi is defined in Eq. (8), where i ¼ 1; 2; . . .; Np . Value xij;k represents the ID of a node on path Pathj that is selected to host fk . We refer to a region as a row in the individual. So, an individual is composed of jDj regions and each region has a length of jF j. In fact, each region is a set of nodes to host F in a source-destination path in the multicast tree. 2

xi1;1 6 . Xi ¼ 6 4 .. xijDj;1

3


xi1;jF j .. 7 .. 7 . 5; 8i ¼ 1; 2; . . .; Np . i


xjDj;jF j

ð8Þ

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In the fitness evaluation, we first check if a solution is feasible according to constraints (5)–(7). A feasible solution is evaluated according to Eq. (4) while an infeasible solution is assigned a sufficiently large value and it will not be involved in VNF placement process. 3.2

Selection, Crossover and Mutation in mGA

In mGA, the tournament selection is adopted, where the tournament size is set larger than two to increase the selection pressure, hence ensuring a faster convergence. Traditional crossover and mutation are not directly applicable to mGA as the individuals are matrix-based but not string-based. In mGA, the crossover is performed to each pair of individuals with a crossover probability Pc . If a pair is selected, a randomly chosen region in one individual and another randomly chosen region in the other individual are swapped, resulting into two offspring individuals. Our crossover helps to produce feasible individuals by avoiding significant destruction to promising genes accumulated during the evolution. Figure 1 shows an example of the proposed crossover operation. The m-th region of X i and the n-th region of Xj are randomly chosen and swapped. Then, the two offspring individuals replace their parent individuals.

Fig. 1. An example of crossover

After crossover, the population undergoes mutation operation, where each position in the individuals is chosen with a mutation probability Pm . Suppose the k-th position of the j-th region in individual Xi , xij;k , is chosen for mutation. Then, a node is randomly j selected from node set VPath and used to replace xij;k .

4 Performance Evaluation In order to verify the effectiveness of mGA, we compare it with three widely used evolutionary algorithms, including population based incremental learning (PBIL), ant colony optimization (ACO) and particle swarm optimization (PSO). There are six instances for experimentation, where three real-world networks from the Internet

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Topology Zoo (http://www.topology-zoo.org) and three random networks are generated by the Random ER Graph Generation Algorithm [8]. The available compute resource of node v, RAvailable ðvÞ, the compute resource consumed by deploying VNF fi ; RConsumed ðfi Þ, and the processing delay caused by fi ; Delayðfi Þ, are uniformly distributed in the ranges of [30, 50] units, [10, 20] units, and [10, 20] ms, respectively. Table 1 shows the test instances and their parameters. In each instance, the multicast service request MR is randomly generated. In each instance, the multicast source and destination nodes are randomly generated, where the number of destination nodes varies from 5 to 9. The number of VNFs is uniformly distributed in the range [5, 8]. Table 1. Test instances and their parameters. Tinet Dfn Tata Random_1 Random_2 Random_3 Nodes 53 58 145 50 150 250 Links 89 87 186 128 419 665 Dest. Nodes 5 8 5 6 7 9 VNFs 8 5 8 5 6 7

In mGA, we set Pc ¼ 0:7 and Pm ¼ 0:05. In PBIL, the learning rate and the mutation probability are set to 0.1 and 0.05, respectively. In ACO, the pheromone evaporation rate is set to 0.5 and the two heuristic factors are set to 0.1 and 0.2, respectively. In the PSO, we set the inertia weight to 0.2 and the two positive constants c1 ¼ c2 ¼ 2, respectively. For all algorithms, we set Np ¼ 100 and Ng ¼ 300. Each algorithm is run 20 times for each instance and the best fitness curve is provided in Fig. 2.

Fig. 2. Best fitness value vs. generation

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In terms of the convergence, PBIL, PSO, and ACO are easily trapped into local optima in early stage of evolution, e.g. they converge around 50th generation. mGA, on the other hand, converges gradually because of the diversity preservation provided by the proposed mutation scheme. It not only helps to explore wider area in the search space but also avoid damaging feasible individuals. Regarding the solution quality, it is no doubt that mGA always obtains the best solution in each instance. This is because the proposed crossover and mutation operations provide efficient global exploration and local exploitation over the search space.

5 Conclusion This paper formulates a multicast-oriented virtual network function placement (MVNFP) problem. First, it adopts Dijkstra’s algorithm to construct a multicast tree with the minimum average propagation delay. Then, the paper deploys virtual network functions (VNFs) onto the tree by a modified genetic algorithm (mGA), where problem-specific individual encoding, crossover and mutation are devised. Experiment results demonstrate the superiority of mGA over a number of well-known evolutionary algorithms in terms of the solution quality and convergence. In the future, we would like to extend the MVNFP problem in the context of wireless and mobile networking environment [9, 10].

References 1. ETSI: Network Functions Virtualisation; Architectural Framework. Standard no. GS NFV 002 v1.1.1. ETSI (2013) 2. Cohen, R., Lewin-Eytan, L., Naor, J.S.: Near optimal placement of virtual network functions. In: Conference on Computer Communications, pp. 1346–1354. IEEE (2015) 3. Khebbache, S., Hadji, M., Zeghlache, D.: Scalable and cost-efficient algorithms for VNF chaining and placement problem. In: Innovations in Clouds, Internet and Networks, pp. 92– 99. IEEE (2016) 4. Rankothge, W., Le, F., Russo, A., Lobo, J.: Optimizing resources allocation for virtualized network functions in a cloud center using genetic algorithms. IEEE Trans. Netw. Serv. Manage. 14(2), 343–356 (2017) 5. Zhang, S.Q., Zhang, Q., Bannazadeh, H.: Routing algorithms for network function virtualization enabled multicast topology on SDN. IEEE Trans. Netw. Serv. Manage. 12(4), 580–594 (2015) 6. Xu, Z., Liang, W., Huang, M.: Approximation and online algorithms for NFV-enabled multicasting in SDNs. In: International Conference on Distributed Computing Systems, pp. 625–634. IEEE (2017) 7. Beasley, D., Bull, D., Martin, R.: An introduction to genetic algorithms. Artif. Life 3(1), 63– 65 (1999)

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8. Batagelj, V., Brandes, U.: Efficient generation of large random networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 71(3), 036113 (2005) 9. Xu, L., Luan, Y., Cheng, X., et al.: WCDMA data based LTE site selection scheme in LTE deployment. In: 1st International Conference on Signal and Information Processing, Networking and Computers, pp. 249–260. CRC Press Taylor & Francis Group, Beijing (2015) 10. Xu, L., Cheng, X., et al.: Mobility load balancing aware radio resource allocation scheme for LTE-advanced cellular networks. In: 16th IEEE International Conference on Communication Technology, pp. 806–812. IEEE Press, Hangzhou (2015)

PM2.5 Concentration Forecast Based on Hierarchical Sparse Representation Rui Zhao1, Bingjian Lu1,2(&), Zhenyu Lu2,3, Hengde Zhang1, and Tianming Zhan4 1

National Meteorological Center, Beijing 100081, China [email protected] 2 School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China 3 The Collaborative Innovation Center on Atmospheric Environment and Equipment of Jiangsu, Nanjing 210044, Jiangsu, China 4 Nanjing Audit University, Nanjing 210044, Jiangsu, China

Abstract. This paper proposes hierarchical sparse representation (H-SRC) to predict PM2.5 Concentration. It selects factors from observational data in Beijing-Tianjin-Hebei. Its time is from January to March in 2013–2017. Then, it constructs 4000 samples of historical databases based on fuzzy C means algorithm (FCM). Input Meteorology factors predicted by Rapid Refresh Multi-scale Analysis & Prediction System-CHEM (RMAPS-CHEM) and European Centre for Medium-Range Weather Forecasts (ECMWF), use the first-level sparse representation to classify test samples and the second-level sparse representation to regress test samples, then it predict the PM2.5 Concentration. Experiment with the data in Beijing-Tianjin-Hebei between January and March, 2018, reveals that the method in this paper can increase the accuracy and reduce mean absolute error. The accuracy by hierarchical sparse representation is 25.28%, 13.34%, 14.28%, 23.08% higher than RMAPS-CHEM in 0–35 lg=m3 , 75–115 lg=m3 , 115–150 lg=m3 , 150–250 lg=m3 , while absolute errors are all lower than RMAPS-CHEM. At the same time, this method is easy to study and is convenience for the analysis of other meteorological data. Keywords: Historical database PM2.5 concentration


Sparse representation
Regress


1 Introduction In recent years, haze pollution in China has shown an overall trend of increase [1]. PM2.5 (particle size less than 2.5 l) as the primary pollutant in the process of heavy haze pollution, has been proved to have significant harm to human health [2]. Since the implementation of the State Council’s air pollution prevention and control plan in 2013, the pollution situation in Beijing-Tianjin-Hebei has been obviously alleviated and the air quality has improved significantly. However, the pollution weather has occurred from time to time. Especially in the “2+26” cities, the pollution phenomenon is more prominent, which is still significantly higher than that in developed countries. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 429–436, 2019. https://doi.org/10.1007/978-981-13-7123-3_50

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Therefore, it has great importance to search for a suitable method to predict air pollution. At present, the North China Regional Meteorological Center introduced the WRFCHEM model and established the North China Regional Meteorological Prediction System (BREMPS) locally [3]. It can simulate the temporal and spatial distribution of regional atmospheric pollutant concentration. However, due to the uncertainties of the emission list and the imperfections of physical and chemical mechanisms, the prediction errors of PM2.5, PM10, O3 and other key pollutants are large [4]. In the recent ten years, machine learning has been rapidly rising and has become the cornerstone of technology in the era of big data. Fan et al. have constructed a space-time prediction model based on the missing value processing algorithm and the cyclic neural network to realize the prediction of air quality [5]. Experimental comparison verified the validity of the space-time prediction framework based on depth learning. Dai et al. combined particle swarm optimization (PSO) and support vector machine (SVM) to establish a rolling prediction model to predict the concentration of PM2.5 in the next 24 h [6]. Although the above methods have achieved good results to some extent, there are still some problems. Such as, the learning speed of neural network method is slow; the support vector machine is difficult to achieve multi-classification. Sparse representation is a focus in the current research. It is especially widely used in computer vision, machine learning and pattern recognition. The representation coefficient of this method contains many information, which is helpful for classifying [7]. Moreover, It is also used for voice signal processing [8] and visual target tracking [9]. The final result of sparse representation is depend on a few coefficients. So, the computational complexity is greatly reduced. Since it doesn’t need to know the exact relationship of dependent variables and independent variables, it is perfect for predicting when the relationship between predictors and prediction objects is ambiguous. This paper uses observational data and two numerical forecast data to predict the PM2.5 concentration. It constructs 4000 samples of historical databases based on fuzzy C means algorithm (FCM). Then, it use meteorological elements and pollutant concentration to predict. It firstly use the first-level sparse representation to divided the test samples into six categories. Then, It use the second-level sparse representation to regress PM2.5 concentration with classification and its corresponding sub databases. By experiments with the data predicted by RMAPS-CHEM and ECMWF in 2018, the method in this paper has proven to have potential in PM2.5 concentration forecast. It can improves forecast accuracy of PM2.5 concentration and is suitable for the analysis of other meteorological data.

2 Information 2.1

Data Sources

Meteorological Data. Air temperature, wind direction, wind speed, humidity and other observational data of meteorological stations in Beijing-Tianjin-Hebei by 3 h between January and March, 2013–2017.

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Pollutant Concentration. The hourly concentration of PM2.5 at 1493 stations announced by China Environmental Monitoring Station were selected from the nearest environmental monitoring stations of the required meteorological stations. The Prediction Field of Meteorological Data. Air temperature, wind direction, wind speed and other meteorological data of Beijing-Tianjin-Hebei between January and March 2018 are predicted by ECMWF and RMAPS-CHEM. The Prediction Field of Pollutant Concentration. RMAPS-CHEM predicts the PM2.5 concentration in Beijing-Tianjin-Hebei between January and March 2018. 2.2

Factor Selection

Cheng et al. found that in most cases the PM2.5 concentration had a good correlation with air temperature, humidity, wind direction and wind speed, and passed the 99.9% confidence level test [10]. Based on the experience of predecessors and forecasters, this paper calculates 10 factors, including air temperature, 24-h variation of air temperature, humidity, wind direction, wind speed and dew point on the ground, air temperature and the dew-point deficit of 700 hpa, air temperature and the dew-point deficit of 850 hpa.

3 Core Algorithm Figure 1 gives the structure chart about predicting PM2.5 concentration forecast by hierarchical sparse representation. This method is mainly include two parts. The first part is classification: it firstly uses observational meteorological elements to construct historical database D. Then input meteorological data predicted by RMAPS-CHEM and ECMWF and historical database D. Finally, it can divided test samples into six categories by first-level sparse representation. The second part is regression: it firstly divides historical database D into six subclass databases. Then it inputs the results of classification and subclass databases. Finally, it can regress PM2.5 concentration by the second-level sparse representation. 3.1

Construct Historical Database

If there are enough samples in the historical database and there are certain structural characteristics, the sample elements can be sparsely represented. It can be seen that one of the main points of sparse representation is to build a suitable database. This paper constructs the historical database with the observational data from January to March of years 2013–2017. If samples’ number in the historical database is too large, processing speed will be impacted. However, if samples’ number in the historical database is too small, sample’s integrity will be impacted. This paper selects about 4000 samples to construct the historical database by doing many experiments. Assume that xi ¼ ½ai1 ; ai2 ;


; ai10 ; zi T , ai1 ; ai2 ;


; ai10 is the elements of a certain day, zi is its corresponding PM2.5 concentration. It is divided into 6 categories according to the PM2.5 concentration (as shown in the Table 1). The number of each class is numb , b ¼ 1; 2;


; 6.

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Observational data (meteorological data and PM2.5 concentration) Historical database D about 4000 samples

First-level sparse representation Subclass historical databases D1, D2, D3, D4, D5, D6

Classify (six subclass)

Second-level sparse representation

Input subclass database

Regress PM2.5 concentrations

Fig. 1. Structure chart of predicting PM2.5 concentration by hierarchical sparse representation Table 1. The classification standard of PM2.5 concentration Air quality index level PM2.5 concentration ðlg=m3 Þ First level 0–35 Second level 35–75 Third level 75–115 Fourth level 115–150 Fifth level 150–250 Sixth level >250

Air pollution level Good Moderate Lightly polluted Moderately polluted Heavily polluted Severely polluted

How to select samples to ensure diversity of samples is very important, after being classified by the PM2.5 concentration. Because it cannot be classified more meticulously from the surface. In 1974, Dunn proposed FCM algorithm. From the most algorithms of cluster, it is a successful one and is widely used in fuzzy cluster. Thus, we use it to classify further data. Each sample is further divided into 100 categories by FCM algorithm. It needs to select the number of each class according to the proportion of each class randomly, which is constantly adjusted in the experiment. Finally, it selects about 4000 samples for constructing the historical database D, D ¼ ½x1 ; x2


x4000 . 3.2

Hierarchical Sparse Representation (H-SRC)

Whether today’s weather and historical weather belong to the same kind, there are certain similarities between them, especially the meteorological data of the same weather in the weather classification. If today’s weather is similar to many historical weather, we can represent today’s weather by those similar historical weather. Now, we

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can use historical PM2.5 concentration to represent the future PM2.5 concentration sparsely. Figure 2 gives the structure diagram of H-SRC. Input meteorological data predicted by RMAPS-CHEM and ECMWF (test sample f) Input historical database D First-level sparse representation Classify f1

f2

f3

Subclass historical database f4

f5

f6

D1

D2

D3

D4

D5

D6

z4

z5

z6

Second- level sparse representation Historical PM2.5 concentration

β1

β2

β3

β4

β5

β6

y4

y5

y6

z1

z2

z3

Regress y1

y2

y3

Fig. 2. The structure diagram of hierarchical sparse representation

It divides historical database D into six categories according to PM2.5 concentration as shown in the Table 1. D ¼ ½D1 ; D2 ;


; D6 , D1  D6 is six subclass databases. The test sample f is composed of meteorological data predicted by RMAPS-CHEM and ECMWF, f ¼ ½a1 ; a2 ;


a10 . Equation (1) is the formula for calculating sparse coefficients. a ¼ argminkf  Dak22 þ kkak1 a

ð1Þ

The result is a ¼ ½a1 ; a2 ;


; a6 . a1  a6 is six types of coefficients corresponding to six subclass historical samples. Equation (2) is the formula for calculating the reconstruction error of each coefficients, c ¼ 1; 2;


; 6. The test samples’ categories are depending on the smallest error. It classifies test samples into six categories that is f ¼ ½f1 ; f2 ;


; f6 . f1 f6 is six subclass test samples. ec ¼ kf  Dc ac k2

ð2Þ

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Equation (3) is the formula for calculating the second sparse coefficients. The second sparse coefficients is the coefficients of subclass test sample f corresponding to its subclass historical database Dk . bk ¼ argminkf  Dk bk k22 þ kkbk k1 bk

ð3Þ

After getting the second sparse coefficients, we use these coefficients and the PM2.5 concentration of subclass historical database to regress PM2.5 concentration yk . zkj represents the PM2.5 concentration in j sample of k subclass historical database. j ¼ 1; 2;


; mk , In it, mk is samples’ number of k subclass historical database Dk . yk ¼

mk X

bkj zkj

ð4Þ

j¼1

4 Experimental Results This paper does the experiment with the meteorological data predicted by RMAPSCHEM and ECMWF to verify the effect of using H-SRC to predict PM2.5 concentration. The data is from Jan. 1 to Mar. 31 in Beijing-Tianjin-Hebei, 2018. 4.1

Contrast Experiment of Observational Data, H-SRC and RMAPSCHEM

This paper does related experiments to evaluate H-SRC. It use the data of 08 h from Jan. 1 to Mar. 31 in 2018 in Beijing-Tianjin-Hebei. Figure 3 is observational data and the results predicted by H-SRC and RMAPS-CHEM at Beijing from January to March in 2018. There are 68 test samples when removing missing data. From Fig. 3, its vertical axis is PM2.5 concentration lg=m3 and its horizontal axis is date. The results of H-SRC are similar to the observational data. It can simulate the PM2.5 concentration better than the forecast results of RMAPS-CHEM, especially in low pollution conditions. 4.2

The Accuracy and Mean Absolute Error of H-SRC and RMAPSCHEM

In order to analyse the forecasting effect of H-SRC, Table 2 gives the forecasting accuracy rate of two forecasting methods for three hours from January to March in 2018 at Beijing, and Table 3 gives the forecasting error of two forecasting methods for three hours from January to March in 2018 at Beijing. From Table 2, the accuracy of H-SRC in 0–35 lg=m3 , 75–115 lg=m3 , 75–115 lg=m3 , 115–150 lg=m3 is 25.28%, 13.34%, 14.28%, 23.08% higher than BREMPS. The accuracy in 35–75 lg=m3 is only 8.23% lower than RMAPS-CHEM. Because it is

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Fig. 3. Results of observational data, H-SRC and RMAPS-CHEM

Table 2. The accuracy of two methods at Beijing from January to March 2018 Methods 0–35 35–75 75–115 115–150 150–250 >250 Hierarchical sparse representation 71.31% 20.34% 26.67% 28.57% 61.54% / RMAPS-CHEM 46.03% 28.57% 13.33% 14.29% 38.46% /

Table 3. The mean absolute error of two methods at Beijing from January to March 2018 ðlg=m3 Þ Methods 0–35 35–75 75–115 115–150 150–250 >250 Hierarchical sparse representation 17.90 44.21 55.51 46.01 69.76 / RMAPS-CHEM 49.52 49.95 82.42 50.09 106.61 /

easily misreported in 75–115 lg=m3 . On the whole, H-SRC has a better forecasting effect, especially when the weather is excellent. From Table 3, the mean absolute error of the six types of pollution in this method is 31.62 lg=m3 , 5.74 lg=m3 , 26.91 lg=m3 , 4.08 lg=m3 , 36.85 lg=m3 lower than BREMPS. Taken together, all mean absolute errors of PM2.5 concentration by this method are less than RMAPS-CHEM. So, the effect of H-SRC is better.

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5 Conclusion This paper proposed hierarchical sparse representation to predict PM2.5 concentration. And it does some experiments to verified its feasibility. Through the contrast experiments with RMAPS-CHEM, we can see that: the accuracy of PM2.5 concentration based on H-SRC is better than RMAPS-CHEM in 0–35 lg=m3 , 75–115 lg=m3 , 115–150 lg=m3 , 150–250 lg=m3 . And the mean absolute errors of PM2.5 concentration forecast in all categories are lower than RMAPS-CHEM. That is to say the PM2.5 concentration forecast based on H-SRC can reduce mean absolute error and increase the accuracy. Acknowledgments. This work has been supported in part by the National Key Research Program of China (Grant No. 2016YFC0203301), the National Natural Science Foundation of China (Grant No. 61773220, 61502206), the Nature Science Foundation of Jiangsu Province under Grant (No. BK20150523).

References 1. Wang, Y.H., Liu, Y.J.: The characteristics of long-term changes of smog and haze in China in the past 50 years and its relationship with atmospheric humidity. Chin. Sci. Earth Sci. 44 (1), 37–48 (2014) 2. Fang, J.X., Zhao, C.Y., Zhu, W.H.: Concentration monitoring and forecasting model of PM_ (2.5) during the heating period of Lanzhou city. Environ. Sci. Technol. 37(4), 80–84 (2014) 3. Liu, H., Rao, X.Q., Zhang, H.D., et al.: Comparative test of the forecast effect of environmental meteorology numerical prediction model. J. Meteorol. Environ. 33(5), 17–24 (2017) 4. Zhao, X.J., Xu, J., Zhang, Z.Y., et al.: Beijing regional environmental meteorology prediction system and PM2.5 forecast inspection. J. Appl. Meteorol. 27(2), 160–172 (2016) 5. Fan, J.X., Li, Q., Zhu, Y.J., et al.: Research on air pollution prediction model based on RNN. Sci. Surv. Mapp. 42(7), 76–83 (2017) 6. Dai, L.J., Zhang, C.J., Ma, L.M.: PM2.5 short-term concentration dynamic prediction model based on machine learning. Comput. Appl. 37(11), 3057–3063 (2017) 7. Akhtar, N., Shafait, F., Mian, A.: Efficient classification with sparsity augmented collaborative representation. Pattern Recogn. 65, 136–145 (2017) 8. Abrol, V., Sharma, P., Sao, A.K.: Greedy double sparse dictionary learning for sparse representation of speech signals. Speech Commun. 85, 71–82 (2016) 9. Abdessamad, J., Eladel, A., Zaied, M.: A sparse representation-based approach for copymove image forgery detection in smooth regions. In: International Conference on Machine Vision, vol. 10341, 1034129 (2017) 10. Chen, X.H., Diao, Z.G., Hu, J.K., et al.: Dynamic-statistical prediction of PM2.5 concentration in China based on CMAQ model and adaptive partial least square regression. J. Environ. Sci. 35(8), 2771–2782 (2016)

Intelligent User Profile Prediction in Radio Access Network Yaxing Qiu(&), Xidong Wang(&), Fengjun Wang(&), and Sen Bian(&) Green Communication Research Center of China Mobile Research Institute, CMCC, Beijing, People’s Republic of China {qiuyaxing,wangxidong,wangfengjun,biansen} @chinamobile.com

Abstract. With increasingly dense deployment of base stations, power consumption, resource utilization, interference management, and user experience optimization in mobile network have become more and more important challenges. The effective prediction of temporal and spatial data traffic distribution, en-powered by intelligent user profile prediction, will be essential. This paper investigates user mobility and user service patterns prediction by generating refined user trajectory and 2-D service feature models. Specifically, an improved Bayesian trajectory prediction strategy coupled with time-domain features extraction used for user service pattern prediction is proposed. Large scale field test using 2300 active mobile devices (users) across 1600 cells in a live network showed promising results of 85% trajectory prediction accuracy and 70% service pattern prediction accuracy. The accurate prediction of user-level behavior pattern is of great significance not only for the improvement of network energyefficiency, but also for the guarantee of user experience and the optimization of network utilization. Keywords: Mobility prediction
Service pattern prediction
Machine learning

1 Introduction As one of the biggest telecom operators in the world, China Mobile has the widest wireless access networks in China, in which the total number of base stations (BSs) has exceeded 3 million by 2016 [1]. For the 5th Generation communication system (5G), more BSs will continue to be installed to support up to 1,000-fold gains in capacity. With increasingly dense deployment of base stations, wireless networks have become more and more advanced and complicated. The wireless networks are generating a large amount of wireless data (such as measurement report, traffic load, resource usages, etc.) all the time, and this drives the wireless communication networks into the era of big data [2]. In practice, the traffic load and resource usages of network are closely related to the behavior pattern of the mobile users. Analyzing users’ behavior pattern based on their interaction with wireless networks is a key problem in network usage mining. Also, user mobility and behavior patterns prediction can significantly

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benefit the resource-constrained network automation, from network planning, network traffic monitoring to network management in mobile networks [3]. Mobility is an inherent characteristic of users in mobile networks. User mobility prediction allows estimating or predicting the location and trajectory of users at the next moment. Nowadays, a number of studies have been proposed to conduct human mobility pattern mining based on human mobility data in many types of wireless networks. In [4], Order-k Markov predictor is used to predict next place on the basis of the sequence of the k-most latest locations in users’ trajectories. Additionally, some prediction methods using machine learning techniques have been suggested as useful approaches to improve the prediction accuracy. One advantage of the learning-based model is that mobile contexts can be quantitatively measured and mapped into a feature space for prediction. Tomar and Verma have suggested the trajectory prediction method using a Support Vector Machine (SVM) and used it as the regression analysis method in [5]. Furthermore, deep learning approach for spatiotemporal modeling and prediction in cellular networks is also proposed with using big system data in [6]. Unfortunately, these methods are challenged by multiple factors, such as low accuracy, high complexity, etc. More importantly, these methods consider only historical trajectory data for prediction without taking into account time factors (i.e., the residence time of a user in a cell), which, however, is an important factor for the data traffic distribution in the wireless network. In this paper, we study the user-level behavior patterns to predict the temporal and spatial data traffic distribution in the wireless network. To model a user profile, user’ mobility and service pattern are investigated. An improved Bayesian trajectory prediction algorithm is proposed to predict the user’s trajectory in the working and rest days, respectively. Also, the user residence time is considered to achieve more accurate trajectory prediction. User service pattern prediction is obtained by combining time feature and the mobility prediction results. Furthermore, instead of using virtual mobile data, our work is based on the real mobile data collected from the mobile networks, which can make our prediction results more meaningful. The contributions of this paper are summarized as below: (1) For user mobility prediction, an improved Bayesian prediction algorithm is conceived to predict the likelihood of the next location for users with considering the user residence time to achieve more accurate location prediction. (2) An innovative approach is designed to predict user service type and traffic by combining with time feature and location prediction results. (3) En-powered by user mobility and service patterns prediction, the effective prediction for temporal and spatial data traffic distribution is carried out.

2 User Profile Prediction Algorithm The user profile prediction algorithm is divided into two parts: user’s location prediction and service pattern prediction. For user’s location prediction, the personal trajectory modeling can be conducted according to the users’ historical trajectory. After modeling, when the system is inputted the continuous trajectory points of a user within a few hours, which cell the user will visit next time will be predicted as the output

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results. For service pattern prediction, when the continuous service information for a period of time (for example, a few hours) is fed into the well trained model, the service type and the traffic used by the user at the next moment can be predicted as the output results. After the above two predictions are effectively combined, for a certain cell we can know how many users will enter and leave at the next moment, and how much data traffic they will bring in or take away. The real-time prediction of the users’ mobility and service pattern will assist in the analysis of data traffic distribution in wireless networks at the near future moment. The algorithm includes three parts (see Fig. 1): offline learning, online prediction and traffic statistics. In the offline learning stage, historical data is used to build prediction models. For online prediction, the real-time data in the process of user movement will be fed into the model that has been well trained in the offline learning stage to obtain the prediction results. Then, the data traffic at the next moment for a cell brought by each user can be summed up to obtain the total traffic. The other Key Performance Indicators (KPI) prediction (for example, PRB resource utilization) can be also made to understand the load condition of the network at the near future moment.

Data collec on Mobile network data

Data preprocessing

Real- me loca on data

Real- me APP type and traffic data

Historical loca on data

Offline learning Loca on data training User meloca on model

Online Predic on User locaton predicton

APP type and traffic predic on

Summarize user traffic volume to predict the cell traffic volume Othre KPI prediton Traffic Sta s cs

Fig. 1. The flowchart of prediction algorithm.

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Mobility Prediction Model

In wireless network, mobile users need to have their existing connections transferred to a different access point (AP) to feed their highly mobile habits. With the movement of users, their connections are constantly being switched from one wireless AP to another. The user trajectory can be obtained by connecting the continuous wireless APs that have been accessed by users. So, the cell (or AP) granularity is taken as the smallest grid unit to analyze and predict the user’s location. The trajectory T1 can be represented as fn1 ; n2 ; n3 ; n6 g by network nodes. A model based Bayesian theory towards this objective was studied in detail to make the user next location prediction. In this model, the user’s previous moving trajectory has been known and is selected as historical trajectory. When the user’s current travel trajectory is Tp , his destination nj can be predicted by using Bayesian algorithm. And then the path with the highest probability of switching from Tp to a location ln under the condition of destination nj will be chosen and the next location of the trajectory ln is the prediction location point. The formula is described as: PðTp jd 2 nj Þ
Pðd 2 nj Þ Pðd 2 nj jTp Þ ¼ PG : k¼1 PðTp jd 2 nk Þ
Pðd 2 nk Þ

ð1Þ

Where G represents the total number of destinations for a certain user. A location where a user resides for more than a certain time can be selected as the destination. The longer the residing time is, the more likely the location is to be a destination, but this method may miss the places where users only stay at a short time. For Pðd 2 nj Þ, it is the priori probability of the destination nj , it can be calculated as Pðd 2 nj Þ ¼ Sd2nj =Stotal

ð2Þ

Where Sd2nj is the total number of the historical trajectories that with the destination nj . Stotal is the total number of historical trajectories. Only a priori probability of the grid nodes that users have ever reached is not zero. That means, only the grid nodes that users have arrived before can be predicted as the user’s next destination. It is important to note that the prior probability and the time are relevant. So, for better location prediction, the trajectory of the user’s working day and the weekend is analyzed and counted, respectively. Where PðTp j d 2 nj Þ is the posteriori probability that indicates the probability of passing through the trajectory Tp when the destination is nj . It can be calculated as: PðTp j d 2 nj Þ ¼

STp ;d2nj Sd2nj

ð3Þ

Where STp ;d2nj refers to the number of trajectories Tp when the destination is nj . For the process of user trajectory prediction, the user’s destination is predicted by real-time trajectory data, and then the cell with the highest handoff probability from the current

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location to the predicted destination is selected. The next location of the trajectory Tp is the prediction point. If the user is at the current location point nc , the destination predicted is nj , there may be different tracks from the current point to the destination, the location transfer probability from nc to nj is Pðnc ! nj Þ ¼ Pðnc ! nc þ 1 Þ
Pðnc þ 1 ! nc þ 2 Þ



Pðnj¼1 ! nj Þ

ð4Þ

The next location of the trajectory with Pðnc ! nj Þ maximum is the predicted location point. According to the above model, the data of the rest day and the data of the working day are divided into two databases, which are trained and predicted respectively. The distinction between the working and the rest day can improve the accuracy of prediction model for it is in line with users’ living habits. 2.2

Service Pattern Prediction Model

According to the burst and continuity of user’s service pattern, we divide user’s service type prediction into type analysis in the moving state and static state to make more accurate prediction. Service type modeling and prediction in motion state: the type of service used by the user is related to his trajectory. We decompose the user’s probability of using certain application (APP) at some time and place to two important variables: APP type and location. After the user position switches from the current point to the next point, the APP that he uses may change. For example, when a user goes to the subway, he will turn on the video and watch the movie, and when he goes down the subway he will turn off the video. This event has a high occurring probability with a specific user. Define the state switching probability of using certain APP when user’s location changes as: Pðqar ! qar0 j ni ! ni þ 1 Þ ¼

Nðqar ! qar0 & ni ! ni þ 1 Þ Nðni ! ni þ 1 Þ

ð5Þ

Where Nðqar ! qar0 & ni ! ni þ 1 Þ refers to the number of events that the APP switches from qar ! qar0 by a user when the location changes from ni ! ni þ 1 . The next location ni þ 1 of user can be obtained by the previous trajectory prediction, and combine the current location ni and the current application status qar the number for handover Nðqar ! qar0 & ni ! ni þ 1 Þ can get. Then the service type with the maximum Pðqar ! qar0 jni ! ni þ 1 Þ will be chosen as the predicted service type. In static state, according to the continuity of business, users’ service type is related to the user’s current location, next time period and the probability of a service type user will use in the next period of time. In this way, we can give the probability that the user will use a certain service type for the next period of time:

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Ni;t;a PAi;t;a ¼ PA a¼1 Ni;t;a

ð6Þ

Among the formula, Ni;t;a is the number of events that the service type a is used by the user at the location ni and time t, and the A refers to the total number of user service types. 2.3

Traffic Statistics Model

The traffic of a certain wireless communication cell is determined by the usage of the users. All the traffic that users used in the cell needs to be counted to know the total traffic. As predicted above, whether a user will enter a cell can be measured by a probability value. Combined with the service type and traffic prediction, which APP is used by each user and how much traffic consumed is also a probability value. So the output of this module is also the probability of the total traffic volume of service APPs at the next time. First, define the threshold for a cell traffic is VTH , the probability of the total traffic is greater than VTH will be calculated in this section. Define the traffic matrix V, in it the matrix vij indicates the traffic of user i uses the service type app j, and i  m; j  n. Matrix U is defined as a probability matrix for the user i to use a APP j, and i  m; j  n. 0

v11 V ¼ @


vm1 0

u11 U ¼ @


u1n

1 v1n


A vmn

ð7Þ

1


um1





A


umn

ð8Þ












So, the traffic of user i is as follows: 0

1 V1 AðjÞ X B V2 C B C¼V
U ¼ vij
uij @...A j¼1 Vm

ð9Þ

The matrix E describes the number of users in the current cell and their state, E ¼ ðe1 ; e2 ; e3 ; . . .e2n Þ 0 1 p1 B p2 C C p¼B @


A pm

ð10Þ

ð11Þ

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The probability matrix corresponding to the user state is defined as P, pi is the probability of user i is in this cell. The total cell traffic S can be calculated as, S ¼ ðV1 ; V2 ; . . .Vm Þ
E ¼ ðS1 ; S2 ; . . .S2n Þ

ð12Þ

In this case, the distribution probability of the total traffic volume of the cell is, 0

1 0 1 S1 p1 B S2 C B p2 C B C B C @ . . . A ! @ . . . A ¼ pðei Þ S2 n p2 n

ð13Þ

The probability that the total traffic volume is greater than VTH in a cell is, pðS [ VTH Þ ¼

2n X

pðei ÞpðSi [ VTH jei Þ

ð14Þ

i¼1

3 Experiments Results To verify the performance of our proposed scheme, we used a wireless trajectory dataset collected from Nanning to perform prediction. The format of input raw data is the detailed XDR. About 2300 mobile devices (users) active in 1600 wireless communication cells in NanNing urban area is chosen as test objects. The size of dataset collected is about 10G each day (including 265 million S1-U HyperText Transfer Protocol (HTTP) data and 223 million S1-U Mobility Management Entity (MME) data). History data of last 14 days is used to generate history training dataset. Every 15 min real time data in testing day is used as test dataset. For privacy security, data is encrypted to ensure that user’s information is not directly involved in the study. To estimate the overall accuracy of our prediction, we select the mobile users by their historical stay time. Similarly, the valid destinations are selected by historical stay number and time threshold. The track prediction result for 1 day (2018-01-23) is shown in Fig. 2. Blue line is the ratio of fully correct prediction. Red line is the ratio of next cell correct prediction. For most of the time, the track prediction accuracy is above 85%. Track prediction accuracy is a little lower in rush hour than other time. The mobile prediction is important to understand users’ distribution in the network. S1-U HTTP data is used for service pattern prediction. To estimate the overall accuracy of service type and traffic prediction, the proportion of correct service type and traffic prediction for all chosen mobile users is evaluated. The prediction result for one day (2018-01-23) is shown in Fig. 3. The overall service type and traffic prediction accuracy is above 70%, and the accuracy is a little lower in rush hour.

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00:00-00:15 00:30-00:45 01:00-01:15 01:30-01:45 02:00-02:15 02:30-02:45 03:00-03:15 03:30-03:45 04:00-04:15 04:30-04:45 05:00-05:15 05:30-05:45 06:00-06:15 06:30-06:45 07:00-07:15 07:30-07:45 08:00-08:15 08:30-08:45 09:00-09:15 09:30-09:45 10:00-10:15 10:30-10:45 11:00-11:15 11:30-11:45 12:00-12:15 12:30-12:45 13:00-13:15 13:30-13:45 14:00-14:15 14:30-14:45 15:00-15:15 15:30-15:45 16:00-16:15 16:30-16:45 17:00-17:15 17:30-17:45 18:00-18:15 18:30-18:45 19:00-19:15 19:30-19:45 20:00-20:15 20:30-20:45 21:00-21:15 21:30-21:45 22:00-22:15 22:30-22:45 23:00-23:15 23:30-23:45

100.00% 95.00% 90.00% 85.00% 80.00% 75.00%

Ratio of Fully Correct Prediction

Ratio of Next Cell Correct Prediction

Fig. 2. Ratio of correct track prediction. Ratio of Correct App Prediction

00:00-00:15 00:45-01:00 01:30-01:45 02:15-02:30 03:00-03:15 03:45-04:00 04:30-04:45 05:15-05:30 06:00-06:15 06:45-07:00 07:30-07:45 08:15-08:30 09:00-09:15 09:45-10:00 10:30-10:45 11:15-11:30 12:00-12:15 12:45-13:00 13:30-13:45 14:15-14:30 15:00-15:15 15:45-16:00 16:30-16:45 17:15-17:30 18:00-18:15 18:45-19:00 19:30-19:45 20:15-20:30 21:00-21:15 21:45-22:00 22:30-22:45 23:15-23:30

82.00% 80.00% 78.00% 76.00% 74.00% 72.00% 70.00% 68.00%

Correct App Prediction Ratio

Fig. 3. Ratio of correct service type and traffic prediction.

4 Conclusions In this study, we have proposed a new algorithm model for user profile prediction. The proposed scheme includes two sub-algorithms: Bayesian theory is used to model the user mobility pattern and an innovative method is designed to make service pattern prediction by combining the time and location features. The proposed strategy has low computational complexity. Field test results show that the accuracy and timeliness of the algorithm is of great significance to understand the temporal and spatial data traffic distribution in the wireless network. Also, the user profile prediction can help operators perceive the personalized service needs of mobile users so that more refined type of operation and maintenance can be carried out.

References 1. China Mobile Communications Corporation: Sustainability Report: Big Connectivity, New Future (2016) 2. Xu, L., Zhao, X., Luan, Y., et al.: User perception aware telecom data mining and network management for LTE/LTE-advanced networks. In: 4th International Conference on Signal and Information Processing, Networking and Computers, pp. 237–245. Springer, Qingdao (2018)

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3. Xu, L., Luan, Y., Cheng, X., et al.: Telecom Big Data based user offloading self-optimisation in heterogeneous relay cellular systems. Int. J. Distrib. Syst. Technol. 8(2), 27–46 (2017) 4. Song, L., Kotz, D., Jain, R., et al.: Evaluating next-cell predictors with extensive Wi-Fi mobility data. IEEE Trans. Mob. Comput. 5(12), 1633–1649 (2006) 5. Tomar, R.S., Verma, S.: Trajectory prediction of lane changing vehicles using SVM. Int. J. Veh. Saf. 5(4), 345–355 (2011) 6. Wang, J., Tang, J., Xu, Z., et al.: Spatiotemporal modeling and prediction in cellular networks: a Big Data enabled deep learning approach. In: INFOCOM IEEE Conference on Computer Communications, pp. 1–9. IEEE (2017)

Realization of the National QPF Master Blender: A Big Data Approach Jian Tang(&), Kan Dai(&), Zhiping Zong, Yong Cao, Couhua Liu, Song Gao, and Chao Yu National Meteorological Center of CMA, Beijing 100081, China [email protected], [email protected]

Abstract. With the development of the weather forecast modernization, forecasters are facing challenges brought by the explosion of meteorological data, the increasing demand of service front-end and the wide use of objective forecasting technology. Tradition quantitative precipitation forecast (QPF) routine, which is mainly based on manually plotting of precipitation areas, can no longer assist forecasters in demonstrating added value at a higher level. To support the forecasters’ central role in the QPF routine, a subjective and objective QPF blender has been designed and developed. This platform helps forecasters to take control of the whole forecast process with the following five steps: selection from massive forecast data, integration of multi-source QPF, adjustment and correction of QPF, grid processing and service product production. The intelligence of the platform is secured by the development of a number of key supporting techniques. Based on the Meteorological Information Comprehensive Analysis and Processing System Version 4 (MICAPS4), the main functions of this QPF platform are realized. The “QPF Master Blender 1.0” version was released and put into operational use in May 2017, which has yielded good feedback and effectiveness. Based on different weather forecast scenarios, five stages of work mode are provided, which are demonstrated by corresponding examples and verifications. At the end of this paper, the future development of the platform is prospected, including the development of numerical model verification tools and the research on the fusion technologies of multi-scale model information. Keywords: Gridded quantitative precipitation forecast Intelligent forecast
Weather forecast scenarios


Big data


1 Introduction Precipitation is one of the most important weather forecast products and plays a key role in many forecast-related scenarios [12, 16]. In response to the increasing demands, national prediction centers all over the world have established quantitative precipitation forecast (QPF) operation. [3, 8, 14, 20, 25] comprehensively reviewed the progress of QPF technology, and pointed out that the continuous development of numerical prediction models and the application of statistical post-processing techniques have led to continuous improvement of QPF accuracy, but the added value which can be provided by forecasters is increasingly limited. In recent years, with the development of © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 446–454, 2019. https://doi.org/10.1007/978-981-13-7123-3_52

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modernization of weather forecasting services, how to maintain the key position of forecasters in QPF operational processes has been challenged in the following three aspects. Firstly, it is the challenge of massive forecast data (Table 1). The modern QPF routine is based on numerical model forecasts. In order to improve the forecasting ability of QPF, the global model of each country is strengthening data assimilation, perfecting the model physical process and upgrading the spatial and temporal resolution, such as the deterministic integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF) whose resolution has reached 9 km/137 layers [15], and the China’s self-developed GRAPES (Global/Regional Assimilation and Prediction System [5, 26]), which has a horizontal resolution of 25 km. In addition, the numerical weather prediction (NWP) cloud that is built in the lead of Shanghai Meteorological Bureau, could provide real-time model forecasts, including GRAPESMESO, GRAPES-RAFS and GRAPES-3 km. Besides, North China, East China and South China regional meteorological centers all have their own high-resolution models. These models have provided important support for the refined weather service in China. Furthermore, in order to provide uncertainty information for QPF and early warnings of extreme weather, the ensemble prediction systems have also been developed rapidly, such as ECMWF [18] and Global ensemble forecasting system of National Centers for Environmental Prediction (NCEP [22, 23]) of the US, China’s T639 ensemble prediction system upgraded in 2014, and the THORPEX Interactive Grand Global Ensemble (TIGGE [2]). In addition to the global ensemble models, the convective-scale ensemble prediction system has become a hot topic in recent researches [10, 11]. And the GRAPES-Meso regional ensemble system of China is also continuously improved [26]. Therefore, with the rapid development of NWP, forecastrelated data has grown by orders of magnitude. At the session of Commission for Basic systems (CBS) of World Meteorological Organization (WMO) in 2016, Ms. Jiao Meiyan, the Deputy Director of China Meteorological Administration, pointed out that the data processed by China meteorological services in 2015 reached 6.54 TB, and it is expected that it will be rising to more than 60 TB by 2020. Thus it poses a huge challenge for forecasters to get effective forecast information from massive data. Table 1. List of data sources of operational numerical weather prediction models available at CMA. See more detail in Tang et al. 2018. Model type Model name Global deterministic CMA GRAPES global model ECMWF high-resolution model NCEP global model Global ensemble ECMWF global ensemble model CMA T639 global ensemble model NCEP global ensemble mode Regional mesoscale CMA GRAPES-MESO model CMA GRAPES-3 km model CMA-Shanghai-MESO 9 km model

Resolution 0.25°/up to 240 h 0.125°/up to 240 h 0.5°/up to 240 h 0.5°/up to 360 h 0.5°/up to 360 h 1.0°/up to 384 h 0.1°/up to 84 h 3 km/up to 36 h 9 km/up to 72 h

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Secondly, the demand for front-ends of QPF products is increasing fast. For example, in hydrological applications, only the QPF with spatial/temporal resolution smaller than 10 km/1 h could meet the requirements of flash flood forecasting [1, 24], and the accuracy is critical [13]. In order to meet the demand, Weather Prediction Center (WPC) of the US has established a complete product system, including the cumulative QPF and probability QPF grid products in the future 3/6/24 h, with a spatial resolution of 2.5 km. The Central Meteorological Observatory (also known as National Meteorological of China, NMC) has also carried out QPF operation for many years, and established a grid-based QPF forecast routine in 2015, using objective technologies to convert the field forecasts into QPF products with a resolution of 5 km [4]. The “Modern Meteorological Forecast Operation Development Plan (2016–2020)” released in 2016 put forward a higher goal, that is requiring the release of higher resolution gridded forecasts with spatial and temporal resolutions of 1–2.5 km and 1 h respectively by 2020, and the forecast accuracy rate of heavy rain should maintain an improvement rate of 10–20% compared with the international advanced models. Traditional QPF routine has been unable to support forecasters to achieve the above objectives. In addition, the widespread use of big data mining techniques in forecasting operations challenges the value of traditional forecasters. For example, [17] evaluated the WPC QPF and indicated that the forecaster did not show an advantage over the objective QPF after the bias correction and downscaling procession. The same situation exists in the QPF service of NMC. For example, the “ensemble optimal quantile” method has approached or slightly exceeded the forecasters’ skill on the Threat Score (TS) [3]. There are three main reasons why the objective forecasting techniques show better scores than forecasters: (1) The traditional value of the forecaster is reflected in the correction of the systematic deviation of the model precipitation level forecast, but the objective QPF method can better replace this work. (2) In addition to the correction of the magnitude of model output, the forecaster also adjusts the location and distribution of the rain area through the analysis of the evolution of the weather systems, which is very difficult, but the TS score is difficult to express or evaluate this part of work. (3) When the heavy rain warning is issued, the forecaster would sacrifice false alarming ratio (FAR) to secure public safeness. Facing with the above challenges, forecasters need to find new added value based on massive information and data mining technology to play a more important role in NWPs. [3] pointed out that the forecasters need to be freed from the traditional manual production, based on the ability to understand the model and the information mining ability from well-designed artificial intelligence, and turn to be scientific decisionmakers. [7] also pointed out that there is an urgent need to develop convenient and efficient forecast editing tools to help forecasters achieve added value. To this end, this study proposes to design a subjective and objective QPF platform to help forecasters cope with multiple challenges. The remainder of this paper is organized as follows. Section 1 introduces the overall design of the platform. Section 2 explains the operational routine based on the platform design. Finally, in Sect. 3 we show the forecasting ideas through case application and verification, and present a summary and future development in Sect. 4.

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2 Operational Routine Design How to integrate massive model forecast data information to obtain accurate QPF products is the primary problem that needs to be solved in the subjective and objective integration of QPF platform. Using different dynamic frameworks and physical parameterizations, model systems from different forecasting centers have assimilated different observations, and therefore they have different advantages and disadvantages for different regions, seasons, steps and weather types. In addition, different data can provide complementary QPF forecast information. For example, global NWPs can provide precipitation information for large-scale weather systems. Ensemble model systems can provide forecast uncertainty and low probability of extreme weather information. High resolution meso-scale model can provide characteristics such as the shape and evolution of the convective-scale precipitation system. At present, leading international weather centers are developing tools that can help forecasters quickly mine massive forecast information. For example, in WPC of the US, the QPF process [19] is done as follows. The forecasters need to verify and evaluate the forecast results of more than 100 numerical models, and use the “WPC MASTER BLENDER” software to rapidly select the model forecasts and give corresponding weights according to verifications. Then the products with relatively coarse resolution are generated. After that, automated post-processing techniques are used to form high-resolution products which are then distributed to regional weather offices. Next, local forecasters could perform grid editing with graphical forecast editors (GFE), and finally return, merge and publish the QPF. In the traditional QPF operation of NMC (see Fig. 1), forecasters usually make QPF products bases on a single model QPF (usually ECMWF as the deterministic model), with other models or objective forecast methods as a reference. Then they use MICAPS to plot contours of different QPF levels and finally convert the contour-QPF into grid products through automated post-processing operations. The disadvantages of this QPF routine including the inability to use massive model data information effectively, the waste of time manually drawing the contour-QPF, the out of control of QPF post process, and the lack of proper tools to make specific products according to users’ requirements. To this end, according to the operational technology framework in shortmedium-range weather forecast proposed by [6, 7], the design of subjective and objective QPF routine is divided into the following five steps. (1) Based on real-time multi-model QPF, appropriate QPF source is selected based on different forecasting steps, seasons and needs. (2) Based on history, recent forecast verification and forecast uncertainty assessment, best guess QPF is obtained by using multi-model QPF integration technology. (3) Based on various weather conceptual models and understanding of model bias, QPF adjustment techniques and contour correction techniques are used to further correct the best guess field. (4) A high-resolution grid QPF is obtained by using the gridded post-processing techniques to downscale the best guess QPF field. (5) Finally, service products are produced according to various service needs, such as heavy-rain warning, typhoon rainfall, process precipitation, station forecast, etc. The process uses multiple intelligence techniques [21] to help forecasters control the entire grid QPF process and rapidly implement the subjective corrections.

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Fig. 1. The comparison between traditional QPF processes and the subjective and objective integrated QPF processes.

QPF Master Blender V1.0 was mounted on MICAPS4 [9]. MICAPS4 provides efficient display and analysis for high-resolution model forecast, and adopts an open framework, which provides very convenient expansion capability and a basis for QPF platform construction. The platform adopts a layered architecture, consists of a multidata source integrated correction module and a grid point post-processing module. The multi-data integration correction module includes five interactive Graphic User Interfaces (GUI): data source selection, multi-source QPF integration, QPF field adjustment, contour generation and modification, and grid QPF post-processing.

3 Case Application and Operational Verification 3.1

Operational Verification

QPF Master Blender V1.0 was officially put into operational use on May 1st, 2017. The quality of the high-resolution grid products output by QPF master blender will be compared with the ECMWF and T639 models through TS score (May 1st to August 31st, 2017). The results show that blender’s TS is 0.204, which has an increase of 22% and 32% compared to ECMWF (0.167) and T639 (0.154), respectively. The increase also is the highest among the past three years. It should be pointed out that the improvement rate of TS compared with raw model is also related to the climate background and the training degree of forecasters, but it also shows the adaptability of QPF master blender to the new operational routine. Forecasters can produce higher resolution QPF products. In addition, the main role of blender is to provide an intelligent forecasting tool.

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User Recommendation Based on Different Forecast Scenarios

After two-year operational use in flood season of 2017 to 2018, according to the forecasters’ intervention in the degree of precipitation prediction, the use of QPF master blender by forecasters can be divided into five different levels of work mode (see Fig. 2): fully automatic stage (L0), data-selecting stage (L1), weight-specifying stage (L2), grid-editing stage (L3) and hand plotting stage (L4). Three different cases are chosen to illustrate how forecasters can adjust their work stages with QPF master blender in different scenarios in order to achieve the best added value. In the L0 scenario, the numerical model is stable and the consistency is high, and the precipitation is relatively weak. Thus forecasters can fully believe the objective methods and carry out the grid QPF generation based on the objective guidance forecast. In the L1 scenario, often an objective method or model exhibits obvious advantages. For weak or medium rainfall processes, forecasters can select priority members from multi-source forecast products based on experience and use them to make gridded products. In the L2 scenario, forecasters need to select multiple forecast data sources, then give them weights and extract effective forecast information based on verification and experience. At this time, the model and objective method often show different abilities. The magnitude and spatial distribution of QPF need to be synthesized. In the L3 scenario, strong or extreme precipitation processes often occur. The integration results do not conform to the forecasters’ conceptual weather model. The forecasters need to apply the point and surface grid adjustment techniques to the whole field or regional adjustment on the basis of multi-source integration. In the L4 scenario, the model predictions are divergent and the integration results are unreasonable. Forecaster need to re-adjust or draw the contours based on optimal guidance. From July 24th to 25th, 2018, the residual circulation of typhoon “Ampil” affected Northeast China, and brought strong precipitation after combining with cold air. By evaluating the earlier stage model and objective product performance, forecasters found that the optimal percentile product and Shanghai-MESO model were stable, and the global model, mesoscale model and objective method forecast were similar. Forecasters integrated the operation by selecting credible model and objective products. After that, the forecast can be completed with a small amount of operations. After inspection, it is found that when the optimal percentile, Shanghai- MESO, GRAPES-MESO and ECMWF have a weight ratio of 4:2.5:2.5:1, the highest TS score can be achieved by theoretical weight integration. Forecasters have a TS score of 0.31 during this forecast and effectively control the FAR. This is an example that forecasters only need to do a small amount of interventions (L2). From June 5th to 6th, 2018, during the heavy precipitation process in South China, the recent model verification showed that NCEP-GFS performed outstandingly. Afterwards, it was also found that it had a good skill of QPF in central and southern Jiangxi and western Fujian. The TS score of ECMWF reached 0.20 and the optimal percentile reached 0.33. However, the model forecasted heavy rainfall poorly in Hainan and required forecasters to intervene. Therefore, after selecting the “optimal” model combination, forecasters still need further intervention, especially the further correction of the strong precipitation in northern Hainan. It was found that when the weight ratio of NCEP-GFS, Shanghai-MESO, GERMAN, GRAPES-MESO was 5:2:2:1, the

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Fig. 2. Forecasters’ different work modes of QPF Master Blender under different forecast scenarios.

theoretical weight integration maximum TS of 0.369 can be achieved, and the forecaster’s TS score reached 0.36. This is a L3 case. From May 16th to 17th, 2018, there were scattered heavy precipitation cases in Huanghuai region. It can be found that almost all global models have missed the process, but the mesoscale models have some reflection on heavy rain. The real-time verification found that the constancy of recent model is poor, and the global models and the mesoscale models are quite different. Then, forecasters need to fully intervene. And this could be a L4 stage case. Forecasters need to complete the operations by completely intervening the forecast through the understanding of the precipitation process.

4 Conclusions and Prospects Facing with the challenges brought by the explosive growth of meteorological data, as well as the increasing demand from front-ends and the wide application of objective forecasting techniques, the traditional QPF operational routine can no longer embody the core value of forecasters. To this end, QPF Master Blender V1.0 was designed and implemented to help forecasters control the entire digitization QPF process from five aspects: forecast big data selection, QPF integration, QPF adjustment and correction, grid post-processing and service product production. Then the value of forecasters could be redefined as “big data mining based on weather understanding”. QPF Master Blender integrates several key technologies: the construction technology of multimodel QPF dataset, including the design of standard data format and storage specification, as well as the conversion technology of the data with different resolutions; multi-model QPF weighting and probability matching technique; various adjustment

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and correction techniques of QPF field, including whole-field frequency adjustment and filter adjustment techniques, single-point and target rain area adjustment techniques, and correction techniques of contour area; spatial downscaling and time splitting techniques. Based on the MICAPS4, the main functions of QPF Master Blender have been realized, and the version 1.0 has been released. In May 2017, “QPF Master Blender 1.0” passed the operational test at NMC and became the main supporting tool for the daily forecast operation, and initially showed good feedbacks and results. The future development of the platform mainly includes the following five aspects. (1) The platform mainly provides intelligent forecasting and editing tools for forecasters. However, if we want to support forecasters to make correct subjective judgments, further development is needed to help forecasters find, track and understand model errors. For example, model verification tools can provide error statistics of historical weather patterns, real-time model verification and uncertainty/stability information of future models. (2) Integration based on weights cannot solve the fusion of models with different scales. For example, if it is necessary to combine the largescale precipitation of the global model with the convective precipitation of the mesoscale model, scale separation and synthesis techniques are needed. (3) For special forecast scenarios such as winter precipitation phase, typhoon rainstorm, complex terrain precipitation, etc., it is necessary to develop targeted data mining and integration techniques to further improve the QPF forecasting capability. (4) Current QPF platform can only provide the most likely forecast. In the future it is necessary to provide corresponding probabilistic forecasts to transmit the uncertain information of forecasts, which is beneficial to users in making scientific decisions. (5) Forecasters can be supported to interpret the forecast results and to issue influence forecast through adding the information of human geography, socio-economic and other information. Acknowledgment. This paper was supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China under grant No. 2017YFC1502004 and the Key Project in the National Science & Technology Pillar Program of China under grant No. 2015BAC03B01.

References 1. Arnaud, P., Bouvier, C., Cisneros, L., et al.: Influence of rainfall spatial variability on flood prediction. J. Hydrol. 260(1–4), 216–230 (2002) 2. Bougeault, P., Toth, Z., Bishop, C., et al.: The thorpex interactive grand global ensemble. Bull. Am. Meteorol. Soc. 91(8), 1059–1072 (2010) 3. Bi, B.G., Dai, K., Wang, Y., et al.: Advances in techniques of quantitative precipitation forecast. J. Appl. Meterol. Sci. 27(05), 534–549 (2016) 4. Cao, Y., Liu, C.H., Zong, Z.P., et al.: State-level gridded quantitative precipitation forecasting system. Meteor. Mon. 42(12), 1476–1482 (2016) 5. Chen, D.H., Shen, X.S.: Recent progress on GRAPES research and application. J. Appl. Meterol. Sci. 17(06), 773–777 (2006) 6. Chen, L.Q., Zhou, X.S., Yang, S.: A quantitative precipitation forecasts method for shortrange ensemble forecasting. J. Nanjing Inst. Meteorol. 28(04), 543–548 (2005)

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7. Dai, K., Cao, Y., Qian, Q.F., et al.: Situation and tendency of operational technologies in short- and medium-range weather forecast. Meteor. Mon. 42(12), 1445–1455 (2016) 8. Ebert, E.E.: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Weather Rev. 129(10), 2461–2480 (2001) 9. Gao, S., Bi, B.G., Li, Y.A., et al.: Implementation and development plan of MICAPS4. J. Appl. Meterol. Sci. 28(05), 513–531 (2017) 10. Gebhardt, C., Theis, S., Krahe, P., et al.: Experimental ensemble forecasts of precipitation based on a convection-resolving model. Atmos. Sci. Lett. 9(2), 67–72 (2008) 11. Golding, B.W., Ballard, S.P., Mylne, K., et al.: Forecasting capabilities for the London 2012 olympics. Bull. Am. Meteorol. Soc. 95(6), 883 (2014) 12. Kim, G., Barros, A.P.: Quantitative flood forecasting using multisensor data and neural networks. J. Hydrol. 246(1–4), 45–62 (2001) 13. Knebl, M.R., Yang, Z.L., Hutchison, K., et al.: Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event. J. Environ. Manage. 75(4), 325–336 (2005) 14. Li, J., Du, J., Chen, C.J.: Introduction and analysis to frequency or area matching method applied to precipitation forecast bias correction. Meteor. Mon. 5, 580–588 (2014) 15. Malardel, S., Wedi, N., Deconinck, W., et al.: A new grid for the IFS. ECMWF Newslett. 146, 23–28 (2016) 16. Messner, J.W., Mayr, G.J., Zeileis, A., et al.: Heteroscedastic extended logistic regression for postprocessing of ensemble guidance. Mon. Weather Rev. 142(1), 448–456 (2014) 17. Novak, D.R., Bailey, C., Brill, K.F., et al.: Precipitation and temperature forecast performance at the weather prediction center. Weather Forecast. 29(3), 489–504 (2014) 18. Palmer, T.N., Molteni, F., Mureau, R., et al.: Ensemble prediction (1993) 19. Petersen, D., Brill, K.F., Bailey, C., et al.: The evolving role of the forecaster at the weather prediction center. In: World Weather Open Science Conference (2014) 20. Qian, Q.F., Zhang, C.A., Gao, S.Z., et al.: Real-time correction method for ensemble forecasting of typhoon tracks. J. Trop. Meteorol. 30(05), 905–910 (2014) 21. Tang, J., Dai, K., Zong, Z.P., et al.: Methods and platform realization of the national QPF master blender. Meteor. Mon. 44(8), 1020–1032 (2018) 22. Toth, Z., Kalnay, E.: Ensemble forecasting at NMC: the generation of perturbations. Bull. Am. Meteorol. Soc. 74(12), 2317–2330 (1993) 23. Toth, Z., Kalnay, E.: Ensemble forecasting at NCEP and the breeding method. Mon. Weather Rev. 125(12), 3297–3319 (1997) 24. Zehe, E., Becker, R., Bardossy. A.: The influence of spatial variability of soil moisture and precipitation on runoff production (2001) 25. Zhang, F.H., Cao, Y., Xu, J., et al.: Application of the logistic discriminant model in heavy rain forecasting. Meteor. Mon. 42(4), 398–405 (2016) 26. Zhang, H.B., Chen, J., Zhi, X.F., et al.: Design and comparison of perturbation schemes for GRAPES_meso based ensemble forecast. Trans. Atmos. Sci. 37(03), 276–284 (2014)

Artificial Intelligence Research on Visibility Forecast Chao Xie and Xuekuan Ma(&) National Meteorological Center, Beijing, China [email protected]

Abstract. The meteorological data in 2000 to 2017 from the China observation meteorological stations were collected for research. The multiple time scales variation characteristics and relations between visibility and meteorological elements were studied to summarize the weather conditions of low visibility weathers. The selecting factors which related to visibility and its change were input into an artificial neural network model for training. The long-term and meticulous visibility forecast of observation stations in China were calculated through the European Centre for Medium-Range Weather Forecasts (ECMWF) data. The error and TS score detection showed that the model had better reference than the China Meteorological Administration Unified Atmospheric Chemistry Environment model (CUACE) in the first half year of 2018. Keywords: Low visibility


Neural network
Model release

1 Introduction Along with the increase of aerosol emissions in various urban areas in China, low visibility weather occurs frequently [1], which not only affects the normal operation of highways and civil aviation, but also causes continuous heavy pollution incidents and endangers public health and safety. Visibility changes have similar regularity and periodicity on a larger time scale, which is mainly affected by climate, topography and other factors, while the frequent fluctuations of visibility in the same time-space region are caused by meteorological and environmental factors [2]. Precipitation [3], boundary layer height, wind direction [4], wind speed [5], temperature [6], relative humidity [7], pollutant concentration [8, 9] and other factors affect the atmospheric extinction characteristics in different ways, and then change the visibility. The optical properties of aerosols are determined by the chemical composition, the particle size, shape and mixed state of the aerosol, and these properties are related to meteorological factors [10]. In recent years, with the increasing scale of industrial production, the hygroscopic growth of pollutants in aerosols was gradually responsible for the frequent occurrence of long-term low visibility weather [11]. Visibility has strong nonlinear characteristics. The Neural Network prediction method directly output the prediction data by inputting various types of related data [12], and uses its better approximation ability for nonlinear data, and can better predict the air quality of a specific city group or a larger area. Fully connected neural network using back propagation algorithm is a mature artificial intelligence method. It can © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 455–461, 2019. https://doi.org/10.1007/978-981-13-7123-3_53

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approximate any nonlinear function through the hidden layer and linear output layer by using signal forward propagation and error back propagation [13]. However, fully connected neural network is subject to more conditions in practical applications due to its shortcomings in convergence, difficulty in training, easy to fall into local minimum, and no feedback in one-way propagation [14].

2 Weather Characteristics in Low Visibility Weather In order to ensure the physical significance of the input factors of the neural network model, the correlation between the meteorological observation factors and visibility of the national meteorological stations from 2000 to 2016 was analyzed. We analyzed nineteen surface and near-surface meteorological observation factors such as pressure, temperature, humidity, wind speed and wind direction and their daily changes by person correlation analysis method. The result showed that the p values were all less than 0.05 and was statistically significant. The stable pressure field and the sparse pressure gradient are beneficial to the formation of low visibility weather. When the pressure is low, the stable pressure field will cover the boundary layer and enrich particulate matter and water vapor in the boundary layer. In this way the possibility of low visibility weather significant increase. The relationship between visibility and temperature change is more complicated. On the one hand, when the pressure change is not obvious, the negative temperature change is mainly caused by radiation cooling, which is conducive to the formation of radiation fog, resulting in a reduction of visibility. On the other hand, when the pressure change is a large positive value, the negative temperature change at this time is mainly caused by the invasion of cold air, resulting in an increasing of visibility. When the temperature in the boundary layer rises, the vertical exchange of the atmosphere is strengthened, and the stability of the atmospheric stratification is reduced, which is conducive to the dilution of the pollutants and the dissipation of the mist. Meanwhile, the relative humidity in the air is lowered resulting in a significant increasing of visibility. Relative humidity has a significant effect on visibility. Water vapor changes visibility by changing the optical properties of aerosols in the boundary layer. On the one hand, the extinction of water vapor itself will reduce visibility, on the other hand, the moisture absorption of particulate matter in the aerosol increases, and the extinction increases significantly, resulting in a reduction of visibility. Wind has a dilution effect on pollutants and water vapor at the horizontal level. It is difficult to appear fog and haze weather in gale weather. However, the blowing sand or floating weather in Northwest China may occur with the increase of wind speed, which significant reducing the visibility. Meanwhile, the topography and the urban underlying surface affected the ground wind field. The effect of diffusion dilution cannot be fully reflected. The wind direction also has an effect on visibility. The warm and humid air current blown from the sea surface to the land area is rich in high water vapor, increasing the possibility of low visibility weather. the bias from the north Although the northerly wind is relatively dry, it is not conducive to low-visibility weather, but the polluted air mass from the north may also increase the aerosol concentration in China and reduce visibility. When the vertical wind shear (WS) near the formation is low, turbulent

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mixing almost does not exist. It’s easy to appear low visibility weather with the vertical stratification in a stable state. In the opposite case, when the vertical WS in the nearsurface layer is large, the vertical layer is in an unstable state. The elements of the boundary layer are evenly mixed and are not prone to low visibility weather. Meanwhile, short-term heavy rainfall, thunderstorm gale and hail weather, which reduces visibility, are prone to occur in the case of high vertical wind shear, so the correlation between wind shear and visibility is affected. The inversion phenomenon in the boundary layer will hinder the vertical convection of the air meanwhile reduce the pollutant capacity of the boundary layer. In this case the pollutants, the combined air and gas bubbles cannot be exchanged vertically and gradually accumulate under the low environmental capacity result in a significantly decreasing of visibility. When the humidity above 850 hPa is high, it indicates that the unstable weather system is developing and then the cloud amount in the sky will increase obviously or precipitation will occur. At this time, there will be no fog. But when the surface humidity is close to saturation and the height above 850 hPa is relatively dry, namely an “upper dry and lower wet” atmosphere structure, which is more prone to foggy weather [15].

3 Construction of the Neural Network Visibility Forecasting System Quality control of the meteorological data in 2000–2017 in China was conducted to remove missing and abnormal values. Input factors are selected after correlation analysis. Linear or nonlinear parameterization of input factors is carried out to speed up the convergence speed of the network and enhance the sensitivity of the neural network to low visibility weather. The three-layer structure and the gradient descent search algorithm were adopted. Based on the empirical formula, the neural network parameters are determined through repeated experiments to ensure reliability, stability and versatility of the neural network. The overall input data is often divided into multiple sets of data and modeled separately. However, this method destroys the integrity of the original data set, especially the ability to fit low visibility weather with small data samples. After repeated experiments, the reliability and stability of the prediction model can be determined, and the “over-fitting” phenomenon of the neural network can be prevented. The reasonable parameters of the final determined neural network are as shown in the Table 1. The number of hidden layer nodes is four layers. The determination of the number of hidden layer nodes is directly related to the rationality of model construction and the final forecasting effect. Increasing the number of hidden layer nodes may improve the convergence accuracy of the network. However, the excessive number of hidden layer nodes will reduce the ability of the network to identify samples with noise and lead to “over-fitting” phenomenon. The variable learning rate and the momentum factor are used to dynamically select the value according to the effect of the last solution in the objective function to shorten the learning time. The ownership value was initialized and then the initial value is randomly selected and adjusted in the operation.

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Parameter The number of hidden layer nodes Learning rate

Normalization of sample data Order of sample data Eliminate interference samples System deviation correction

Principle Balance of generalization ability and fit ability

Result Four

Learning rate a and momentum factor b dynamically take values to shorten the network learning time Avoid the saturation region of the Sigmoid function Effectively to improve training speed The network has invalid sample sets with continuous features Samples with too large fitting errors should be rejected, and excessive rejection will result in poor representativeness of the sample set Positive deviation for low-visibility fitting results and negative bias for high visibility

Dynamic value

Input value is between 0.2 and 0.8 Random sort The rejection rate is less than 5% of the total

Calculate the fitting deviation between the partitions of historical data

After the stability experiment the neural network forecasting model was determined. Ground monitoring meteorological data such as temperature, pressure, humidity, wind speed and the ECMWF high-altitude data such as humidity, wind speed were input to the model so as to obtain the visibility forecast of 10 days with a 3 h interval.

4 Results After the establishment of the model, the forecast effect of the visibility of the national meteorological stations from January 1, 2008 to June 30, 2008 was tested. The daily average errors of forecast data and corresponding observation data were compared. The Mean absolute error (MAE), Root mean square error (RMSE) and System error (SE) were utilized to test the accuracy and generality of the forecasting model. Meanwhile, the CUACE visibility forecast model of 3 days were introduced to this test by the same method. The test results were shown in Table 2. As shown in Table 2, the neural network model had a better performance than the CUACE model in Absolute error test and Root mean square error test. The accuracy of the neural network model was maintained at a high level during the 10-day forecast process, and gradually weakened by the extension of the forecast time. This is due to the model belongs to off-line model, the accuracy of ECMWF data itself decreases with the extension of prediction time and the time series iteration caused by the prolongation of forecast date in the process of neural network modeling leads to the decrease of forecast precision. The neural network model also had a better performance than

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Table 2. Error analysis of ANN visibility (outside the brackets) and CUACE forecast visibility (outside the brackets) to monitoring data Error MAE

024 h 3.90 (5.51) RMSE 5.06 (6.86) SE –1.41 (2.94)

048 h 4.03 (5.55) 5.25 (6.95) –1.74 (2.90)

072 h 4.07 (5.49) 5.31 (6.93) –1.76 (3.08)

096 h 120 h 144 h 168 h 192 h 216 h 240 h 4.13 4.18 4.25 4.34 4.52 4.52 4.73 5.37

5.41

5.50

5.58

5.78

5.78

6.01

–1.83 –1.86 –1.88 –1.89 –1.90 –1.97 –2.00

CUACE model in System error test. It is shown that the model maintains good stability in multi-station and multi-time prediction. The visibility prediction of neural network is less than the actual value, mainly due to the systematic error of ECMWF data. In order to highlight the forecasting effect of the neural network model on the difference visibility level, the average visibility of meteorological stations during the period from January 1, 2018 to June 30, 2018 was classified according to less than 2 km, 2–5 km and 5–10 km, respectively. The daily average TS score of 1–10 days is the correct number of predictions divided by the number of correct predictions, the number of empty reports, and the number of false negatives. Meanwhile, the CUACE visibility forecast model of 3 days were introduced to this test by the same method. The test results were shown in the Table 3. Table 3. Forecast TS score of ANN visibility (outside the brackets) and CUACE forecast visibility (outside the brackets) TS score

> > If k is < ix gi ¼ If kix is > > > : If k is ix

better than Kih ; then gi ¼ 1 worse than Kic ; then gi ¼ 0 jKix Kiz j better than Kiz ; and worse than Kih ; then gi ¼ jK  0:6 þ 0:4 ih Kiz j ix Kic j better than Kic ; and worse than Kiz ; then gi ¼ jK jKiz Kic j  0:4

ð3Þ Based on evaluation weights di, putting the evaluation ideas of interval scoring method and interval increment method into the evaluation process, we obtain the service perception scoring results. Video Perception Score ¼

n X

di  gi

ði ¼ 1; 2; . . .; nÞ

ð4Þ

i¼1

gi is for the evaluation coefficient of KQI index, di is for the evaluation weights.

4G Video Service Stalling Perception Evaluation and Optimization

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3 Model Verification 3.1

Analysis Object

We select 38,441 cells with the poor stalling rate as objects for model verification. 3.2

Weight

Firstly, the stalling rate is calculated by the segment, and then the correlation coefficient is obtained by means of calculation according to Formula (1). Finally, five KQIs whose absolute values of correlation coefficient are >0:8 are obtained (Tables 1 and 2). Table 1. The correlation coefficient of selected KQIs Indicator

Downlink RTT delay

Streaming media valid download rate

Downlink TCP retransmission rate

Proportion of the low video download rate/Video bitrate sampling

Proportion of low rate sampling

The absolute correlation coefficient

0.88

0.88

0.84

0.82

0.81

Calculate the weight of each KQI based on Formula (2). Table 2. The Weight of Each KQI KQI

Proportion of low rate sampling

Weight

19.04

3.3

Proportion of the low video download rate/Video bitrate sampling 19.34

Downlink RTT delay

20.81

Streaming media valid download rate 20.88

Downlink TCP retransmission rate 19.93

Threshold

We collect statistics on selected KQIs based on the number of cells, and then we obtain the distribution curve. Based on the evaluation threshold, the grading threshold of each KQI is as Table 3. Table 3. The grading threshold of each KQI KQI Proportion of the low video download rate/Video bitrate sampling Downlink RTT Delay Proportion of low rate sampling Downlink TCP Retransmission Rate Streaming Media Valid Download Rate

Good 0.01%

Median 0.02%

Poor 0.03%

80 ms 0.02% 0.04% 6 Mbps

140 ms 0.05% 0.08% 4.5 Mbps

200 ms 0.08% 0.14% 3 Mbps

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Complete the perception score of all cells by referring to the evaluation model, and then calculate the stalling rate of each cell by segment to obtain the fitting curve of the score and the stalling rate (Fig. 3).

Fig. 3. The fitting curve of the perception score and stalling rate

The mapping between the score model and the actual stalling rate is high. The stalling rate corresponding to the curve 60 points is 1.90%, and the average stalling rate 1.74% corresponds to 70 points. There is an obvious correlation between two curves. For 38441 cells, the precision rate is 73% and the recall rate is 80.96%. Based on the traditional analysis method, this is a good result. The scope and accuracy of the check are considered, which proves that the evaluation model is effective and feasible.

4 Optimization Effect During network optimization, the video stalling rate evaluation model is used to evaluate video services based on the service IP address, Gateway, transmission IP RAN, and wireless network cells. We can identify network elements with poor quality based on scores. The score of IPRAN-22 on the transmission ring is significantly lower than that on others. The cause is that the network delay increases due to high transmission load. After the bandwidth is expanded, the indicator becomes normal (Table 4). Table 4. The score of IPRAN-22 on the transmission ring IPRAN no.

Streaming media valid download rate

Downlink Proportion Proportion of the Downlink TCP Evaluation RTT delay of low rate low video download retransmission score sampling rate/Video bitrate rate sampling

IPRAN-22 IPRAN-125

2.43 Mbps 216 ms 4.85 Mbps 55 ms

10.09% 4.43%

1.82% 0.66%

54.00% 5.00%

28.74 87.39

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Based on the evaluation of wireless network cells, TOP 1,000 cells with poor scores are filtered out. We associate 4G network performance counters to locate the problem, and implement optimization by means of carrier capacity expansion, parameter optimization, and radio frequency adjustment. Then, the stalling rate of Tencent video services is improved by 0.27PP, greatly improving work efficiency (Fig. 4).

Stalling Rate of Tencent Video 2.45%

2.50% 2.40% 2.30%

2.18%

2.20% 2.10% 2.00% Before optimization

After optimization

Fig. 4. The optimization effect of tencent video stalling rate

5 Conclusion This paper presents an effective and feasible evaluation model of 4G mobile video service perception, based on the network operation and user service big data analysis. With the application of the model, the video service stalling problem evaluation and locating efficiency is greatly improved. In addition, it supports efficient implementation of optimization and improves user experience in 4G network video services. At the same time, it provides a valuable reference for E2E (end to end) perception evaluation and optimization.

References 1. China Internet Network Information Center: The 42nd China Statistical Report on Internet Development, July 2018 2. Xu, L., Zhao, X., Luan, Y., et al.: User perception aware telecom data mining and network management for LTE/LTE-advanced networks. In: 4th International Conference on Signal and Information Processing, Networking and Computers, pp. 237–245. Springer, Qingdao (2018) 3. Xu, L., Zhao, X., Yu, Y., et al.: Data mining for base station evaluation in LTE cellular systems. In: 3rd International Conference on Signal and Information Processing, Networking and Computers, pp. 356–364. Springer, Chongqing (2017) 4. Xu, L., Luan, Y., Cheng, X., et al.: WCDMA data based LTE site selection scheme in LTE deployment. In: 1st International Conference on Signal and Information Processing, Networking and Computers, pp. 249–260. CRC Press Taylor & Francis Group, Beijing (2015)

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5. Chen, C., Ke, J., Qin, D.: Discussion on Video Service Experience Evaluation and Optimization. Designing Techniques of Posts & Telecommunications (2017) 6. Xu, L., Luan, Y., Cheng, X., et al.: Telecom big data based user offloading self-optimisation in heterogeneous relay cellular systems. Int. J. Distrib. Syst. Technol. 8(2), 27–46 (2017) 7. Xu, L., Cheng, X., et al.: Mobility load balancing aware radio resource allocation scheme for LTE-advanced cellular networks. In: 16th IEEE International Conference on Communication Technology, pp. 806–812. IEEE Press, Hangzhou (2015) 8. Abdellah, S., Sara, M., El-Houda, MN., Samir, T.: QoS and QoE for mobile video service over 4G LTE network. In: Computing Conference, pp. 1263–1269 (2017) 9. Liu, L., Zhou, WA., Song, J.: Quantitative customer perception evaluation for telecommunication service. In: International Conference on Pervasive Computing & Applications, vol. 2, pp. 912–915 (2009)

Application of the Bayesian Processor of Ensemble to the Combination and Calibration of Ensemble Forecasts Yi Wang1, Xiaomei Zhang2(&), and Zoltan Toth3 1

2

National Meteorological Centre, China Meteorological Administration, Beijing 100081, China Public Meteorological Service Centre, China Meteorological Administration, Beijing 100081, China [email protected] 3 Global Systems Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA

Abstract. Ensemble forecasts are developed to assess and convey uncertainty in weather forecasts. Unfortunately, ensemble prediction systems (EPS) usually underestimate uncertainty and thus are statistically not reliable. In this study, we apply the Bayesian Processor of Ensemble (BPE), which is an extension of the statistical post-processing method of Bayesian Processor of Forecasts (BPF) to calibrate ensemble forecasts. BPE is performed to obtain a posterior function through the combination of a regression-based likelihood function and a climatological prior. The method is applied to 1–10 day lead time EPS forecasts from the NCEP Global Ensemble Forecast System (GEFS) and the Canadian Meteorological Centre (CMC) of 2-m temperature at 24 stations over the continental United States (CONUS). Continuous rank probability score is used to evaluate the performance of posterior probability forecasts. Results show that post-processed ensembles are much better calibrated than the raw ensemble. In addition, merging two ensemble forecasts by incorporating the CMC ensemble mean as another predictor in addition to GEFS ensemble forecasts is shown to provide more skillful and reliable probabilistic forecasts. BPE has a broad potential use in the future given its flexible framework for calibrating and combining ensemble forecast. Keywords: Ensemble forecasting


Statistical post-processing

1 Introduction Ensemble prediction systems (EPS) were developed to quantify the uncertainty in numerical weather forecasts. However, EPS’ are often under-dispersed and tend to be biased [1]. To address these issues, a variety of statistical postprocessing methods for ensemble forecasts have been proposed [2]. These methods produce a probability density function (PDF) for the weather variable of interest, which generally outperforms the raw ensemble in terms of satisfying the underlying goal of “maximizing sharpness subject to calibration” [3]. The Meteorological Development Laboratory © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 487–494, 2019. https://doi.org/10.1007/978-981-13-7123-3_57

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(MDL) of National Weather Service (NWS) has developed a MOS-based technique called ensemble model output statistics (EMOS) [4]. There also exist alternative approaches such as Nonhomogeneous Gaussian regression (NGR) [5], extended logistic regression [6] and decaying-average bias correction [7]. In the context of Bayes theory, Bayesian model averaging (BMA) [8] is a widely used postprocessing method. However, the main difficulty with the BMA is that many parameters and weights are needed to be estimated. Hamill [9] discussed issues of disadvantage of BMA method related to overfitting. Hodyss [10] pointed out the tendency of overweighting climatological information in BMA. By implementing the Bayes’ basic rule, Krzysztofowicz and Evans [11] introduced the Bayesian Processor of Forecasts (BPF). This method firstly transformed climatological data of predictand to a normal distribution which provides the prior. Then the prior was updated based on a PDF estimated through a correction of the current forecasts using the regression relationships estimated from the joint sample of transformed forecasts and observations. Rather than attempting to calibrate the forecast solely based on the recent forecast-observation pairs, BPF aims to update the climatological prior with the likelihood. Thus information from climatic sample and joint sample is optimally fused. Merging different EPS’ into multi-center ensembles has recently gained increased interest, since these combined ensemble forecasts improve the skill of the single probabilistic forecasts. One example is the North American Ensemble Forecasting System (NAEFS [12]), merging the NCEP and the Canadian Meteorological Centre (CMC) ensemble. In this paper, we use the Bayesian Processor of Ensemble (BPE) that was recently developed by Krzysztofowicz [13] as an extension of BPF to utilize uncertainty information from EPS. 2-m temperature (T2M) data are used in this study for application of BPE to ensemble forecast calibration and combination. The structure of the article is as follows: Sects. 2 and 3 describe the data and the BPE methodology used. Section 4 illustrates the application of BPE to T2M ensemble forecasts from NAEFS and assesses the skill and reliability of calibrated ensemble forecasts, while Sect. 5 offers a summary and some discussion.

2 Observations and NAEFS Forecast Data The observational temperature data set that is used in this study was provided by MDL of NWS. It contains T2M observations from 24 stations over the conterminous United States (CONUS) (Fig. 1) during 1977–2010. The NAEFS combines the National Centers for Environmental Prediction’s (NCEP) Global Ensemble Forecast System (GEFS) with the Canadian Meteorological Center (CMC)’s ensemble prediction system. Both GEFS and CMC systems contain a control forecast and 20 perturbed members. All data were taken from the 0000 UTC runs with lead time from 24 to 240 h. The gridded ensemble forecasts were interpolated to the 24 station sites. In this study we focus on the warm half year season of April– September. Similar results were found for the cold half year of October–March (not shown). Two seasons were used for development of BPE (2008, 2009) and the third season (2010) was used for cross-validation.

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Fig. 1. Spatial distribution of 24 stations over CONUS

3 Methodology The idea of the Bayesian Processor of Ensemble (BPE) is to build a likelihood function from the joint sample of ensemble forecasts and observations, and to combine it with a prior distribution to obtain the posterior distribution of the predictand. According to Bayes theorem: £ðwÞ ¼

f ðXjwÞgðwÞ kðXÞ

ð1Þ

Here gðwÞ is the prior density function of the predictand (I.e., climatological distribution); f ðXjwÞ is called the likelihood function of the predictand and describes the conditional probability density of the forecast given an observed value; kðXÞ is the expected density function of the predictor vector X and £ðwÞ is the posterior density function. Thus, the BPE includes three components: (i) estimation of the prior distribution, (ii) modeling of the likelihood function, and (iii) determination of the posterior distribution. Details of the procedures are found in Krzysztofowicz [13]. Prior distribution for the predictand Prior distribution is based on a long climatic sample. In this study we use 30 years from 1977 to 2006. To increase the sample size, data are pooled from consecutive 5 days. The daily time series are standardized to guarantee the stationarity of the first two moments. Seasonality effect is also removed after standardization. Instead of estimating a different distribution function for every day of the season, it is sufficient to estimate just one distribution function. From a set of candidates, the Weibull distribution is chosen as it fits the sample best (not shown). 3.1

Modeling of the Likelihood Function

A joint sample of predictand (wk , observed T2M) and predictor (xk , ensemble forecast) is retrieved from the observational and ensemble forecast datasets. First, each joint realization (xk , wk ) is standardized using the climatic mean mk and the climatic standard deviation Sk for day k:

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x0k ¼

xk  mk 0 wk  mk ; wk ¼ Sk Sk

ð2Þ

Next step is to model the stochastic dependence between the standardized forecast and predictand. Here we employ the meta-Gaussian likelihood model of Krzysztofowicz and Kelly [13] using the normal quantile transform (NQT): Z ¼ Q1 ðK 0 ðx0 ÞÞ;

V ¼ Q1 ðG0 ðw0 ÞÞ;

ð3Þ

where G0 is the prior distribution, K 0 denotes the marginal distribution function of the standardized forecast x0 ; Q is the standard normal distribution function, and Q1 is its inverse. The NQT guarantees that each marginal distribution function is Gaussian in the sample space of the transformed variates (Z, V). The likelihood parameters a, b and r2 can be estimated through: EðZÞ ¼ aV þ b,VarðZÞ ¼ r2

3.2

ð4Þ

Determination of the Posterior Distribution

After the likelihood parameters have been estimated, the posterior parameters can be calculated: A¼

a2

a ab r2 ;B ¼ 2 and T 2 ¼ 2 2 2 a þr þr a þ r2

ð5Þ

The posterior distribution is obtained by simply combining the prior with the likelihood function, where the posterior distribution function £ of w, is specified by the equation 

  1  1 1 Q ðGðwÞÞ  AQ ðKðxÞÞ  B £ðwÞ ¼ Q T

3.3

ð6Þ

Assessment of Forecast Skill and Reliability

To assess the performance of the calibrated ensemble forecasts, the Continuous Ranked Probability Score (CRPS) is used [14]. CRPS compares the difference between cumulative distribution functions (CDFs) of the forecast with the observation. The CRPS is a negatively oriented score; hence the lower the CRPS, the better the probabilistic system performs. Users might prefer a reliable system over a sharper forecast which is not reliable. In this paper, reliability is assessed through the decomposition of CRPS into reliability and potential components proposed by Hersbach [15]:

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CRPS ¼ Rel þ Potential

ð7Þ

The reliability component Rel measures the ability of the EPS to be statistically consistent by comparing the observed frequency of events with their forecast probability. When perfectly calibrated (i.e., zero Rel error), a given EPS system would reach its lowest, Potential CRPS value, which is also a function of the observational data.

4 Results The BPE coefficients are developed and then applied for each station and each lead time independently. Two seasons of a joint sample (2008, 2009) is used as training data, and a third year (2010) is used as the cross-validation data. BPE outputs calibrated distribution functions, quantile functions, density functions, and ensemble members. Here we use 11 posterior quantiles (0.05, 0.1, 0.2, … 0.9, 0.95) for verification. The verification is carried out for all sites and CRPS and its reliability component is then averaged over the 24 stations and the entire verification period. To assess the effect of merging different ensemble forecast systems, we designed and tested two configurations of BPE with different potential predictors for the multilinear regression for central tendency of the likelihood function. Table 1 gives the configuration name and the potential predictors. For simplicity, both configurations use the GEFS ensemble as a predictor for both the central tendency and uncertainty. The GEFS control is also offered as a predictor of central tendency in both configurations, while BPE-GEFS_CMC also adds the CMC ensemble mean as a potential predictor. Table 1. Configuration of BPE with different potential predictors for the multi-linear regression for central tendency of likelihood function Model name Potential predictors of T2M BPE-GEFS GEFS Control Member, GEFS Ensemble Mean BPE-GEFS_CMC GEFS Control Member, GEFS Ensemble Mean, CMC Ensemble Mean

The reliability component of CRPS is displayed in Fig. 2b. Short range raw 2mT GEFS forecasts exhibit a large (up to 3.5 F) calibration error over both the dependent (2008–2009) and independent (2010) verification periods. This is due to the lack of initial surface perturbations in the GEFS system, highlighting the need for the statistical calibration of raw ensemble forecasts. Due to their miscalibration, CRPS (Fig. 2a) for the raw short range GEFS is also high (above 4.5 F). This leads to the unusual, mostly flat raw GEFS CRPS curves. In contrast, the calibration component of the BPE-GEFS system is significantly reduced to around 0.15 F at all lead times, both for the dependent and independent samples. Therefore, post-processed ensemble forecasts accurately represent the uncertainty and are very reliable. This is due to the self-calibration property of the Bayesian

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Fig. 2. (a) CRPS and (b) its decomposition in reliability of raw GEFS and BPE-GEFS calibrated ensemble forecast averaged over 24 stations during summer half year for training sample (2008/09) and independent sample (2010).

formulation used in BPE [16]. Correspondingly, CRPS for the BPE-GEFS system is significantly lowered, starting from above 2 F and reaching just below 4 F at longer lead times. As with the reliability component, BPE-GEFS exhibits similarly low scores for CRPS over the dependent and independent verification periods (cf. red vs blue dotted curves in Fig. 2a), indicating a robust performance with no apparent overfitting. BPE is set up to combine forecasts from any number of systems. When the CMC ensemble mean is included as an additional potential predictor for central tendency (BPE-GEFS_CMC) beyond the GEFS ensemble and control forecasts (BPE-GEFS), CRPS is generally lowered (except at Day 9 lead time for 2010), thus extending predictability by 12–24 h (Fig. 3a). The combined BPE-GEFS_CMC configuration displays calibration scores even better than the already low values found for BPE-GEFS (Fig. 3b).

Fig. 3. (a) CRPS and (b) its decomposition in reliability of BPE-GEFS, BPE-GEFS_CMC, calibrated ensemble forecast averaged over 24 stations during summer half year for training sample (2008/09) and independent sample (2010).

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The above verification results indicate the value of post-processing raw ensemble forecasts with BPE in terms of enhanced skill and reliability. Encouragingly, the results on dependent data carry over to an independent verification period. Since BPE can combine independent input information from multiple forecast systems, it can be used to merge output from multiple ensemble forecast systems.

5 Discussion and Conclusion A post-processing method called Bayesian Processor of Ensemble (BPE) is utilized to calibrate ensemble forecasts. The scheme is based on Bayes basic rule. The BPE technique includes three steps: (i) estimation of the prior distribution, (ii) modeling of the likelihood function, and (iii) determination of the posterior distribution. An appealing aspect of BPE is that it leverages a long-term climatology thus reducing the need for large training datasets (i.e., reducing the demand on the costly generation of hind-cast datasets). It also avoids some other disadvantages of the BMA approach. We applied the BPE technique to GEFS control and ensemble 2-m temperature forecasts. Results obtained for 24 stations over the CONUS indicate that BPE produces forecasts with negligible calibration error that are also significantly more skillful than the raw ensemble forecasts. When the CMC ensemble mean forecast is also considered as a central tendency predictor, BPE gains 12–24 h additional skill beyond using GEFS input only. BPE can also be configured to use additional predictors from any number of single value or ensemble forecasts, as well as “auxiliary” predictors (i.e., forecast variables other than the predictand, e.g., 850 hPa T and/or 1000/500 hPa thickness for predicting 2m T). Given its flexible framework and multiple output formats, BPE can be used in a multitude of applications, including the blending and calibration of information from multiple forecast systems. Acknowledgements. We thank Kevin Kelleher, former Director of GSD/ESRL/NOAA for his support. The work was sponsored by the National Key Research and Development Program of China (2017YFC1502004) and “Key technology research on medium range forecast” (2015BAC03B01) project. The detailed algorithm of the BPE was developed by Professor Krzysztofowicz of the University of Virginia and coded by Geary Layne of GSD. We acknowledge discussions with Mark Antolik and Jeffrey Craven of MDL. Data were kindly provided by John Wagner and Carly Buxton of MDL.

References 1. Hamill, T.M., Colucci, S.J.: Verification of Eta-RSM short-range ensemble forecasts. Mon. Weather Rev. 125, 1312–1327 (1997) 2. Wilks, D., Hamill, T.: Comparison of Ensemble-MOS Methods using GFS Reforecasts. Mon. Weather Rev. 135, 2379–2390 (2007) 3. Gneiting, T., Balabdaoui, F., Raftery, A.E.: Probabilistic forecasts, calibration and sharpness. Roy. Stat. Soc. 69, 243–268 (2007)

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4. Glahn, H.R., Peroutka, M.R., Wiedenfeld, J., Wagner, J.L., Zylstra, G., Schuknecht, B., Jackson, B.: MOS uncertainty estimates in an ensemble framework. Mon. Weather Rev. 137, 246–268 (2009) 5. Gneiting, T., Raftery, A.E.: Weather forecasting with ensemble methods. Science 310, 248– 249 (2005) 6. Wilks, D.S.: Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteorol. Appl. 16, 361–368 (2009) 7. Cui, B., Toth, Z., Zhu, Y., Hou, D.: Bias correction for global ensemble forecast. Weather Forecast. 27, 396–410 (2012) 8. Raftery, A.E., Gneiting, T., Balbdaoui, F., Polakowskil, M.: Using Bayesian model averaging to calibrate forecasts. Mon. Weather Rev. 133, 1155–1174 (2005) 9. Hamill, T.M.: Comments on ‘‘Calibrated surface temperature forecasts from the Canadian ensemble prediction system using Bayesian Model Averaging’’. Mon. Weather Rev. 135, 4226–4236 (2007) 10. Hodyss, D., Satterfield, E., McLay, J., Hamill, T.M., Scheuerer, M.: Inaccuracies with multimodel postprocessing methods involving weighted, regression-corrected forecasts. Mon. Weather Rev. 144, 1649–1668 (2016) 11. Krzysztofowicz, R., Evans, W.B.: Probabilistic forecasts from the national digital forecast database. Weather Forecast. 23, 270–289 (2008) 12. Candille, G.: The multi-ensemble approach: the NAEFS example. Mon. Weather Rev. 137, 1655–1665 (2009) 13. Krzysztofowicz, R.: Algorithms Implementing Bayesian Processor of Ensemble (BPE), p. 75 (2016). Available upon request (2016) 14. Matheson, J.E., Winkler, R.L.: Scoring rules for continuous probability distributions. Manage. Sci. 22, 1087–1096 (1976) 15. Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for ensemble prediction systems. Weather Forecast. 15, 559–570 (2000) 16. Krzysztofowicz, R.: Decision criteria, data fusion, and prediction calibration: a Bayesian approach. Hydrol. Sci. J. 55, 1033–1050 (2010)

Initial Analysis of the Cell Selection Progress in SA of 5G NR Zetao Xu1(&), Yang Zhang2, Ao Shen1, Bao Guo3, Yuehua Han3, and Yi Liu4 1

China Mobile Group Design Institute Co., Ltd., Beijing, China [email protected] 2 Department of Networks, CMCC, Beijing, China 3 China Mobile Group Shanxi Co., Ltd., Taiyuan, China 4 China Mobile Group Shandong Co., Ltd., Jinan, China

Abstract. As the commercialization of the 5th generation mobile networks is approaching globally, 5G NR standard is deemed as a competitive candidate who abstracts a mass of concern from the industry. However, there are still some blurry conceptions and issues regarding real network deployment needed to be further studied. 5G NR standardization claims there are non-standalone (NSA) and standalone (SA) network deployment schemes, respectively. No matter how it would be chosen, the UE cell selection mechanism is an inevitable fundamental process for end-to-end communication service, which needs to be further concerned. For the purpose of network operators’ daily optimization & management in the future stage, this paper briefly analyzed UE cell selection process for NSA and SA networking scheme, respectively. Keywords: 5G new radio (NR)


Non-standalone (NSA)
Standalone (SA)

1 Introduction With the acceleration of the commercial pace of the 5th generation mobile networks worldwide, ultra-high speed, ultra-high spectral efficiency, ultra-low latency and massive connectivity based on massive IoT have become the consensus of the next generation broadband mobile communication networks. 5G NR technology adopts application-oriented design ideas in the formulation of standards, and adapts to the differentiated networking requirements through flexible and variable system-level parameter adjustment. The 5G NR has attracted wide attention from domestic and foreign operators due to its targeted design for new spectrum (millimeter wave). In the standard setting phase, 5G NR has formulated two networking modes according to the actual networking development requirements. One is non-standalone networking mode that needs to be combined with the existing 4G network, and the other is standalone networking mode that can be independently deployed and operated. As the development of standardization, the related networking technology details are still being improved. No matter which networking mode is adopted, the UE cell selection mechanism is the basic process for implementing end-to-end communication in any communication system, and it is also an important entry point for subsequent network © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 495–504, 2019. https://doi.org/10.1007/978-981-13-7123-3_58

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operation and maintenance optimization to enhance user perception. This paper is based on the preliminary analysis of the UE cell selection mechanism in the nonstandalone and standalone networking modes.

2 5G NR Non-standalone Networking and Standalone Networking Technology 2.1

5G NR Non-standalone Networking Technology

In order to meet the ever-expanding global connectivity needs and provide diversified service experience, 5G NR has re-designed a new 5G unified air interface design based on OFDM technology. In addition to new spectrum usage, greater air interface transmission bandwidth, new waveform modulation and channel coding techniques, CUDU separation architecture, new multi-antenna array technology and other new features, one of the most noteworthy features is that it provides a flexible frame design, and differentiated services are provided through flexible and tunable system-level parameters for different applications (eMBB, URLLC, mMTC). Based on the flexible framework design concept, 5G NR defines two networking architectures, one is nonstandalone networking mode, and the other is standalone networking mode. Among them, the non-standalone networking mode relies on the existing 4G LTE network for fusion networking. This solution leverages the existing 4G core network and transmission network to pass the dual connection in the form of “subcarrier” (E-UTRA NR Dual Connectivity, EN-DC) provides high-speed data services. The schematic diagram of the LTE-Assisted 5G NR networking architecture is shown in Fig. 1.

Fig. 1. 5G NR non-standalone networking architecture.

In addition to the two NSA candidate schemes illustrated in Fig. 1, there are some other candidates. However, it is generally believed that the candidate of the operator’s

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initial most feasible system is the one on the left of Fig. 1 [1]. This paper does not cover the other candidates. We can choose between co-site and non-co-site for NSA networking deployment. 2.2

5G NR Standalone Networking Technology

The ultimate form of any kind of communication system evolution is to provide information services independently, and 5G systems are no exception. 5G adopts the standalone networking architecture, which has produced a series of changes from the access side to the core network element. The core network adopts a service-based “busbased” network architecture (SBA). A new interface form is defined between the access network, the core network and the access network element. The access network can also be separated according to the Central Unit (CU) and the Distributed Unit (DU) subnet to promote the segmentation of the protocol stack and further decoupling of the system software and hardware, business and resources, and achieving processing delay and optimization improvement of system performance [2]. It is shown in Fig. 2.

Fig. 2. 5G NR standalone networking architecture diagram (including CU/DU separation).

The 5G NR still follows the design concept of control plane and user plane separation in LTE. The SDAP sublayer is added to the user plane to provide the QoS mapping relationship between the core network and the data radio bearer (DRB).

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SA and NSA Technology Comparison

For individual terminals, the NSA has excellent downlink rate due to 4G/5G dual connectivity. According to relevant statistics, NSA is better than SA7%. Because of independent 5G dual-issue, the uplink peak rate is better than NSA, field test verification. The uplink rate SA is 87% better than the NSA, but the uplink edge rate is relatively low. Coverage performance: The NSA can achieve continuous coverage with the existing 4G network, and can quickly perform 5G deployment. The dual connectivity technology can be seamlessly switched to ensure service connectivity. The SA mode is relatively high due to the 5G frequency band. Small, initial continuous coverage construction costs are high, and it is necessary to perform interoperability between 5G and 4G through reselection and handover, and business continuity is relatively poor. Voice capability: NSA relies on dual connectivity technology to inherit 4G existing voice solution VoLTE/CSFB; SA adopts 4/5G loose coupling, relies on interoperability, voice solution uses voice to fall back 4G and 5G to carry voice VoNR, Vo5G performance Depends on the 5G coverage level. Service Capability: The NSA is limited by the EPC capability of the existing 4G core network, and cannot provide 5G new services, such as network slicing related services; SA supports 5G new services, such as eMBB, mMTC and uMTC, to facilitate the expansion of vertical industries and meet each Diversified needs of users of class scenarios.

3 5G NR Standalone Networking Process 3.1

UE Initial Search Process

The process of 5G UE power-on network includes PLMN search (cell search), random access, ATTACH, public process and other sub-processes. After the UE is powered on, it first enters the PLMN search (cell search) process, in order to search the network and obtain downlink synchronization with the network. Random access is to resolve competition between different UE and achieve uplink synchronization. The ATTACH process establishes the same mobility context between the UE and the core network, the default bearer of the terminal, and obtain the IP address assigned by the network. The public process includes the authentication process and the secure mode process. The Channel Raster is fixed at 100 kHz in LTE, and different Channel Raster are defined in different frequency bands in NR, as shown in Table 1. The channel bandwidth is large in NR, and the UE performs synchronization signal search according to the Channel Raster, and the delay is long. Thus NR introduces a synchronous Raster, and the synchronization signal is placed according to the synchronous Raster. The UE of any communication system needs to synchronize with the network when it is powered on. The synchronization is to obtain network information more accurately. Synchronization at power-on refers to downlink synchronization. Downlink synchronization involves two processes, frequency synchronization and time synchronization. Generally, there is frequency synchronization before time synchronization. Nearly 30 working bands are defined in the 5G NR. In order to avoid the synchronization of the

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full band (FR1: 450 MHz–6000 MHz; FR2: 24250 MHz–52600 MHz), the UE can synchronize downward frequency according to the pre-built operating frequency band of the country’s operator, thereby shortening the initial boot cell search time. The 5G NR terminal is step-locked in accordance with the accuracy of the synchronization raster in frequency synchronization. The 5G NR sync grid is not fixed with 100 kHz as the synchronization precision in LTE/NB-IoT, but combined with the calculation formula of GSCN (global synchronization channel number) and center frequency point of SSB [3, 4]. It is worth mentioning that the UE needs to further clarify the center frequency by determining the location of the 20 PRBs in the frequency domain occupied by the SSB. Since the OFDM subcarrier spacing of the 5G NR is flexible, the UE needs to make a traversal search attempt according to the subcarrier spacing corresponding to the different working frequency bands to determine the actual subcarrier spacing of the SSB in the initial downward synchronization. After locking SSB to achieve frequency domain synchronization, UE can achieve time domain synchronization of symbol level precision by combining carrier frequency and acquired subcarrier spacing [5]. Table 1. Channel Raster in different bands in NR. NR operating band DF_raster (kHz) n1 100 n5 100 n8 100 n75 100 n77 15 n78 15 n257 60

The UE cell needs to perform downlink synchronization to obtain downlink timing to correctly demodulate the broadcast system message sent at a specific moment. In LTE, the UE acquires the synchronization accuracy of the symbol level in the subframe by successfully demodulating the primary synchronization signal (PSS) and the secondary synchronization signal (SSS). After obtaining downlink synchronization, a series of system messages (MIB, SIB1, SI) are demodulated for cell selection process [6, 7]. In the non-standalone networking mode, the 5G NR adopts the EN-DC dualconnection scheme to provide data services. In this scheme, the UE does not need to perform cell selection, but needs to obtain physical control channel configuration information. Therefore, the downlink time-frequency domain synchronization process is still needed. In addition, due to the “bundled” special design structure of the SS/PBCH block and the UE needs to periodically update the network side information, although the design MIB message can be reserved, the UE does not need to decode the MIB message content. And the system design does not need to separately design SIB1 and

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other SI system messages for the EN-DC data transmission mode of the nonindependent networking. Of course, the system design can also perform SIB1 design for compatibility with the independent networking mode, but the UE cannot listen to the decoding. 3.2

Synchronization Signal Structure

The synchronization signal (SS) and the PBCH appear in groups according to a certain time-frequency domain resource relationship in 5G NR. The SS/PBCH (SSB) block of each cell can be independently and flexibly configured in the full bandwidth timefrequency domain location [8]. This design not only reflects the flexibility of 5G NR in resource utilization, but also allows operators to customize network planning based on such flexible design to further improve the reliability of SS/PBCH decoding. The SS/PBCH block occupies 4 OFDM symbols in the time domain, and is indexed from 0 to 3 in the SS/PBCH block, while the frequency domain occupies the frequency domain width of 20 RB, occupying up to 240 subcarriers (each RB contains 12 subcarriers in 5G), the frequency domain RB index and the subcarrier index can be respectively identified in ascending order from 0. It is shown in Fig. 3. 239

P B C H

192 182 Subcarrier Number

P S S

P B C H

S S S

P B C H

56 47

P B C H

0 0

1

2

3

OFDM symbol number

Fig. 3. SSB time and frequency domain structure.

The UE first searches for the PSS primary synchronization signal, and the PSS length of the NR is 127 pseudo-random sequence, using the frequency domain BPSK M sequence. The PSS maps to consecutive 127 subcarriers in the middle of 12 PRBs, occupies 144 subcarriers, and performs guard intervals on both sides without transmitting power. After the UE searches for the PSS, the subcarrier spacing of the SSB can be obtained. The SSS frequency domain is similar to the PSS. It maps to 127 subcarriers in the middle of 12 PRBs and occupies 144 subcarriers. After the UE searches for SSS, it can

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ð1Þ

obtain NID . The only physical layer cell ID in NR is determined as the following formula: ð1Þ

ð2Þ

cell ¼ 3NID þ NID ; NID

ð1Þ

NID 2 f0; 1; . . .; 335g;

ð2Þ

NID 2 f0; 1; 2g

ð1Þ

The different symbol positions occupied by PSS, SSS, PBCH, and DM-RS are cell mod 4. given in Table 2, where v ¼ NID Table 2. Resource allocation in SS/PBCH block. Channel or signal PSS SSS Set to 0 PBCH

DM-RS for PBCH

OFDM symbol number l relative to the start of an SS/PBCH block 0 2 0 2 1, 3 2 1, 3 2

Subcarrier number k relative to the start of an SS/PBCH block 56, 57, …, 182 56, 57, …, 182 0, 1, …, 55, 183, 184, …, 236 48, 49, …, 55, 183, 184, …, 191 0, 1, …, 239 0, 1, …, 47, 192, 193, …, 239 0 þ v; 4 þ v; 8 þ v; . . .; 236 þ v 0 þ v; 4 þ v; 8 þ v; . . .; 44 þ v 192 þ v; 196 þ v; . . .; 236 þ v

4 The Related Parameters of 5G NR Resident Network Process 4.1

The Content of MIB in PBCH Channel

The NR cell network side needs to exchange control plane information with the UE. In the initial boot process, the UE needs to perform the cell selection process. Therefore, the entire system message design includes not only the MIB message block but also the SIB1 of the cell selection related parameters and other optional type system messages (SIBX). The UE obtains the MIB message by synchronous decoding SSB in the timefrequency domain (as shown in Fig. 4). It is extremely important to set some systemlevel parameters in the cell planning of 5G NR independent networking. If the setting is abnormal, the UE may not be able to perform network residing [9, 10]. 4.2

The Related Parameters of RMSI

After the UE obtains the SSB block information, it also needs to obtain some necessary system information to complete the camped cell and the initial access. The necessary system information becomes RMSI (Remaining Minimum System Information) in the NR. In the current R15 version, the RMSI can be considered as an SIB1 message in

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Z. Xu et al. -- ASN1START -- TAG-MIB-START MIB ::= systemFrameNumber subCarrierSpacingCommon ssb-SubcarrierOffset dmrs-TypeA-Position pdcch-ConfigSIB1 cellBarred intraFreqReselection spare }

SEQUENCE { BIT STRING (SIZE (6)), ENUMERATED {scs15or60, scs30or120}, INTEGER (0..15), ENUMERATED {pos2, pos3}, INTEGER (0..255), ENUMERATED {barred, notBarred}, ENUMERATED {allowed, notAllowed}, BIT STRING (SIZE (1))

-- TAG-MIB-STOP

Fig. 4. System parameters included in MIB message.

LTE, mainly transmitted through the downlink PDSCH channel, and the PDSCH channel needs the DCI of the PDCCH to be scheduled. The UE needs to obtain the PDCCH channel information of the scheduled RMSI in MIB, perform blind detection on the PDCCH to obtain the RMSI. This information in the MIB is the pdcchConfigSIB1 field [11]. The parameter ssb-SubcarrierOffset in the MIB message can be used to confirm whether the current SSB has a common CORSET (control resource set) in the frequency domain, that is, the Type0-PDCCH common channel is configured. Therefore, it can be determined whether the frequency domain of the current SSB is configured with SIB1. When the UE detects that the current SSB in the frequency domain is not configured with SIB1 under certain conditions, the parameter pdcch-ConfigSIB1 contained in the MIB message can be used to detect whether the Type0-PDCCH public search space provides CORSET in the frequency domain of the next SSB and the biased SSB. If the UE still does not detect the public CORSET, it can be considered that the cell is not configured with SIB1, and then the cell search procedure for the corresponding frequency point obtained by the previous frequency-locked SSB will be abandoned [12]. The PDCCH channel in the NR corresponds to multiple search spaces, including common search space and UE-specific search space. The Common Search space of Type 0 is only used for RMSI scheduling. In NR, CORESET (Control Resource SET) is introduced for the physical Resource SET for the PDCCH channel. One cell PDCCH channel corresponds to multiple CORESET sets, and the CORESET set has an ID number, where CORESET 0 represents a physical resource set corresponding to the search space. 4.3

The Related Parameters of SSB

In the 5G NR non-standalone networking solution, the NR cell network side does not need to exchange control plane messages with the UE, while the data transmission of

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the 5G NR cell user plane can be independently scheduled by listening to the physical control channel. The UE may acquire the 5G NR system configuration parameter by using the camped 4G LTE cell RRC reconfiguration message. The LTE cell configures the system configuration parameter of the 5G NR as the second cell group related parameter of the EN-DC, and encapsulates it into an 8-byte string group for transmission. The UE can quickly obtain the SSB message by decoding these auxiliary configuration information. These auxiliary information mainly involves the following four aspects: Subcarrier interval (numerology) information of SSB: 5G is flexible and configurable for OFDM system level parameter subcarrier spacing, and subcarrier spacing of SSB can only be configured for 15 kHz or 30 kHz (carrier frequency 6 GHz); Frequency domain location information of the SSB: the starting node of the frequency domain (absoluteFrequencyPointA), the frequency domain location of the SSB (absoluteFrequencySSB, defined as the no. 0 subcarrier of the no. 10 index RB in SSB), the frequency band (frequencyBandList) and actual carrier start position (offsetToCarrier) or bandwidth (carrierBandwidth) based on different subcarrier spacing; Time domain location information of SSB: The SSB can repeatedly transmit at a specific candidate position within a half-frame (5 ms), and the number of repetitions and whether the candidate time domain position is actually configured by the SSB can be known by the parameter ssb-PositionsInBurst. If the candidate location is not configured with SSB, the frequency domain location of the corresponding SSB does not need to be used for other channels or signal transmission. At the same time, the auxiliary time domain information also includes the repetition period (ssbperiodicityServingCell) for configuring the SSB half-frames, and the default value is repeated every half frame (5 ms).

5 Conclusion This paper introduces two network architectures of 5G non-standalone and standalone networking, and focuses on the UE cell search process in 5G standalone networking mode. At the same time, it explains the system-level parameters that need to be paid attention to in the open network optimization. Generally, when the UE is powered on for the first time, it does not know the bandwidth and frequency of the network. The UE repeats the basic cell search process and traverses the frequency points of the entire spectrum to try to demodulate the synchronization signal. This process is time consuming, but the general time requirements are not strict. The UE initialization time can be shortened by some methods, such as the UE storing the previous available network information, and searching for these networks and frequency points after booting. Once the UE searches for the available network, the UE first demodulates the PSS, implements symbol synchronization, and obtains the ID of the cell group. Secondly, it demodulates the SSS, implements frame synchronization, acquires the cell group ID, and combines the ID of the cell group to obtain the PCI of the cell. Get the service cell ID. The UE will demodulate the downlink broadcast channel PBCH to obtain system information such as system bandwidth and number of transmitting

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antennas; after receiving the PBCH, the UE also receives system messages transmitted on the PDSCH. Finally get the complete system message.

References 1. Zhang, J., Xie, W., Yang, F.: Architecture and solutions of 5G ultra dense network. Telecommun. Sci. 44–51 (2016) 2. Liu, Y., Li, X., Ji, H.: Key technologies of network self-organization in 5G ultra-dense scenarios. Telecommun. Sci. 44–51 (2016) 3. Chen, M.: New network planning technology for 5G ultra-dense networking. Mob. Commun. 28–29 (2016) 4. Lei, Q., Zhang, Z., Cheng, F.: 5G radio access network architecture based on C-RAN. Telecommun. Sci. 106–115 (2015) 5. 3GPP TS 38.331 V15.2.0 NR; Radio Resource Control (RRC) protocol specification (Release 15) 6. Zhou, H.: 5G wireless access network architecture design. Electron. Sci. Technol. 102–105 (2017) 7. 3GPP TS 38.101-1 V15.2.0 NR; User Equipment (UE) radio transmission and reception; Part 1: Range 1 Standalone (Release 15) 8. 3GPP TS 38.213 V15.2.0 NR; Physical layer procedures for control (Release 15) 9. Zhang, J., Xie, W., Yang, F.: Mobile edge computing and application in traffic offloading. Telecommun. Sci. 32, 132–139 (2016) 10. Bai, L., Liu, T., Yang, C.: Interference coordination method and performance analysis in ultra-dense network. Sig. Process. 1263–1271 (2015) 11. Xu, L., Cheng, X., et al.: Mobility load balancing aware radio resource allocation scheme for LTE-advanced cellular networks. In: 16th IEEE International Conference on Communication Technology, pp. 806–812. IEEE Press, Hangzhou (2015) 12. Xu, L., Luan, Y., Cheng, X., Cao, X., Chao, K., Gao, J., Jia, Y., Wang, S.: WCDMA data based LTE site selection scheme in LTE deployment. In: 1st International Conference on Signal and Information Processing, Networking and Computers, pp. 249–260. CRC Press Taylor & Francis Group, Beijing (2015)

NB-IoT Network and Service Collaborative Optimization Pengcheng Liu1(&), Bao Guo2, Yang Zhang3, Yuehua Han2, Yi Liu4, and Guozhi Wang5 1

China Mobile Group Design Institute Co., Ltd., Beijing, China [email protected] 2 China Mobile Group Shanxi Co., Ltd., Taiyuan, China 3 Department of Networks, CMCC, Beijing, China 4 China Mobile Group Shandong Co., Ltd., Jinan, China 5 China Mobile Group Zhejiang Co., Ltd., Hangzhou, China

Abstract. The preliminary construction of the NB-IoT network has been completed at this stage. The overall coverage is good, but there are more partial coverage holes, insufficient coverage of indoor difficult scenes, prominent interference problems, and overlapping coverage. At present, the industry application of NB-IoT is low in maturity, and the application of the industry is in its infancy. Different terminal manufacturers have different specifications, which leads to major problems in the deployment process of network deployment. This article analyzes the NB-IoT network related indicators according to different business characteristics according to the main types of business of the Internet of Things, analyzes the coverage performance of such services according to test data and statistical data, and conducts in-depth coverage analysis on the covers of manholes and poles. Overlapping coverage analysis gives an optimized solution to the business model with relatively concentrated service access. Keywords: Deep coverage


Independent antenna feeder
Access capability

1 Introduction NB-IoT network construction is to build a high-quality, high-quality, urban-wide continuous coverage of the IoT bearer network, supporting the national big connection strategy, but limited by external strong interference, site alarms and hardware failures, single check network process needs to be improved For other reasons, local areas have not yet formed continuous coverage. At present, the application maturity of NB-IoT industry is low, and the industry application is in its infancy. The terminal specifications of different manufacturers are different, and the receiving sensitivity is different. This leads to major problems in the network deployment and the signal strength of different terminal access networks. The signal quality requirements are very high, otherwise it will not be able to access the network [1, 2]. At present, the NB-IoT network construction process has discovered five major problems: the network interference problem is prominent, the overlapping coverage is serious; the overall coverage is good, but the local coverage is more empty; the indoor © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 505–515, 2019. https://doi.org/10.1007/978-981-13-7123-3_59

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difficult scene depth coverage problem; the concurrent capacity bottleneck; To be improved. In addition, the Internet of Things service based on NB-IoT technology has the characteristics of “large difference in service model, large difference in service level, high depth coverage requirement, and long terminal silence”. At this stage, the NB-IoT network is in the early stage of construction, commercial terminals have not yet been popularized, and the business volume is small, and the optimization experience that can be used for reference is lacking. It is urgent to carry out targeted network and business collaborative optimization research [3]. Based on the main business types of the current Internet of Things, this paper analyzes the NB-IoT network association indicators according to different service characteristics, analyzes the coverage performance of such services based on test data and statistical data, and performs deep coverage analysis on the manhole cover and the light pole business respectively. Overlap coverage analysis, corresponding optimization analysis of business models with relatively concentrated business access [4].

2 NB-IoT Services Coverage Research 2.1

NB-IoT Business Scenario and Related Indicators

At present, the main business types of NB-IoT include water meter, street light, bicycle and intelligent parking service. According to the data reporting method (data reporting, data real-time interaction) and mobile mode, the NB-IoT network coverage is adopted. Types (indoor, outdoor, subsurface, and mobile) can be roughly divided into four types of business: type 1, fixed monitoring, such as smart meter reading, intelligent parking, manhole cover monitoring, etc.; type 2, fixed control Classes, such as smart street lights, self-service washing machines, etc.; type 3, mobile monitoring, such as logistics monitoring, chronic disease management, etc.; type 4, mobile control, such as shared bicycles, mobile POS machines, children’s watches, etc. In order to accurately evaluate the performance of the NB-IoT service, according to the current test data and statistical data, the NB-IoT service indicators such as the connection class, the attachment class, and the rate class are selected, and the benchmark value of the service indicator and the corresponding RSRP and SINR are obtained through test analysis. The value provides basic data support for the study of subbusiness coverage standards [5, 6]. Fixed Monitoring Class. Such services mainly include meter reading, manhole cover, hydrological monitoring and other services, and are sensitive to network coverage, service connectivity and power consumption indicators. Involved indicators: Attach success rate, RRC connection success rate, and uplink rate. Fixed Control Class. Such services mainly include smart light poles, smart security, smart home and other services, which are sensitive to network coverage, power consumption and data related indicators. Involved indicators: Attach success rate, RRC connection success rate, uplink rate, and downlink rate.

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Mobile Monitoring. Such services mainly include services such as personnel and item location tracking, and are sensitive to network connectivity, power consumption, and mobility metrics. Involved indicators: Attach success rate, RRC connection success rate, uplink rate, and downlink rate. Mobile Control Class. Such services mainly include services such as shared bicycle, smart wear, and mobile payment, and are sensitive to network connectivity, service connectivity, power consumption, and mobility metrics. Involved indicators: Attach success rate, RRC connection success rate, RRC connection delay, uplink rate, downlink rate. 2.2

NB-IoT Business Scenario Coverage Standard

Through the standard coverage method of service coverage, the minimum coverage standards of the four types of services are obtained respectively. Through analysis, the fixed control services have the highest coverage requirements, and the fixed monitoring services are relatively low. Combined with business scenario coverage standards, it can provide guidance for subsequent NB-IoT network construction and optimization. Fixed Monitoring Coverage Standard is RSRP: –112.9 dBm, SINR: 1.3 dB. The main data transmission mode of the fixed monitoring service is periodic or triggering data reporting, occasional data configuration and other data delivery requirements, and the power saving mode is usually set. In response to this feature, in order to ensure business continuity, the RSRP indicator is the focus of optimization. Fixed Control Coverage Standard is RSRP: –108.6 dBm, SINR: 0.5 dB. The fixed control service is mainly for periodic or triggering terminal status data reporting and platform command data receiving. In order to ensure that the platform side can monitor the terminal in real time, the uploading information is usually set frequently. For this feature, SINR is the focus of optimization for this scenario. Mobile Monitoring Coverage Standard is RSRP: –110.3 dBm, SINR: 0.9 dB. Mobile monitoring service terminals are usually deployed in moving targets such as vehicles, machinery, and personnel to realize remote monitoring of target status and location information, and there are two-way requirements for data reporting and delivery. The RSRP and SINR indicators in this scenario are the focus of optimization. The Coverage Standard of Mobile Control is RSRP: –100.7 dBm, SINR: 0.7 dB. The mobile control service is usually worn by people, and the terminal monitoring and remote control are realized through the NB-IoT communication module in the terminal. Due to the characteristics of the service type, the scenario is sensitive to coverage and rate. Therefore, the RSRP and SINR indicators in this scenario are the focus of optimization.

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3 Optimization Analysis of Depth Coverage 3.1

Depth Coverage Assessment Method

The NB-IoT network protocol stipulates that the terminal does not report the MR and cannot evaluate the deep coverage status through the MR data. The coverage performance can be evaluated by the PH level information carried by the NB-IoT terminal in the MSG3 message [7]. Road loss estimation: According to the PH level information carried by the terminal in the MSG3 information, a specific value is assigned to each PH through its corresponding range for path loss estimation. The formula is as follows: PH ¼ Pcmax  P0Npusch þ a  PLUL




ð1Þ

Pcmax is the maximum transmit power of the terminal, P0-Npusch is the initial target power of the UE, PL is the path loss, and a is the road loss compensation weight, which is usually 0.7–0.8. According to the calculated uplink path loss, it considers the difference between the uplink and downlink frequencies, and estimates the downlink path loss, and further approximate the RSRP of the location of the UE. The formula is as follows: RSRP ¼ RSTXPwr  ðD þ PLUL Þ

ð2Þ

Among them, RSTXPwr is the reference signal power, and D is the correction value for the uplink path loss. In the NB-IoT network planning and design, GSM (900 MHz) is planned according to 1:4 site, and the depth coverage requirement scenario can adopt 1:2, 1:1 planning scheme, and on-demand planning to ensure good coverage of the NB-IoT network; The antenna uses the 2T4R deployment principle to replace the site with 2T4R antenna feeds to improve the uplink performance of the NB-IoT network. NB and GSM independent antenna feeder systems should be deployed in key areas and overlapping coverage areas to ensure that NB optimization does not affect GSM network coverage. The outdoor road edge coverage RSRP is not lower than –84dBm, and the SINR is not lower than –3 dB. It is expected that no less than 30 dB penetration loss can be reserved to meet the general scene depth coverage requirements [8]. 3.2

NB-IoT 1: N Networking Mode

At present, the NB-IoT network is mainly based on macro station coverage, and is planned in a 1:4 ratio with the GSM station, which can solve about 90% indoor general scene coverage [9]. However, some IoT terminal modules are concentrated in corridors, basements, etc., and have a fixed position, which requires high depth coverage. The 19 sites of the NB-IoT network are selected as the 1:N pilot area of the GSM site. The RSRP test results are shown in Fig. 1.

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Fig. 1. NB-IoT 1: N mode coverage performance comparison

Tests show that NB-IoT is 1:1 compared with GSM networking mode 1:2, RSRP is about 4–7 dB, and 1:4 is about 10–12 dB lower than 1:1. However, in the 1:2 networking mode, the SINR is 1.5–2 dB higher than the 1:1, and the SINR is 0.6–2.4 dB higher than the 1:4. The pairs under different load loads are shown in Table 1. Table 1. Comparison test of NB-IoT and GSM 1:N networking Terminal drive test 1:1 no load Average RSRP Edge 5% RSRP Average SINR Edge 5% SINR

1:1 no load –78.7 –100 8.9 –6.7

1:2 no load –85.9 –102.3 10.9 –5.8

1:4 no load –89.8 –107.1 6.5 –8.7

1:1 with load –77.1 –96.1 6.1 –7.5

1:2 with load –80.1 –98.6 7.5 –8

1:4 with load –89.3 –105.9 6.8 –7.4

Tests show that NB-IoT in 1:4 network can reach the same level of GSM coverage and has certain wear and tear redundancy. If 1:1 networking is adopted, not only the investment is too large, but also the problem that the network noise rise is obvious and the interference control is difficult. In the initial stage of network construction, NBIoT’s coverage enhancement technology can be fully utilized to achieve basic coverage of NB-IoT compared to the GSM900 macro station’s 1:4 mode, which can support a

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wide range of business development needs and effectively control investment risks. It should be noted that when 1:4 networking, NB-IoT can basically meet indoor demand, 95% coverage can reach –107 dBm, and receiving sensitivity has 25 dB surplus, which can meet basic indoor coverage requirements, and some deep coverage can be Deepen coverage based on actual conditions. 3.3

NB-IoT Independent Antenna Feeder

The NB-IoT network has basically achieved continuous urban coverage, but the NBIoT network structure is quite different from the traditional LTE/GSM network. Due to the difference in NB-IoT and GSM site size and coverage targets, the site size of the NB-IoT network. It is about 1/4 of GSM, and the cell coverage of the two is quite different. Therefore, the optimal setting of the antenna parameters will be different [10, 11]. The NB-IoT independent antenna feeder network is beneficial to the optimization of network coverage quality. The coverage performance comparison between NB-IoT total antenna feed and independent antenna feed mode is shown in Fig. 2.

Fig. 2. Comparison of coverage performance between NB-IoT total antenna feed and independent antenna feed.

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The test results show that the outdoor road coverage, the NB-IoT independent antenna feeder has a RSRP increase of 0.86 dB and a SINR increase of 1.67 dB. The test pairs in the outdoor coverage room are shown in Table 2. Table 2. Comparison of indoor coverage performance of NB-IoT independent antenna feeder Outdoor covered indoor

RSRP Total Independent antenna antenna –111.48 –113.87 –86.135 –93.888 –81.393 –83.79

**Department **Big hotel **Professional Engineering College ** Hotel –96.108 –99.906 **University –120.26 –117.46 underground garage University cafeteria –91.887 –93.553 Average –97.877 –100.41

Gain 2.3895 7.753 2.397 3.79798 –2.8064 1.6655 2.53276

SINR Total antenna 8.884 5.1665 12.9915

Independent antenna 7.2475 0.349 6.9085

Gain 1.6365 4.8175 6.083

7.45429 3.37667 1.93071 –0.3179

4.07762 2.24857

6.306 7.12217

1.9105 3.46228

4.3955 3.65988

The test results show that the outdoor coverage room, the NB-IoT independent antenna feed has a 2.5 dB increase in RSRP and a 3.5 dB increase in SINR. Summary: Under the NB-IoT independent antenna feed scenario, the RF optimization adjustment scheme will not be affected by the different coverage targets of GSM and FDD networks, and no joint optimization is needed. The azimuth can be independently combined with the requirements of NB-IoT network coverage optimization. The adjustment of the mechanical downtilt angle, the electronic downtilt angle and even the antenna hanging height optimizes the RF optimization effect. According to field test research, the independent antenna feeder network has a significant effect on the optimization of depth coverage.

4 Collaborative Optimization Based on Business Characteristics 4.1

Internet of Things Business Characteristics

The Internet of Things business itself has the characteristics of “large difference in business model, large difference in service level, and high demand for concurrent users in the industry”. From the statistics of the NB-IoT test area of the current network, the statistical results of different business models show large differences. Collecting the maximum number of users in a single time period in 24 h of the test area, it can be seen that the statistical data is consistent with the online time of the water meter, as shown in Fig. 3.

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Fig. 3. NB-IoT single-time maximum number of users.

The maximum number of users in the 24 h of a single cell in the test area is collected. It can be seen from that the statistical data is consistent with the on-line time of the street light, as shown in Fig. 4.

Fig. 4. NB-IoT single cell maximum number of users in each period.

4.2

NB-IoT Access Capability Analysis

The terminal accesses the NB-IoT network by two processes: first accessing the network and then allocating resources. The random access capability depends on the Msg3 message processing capability; the ability to allocate resources depends on the NB-IoT schedulable resources. There are two main factors affecting the concurrent access processing capability: one is the PRACH period size, and the other is the allocation of traffic channel resources in the access process [12]. When the PRACH period is 320 ms, the theoretical maximum concurrent access user is 37.5. The theoretical maximum number of concurrent access users is calculated as follows: Theoretically concurrent access to the largest number of users duration 1000 ms ¼  PRACH number of subcarriers ¼  12 ¼ 37:5 PRACH period 320 ms

ð3Þ

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NB-IoT network defaults that the PRACH channel occupies 12 subcarriers, and each user access occupies one subcarrier. Under the better conditions of the wireless environment, 12 terminals initiate random access at the same time, and the RRC access time to all terminals is 0.93 s. The Msg2 to Msg5 resource allocation process takes 0.92 s, and the 1 s supports up to 12 concurrent users. From this point of view, concurrent access is mainly limited by the resource allocation in the process of Msg2 to Msg5. The NB-IoT access capability mainly considers two aspects: one is the maximum number of RRC connected users, and the other is the number of valid RRC connected users. At present, the maximum number of RRC connected users of Huawei and ZTE is 600. Through calculation, the maximum number of valid RRC connected users in 100 ms is 150, calculated as follows: The maximum effective number of users in the uplink theory. In the Single-tone 15 kHz subcarrier mode, each user occupies one RU, and the resources occupied by the PRACH are not considered. Maximum number of valid usersð100 msÞ

¼

ð4Þ NBIoT frequency domain bandwidth duration 180 kHz 100 ms RU frequency domain bandwidth  RU duration ¼ 15 kHz  8 ms ¼ 150

In the simulation calculation, the maximum number of valid RRC connected users is 50, which is calculated as follows: Simulate the maximum number of effective users. Under the better conditions of the wireless environment, the Single-tone 15 kHz subcarrier mode is used for uplink scheduling simulation. Each uplink user occupies an average of 2.5 RUs. Considering the PRACH resource consumption and scheduling efficiency, the 8 ms average scheduling is calculated. The number of uplink users is four. Number of valid RRC connected users ð100 msÞ ¼ 100 ms=8 ms * 4 ¼ 50

ð5Þ

In theory, each uplink user occupies 1 RU. In fact, the row user occupies an average of 2.5 RUs because the number of valid users decreases to 50. One RU frequency domain is 15 k and the time domain is 8 ms. Summary: NB-IoT access capability is mainly limited by the capability of concurrent access processing. Constrained concurrent access will generate a large amount of retransmission signaling, causing signaling avalanche. The research and verification of the concurrent access processing capability of NB-IoT base stations under different coverage levels is carried out. By verifying the concurrent access test of 60 terminals, the access can be concurrently transmitted under coverage level 0 and coverage level 1, and only 36 concurrent accesses can be accessed under coverage level 2.

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5 Conclusion NB-IoT network construction is initially completed, but the overlapping coverage and interference problems are prominent; the overall coverage quality is up to standard, but there are still coverage holes and continuous weak coverage sections; at this stage, NBIoT is mainly covered by macro stations, which can solve the problem. 90% indoor general scene coverage, however, some indoor complex and difficult scenarios need to further study the low-cost, easy-to-deploy NB indoor scene coverage scheme. In the optimization analysis of the problems that have occurred in NB-IoT, it can be found that the terminal management and control capabilities are lacking. Each terminal manufacturer can define the frequency and time of the terminal access network. If a large number of terminals simultaneously access the network, it will lead to network congestion and even network congestion. Signaling storm; when the market is required to promote the business, the traction customer is aligned with the NB-IoT platform, and the technical department communicates with the industry manufacturers before the business application commercial. In addition, end-to-end analysis methods are lacking: the enabling of industry terminals, in addition to the ability of operators to end-to-end pipelines, depends on the ability of the manufacturer to manage the platform. For example, the lamp management platform fails to control the lights, and the motorcycles cannot be reported. Cycling status, etc.; end-to-end analysis means should be implemented and improved as soon as possible to clarify the problem demarcation. For nonnetwork reasons, urge customers to check.

References 1. He, X., Song, L.: NB-IoT uplink coverage performance based on rate requirements. Telecommun. Sci. 149–156 (2016) 2. Hao, H., Ye, L., Noya, Z.: Capacity performance analysis of NB-IoT independent deployment. Mobile Commun. 78–84 (2017) 3. Guo, B., Liu, Y., Zhang, Y.: Discussion on NB-IoT wireless throughput and low power consumption technology. Mobile Commun. 84–89 (2017) 4. Xu, L., Chen, Y, Schormans, J., et al.: User-vote assisted self-organizing load balancing for OFDMA cellular systems. In: 22nd IEEE International Symposium on Personal Indoor and Mobile Radio Communications, pp. 217–221. IEEE Press, Toronto (2011) 5. Chao, Z., Gao, Y., Ding, H.: Analysis of NB-IoT performance. Mobile Commun. 47–52 (2017) 6. Feng, C.: Discussion on the strategy of narrowband internet of things deployment. Mobile Commun. 64–68 (2017) 7. Yan, S., Huang, J.: NB-IOT coverage analysis based on theory and measurement. Mobile Inf. 72–74 (2016) 8. Wang, Y.: NB-IoT technology and network deployment. Electron. Test. 60–61 (2017) 9. Ritao, C., Anda, D., Fanli, M.: Preliminary study on planning objectives and planning of NB-IoT. Telecommun. Sci. 137–143 (2016) 10. Liu, Y., Dong, J., Liu, N.: Key technology and planning simulation method of NB-IoT. Telecommun. Sci. 144–148 (2016)

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11. Zhang, J.: Research on NB-IoT deployment strategy of China mobile. Mobile Commun. 25–30 (2017) 12. Xu, L., Zhao, X., Luan, Y., et al.: User perception aware telecom data mining and network management for LTE/LTE-advanced networks. In: 4th International Conference on Signal and Information Processing, Networking and Computers, pp. 237–245. Springer, Qingdao (2018)

A Gender and Age Prediction Algorithm Using Big Data Analytic Based on Mobile APPs Information Jie Gao(&), Tao Zhang, Jian Guan, Lexi Xu, and Xinzhou Cheng Research and Development Centre of Big Data, Unicom Network Technology Research Institute China, Beijing, People’s Republic of China {gaojie49,zhangt176,guanj9,xulx29, chengxz11}@chinaunicom.cn

Abstract. In the current society, almost everyone can’t do without a mobile phone. As the rapidly expansion of smartphone and app market in recently years, the current 35%–40% penetration of smartphone in the mobile phone market will reach to 60% by the year 2019. The customers use their mobile phones to browse internet, have chat and play popular game almost at anywhere and anytime. As a result, mobile phone carries almost all of a person’s behavior and preferences. In that way, user’s personal information such as gender and age, demographic attribute that is frequently used in precision marketing, can be accurately predicted. In this paper, a gender and age prediction algorithm (GAPA) is proposed to predict user’s gender and age by using established supervised machine learning. The numerical results show that the algorithm proposed in this paper is high-efficiency and is able to control the loss function near 2–3. Keywords: Big data
Data mining
Machine learning


Prediction algorithm

1 Introduction As the rapidly development of telecommunications and smartphone, almost everyone can’t do without the personal mobile phone. From Ericsson Mobility Report, it show that during nearly development of 5 years, the smartphone penetration in the mobile market will increase from 30% to 60% by the year 2019. People use their mobile phones to browse internet, have chat and play popular game almost every day. As a result, the mobile phone carries nearly all of a person’s behavior and preferences. The record data, such as installed APP list, APP usage record, type and price of the mobile phone which are collected by tracing platform will contain abundant information of the customers. In that way, user’s personal information such as gender and age can be accurately predicted by using machine learning technology. This information can be widely used to provide personal targeted advertising. It can not only help APP companies understand their users’ behavior characteristics, iterate products, but also help enterprises to more accurately deliver Internet advertising and save advertising costs. Recently, in [1], the author proposes that the behavioral targeted advertisements could © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 516–524, 2019. https://doi.org/10.1007/978-981-13-7123-3_60

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improve the click-through-rates of advertisement effectively. To achieve this objective, big data and machine learning technology have the ability to provide nearly real-time solutions for processing the huge amount of data collected from tracing platform. There have several famous supervised algorithms in machine learning technology such as Support Vector Machines (SVM), Logistic Regression (LR), and Decision Trees, i.e. The gender of customers is divided into male and female. The information of age can be represented by 10 years per group. From the above, the prediction of gender and age can be converted as a problem of classification. There are also some classical algorithms to solve the problem of classification, such as Decision Trees, GBDT (Gradient Boosting Decision Tree), and XGBoost algorithm, i.e. For example, XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework [2]. Later after XGBoost, LightGBM was proposed by Microsoft to improve the performance of boosting algorithms [3]. As a result, it can reduce the calculation cost of split gain and use histogram subtraction for further speedup. Based on the machine learning algorithms mention above, we develop a framework to estimate the gender and age of mobile users by the installation and usage of APPs. This paper is organized as follows, in Sect. 2, researches related to prediction of user’s information are discussed. Section 3 shows the scheme of GAPA algorithm. Section 4 describes the numerical results of the accurate scheme by analyzing the tracing data. At last, conclusions of this paper are given in Sect. 5.

2 Relevant Work To the best of our known, the feasibility of demographic inference through various tracing data of the customers has been proposed many times in the past. For example, the authors of [4] proposed a solution of predicting gender, age and religion tendency of the mobile users based on the search queries from SNS, such as Facebook, i.e.. In [5], the authors developed a scheme to predict demographics such as relationship, age and gender. The scheme is not only based on the behavioral features of application usage, voice call usage, and SMS usage, but also refers to the environment features, such as Bluetooth and WiFi devices detected per day on mobile phones. Also in [6], Suranga gave a warning that there will be multiple privacy and security issues with the data collection through over-permission platform and share with other companies. In order to verity the affection, the authors presented a framework to predict mobile users’ gender based on installed APPs simply and the accuracy could reach around 70% in the numerical results. Mobile phones are widely used worldwide [7–9]. The proposed gender and age prediction algorithm is based on the LightGBM method by collecting the tracing data such as installed APPs, records of APPs usage, type and price of the mobile phone. In order to enhance the accuracy of prediction, cross verification scheme is applied in the proposed GAPA. Through repeated iterative calculations based on the training data set, we use the most accurate set of the features’ importance to train the forecast data set. Then we check the accuracy of this calculation model by the log-loss function.

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3 A Gender and Age Prediction Algorithm Based on Machine Learning In order to enhance the accuracy of prediction, LightGBM algorithm and cross verification scheme is applied in the proposed GAPA. Figure 1 presents the process of GAPA and the whole process can be divided into five steps: data collection, feature engineering, model training, cross verification and results evaluation.

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Fig. 1. The flowchart of GAPA

3.1

Step 1: Collection

The first step is data collection, the collector in the tracing platform will gather user’s mobile information, such as user ID, mobile brand, mobile sub-brand, mobile price, gender and age. Of course, we set the data cluster with gender and age as the training set, and set the data cluster without gender and age information as the forecast set. In this study, we collect nearly 73000 android users’ information, and out of 50000 android users who provide the information of gender and age. We set this data of users as training set. In this data set, there are 32324 (64.6%) male and 17676 (35.4%) are female. Beside the user’s mobile information, user’s APP information is also collected by the collector. It contains user ID, APP, APP series, APP sub-series, start time and end time record of the APP usage. Tables 1 and 2 describe the fragmentary of the two kind of data collected from tracing platform. In the next step, we will separate this data and promote the feature engineering which is the key processing of GAPA. Table 1. User’s mobile information User ID Brand Sub brand Price Gender (training) Age (training) f69cdc Samsung GT-I9507 V 2538 1 3

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Table 2. User’s APP information User ID APP Series Sub series Start time End time 10125 8c4ac9e Financial Investment and finance 2018-6-1:8:00:00 2018-6-1:8:00:00

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Step 2: Feature Engineering

Step 2 is feature engineering, which is the most important segment in each machine learning project. In this process, we need to pick up features which have the most influential and discriminative ability to classify and identify the user’s information. In a word, the more studies we do in the step, the more accuracy result we will get from the machine learning algorithm. First of all, we need to have statistical analysis of the training set which have 50000 users’ data. Before the study, we set a regulation for the target features, gender and age. It is presented in Table 3. Table 3. Regulation for gender and age Gender Index Age Male 1 0–10 10–20 Female 2 20–25 25–30

Index 0 1 2 3

Age 30–35 35–40 40–45 45–50

Index 4 5 6 7

Age Index 50–60 8 60–70 9 70–80 10

We represent the male and female as 1 and 2, we divide the age as 10 years for each group. For example, the users who are in the 25–30 group will be represented by 3 in the age feature. 3.2.1 Basic Features Basic features show the rule of statistics for the basic information in the training set. Figure 2 describes the distribution of mobile brand in training set, it is obviously that no matter in male and female, Xiaomi, Samsung and Huawei are the top three market share in the android smartphone. The occupation ratio of Xiaomi is 20.5% and 18.7% in male and female separately. In Fig. 3, the distribution of age in different mobile brand is shown. From the figure, we can find a rule that the top three market share brands have intensified competition for the young consumers below 30 years old. 3.2.2 APP Features APP features describe the installed and usage statistical rules in the training set. Figure 4 shows the distribution of installed APP in different gender. For the male in left figure, the top three favorites APPs are social, mobile shopping and App-Manager. On the other side, social, mobile shopping and physical health are the most favorites APPs amount female customers. This market rule is formed by the different living habit, way of thinking and physiologic structure.

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Fig. 2. The distribution of mobile brand in training set

Fig. 3. The distribution of age in different mobile brand

Fig. 4. The distribution of installed APP in different gender

What is more, we select game and physical health as the representative APP series for different gender to analyze the rule more deeply. In Fig. 5, the physical health series is a very popular series amount the female consumers during 20 to 50 years old. The penetration of game series demonstrate a young tendency in male group, as the users below 40 years will be more likely to install and play for fun.

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Fig. 5. The distribution of physical and game in different gender

From the above analysis, we chose TF-IDF (Term Frequency–inverse Document Frequency) algorithm which is a very famous algorithm is information retrieval to process the installed APPs information which is a kind of literal-type data. TF-IDF is an algorithm that is intended to reflect how significant a word that is to a document in a collection or corpus. In the case of the TF, the simplest choice is to use the raw count of a term in a document. It can be described as follow: ni;j TFi;j ¼ P k ni;j

ð1Þ

where ni;j is the number of times that term t occurs in document d, the denominator is the total number of words in d. The inverse document frequency is a measure of how much information the word provides. It can be described in (2): ITFi = log

jDj jfj : ti 2dj gj

ð2Þ

where |D| is the total number of documents in the corpus, {j: ti 2 dj} is the number of documents where the term t appears. Then TF-IDF is calculated as (3): TF  ITFi;j = TFi;j * IDFi

ð3Þ

The TF-IDF value will increase as the number of times that a word appears in the corpus proportionally. 3.3

Step 3: Model Training

As mention above, GAPA is based on big data and machine learning technology, we studied GBDT, Xgboot and LightGBM, which are famous algorithm in machine learning. After comparison of the accuracy and complexity, we chose LightGBM as the

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core algorithm of GAPA. The parameters for the lightGBM in Python is set as follow (Table 4): Table 4. Regulation for gender and age Boosting_type Max_depth Metric Num_class Objective Random_state gbdt 3 Multi_logloss 22 Multiclass 666

3.4

Step 4: Cross Validation

In this step, cross validation scheme is used to optimize the parameters of iteration trees in LightGBM algorithm. Cross validation is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. We fold the training data into 5 pieces, 1 piece for training and the rest for validation. 3.5

Step 5: Results Evaluation

In order to evaluate the performance of the built model, we use loss-log function to profile the accuracy of the prediction result. The loss-log function is presented in formula (4): Loss ¼ 

N X 22 1X yij lnðpij Þ N i¼1 j¼1

ð4Þ

where i is the number of users in forecast set, j is the different group of users divided by gender and age, yij is whether a user is in the group of j or not, and pij is the probability calculated by the GAPA for each user. In a solution, the Loss-log is the cumulative sum of the error between the reality and the prediction result. Apparently, 0 is the ideal target of the proposed algorithm.

4 Numerical Result In this paper, the case analysis of machine learning is based on the user’s mobile information collected by tracing platform. The data sets is processed and analyzed by Sklearn in the Python environment (Fig. 6). From the above, we can observe a disciplinary rule that the performance of GAPA will be improved as the accumulation of features. Because all the basic features are gains for the Sklearn algorithms. The inflection point happens when we add the ‘Top30–40 apps is installed’ features in the algorithms. Because this ten features contain some interferential features that is an interference to classify the forecast data set. The best result in our experiment is 2.67 by using the LightGBM algorithm.

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Fig. 6. The distribution of precision for different algorithms

5 Conclusions In this paper, a gender and age prediction algorithm based on big data analytic and machine learning is proposed for study. The proposed framework can predict the user’s information by machine learning algorithm with accuracy of 2.67. The proposed algorithm considers more aspects and features than the algorithms in [4–6]. Also, the precision can be improved unceasingly. In the end, we give the performance of GAPA is significant by analyzing the collected data in the last part. And the results show that the GAPA scheme can be generalized in the area of targeted advertising.

References 1. Jakir, K., Fenil, A., Mithila, S.: Different approaches and methods for targeted advertisements by predicting user’s behavioral data and next location. In: Conference 2018, ICISC, pp. 1345– 1350. IEEE (2018) 2. Chen, T., Carlos, G.: Xgboost: a scalable tree boosting system. In: Conference 2016, ACM, pp. 785–794. IEEE (2016) 3. Ke, G., Meng, Q., Finley, T.: LightGBM: a highly efficient gradient boosting decision tree. In: Conference 2017, NIPS, pp. 342–353. NIPS (2017) 4. Bi, B., Shokouhi, M., Kosinki, M.: Inferring the demographics of search users: Social data meets search queries. In: Conference 2013, World Wide Web, pp. 131–140. IEEE (2013) 5. Aarthi, S., Bharanidharan, S., Saravanan, M.: Predicting customer demographics in a mobile social network. In: Conference 2011, International Conference on Advances in Social Networks Analysis and Mining, pp. 553–554. IEEE (2011) 6. Chen, J., Wang, C., He, K.: Semantics-aware privacy risk assessment using self-learning weight assignment for mobile apps. IEEE Trans. Dependable Secur. Comput. pp, 1 (2018) 7. Xu, L., Luan, Y., Cheng, X.: Telecom big data based user offloading self-optimisation in heterogeneous relay cellular systems. Int. J. Distrib. Syst. Technol. 8, 27–46 (2017)

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8. Xu, L., Cheng, X., Chen, Y., Chao, K., Liu, D., Xing, H.: Self-optimised coordinated traffic shifting scheme for LTE cellular systems. In: 1st EAI International Conference on SelfOrganizing Networks, pp. 67–75. Springer press, Beijing (2015) 9. Xu, L., Zhao, X., Luan, Y.: User Perception aware telecom data mining and network management for LTE/LTE-advanced networks. In: 4th International Conference on Signal and Information Processing, Networking and Computers, pp. 237–245. Springer press, Qingdao (2018)

Medium-Extended-Range Weather Forecast Based on Big Data Application Yong Li, Wei Huang(&), Zhengguang Hu, Huafeng Qin, and Menglei Xu National Meteorological Center, Beijing, China [email protected]

Abstract. The National Meteorological Center initially completed the construction of the Medium-Extended-Range Weather Forecast (MERWF) operational system in 2018. The system uses browser/server system architecture to support concurrent operation of hundreds of terabyte real-time and historical data, through the introduction of large data core technologies such as distributed storage and distributed computing. The key technical problem of MERWF, which is the fusion of real-time data and historical data, is solved. It greatly improves the efficiency of data access and display, and realizes the development of MERWF technology products based on the big data analysis and the effective extraction of predictable information. Based on big data analyses, an application technology system of MERWF is then established for the first time in national business department, to meet the objective and intelligent needs of modern meteorological business. Keywords: Big data


Medium-Extended-Range
Forecast
Application

1 Introduction With the development of modern weather forecast services, the amount of data in all fields of meteorological services, including meteorological observations, numerical model simulations, objective derivatives and diversified services, has grown geometrically. The massive data is used in MERWF services, which constitutes a big data environment. At present, the national forecast operation system is the Meteorological Information Combine Analysis and Process System (MICAPS) developed by the China Meteorological Administration (CMA). The system has been developed to the fourth edition and has begun to support the storage and access of massive real-time data. But the fusion of real-time data and historical data, as well as the big data analysis technical integration, still cannot satisfy the modern MERWF. Its existing client/server infrastructure is also not conducive to effectively integrating the multitudinous supporting technologies and products developed in recent years. There is very little time for forecasters to use big data analysis and fine-tuning. At the same time, the current MERWF is still challenged by the limitations of basic theory, model level, Supported by the National Science and Technology Support Program of China (2015BAC03B07). © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 525–533, 2019. https://doi.org/10.1007/978-981-13-7123-3_61

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predictability, etc., which has become a hindrance for forecasters to efficiently correct and integrate multi-source forecast information. It makes forecasters unable to get more forecast information and conduct sufficient data mining in a limited time and has seriously affected the efficiency of work. Therefore, according to the needs of business development, it is urgent to build a new generation operational system which could support the integrated business processes and big data analysis, and this is exactly what this paper will introduce.

2 The Construction Background of MERWF Operational System 2.1

Big Data Characteristics of MERWF

The MERWF covers the next 4–30 days. Since the atmospheric system is a highly nonlinear chaotic system, the uncertainty of forecast increases rapidly with the increase of leading time. The ensemble forecast, which would usually produce a large amount of data, is widely used to deal with this long-range nonlinear uncertainty. Meanwhile, in order to reflect abnormal weather signals such as weather trends, important weather processes, key circulation systems, monsoons and rainy seasons, it is necessary to apply a large number of historical data and derived data. Such operations, including multi-type and high-dimensional data processing, data value mining, and efficient interactive operations, which are all based on the massive data, have gradually become the core of data analysis application on MERWF. 2.2

Issues and Goals of Big Data Application

At present, the national meteorological department has built a server cluster system for hundreds of concurrent users, which can accommodate meteorological real-time data in the order of 102 TB. It basically realizes the real-time storage, read and write functions of high-resolution observation and numerical model data [1–3]. But the system cannot meet the specialized needs of medium-extended-range weather forecasts and services. The main disadvantages are as follows. First, the storage and processing of massive historical data is insufficient. Second, the interpretation application on MERWF is not enough. Third, it is unable to extract the high-value information based on the big data comprehensive application, such as the predictability. Our goal is to build an operation system based on big data applications that can meet MERWF. So, we need to build a big data distributed application environment and interactive display platform, covering meteorological and user databases, wherein the meteorological databases include real-time databases and historical databases. Furthermore, the MERWF technology based on big data analysis should be integrated, and then we could realize the forecast technical process with monitor, forecast and service integrated together. Thus, establish intensive, objective and intelligent weather forecast to satisfy the demands of modern meteorological business development.

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3 System Architecture of MERWF The MERWF operational system is a specialized big data application system built on the MICAPS4 network platform [3]. It adopts the browser/server system architecture and chiefly includes three parts: basic environment of big data, big data processing system and front-end interactive analysis display (Fig. 1).

Front-end interactive analysis display The MERWF analysis product display

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Basic environment of big data Real-time database Cassandra Basic geographic information

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Fig. 1. System architecture of MERWF.

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Basic Environment of Big Data

The data sources of the operation system mainly include meteorological service data in China Integrated Meteorological Information Sharing System (CIMISS), basic geographic information service data, and local applied meteorological service data. These data can be divided into real-time data and historical data. Real-time data uses Cassandra as the storage system [2]. Cassandra is a point-to-point distributed system based on key-value, which is suitable for multi-dimensional data space. Historical data is principally stored in the memory database (Timesten). That is, the data is directly manipulated in the memory, and the database processing speed is more than 10 times faster than the traditional disk storage. At the same time, in view of the characteristics that the MERWF involves a complex and highly correlated data model, a relational

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database management system (MySQL) is used to satisfy this requirement. The system builds comprehensively distributed database architecture by introducing a variety of databases, which basically meets the storage and access requirements of massively concurrent system. 3.2

Big Data Processing System

We have adopt STORM [4], a distributed real-time big data processing framework, to expand the functions of the MERWF operational system, such as the big data preprocessing system and product processing system. The forecast operational system also realizes the unified and efficient processing of meteorological data analysis, memory data structure organization, data analysis, graphic process and product output. Meanwhile it supports the computational processing node parallelism and functional multiplexing. As an open source distributed computing system, STORM can handle large data streams easily and reliably, and support applications such as real-time analysis, online machine learning and continuous computing [4, 5]. It especially suits to the calculation and analysis of meteorological data characterized by massive, multi-source and real-time, etc. It also can guarantee the efficient real-time network publishing of basic data, and the real-time distributed processing requirements of basic meteorological elements could also be satisfied. The distributed processing program of the MERWF operational system uses realtime message monitoring to record the arrival, update and deletion of various meteorological data information, as well as the front-end interactive display of graphical interface and the operation applications of statistical analysis. These instant messages will enter the memory database (REDIS) as a message index queue. REDIS has an efficient indexing Key-Value performance for massive data [6]. The topology of the STORM cluster will immediately obtain the data index from the memory database (REDIS) message queue. Each topology will rapidly find the corresponding data according to the index and enter the subsequent unit to calculate and analyze the data. 3.3

Front-End Interactive Analysis and Display

The browser of the MERWF operational system is based on the HTML5 network platform. The Canvas and WebGL drawing interfaces provided by HTML5 have strong performance on real-time rendering of vector data and raster data. WebWorker parallel processing can provide a smoother interface response when performing complex analysis operations. HTML5 has transformed Web browsers from simple rendering into a runtime environment that provides rich interactive applications in numerous fields [7], and interactive analysis display is exactly the basic requirements for visualizing MERWF products. Real-time operations including multi-layer overlay, map zooming and roaming, projection switching, display mode switching, statistical analysis, etc. have high requirements on the processing speed of the system. After testing, the average response time of the systematic front-end display product is not more than 1 s. In addition, in order to further reduce the processing pressure on the webpage side, the system uses pre-processing drawing on the server pretreatment for common graphic products by means of algorithms or software like Python, Grads, etc.

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4 MERWF Technology Based on Big Data Analysis The difficulty of MERWF techniques is to effectively excavate the predictable information, which is hidden in interrelated historical and real-time data. The MERWF operational system firstly uses the big data comprehensive application analysis as the core of platform construction. Based on big data analysis, the operational system integrates techniques such as abnormal weather forecast information extraction, extreme weather process forecast, statistical post-processing forecast and machine deep learning. 4.1

Technology of Abnormal Weather Forecast Information Extraction

Based on the basic principles of meteorology, the real-time observation data, numerical models, historical observations, and reanalysis data are interpreted and applied. Through multi-element and multi-level conjoint analysis, the spatio-temporal distribution anomaly signals of large-scale circulation are effectively extracted, such as an index sequence that reflects the abnormal evolution of the atmospheric circulation; the anomaly, the standardized anomaly, and the abnormal percentile that reflect the spatial distribution anomalies of the surface elements. 4.2

Forecast Technology of Extreme Weather Process

By use of an objective identification method, Li et al. [8] established a historical case database of extreme precipitation processes in specific seasons and regions. Through the analysis of big data in high, middle and low level circulation fields, the key circulation indexes were extracted. We can select a variety of eigenvalues for each characteristic index. The eigenvalues of different indexes can be freely combined, which is equivalent to constructing a massive retrieval model. According to the predictability of the model prediction, a certain priority can be set for objective automatic retrieval. Figure 2 shows the specific forecasting technology flow.

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Fig. 2. Technology flow of extreme weather forecast.

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Statistical Post-processing Forecast Technology

For different forecast objects, the factors with high correlations are selected, and the big data are applied to traditional statistical post-processing techniques such as Chebyshev [9], natural orthogonal decomposition [10], Logistic correction [11], ingredients based method, frequency matching, and ensemble optimal percentile [12, 13], multi-mode integration [14], and prediction techniques like multivariate time-delay regression model, multivariate time-delay regression/principal component complex autoregressive model, etc. [15], to construct training model and produce forecasts with ensemble forecast data. 4.4

Machine Deep Learning Technology

As the core of artificial intelligence, machine deep learning has developed rapidly in many fields. Its advantages lie in the ability of automatically acquiring data features through learning algorithms. At present, the theory and method of machine deep learning have been initially applied to the field of meteorological forecast. For example, Bayesian classifier [16], neural network [17], clustering [18], support vector machine (SVM) [19] and other algorithms are used to analyze and forecast meteorological elements and circulations. Some of these products have been initially applied in MERWF operational system, such as precipitation correction products (Fig. 3) based on SVM-Multi-Mode Integration (SVM_MEF). It can be seen from the figure that

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Fig. 3. (a) Observational precipitation on June 23th, 2012, and the corresponding (b) SVM_MEF forecast, (c) EC_C forecast, and (d) EC_M forecast [19].

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compared with control forecast (EC_C) and ensemble average forecast (EC_M) in the European Centre for Medium-Range Weather Forecasts, forecast effect of SVM_MEF is significantly better.

5 System Function and Interface Design The browser of MERWF operational system includes multi-source big data, subjective and objective comprehensive analysis and forecast, service and evaluation, etc. It is divided into 8 functional modules: historical background, monitoring, numerical model interpretation, predictability analysis, objective forecasting, business evaluate, product production, and customer service (Fig. 4). Each module has a unified application logic relationship, which is consistent and reflects the professional technical process of MERWF services.

Service and evaluation product production customer service

Multi-source big data

Comprehensive

Big data analysis analysis and forecast and application

historical background monitoring numerical model interpretation

predictable analysis objective forecast business evaluate

Fig. 4. The function modules of the MERWF operational system browser.

The visual user interface contains three parts: the functional area, the attribute area, and the display area. The function area is located at the top of the page, including the system function main menu and sub menu. The attribute area is situated on the left side of the page, and is flat-designed for different modules, including data attribute characteristics such as mode type, meteorological element, geographic range, etc. The display area

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is located in the right side of the page, which displays product information such as graphics, text, and corresponding attribute operations. The MERWF usually need to display multi-dimensional data comprehensively. The visual products with high business value should be produced through analysis, reconstruction, and information extraction. Products often contain a large amount of data such as historical data, real-time data, forecast data and inspection data, which can reflect the changing trend, abnormal signals and predictability of the weather system.

6 Summary and Outlook The MERWF operational system uses distributed data storage and distributed computing technology, and significantly improves the application capability and access efficiency on multi-type, high-dimensional, and complex-related meteorological data. The system has realized efficient integration of massive real-time data and historical data, as well as the integration of big data with forecasting techniques. The specialized big data application system of MERWF is initially constructed. Furthermore, the MERWF business process which integrates monitoring, forecast and service is also established. The system has been applied in the national forecast business department and some provincial meteorological business departments, which has shown good versatility and stability. However, big data applications are still focused on big data mining within the meteorological industry. In the future, it will gradually expand to cross-industry data and form a broader data application platform. We will focus on such intelligent development directions, including forecast technology integration, efficient function expansion, improving on system performance, personalized service and convenient management. With the continuous development and improvement of the system, it could efficiently support the development of national MERWF services.

References 1. Wang, R., Huang, X., Zhang, B., et al.: Design and implementation of a real time parsing and storage system for massive meteorological data. Comput. Eng. Sci. 37(11), 58–64 (2015) 2. Wang, R., Wang, J., Huang, X., et al.: The architecture design of MICAPS4 server system. J. Appl. Meteorol. Sci. 29(1), 1–12 (2018) 3. Hu, Z., Gao, S., Xue, F., et al.: Design and implementation of MICAPS4 web platform. J. Appl. Meteorol. Sci. 29(1), 45–56 (2018) 4. STORM Documentation 1.1.0. http://storm.apache.org. Accessed 30 June 2016 5. Sun, D.W., Zhang, G.Y., Zheng, W.M.: Big data stream computing: technologies and instances. J. Softw. 25(4), 839–862 (2014) 6. Lang, H., Ren, Y.: a fast search algorithm based on redis emory database. Comput. Appl. Softw. 33(5), 40–43 (2016) 7. Wu, L., Zhang, F.: A study on WebGIS client based on HTML canvas. Geomat. World 7(3), 78–82 (2009)

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8. Li, Y., Jin, R., Zhou, N., et al.: An analysis on characteristics of heavy rainfall processes during the Meiyu season in Jianghuai region. Acta Meteorologica Sinica 75(5), 717–728 (2017) 9. Li, Y., Jin, R., Lin, Y.: The forecast of dekad temperature abnormal of china in winter. Meteorol. Mon. 25(7), 41–45 (1999) 10. Huang, W., Zhang, H., Bao, Y., et al.: Objective forecast of medium-term average temperature anomalies based on EOF technique. J. Meteorol. Sci. 37(4), 461–566 (2017) 11. Zhang, F., Cao, Y., Xu, J., et al.: Application of the logistic discriminant model in heavy rain forecasting. Meteorol. Mon. 42(4), 398–405 (2016) 12. Dai, K., Cao, Y., Qian, Q., et al.: Situation and tendency of operational technologies in shortand medium-range weather forecast. Meteorol. Mon. 42(12), 1445–1455 (2016) 13. Chen, B., Dai, K., Guo, Y., et al.: Precipitation verification and analysis of ECMWF ensemble statistic products in 2013 flooding season. Torrential Rain Disasters 34(1), 64–73 (2015) 14. Du, J., Chen, J.: The corner stone in facilitating the transition from deterministic to probabilistic forecasts-ensemble forecasting and its impact on numerical weather prediction. Meteorol. Mon. 36(11), 1–11 (2010) 15. Yang, Q.: Prospects and progresses in the research of the methods for 10–30 days extendedrange weather forecast. Adv. Earth Sci. 30(9), 970–984 (2015) 16. He, W., Kong, M., Zhao, H.: Research on meteorological prediction with Bayesian classifier. Comput. Eng. Des. 28(15), 3780–3782 (2007) 17. Ma, X.-K., Cai, X.-N., Yang, G.-M., et al.: Study on fog synoptic characteristics and fog forecast method in Chongqing. Clim. Environ. Res. 12(6), 795–803 (2007) 18. Huang, W., Niu, R.: The medium-term multi-model integration forecast experimentation for heavy rain based on support vector machine. Meteorol. Mon. 43(9), 1130–1137 (2017) 19. Roushangar, K., Alizadeh, F.: A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique. J. Mt. Sci. 15(7), 1481–1497 (2018)

Application Research of Big Data in Heavy Rainfall Forecast Model in Meiyu Season Shan Yin(&), Jie Ma(&), Ronghua Jin, and Ningfang Zhou National Meteorological Center, Beijing 100081, China [email protected], [email protected]

Abstract. In this paper, 33 classic Meiyu precipitation processes in recent 30 years are selected by using historical observation big data, and the inter-annual variation characteristics of heavy rainfall location are analyzed by descriptive big data analysis method. Besides, it is verified that there is a close connection between rainfall location and 500 hPa 5840 geopotential meter isoline. However, serious errors appeared in the forecast model during the medium-range precipitation forecast (4–10 days) from June 30th to July 4th, 2016. Therefore, in this paper, the causes of errors are analyzed by diagnostic big data analysis method using European Centre for Medium-range Weather Forecasts (ECMWF) ensemble forecast data. The results show that the premise for an accurate forecast by the classic forecast model is that, the heavy precipitation process must be accompanied by a southward-moving cold air. As the precipitation was a warm area rainfall in the monsoon region, errors were caused by the lack of high-level cold air participation. On one hand, this study proves the important impact of southward-moving cold air on the accuracy of rain belt location forecast. On the other, it will undoubtedly serve as an important reference for the subjective correction of the rain belt location in the forecast operation. Keywords: Big data
Rain belt during Meiyu season 5840 geopotential meter isoline
Monsoon
Jet




1 Introduction Meiyu rainfall generally occurs over the Yangtze-Huaihe River Valley (YHRV) from the beginning of June to July each year. Therefore, it is also known as the Jianghuai Meiyu which accomplished by frequent heavy rain, large precipitation and concentrated rainy days. According to statistics, the precipitation of the Jianghuai Meiyu accounts for about 45% of total summer rainfall. Studies have shown that the Meiyu rain belt usually shows a quasi-zonal direction around 30°N in eastern China, presenting a long strip shape [1, 2], while it shows an east-northeast direction in the Korean Peninsula and Japan [3, 4]. Therefore, the location and residence time of the Meiyu rain belt often determine the spatial distribution variability of precipitation over the YHRV. In addition, due to the narrow shape of the YHRV, the location of the rain belt is often the key and also the difficulty in the weather and climate forecast operation. Previous studies have paid great attention to the Meiyu rain belt. Some discussed the causes of the rain belt from the perspective of Meiyu front structural characteristics [3]. Some studied the © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 534–541, 2019. https://doi.org/10.1007/978-981-13-7123-3_62

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variation characteristics of rain belt location, range and intensity on inter-annual and inter-decadal time scales [5]. Besides, some other researchers discussed the factors affecting the location of the rain belt, such as the winter sea temperature and the various subsystems of the summer monsoon circulation [6, 7]. The relationship between the westerly jet and Meiyu rainfall shows that when the westerly jet is strong (weak), the rain belt locates further south (north) [8]. In the short-range and medium-range (1–10 days) forecasts, forecasters often refer to the 500 hPa 5840 geopotential meter isoline when considering the Meiyu rain belt location over YHRV, because the isoline usually corresponds to the Meiyu front location [9]. From June 30th to July 4th, 2016, the strongest precipitation process occurred since the beginning of the Meiyu season this year. The middle and lower reaches of Yangtze River MLYR suffered heavy rainfall with an extreme precipitation of 800–948 mm observed in some areas of Huanggang, Hubei Province. It is shown by post analyses that compared with observations, the rain belt intensity in short-range and mediumrange subjective and objective forecast is obvious weaker, and the east part of the rain belt is forecasted to be in further north area. The updating forecasts all indicated that the main rain belt would center in MLYR, the Huaihe Valley and the Huanghuai area. In observations, the heavy rainfall located along the Yangtze River and presented a typical quasi-east-west Meiyu rain belt. However, both the numerical models and the subjective forecast indicated that the heavy rainfall center would be near the Huaihe River (figure omitted), where was about one latitude further north than the observations. According to the forecast, the flood control in the Huaihe Basin would be quite challenging because of high population density and lack of the estuary. Therefore, the impact of the error in the rain belt location forecast is great, although it is only one latitude further north. In view of this, it is necessary to clarify the reasons for the rain belt location forecast error. In the following, the big data approach will be used for the study of the aforementioned issue.

2 Data and Methodology The grid precipitation data provided by the National Meteorological Information Center (from early June to the middle of July during 1980–2009), is used to analyze the distribution of pentad rainfall in Meiyu seasons over the past 30 years. 33 precipitation processes with quasi-east-west rain belt are selected. At the same time, the NCEP/NCAR reanalysis data [10] is used to analyze the atmospheric circulation during the same period. To explore the mechanism of the precipitation process from June 30th to July 4th, 2016, both ECMWF ensemble forecast data from June 21st to July 4th, 2016 and the daily rainfall data observed at 20 o’clock every day are used. Here we defined the position of the zonal-mean precipitation maximum in the middle-lower Yangtze River reaches from 110° to 120°E as the location of the rain belt for the period from June 30th to July 4th.

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3 Characteristics of Rain Belt Distribution Figure 1 shows the average horizontal distribution of 33 precipitation processes. Here, the area where the rainfall exceeds 100 mm is regarded as the range of the rain belt. On average, the rain belt was within 27.5°–31°N and exhibited a quasi-zonal distribution (Fig. 1a). In addition, two precipitation centers were located in the east and west respectively, with the maximum rainfall intensity about 180 mm/day, reaching the magnitude of large rainstorm. From the zonal average of precipitation within 110°– 120°E (Fig. 1b), it can be seen that the intensity curve and the range covered by 100 mm can well represent the area of the rain belt in Fig. 1a. Showing a sinusoidal distribution, the curve has only one maximum value located at 28.5°N, similar to the latitude of the maximum rainfall intensity in Fig. 1a. Based on the above analyses, the rain belt location for each precipitation process could be defined as the latitude of the maximum cumulative rainfall between 110°E and 120°E. Figure 2 shows the variations of rain belt locations of 33 precipitation processes. From the figure, the southernmost rain belt was at 27.75°N (the second process), and the northernmost one at 35.75°N (the 31st process). It can be seen from Fig. 2 that after normalized, the rain belts of 16 processes were located to the south of the 0 line and the rest were located to the north of the 0 line. These rain belt locations were basically symmetrical.

Fig. 1. (a) The average precipitation distribution of 33 precipitation processes; (b) the zonal average of the precipitation in Figure (a).

Fig. 2. The variation characteristics of rain belt locations of 33 precipitation processes. The histogram shows the rain belt location corresponding to the right coordinate, with its average value denoted by the red dotted line; the short-dotted line with + is the normalized series corresponding to the left coordinate.

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4 Main Factors Affecting the Rain Belt Location Based on previous studies and forecasters’ experience, it is considered that the location of 200 hPa westerly jet, South Asia High, 500 hPa 5840 geopotential meter isoline, 850 hPa and 700 hPa shear line, as well as the intensity of West Pacific subtropical high (WPSH) are all related to the rain belt location. So, is there still a close relationship between the precipitation process in quasi-zonal rain belt and these factors? The correlation coefficients between the rain belt location and 200 hPa maximum wind speed center, the location of its corresponding zero wind speed line, and the 5840 geopotential meter isoline are calculated. The corresponding correlation coefficients are 0.49, 0.63 and 0.67 respectively, and reach the 99%, 99.9% and 99.99% significance levels respectively. Therefore, the above high-level circulation systems have significant influences on the location of the typical quasi-east-west Meiyu rain belt.

Fig. 3. The comparisons between rain belt locations and meridional wind zero isotaches at (a) 500 hPa, (b) 600 hPa, (c) 700 hPa, and (d) 850 hPa during 33 precipitation processes.

The rain belt locations and shear lines at different levels during 33 precipitation processes are further compared and analyzed (Fig. 3). Here we use the location of the zero isotach of the meridional wind as the shear line. The results show that the 850 hPa shear line was often absent, indicating that the rainfall was mainly warm area precipitation without cold air in low level. Thus the distinct wind shear was hard to find. Although similar situations could be detected in the middle and upper levels, shear lines become more and more obvious with the rise of height. For example, distinct shear lines can be found easily at 700 hPa in the first 26 cases, and they are found in almost all the cases at 500 hPa. On one hand, it is revealed that the system in high level is often more stable than the lower one. On the other, it shows that the cold air is weak in most cases and not obvious in low levels. In general, the shear line does determine the location of the rain belt in some cases. At 700 hPa to 500 hPa, the correlation coefficients between shear lines and the rain belt locations all exceed 0.52. However,

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this relationship is not stable, especially for 850 hPa – the reference level often used in the operational forecast of central and eastern China. In the past forecasts, many front-line forecasters often take the location of low level (850 hPa and 700 hPa) shear line and middle level (500 hPa) 5840 geopotential meter isoline into serious consideration when forecasting the rain belt location during heavy rainfall. When it comes to the medium-range forecast, most of them only focus on the location of the 5840 geopotential meter isoline. It is revealed that with the lengthening of the forecasting time, the middle and high level systems and their relationships with the rain belt locations are generally more stable than that of the low-level ones.

5 Case Analysis The Meiyu season in 2016 was from June 19th to July 17th. Six heavy precipitation processes occurred during this period, of which the one from June 30th to July 4th was the strongest and most influential. However, verification shows that serious errors appeared in the forecasting rain belt location and heavy rain center by both numerical models and subjective forecasts. The average circulation situation during the precipitation from June 30th to July 4th reflects that cooperating with WPSH, the summer monsoon extended from the northwest side of the WPSH to the Jianghuai and Huanghuai areas (figure omitted). A wide low trough controlled the mid-high latitudes of Asia. Blocking highs were observed in both of the Ural Mountains on the west side of the low trough and the Okhotsk Sea on the east side. This circulation pattern is often referred to as “two ridges and one trough” in daily forecast operation. It is one of the typical circulation patterns in the Meiyu season, indicating that the circulation situation is relatively continuous and stable. This precipitation process lasted for 5 days with the cumulative rainfall of 100–250 mm in MLYR, some areas reaching 300–400 mm, and parts of areas exceeding 600 mm. It increased the severity of flood control in the Yangtze River Basin. The medium-range subjective forecast (5 days earlier before the end of the process) showed that the heavy rainfall center of the precipitation process would be along the Huaihe River, which is obviously one latitude further north than the observation. Referring to the experiences using the characteristic line (5840geopotential meter isoline) commonly in the forecast, the sliding correlation between the rain belt location and 5840 geopotential meter isoline during the Meiyu season is analyzed (Fig. 4). It can be seen from Fig. 4a and b that there was a certain correspondence between the rain belt location variation and the correlation coefficient trend before June 30th. The correlation coefficient increased (decreased) when the rain belt moved to the north (south). When it comes to July 1st, the coefficient between them decreased rapidly, revealing that the 5840 geopotential meter isoline could no longer indicate the rain belt location, and the 500 hPa circulation system could not dominate the rain belt location any more, like what it had done in several previous precipitation processes. To find out why the rain belt location could no longer be forecasted by 5840 geopotential meter isoline, the average rainfall in MLYR (26–32°N, 115–120°E) is defined as the rainfall intensity index in the key area, and the correlation between the

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Fig. 4. (a) The sliding correlation coefficient of the rain belt location and 5840 geopotential meter isoline from June 18th to July 4th, 2016; (b) 110°–120°E zonal average precipitation.

Fig. 5. The correlation coefficients between the rainfall intensity forecasted on the 30th and height field at (a) 500 hPa, (b) 700 hPa, (c) 850 hPa and (d) 925 hPa. The shadow denotes the areas passing 90% confidence level, and the isoline is the average 500 hPa geopotential height during the precipitation process.

rainfall intensity index based on the ensemble forecast and the 500–850 hPa geopotential height is analyzed (Fig. 5). Figure 5a shows that the correlation coefficient in MLYR does not pass the 90% confidence level test, indicating that there is no obvious relationship between the rainfall intensity and 500 hPa geopotential height, or the 5840 geopotential meter isoline. However, in the lower levels (700–925 hPa), the correlation is significant with the coefficient passing the 90% confidence level test, revealing that the heavy rainfall or location variation during the precipitation process is only closely related to the circulation below 700 hPa, while the influence of the higher layer (500 hPa) circulation is relatively weak. Further analysis also shows that the relevant significant regions from 700 hPa to 925 hPa in Fig. 5a–c are located on the northwest side of WPSH, i.e. the active southwest monsoon region in the low level, indicating that the southwest monsoon has

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played a significant role in the intensity variation of this precipitation process. Figure 6 shows a vertical section along 115–120°E, from which it can be seen that the northerly wind (cold air from the north) generally influences the area to the north of 35°N and slopes northward with the rise of height during this rainfall. Therefore, the cold air was far from the heavy rainfall center, and its contribution was insignificant because it was a warm area precipitation in the monsoon region. It is quite different from the average circulation pattern of 33 processes in Fig. 6b. In 33 classic Meiyu precipitation processes, around the strong rainfall center (30–31°N), there were both low-level shear lines (the interface of east and west wind) and middle-low-level (850–500 hPa) cold and warm air confluence (the interface of south and north wind) (Fig. 6b). From June 30th to July 4th, no northerly cold air participated in the precipitation process, resulting in deviations between the actual rain belt location and the classic forecast model results (or 33 process averages). In the classic forecast model, the rain belt central location (5840 geopotential meter isoline) is generally in the cold and warm air convergence zone accompanied by a strong convergence rise, which is conducive to heavy rain (Sugimoto et al. 2003).

Fig. 6. (a) The vertical section of 115–120°E averaged vectorial wind field, the zonal wind (red contour), meridional wind (shaded) in the middle and low levels from June 30th to July 4th, 2016; (b) the same as (a) except for 33 historical cases; (c) the heavy rainfall center variation of 33 historical cases.

6 Conclusion In this paper, the big data analysis method is used to explore the variation characteristics of the rain belt location during the typical Meiyu season in the past 30 years. The results show that the rain belt location is closely related to the 500hpa 5840 geopotential meter isoline, which is in line with the forecast operation experience. However, errors appear in the forecast of the precipitation process from June 30th to July 4th, 2016 by the classic forecast model, indicating that there are still some limitations in it.

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The results of this paper show that the northerly cold air only appeared in the middle and upper troposphere during the heavy precipitation process from June 30th to July 4th, 2016. There was a certain distance between the cold air and the rain belt center, that is, the heavy precipitation process was not caused by the confluence of cold and warm air. Considering the location of WPSH, it can be seen that this precipitation process occurred in the warm area of the monsoon region, which was caused by the convergence of warm air. Under this formation mechanism, the 5840 geopotential meter isoline of the classic forecast model loses its forecasting ability. Instead, cold air participation in the precipitation process is the key factor determining the accuracy of the forecast model. On one hand, this conclusion is conducive to enriching and deepening the forecaster’s understanding of the rain belt location variation during the Meiyu season. On the other, it provides important reference for forecast correction. There are still many problems in the paper that need further study. For example, why did the numerical model fail in the rain process forecast? What are the reasons for the difference in the performance of different numerical models? These will be discussed in the future work. Acknowledgement. This work was supported by grants of the National Natural Science Foundation of China (41575066) and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAC03B04, 2015BAC03B06 & 2015BAC03B07).

References 1. Si, D., Ding, Y.H., Liu, Y.J.: Decadal northward shift of the Meiyu belt and the possible cause. Chin. Sci. Bull. 55(1), 68–73 (2010) 2. Xu, W.G., Jiang, J.: Characteristics of the rain belt of Meiyu between inter-annual and interdecadal climate variations. J. Nanjing Univ. 40(3), 292–303 (2004) 3. Ding, Y.H., Liu, J.J., Sun, Y., et al.: A Study of the Synoptic-Climatology of the Meiyu System in East Asia. Chin. J. Atmos. Sci. 31(6), 1082–1101 (2007) 4. Oh, J.H., Kwon, W.T., Ryoo, S.B., et al.: Review of the research on Changma and future observational study (KORMEX). Adv. Atmos. Sci. 14(2), 207–222 (1997) 5. Wei, F.Y., Xie, Y.: Interannual and interdecadal oscillations of meiyu over the middle-lower reaches of the changjiang river for 1885–2000. Q. J. Appl. Meteorol. 16(4), 492–499 (2005) 6. Zong, H.F., Zhang, Q.Y., Chen, L.T.: Temporal and spatial variations of precipitation in Eastern China during the Meiyu period and their relationships with circulation and sea surface temperature. Chin. J. Atmos. Sci. 30(6), 1189–1197 (2006) 7. Su, T.H., Xue, F.: The intraseasonal variation of summer monsoon circulation and rainfall in East Asia. Chin. J. Atmos. Sci. 34(3), 611–628 (2010) 8. Sampe, T., Xie, S.P.: Large-scale dynamics of the Meiyu-baiu rainband: environmental forcing by the westerly jet. J. Clim. 23, 113–134 (2010) 9. Sugimoto, S., Hirakuchi H.: Simulation of precipitation caused by a Baiu front: an evaluation study with radar data. In: Weather Radar Information and Distributed Hydrological Modelling, vol. 282, pp. 51–58. IAHS Publication (2003) 10. Kalnay, E., Kanamitsu, M., Kistler, R., et al.: The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996)

Predictability of an Extreme Rainfall Event in North China Jie Ma1(&), Shan Yin1(&), Lijun Jin2, and Ronghua Jin1 1

2

National Meteorological Center, Beijing, China [email protected], [email protected] Hydrology Bureau, Yellow River Conservancy Commission, Zhengzhou, China

Abstract. In this paper, the predictability of an extreme rainfall event in North China is discussed by diagnostic big data analysis method. The data of the event (July 18th–20th, 2016) is from the deterministic forecast and ensemble forecast of the European Centre for Medium-Range Weather Forecasts (ECMWF). The results show that, the rainfall event is characterized by evident variations of the precipitation magnitude and rainfall belt location from July 18th to 19th. The rain belt moved almost 10 degree northward, bringing significant heavy rainfall to North China, thus it caused a big challenge for forecast operation. Further analyses reveal that in this event, the predictability of the rain belt location was determined by the strength of blocking high system over the Bohai Sea (120–130°E). The blocking high system above the Bohai Sea was favorable for the low vortex over North China to move northward slowly. Meanwhile, the eastward-moving vortex and the blocking high strengthened the southwest wind in the low levels, increased the precipitation magnitude, and drove the rain belt to move further northward in North China. Furthermore, the comparison between the deterministic forecasts and ensemble forecasts with different leading times demonstrates that the maximum forecast leading time for the rain belt location in the deterministic model is 96 h, while that in the ensemble model is 120 h. Keywords: Predictability Blocking high


Extreme rainfall event
Rain belt location


1 Introduction From July 18th to 20th, 2016, the Sichuan Basin, the area from Jianghan to North China, and the Huanghuai area suffered from the strongest rainfall process since the flood season in 2016. The rainfall in 22 counties and cities such as Daxing of Beijing City and Jingjing of Hebei Province broke the historical records. The heavy rainfall also caused disasters such as mountain torrents, landslides, debris flow and urban waterlogging in provinces like Hubei, Hunan, Hebei, Shanxi and Guizhou, resulting in many casualties. The heavy rainfall event is characterized by two remarkable features. First, the rain area jumped rapidly from the Yangtze River Basin to North China, which connected two rainy seasons – the Jianghuai Meiyu season and North China rainy season. It can be seen that the rain belt moved drastically on the 18th and the 19th, © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 542–550, 2019. https://doi.org/10.1007/978-981-13-7123-3_63

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moving from 28°N (the Yangtze River Basin) to the vicinity of 38°N (North China), crossing 10 latitudes northward within two days. The rainfall intensity also increased significantly on the 19th, reaching the magnitude of rainstorm. In comparison, the change of the rainfall zone showed a slow v-shaped variation in the east-west direction, extending from east to west from the 16th to 19th. After reaching the largest magnitude on the 19th, the rain zone began to move back from west to east (Fig. 1c). Another feature is that on the initial stage, large errors appeared in the forecasts from both the ECMWF numerical model (Fig. 1d), which generally performed perfectly, and the subjective forecast (figure omitted). Figure 1d shows the forecast of the rain belt location from 1200 UTC July 19th to 1200 UTC July 20th (hereinafter referred to as the 20th) from different initial forecast times. It can be seen that before the 16th, all the forecasts indicated that the rain belt was within the area from the middle and lower reaches of the Yangtze River to the Huanghuai area (30–35°N), and the average rainfall intensity was weak. After that, the forecasted rain belt moved to around 40°N, revealing that the ECMWF deterministic model gave the information 96 h in advance that the rain belt would jump northward, while it failed to grasp this feature before that time point.

Fig. 1. (a) Distribution of cumulative precipitation in North China and other places from July 18th to 20th, 2016. (b) Time series of zonal averaged precipitation in 110–120°E from July 16th to 22th. (c) Time series of meridional averaged precipitation in 26–42°N from July 16th to 22th. (d) Rain belt location forecast on July 20th by ECMWF deterministic model initialized at 1200 UTC from July 10th to 19th.

Previous studies have pointed out that the rain belt jumps northward two times and stays in three quasi-stationary phases every year in China. And it moves northward periodically [1–4]. Among them, the third stagnation period from mid-July to midAugust represents the beginning of the rainy season in North China and Northeast China. During this heavy rainfall process, the rain belt jumped from the Yangtze River Basin to North China, indicating the end of the Jianghuai Meiyu and the beginning of the rainy season in North China. However, in most medium-range forecasts, subjective

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or objective, there are obvious errors, especially in the forecast of the rain belt location. So, what causes these forecast errors? How long is the predictability of this extreme rainfall process by numerical model? In this paper, to solve the above problems, the causes of the dramatic rain belt location variation in a short period of time and its mechanisms are analyzed. Besides, the big datasets of deterministic forecast and ensemble forecast are used to discuss the predictability of the rainfall process in the medium-range forecast.

2 Data and Method It is generally believed that the difference in the initial fields is an important reason for forecast errors of the numerical model. Ensemble numerical forecast provides an effective way to correct initial bias and model errors. Compared with the deterministic forecast, ensemble numerical forecast can improve the effectiveness and reliability of the forecast by providing probabilistic prediction involving multiple members or possible scenarios through characterizing and describing the uncertainty of initial filed [5–7]. Some members of the ensemble forecast may capture some “missing” information to help forecasters, decision makers and the public make better decisions. Big data set of ensemble forecast has also been widely used in recent years due to its great application value in operational weather forecast and forecast theory research. Therefore, forecasting and analysis field in ECMWF deterministic model (spatial resolution: 2.5°  2.5°, forecasting time: 240 h), and the big data set from ECMWF ensemble forecast model (spatial resolution: 0.5°  0.5°, forecasting time: 360 h with 51 members) are used to analyze the atmospheric circulation and the predictability of this heavy rainfall process by statistical methods such as empirical orthogonal function (EOF), correlation analysis and composite analysis. In addition, the NCEP/NCAR rainfall reanalysis data with a spatial resolution of 0.5°  0.5° is selected to verify the forecast results. The location of blocking high is quantified as a blocking high north boundary position index, defined as the averaged latitude of the 500 hPa 5880 gpm isoline in 120–130°E. The rainstorm north boundary position index is defined as the zonal averaged 50 mm precipitation north boundary position in 110–120°E.

3 Analysis of the Atmospheric Circulation Background and Key Impact System Sensitivity Through the analyses of circulation background, it can be seen that the low vortex system of the lower layer (850 hPa), the monsoon and West Pacific Subtropical High (WPSH) are the main circulation systems affecting the rainfall process (figure omitted). The low vortex system gradually moved northward when it moved eastward along the edge of WPSH, so the main rainfall area was obviously related to the WPSH north boundary position. Figure 2a shows the changes of WPSH from July 18th to 20th. It can be seen that with the eastward movement of the low vortex system in the middle and lower reaches of the Yangtze River, WPSH began to extend westward and move

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northward. Its western ridge point extended from 125°E to 110°E, and the 5880 gpm isoline above the 120–130°E region (often used to define the north boundary of WPSH) also jumped from 30°N to 40°N. Near the Bohai Sea and the East China Sea, a high pressure ridge was formed, which slowed down the low vortex moving eastward and northward. Therefore, the rainfall was prolonged and the accumulated rainfall increased obviously in North China, the Huanghuai area and the nearby areas. The heaviest rainfall in North China during this rainfall process is on July 20th, 2016, and North China is the region with the greatest error in precipitation forecasting. Thus, the region of 38–42°N and 115–120°E is selected as the key area for the following analyses. The correlation between the average rainfall intensity in the key area and 500 hPa geopotential height field is calculated (Fig. 2b). Two systems affecting the variation of the rainfall intensity in the key area are found: the blocking high in 120–130°E and the low vortex in 110–120°E, both of which have passed the 99.9% confidence test. It also confirms that the strong blocking high is favorable for heavy rainfall in North China, and the effect of the low vortex is reflected in the inverted trough on the north side.

Fig. 2. (a) The variations of the WPSH position from July 18th to 20th. The black, red, and green lines represent the 18th, 19th and 20th respectively. (b) Distribution of the correlation coefficients between the average rainfall intensity in the key area (38–42°N, 115–120°E) and 500 hPa geopotential height field (shaded means the passing of 99.9% confidence test) from ECMWF ensemble forecast initialized at 1200 UTC July 16th. The contour is 500 hPa geopotential height on the 20th.

In addition, from the composite analysis of 850 hPa wind field based on the blocking high north boundary position index, it can be seen that when the high located to the north of its normal position, a cyclonic anomalous wind field was detected in North China (Fig. 3a), increasing the low vortex intensity (Fig. 3d). It was favorable for the enhancement of the southwest monsoon and pushed the low-level jet northward (Fig. 3a and c), and vice versa (Fig. 3b). The comparison of the north boundary position index series of blocking high and the rainstorm shows that their variation trends were consistent, and their correlation coefficient reached 0.61 (passed the 99.9% confidence test). It is revealed that in 110–120°E, the average rain area location and intensity variation were obviously affected by the geopotential height field of the region (30–45°N, 120–130°E).

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Fig. 3. Composite analyses of 850 hPa wind field based on blocking high north boundary position index on July 20th from ECMWF ensemble forecast initialized at 1200 UTC July 16th (shaded represents the passing of 99.9% confidence test), for (a) high index members, (b) low index members, (c) difference between high and low index members, and (d) ensemble mean wind anomalies.

4 Analyses of the Predictability As the position variation of the east section of WPSH has a significant impact on the rain belt location during the heavy rainfall, it is necessary to analyze the causes for the deviation of the forecasted rain belt location from the geopotential height field of 120– 130°E. Is the failure of predicting the variation characteristics of WPSH in 120–130°E the reason for the omission of the sudden northward jump of the rain belt? First, the ECMWF deterministic forecasts are verified. Figure 4 shows the ECMWF deterministic forecasts of WPSH on July 20th, which were initialized on July 13th to 19th. It can be seen that the variation of WPSH north boundary in 110–120°E was obviously smaller than that in 120–130°E, and it was very close to the observations. The results show that the subjective and objective forecasts of the western part of the rain belt (110–115°E) were closer to the observations. However, the subjective and objective prediction errors in the eastern part of the rain belt (115–120°E) were obvious, which might be caused by the great impact of the WPSH in 120–130°E on the east part of the rain belt. From July 13th to 19th, with the approaching of time, the range of WPSH near 120–130°E predicted by the model continuously adjusted, jumping from 32°N to 40°N. The model shows great uncertainty for the prediction of WPSH in this region (Fig. 4a). In Fig. 4a, the WPSH on 20th predicted by the model initialized on July 13th to 15th was in the south thus the moving path and influence range of the low vortex were both in the south, and the overall rainfall area and the strong rainfall center were in the south of North China. The forecasted WPSH on 20th

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initialized on July 16th to 18th began to adjust toward the “blocking situation”, so the predicted rainfall area gradually moved northward with an enhanced intensity. Therefore, from the perspective of forecasting time, only the ECMWF deterministic forecast initialized on July 16th has reference values. It can be seen that the rainfall in key areas on 20th (North China and nearby areas) initialized on July 16th was significantly enhanced when compared with that initialized on July 12th–15th. With the previous analyses of the rain belt’s northward jumping, it can be seen that the maximum forecast leading time of ECMWF deterministic model is only 96 h for the rain belt location.

Fig. 4. (a) The ECMWF deterministic forecast of WPSH on the 20th initialized at 1200 UTC every day from July 13th to 19th, and the observations on the 20th. The contour number is the corresponding starting date; (b) the rainfall intensity forecast for key areas on July 20th by ECMWF deterministic model initialized at 1200 UTC every day from July 12th to 17th.

Based on the empirical orthogonal function (EOF) analysis of rainfall field on July 20th initialized at 1200 UTC on July 12th, the main distribution mode presented northeast-southwest direction (the first principal component with an interpretation variance of 21.9%) (Fig. 5a), and the main rainfall centers were located in the Huanghuai and Jianghuai areas. It represented the forecast trend of most members, that is, the rain belt moved northward while moving eastward, but most of its north boundary was within the Huanghuai area (represented by member 1 and 4). The correlation analysis between the time coefficient of the first principal component and the 500 hPa height field show that there is a significant correlation between the main rainfall mode and the 500 hPa geopotential height field in the region (120–130°E, 35– 45°N), indicating that the variation of the 500 hPa height field has a significant influence on the rain belt distribution. The results are consistent with our analysis of the circulation background field. Figure 5b shows another rain distribution type, in which the rain belt was carried northward to the north of Huanghuai-Jianghuai area, while the middle and lower reaches of the Yangtze River were negative, indicating that the rainfall was less and the Meiyu season tended to end. It corresponds to members of 2/6/14/24. The third mode explanation variance is small and it is unnecessary to be analyzed here. We also analyze the distribution characteristics of 500 hPa geopotential height fields and the amount of precipitation on July 20th when the time coefficients of the

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Fig. 5. EOF analysis of the precipitation on 20th from the ECMWF ensemble forecast initialized at 1200 UTC July 12th, for (a) the first principal component and (b) the second principal component. (c) The distribution of correlation coefficients between the time coefficients of the first principal component and the synchronous 500 hPa height field.

above modes are greater than 1.5 standard deviations (figure omitted). It is found that when the first and second modes are strong, the distribution of rain areas is similar except for the north boundary of rainstorm area. The second mode rain area is located in the further northeast area. It indicates that the heavy rainfall center moves faster, and its main body would move eastward into the sea. But the heavy rainfall center in the first mode would continue to move northward. It also explains why the first mode presents an east-west dipole distribution. The difference between the two modes might be attributed to the development of blocking high over 120–130°E in the east side of the rain area. Although the blocking high situations are presented in both the first and second modes, the intensities of these two are different. It shows that the strong rainfall area is more sensitive to the change of blocking high intensity. In comparison, the rain belt forecasted by ECMWF deterministic model is still located in the Yangtze River valley on July 20th, indicating that the forecast error from deterministic model is greater. Figure 6 shows the first two modes of the forecasted rainfall on the 20th initialized every day at 1200 UTC from July 13th to 16th, 2016. With the approaching of leading time, the rain belt of the first mode (Fig. 6a, c, e, g) and its north boundary continuously move to the northeast, indicating the northward movement of the forecasted rain belt, which is closer to the observations. The explained variance of the first mode varies little with time, and the significant correlation region between the first mode time coefficient and the height field is similar to that of Fig. 5c, indicating that the ensemble forecast is stable for the first mode. Compared with the first mode, the horizontal distributions of the second mode change obviously with the approaching of leading time (Fig. 6 b, d, f, h). In the forecasts initialized on the 13th and 14th, the second mode is mainly located in the north part of the Jianghuai and the Huanghuai areas. In the forecasts initialized on the 15th and 16th, the main rain areas in the second mode jumped northward to North China. Therefore, the ensemble forecast initialized on the

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15th can be used as a reference as it reveals that the rain belt would jump northward to North China and nearby areas during this rainfall process.

Fig. 6. The first mode (a, c, e, g) and the second mode (b, d, f, h) of EOF based on the 20th rainfall forecasted by ECMWF ensemble model initialized at 1200 UTC July 13th (a and b), 14th (c and d), 15th (e and f) and 16th (g and h). The explained variance is given in the lower right corner.

In conclusion, from the point of view of forecast leading time, compared with the deterministic forecasts (started from the 16th), the ensemble forecasts (started from the 15th) could give a more reasonable forecast 24 h in advance, so it has a significant advantage in forecasting. The analyses of the first two modes of EOF reveal the uncertainties of rain belt location prediction and the causes, which is of important reference value for improving the medium-range forecast.

5 Conclusions In this paper, the predictability of medium-range forecast for an extreme rainfall event from July 18th to 20th, 2016 is discussed. In this event, both subjective and objective forecasts show great errors on the forecast of the north boundary position of the rain belt. To find out the reason, the atmospheric circulation factors influencing the rain belt location variation are firstly analyzed. The results show that the intensity variation of blocking high in 120–130°E greatly affects the rain belt location in 110–120°E. When the blocking high is stronger, the average 5880 gpm isoline in 120–130°E is in a further north area, so is the rain belt in 110–120°E. Then, the rainfall intensity in North China is also higher. The further study of atmospheric circulation shows that when the blocking high is stronger, the low vortex system in North China is stronger, and the southwest airflow between them is obviously stronger, which is favorable for the northward transport of water vapor. Therefore, it leads to the more north location of rain belt in the East China and the strong rainfall in North China. Based on the characteristics of heavy rainfall

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and the forecast of the above-mentioned atmospheric circulation factors, the predictability of extreme rainfall in North China is discussed by big data set of ECMWF ensemble and deterministic model forecasts. The results show that the maximum forecast leading time for the rain belt location by deterministic model is 96 h, while it can reach 120 h by ensemble model. Acknowledgement. This work was supported by grants of the National Natural Science Foundation of China (41575066) and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAC03B04, 2015BAC03B06 & 2015BAC03B07).

References 1. Tao, S.Y.: Torrential Rain in China. Science Press, Beijing (1980) 2. Guo, Q.Y., Wang, J.Q.: Interannual variations of rain spell during predominant summer monsoon over China for recent thirty years. Acta Geogr. Sin. 36(2), 187–195 (1981) 3. Ding, Y.H.: Summer monsoon rainfalls in China. Q. J. R. Meteorol. Soc. 70, 373–396 (1992) 4. Ding, Y.H., Li, C.Y., He, J.H., et al.: South China Sea Monsoon Experiment (SCSMEX) and the East Asian monsoon. Acta Meteorologica Sin. 62(5), 561–586 (2004) 5. Matsueda, M., Tanaka, H.L.: Can MCGE outperform the ECMWF ensemble? SOLA 4, 77–80 (2008) 6. Park, Y.Y., Buizza, R., Leutbecher, M.: TIGGE: preliminary results on comparing and combining ensembles. Q. J. R. Meteorol. Soc. 134(637), 2029–2050 (2008) 7. Johnson, C., Swinbank, R.: Medium-range multi-model ensemble combination and calibration. Q. J. R. Meteorol. Soc. 135(640), 777–794 (2009)FloatPlacement>

Research on Visibility Forecast Based on LSTM Neural Network Yuliang Dai1(&), Zhenyu Lu1,2, Hengde Zhang3, and Tianming Zhan4 1

2

School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China [email protected] Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing 210044, Jiangsu, China 3 National Meteorological Center, Beijing 100081, China 4 Nanjing Audit University, Nanjing 210044, Jiangsu, China

Abstract. For series problems in the meteorological field, the long-short-term memory neural network (LSTM) model is applied to the visibility forecast in the Beijing, Tianjin and Hebei region. First of all, the historical meteorological data during the months (Oct.-to-Dec. and Jan.-to-Feb.) of years 2015–2016 in the Beijing, Tianjin and Hebei region is selected as a dataset. Then, the Pearson Correlation Coefficient method is applied to select meteorological factors that have main influence on visibility to construct the training set, and adjust the network model parameters to train the neural network, and establish the input meteorological factors and the visibility of the output. Finally, European Centre for Medium-Range Weather Forecasts (ECMWF) data of the Beijing, Tianjin and Hebei region from October to December in 2017 is used to test the forecast effect of the LSTM model, and compared with the prediction results of the BP neural network. The result shows the visibility forecast based on the LSTM model is significantly better than BP neural network. The TS score in 0–1 km is 0.22, and its error is 0.34 km. The TS score in 1–10 km is 0.51, and its error is 2.18 km. The TS score above 10 km is 0.38, and its error is 6.07 km Keywords: Visibility forecast


Neural network
LSTM

1 Introduction Atmospheric visibility refers to the maximum horizontal distance of a target object that can be seen by a person with normal vision under the current weather conditions [7]. Visibility as an important physical parameter for characterizing atmospheric transparency reflects air pollution and atmospheric environmental quality in the region. When continuous and wide-ranging low-visibility weather conditions occur, it often causes frequent occurrences of traffic accidents and delays in aircraft movements, causing huge losses to people’s lives and property. Therefore, accurate forecasting of low visibility weather is a difficult problem in meteorological work and plays a positive role in preventing the occurrence of various traffic accidents.

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 551–558, 2019. https://doi.org/10.1007/978-981-13-7123-3_64

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Atmospheric visibility mainly depends on air pollution and meteorological conditions [9]. The air pollution caused by the rise of the industrial and transportation industries has led to the accumulation of particulate matter in the air, which has significantly reduced atmospheric visibility through extinction [1]. The effects of changing of relative humidity, wind direction, and wind speed on atmospheric visibility are reflected in the fact that atmospheric aerosol particles can moisture absorption grow through the moisture in the air and change their optical characteristics, thereby affecting visibility; the spread of pollutants in the air is poor, and particulate matter is constantly increasing, which also reduces visibility. The forecasting methods of visibility are roughly divided into three categories at present. One is based on the method of meteorological analysis. For example, Yin [2] used weather maps to analyze the weather conditions to determine the visibility level of future haze weather. Such methods have strong theoretical knowledge and rich experience requirements for forecasters engaged in this business. The second category is the method of historical data statistics, including non-parametric modeling, linear regression, etc. For example, Zhai [3] used the method of the support vector machine to analyze the characteristics of haze weather to predict visibility. This method has strong dependence on meteorological data and geographical location, and models built by this method are not easy to generalize and apply to the business. The third type is the method of numerical model, which establishes the visibility forecasting system of haze weather through atmospheric chemistry model, such as the CUACE model of the Chinese Academy of Meteorology, this method has a clear physical meaning, but has strong requirements for hard conditions such as computing resources, and there will be some statistical bias compared with the observed values. The neural network method has developed rapidly in various fields recent years, and many scholars have applied it to the meteorological field [5]. The long-short-term memory neural network (LSTM) is a variant of the recurrent neural network (RNN). Recurrent neural network can be regarded as multiple copies of the same neural network. Information of each neural network module will be transmitted in turn, and the time-order can memorize the data. However, in the recurrent neural network, there are problems of gradient explosion and gradient disappearance, which makes it impossible to deal with time series with too long delay [8]. The LSTM model is a new network structure improved on the basis of the recurrent neural network, solved the problems of gradient explosion, gradient disappearance and the lack of long-term memory in the traditional recurrent neural network. Therefore, the LSTM model is better than the traditional model to solve the time series problem with missing values [10], and establish an effective space-time forecasting framework.

2 Model Data 2.1

Data Source

Meteorological stations in Beijing, Tianjin and Hebei region are located in the North China Plain. They are affected by the East Asian monsoon climate, with similar climatic background and the same range of relative humidity, so similar weather

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phenomena are also found in the same weather conditions. The data used in this paper is the conventional ground observation data and high-altitude data from October to February of 2015–2016, including wind speed, relative humidity, temperature, and so on. The model is trained by constructing a historical database of the conventional ground and high-altitude data, and the data derived from the EC model with the same elements as above from January to February and October to December in 2017 is used to forecast. 2.2

Factor Analysis

There are many factors affecting visibility. In order to predict the visibility of the next day more accurately, the correlation coefficient method is needed to select physical quantity factors that are highly correlated with visibility as the input factor of the forecast model. Choosing a highly correlated feature factor is conducive to constructing a faster, lower-cost model and improving the accuracy of the prediction. This paper applies the Pearson Correlation Coefficient, which is a statistic used to reflect the linear correlation between two variables. Equation as follows. qX;Y ¼

COVðX; YÞ EððX  lX ÞðY  lY ÞÞ EðXYÞ  EðXÞEðYÞ ¼ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffipffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rX rY rX rY EðX 2 Þ  E 2 ðXÞ EðY 2 Þ  E 2 ðYÞ ð1Þ

The value interval is [–1, 1], a complete negative correlation is denoted by –1, a complete positive correlation is denoted by +1, and a non-linear relationship is denoted by 0. Table 1 gives the factors selected based on the calculation results. Table 1. Factors of visibility forecast based on LSTM Factors 08h humidity The difference between 08h temperature and dew point Up dry and down wet wrh 08h 950 hpa horizontal component of wind speed 14h humidity 08h 900 hpa temperature The difference between 08h temperature and 14h dew point 08h 925 hpa temperature 08h 850 hpa vertical component of wind speed Difference between 08h 850 hpa and ground temperature 08h atmospheric pressure

Absolute value of correlation coefficient 0.62 0.60 0.57 0.49 0.45 0.44 0.41 0.40 0.39 0.39 0.38 0.36

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3 Model Structure Neural network is a deep learning technique which simulates the structure of biological neural network and is used to model nonlinear statistical data [4]. The neural network is connected by a large number of artificial neural units in a prescribed manner, including an input layer, a hidden layer, and an output layer, and external input information is dynamically processed by adjusting the structural parameters of the internal unit [6]. The LSTM model is a new network structure, which solves the lack of long-term memory in the traditional recurrent neural network. It contains a memory unit and three control gates, namely the forget gate, the input gate and the output gate, to realize storage and control of information (Fig. 1). ht

ht-1

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A Xt-1

ht+1

σ

σ

tanh

A

σ

Xt

Xt+1

Fig. 1. The structure of LSTM

The forget gate determines some of the information that is discarded from the cell state. The gate reads the output of the previous layer and the input of the current time point, and outputs a value between 0 and 1 to the current cell state. Completely reserved with the value 1 and completely discarded with the value 0. The calculation formula is: ft ¼ rðWf  ½ht1 ; xt  þ bf Þ

ð2Þ

Input gate determines the amount of new information added to the cell state. The implementation of the gate requires two steps: First, the sigmoid activation function determines the information that needs to be updated, and the new candidate vectors will be generated by the tanh activation function and added to the state ct . The calculation formula for this process is: it ¼ rðWi  ½ht1 ; xt  þ bi Þ

ð3Þ

~ct ¼ tanhðWc  ½ht1 ; xt  þ bc Þ

ð4Þ

Then, the cell’s state needs to be renewed. Multiply the forget gate ft with the state ct1 of the old cell, discard the information that needs to be discarded, and add a new candidate vector. The calculation formula is:

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ct ¼ ft  ct1 þ it  ~ct

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ð5Þ

Output gate controls the influence of long-term memory on current output. This output is not only based on cell status, but also a filtered version. A sigmoid activation function is used as a valve to selectively output part information of the cell’s state, and the tanh activation function is used to scale the value of the cell’s state between –1 and 1, and multiply the output of the sigmoid activation function with this value. It will ultimately determine which part of the cell’s information will be output. The calculation formula of this gate is: ot ¼ rðWo  ½ht1 ; xt  þ b0 Þ

ð6Þ

ht ¼ ot  tanhðct Þ

ð7Þ

4 Experimental Analysis After the model was established, the data of meteorological stations in Beijing, Tianjin and Hebei region from October to December of 2017 was predicted by the LSTM model. Effect of the model is verified, and compared with the results of the BP neural network. In order to evaluate the prediction effect at different levels of visibility, classification is performed according to the intervals of 0–1 km, 1–5 km, 5–10 km and above 10 km. The model effect is analyzed by the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) of the predicted value and visibility. From Table 2, the mean absolute error (MAE) and the root mean square error (RMSE) between the forecast results of LSTM model and the observed results of visibility are smaller than those of BP neural network model. Among them, the root mean square error (RMSE) of the LSTM model is 4.81 km, which is 0.25 km lower than the BP neural network. It shows that predicted values of LSTM model is highly fitted to the observed values, and the stability is good. At the visibility levels of 0– 1 km, 1–5 km and 5–10 km, the mean absolute errors of LSTM model are 0.37 km, 1.25 km and 1.57 km, respectively, which are 1.56 km, 1.58 km and 2.33 km lower than those of BP neural network method. From the perspective of the mean absolute error, when the visibility level is below 10 km, the deviation between predicted value and observed value is less than forecast result of the level above 10 km, which indicates that the prediction effect of the two models below 10 km is stable and higher than the forecast result of the visibility above 10 km. Define the TS score, TS = accuracy/(accuracy + empty + missing). Take visibility less than 1 km as an example. Accuracy means that the observed value is less than 1 km and the forecast result is less than 1 km. Empty means that the observed value is greater than 1 km and the forecast result is less than 1 km. Missing means that the observed value is less than 1 km and the forecast result is larger than 1 km. At the same time, the predicted value of the BP neural network model are compared, as shown in Table 3.

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Methods

RMSE 4.81

0– 1 km/MAE 0.37

1– 5 km/MAE 1.25

5– 10 km/MAE 1.57

LSTM neural network BP neural network

10 km/MAE 6.07

5.06

1.93

2.83

3.90

7.96

Table 3. TS score of LSTM and BP neural network on forecast visibility Methods 0–1 km/TS 1–5 km/TS 5–10 km/TS 10 km/TS LSTM neural network 0.22 0.27 0.23 0.38 BP neural network 0.16 0.25 0.22 0.21

From the data of Table 3, the TS scores of this method in 0–1 km and above 10 km are 0.22 and 0.38, and the forecasting effect is obviously better than BP neural network method, which are 0.06 and 0.17 greater than that of BP neural network method. Experimental results in 1–5 km and 5–10 km show that the difference between the two is only 0.02 and 0.01. In this interval, the prediction accuracy of the two methods is relatively close. On the whole, the LSTM model has a better application effect in the low visibility of 0–1 km and the high visibility above 10 km. In order to visually value the forecasting effect of the LSTM model, the heavy pollution weather data of Beijing, Tianjin and Hebei region in November 6, 2017 is selected for testing. The missing data is removed, and a total of 159 test samples are tested. As shown in Fig. 2, the predicted results are visually compared with the observed results.

Actual situa on

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25 20 15 10 5 54406 54431 54594 54528 53392 53593 53692 53773 53792 53886 53896 54308 54408 54434 54503 54519 54534 54604 54612 54621 54632 54705 54717

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Fig. 2. The comparison of predicted values of LSTM model and observed values

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The red and blue in the line chart represent the true and predicted values of visibility respectively. As shown in the figure, the forecast effect of the model in the 1– 10 km is slightly better than the forecast effect of the model in the 0–1 km, mainly due to the lack of heavy-pollution, low-visibility weather data samples, resulting in insufficient training of the model. From the whole point of view, the predicted results of the model fit well with the observed values of visibility, and even when the visibility is extreme, the relevant information can also be captured.

5 Conclusion The model of visibility prediction based on LSTM recurrent neural network proposed in this paper is an effective model to deal with time series. Based on the historical meteorological data of Beijing, Tianjin and Hebei region from October to February of 2015–2016, meteorological factors that highly correlated with visibility are selected with the Pearson Correlation Coefficient method to build the data set. And weighted parameters of the model are trained to predict the visibility. With the comparison and analysis of the experiment, the following conclusions are obtained: (1) The LSTM model shows good stability in the prediction of multiple sites under the same climate background. (2) Due to few weather data samples with heavy pollution and extremely low visibility, the TS score of the LSTM model in 0–1 km is lower than 1–10 km. It can be seen that increasing the training samples with low visibility in the later period is beneficial to improve the effect of model in 0–1 km. (3) The visibility forecast skills based on LSTM neural network are better than that of BP neural network. The TS score of visibility in 0–1 km is 0.22, and the mean absolute error is 0.34 km. The TS score of visibility in 1–5 km is 0.27, and the mean absolute error is 1.25 km. The TS score of visibility in 5–10 km is 0.22, and the mean absolute error is 1.57 km. The TS score of visibility above 10 km is 0.38, and the mean absolute error is 6.07 km. It can be seen that the LSTM model has higher application value in visibility forecasting and can obtain more accurate results. Acknowledgments. This work has been supported in part by the National Natural Science Foundation of China (Grant No. 61773220), the National Key Research Program of China (Grant No. 2016YFC0203301), the Nature Science Foundation of Jiangsu Province under Grant (No. BK20150523).

References 1. Wang, Z., Li, J., Wang, Z., et al.: Numerical simulation and control countermeasures of strong haze pollution in central and eastern China in January 2013. Chin. Sci. Earth Sci. 1, 3–14 (2014) 2. Yin, S., He, L.: Analysis of atmospheric circulation and weather in January 2015. Meteorological 4, 514–520 (2015) 3. Zhai, X., Long, Y., Xiao, Z.: Haze weather feature analysis and visibility forecast based on support vector machine in Wuhan. Resour. Environ. Yangtze River Basin 12, 1754–1761 (2014)

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4. Zhang, W., Wang, Z., An, J., et al.: Using BP neural network to improve the prediction effect of real-time forecasting system for air quality of Olympic games. Climatic Environ. Res. 5, 595–601 (2010) 5. Li, Y., Liu, D., Jin, L., Gao, Y.: Application of BP neural network model in Chongqing drought forecast. Meteorology 12, 14–18 (2003) 6. Cai, Z., Han, S., Yao, Q., Zhang, M.: Study on weather visibility prediction of Tianjin based on BP neural network, pp. 2848–2854. Chinese Academy of Environmental Sciences (2016) 7. Liu, D., Hu, B., Yuan, Y., Zhang, H.: Application of least squares support vector machine in visibility prepackage, pp. 1386–1391. Meteorological Society of China (2009) 8. Han, W., Wu, Y., Ren, F.: Air pollution prediction based on full connection and LSTM neural network. Geogr. Inf. World 3, 34–40 (2018) 9. Jiang, J., Zhang, G., Gao, J.: Main influencing factors of atmospheric visibility in Beijing. J. Appl. Meteorol. 2, 188–199 (2018) 10. Fan, J., Li, Q., Zhu, Y., Hou, J., Feng, W.: Research on space-time prediction model of air pollution based on RNN. Sci. Mapp. Sci. 7, 76–83 (2017)

Application of Artificial Intelligence on the Image Identification of Icing Weather Phenomena Xiaoyu Huang1, Chengzhi Ye2(&), Ronghui Cai2, Yao Zhang3, Lianye Liu2, and Chenghao Fu2 1

3

National Meteorological Center, Beijing 100081, China [email protected] 2 Hunan Meteorological Office, Changsha 41007, China [email protected] Beijing Woquxiu Science and Technology Ltd., Beijing 100101, China

Abstract. Based on field experiments at Nanyue Mountain Meteorological Station and Huaihua National Reference Climatological Station in Hunan Province, the camera images of icing weather phenomena, such as glaze, rime and mixing rime, are collected minutely from January to March in 2018. The convolution neural network technology is employed for modelling and training using the camera images of the icing field experiment at Nanyue station, and the results of identification are examined by the camera images. Furthermore, based on deep learning, the environmental layout requirements of ice accretion image identification are discussed. The main conclusions are as follows. When identifying icing weather phenomena at Nanyue station, the probability of correction (PC) is 99.21%, the false acceptance rate (FAR) is 0.28%, and the probability of omission (PO) is 0.51%. The probability of icing identification increases significantly in the initial stage of ice accretion, while that in the sustained stage is stably around 99.0%, and in the dissipation stage it gradually decreases. False acceptance and omission occur occasionally during the initiation and dissipation stages, the transition period between daytime and night, and the nighttime when the pictures are not clear enough. The test results show that the artificial intelligence identification model established in this paper can extract the key features of icing in different stages of an icing lifetime, and the identification result is good. In addition, the false acceptance and omission can be further eliminated by using the meteorological conditions criteria and judging the consistency of identification. This method can provide important technical support for the automatic observation of icing weather phenomena. Keywords: Icing weather phenomena Automatic identification


Artificial intelligence


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1 Introduction Surface observation is the basis of weather monitoring, weather forecast [1–3], analysis and prediction on climate [4, 5], and other works. With the advancement of observation automation and structural reform of vocational work in China, most of the surface observational projects have realized automation. It greatly reduces the workload of observers and makes it possible for the observers to focus on the operation system guarantee and data processing, and to realize the unattended operation of surface observation. However, at present, many weather phenomena, such as cloud cover, cloud height, ground condensation and obstruction to vision, still need manual observation. The disadvantages of strong subjectivity, low observation frequency, sparse distribution of stations, high input and maintenance costs are becoming increasingly prominent, which seriously affects the effectiveness of observation automation [6–8]. In recent years, the image recognition technology based on deep learning has developed greatly. Research on deep learning by Hinton et al. [9, 10] leads many scholars to find new research directions and targets, significantly improving the timeliness and accuracy of deep learning [11]. Convolutional neural network (CNN) is an algorithm of deep learning, which can realize local connection and parameter sharing, thus reducing the number of parameters, calculation difficulty and time [12]. It is a multi-layer feedforward neural network specially designed for processing twodimensional input data [13], with strong fault tolerance, self-learning ability and parallel processing ability. Krizhevsky et al. [14]. proposed a deep learning algorithm based on the core of CNN and won the champion of the 2012 ImageNet Competition with a much lower error rate than second place. After that, the research and application of CNN have increased exponentially and they are widely used in classifying problems of various fields. However, the relative researches on the identification and application of the weather phenomena are few. According to the characteristics of icing, 47 layers of neural network are designed to analyze the existence of icing and its key features in different stages of development, so as to automatically identify the phenomena of icing. The weather phenomena image recognition method in the basis of deep learning is similar to the observers’ visual observation in principle, avoiding the problems such as small sample numbers, complex relationships between meteorological elements and the inconspicuous convergence of model in the process of establishing the statistical identification model. It is expected to obtain more continuous and quantitative meteorological observation information. Ice accretions, such as surface glaze, rime and mixing rime, are the major weather phenomena causing freezing disaster in China, which have great impact on industrial and agricultural production and socioeconomic development. Especially during January 10–February 2 in 2008, southern China suffered from a rare synoptic process of continuous low temperature, rain, snow and frost [15–17]. According to statistics, more than 100 million people were affected by this disastrous synoptic process, and the direct economic losses exceeded 110 billion yuan. Therefore, the observation of glaze and rime plays an important role in meteorological disaster prevention and mitigation. At present, the observation of glaze and rime is an artificial observation task. Since January 1 2014, the domestic meteorological stations had stopped the night observation of

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glaze and rime, and planned to cancel the artificial observation task from 2019. In the absence of automatic observation equipments for glaze and rime, it is necessary to carry out the study of glaze and rime image recognition algorithm based on deep learning, in order to satisfy the needs from weather forecast and service. Based on the field experimental data of icing weather phenomena, including glaze, rime and mixing rime at Nanyue Mountain Meteorological Station (Nanyue station) and Huaihua National Reference Climatological Station (Huaihua station) in Hunan Province, the layout environment requirements of icing weather phenomenon image identification based on deep learning, are discussed in this paper. By using the experimental pictures collected at Nanyue station, the CNN deep learning is used to model and train, and it is tested with the data at the same station. This method provides technical support for the automatic observation of icing weather phenomena, and also plays a positive role in effectively promoting the automation of surface observation and realizing the unattended operation of surface observation.

2 Analysis of Observation Environment The Nanyue Mountain is situated in the Hengshao Basin of Hunan Province, located in the Nanyue District of Hengyang City. Nanyue station (27° 18′N, 112° 42′E) is located on Wangritai mountaintop in Nanyue Mountain Scenic Area. The altitude of the observation site is 1265.9 m, and the horizontal distance to Zhurong peak (1300 m) is only 400 m. Nanyue station is a high-altitude station, and in winter there are many types of icing condensation weather, such as glaze, rime and mixing rime. HuaiHua station (27° 34′N, 110° 00′E) with higher elevation and much more glaze in winter is situated in the eastern part of Yunnan-Guizhou Plateau. The two stations can provide abundant sample data for the research on artificial intelligence identification technology of image recognition based on deep learning. The field experiment used hikvision DS-2CD5028FWD/E-IRA web cameras with 2 million black-light. The cameras have functions of network shield integration, ultrawide dynamic infrared camera shooting, human eye bionic technology, multi-spectral imaging technology and so on. The image size is 1920 * 1080 pixels. Due to the infrared function of the camera, light was not filled at night during the experiments. In the local observation field at Huaihua station, the camera was fixed only once so its stability was poor, and the observation scene experienced many changes during the period of the experimental observation (Fig. 1). Except the glaze observed in Fig. 1B, the glaze and rime cannot be clearly shot in Fig. 1A, C and D because of their long focal length, and even in Fig. 1B the observation time is very short. Therefore, the observation environment at Huaihua station is not conducive to automatic camera observation of ice accretion. Nanyue station is a high-altitude station, and the observation environment is harsh with high winds and thunderstorms. Therefore, the camera was reinforced twice and its stability was improved significantly. The scene was fixed with a dense forest behind the ice shelf, and there were icing wires, stainless steel, porcelain, bamboo poles, and screw steel, etc. on the shelf. The stainless steel was painted red to test the ice accretion on different media surfaces (Fig. 2). Comparing and analyzing the field experiment

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Fig. 1. Observation background of HuaiHua Station

pictures and artificial observations, it can be concluded that metal has small heat capacity, poor hydrophilicity, fast cooling and heating speed. So it is easy to form icing, but fast for melting. While vegetation has large heat capacity, good hydrophilicity, slow cooling and heating speed, and the melting speed of icing is obviously lower than that on metal. The impact of different colors on icing is not obvious. For example, the beginning of ice accretion on the metal icing shelf at 0:43 on January 25 (Fig. 2A) is basically the same as the artificial observation. The end time of ice accretion on the metal ice shelf is 12:30 on February 7, and the ending of vegetation ice accretion is 11:43 on February 9. The former is 40 h earlier than the latter (Fig. 2B, C), and the latter is basically consistent with the artificial observation. Consequently, the icing shelf and the tall dense vegetation background are the necessary conditions for the observation of ice accretion, and the observation at Nanyue station is consistent with the artificial observation. During the field experimental observation period (from January 22 to March 1 2008), few icing pictures were observed at Huaihua station, but three icing synoptic processes occurred at Nanyue station. The specific times of artificial observation were: the mixing rime process from 0:40 on January 25 to 12:30 on February 9 (hereinafter referred to as process 1); the rime process from 8:46 on February 10 to 17:45 on February 11 (process 2); the mixing rime process from 19:30 on February 20 to 10:17 on February 23 (process 3). During the observation period, the equipment was damaged due to a lightning strike. The data of February 16–21 and February 29–30 were missed and icing data in the beginning period of Process 3 was absent. In brief, because of good observational environment and large sample size, the data at Nanyue station were selected for modeling.

Fig. 2. Formation and melting process of icing (A: 0:43 on January 25; B: 12:03 on February 7; C: 11:43 on February 9)

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3 Analysis of Observation Data The sample data for modeling and training are camera pictures photographed at Nanyue station every 20 min from 07:00 on January 23 2018 to 23:00 on March 1 2018. 1789 pictures are selected as training samples after decontamination. Ice accretion during the daytime (07:00a.m.–18:59) and the night (19:00–06:59a.m.) at Nanyue station are respectively shown in Fig. 3A and B. The pictures of the daytime are significantly better than those of the night. The blurry pictures are very unclear (Fig. 3C) mainly due to the changes of night camera mode after February 8, thus there are only the daytime pictures after February 8. The pictures at Nanyue station during January 23–March 1 2018 are selected as the testing samples, which are not duplicated and independent with the training samples.

Fig. 3. Contrast of mixing rime between day and night (A: 14:43 on February 6; B: 23:03 on February 6; C: 04:00 on February 25)

Because of light, focus, weather and other reasons, the sizes of the photographs are different. In order to calculate conveniently, we uniformly use the numpy, which is a math library of python, to convert the picture into 299 * 299 pixels (not shown).

4 Technology and Methods In this paper, after pre-processing and simply tagging the start and end time of ice accretion, the training model automatically captures the shape, color, texture and other key information of the ground icing through neural network, and directly extracts information features from the whole picture instead of manual marking. 4.1

Introduction of Training Model

The more-advanced Google Inception Net training model is adopted in this research, and the 47-layer Inception V3 (hereinafter, V3) network is used for network training, while training samples are randomly divided into test samples and training samples at the ratio of 1:4.

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4.2

Model Training

Using tf.contrib.slim, based on the V3 network structure, the full connective layer is deleted to reduce the scale of parameters and enhance the generalization ability of the network. The entire CNN is customized and its structure is shown in Table 1. Table 1. V3 Structure table Typer Convo Convolution Convolution Pooling Convolution Convolution Pooling Inception modules Inception modules Inception modules Pooling Linear Convolution Softmax

Kernel patch/stride or remarks 3  3/2 3  3/1 3  3/1 3  3/2 1  1/1 3  3/1 3  3/2 3 inception modules 5 inception module 3个 inception module 88 Logits 1  1/1 Classifier

Input size 299  299  3 149  149  32 147  147  32 147  147  64 73  73  64 71  71  80 35  35  192 35  35  256 17  17  768 8  8  1280 8  8  2048 1  1  2048 1  1  1000 1  1  1000

The training process of CNN is divided into two stages. The first stage is disseminating data from the low level to the high level, or the forward propagation stage. Another stage is when the results of the forward propagation do not correspond with expectations, the error is propagated from the high level to the low level, or the back propagation stage. The main parameters of the model are shown in Table 2. Training steps denote the steps must be run before the end of training. Learning_rate denotes the speed at which the parameters could reach optimal values. Train_batch_size is the number of pictures trained at a time. Validation_batch_size is the number of pictures used in the evaluation batch. Eval_step_interval denotes the time interval of estimating the training results. Table 2. List of training parameters Training steps Learning rate Train batch size Validation batch size Eval step interval 4000 0.01 100 100 10

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5 Result Analysis The test pictures independent of training samples after decontamination at Nanyue station are used to test the training model. The probability greater than or equal to 50% indicates ice accretion, whereas that less than 50% indicates no ice accretion. 5.1

Image-Testing Results at Nanyue Station

One fifth of the 1789 samples that is 358 samples without training are tested. The PC of identification is 98.88%, the FAR is 0.28%, and the PO is 0.84%. In addition, 1773 samples without training during January 23–March 1 2018 are used to verify the model. The results are shown in Fig. 4. The PC is 1759/1773 = 99.21%, the FAR is 5/1773 = 0.28%, and the PO is 9/1773 = 0.51%. The results are basically consistent with that from the random test of model. The three icing processes can be clearly analyzed in Fig. 4. The duration of process 1 is the longest, and those of the second and third processes are shorter. During the icing formation period the probability increases gradually with the accretion of the condensate, but it is fluctuant. 100

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Fig. 4. Time series of icing identification probability at Nanyue Station from 7:23 January 23 to 12:20 March 1, 2018

(The blue line expresses the probability of icing identified by the model of each time level; and the red line indicates the start and end time of the three icing processes.) Figure 5 shows the pictures of the three processes in the icing stable period respectively, and all the probabilities are greater than 99.3%. There are many condensates in the stable periods of process 1 and process 3, the probability is stably more than 99%, just like quasi-straight line. Process 2 is a rime process with shorter icing duration and less condensate, thus the probability fluctuates (Fig. 4). During the process of icing melting, the probability gradually decreases. The amount of condensate in the process 1 is maximum, and it began to melt on Feb 7; the melting time is longer and the probability curve decreases slowly. Process 3 is similar to process 1, but the condensate is less than process 1, and the melting speed is relatively faster, the probability curve decreases more rapidly. Process 2 is a rime

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Fig. 5. Pictures of icing stationary phase in the three processes (A: 13:23 on January 31 (99.9%); B: 7:03 on February 11 (99.3%); C: 13:43 on February 22 (99.9%))

process with short duration, the melting speed is the fastest and the probability curve drops rapidly (Fig. 4). There are 9 omissions in the testing pictures, and the probability is less than 50%. The omission usually occurs at the beginning or near the end of the ice accretion due to the small amount of condensate and the indistinct characteristics of the ice accretion (Fig. 6A and B). In addition, when the condensate is little or during the transition of camera modes between daytime and night, omissions could occur. Under these two situations, it is also difficult to identify even by manual observation, so, such cases are few and generally have little impact on the observations.

Fig. 6. Omissions of ice accretion (A: 7:03 on February 9 (41.84%); B: 15:43 on February 11 (36.29%); C: 18:43 on February 22 (23.63%))

There are 5 false accepted pictures in the testing pictures (the probability is greater than 50%). The false accept mainly appears in the beginning or at the end of the process. In Fig. 7A, the icing has almost melted, but condensation still exists on the ground, and the probability of false acception is 50.3%. Because of the decrease of visibility before the rime formation in Fig. 7B, the image is blurred and the FAR reaches 85.65%. Due to the discontinuity of false accept pictures and the low FAR, it has little effect on the observations. In summary, the PC of identification is more than 98%. The training model can extract the characteristics of ice accretion very well and identify the icing weather phenomena effectively. Although the probabilities of false and missing identification are very low, PC can be further improved by the following measures. On one hand, the meteorological conditions can be included to eliminate the false identification. On the other, because the ice accretion is a continuous weather phenomenon, the false acception and omission can be further eliminated according to the consistency of the identification criteria.

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Fig. 7. False identification of ice accretion (A: 15:23 on February 9 (50.3%); B: 8:03 on February 10 (85.65%))

6 Conclusion and Discussion In this paper, the camera images of the icing field experiment at Nanyue station and Huaihua station are used to model the testing images at Nanyue station by CNN technology, and the testing data are used to verify the model. On this basis, the environment conditions for collecting images used for identification of icing weather phenomena by artificial intelligence are discussed. The main conclusions are as follows. (1) Comparing and analyzing the experimental pictures at Nanyue station and Huaihua station, the environmental conditions for the image acquisition equipment of Nanyue station are similar to manual observation environment and are better than that of Huaihua station. The Nanyue station is suitable for the automatic observation of icing weather phenomena, including glaze, rime, mixing rime and so on. (2) Independent samples are used to verify the effect of the icing training model at Nanyue station. The PC is 99.21%, the FAR is 0.28%, and the PO is 0.51%. The probability of ice accretion during the formation period ascends, and the probability in the stable period is greater than 99.3%. But, it decreases in the melting period. It shows that the training model can extract the characteristics of icing weather phenomena very well, and the identification effect of icing is quite good. However, in the initial stage of icing formation, the end stage, the transition time of camera modes between daytime and night, or when the pictures are unclear at night, the false acceptance or omission could occasionally occur. It is recommended to set supplementary lighting at night for increasing the clarity of the pictures. (3) Artificial intelligence image recognition method based on deep learning can identify the icing weather phenomena very well, and can eliminate the false acceptance and omission by including the meteorological conditions criteria and judging the consistency of identification. Glaze, rime and mixing rime can be distinguished by precipitation, temperature, relative humidity, visibility and other meteorological parameters.

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(4) Image identification based on deep learning needs to be trained by a lot of image samples from different fields, backgrounds and illumination conditions. The quantity of the image samples and the precision of the annotations probably affect the image recognition effect. In this study, the field testing duration is relatively short. The corresponding image acquisition standards and specifications will be studied according to the needs of deep-learning image identification technology and conditions of meteorological operation. The artificial intelligence model should be further optimized and improved. Acknowledgement. This research was funded by the Small Business Construction Project of China Meteorological Administration (2018) “Comprehensive Meteorological Observation Intelligent Analysis and Identification System Construction” (QXPG20174022) and Special Project for Capacity Building of Meteorological Forecasting of Hunan Meteorological Bureau (2016-2017) “Meteorological element product improvement based on multi-source data fusion (YBNL16-04)”.

References 1. China Meterological Administration. The criterion of surface meteorological observation, pp. 21–27. China Meteorological Press, Beijing (2003) 2. Huang, X.Y., Li, Z.X., Li, C., et al.: Analysis on extreme freeze catastrophic weather of Hunan in 2008. Meteorol. Mon. 34(11), 47–53 (2008) 3. Hu, W.D., Yang, K., Huang, X.Y., et al.: Analysis on a severe convection triggered by gust front in Yinchuan with radar data. Plateau Meteorol. 34(5), 1452–1464 (2015) 4. Wang, Z.Y., Ding, Y.H., He, J.H., et al.: An updating analysis of the climate change in China in recent 50 years. ACTA Meteorol. Sin. 62(2), 228–236 (2004) 5. Liang, S.J., Ding, Y.H., Zhao, N., et al.: Analysis of the interdecadal changes of the wintertime surface air temperature over mainland China and regional atmospheric circulation characteristics during 1960–2013. Chin. J. Atmos. Sci. 38(5), 974–992 (2014) 6. Xing, H.Y., Zhang, J.Y., Xu, W., et al.: Development and prospect of automatic meteorological observation technology on the ground. J. Electron. Measur. Instrum. 31 (10), 1534–1542 (2017) 7. Ma, S.J., Wu, K.J., Chen, D.D., et al.: Automated present weather observing system and experiment. Meteorol. Mon. 37(9), 1166–1172 (2011) 8. Liu, L.Y., Lan, M.C., Zhu, X.W., et al.: The Comparative analysis of two cloud products of FY2G satellite in Hunan Province. Torrential Rain Disasters 36(2), 164–170 (2017) 9. Hinton, G.E., Osindero, S., TeH, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527 (2006) 10. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(28), 504–507 (2006) 11. Yu, B.,Li, S., Xu, S.X., et al.: Deep learning: the key to open big data era. J. Eng. Stud. no. 3, 233–243 (2014) 12. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009) 13. Chan, C.H., Pang, G.K.H.: Fabric defect detection by Fourier analysis. IEEE Trans. Ind. Appl. 36(5), 1267–1276 (2000)

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14. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Image net classification with deep convolutional neural networks. In: Proceedings of International Conference on Neural Information Processing System, pp. 1097–1105 (2012) 15. Wang, Z.Y., Zhang, Q., Chen, Y., et al.: Characters of meteorological disasters caused by the extreme synoptic process in early 2008 over China. Clim. Chang. Res. 4(2), 63–67 (2008) 16. Gang, H., Chen, L.J., Jia, X.L., et al.: Analysis of the severe cold surge, ice-snow and frozen disasters in South China during january 2008: II possible climatic causes. Meteorol. Mon. 34 (4), 101–106 (2008) 17. Ye, C.Z., Wu, X.Y., Huang, X.Y.: A synoptic analysis of the unprecedented severe event of the consecutive cryogenic freezing rain in Hunan Province. Acta Meteorologica Sin. 67(3), 488–500 (2009)

The Realization Path of Network Security Technology Under Big Data and Cloud Computing Nan Kang1(&), Xuesong Zhang1(&), Xinzhou Cheng2(&), Bingyi Fang1(&), and Hong Jiang1(&) 1 Unicom Cloud Data Limited Liability Company, China United Network Communications Corporation, Beijing 100084, People’s Republic of China {kangnan,zhangxs18,fangby2}@chinaunicom.cn, [email protected] 2 Network Technology Research Institute, China United Network Communications Corporation, Beijing 100048, People’s Republic of China [email protected]

Abstract. This paper studies the cloud and big data technology based on the characters of network security, including virus invasion, data storage, system vulnerabilities, network management etc. It analyzes some key network security problems in the current cloud and big data network. Above all, this paper puts forward technical ways of achieving network security. Cloud computing is a service that based on the increased usage and delivery of the internet related services, it promotes the rapidly development of the big data information processing technology, improves the processing and management abilities of big data information. With tie rapid development of computer technology, big data technology brings not only huge economic benefits, but the evolution of social productivity. However, serials of safety problems appeared. How to increase network security has been become the key point. This paper analyzes and discusses the technical ways of achieving network security. Keywords: Network security


Big data
Cloud

1 Introduction Cloud computing is a kind of widely-used distributed computing technology [1–3]. Its basic concept is to automatically divide the huge computing processing program into numerous smaller subroutines through the network, and then hand the processing results back to the user after searching, calculating and analyzing by a large system of multiple servers [4–6]. With this technology, web service providers can process tens of millions, if not billions, of information in a matter of seconds, reaching a network service as powerful as a supercomputer [7, 8]. Cloud computing is a resource delivery

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and usage model, it means get resource (Hardware, software) via network. The network of providing resource is called ‘Cloud’. The hardware resource in the ‘Cloud’ seems scalable infinitely and can be used whenever [9–11]. Cloud computing is the product of the rapid development of computer science and technology. However, the problem of computer network security in the background of cloud computing brings a lot of trouble to people’s life, work and study [12–14]. Therefore, scientific and effective management measures should be taken in combination with the characteristics of cloud computing technology to minimize the risk of computer network security and improve the stability and security of computer network. This paper briefly introduces cloud computing, analyzes the network security problem of computer under cloud computing, and expounds the network security protection measures under cloud computing.

2 Model Construction of Cloud Computing Technology in Data Processing Processing data by cloud computing can save the energy expenditure and reduce the dealing cost of big data, so that it can improve the healthy development of cloud computing technology. Analyzing big data by cloud computing technology can be shown by a directed acyclic data flow graph G ¼ ðV; EÞ, and the cloud service module in the Parallel selection mechanism is made up by a serial group V ¼ fiji ¼ 1; 2; . . .; vg and a serial of remote data transfer hidden channels E ¼ fði; jÞji; j 2 Vg. Assuming the date transmission distance of the data flow model in C=S framework is T0 þ B þ is þ Td þ ji þ 1\T0 þ B þ is þ Td. The directed graph model GP ¼ ðVP; EP; SCAPÞ explanation, EP represent LKSET, the VP cross channel bearing the physical node set, the SCAP explains the quantity of data unit of physical node. Besides, assuming undirected graph GS ¼ ðVS; ES; SARSÞ expresses data packet markers input by application. The process of link mapping between cloud computing components and overall architecture can be explained by: eS ¼ PðvS ! vtÞ; eS 2 ES; ð vs; vtÞ 2 VS

ð1Þ

For the different customer demands, building an optimized resource-allocated model to build the application model that processed by big data. The built-in network link structure for big data information processing as follows:

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Fig. 1. Built-in network link structure for big data information processing

In Fig. 1, the ith transmission package in the cloud computer is ith. Let Ti represent the transmission time of ith. The interval of Component is mapped to thread or process is showed by ji ¼ Ti  Td , when ji ¼ Ti  Td in the range of (−∞, ∞), the weight of node I is Wi which computing time, the detail application model of big data information processing is shown in Fig. 2

Fig. 2. The application model of the big data information processing

3 The Formulation of Cloud Computing in Computer Data Processing In the mobile cloud system model, the grid architecture that relies on local computing resources and the wireless network to build cloud computing, which will select the components of data flow graph to migrate to the cloud, Computer data processing cloud

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computing formula modeling, fGðV; EÞ; si; di; jg is the given data flow applications, assuming that the channel capacity is infinite, the problem of using cloud computing technology to optimize big data information processing is described as follows maxmax TP ¼ xi;yi;jxi;yi;j

1 ; i; j 2 f0; 1;


; v þ 1g tp

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Among them: tp ¼ maxfmax maxðxi : i2v

i2v

si X di;j ðxi  xj Þ2 Xi Þ; max max ð Þg gp i2v yi;j ði;jÞ2E ði;jÞ2E

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The energy overhead of data flow migrating between groups in mobile cloud computing is described as: k ¼ Intð

nQ Þþ1 1Q

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4 Main Characteristics of Network Security Technology 4.1

Security

In the context of big data, cloud computing, users can save the data in the cloud and then process and manage the data. Compared with the original network technology, it has certain data network risks, but its security coefficient is higher. Cloud security technology can utilize modern network security technology to realize centralizing upgrade and guarantee the overall security of big data. Since the data is stored in the cloud, enhancing the cloud management is the only way to ensure the security of the data. 4.2

Convenience

Big data stored in the cloud usually affects network data. Most enterprises will connect multiple servers so as to build computing terminals with strong performance. Cloud computing itself has the convenience. Customers of its hardware facilities do not need to purchase additional services. They only need to purchase storage and computing services. Due to its particularity, cloud computing can effectively reduce resource consumption and is also a new form of energy conservation and environmental protection. 4.3

Participatory

When local computers encounter risks, data stored in the cloud will not be affected, nor will it be lost, and at the same time these data will be shared. The sharing and transfer of raw data is generally based on physical connections, and then data transfer is implemented. Compared with the original data research, data sharing in big data cloud computing can be realized by using the cloud. Users can collect data with the help of various terminals, so as to have a strong data sharing function.

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5 Security Issues 5.1

System Vulnerabilities

Most computer networks have risks from system vulnerabilities. Criminals use illegal means to make use of system vulnerabilities to invade other systems. System vulnerabilities not only include the vulnerabilities of the computer network system itself, but also can easily affect the computer system due to the user’s downloading of unknown plug-ins, thus causing system vulnerability problems. 5.2

Virus Invasion

With the continuous development of the network, its virus forms are also diverse, but mainly refers to a destructive program created by human factors. Due to the diversity of the virus, the degree of impact is also different. Customer information and files of enterprises can be stolen by viruses, resulting in huge economic losses, and some of the viruses are highly destructive, which will not only damage the relevant customer data, but also cause network system paralysis. 5.3

Data Storage

In the context of big data cloud computing, external storage of the cloud computing platform can be realized through various distributed facilities. The service characteristic index of the system is mainly evaluated through high efficiency, security and stability. Storage security plays a very important role in the computer network system. Computer network system has different kinds, large storage, the data has diversified characteristics. The traditional storage methods have been unable to meet the needs of social development. Optimizing the data encryption methods cannot meet the demand of the network. The deployment of cloud computing data and finishing need data storage has certain stability and security, to avoid economic losses to the user. 5.4

Network Management

In order to ensure data security, it is necessary to strengthen computer network management. All computer managers and application personnel are the main body of computer network security management. If the network management personnel do not have a comprehensive understanding of their responsibilities and adopt an unreasonable management method, data leakage will occur. Especially for enterprise, government and other information management, network security management is very important. In the process of application, many computers do not pay enough attention to network security management, leading to the crisis of computer intrusion, thus causing data exposure problems.

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6 Ways to Achieve Network Security 6.1

Save and Encrypt Data

One of the main factors influencing the big data cloud save system is data layout. Exploring it at the present stage is usually combined with the characteristics of the data to implement the unified layout. Management and preservation function are carried out through data type distribution, and the data is encrypted. The original data stored in more than one cloud, different data management level has different abilities to resist attacks. For cloud computing, data storage, transmission and sharing can apply encryption technology. During data transmission, the party receiving the data can decrypt the encrypted data, so as to prevent the data from being damaged or stolen during the transmission. 6.2

Build Network Walls

The intelligent firewall can identify the data through statistics, decision-making, memory and other ways, and achieve the effect of access control. By using the mathematical concept, it can eliminate the large-scale computing methods applied in the matching verification process and realize the mining of the network’s own characteristics, so as to achieve the effect of direct access and control. The intelligent firewall technology includes risk identification, data intrusion prevention and outlaw personnel supply warning. Compared with the original firewall technology, the intelligent firewall technology can further prevent the network system from being damaged by human factors and improve the security of network data. 6.3

Introduction of Encryption Protection Technology

The system encryption technology is generally divided into public key and private key with the help of encryption algorithm to prevent the system from being attacked. Meanwhile, service operators are given full attention to monitor the network operation and improve the overall security of the network. In addition, users should improve their operation management of data. In the process of being attacked by viruses, static and dynamic technologies are used. Dynamic technologies are efficient in operation and can support multiple types of resources.

7 Use Case: Shenzhen E-Government Resource Center Security Isolation System Safety isolation system is usually called virtualizes distributed firewalls (VDFW). It made up of security isolation system centralized management center and security service virtual machine (SVM). The main role of this system is to achieve network security. The key functions of the system are as follows.

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7.1

Access Control

Access control functions analyze source/destination IP addresses, MAC address, port and protocol, time, application characteristics, virtual machine object, user and other dimensions based on state detection access control. Meanwhile, it supports many functions, including the access control policy grouping, search, conflict detection. 7.2

Intrusion Defense

Intrusion prevention module judge the intrusion behavior by using protocol analysis and pattern recognition, statistical threshold and comprehensive technical means such as abnormal traffic monitoring. It can accurately block eleven categories of more than 4000 kinds of network attacks, including overflow attacks, RPC attack, WEBCGI attack, denial of service, trojans, worms, system vulnerabilities. Moreover, it supports custom rules to detect and alert network attack traffic, abnormal messages in traffic, abnormal traffic, flood and other attacks. 7.3

Malicious Code Protection

It can check and kill the Trojan, worm, macro, script and other malicious codes contained in the email body/attachments, web pages and download files based on streaming and transparent proxy technology. It supports FTP, HTTP, pop3, SMTP and other protocols. 7.4

Apply Identification

It identifies the traffic of various application layers, identify over 2000 protocols; its built-in thousands of application recognition feature library.

8 Conclusion This paper studies the cloud and big data technology. In the context of large data cloud computing, the computer network security problem is gradually a highlight, and in this case, the computer network operation condition should be combined with the modern network frame safety technology, so as to ensure the security of the network information, thus creating a safe network operation environment for users.

References 1. Zhou, X., Lu, L.: Application and operation of computer network security prevention under the background of big data era. Netw. Secur. Technol. Appl. 05, 24–30 (2017) 2. Sun, H., Jia, R.: Research on enterprise network information security technology system in the context of big data. Commun. Technol. 50(02), 334–339 (2007) 3. Xu, L., Cheng, X., Chen, Y., Chao, K., Liu, D., Xing, H.: Self-optimised coordinated traffic shifting scheme for LTE cellular systems. In: 1st EAI International Conference on SelfOrganizing Networks, pp. 67–75. Springer Press, Beijing (2015)

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4. Gao, M.: Network security technology in big data environment. Inf. Commun. 01, 158–159 (2017) 5. Xu, L., Zhao, X., Yu, Y., et al.: Data mining for base station evaluation in LTE cellular systems. In: 3rd International Conference on Signal and Information Processing, Networking and Computers, pp. 356–364. Springer Press, Chongqing (2017) 6. Xu, L., Chen, Y., Schormans, J., et al.: User-vote assisted self-organizing load balancing for OFDMA cellular systems. In: 22nd IEEE International Symposium on Personal Indoor and Mobile Radio Communications, pp. 217–221. IEEE Press, Toronto (2011) 7. Wang, F.: Discussion on network information security in the context of big data. Digital Technol. Appl. 05, 210 (2016) 8. Xu, L., Luan, Y., Cheng, X., et al.: Telecom big data based user offloading self-optimisation in heterogeneous relay cellular systems. Int. Jo. Distrib. Syst. Technol. 8(2), 27–46 (2017) 9. Zhou, H.: Application of cloud computing technology in computer secure storage. Netw. Secur. Technol. Appl. (10):78–79 (2017) 10. Xu, L., Zhao, X., Luan, Y., et al.: User perception aware telecom data mining and network management for LTE/LTE-advanced networks. In: 4rd International Conference on Signal and Information Processing, Networking and Computers, pp. 237–245. Springer Press, Qingdao (2018) 11. Xu, L., Luan, Y., Cheng, X., Xing, H., Liu, Y., Jiang, X., Chen, W., Chao, K.: Selfoptimised joint traffic offloading in heterogeneous cellular networks. In: 16th IEEE International Symposium on Communications and Information Technologies, pp. 263–267. IEEE Press, Qingdao (2016) 12. Huang, Y.: Network information security control mechanism and evaluation system in the context of big data. Inf. Comput. (Theor. Ed.) 20, 201–202 (2016) 13. Xu, L., Cheng, X., et al.: Mobility load balancing aware radio resource allocation scheme for LTE-advanced cellular networks. In: 16th IEEE International Conference on Communication Technology, pp. 806–812. IEEE Press, Hangzhou (2015) 14. Xu, L., Luan, Y., Cheng, X., et al.: WCDMA data based LTE site selection scheme in LTE deployment. In: 1st International Conference on Signal and Information Processing, Networking and Computers, pp. 249–260. CRC Press Taylor & Francis Group, Beijing (2015)

Rate Control in HEVC: A Survey Jiaqi Zou1,2,3(&) and Bingyi Li1,2,3 1 National Engineering Laboratory for Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing, China [email protected] 2 Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China 3 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

Abstract. Rate control is an important tool in video coding. So far, R-k based rate control algorithm for rate control is widely used in High Efficiency Video Coding (HEVC). In this paper, we present a survey on the algorithm of R-k based rate control process, including bit allocation and rate control coding by parameters calculation. Firstly, we provide bit allocation method at three levels: group of pictures (GOP), frame, and basic unit level. Secondly, the methodologies of coding parameters calculation are presented. Then, we summarize the recent research achievements and future directions and finally open problems are discussed. Keywords: Rate control


HEVC
R-k model

1 Introduction Rate control is of vital importance in video applications, particularly in which that constrained by bandwidth requirements, such as real-time video services. Due to the importance of rate control, a large number of rate control algorithms have been proposed in HEVC. In general, rate control algorithms include into two steps. The first one is bit allocation. In GOP, frame, and BU level, an appropriate amount of bits is allocated. And then, in the second step, the algorithm is required to achieve the allocated number of bits, with calculation of the coding parameters required [1]. Overall, the proposed rate control algorithms can be categorized into two kinds. One assumes that R and Q are closely related, including Q-domain algorithm [2] and q-domain algorithm [3]. The other believes that there is a closer connection between k and R, such as k-domain algorithm that is usually adopted in HEVC. In this paper, we concentrate on k-domain rate control algorithms. In Sect. 2, we introduce algorithms in each bit allocation level, including GOP, frame, and basic unit level. In Sect. 3, we introduce methodologies of coding parameters calculation. In Sect. 4, the future of rate control in HEVC is discussed and more insights might be provided for the design of rate control in HEVC.

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 578–583, 2019. https://doi.org/10.1007/978-981-13-7123-3_67

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2 Bit Allocation In most rate control models, bit allocation is adapted in three levels: GOP level, frame level, and basic unit level. For HEVC, one basic unit can be divided into one or more coding units (CU). Except three levels above, bit allocation for the first picture is one special level as encoder is hardly to know the first picture’s information and allocate a proper number of bits and in most cases, the encoding parameters are appointed by users. 2.1

GOP Level

At GOP level, [2] uses the same bit allocation strategy as the strategy in H.264/AVC [5]. However, the GOP structure in HEVC is different from H.264/AVC, so the previous GOP level bit allocation method is not suitable. Thus, [1] uses a sliding window to smooth the bitrate adjustment in GOP level. By using the sliding window, this algorithm can allocate more bits for current GOP if the previous GOPs have been allocated fewer bits and vice versa. In [4] the bits allocated for each GOP are uniformed, which is suitable for low delay configuration. [6] proposes a new GOP level algorithm that matches with the encoding structure of HEVC, and the filling degree of encoder buffer is nearly reduced to zero. Figure 1 show the new open-GOP coding framework.

Fig. 1. Figure gives a demonstration of the intra-frame period of four GOPs with a size of eight frames. The I frame is represented by a black bar (the last one in GOP0). In this picture, only GOP0 consists of an I frame.

2.2

Frame Level

[7] proposes a rate control method in frame-level, which is based on textured and nontextured models. In [8, 9], distinct R-k models are allowed to use in different regions of a frame, and a higher bit rate is allocated in the region of interest (ROI) as while as holding the overall bit rate approaching target number. Besides, [10] proposes an algorithm of lossless ROI in intra-coding in rate control. And [11] proposes a method for intra-coding based on block predictive transform coding (PTC) that approximates the rate-distortion (RD) curve linearly in every frame. [12] studies the influence of the size of encoding units on encoding efficiency and complexity in HEVC intra prediction using off-line and on-line training machine learning models. We can see that in frame level, most of the algorithms proposed recently tend to allocate different bits by analyzing the different characteristics of regions and intra coding is increasingly popular.

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BU Level

In HEVC, one BU can consist of one or more CUs. In BU level, one important topic is how to divide CUs and code in different CU. [13] proposes an algorithm which is able to decide the size of CU in a fast way that terminates the homogeneous CUs. After that, for the rest CUs, it used two linear support vector machines (SVMs) to decide CU division and termination. [7] categorizes the depth of CUs into three levels including low-texture, medium-texture and high-texture level and take the distinguish statistical characteristics into consideration and use Laplacian probability density function to calculate the transform ratio residues. Besides, in order to find the best coding unit, [14] proposes a convolutional neural network (CNN) aimed to classify units. The algorithm can learn the region-wise images’ characteristics.

3 Rate Control Coding After allocating appropriate bits for each level, the encoder is required to achieve allocated bits by adjusting coding parameters. Coding parameters calculation is usually adapted by the bit allocation strategy. Coding strategies based on R-k model which approximate the rate of the RD curve while taking the difference between intra-frame and inter-frame into account, which have been proved a good performance. Figure 2 gives a typical demonstration of R-D curve in videos.

Fig. 2. A typical operational RD curve.

In R-k model, it’s of vital importance to accurately calculate the value of k, because k largely depends determines the accuracy of algorithm. For this reason, the R-k based rate control algorithm designs a special methodology to calculate the coding parameters and update the value of k in encoding procedure by using trained data [15]. [13] proposed a new algorithm to calculate k value, which does not need trained models. Instead, it uses piecewise linear approximations. In each frame, it linearly approximate k by the actual rate and distortion of coded blocks.

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After the value of k is determined, other coding parameters including QP can be calculated by exhaustive RDO search. In [9] a multi-R-k model is proposed. Considering the different characteristics of different regions, it uses three different R-k models to approximate the RD curve, and achieves better accuracy in high-dynamic range. In the proposal of [8], the models of CUs from the region-of-interest is independent from the non-region-of-interest. Their parameters are computed separately. And [17] improves the performance of error tolerant algorithms in [15] by considering region-ofinterest information in rate control for each frame. In [16], a QP accommodation algorithm is proposed. This algorithm is based on a header bits’ map that is calculated by convolutional neural network and has low complexity.

4 Future Topics As we mentioned above, a lot of works based on R-k model aim to allocate appropriate bit rate and achieve the target bit by updating parameters, and they are proved to achieve good performance. In general, recent works on rate control concentrate on two subjects: one is to consider special rate control strategies for special situation, such as [9] concentrating on high dynamic range imaging (HDR), [18–20] aimed to screen content coding (SCC), and the proposal in [21] applying for 3-D HEVC; the other is to apply distinct R-k models for different parts and compute coding parameters separately, such as proposals in [8–11]. We believe in the future, research on special situation will go deeper, with more analyses and better development on SCC, 3-D, HDR and so on. Multi-R-k model may also result in wider application. Moreover, although current rate control algorithms in HEVC have a good capability, they have not been accurately designed for coding intra frames. Thus their accuracy in achieving a target bit rate cannot reach a high level. More algorithms of intra-coding may be proposed. Besides, with the development of deep-learning, some algorithms based of neural network have been applied in video coding. The proposed algorithms perform good accuracy and low complexity in simulation. We believe deep-learning in HEVC may be another hot topic in the next few years.

5 Conclusion In this paper, we present a survey for rate control in HEVC from the perspective of bit allocation and coding parameters calculation. There are kinds of bit allocation strategies at each bit allocation level, and methodologies of coding parameters calculation are adapted accordingly. With tractable theoretical analysis, more insight might be provided for the design of video coding, especially for special situation video coding and multiple coding models building.

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Acknowledgment. This work is supported by National Natural Science Foundation of China (Project 61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.

References 1. Li, B., Li, H., Li, L.: Lambda-domain rate control algorithm for high efficiency video coding. IEEE Trans. Image Process. 23(9), 3841–3854 (2014) 2. Choi, H., Nam, J., Yoo, J., Sim, D., Bajic, I.: Rate control based on unified RQ model for HEVC. Document Rec. JCTVC-H0213, San Jose, February 2012 3. Wang, S., Ma, S., Wang, S., Zhao, D., Gao, W.: Quadratic q-domain based rate control algorithm for HEVC. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, pp. 1695–1699 (2013) 4. Wang, S., Ma, S., Wang, S., Zhao, D., Gao, W.: Rate-GOP based rate control for high efficiency video coding. IEEE J. Sel. Top. Signal Process. 7(6), 1101–1111 (2013) 5. Wiegand, T., Sullivan, G.J., Bjontegaard, G., Luthra, A.: Overview of the H.264/AVC video coding standard. IEEE Trans. Circuits Syst. Video Technol. 13(7), 560–576 (2003) 6. Song, F., Zhu, C., Liu, Y., Zhou, Y., Liu, Y.: A new GOP level bit allocation method for HEVC rate control. In: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Cagliari, pp. 1–4 (2017) 7. Lee, B., Kim, M., Nguyen, T.: A frame-level rate control scheme based on texture and nontexture rate models for high-efficiency video coding. IEEE Trans. Circuits Syst. Video Technol. 1–14 (2013) 8. Meddeb, M., Cagnazzo, M., Pesquet-Popescu, B.: Region-of-interest based rate control scheme for high-efficiency video coding. In: Proceedings of the IEEE International Conference on Acoustics, Speech Signal Processing (ICASSP), pp. 7338–7342, May 2014 9. Perez-Daniel, K.R., Sanchez, V.: Luma-aware multi-model rate-control for HDR content in HEVC. In: IEEE International Conference on Image Processing (ICIP), Beijing, pp. 1022– 1026 (2017) 10. Sanchez, V., Aulí-Llinàs, F., Vanam, R., Bartrina-Rapesta, J.: Rate control for lossless region of interest coding in HEVC intra-coding with applications to digital pathology images. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, pp. 1250–1254 (2015) 11. Sanchez, v: Rate control for HEVC intra-coding based on piecewise linear approximations. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, pp. 1782–1786 (2018) 12. Erabadda, B., Mallikarachchi, T., Kulupana, G., Fernando, A.: Machine learning approaches for intra-prediction in HEVC. In: IEEE 7th Global Conference on Consumer Electronics (GCCE), Nara, pp. 206–209 (2018) 13. Zhang, T., Sun, M., Zhao, D., Gao, W.: Fast intra-mode and CU size decision for HEVC. IEEE Trans. Circuits Syst. Video Technol. 27(8), 1714–1726 (2017) 14. Kuanar, S., Rao, K.R., Conly, C.: Fast Mode decision in HEVC intra prediction, using region wise CNN feature classification. In: IEEE International Conference on Multimedia & Expo Workshops (ICMEW), San Diego, pp. 1–4 (2018) 15. Li, B., Li, H., Li, L.: Adaptive bit allocation for R-lambda model rate control in HM, in JCTVC M0036. In: 13th Meeting of Joint Collaborative Team on Video Coding of ITU-T SG 16 WP 3 (2013)

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16. Katayama, T., Song, T., Shimamoto, T.: QP adaptation algorithm for low complexity HEVC based on a CNN-generated header bits map. In: IEEE 8th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, pp. 1–5 (2018) 17. Maung, H., Aramvith, S., Miyanaga, Y.: Improved region-of-interest based rate control for error resilient HEVC framework. In: IEEE International Conference on Digital Signal Processing (DSP), Beijing, pp. 286–290 (2016) 18. Guo, Y., Li, B., Sun, S., Xu, J.: Rate control for screen content coding in HEVC. In: IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1118–1121, May 2015 19. Guo, Y., Li, B., Sun, S., Xu, J.: Rate control for screen content coding based on picture classification. In: Visual Communications and Image Processing (VCIP), pp. 1–4, December 2015 20. Xiao, J., Li, B., Sun, S., Xu, J.: Rate control with delay constraint for screen content coding. In: IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, pp. 1–4 (2017) 21. Song, Y., Jia, K., Wei, Z.: Improved LCU level rate control for 3D-HEVC. In: Visual Communications and Image Processing (VCIP), Chengdu, pp. 1–4 (2016). https://doi.org/ 10.1109/vcip.2016.78054783

Screen Content Coding: A Survey Bingyi Li1,2,3(&) and Jiaqi Zou1,2,3 1 National Engineering Laboratory for Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing, China [email protected] 2 Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China 3 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

Abstract. Screen content is partly driven by the rapid development of screen applications such as wireless display, screen sharing, cloud computing and gaming, etc. Different from camera-captured content, screen content has relatively bad continuity in spatiotemporal domain and severe movements or sudden changes may occur in continuous pictures. Owing to the special characteristics of screen content, conventional coding tools including High Efficiency Video Coding (HEVC) standard are unsuitable for screen content coding (SCC). A SCC extension to HEVC was brought out and developed to meet the demand of screen content coding. This paper provides a brief survey on the main coding tools in the HEVC-SCC extension. Screen content sequences also pose challenges on transmission due to its discontinuity. The alternate presentation of moving and stationary pictures makes the exploitation of bandwidth a technical difficulty. This paper introduces the improvements of SCC rate control in HEVC with better transmission performance and more efficient bandwidth utilization. Keywords: Screen Content Coding (SCC)
High Efficiency Video Coding (HEVC)
Video coding


Rate control

1 Introduction The early development of the well-known High Efficiency Video Coding (HEVC) and H.264/AVC [1] is primarily concentrated on camera-captured content sequences. However, the phenomenon that not just camera-captured content is displayed on video devices has become popular in recent years. This video sequence containing a large amount of moving or stationary pictures, text, together with camera-captured content is mostly used in specific situations such as remote computer desktop access, wireless displays, and video conferencing screen sharing [2]. This kind of content is called screen content, which shows very different characteristics in comparison with that captured by camera. The sequence captured by the camera has relatively good continuity in the spatiotemporal domain. While in the sequence of screen content, timely continuous pictures may show significant movement or sudden change, and spatially, a single image may have large flat areas with high contrast and sharp edges. Due to these © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 584–592, 2019. https://doi.org/10.1007/978-981-13-7123-3_68

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different characteristics, coding tools initially proposed for camera-captured sequences are unsuitable for screen content sequences. Since screen content has particular features such as repetitive patterns, no noise from sensors, delimited color categories, and the same regions or slices in one sequence, the coding performance and compression efficiency can be significantly improved if these characteristics properly leveraged. Screen content was taken into consideration during the development of the first edition of the High Efficiency Video Coding (HEVC) standard, known simply as HEVC version 1 [3], which was settled down in January 2013, and HEVC range extensions (HEVC-RExt) [4], which was developed after HEVC version 1. Although screen content was not the key point of these standards which targeted camera-captured content, there are coding tools designed for screen content as well. In HEVC version 1, a Transform Skip Mode [5–8] is introduced due to the sharp, irregular edges and shapes in screen content images. This feature leads to residual signals already sparse after the prediction procedure because the background of screen content can be accurately predicted while the foreground not due to its irregularity. If residuals still transform as the conventional coding structure under such circumstances, energy will be spread out rather than being compacted, which destroys the sparsity and will have bad influence on the next procedure of entropy coding. Therefore, skipping transform is adopted and turns out to be a good improvement for coding efficiency in screen content sequence. In HEVC-RExt, several improvements are made for the Transform Skip Mode [9–12] and other tools targeted screen content coding are added such as Residual Differential Pulse Code Modulation (RPDCM) [13–15], which was introduced for intra lossless coding and later extended to inter coding and lossy coding, Cross-Component Prediction (CCP) [16], which helps coding efficiency for videos with RGB format, the domain which screen content is exactly captured in, and so on. In January 2014, a series of demands raised for an extension of HEVC for screen content coding was published [2]. In July 2014, the HEVC-SCC extension Draft Text 1 [17] was published. Up to present, SCC has been added in the fourth version of HEVC. The rest of the paper is organized as follows: Sect. 2 briefly describes the coding tools included in HEVC-SCC extension. Section 3 presents the technical difficulty in transmission of screen content sequence and the improvements in screen content coding rate control. Finally, Sect. 4 concludes the whole paper.

2 HEVC-SCC Extension As an extension of HEVC standard, HEVC-SCC extension mainly focuses on the application of screen content video, inheriting the coding tools of HEVC version 1 [3] and HEVC RExt [4] as the basis of its development. Following are several main coding tools proposed and incorporated in HEVC-SCC extension. 2.1

Intra-Block Copy (IBC)

In modern video coding, motion compensation plays an essential role. In order to exploit the bandwidth of the video signal, the correlation between adjacent images has been studied. The method of block matching and copying in one image originated from

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this concept. When this concept is applied to the video captured by the camera, it turns out not very successful because text patterns in adjacent areas of space may share similarity with ongoing coding blocks, while often change as space steadily changes. Therefore, accurate match for blocks is hard to find in the same image, thus limiting the improvement of coding performance. However, for screen content, it is difficult to find spatial correlation between pixels in the same image. For a typical screen content sequence, repeated patterns frequently occur in the same image. Therefore, block copying in a single image is possible and turns out very efficient. Therefore, Intra-Block Copy mode is proposed as a new prediction mode to take advantage of the particular feature. In this mode, the blocks which have previously been reconstructed are used for predicting the prediction unit (PU) in the same image. The relative displacement from the position of the present PU to the reference block position is shown by the displacement vectors (block vectors or BVs). Then, the compensated prediction error is coded in the same way as the residual in HEVC version 1 [3]. An illustration of IBC is shown in Fig. 1.

Fig. 1. An illustration of IBC.

In [18], an IBC fulfillment is integrated into a software platform, which is founded for the generation development of the next video coding standard and hosted by Joint Video Expert Group (JVET). The simulation results in [18] show that IBC can remain great improvement on coding efficiency in screen content test sequences in the latest JVET with all state-of-the-art coding tools beyond HEVC enabled. 2.2

Palette Mode

In addition to repeated text patterns in one image, another unique feature of screen content is that the number of colors used in blocks is statistically smaller than that in camera-captured content of the same size. For example, one coding block usually only contains the color of foreground and background text in a textual screen content picture. Sometimes text characters have random patterns, which makes it difficult for the current encoding block to find out matching blocks from previously encoded pictures. In this case, efficient compression using directed local intra prediction is also challenging. Palette Mode is a proposed coding tool and has been proved effective in processing this type of source.

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Fig. 2. An example of palette mode.

In short, palette coding is a prediction method based on colors [19, 20]. All the pixels of the encoding block (the values of three components, R/G/B or Y/Cb/Cr) are categorized as a list of colors. The main color is the representative color, which occurs frequently in blocks. For each coding unit (CU) in palette mode, an index table of color is created. Every index entry in the table is combined with three sample values (R/G/B or Y/Cb/Cr). Except barely used pixels, almost all pixels are transformed into matching indexes in CU. These rare pixels are called escaped pixels. They are set apart in the index table and unable to be quantized to dominant colors with ESCAPE symbols marked. Actual values of these escaped pixels are explicitly signaled. These indexes, ESCAPE included, are run-length coded, predicting from the upper or left neighbor. An illustration of Palette Mode is shown in Fig. 2. [21] proposed a pre-decision approach for fast palette mode on the basis of analyzing color complexity in video data. The approach saved approximately 74.24% computation in game video experiments compared with HEVC-SCC palette mode. Meanwhile, the bjøntegaard delta bit rate (BDBR) only increases about 0.36% and the bjøntegaard delta peak signal-to-noise rate (BD-PSNR) averagely decreases 0.03 dB. 2.3

Adaptive Color Transform (ACT)

Adaptive Color Transform (ACT) performs on the coding unit (CU) level. In this CU, a flag which occupies 1 bit is used to indicate whether a color space transform is conducted for each predicted residual pixel. The goal of ACT is that there to be a certain correlation among different components of the same pixel for a given color space (for example, RGB space). Even after the prediction from adjacent spatial or temporal pixels, the correlation still exists among residual pixel components. Color space transform may help compact energy and thus improve coding performance. ACT is used both for lossy [22] and lossless [23] coding.

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Adaptive Motion Vector Resolution (AMVR)

The movements in screen content pictures are supposed to have an integer displacement. Thus, the aliasing effect caused by motions happened in time domain sampled by camera may be invalid. If motion compensation is skipped, the bits for presenting motion vectors can thus be saved. Yet, the method is obviously unsuitable for cameracaptured content due to the use of integer motion vector resolution. A flag placed on the slice level is proposed in [24] to indicate the resolution mode (integer or quarter pixel) the motion vector is at in this slice. On the decoder side, if integer motion vectors are used in slices, decoded motion vectors should be left moved by two. For the design of the encoder, different methods are suggested to decide if the integer motion is suitable for the ongoing coding slice. In the two-channel method, integer motion and quarter-pixel motion are used to encode the current slice twice, and then the method with better rate-distortion (RD) performance is chosen. The encoder time is doubled as a result. Pre-analysis of slice content is conducted by using the original pixel in the alternative method. The percentage of 8  8 homogeneous block is calculated. Isomorphic blocks are defined as slices that match perfectly in the first reference image in List 0, or they have no text pattern (the whole block is a value). If the estimated percentage of 8  8 uniform blocks exceeds the predefined threshold, a flag on the slice level will be used for integer motion. Then, the dual-pass coding procedure can be avoided, and most of the coding performance gains in the dual-pass method can thus be maintained (BD rate savings from 3 to 4%) for 1080p test sequences. Pay attention that for animated video or camera-captured video sequences, no giant benefits for coding performance are observed with this tool.

3 SCC Technical Difficulty and Improvement 3.1

SCC Technical Difficulty

Screen content has very different characteristics in comparison with camera-captured content. The sequence captured by the camera has relatively good continuity, while screen content sequence has poor correlation between previous and subsequent frames. The probability of mutation is much greater than that of natural images captured by camera. Due to the discontinuity of the screen content sequence, some complex frames may consume too many bits in the coding process, which may affect the coding of subsequent frames and cause the problem of out-of-control rate in the whole video coding process, thus leading to unstable transmission and inappropriate use of bandwidth. Therefore, existing rate control schemes primarily designed for camera-captured content in HEVC turn out unsuitable for screen content coding and improved bit allocation and rate control schemes are required for better transmission performance and more efficient bandwidth utilization for SCC. [25] analyzed the characteristics of each frame of screen content sequence and proposed a rate control optimization scheme for screen content coding based on HEVC. Through modeling and analysis, abrupt pictures in a screen content sequence are predicted effectively, and a compensation window is added to ensure the video quality

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under the premise of bitrate control. In order to better allocate resources and avoid bitrate runaway, [25] also introduces sliding window bit allocation instead of traditional GOP bit allocation, and achieves good results. 3.2

SCC Rate Control in HEVC

To describe the improved SCC Rate Control in HEVC, conventional rate control mode in HEVC should be introduced first. Common rate control scheme is divided into two steps: bit allocation and bit control. First, proper bits are allocated to every specific level, which is group of picture (GOP) level, picture level and coding unit (CU) level. After properly allocating the bits, the second part is adjusting parameters during coding process to make actual consumed bits close to the previously allocated target bits. In the improved SCC Rate Control scheme [25], because there exists many stationary frames in screen content sequence which consume very few bits besides abrupt frames, a sliding window bit allocation is used in the first step to replace the traditional GOP-level bit allocation in order to make full use of bandwidth resources. The analysis of each image’s complexity in the screen content sequence is done within the sliding window. Bit allocation will be conducted according to the complexity analysis of each image. Due to the long sliding window, resources can be allocated uniformly, so that existing resources can be distributed more optimally. Meanwhile, abrupt pictures are predicted and specially processed in the sliding window. If the current frame is an abrupt frame, the coding parameters will be adjusted as the second step according to the allocation of bits and actual consumption bits in the coding process, so that the parameters can get the optimal value quickly and converge in the iteration process. After the processing of abrupt pictures, a compensation window is added to compensate quality loss due to the bitrate control so as to improve the quality of the whole sequence. The flow chart of the improved SCC Rate Control scheme is illustrated in Fig. 3. [26] proposed another rate control scheme targeting on the frame level. Firstly, to present the similarity among continuous frames in SCC sequences, inter frame correlation (IFC) is calculated. Frames are further divided into two major kinds - key frames (KFs) and non-key frames (NKFs), on the basis of IFC. Secondly, based on IFC and Hypothetical Reference Decoder (HRD), an effective bit allocation scheme designed for KFs and NKFs is proposed, which can guarantee R-D performance well. Finally, a rate-quantization (R-Q) model for KFs and NKFs is established with the features of these two kinds of frames taken into consideration. Therefore, the rate control scheme proposed in [26] is able to achieve accurate bit allocation on the frame level. This method has low coding delay and low coding complexity burden, which enables it to be applied in real screen sharing scenarios. Results from experiments show that this method achieves more accuracy with better R-D performance than the rate control scheme recommended in HEVC. In detail, the mismatch of bit rate is less than 1.4% on average and R-D performance improves by more than 19% in general.

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Fig. 3. The flow chart of the improved SCC rate control scheme.

4 Conclusion Coding tools added in HEVC-SCC extension which were primarily designed for screen content coding have made great improvements in SCC. With new coding tools under development, screen content coding is expected to achieve higher efficiency. In rate control of screen content coding, aiming at the discontinuity of screen content sequence, an abrupt frame prediction mechanism is added. Meanwhile, the bit allocation and parameter updating in bitrate control are improved. The rate runaway is prevented and the coding quality is guaranteed, thus improving the coding efficiency in SCC. Acknowledgment. This work is supported by National Natural Science Foundation of China (Project 61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.

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References 1. Advanced Video Coding for Generic Audiovisual Services, ITU-T and ISO/IEC JTC1, document ITU-T Rec. H.264 and ISO/IEC 14496-10, May 2003 2. Yu, H., McCann, K., Cohen, R., Amon, P.: Requirements for an Extension of HEVC for Coding of Screen Content, ISO/IEC JTC 1/SC 29/WG 11, document MPEG2014/N14174, San Jose, CA, USA, January 2014 3. Sullivan, G.J., Ohm, J., Han, W.-J., Wiegand, T.: Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2012) 4. Boyce, J., et al.: Edition 2 Draft Text of High Efficiency Video Coding (HEVC), Including Format Range (RExt), Scalability (SHVC), and Multi-View (MV-HEVC) Extensions, document JCTVC-R1013, Sapporo, Japan, July 2014 5. Narroschke, M.: Extending H.264/AVC by an adaptive coding of the prediction error. In: Proceedings of 25th Picture Coding Symposium (PCS), O5–3. Beijing, China, April 2006 6. Lan, C., Xu, J., Sullivan, G.J., Wu, F.: Intra Transform Skipping, document JCTVC-I0408, Geneva, Switzerland, April 2012 7. Peng, X., Lan, C., Xu, J., Sullivan, G. J.: Inter Transform Skipping, document JCTVCI0408, Stockholm, Sweden, July 2012 8. Cohen, R., Vetro, A.: AHG8: Impact of Transform Skip on New Screen Content Material, document JCTVC-L0428, Geneva, Switzerland, January 2013 9. Peng, X., Xu, J., Guo, L., Sole, J., Karczewicz, M.: Non-RCE2: Transform Skip on Large TUs, document JCTVC-N0288, Vienna, Austria, July 2013 10. An, J., Zhao, L., Huang, Y.-W., Lei, S.: Residue Scan for Intra Transform Skip Mode, document JCTVC-J0053, Stockholm, Sweden, June 2012 11. He, D., Wang, J., Martin-Cocher, G.: Rotation of Residual Block for Transform Skipping, document JCTVC-J0093, Stockholm, Sweden, June 2012 12. Peng, X., Li, B., Xu, J.: On Residual Rotation for Inter and Intra BC Modes, document JCTVC-O0186, Geneva, Switzerland, October 2013 13. Lee, S., Kim, I.-K., Kim, C.: AHG7: Residual DPCM for HEVC Lossless coding, document JCTVC-L0117, Geneva, Switzerland, January 2013 14. Joshi, R., Sole, J., Karczewicz, M.: AHG8: Residual DPCM for Visually Lossless Coding, document JCTVC-M0351, Incheon, Korea, April 2013 15. Naccari, M., Mrak, M., Gabriellini, A., Blasi, S., Izquierdo, E.: Inter-Prediction Residual DPCM, Incheon, Korea, document JCTVC-M0442, April 2013 16. Nguyen, T., Khairat, A., Marpe, D.: Non-RCE1/Non- RCE2/AHG5/AHG8: Adaptive InterPlane Prediction for RGB Content, document JCTVC-M0230, Incheon, Korea, April 2013 17. Joshi, R., Xu, J.: HEVC Screen Content Coding Draft Text 1, document JCTVC-R1005, Sapporo, Japan, July 2014 18. Xu, X., Li, X., Liu, S.: Intra block copy for next generation video coding. In: ICMEW (2018) 19. Guo, L., Pu, W., Zou, F., Sole, J., Karczewicz, M., Joshi, R.: Color palette for screen content coding. In: 2014 IEEE International Conference on Image Processing (ICIP), 5556–5560 October 2014 20. Onno, P., Xiu, X., Huang, Y.-W., Joshi, R.: Suggested Combined Software and Text for Run-Based Palette Mode. JCTVC-R0348, July 2014 21. Liu, Y., Fang, C., Sun, J., Huang, X.: Fast Palette Mode Decision Methods for Coding Game Videos with HEVC-SCC. IEEE Trans. Circuits Syst. Video Technol. (2018)

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22. Zhang, L., et al.: SCCE5 Test 3.2.1: In-Loop Color-Space Transform. JCTVC-R0147, July 2014 23. Henrique Malvar, S., Sullivan, G.J., Srinivasan, S.: Lifting-based reversible color transformations for image compression. In: SPIE Proceedings, 7073 (2008), https://doi. org/10.1117/12.797091 24. Li, B., Xu, J., Sullivan, G., Zhou, Y., Lin, B.: Adaptive Motion Vector Resolution for Screen Content. JCTVC-S0085, October 2014 25. Guo, Y., Li, B., Sun, S., Xu, J.: Rate control for screen content coding in HEVC. In: IEEE International Symposium on Circuits & Systems, September 2015 26. Wang, S., Li, J., Wang, S., Ma, S., Gao, W.: A frame level rate control algorithm for screen content coding. In: ISCAS, May 2018

Intelligent Fitness Trainer System Based on Human Pose Estimation Jiaqi Zou1,2,3(&), Bingyi Li1,2,3, Luyao Wang3, Yue Li4, Xiangyuan Li4, Rongjia Lei1,2,3, and Songlin Sun1,2,3 1

National Engineering Laboratory for Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing, China [email protected] 2 Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China 3 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China 4 School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China Abstract. With the popularization of health concept, the demand of fitness trainer system has increased. However, the existent trainer systems only provide motion demonstration but lack users’ motion feedback. This paper designs and implements intelligent fitness trainer system based on human pose estimation, which not only shows fitness training courses but also provides motion correction. The system obtains users’ motion data by optical camera, and then applies human pose estimation, finally providing motion correction advice. In this paper, we present the system design on hardware and software, and introduce the applied human pose estimation algorithm in detail. The field trail results show that the system exerts a good influence on fitness training. Keywords: Fitness trainer


Human pose estimation
Deep-learning

1 Introduction 1.1

Research Background and Meaning

Yoga, Taijiquan and other fitness exercises are becoming more and more popular, but incorrect exercise cannot achieve fitness effects as well as be harmful to human body. For this reason, professional guidance is indispensable. However, personal coaches are expensive and time-intensive, with a big contrast between the strong demand for fitness and the scarcity of professional guidance. In our trainer system, we implement a lowcost, easy-to-deploy fitness self-learning and evaluation system to help fitness practitioners self-learn correct movements. Intelligent fitness trainer system can meet the four major requirements of users: Learn fitness movements correctly. The system contains yoga, Taijiquan and other standard fitness courses, recording the fitness process of users, capturing fitness movements, and carrying out corrective reminders. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 593–599, 2019. https://doi.org/10.1007/978-981-13-7123-3_69

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Convenient deployment. The system can be deployed conveniently in the gym and other application scenarios, which satisfies the freedom of fitness time and space for users. The personalized needs of users. The system can intelligently analyze the different characteristics of multiple fitness practitioners, give personalized guidance suggestions. The needs of gym venues. Gym managers are eager to improve the scientific and technological level of gym, and intelligent functions can attract a larger number of users. 1.2

Research Background and Meaning

So far, motion capture technology is mainly divided into four categories: mechanical, acoustic, electromagnetic and optical and infrared thermal imaging technology. These technologies have their own merits, and are widely used in film and television works modeling, interactive games, athletes training and other scenarios. Considering the convenience and cost of deployment, our system chooses optical camera and motion capture based on deep-learning human pose estimation algorithm.

2 Intelligent Fitness Trainer System 2.1

System Architecture

The technical framework of the system is divided into four layers; Fig. 1 shows the system architecture.

Fig. 1. System architecture.

Hardware Layer. It consists of camera, display screen, server and user terminal. The camera is responsible for obtaining user’s facial information and user’s action resource data. The display screen is used to show the user’s action in real time, and hide the

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camera position. The user can practice the action in front of the display screen without looking for the camera position. Server is our data processing center. We build user database, implement alphapose platform for human pose estimation and design movements evaluation and correction algorithm. User terminals display various services to users and display information processing. User’s historical information and operation behavior will be displayed and output in the terminal. Data Layer. Data layer is divided into three parts: image data, course data and user data. User data includes user’s face data, physical fitness data, history course data and score data. Users can freely access personal data after matching face information correctly (in the future, they can support sharing or backup operations). Image resources are user posture information acquired by camera, which is uploaded to the server during user fitness process. After image data processing, the system will give a score and update user data. Application Layer. The application layer provides various services, including training courses selection and allow users to choose and match freely. Physical fitness analysis and recommendation of personalized courses or course packages are provided; realtime analysis and correction of user posture will be carried out during training. After training, the training will be scored and guidance suggestions will be put forward. Users can also view their history courses. Presentation Layer. The presentation layer is divided into three parts, web end, APP and desktop client. 2.2

Introduction

Intelligent fitness trainer system is a self-learning and evaluation system for fitness. It has the functions of motion capture, correction reminder, face recognition, behavior recording and so on. It is suitable for gyms. The system achieves the self-learning by motion capture, correcting and reminding the fitness worker according to the standard movement coursed, evaluating the fitness worker after the completion of fitness by intelligent learning, perfecting personal fitness files through face recognition and behavior records, and giving fitness suggestions by using recommendation algorithm. The system aims to provide users with personalized fitness guidance, develop good sports habits, while increasing their interest. And the physical map of our system is shown in Fig. 2. 2.3

Principle

Figure 3 shows the principle and working process. Firstly, we use the camera to recognize the users by face recognition. The server can provide the user database information, understand its usage habits, and recommend courses from the course resource database, and give a pre-warning of the habitual errors in the user’s history.

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Fig. 2. Physical map.

Fig. 3. Working process.

After the motion starts, the system will call the system motion module. First, the server reads the images captured by the camera and process them frame by frame. By deep learning alpha pose algorithm, human pose estimation is applied, human body is recognized, key points of human joints are marked, and position information of user joints in each frame is obtained. Then, it compares with the standard action to realize the action correction. The results are shown in Fig. 4. At the end of the whole exercise process, we will generate feedback reports, including details of movement errors, corrective guidance and motion accuracy scores, maximum error points, best progress points, calorie consumption, etc., to enhance the users’ sense of fitness achievement and interest in fitness.

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Fig. 4. Recognition results.

3 Key Technology In our system, the key technology is human pose estimation, which determines the accuracy of pose recognition and has vital effect on movement correction. Human pose estimation is a popular topic of computer vision. Compared with traditional algorithms, algorithms based on deep learning train the networks with a large amount of images, learn features from global space. It needn’t detect or recognize the local feature of objects and has good robustness [1]. In practice, it is more challenging to recognize multiple people’s postures than to recognize individual people’s postures in images. [2, 3]. In general, two frameworks for this problem have been proposed: one is a two-step framework [4], which first detects human bounding boxes and then the posture in each box is estimated independently; the other is a part-based framework [5–7], which detects body parts firstly and then assemble the detected body parts into multiple human postures. As for video scene reconstruction framework, [9] proposes a framework for collaborative tracking of multiple human objects and estimating their three-dimensional posture. [10] eliminates the re-projection error, avoids the optimization of noise observation, and introduces geometric constraints on prior knowledge composed of human joints at reference points. We apply the two-step framework. We apply a regional multi-person pose estimation (RMPE) framework proposed in [8]. And the framework is shown in Fig. 5.

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Fig. 5. A regional multi-person pose estimation (RMPE) framework.

4 Conclusion In this paper, we introduce a kind of intelligent fitness trainer system based on human pose estimation. We use the optical camera to capture users’ movement, and achieve pose estimation by regional multi-person pose estimation framework. The field trail shows that our system has a good influence on fitness training, especially on accuracy of movements. Besides, although our method makes some efforts in improvement of RMPE algorithm, more work still need to be done in deeper research. Acknowledgment. This work is supported by National Natural Science Foundation of China (Project61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.

References 1. Liu, Y., Xu, Y., Li, S.: 2-D human pose estimation from images based on deep learning: a review. In: 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi’an, pp. 462–465 (2018) 2. Sapp, Toshev, A., Taskar, B.: Cascaded models for articulated pose estimation. In: European Conference on Computer Vision (ECCV), pp. 406–420. Springer (2010) 3. Sun, M., Kohli, P., Shotton, J.: Conditional regression forests for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3394–3401. IEEE (2012) 4. Gkioxari, G., Hariharan, B., Girshick, R., Malik, J.: Using k-poselets for detecting people and localizing their keypoints. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3582–3589 (2014) 5. Chen, X., Yuille, A. L.: Parsing occluded people by flexible compositions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3945–3954 (2015) 6. Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B.: Deepcut: joint subset partition and labeling for multi person pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016 7. Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., Schiele, B.: DeeperCut: a deeper, stronger, and faster multi-person pose estimation model. In: European Conference on Computer Vision (ECCV), May 2016 8. Fang, H., Xie, S., Tai, Y., Lu, C.: RMPE: regional multi-person pose estimation. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 2353–2362 (2017)

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9. Tang, Z., Gu, R., Hwang, J.: Joint multi-view people tracking and pose estimation for 3D scene reconstruction. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, pp. 1–6 (2018) 10. Takahashi, K., Mikami, D., Isogawa, M., Kimata, H.: Human pose as calibration pattern: 3D human pose estimation with multiple unsynchronized and uncalibrated cameras. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, pp. 1856–18567 (2018)

The Overview of Multi-person Pose Estimation Method Bingyi Li1,2,3(&), Jiaqi Zou1,2,3, Luyao Wang3, Xiangyuan Li4, Yue Li4, Rongjia Lei1,2,3, and Songlin Sun1,2,3 1

National Engineering Laboratory for Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing, China [email protected] 2 Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China 3 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China 4 School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

Abstract. Research on Multi-person pose estimation is partly improved by deep learning and the computer vision. Multi-person pose estimation is expected to be involved in many applications, such as fitness training, pedestrian recognition, military training, and so on. The prospect of multi-person estimation development is promising and challenging. This paper provides a brief survey on four major multi-person pose estimation methods – DeepCut, DeeperCut, OpenPose and AlphaPose, and presents the advantages and disadvantages of these methods. Keywords: Multi-person pose estimation
DeepCut
DeeperCut
OpenPose
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1 Introduction Multi-person pose estimation concentrates mainly on the discovery of body parts of individuals [1–3] by locating anatomical key points and “parts”. The extrapolation of multiple people’s pose in images is challenging, especially those of socially engaged individuals. First, the number of people contained in one image is unknown and people are likely to appear at any position in the image. Second, spatial overlapping may occur due to human-to-human interaction, which makes it difficult to distinguish each part from each individual. Third, runtime complexity increases as the number of people contained in the image gets larger, which poses great challenges to real-time performance. For multi-person pose estimation, there exists two major methods. One is the topdown method. This method detects multiple people, and then estimates each person’s pose. The detection method can be combined with a single person’s pose estimation. The other one is the bottom-up method: after detecting the joint points, which person each joint belongs to will be determined. © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 600–607, 2019. https://doi.org/10.1007/978-981-13-7123-3_70

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Common top-down methods for multi-person pose estimation [4–6] are to use person detectors, within which the estimation for single-person pose [7–9] is adopted. The major problem of the top-down method is that if a person detector goes wrong, there is no means of recovery. Due to the principle of this method, the running time is in proportion to the number of people. Bottom-up methods, by contrast, are more robust in theory and tend to weaken the relationship between the number of people in the image and the runtime complexity. However, the bottom-up method does not utilize global context directly from other individuals. Bottom-up approaches [10, 11] in previous practice did not keep efficiency advantages because of the costly global inference in eventual parsing. For example, [10] proposed a bottom-up approach to detect candidates and associate them with individuals. [11] developed on the basis of [10] with more solid partial detectors featured by Residual Neural Network (ResNet) [12] and pairwise scoring algorithm dependent on images, which greatly improves the runtime but needs a separate logistic regression due to the difficulty for pairwise representation to regress accurately. The rest of the paper is organized as follows: Sect. 2 introduces two top-down methods, DeepCut and DeeperCut. Section 3 briefly describes a bottom-up method OpenPose. Section 4 presents the method AlphaPose used in our Intelligent Fitness Trainer System. Finally, Sect. 5 concludes the whole paper.

2 DeepCut and DeeperCut 2.1

DeepCut

In [10], the top-down method is used to estimate the pose of many people. The socalled top-down method is, first, to use Convolutional Neural Networks (CNN) to detect human body, that is, body part candidates, and second, to determine whom these joint points belong to. Finally, the Integer Linear Programming (ILP) optimization model is used to estimate attitude. There are overlapping parts in the execution order of the two steps. The DeepCut model is illustrated in Fig. 1. Firstly, CNN is used to extract body parts candidates. Each candidate region corresponds to a joint point. Each joint point acts as a node in the graph. All the nodes represented by these candidate joints constitute a complete graph, as shown in dense graph in Fig. 1. The correlation between nodes is used as the weight between nodes in the graph. At this time, it can be regarded as an optimization problem. The joints belonging to the same person (the nodes in the figure) can be grouped into a group with each person as a separate class. Meanwhile, another branch needs to mark the detected nodes to determine which part of the human body they belong to. Finally, each person’s posture estimation is made up of classified people and tagged parts. The advantages of the DeepCut model can be summarized in three points. The first is that the problem of multi-person pose estimation can be solved in the case of number position. The distribution of each person’s nodes can be obtained by classification. Secondly, by clustering graph-theoretic nodes, non-maximum suppression is effectively used. Thirdly, the optimization problem is expressed as an ILP problem, which can be effectively solved by mathematical methods.

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Fig. 1. DeepCut model.

However, the computational complexity of DeepCut is very costly due to the use of adaptive fast R-CNN for body detection and ILP for pose estimation, so the following method DeeperCut is an accelerated implementation of DeepCut. 2.2

DeeperCut

[11] proposed an improved method, DeeperCut, based on DeepCut. The improvements are mainly on the following two aspects: (1) Using the latest residual net to extract body part, which makes the outcome more accurate and the precision higher. (2) Using image-Conditioned Pairwise Terms method, which can compress the nodes of many candidate regions to fewer nodes, and that is the major reason why DeeperCut can be stronger and faster than DeepCut. The principle of the image-Conditioned Pairwise Terms method is to judge whether the candidate nodes are the same important node by the distance between them.

3 Multi-person Pose Estimation Using OpenPose Top-down methods, such as DeepCut and DeeperCut introduced in Sect. 2, detect people first and then perform pose estimation. Under such circumstances, when people are close to each other, the method of detecting human body is ineffective, and the more the people there are, the more time it takes to detect, leading to real-time effect unable to be achieved. Therefore, [13] proposed a method using bottom-up method on OpenPose. This method presents an effective method to detect the two-dimensional pose of multiple people in an image. The method utilizes nonparametric representation called Part Affinity Fields (PAFs) to connect body parts with individuals. It also encodes global

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context and allows greedy bottom-up parsing steps, which achieves high accuracy as well as real-time performance regardless of the number of people in the image. This structure is designed to learn part positions and association by two branches of the prediction process in the same sequence. The approach proposed in [13] ranks first in the first COCO 2016 Key points Challenge. The model of real-time multi-person 2D pose estimation using part affinity fields is shown in Fig. 2.

Fig. 2. The model of real-time multi-person 2D pose estimation using PAFs [13].

The main ideas applied in this method are as follows. The first idea is using confidence maps for joint detection. Each joint corresponds to a confidence map. Each pixel has a confidence level. Values of each point in the confidence map is related to the distance of ground truth. For multi-person detection, K-person confidence maps are merged to get the maximum of each person at that point. The maximum rather than the average is used here because the accuracy is not affected even if the peak value is very close. In the test phase, non-maximum suppression is used to obtain candidates for body parts. The second idea is using Part Affinity Fields (PAFs) for body composition. For multi-person problem, the parts of different people are detected, but each person’s body needs to be separately combined to form a full-body, where the method PAF is used. The advantage of this method is to include both location and direction information. Each limb has an affinity region between two related body parts, and each pixel has a description direction of a 2D vector. If there are more than one person overlapping at a certain point, the vector sum of these individuals is divided by the number of people. After obtaining confidence maps and PAFs, how to use such information to find the optimal connection between two body-parts should be taken into consideration, which is transformed into a graph theory problem. Hungarian algorithm used here is the third idea. The nodes in the graph are the detection candidates in the body part, and the edges are the optimal connections of these candidates. The weight on each edge is the aggregation of affinity regions. So the next problem is to find a group of connections so that no two edges share a node, that is, to look for a way of connection for edges with largest weight.

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As a supplement, [14] proposed a method referred to as the Magnify-Net for multiperson 2D pose estimation. This method aimed to present a solution to the bottleneck problem of mean average precision (mAP) versus pixel error. Another bottom-up approach is proposed in [15], within which a limb detection heat maps is introduced as a body joint pairs connection representation. For multi-person 3D pose estimation in addition to 2D pose estimation, [16] proposed a novel single-shot method in natural sequences.

4 Multi-person Pose Estimation Using AlphaPose The methods DeepCut [10], DeeperCut [11] and OpenPose [13] introduced in Sect. 2 and Sect. 3 all have the problem that if two people are very close, they are prone to get ambiguity, whether they are top-down methods or bottom-up methods. In addition, because they depend on the relationship between the two components, they lose access to global information. Due to this problem, [17] proposed an improved method based on top-down methods. [17] aimed to detect the correct pose of human body, even if the inaccurate area frame is detected in the first step. To illustrate the problems of previous algorithms, Faster-RCNN and Single Person Pose Estimation (SPPE) Stacked Hourglass are used for experiments. The main problems are location recognition errors and redundancy. Actually, SPPE is easily affected by area frame errors that redundant area frames may create redundant poses. Therefore, a Regional Multi-Person Pose Estimation (RMPE) framework is introduced to improve SPPE-based performance. Adding symmetric spatial transformer network (SSTN) to SPPE structure can pick out human body parts in inaccurate region frames with high quality. Parallel SPPE branches (SSTNs) are used to optimize their networks. Parametric pose non-maximum suppression (NMS) is used as a solution to redundancy detection. In this structure, an ordinary pose distance measurement scheme is applied to compare the similarity between poses. Data-driven method is used for the optimization of distance parameters. Finally, Pose-Guided Proposals Generator (PGPG) is introduced to enhance training data, and learn the descriptive information of different poses in output results to imitate the generation process of the human body region frame and further produce a larger training dataset. In details, the method is made up of three parts. The first part is Symmetric STN and Parallel SPPE. The human body region frame obtained by target detection algorithm is not very suitable for SPPE in that SPPE algorithm is trained on a single image and is rather sensitive to location errors. The effect of SPPE can be effectively improved by micro-transformation and pruning. SSTN + Parallel SPPE can effectively enhance the effect of SPPE under imperfect human body area detection results. The structure is shown in Fig. 3. After STN + SPPE + SDTN, the inaccurate detection frame first conducts pose estimation and maps the estimation results to the original map, so as to adjust the original frame and make the frame become accurate.

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Fig. 3. An illustration of SSTN architecture [17].

The second part is Parametric Pose NMS. Human location inevitably results in redundant detection frames and redundant attitude detection. Due to this phenomenon, [17] proposed a non-maximum suppression for pose to eliminate redundancy. First, the pose of maximum confidence is selected as the reference, and the region frame close to the reference is eliminated according to the elimination criterion. This process is repeated several times until redundant recognition boxes are eliminated and each recognition box is unique (no overlap beyond the threshold). The third part is Data Augmentation. For Two-Stage pose estimation (first locating the region, then locating the pose points), proper data enhancement can help SSTN + SPPE to adapt to imperfect human body region location results. Otherwise, the model may not be well adapted to strange human body location results when it is running in the test phase. An intuitive method is to use the detected area frame in the training phase. However, target detection only produces a localization area for a person. By using the generated human body localization, a certain effect can be achieved. Because the real location of each person and the detected location box have already been generated, a large training set of samples can be created by using the same samples as the human body test results. Through this technology, system performance can be further improved. The results of the four methods – DeepCut [10], DeeperCut [11], OpenPose [13], AlphaPose [17] on the MPII Human Pose dataset is presented in Table 1. The statistics in the table illustrate that AlphaPose is 17 mean average precision (mAP) higher than DeepCut and DeeperCut. Quantitative results on full testing set and subset of 288 testing images used in [10]. It is noteworthy that the average accuracy of AlphaPose in recognizing complex joints such as wrist, elbow, ankle and knee is 72 mAP, which is 20 mAP higher than the previous latest results. The final wrist accuracy of AlphaPose is 70.4 mAP and knee accuracy is 73 mAP. Some results of AlphaPose is shown in Fig. 4. These results show that AlphaPose can accurately predict the attitude of multiperson images.

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B. Li et al. Table 1. MPII multi-person test set results [13, 17] Method Head Subset of 288 images DeepCut [10] 73.4 DeeperCut [11] 87.9 OpenPose [13] 93.7 AlphaPose [17] 89.3 Full testing set DeeperCut [11] 78.4 OpenPose [13] 91.2 AlphaPose [17] 88.4

Shoulder as in [10] 71.8 84.0 91.4 88.1 72.5 87.6 86.5

Elbow Wrist Hip Knee Ankle MAP S/image 57.9 71.9 81.4 80.7

39.9 63.9 72.5 75.5

56.7 68.8 77.7 73.7

44.0 63.8 73.0 76.7

32.0 58.1 68.1 70.0

54.1 71.2 79.7 79.1

57995 230 0.005 1.3

60.2 77.7 78.6

51.0 66.8 70.4

57.2 52.0 75.4 68.9 74.4 73.0

45.4 61.7 65.8

59.5 75.6 76.7

485 0.005 1.5

Fig. 4. Some results of AlphaPose’s prediction models [17].

5 Conclusion In test phases, DeepCut, DeeperCut and OpenPose frequently have errors in pose location and joints connection, while AlphaPose perform better with less errors and faster runtime speed. AlphaPose is a relatively accurate multi-person pose estimation tool compared with previous methods from the robustness of its algorithm. AlphaPose can estimate poses in pictures, videos or multiple pictures, and track motion in pictures. It has different output forms, such as PNG, JPG and AVI, which have key image forms, as well as JSON format, which makes it a popular tool for many applications.

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Acknowledgment. This work is supported by National Natural Science Foundation of China (Project61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.

References 1. Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. In: IJCV (2005) 2. Andriluka, M., Roth, S., Schiele, B.: Monocular 3D pose estimation and tracking by detection. In: CVPR (2010) 3. Andriluka, M., Roth, S., Schiele, B.: Pictorial structures revisited: people detection and articulated pose estimation. In: CVPR (2009) 4. Pishchulin, L., Jain, A., Andriluka, M., Thormahlen, T., Schiele, B.: Articulated people detection and pose estimation: reshaping the future. In: CVPR (2012) 5. Gkioxari, G., Hariharan, B., Girshick, R., Malik, J.: Using kposelets for detecting people and localizing their keypoints. In: CVPR (2014) 6. Sun, M., Savarese, S.: Articulated part-based model for joint object detection and pose estimation. In: ICCV (2011) 7. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: ECCV (2016) 8. Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: CVPR (2016) 9. Ouyang, W., Chu, X., Wang, X.: Multi-source deep learning for human pose estimation. In: CVPR (2014) 10. Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B.: Deepcut: joint subset partition and labeling for multi person pose estimation. In: CVPR (2016) 11. Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., Schiele, B.: Deepercut: a deeper, stronger, and faster multi-person pose estimation model. In: ECCV (2016) 12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016) 13. Cao, Z., Simon, T., Wei, S., Sheikh, Y.: Real-time multi-person 2D pose estimation using part affinity fields. In: CVPR (2017) 14. Wang, H., An, W.P., Wang, X., Fang, L., Yuan, J.: Magnify-net for multi-person 2D pose estimation. In: ICME (2018) 15. Chen, X., Yang, G.: Multi-person pose estimation with LIMB detection heatmaps. In: ICIP (2018) 16. Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Sridhar, S., Pons-Moll, G., Theobalt, C.: Single-shot multi-person 3D pose estimation from monocular RGB. In: 3DV (2018) 17. Fang, H., Xie, S., Tai, Y., Lu, C.: RMPE: regional multi-person pose estimation. In: ICCV (2017)

A Network Information Data Protection Scheme Based on Superposition Encryption Liu Zhe1,2(&) 1

2

School of Information and Electronics, Beijing Institute of Technology, Beijing, China [email protected] General Office of Supreme People’s Procuratorate, Beijing, China

Abstract. The wide application of virtualization, big data, cloud storage and other technologies has made the data centralized storage increase security risks and leakage risks. For the problem of internal personnel using legal identity and role to steal internal information, this paper proposes a network information data protection architecture based on superposition encryption from the process of data generation, transmission, storage and processing. Based on the framework, including information storage protection based on symmetric cryptography and information transmission protection based on hybrid cryptography, a network information data protection scheme based on superimposed encryption is designed. Finally, the security of the scheme is proved. Keywords: Centralized storage
Information encryption Data protection
External attacks
Internal stealing




1 Introduction With the development of big data, cloud storage, virtualization and other technologies, great changes have taken place for the information application model, in which a large number of data are centralized on the server, and the end users retrieve data from the server through application systems or virtualization technologies. This application mode enables information to be shared more fully, greatly improving work efficiency, but also bringing greater risks and hidden dangers to security and confidentiality, which is partly due to information attacks through external attacks on the network, on the one hand, due to internal personnel using work convenience to steal information. In this context, many industries and enterprises in many fields at home and abroad are facing increasingly severe problems of data information security, and many scholars are constantly exploring solutions for data security protection in the era of big data. Paper [2] analyzes the security requirements of data services and studies the existed data protection scheme and mechanism. Combining the cloud service characteristics and data services process, a secure solutions for data service in cloud needs to meet the requirement: re-encryption method under trusted cloud service providers. Paper [3] proposes the security framework based on the integration of cloud and client, the cloud cooperate with the client each other to achieve the protection for user data in the cloud, that is, the cloud will be responsible for protecting the security of the user © Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 608–615, 2019. https://doi.org/10.1007/978-981-13-7123-3_71

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data in the cloud, meanwhile the client will be responsible for verifying and proving the data safety. Yan et al. [4] discusses various aspects of cryptography and data security in cloud computing, including secure cloud data storage, cloud data privacy protection, trusted cloud data management and cryptography related to cloud data security. Paper [5] brings a critical comparative analysis of cryptographic defense mechanisms, and explores research directions and technology trends to address the protection of outsourced data in cloud infrastructures. Paper [6] proposes advanced algorithm for cryptography which is totally depend on hashing function technique to generate a secret key which is further used to encrypt and decrypt the important information. Rajesh et al. [7] proposes a well-organized privacy preservation data-mining scheme with data-mining perturbation merged approach, and uses the association rules with cryptography techniques. Paper [8] presents AES4SeC, a security scheme fully constructed over cryptographic pairings. Paper [9] introduces an algorithm-agnostic solution that provides both data integrity and confidentiality protection at the disk sector layer. Yi et al. [10] proposes an efficient hardware architecture based on multivariate scheme for storage devices. For the problem of internal personnel using legal identity and role to steal internal information, this paper proposes a network information data protection system architecture based on superposition encryption, which includes information storage protection based on symmetric cryptography and information transmission protection based on hybrid cryptography, then a network information data protection scheme based on superimposed encryption is designed. Finally, the security of the scheme is proved. The scheme achieves effective protection on the basis of satisfying the normal application of information technology, which can ensure the security of information data, especially the centralized storage data in the network.

2 Network Information Data Protection Architecture Based on Superposition Encryption At present, the information encryption protection generally adopts symmetric cryptography and hybrid cryptography. In order to ensure that files can be reused and shared by multiple users when using symmetric cryptography, each file or each user shares the same (or the same group) key for encryption and decryption; since the key is known to all users, it cannot effectively prevent the “insiders” stealing behavior, which has shortcomings in security. Hybrid cryptography solves the key transmission problem very well, realizes “one text and one key”, and can solve the “insiders” stealing behavior, which ensures high information security; the symmetric cryptographic key in the hybrid cryptography needs to be protected by digital envelope technology, which is very effective for one-to-one file transfer applications such as e-mail and file exchange, but not for information sharing applications. In order to solve the above problems of using symmetric cryptography and hybrid cryptography separately, this paper designs a cryptography protection architecture. As shown in Fig. 1, the two cryptographic technologies are combined using their respective advantages to protect information in storage and transmission stages through different cryptographic technologies, which not only effectively utilizes the efficiency

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and flexibility of symmetric cryptography, but also fully utilizes the security of hybrid cryptography. In the case of ensuring the security of information storage, the application is more flexible and meets actual needs.

Fig. 1. Storage encryption

This cryptographic protection method uses symmetric cryptography protection for storage and hybrid cryptography protection for transmission. In order to ensure the security of information storage, an address conversion table can be set up to convert the actual storage address of the information and then provide it to the outside, which can more effectively protect the security of information storage. In addition, information with different information sensitivity levels can be stored in in different partitions, and the information sensitivity level is corresponding to the user’s reading information rights, which can effectively realize the control of the user’s reading information rights by establishing the corresponding relationship between the asymmetric cryptographic public key in the hybrid cryptography and the storage area where the user can access the information.

3 Information Storage Protection Based on Symmetric Cryptography The information is stored centrally on the server and protected by symmetric cryptography, where the information plaintext is converted into cipher text when stored, and then converted into plaintext after being read from the storage device. 3.1

Using Symmetric Cryptography to Encrypt and Decrypt Information Storage

In order to improve the security of encrypted information storage and avoid the overall information leakage problem, the symmetric cryptography of information storage can use different keys for different partitions, that is, the storage is divided into partitions, and use different keys for different partitions. In addition, the information partition storage can set the protection strength according to the sensitivity of the information,

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perform finer-grained area division, and use more intensive encryption algorithms or keys. This fine-grained use of symmetric cryptography encrypts the stored files, which can achieve “one thing (text) and one key” under the highest security. 3.2

Configuration of Encryption and Storage Devices

If the storage partition block (area) uses a key or a “one text one key” method, then a key table needs to be set up to establish the corresponding relationship. In order to increase security, encryption and decryption devices and storage devices should be separated. The corresponding structure is shown in Fig. 2.

Storage

Corresponding Table for Storage Address and Key

Symmetric Encryption and Decryption

Address Conversion Table

Fig. 2. Storage address conversion

3.3

Storage Address Conversion

In order to further increase the security of information storage, the actual storage address of information in the storage device can not be clear to the outside. To realize such address conversion, an address conversion table should be set up to correspond the storage address to the address provided to the outside, and the corresponding configuration structure is as shown in Fig. 3.

Fig. 3. Encryption and decryption of the server information

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4 Information Transmission Protection Based on Hybrid Cryptography Hybrid cryptography is only used during the information transmission protection, and the symmetric cryptographic key is used once and randomly generated for each time. Thus, the symmetric cryptographic keys are different for each transmission even for the same file, thus achieving “one person one key” and “one thing one key” and “one time one key”. 4.1

Using Hybrid Cryptography to Encrypt Transmission Information

Firstly, use symmetric cryptography to encrypt information, that is: C1 ¼ E0K0 ðPÞ where Kʹ is a randomly generated key, Eʹ is a symmetric cryptographic encryption operation, C1 is a symmetrically encrypted information ciphertext, and P is a information plaintext. Secondly, use asymmetric cryptography to encrypt the symmetric cryptographic keys, that is: 

M ¼ Ek y0 ðKÞ Where y2Bn, Bn is the user set, k°yʹ is the public key (encryption key) for user y, E° is a asymmetric cryptographic encryption operation; M is the ciphertext of the symmetric cryptographic key encrypted by the asymmetric cryptographic operation. Finally, the information ciphertext encrypted by the symmetric cryptography and the symmetric key encrypted by the asymmetric cryptography are superimposed. f:C ¼ C1 þ M

4.2

Use Hybrid Cryptography to Decrypt the Transmitted Information Ciphertext

Decryption is an inverse operation of encryption. First, the information ciphertext is decomposed, that is: f 1 :C1 ¼ C  M, M ¼ C  C1 This is the inverse operation of C = C1 + M, obtaining C1 and M.

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Secondly, use the asymmetric cryptography to decrypt the symmetric cryptographic key, that is: 

K0 ¼ Dk y ðMÞ Where y 2 Bn, Bn is the user set; k°y is the private key (decryption key) of the user y; Kʹ is the symmetric cryptographic key; M is the ciphertext of the symmetric cryptographic key encrypted by the asymmetric cryptography. Finally, use the asymmetric cryptography decrypt the obtained symmetric cryptographic key, and decrypt the information ciphertext to obtain the information plaintext, that is: P ¼ D0K0 ðC1 Þ Where C1 is the information ciphertext encrypted by the symmetric cryptography, P is the information plaintext, Dʹ is a symmetric cryptographic encryption operation, and K is the symmetric cryptographic key. The decryption process of the hybrid cryptography is the inverse operation of the hybrid encryption process, but the key used for the asymmetric cryptographic decryption in the decryption process is the user’s private key, so it is not necessary to retrieve the user’s public key information base as in the case of server encryption. 4.3

System Implementation

When using hybrid cryptography to protect the information transmission, the settings of the server and the client devices are basically the same, except that a public key table is added for the server. The specific system configuration diagram is shown in Figs. 4 and 5.

Fig. 4. Encryption and decryption of the user information

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Fig. 5. Overall architecture and implementation

5 Conclusion The superposition of the symmetric cryptography and the hybrid cryptography is used to protect information, which solves the problem of security and confidentiality in information centralization to a certain extent, but also affects the normal implementation of some functions in application, such as the information retrieved by the

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application system is ciphertext data, which cannot be counted and queried. This cryptography protection mode is mainly based on the protection of information including core sensitive content, such as daily office files, which is more suitable for the protection of unstructured data such as files, audio and video. For the information security and confidentiality requirements in information application, this paper proposes a cryptographic protection scheme based on the superposition encryption of the symmetric cryptography and the hybrid cryptography, which combines “one person one key” and “one text one key” to ensure the security of information transmission due to the use of the hybrid cryptography; storage and transmission encryption are separated from each other due to the use of superposition encryption. This paper demonstrates the feasibility of the scheme from system construction, scheme design, technology implementation, security protection, etc., which can meet the information security protection in the information application of government agencies and other units, and can effectively guarantee information security of the application modes of virtualization, big data, cloud storage.

References 1. Sibi Chakkaravarthy, S., Sangeetha, D., Venkata Rathnam, M., Srinithi, K., Vaidehi, V.: Futuristic cyber-attacks. Int. J. Knowl.-Based Intell. Eng. Syst. 22(3), 195–204 (2018) 2. GuXin.: Studies on trusted secure data services under cloud environment. Wuhan University (2013) 3. MaWenqi.: Research on the cloud and client fusion based user’s data security technology in cloud storage. National University of Defence Technology (2016) 4. Yan, Z., Deng, R.H., Varadharajan, V.: Cryptography and data security in cloud computing. Inf. Sci. 387, 53–55 (2017) 5. Kaaniche, N., Laurent, M.: Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms. Comput. Commun. (2017). S014036641730796X 6. Kumar, M.: Advanced RSA cryptographic algorithm for improving data security. In: Golden Jubilee Convention CSI- on “digital Life.” December 2018 7. Rajesh, N., Selvakumar, A.A.L.: Association rules and deep learning for cryptographic algorithm in privacy preserving data mining. Cluster Comput. 2, 1–13 (2018) 8. Morales-Sandoval, M., Gonzalez-Compean, J.L., Diaz-Perez, A., et al.: A pairing-based cryptographic approach for data security in the cloud. Int. J. Inf. Secur. 17(4), 441–461 (2018) 9. Broz, M., Patocka, M., Matyas, V.: Practical cryptographic data integrity protection with full disk encryption extended version (2018) 10. Yi, H., Nie, Z.: Towards data storage in cryptographic systems: an efficient hardware architecture based on multivariate scheme for secure storage applications. Cluster Comput. (2018)

Author Index

A An, Linchang, 462 An, Zhou, 212, 343, 371 B Bai, Xinping, 471 Bian, Jiang, 371 Bian, Sen, 437

Du, Jianbo, 317 Duan, Chao, 227 E E., Wei, 241, 268 F Fan, Hanchao, 380 Fang, Bingyi, 570 Fang, Lingfeng, 22 Fang, Nan, 227 Feng, Chengyu, 115 Feng, Shuo, 317 Fu, Chenghao, 559 Fu, Dawei, 291, 317 Fu, Meixia, 22, 69

C Cai, Ronghui, 559 Cao, Chenlei, 139 Cao, Lu, 180 Cao, Yong, 446 Chen, Juan, 411 Chen, Na, 69 Chen, Qing, 139 Chen, Shijie, 291, 300 Chen, Xi, 310 Cheng, Chao, 188 Cheng, Cheng, 154 Cheng, Wei, 180 Cheng, Xinzhou, 516, 570 Cui, Xiaoyi, 49 Cui, Yufu, 260

G Gao, Hongtao, 352 Gao, Jie, 516 Gao, Ru, 124 Gao, Song, 446 Gao, Yang, 180 Guan, Jian, 516 Guo, Bao, 495, 505

D Dai, Kan, 446 Dai, Yuliang, 551 Deng, Xiangjin, 249 Ding, Zhenpeng, 343 Ding, Zhimin, 227 Dong, Jiangbo, 78 Dong, Mingtao, 32

H Han, Dong, 124 Han, Jindong, 32 Han, Xiaojun, 291 Han, Yuehua, 495, 505 Hao, Jianing, 154 He, Yongcong, 188 He, Yuan, 32

© Springer Nature Singapore Pte Ltd. 2019 S. Sun et al. (Eds.): ICSINC 2018, LNEE 550, pp. 617–620, 2019. https://doi.org/10.1007/978-981-13-7123-3

618 He, Zhengwen, 268 Hu, Hongning, 105 Hu, Lin, 291, 317 Hu, Xuhua, 343 Hu, Zhengguang, 525 Huang, Jianhua, 3 Huang, Wei, 525 Huang, Xiaokai, 360 Huang, Xiaoyu, 559 Huang, Yufeng, 173 Huang, Zhongjie, 69 J Jia, Xiaodong, 100, 260 Jiang, Guoqiang, 100 Jiang, Hong, 570 Jiang, Yu, 146 Jiang, Zhiguang, 326 Jiao, Ronghui, 380, 391 Jin, Lijun, 542 Jin, Ronghua, 534, 542 Jin, Shengyi, 249 Jing, Quan, 276 Jing, Xiaojun, 32 K Kang, Nan, 570 Kong, Peng, 131 L Lei, Rongjia, 593, 600 Lei, Yong, 146, 310 Li, Bingyi, 578, 584, 593, 600 Li, Fangyong, 360 Li, Haibo, 139 Li, Hao, 336 Li, Luming, 91 Li, Nan, 227 Li, Xiangyuan, 593, 600 Li, Xiao, 54 Li, Xiaojuan, 115 Li, Yan, 139 Li, Yifan, 164 Li, Yong, 525 Li, Yongchang, 154 Li, Yue, 593, 600 Li, Zhidong, 219 Liang, Deyin, 310 Liang, Yin, 400 Liu, Bo, 13 Liu, Couhua, 446

Author Index Liu, Fei-hu, 180 Liu, Guoyu, 173 Liu, Hongjin, 13 Liu, Jiang, 380 Liu, Li, 154, 227 Liu, Lianye, 559 Liu, Pengcheng, 505 Liu, Shufen, 13 Liu, Wei, 78 Liu, Yaxi, 78 Liu, Yi, 495, 505 Liu, Yiming, 146 Liu, Yuhao, 69 Lu, Bingjian, 429 Lu, Chengzhi, 317 Lu, Pengluo, 154 Lu, Wen-gao, 124 Lu, Yi, 173 Lu, Zhenyu, 429, 551 Lv, Jingyang, 49, 54, 61 Lv, Mengyao, 462 Lv, Qianqian, 227 Lv, Zhongliang, 471

M Ma, Jie, 534, 542 Ma, Xiaomei, 69 Ma, Xuekuan, 455 Ma, Yan, 41 Mao, Yilan, 310 Meng, Weiqiang, 3 Miao, Qi, 343, 371 Mo, Fan, 276

N Ni, Kaili, 69 Niu, Jiaxiang, 91 P Pan, Huifang, 212 Pan, Li, 146 Peng, Liqiang, 284 Q Qiao, Lei, 13 Qin, Huafeng, 525 Qin, Suran, 380, 391 Qiu, Yaxing, 437 Qu, Quanyou, 310

Author Index R Ran, Qianhan, 22 Rao, Xiaoqin, 462 Ren, Liang, 188 Ren, Lu, 352 Ren, Yebing, 78 Ren, Zhenyue, 173 Ren, Zhong, 291 Ru, Xiaoyi, 343, 371 S Shen, Ao, 495 Shi, Haitao, 352 Shi, Jian, 100 Shi, Jianmin, 380 Shi, Wei, 249 Song, Ding, 391 Song, Lei, 124 Song, Wenlong, 212 Sun, Bo, 219 Sun, Gang, 360 Sun, Songlin, 22, 69, 593, 600 Sun, Tengfei, 164 Sun, Zhelei, 115, 284 T Tan, Tian, 100 Tang, Jian, 446 Tang, Yang, 41 Tian, Hexiang, 131 Tong, Yelong, 164 Toth, Zoltan, 487 W Wan, Yuting, 105 Wang, Fengjun, 437 Wang, Guozhi, 505 Wang, Haobin, 78 Wang, Hui, 471 Wang, Luyao, 593, 600 Wang, Qiang, 180, 204, 400 Wang, Qianying, 276 Wang, Teng, 195 Wang, Wenping, 146 Wang, Xiaonan, 131 Wang, Xidong, 437 Wang, Xingyan, 400 Wang, Xinhan, 420 Wang, Yi, 487 Wei, Heng, 479 Wei, Huangfu, 78 Wu, Feixia, 3 Wu, Lequn, 300

619 X Xia, Jixia, 360 Xie, Chao, 455 Xie, Hongrui, 61 Xing, Huanlai, 411, 420 Xu, Jian, 13 Xu, Kai, 336 Xu, Lexi, 411, 516 Xu, Menglei, 525 Xu, Ran, 462 Xu, Zetao, 495 Xue, Feng, 173 Y Yan, Hongcheng, 91 Yang, Chunsheng, 173 Yang, Feng, 188 Yang, Hai, 411, 420 Yang, Hongyu, 291, 317 Yang, Hua, 13 Yang, Liuqing, 115, 284 Yang, Yi, 300 Yao, Jian, 3 Yao, Meng, 249 Ye, Chengzhi, 559 Yin, Jinsong, 479 Yin, Shan, 534, 542 You, Yanan, 131 Yu, Chao, 446 Yu, Lei, 164 Yu, Wei, 343 Yuan, Lili, 391 Yuan, Yi, 212, 343 Yun, Lei, 391 Z Zeng, Chunping, 100, 260 Zhan, Tianming, 429, 551 Zhang, Bihui, 462 Zhang, Boyin, 400 Zhang, Chenhua, 300 Zhang, Hengde, 429, 462, 551 Zhang, Jingjing, 105 Zhang, Lei, 219 Zhang, Lijian, 326, 336 Zhang, Miao, 227 Zhang, Qiang, 131 Zhang, Rui, 91 Zhang, Shao-po, 124 Zhang, Shuai, 219 Zhang, Tao, 115, 516 Zhang, Tianhang, 462 Zhang, Tianwei, 284

620 Zhang, Xiaofeng, 352 Zhang, Xiaomei, 487 Zhang, Xuesong, 570 Zhang, Yahang, 91 Zhang, Yang, 249, 495, 505 Zhang, Yao, 559 Zhao, Bingxin, 300 Zhao, Jianwu, 391 Zhao, Jinchen, 317 Zhao, Na, 380 Zhao, Ning, 241, 268

Author Index Zhao, Rui, 429 Zhao, Zhihui, 249 Zhe, Liu, 608 Zheng, Xiaoxia, 284 Zheng, Yanhong, 249 Zhou, Ningfang, 534 Zhu, Chunying, 380 Zhu, Hui, 479 Zhu, Jun, 154 Zong, Zhiping, 446 Zou, Jiaqi, 578, 584, 593, 600