Advances in Intelligent Information Hiding and Multimedia Signal Processing: Proceeding of the 16th International Conference on IIHMSP in conjunction ... (Smart Innovation, Systems and Technologies) [1st ed. 2021] 981336419X, 9789813364196

Table of contents :
Conference Organization
Conference Founders
Honorary Chairs
Advisory Committee
General Chairs
Program Chairs
Publication Chairs
Special Session Chairs
Special Track Co-chairs
Electronic Media Chairs
Finance Chairs
Local Organization Chairs
Program Committee
Committee Secretaries
Preface
Contents
About the Editors
Modeling and Simulation for Battery Energy Storage System Participating in Power System Frequency Modulation Based on Power System Analysis Software Package
1 Introduction
2 Battery Energy Storage System Model
2.1 Mathematical Model of BESS
2.2 PSASP Model of Energy Storage System
3 Example Analysis
3.1 Model Validity Verification
3.2 Simulation of Power System Frequency Modulation Based on Energy Storage Model
4 Conclusion
References
CNN Character Recognition Model for 3D Integral Image Character
1 Introduction
2 Method and Dataset
2.1 Fonts and Characters Collection
2.2 Generate Elemental Images
2.3 Generate Integral Images
3 Result
3.1 Training Result
3.2 Evaluation Result
4 Conclusion
References
Using Virtual Machines in Computer Networking Class
1 Introduction
2 Research Methodology
2.1 Network Laboratory Design
2.2 An Implemented Laboratory Design
2.3 Virtual Machine
2.4 Virtualization
2.5 A Full Virtualization
2.6 Para-Virtualization
2.7 OS-Layer Virtualization
2.8 Virtual Network Infrastructure
3 Experimental Result
3.1 Classroom Environment
3.2 Computer Used in Laboratory Work
3.3 Software Used in Laboratory Work
3.4 Laboratory Practice 1
3.5 Laboratory Practice 2
3.6 Laboratory Practice 3
3.7 Result
4 Conclusion
References
Usability Evaluation of Mobile Luxury Brand Websites Based on the Analytic Hierarchy Process
1 Introduction
2 Related Work
2.1 Research on Usability Evaluation
2.2 Analytic Hierarchy Process
2.3 Research Model of Usability Evaluation
3 Experiment Design
3.1 Experimental Subject
3.2 Experimental Methods
4 Results and Discussion
5 Conclusion
References
Study of Low-Cost-Based Smart Home Control Using IoT Powered by Photovoltaic Cells
1 Introduction
2 Proposed Smart Home System
2.1 Photovoltaic Cell Design
2.2 Servomotor Design
2.3 Microcontroller Design
2.4 Relay Design
2.5 Battery Design
2.6 Load Design
3 Efficiency of Photovoltaic Cell
3.1 Microcontroller (ESP-8266) Setting
3.2 Relay Setting
3.3 Control Setting
3.4 System Test Results and Analysis
4 Conclusion
References
Design of Combined Cycle Gas Turbine and IoT in Production Electricity from KIVU Lake Methane Gas
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusion and Suggestion
References
An Implementation of Ionic-Based Hybrid Mobile Application for Controlling Bluetooth Low-Energy-Based Humidifier Device
1 Introduction
2 Methods
2.1 Bluetooth Low Energy
2.2 Ionic Framework
3 Proposed Solution
4 Implementation
5 Conclusion
References
Study of Assessing the Stability of Rwanda’s Power System from Big Data Based on Power Generation
1 Introduction
2 Methodology
3 Current Situation of Rwanda Power System Sector
4 Expected Future Status of Rwanda Power System Sector
5 Rwanda General Energy Source Sector
6 Rwanda Power Blackout Experience
7 Discussion and Recommendations
8 Conclusion
References
Image Feature Detection and Clustering for UAV Multiple Obstacles Avoidance
1 Introduction
2 Related Works
3 Method
4 Experimental Results
5 Conclusion
References
Sentiment Analysis for Mongolian Tweets with RNN
1 Introduction
2 Related Work
3 Methodology
3.1 Deep Learning
3.2 Multilayer Perceptron
3.3 Recurrent Neural Network
3.4 Word Embedding
3.5 Preprocessing
4 Experiments and Results
4.1 Environment Setup
4.2 Model Implementation
4.3 Experiments and Results
5 Conclusion
References
Thin Point Light Source Display
1 Introduction
2 Conventional Point Light Source Display
2.1 Point Light Source Display
2.2 Calculation to Create an Elemental Image of the PLS Display
2.3 The Viewing Angle and Resolution of a Conventional PLS Display
3 Proposed Method
3.1 The Proposed Method of PLS Display
3.2 Calculation to Create an Elemental Image of the Proposed PLS Display
3.3 The Viewing Angle and Resolution of the Proposed PLS Display
4 Experimental Result
4.1 Experimental Result of Parallel Ray and PLSs
4.2 Experimental Result of Conventional PLS Display
4.3 Experimental Result of the Proposed Thin PLS Display
5 Conclusion
References
Dynamic Token Distribution Model for Privacy Protection of Mobile Users
1 Introduction
2 The Principle of Token-D Model
2.1 Privacy Protection
2.2 Token-D Model Architecture
3 Token Distribution Process
3.1 Synchronous Update
3.2 Validation Mechanism and Contingency Plan
3.3 Three-Party Interaction Mode
4 Evaluation Analysis
5 Conclusion
References
Cascading Fault Prevention of Power Grid Based on Key Power Generation Nodes
1 Introduction
2 The Safety Level Index of Power Grid Cascading Trip
3 Solution Process of the Model
4 Example Analysis
5 Conclusion
References
Optimal Power Generation Output Considering Cascading Failure
1 Introduction
2 Severity Evaluation Index of Interlocking Disturbed Branch
3 Optimization Model Solution Based on PSO
4 Example Analysis
5 Conclusions
References
A Survey of Common IOT Communication Protocols and IOT Smart-X Applications of 5G Cellular
1 Introduction
2 Internet of Things Communication Protocols
2.1 Bluetooth LE
2.2 Zigbee
2.3 Z-Wave
2.4 Wi-Fi
3 5G Cellular
4 Near-Field Communication (NFC)
5 LoRaWAN
6 IOT-X Applications
6.1 Smart Cities
6.2 Smart Energy and The Smart Grid
6.3 Smart Home, Smart Buildings, and Infrastructure
6.4 Smart Industrial Manufacturing
6.5 Smart Health
6.6 Smart Farming
7 Discussion
8 Conclusion
References
Study of Thermal Power Plant’s Intelligent Fire Detection and Suppression System Via Wireless Sensor Network and Carbon Capture and Storage Technology
1 Introduction
2 Proposed Fire Alarm System Design
3 Experimental Design and Verification
3.1 Wireless Sensor Network
3.2 Manual Initiating Device
3.3 Technology of Carbon Storage and Capture
3.4 Codes, Standards, and Regulations of Fire
4 Result Analysis and Discussion
5 Conclusion and Suggestion
References
Game-Theoretic Decision-Making Analysis on Antivirus
1 Introduction
2 Literature Review
3 Game Theory and Mixed Strategy Nash Equilibrium
4 Antivirus Report with Performance Test
4.1 Game-Theoretic Model and Analysis
5 Results and Conclusion
References
A New JPEG Encryption Scheme Using Adaptive Block Size
1 Introduction
2 Proposed Scheme
2.1 Transformation Stage Encryption
2.2 Block Permutation
2.3 Entropy Coding Stage Encryption
3 Experimental Result and Analysis
3.1 Perceptual Security
3.2 Brute-Force Attack
3.3 Key Sensitivity Analysis
3.4 Statistical Attack
3.5 Compression Performance
4 Conclusion
References
Study of Smart Decorating Machine on Cake Patten
1 Introduction
2 System Hardware and Software Design
2.1 Linkage Mechanism
2.2 G-Code Introduction and Analysis
2.3 Linear Interpolation Algorithm
2.4 System Main Program
3 System Integration and Testing
3.1 Accuracy Test of Three-Axis Linkage Device
3.2 Discharge Device Status Test
3.3 Decorating Function Test
3.4 Data Analysis and Discussion
4 Conclusion and Suggestion
References
Study of Advanced Low-Cost Smart Prepaid Electricity Meter Using Arduino and GSM
1 Introduction
2 Literature Reviews
3 Proposed System
4 Hardware Implementation
4.1 Controller of Arduino UNO
4.2 Module of GSM
4.3 Relay Module
4.4 Energy Meter
5 Working System and Experimental Results
6 Conclusion
References
Study of Integrating the Data Fusion Method for Reducing and Preventing Road Accidents Occur at Blackspots Places in Third World Countries
1 Introduction
2 Problem Description
3 Methodology
3.1 Filter of Kalman
3.2 Data Fusion Flow Chart and Government Intervention
4 Simulation and Discussion
5 Conclusion
References
Study of 2D SubMarine Tracking with Complete Worked Out Example Based on Kalman Filter
1 Introduction
2 Methodology
3 Complete Example Worked Out
3.1 Predicted State
3.2 The Initial Covariance Matrix
3.3 The Covariance Matrix of Predicted Process
3.4 Calculating the Kalman Gain
3.5 The New Observation
3.6 Calculating the Current State
3.7 Update the Covariance Matrix with the Overlapping Process for the Next Stage
4 Results and Discussions
5 Study Conclusion
References
Retina Macular Edema and Age-Related Macular Degeneration Feature Recognition Method Based on the OCT Images
1 Introduction
2 Data Preprocessing
3 Convolutional Neural Networks and Occlusion Sensitivity
3.1 Overview
3.2 OCC-DME Algorithm Process
4 Experimental Results and Discussion
4.1 Dataset
4.2 OCC-DME and SVM Algorithm Accuracy Comparison
4.3 Discussion
5 Conclusion
References
Gene Expression PPI Network Clustering Analysis Between Endometrial Cancer and Ovarian Cancer
1 Introduction
2 Gene Data Clustering Analysis
2.1 Motivation
2.2 Preprocessing
3 PPI Network Analysis
3.1 Homologous and Heterogeneous Genes PPI Network
3.2 Dedicated Ovarian Cancer Gene Analysis
4 Evaluation Analysis
5 Conclusion
References
Automatic Identification and Classification Method for Diabetic Retinopathy FFA Image Processing
1 Introduction
2 DR Classification Method
3 SADRD Algorithm
3.1 Preprocessing
3.2 Standard Vectors
3.3 Multi-class Support Vector Machine
3.4 Algorithm Description
4 Evaluation
5 Conclusion
References
Emotional Expression Analysis Based on Fine-Grained Emotion Quantification Model Via Social Media
1 Introduction
2 Motivation
3 Fine-Grained Emotion Quantification Model
3.1 Preprocessing
3.2 Quantification of Emotional Expression
3.3 Emotional Expression Analysis Algorithm
4 Experiment and Results
5 Conclusion
References
Fuzhou PM2.5 Prediction and Related Factors Analysis
1 Introduction
2 Materials and Methods
2.1 Study Sites
2.2 LSTM
2.3 PCC
3 Results and Discussions
4 Conclusion
References
An Improved Whale Optimization Algorithm and Its Application to Power Generation in Cascade Reservoir
1 Introduction
2 Reservoir Optimal Operation Model
2.1 Power Generation Formula
2.2 Restrictions
3 Adaptive Perturbation Whale Optimization Algorithm
3.1 Nonlinear Time-Varying Adaptive Weights
3.2 Differential Variation Perturbation Factor
3.3 Improved Spiral Update Method
3.4 APWOA Algorithm Flow
4 Experimental Results
4.1 Test on Standard Function
4.2 Test on Reservoir Power Generation
5 Conclusions
References
Prediction of Hypertension Using Deep Autoencoder-Based Feature Representation
1 Introduction
2 Materials and Method
2.1 Data
2.2 Method
3 Experiment
4 Results
5 Conclusion
References
The Prediction Model for High-Risk Patient with Liver Cancer Based on Classification Approaches
1 Introduction
2 Related Works
3 Methods and Experimental Results
3.1 Classification Methods
3.2 Performance Evaluation Methods
3.3 Experimental Results
4 Conclusion
References
Improve the Fingerprinting Algorithm Based on Affinity Propagation Clustering to Increase the Accuracy and Speed of Indoor Positioning Systems
1 Background
2 Related Works
3 Algorithms Using Affinity Propagation Clustering Method
3.1 Clustering to Improve Speed
3.2 Clustering to Improve Quality
3.3 Test Scenarios
4 Results and Evaluation
5 Conclude
Reference
Avoid Selection Bias in Observational Study Based on Health Big Data
1 Introduction
1.1 Epidemiology Study
1.2 Selection Bias
2 Prevention Methods
2.1 Systematic Prevention
2.2 Matching Method
2.3 Propensity Score
3 Conclusion
References
Caffeine Drinks and the Risk of Cancer: A Review
1 Introduction
2 Caffeine Drinks and the Risk of Cancer
2.1 Breast Cancer
2.2 Colorectal Cancer
2.3 Head and Neck Cancer
2.4 Liver Cancer
2.5 Ovarian Cancer
2.6 Prostate Cancer
3 Conclusion
References
Association Rule Mining Method to Predict Coronary Artery Disease: KNHANES 2016–2018
1 Introduction
2 Materials and Methods
2.1 Data Preprocessing
2.2 Feature Selection
2.3 Association Rule Mining
3 Experiments and Results
3.1 Data
3.2 Variable Selection Result
3.3 Discovered Association Rule
4 Conclusion
References
Framework Design of Anti-online Learning Anomie Behavior System
1 Introduction
2 Design Steps and Principles
3 System Design
3.1 Information Collection During Registration
3.2 Supervision and Intervention During Learning Process
3.3 Monitoring During Examination
4 Experiment
4.1 Design of the Information Feedback Function
4.2 Issues and Adjustment
5 Application Prospects and Future Work
References
Research on Intelligent Scene Generation Based on Unity3D
1 Introduction
2 Related Work
2.1 Design Composition of Scene Intelligent Generation
2.2 Access to Needed Material from Public Resource Sharing Platforms Such as Asset Store
3 Introduction of Key Algorithms
3.1 Material Object Generation
3.2 Use of Arrays to Determine the Reasonableness of Objects
3.3 Example Objects Using Array Elements
4 Experimental Results
4.1 Scene Generation Process
5 Summary and Prospect
5.1 Summary
5.2 Outlook
References
A Study of Guide Interpretation of Haihunhou Pavilion in Nanchang in the Age of Artificial Intelligence
1 Artificial Intelligence and Machine Translation
1.1 Artificial Intelligence
1.2 Machine Translation
1.3 Translation Machine
2 Guide Interpretation and Machine Interpretation
2.1 Guide Interpretation
2.2 Studies on Machine Interpretation
3 Guide Interpretation of Haihunhou Pavilion in Nanchang
3.1 The Site of Haihunhou
3.2 Guide Interpretation of Haihunhou Pavilion in Nanchang
4 Conclusion
References
Detection Method for Crowd Abnormal Behavior Based on Long Short-Term Memory Network
1 Introduction
2 Methodology
2.1 Improved ViBe Foreground Extraction Algorithm
2.2 Focal Loss Function Optimization LSTM Algorithm
3 Experiment
3.1 Analysis of Results
4 Conclusion
References
Research of Software Testing Technology Based on Statechart Diagram
1 Introduction
2 The Test Based on Statechart Diagram
2.1 Extended Finite State Machine
2.2 UML Statechart
2.3 Test Coverage Criteria
2.4 Minimum Test Cases
2.5 Test Case Generation Strategy
3 Case Analysis
3.1 Statechart Diagram Modeling
3.2 Building EFSM
3.3 To Generate Test Tree and Test Path
3.4 Generate Minimal Test Path Set
3.5 Generate Test Cases
4 Conclusion
References
Research on Intelligent Optimization Method of Grid Communication Server Based on Support Vector Machine
1 Introduction
2 Thread Pool Tuning Model Based on Support Vector Machine
2.1 Thread Pool Performance Model
2.2 Thread Pool Tuning Model Based on Support Vector Machine
3 Support Vector Machine Model Based on Improved Fluid Search Optimization Algorithm
4 Experimental Results
4.1 Experimental Data and Environment
4.2 SVM Training Results with Improved Fluid Search Optimization
4.3 Server Performance Test Experiment
5 Conclusion
References
Research on Optimization Model for Thread Pool Performance on Grid Information Server
1 Introduction
2 Thread Pool Performance Analysis
3 Modeling Thread Pool Performance
4 Thread Pool Performance Experiment
4.1 Throughput Affects Thread Pool Performance
4.2 Task Operation Time Affects Thread Pool Performance
4.3 Task Wait Time Affects Thread Pool Performance
5 Conclusion
References
Network Security Situation Assessment of Power Information System Based on Improved Artificial Bee Colony Algorithm
1 Introduction
2 Design of Network Security Situation Assessment Model Based on Neural Network
3 Network Security Situation Assessment Method Based on Improved Artificial Bee Colony Algorithm
4 Experimental Results
4.1 Setting up the Experimental Environment
4.2 Analysis of Experimental Results
5 Conclusion
References
A Brief Survey on Recent Advances of Object Detection with Deep Learning
1 Introduction
2 Detection Frameworks
2.1 Two-Stage Detection Frameworks
2.2 One-Stage Detection Frameworks
3 Object Detection Challenges and Solutions
4 Object Detection Data Set, Metrics, and Applications
4.1 Object Detection Data Set
4.2 Metrics
4.3 Applications
5 Conclusion and Future Directions
References
Research on the Method of Neural Network Switchgear Portrait Based on Sequence Clustering
1 Introduction
2 Data Acquisition and Pretreatment
2.1 Data Collection and Serialization
2.2 Time Tagging of Signal Sequence
3 Switchgear Portrait Based on Aggregation Technology
3.1 Algorithm Steps
3.2 Construction of Switchgear Portrait Model
3.3 Verification of the Switchgear Portrait Algorithm
4 Experimental Results and Conclusion
4.1 Experimental Results
4.2 Conclusion
References
Detection of False Data Injection Attack in Power Grid Based on Machine Learning
1 Introduction
2 Feature Extraction of Attack Detection Data
2.1 Outlier Score Extraction Based on Isolation Forest
2.2 Feature Extraction Method of Iforest-LLE Measurement Data
3 Attack Detection Model Based on GBDT
4 Simulation Experiment and Example Analysis
4.1 Experiment Preparation
4.2 The Construction of False Data Injection Attack Vector
4.3 Experimental Results and Analysis
5 Conclusion
References
Demand Response Strategy Model Based on User Satisfaction
1 Introduction
2 Intelligent Family Microgrid Load Analysis
2.1 Load Model
2.2 Load Characteristic Constraints
3 A Demand Response Strategy for Household Microgrid
3.1 The Key Technology of Demand Response Strategy Model
3.2 Bacterial Colony Chemotaxis Hybrid Algorithm
4 Instance Validations
4.1 User’s Satisfaction Verification
4.2 Algorithm Verification
4.3 Verification of Demand Response Model
5 Conclusion
References
Low-Complexity MMSE Precoding Based on SSOR Iteration for Large-Scale Massive MIMO Systems
1 Introduction
2 System Model of Massive MIMO
3 MMSE Precoding Algorithm Based on SSOR Iteration
3.1 Classical MMSE Precoding
3.2 SSOR-based Precoding Algorithm
3.3 Relaxation Parameter
3.4 Complexity Analysis
4 Simulation Results
5 Conclusion
References
Design of Intelligent Substation Communication Network Security Audit System
1 Introduction
2 IEC61850
2.1 Substation Event Message GOOSE
2.2 Sample Value Message SV
2.3 Manufacturing Message Specification MMS
2.4 Summary of Intelligent Substation Communication Security Threats
3 Security Audit System Design
3.1 Overall Architecture Design
3.2 Data Communication Engine
3.3 Decoding Engine
3.4 Abnormal Detection
4 Conclusion
References
Research on Security Auditing Scheme of Intelligent Substation Communication Network
1 Introduction
2 Introduction to Traditional Security Audit Program
3 IEC61850
4 Research Program
4.1 SCD File Parsing
4.2 Network Traffic Analysis
4.3 Security Baseline Establishment
4.4 Threat Analysis Alarm
5 Conclusion
References
Design of Radio Frequency Energy Harvesting System
1 Introduction
2 Design of RFEH System
2.1 Harvesting Antenna Design
2.2 Matching Circuit
2.3 Design of Rectifier Booster Circuit
3 Experimental Results and Analysis
4 Conclusion
References
An Encryption Method of Power Cloud Data Based on n-RSA
1 Introduction
2 Power Cloud Security Technology
3 RSA Encryption Algorithm Introduction
4 n-RSA Encryption Algorithm Introduction
4.1 Prime Selection
4.2 Key Generation
5 Security Proof of n-RSA Algorithm in Power Cloud
6 Conclusion
References
K-Means-Based Method for Identifying Characteristics of Wireless Terminal Equipment in Power System
1 Introduction
2 Characteristic Behavior Recognition of Wireless Terminal Equipment in Power System
2.1 Basic Principles of Characteristic Behavior Recognition
2.2 The Overall Process of Characteristic Behavior Recognition
2.3 Basic Principles of Improved K-Means Algorithm
3 Experimental Simulation and Analysis
4 Conclusion
References
Security Transmission Technology of WSN Based on Trust Management Mechanism in Power System
1 Introduction
2 WSN Security Technology
2.1 Cryptography
2.2 Key Management
2.3 Secure Routing
3 Trust Management Mechanism
4 Trust Management Mechanism Based on Residual Energy
5 System Simulation Analysis
6 Conclusion
References
Survey of Attack Detection and Defense Technologies in Wireless Sensor Networks
1 Introduction
2 Wireless Sensor Networks Attack Methods and Their Detection and Defense Technologies
2.1 Camouflaged WSN Attacks
2.2 Non-camouflaged Attacks
3 Problems and Prospects
4 Conclusion
References
A Wireless Hijack Attack on Power Consumption System of Power Metering Automation
1 Introduction
2 Introduction of Power Consumption Side of Power Metering Automation
2.1 Operation Mechanism of Power Side System of Metering Automation System
2.2 Power Side System Protocol of Power Metering Automation
3 Safety Analysis of Power Consumption Side of Metering Automation System
3.1 No Encrypted Transmission and Simple Verification Mechanism
3.2 One-Way Identity Authentication Mechanism
3.3 One-Way Authentication of GSM Network
4 Wireless Hijacking Attack Experiment of Power Side System of Measurement Automation
4.1 Experimental Environment
4.2 Experiment Process
5 Experimental Results
6 Conclusion
References
Research on Real-Time Deformation Measurement of Structural Frame Based on Data Driven
1 Introduction
2 Deformation Measurement of Structural Frame
3 Algorithm for Deformation of Structure Frame
3.1 Data-Driven Methods
3.2 kNN for Deformation of Structural Frame
4 Example
5 Experiment
6 Conclusion
References
A Sensor Fusion Method for In-Station Articulation of Train
1 Introduction
2 Related Work
3 Sensors Calibration
3.1 Camera Calibration
3.2 Joint Calibration
4 Radar Signal Processing
4.1 Signal Preprocessing
4.2 Signal Modification Based on Improved Kalman Filter
5 Over-All Experiment
6 Conclusion and Outlook
References
Intelligent Fault Diagnosis Using Limited Data Under Different Working Conditions Based on SEflow Model and Data Augmentation
1 Introduction
2 Theoretical Background
2.1 Normalizing Flow
2.2 Squeeze-and-excitation Networks (SEnets)
3 Design of Proposed Model and System Framework
3.1 Model Training of Proposed Frame
4 Experiments Analysis
4.1 Introduction of Datasets and Compared Augmentation Method
4.2 Compared Augmentation Techniques
4.3 Results on CWRU Dataset
5 Conclusions
References
The Unified Framework of Deep Multiple Kernel Learning for Small Sample Sizes of Training Samples
1 Introduction
2 Proposed Depth-Width-Scaling Deep Kernel Learning Network
2.1 Framework
2.2 Algorithm
3 Experiments
4 Conclusion
References
Quasiconformal Mahalanobis Distance-Based Kernel Mapping Machine Learning for Hyperspectral Data Classification
1 Introduction
2 Proposed Algorithm
3 Experiments and Analysis
4 Conclusion
References
Research on Time-Delay Estimation of PMSM Driving System Based on RLS Method
1 Introduction
2 PMSM Driving System Model with Time-Delay Factor
3 Time Delay Parameter Estimate
3.1 Principle of Recursive Least Square (RLS) Method
3.2 Time Delay Parameter Estimation
4 Simulation Verification and Analysis
5 Conclusion
References
Cache Learning Method for Terrific Detection of Atrial Fibrillation
1 Introduction
2 Related Work
3 Cache Learning Method
3.1 Preliminary Learning
3.2 Screening
3.3 Development
3.4 Elimination
4 Experimental Work
5 Conclusion
References
Enhanced the Depth of Integral Image Display by Using Barrier Array
1 Introduction
2 Depth Range of InIm Display
3 Proposed Method
4 Experimental Results
4.1 Effect of Barrier on the Depth
4.2 Enhanced the Depth Range of the 3-D Display by Using Barrier Array
5 Conclusion
References
Author Index

Citation preview

Smart Innovation, Systems and Technologies 211

Jeng-Shyang Pan · Jianpo Li · Oyun-Erdene Namsrai · Zhenyu Meng · Miloš Savić   Editors

Advances in Intelligent Information Hiding and Multimedia Signal Processing Proceeding of the 16th International Conference on IIHMSP in Conjunction with the 13th International Conference on FITAT, November 5–7, 2020, Ho Chi Minh City, Vietnam, Volume 1

Smart Innovation, Systems and Technologies Volume 211

Series Editors Robert J. Howlett, Bournemouth University and KES International, Shoreham-by-sea, UK Lakhmi C. Jain, KES International, Shoreham-by-Sea, UK

The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form. Volumes on interdisciplinary research combining two or more of these areas is particularly sought. The series covers systems and paradigms that employ knowledge and intelligence in a broad sense. Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community. It also focusses on the knowledge-transfer methodologies and innovation strategies employed to make this happen effectively. The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities. The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions. High quality content is an essential feature for all book proposals accepted for the series. It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles. Indexed by SCOPUS, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago, DBLP. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/8767

Jeng-Shyang Pan · Jianpo Li · Oyun-Erdene Namsrai · Zhenyu Meng · Miloš Savi´c Editors

Advances in Intelligent Information Hiding and Multimedia Signal Processing Proceeding of the 16th International Conference on IIHMSP in Conjunction with the 13th International Conference on FITAT, November 5–7, 2020, Ho Chi Minh City, Vietnam, Volume 1

Editors Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao, Shandong, China

Jianpo Li Northeast Electric Power University Jilin, China Zhenyu Meng Fujian University of Technology Fuzhou, China

Oyun-Erdene Namsrai National University of Mongolia Ulaanbaatar, Mongolia Miloš Savi´c University of Novi Sad Novi Sad, Serbia

ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-981-33-6419-6 ISBN 978-981-33-6420-2 (eBook) https://doi.org/10.1007/978-981-33-6420-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed 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

Conference Organization

Conference Founders Jeng-Shyang Pan (Shandong University of Science and Technology) Lakhmi C. Jain (University of Canberra/Bournemouth University) Keun Ho Ryu (Chungbuk National University/Ton Duc Thang University/Chiang Mai University) Oyun-Erdene Namsrai (National University of Mongolia)

Honorary Chairs Le Vinh Danh (Ton Duc Thang University) Chin-Chen Chang (Feng Chia University) Lakhmi C. Jain (University of Canberra/Bournemouth University) Goutam Chakraborty (Iwate Prefectural University)

Advisory Committee Yanja Dajsuren (Tu/E) Kebin Jia (Beijing University of Technology) Li-Hua Li (Chaoyang University of Technology) Yanjun Peng (Shandong University of Science and Technology) Ioannis Pitas (Aristotle University of Thessaloniki) Renjie Song (Northeast Electric Power University) Yoiti Suzuki (Tohoku University) Yao Zhao (Beijing Jiaotong University)

v

vi

Conference Organization

General Chairs Pham Van Huy (Ton Duc Thang University) Jong Yun Lee (Chungbuk National University) Chin-Feng Lee (Chaoyang University of Technology) Jianpo Li (Northeast Electric Power University) Oyun-Erdene Namsrai (National University of Mongolia)

Program Chairs Le Anh Cuong (Ton Duc Thang University) Suvdaa Batsuuri (National University of Mongolia) Nipon Teera-Umpon (Chiang Mai University) Ling Wang (Northeast Electric Power University) Ching-Yu Yang (National Penghu University of Science and Technology)

Publication Chairs Hoang Van Dung (Ton Duc Thang University) Zhenyu Meng (Fujian University of Technology) Jeng-Shyang Pan (Fujian University of Technology) Keun Ho Ryu (Ton Duc Thang University/Chungbuk National University/Chiang Mai University)

Special Session Chairs Aziz Nasridinov (Chungbuk National University) Yongjun Piao (Nankai University)

Special Track Co-chairs Track 1: Technologies for Next Generation Network Environments Tsu-Yang Wu (Shandong University of Science and Technology) Huynh Ngoc Tu (Ton Duc Thang University) Track 2: Intelligent for Manufacturing Kuo-Chi Chang (Fujian University of Technology)

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Yuh-Chung Lin (Fujian University of Technology) Kai-Chun Chu (National Central University) Track 3: Pattern Recognition and Computational Intelligence Quoc-Tao Ngo (Vietnam Institute of Information Technology of Vietnamese Academy of Science and Technology) Huu-Quynh Nguyen (Thuyloi University) Truong-Giang Ngo (University of Manage and Technology) Track 4: Wireless Networks and Its Application Ling Wang (Northeast Electric Power University) Jianpo Li (Northeast Electric Power University) Track 5: Data Mining and Applications in Biology and Medicine Xun Jin (Tianjin Medical University) Yongjun Piao (Nankai University) Track 6: Big Data and Its Applications Young-Ho Park (Sookmyung Women’s University) Aziz Nasridinov (Chungbuk National University) Track 7: Intelligent Services and Data Management Kwang Woo Nam (Kunsan National University) Seong Ho Lee (Electronics and Telecommunications Research Institute) Track 8: Medical Informatics and Big Data Hyo Soung Cha (National Cancer Center) Kwang Sun Ryu (National Cancer Center) Track 9: Statistical Analysis and Data Mining Oyun-Erdene Namsrai (National University of Mongolia) Erdenebileg Batbaatar (Chungbuk National University) Track 10: Deep Learning For Intelligent Systems Le Anh Cuong (Ton Duc Thang University)

Electronic Media Chairs Ganbat Baasantseren (National University of Mongolia) Erdenebileg Batbaatar (Chungbuk National University) Vu Dinh Hong (Ton Duc Thang University) Jieming Yang (Northeast Electric Power University)

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Finance Chairs Truong Dinh Tu (Ton Duc Thang University) Meijing Li (Shanghai Maritime University) Khishigsuren Davagdorj (Chungbuk National University)

Local Organization Chairs Tran Trong Dao (Ton Duc Thang University) Dang Minh Thang (Ton Duc Thang University) Duong Huu Phuc (Ton Duc Thang University)

Program Committee Mohamed Ezzeldin A.Bashir (A’lsharqiyah University) Tom Arbuckle (University of Limerick) Sansanee Auephanwiriyakul (Chiang Mai University) Khuyagbaatar Batsuren (National University of Mongolia) Suvdaa Batsuuri (National University of Mongolia) Enkhtuul Bukhsuren (National University of Mongolia) Erwin Bonsma (Philips) Eun-Jong Cha (Chungbuk National University) Hyo Soung Cha (National Cancer Center) Basabi Chakraborty (Iwate Prefectural University) Goutam Chakraborty (Iwate Prefectural University) Jeong Hee Chi (Konkuk University) Young Sung Cho (Information Research Company) Lodoiravsal Choimaa (National University of Mongolia) Anour Dafaalla (City College) Garmaa Dangaasuren (National University of Mongolia) Dang Minh Thang (Ton Duc Thang University) Nan Ding (Dalian University of Technology) Razvan Dinu (Philips) Duong Huu Phuc (Ton Duc Thang University) Sang Hun Han (Hangil Software) Herman Hartmann (University of Groningen) Hoang Do Thanh Tung (Vietnam Institute of Information Technology of Vietnamese Academy of Science and Technology) Hoang Van Dung (Ton Duc Thang University) Bu Hyun Hwang (Chonnam National University)

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Jeong Hee Hwang (Namseoul University) Jeong Kyeong Ja (Chungchung University) Purev Jaimai (National University of Mongolia) Seon-Phil Jeong (United International College) Wanchang Jiang (Northeast Electric Power University) Ohyun Jo (Chungbuk National University) Kwang Su Jung (National Institute of Health) Byungchul Kim (Baekseok University) Kyung-Ah Kim (Chungbuk National University) Yang-Mi Kim (Chungbuk National University) Taeil Kwon (Bigsun Co. Ltd) Le Anh Cuong (Ton Duc Thang University) Le Van Vang (Ton Duc Thang University) Bum Ju Lee (Korea Institute of Oriental Medicine) Eunji Lee (Soongsil University) Heon Gyu Lee (Gaion Company) Jong Yun Lee (Chungbuk National University) Sanghyuk Lee (Xi’an Jiaotong-Liverpool University) Yongmi Lee (Chungbuk National University) Dingkun Li (Shanghai Tongji University) Jianpo Li (Northeast Electric Power University) Meijing Li (Shanghai Maritime University) Peipei Li (Van andel Institute) Gang Liu (Xidian University) Ran Ma (Shanghai University) Bayarpurev Mongolyn (National University of Mongolia) Tsendsuren Munkhdalai (Microsoft Research) Kwang Woo Nam (Kunsan National University) Aziz Nasridinov (Chungbuk National University) Goce Naumoski (Bizzsphere) Oyun-Erdene Namsrai (National University of Mongolia) Nguyen Chi Thien (Ton Duc Thang University) Incheon Paik (The University of Aizu) Hyun Woo Park (National Cancer Center) Pham Van Huy (Ton Duc Thang University) Minghao Piao (Chungbuk National University) Yongjun Piao (Nankai University) Gouchol Pok (Pai Chai University) Keun Ho Ryu (Ton Duc Thang University/Chungbuk National University/Chiang Mai University) Kwangsun Ryu (National Cancer Center) Supatra Sahaphong (Ramkhamhaeng University) Eun-Young Shin (Chungbuk National University) Jung Hoon Shin (Chunbuk National University) Moon Sun Shin (Konkuk University)

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Ho Sun Shon (Chungbuk National University) Weifeng Su (Bnu-Hkbu United International College) Tran Thanh Phuoc (Ton Duc Thang University) Truong Dinh Tu (Ton Duc Thang University) Nipon Theera-Umpon (Chiang Mai University) Vo Hoang Anh (Ton Duc Thang University) Vu Dinh Hong (Ton Duc Thang University) Vu Thi Hong Nhan (Vietnam National University, Hanoi) Jingdong Wang (Northeast Electric Power University) Ling Wang (Northeast Electric Power University) Eunjoo Yang (National Institute of Health) Bold Zagd (National University of Mongolia) Tiehua Zhou (Northeast Electric Power University) Xinxin Zhou (Northeast Electric Power University)

Committee Secretaries Tsatsral Amarbayasgalan (Chungbuk National University) Enkhtuul Bukhsuren (National University Of Mongolia) Dung Cam Quang (Ton Duc Thang University) Wenqiang Liu (Northeast Electric Power University) Huilin Zheng (Chungbuk National University)

Conference Organization

Preface

Welcome to the 16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2020) in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools (FITAT 2020) held in Ho Chi Minh City on November 5–7, 2020. IIH-MSP 2020 and FITAT 2020 are technically co-sponsored by Ton Duc Thang University in Vietnam, Shandong University of Science and Technology in China, Northeast Electric Power University in China, Chungbuk National University in South Korea, National University of Mongolia in Mongolia, and Fujian University of Technology in China. Both conferences aim to bring together researchers, engineers, and policymakers to discuss the related techniques, to exchange research ideas, and to make friends. We received a total of 238 submissions. Finally, 126 papers are accepted after the review process. The keynote speeches are kindly provided by Prof. Nipon TheeraUmpon (Chiang Mai University) on “A.I. in biomedicine,” Prof. Ho Tu Bao (Emeritus of JAIST) on “learning from medical data,” Prof. Junbao Li (Harbin Institute of Technology) on “embedded artificial intelligence,” and Prof. Limsoon Wong (National University of Singapore) on “some opinion and advice on machine learning in population-based genomic medicine.” We would like to thank the authors for their tremendous contributions. We would also express our sincere appreciation to the reviewers, program committee members, and the local committee members for making both conferences successful. Especially, our special thanks go to Prof. Keun Ho Ryu and Prof. Jeng-Shyang Pan for the efforts and contribution from them to make IIH-MSP 2020 and FITAT 2020 possible. Finally, we would like to express special thanks for Ton Duc Thang University, Shandong University of Science and Technology, Northeast Electric Power University, Chungbuk National University, National University of Mongolia, Chaoyang University of Technology, Fujian Provincial Key Lab of Big Data Mining and Applications,

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and Institute of Artificial Intelligence, and Fujian University of Technology for their generous support in making IIH-MSP 2020 and FITAT 2020 possible. Qingdao, China Jilin, China Ulaanbaatar, Mongolia Fuzhou, China Novi Sad, Serbia October 2020

Jeng-Shyang Pan Jianpo Li Oyun-Erdene Namsrai Zhenyu Meng Miloš Savi´c

Contents

Modeling and Simulation for Battery Energy Storage System Participating in Power System Frequency Modulation Based on Power System Analysis Software Package . . . . . . . . . . . . . . . . . . . . . . . . . Pengcheng Cao, Peiqiang Li, Peidong Sun, Zhongkai Zhang, and Kuo-Chi Chang CNN Character Recognition Model for 3D Integral Image Character . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zorig Badarch, Battogtokh Jigjidsuren, Nomin-Erdene Dalkhaa, and Ganbat Baasantseren Using Virtual Machines in Computer Networking Class . . . . . . . . . . . . . . S. Batbayar, Oyun-Erdene Namsrai, Sh. Bat-Ulzii, and N. Munkhtsetseg Usability Evaluation of Mobile Luxury Brand Websites Based on the Analytic Hierarchy Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wen Qi and Paite Yang Study of Low-Cost-Based Smart Home Control Using IoT Powered by Photovoltaic Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shoaib Ahmad, Kuo-Chi Chang, Tien-Wen Sung, Kai-Chun Chu, Yu-Wen Zhou, Abdalaziz Altayeb Ibrahim Omer, Governor David Kwabena Amesimenu, and Fu-Hsiang Chang Design of Combined Cycle Gas Turbine and IoT in Production Electricity from KIVU Lake Methane Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . Joram Gakiza, Kuo-Chi Chang, Kai-Chun Chu, Hsiao-Chuan Wang, Governor David Kwabena Amesimenu, Shoaib Ahmad, Shamim MdObaydul Haque, and Fu-Hsiang Chang An Implementation of Ionic-Based Hybrid Mobile Application for Controlling Bluetooth Low-Energy-Based Humidifier Device . . . . . . . Dongoh Jung, Khongorzul Munkhbat, Jong Yun Lee, and Keun Ho Ryu

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Study of Assessing the Stability of Rwanda’s Power System from Big Data Based on Power Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . Gilbert Shyirambere, Kuo-Chi Chang, Kai-Chun Chu, Hsiao-Chuan Wang, AbdalazizAltayeb Ibrahim Omer, Governor David Kwabena Amesimenu, and Fu-Hsiang Chang Image Feature Detection and Clustering for UAV Multiple Obstacles Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Baohua Zhao, Tien-Wen Sung, and Xin Zhang

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Sentiment Analysis for Mongolian Tweets with RNN . . . . . . . . . . . . . . . . . . Orgilbat Ariunaa and Zoljargal Munkhjargal

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Thin Point Light Source Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nomin-Erdene Dalkhaa, Bymba-Ochir Chagnaadorj, Choijamts Namsraijaw, and Ganbat Baasantseren

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Dynamic Token Distribution Model for Privacy Protection of Mobile Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tie Hua Zhou, Kai Tai Gao, Yu Lu, and Ling Wang Cascading Fault Prevention of Power Grid Based on Key Power Generation Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kuo-Chi Chang, Jie Luo, Hui-Qiong Deng, Qin-Bin Li, Rong-Jin Zheng, and Pei-Qiang Li

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Optimal Power Generation Output Considering Cascading Failure . . . . 106 Hui-Qiong Deng, Jie Luo, Qin-Bin Li, Rong-Jin Zheng, Pei-Qiang Li, and Kuo-Chi Chang A Survey of Common IOT Communication Protocols and IOT Smart-X Applications of 5G Cellular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Kai-Chun Chu, Elias Turatsinze, Kuo-Chi Chang, Yu-Wen Zhou, Fu-Hsiang Chang, and Ming-Tsung Wang Study of Thermal Power Plant’s Intelligent Fire Detection and Suppression System Via Wireless Sensor Network and Carbon Capture and Storage Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Kuo-Chi Chang, Governor David Kwabena Amesimenu, Tien-Wen Sung, Kai-Chun Chu, Fu-Hsiang Chang, Hsiao-Chuan Wang, Tsui-Lien Hsu, and Ming-Tsung Wang Game-Theoretic Decision-Making Analysis on Antivirus . . . . . . . . . . . . . . 133 Sang-Hoon Lee and Tae-Sung Kim A New JPEG Encryption Scheme Using Adaptive Block Size . . . . . . . . . . 140 Peiya Li, Jiale Meng, and Zefan Sun

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Study of Smart Decorating Machine on Cake Patten . . . . . . . . . . . . . . . . . . 148 Shi-Jie Jiang, Kuo-Chi Chang, Hong-Jiang Wang, Kai-Chun Chu, Hsiao-Chuan Wang, and Fu-Hsiang Chang Study of Advanced Low-Cost Smart Prepaid Electricity Meter Using Arduino and GSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 Abdalaziz Altayeb Ibrahim Omer, Kuo-Chi Chang, Hui-Qiong Deng, Kai-Chun Chu, Governor David Kwabena Amesimenu, Yu-Wen Zhou, and Fu-Hsiang Chang Study of Integrating the Data Fusion Method for Reducing and Preventing Road Accidents Occur at Blackspots Places in Third World Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Kai-Chun Chu, Gilbert Shyirambere, Kuo-Chi Chang, Hsiao-Chuan Wang, Governor David Kwabena Amesimenu, Fu-Hsiang Chang, and Shoaib Ahmad Study of 2D SubMarine Tracking with Complete Worked Out Example Based on Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 Kuo-Chi Chang, Joram Gakiza, Kai-Chun Chu, Hsiao-Chuan Wang, Tsui-Lien Hsu, Governor David Kwabena Amesimenu, and Fu-Hsiang Chang Retina Macular Edema and Age-Related Macular Degeneration Feature Recognition Method Based on the OCT Images . . . . . . . . . . . . . . . 188 Ling Wang, Wen Ce Xie, Tong Li, Yi Min Liu, and Tie Hua Zhou Gene Expression PPI Network Clustering Analysis Between Endometrial Cancer and Ovarian Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 Tie Hua Zhou, Wei Jian Pu, Hua Xie, Li Yan Zhang, and Ling Wang Automatic Identification and Classification Method for Diabetic Retinopathy FFA Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 Tie Hua Zhou, Yi Min Liu, Wen Ce Xie, Hong Na Li, and Ling Wang Emotional Expression Analysis Based on Fine-Grained Emotion Quantification Model Via Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Ling Wang, Hang Yu Liu, Wen Long Liang, and Tie Hua Zhou Fuzhou PM2.5 Prediction and Related Factors Analysis . . . . . . . . . . . . . . . 219 Wen-Ji Zhang, Li-Wen Chen, Yao Zhou, Ri-Jing Zheng, and Kuo-Chi Chang An Improved Whale Optimization Algorithm and Its Application to Power Generation in Cascade Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 Ji-Xiang Lü, Li-Jun Yan, Tien-Szu Pan, Shu-Chuan Chu, Jeng-Shyang Pan, Xian-Kang He, and Kuo-Chi Chang

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Prediction of Hypertension Using Deep Autoencoder-Based Feature Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 Hyun Woo Park, Yul Hwangbo, and Keun Ho Ryu The Prediction Model for High-Risk Patient with Liver Cancer Based on Classification Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 Kwang Sun Ryu, Ha Ye Jin Kang, Sang Won Lee, Young Ha Hwang, Na Young You, Jae Ho Kim, Kui Son Choi, and Hyo Soung Cha Improve the Fingerprinting Algorithm Based on Affinity Propagation Clustering to Increase the Accuracy and Speed of Indoor Positioning Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Binh Ngo Van, Vuong Quang Phuong, and Hoang Do Thanh Tung Avoid Selection Bias in Observational Study Based on Health Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 Sang Won Lee, Kwang Sun Ryu, Jae Ho Kim, Na Young You, Ha Ye Jin Kang, Yong Ha Hwang, Kui Son Choi, and Hyo Soung Cha Caffeine Drinks and the Risk of Cancer: A Review . . . . . . . . . . . . . . . . . . . 268 Jae Ho Kim, Kwang Sun Ryu, Sang Won Lee, Na Young You, Ha Ye Jin Kang, Yong Ha Hwang, Kui Son Choi, and Hyo Soung Cha Association Rule Mining Method to Predict Coronary Artery Disease: KNHANES 2016–2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 Na Young You, Kwang Sun Ryu, Jae Ho Kim, Ha Ye Jin Kang, Sang Won Lee, Kui Son Choi, and Hyo Soung Cha Framework Design of Anti-online Learning Anomie Behavior System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Shutang Liu, Jie Xu, Shiji Feng, Youguo Liao, and Junhua Li Research on Intelligent Scene Generation Based on Unity3D . . . . . . . . . . . 289 Lingbin Zheng, Menghua Li, Yuxi Gao, Hang Chen, and Fuquan Zhang A Study of Guide Interpretation of Haihunhou Pavilion in Nanchang in the Age of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . 298 Jun Tan and Weiwei Zhou Detection Method for Crowd Abnormal Behavior Based on Long Short-Term Memory Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Bo Meng, Li Wang, and Dong Wei Li Research of Software Testing Technology Based on Statechart Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Cuijuan Chen and Wenru Lin Research on Intelligent Optimization Method of Grid Communication Server Based on Support Vector Machine . . . . . . . . . . . . 323 Xiaohui Zhu, Lu Ji, Huijing Bi, and Xiaobo Zhao

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Research on Optimization Model for Thread Pool Performance on Grid Information Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 Xiaobo Zhao, Xiaohui Zhu, Kenan Yi, and Zihang Jia Network Security Situation Assessment of Power Information System Based on Improved Artificial Bee Colony Algorithm . . . . . . . . . . . 340 Lifang Gao, Zhihui Wang, Huifeng Yang, Shaoying Wang, Qimeng Li, Shaoyong Guo, and Chao Ma A Brief Survey on Recent Advances of Object Detection with Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348 Ruo-Bin Wang, Dang-Kang Yan, and Lin Xu Research on the Method of Neural Network Switchgear Portrait Based on Sequence Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Yong Wang, Han Liu, Yang Jiao, and Jianfei Chen Detection of False Data Injection Attack in Power Grid Based on Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Xiaoli Guo, Shiyuan Wang, Yuhan Sun, Tieli Sun, Li Feng, and Zhexing Jin Demand Response Strategy Model Based on User Satisfaction . . . . . . . . . 372 Xiaoli Guo, Yuhan Sun, Li Feng, Chaoyang Qu, and Tieli Sun Low-Complexity MMSE Precoding Based on SSOR Iteration for Large-Scale Massive MIMO Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 Jianpo Li, Saeed I. A. Saeed, Tao Yang, Yan Xie, and Guoge Zhang Design of Intelligent Substation Communication Network Security Audit System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Wenting Wang, Xin Liu, Xiaohong Zhao, Yang Zhao, Rui Wang, and Jianpo Li Research on Security Auditing Scheme of Intelligent Substation Communication Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 Wenting Wang, Guilin Huang, Xin Liu, Hao Zhang, Rui Wang, and Jianpo Li Design of Radio Frequency Energy Harvesting System . . . . . . . . . . . . . . . . 407 Xing Liu, Jihai Yang, Tao Yang, Jun Gao, and Jianpo Li An Encryption Method of Power Cloud Data Based on n-RSA . . . . . . . . . 416 Yong Wang, Qiang Ma, Lei Li, Ti Guan, Yujie Geng, Shuowang Yao, and Jianpo Li K-Means-Based Method for Identifying Characteristics of Wireless Terminal Equipment in Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Yueqin Yin, Zhantu Zhang, Huajian Zhang, Shengze Sun, and Jianpo Li

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Security Transmission Technology of WSN Based on Trust Management Mechanism in Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 Yong Wang, Lei Li, Qiang Ma, Ti Guan, Yujie Geng, Shici Li, and Jianpo Li Survey of Attack Detection and Defense Technologies in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440 Jianpo Li, Shici Li, Tao Yang, Yan Xie, and Guoge Zhang A Wireless Hijack Attack on Power Consumption System of Power Metering Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 Xiao Yong, Jin Xin, Feng Junhao, and Zhang Zitong Research on Real-Time Deformation Measurement of Structural Frame Based on Data Driven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 Zhou Yan, Jun Weng, and Qing Wang A Sensor Fusion Method for In-Station Articulation of Train . . . . . . . . . . 467 Zhao-Qing Liu, Xing-Yuan Song, Yi-Hao Chen, and Zhen-Ni Yang Intelligent Fault Diagnosis Using Limited Data Under Different Working Conditions Based on SEflow Model and Data Augmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 Sijue Li, Gaoliang Peng, Daoyong Mao, Zhiyu Zhu, Mengyu Ji, and Yuanhang Chen The Unified Framework of Deep Multiple Kernel Learning for Small Sample Sizes of Training Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Jing Liu, Tingting Wang, and Yulong Qiao Quasiconformal Mahalanobis Distance-Based Kernel Mapping Machine Learning for Hyperspectral Data Classification . . . . . . . . . . . . . . 494 Jing Liu and Yulong Qiao Research on Time-Delay Estimation of PMSM Driving System Based on RLS Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 Zhong-zhen Chen, Dong-wei He, Li-sang Liu, Jian-xing Li, and Kuo-Chi Chang Cache Learning Method for Terrific Detection of Atrial Fibrillation . . . . 512 Mohamed Ezzeldin A. Bashir, Abdul Hakim H. M. Mohamed, Akbar Khanan, Fadi Abdel Muniem Abdel Fattah, Ling Wang, and Keun Ho Ryu Enhanced the Depth of Integral Image Display by Using Barrier Array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520 Yulian Cao and Ganbat Baasantseren Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 529

About the Editors

Jeng-Shyang Pan received the B.S. degree in Electronic Engineering from the National Taiwan University of Science and Technology, in 1986, the M.S. degree in Communication Engineering from National Chiao Tung University, Taiwan, in 1988, and the Ph.D. degree in Electrical Engineering from the University of Edinburgh, UK, in 1996. He is currently the Professor in Shandong University of Science and Technology. He joined the Editorial Board of Journal of Network Intelligence, the Journal of Computers, and the Chinese Journal of Electronics. His current research interests include soft computing, information security, and signal processing. Jianpo Li received his B.S., M.S., and Ph.D. from the Department of Communication Engineering, Jilin University, China, in 2002, 2005, and 2008, respectively. In 2008, he joined the School of Information Engineering, Northeast Electric Power University (NEEPU). He was Visiting Scholar with New York University in 2013 and the University of Ottawa in 2016. Now, he is Full Professor and Vice-Dean of the School of Computer Science, NEEPU. He has published more than 70 research papers and has 16 patents. His research interests focus on wireless sensor networks, intelligent signal processing, 5G, and wireless power transmission. Prof. Dr. Oyun-Erdene Namsrai is Full Professor and Head of the Department of Information and Computer Science at the National University of Mongolia (NUM). In 2008, she received her Ph.D. in Computer Science from Chungbuk National University (CBNU) and in recent years worked as Visiting Professor at the CBNU, Republic of Korea. She has been working as Active Member of the Information Technology Professionals Examination Council (ITPEC) since 2008. Prof. Namsrai has served on numerous Organizing and Program Committees of international conferences such as ICISCA, ICW, ICCS, ICAST, ISPM, MMT, DBMI, and FITAT. Her research interests include core database paradigms and temporal databases, spatiotemporal database, temporal GIS and stream data processing, data structure, knowledgebase information retrieval, database security, data mining, bioinformatics, and biomedical informatics.

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About the Editors

Zhenyu Meng received the B.S., M.Phil., and Ph.D. Degrees in Computer Science from Shandong Normal University, Harbin Institute of Technology Shenzhen Graduate School, Harbin Institute of Technology Shenzhen in 2008, 2011, and 2018, respectively. Currently, he is Director of the Institute of Artificial Intelligence, Fujian University of Technology, and Professor in Fujian Key Provincial Key Laboratory of Data Mining and Application, Fujian University of Technology. He also serves as Reviewer of several JCR Q1 SCI journals such as IEEE T EC, IEEE T CYB., INF. SCI., KBS, ASOC, SWEVO, ESWA, IEEE Access, EAAI, and several Chinese SCI journals. His research interest includes evolutionary computation and vehicle navigation. Miloš Savi´c is Assistant Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, where he received his B.Sc., M.Sc., and Ph.D. degrees in the field of computer science. His research interests are in the field of complex network analysis, intelligent systems, graph-based machine learning, and scientometrics.

Modeling and Simulation for Battery Energy Storage System Participating in Power System Frequency Modulation Based on Power System Analysis Software Package Pengcheng Cao1(B) , Peiqiang Li1 , Peidong Sun1 , Zhongkai Zhang1 , and Kuo-Chi Chang1,2,3,4 1 School of Information Science and Engineering, Fujian University of Technology, Fuzhou,

China [email protected] 2 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China 3 College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 4 Department of Business Administration, North Borneo University College, Kota Kinabalu, Sabah, Malaysia

Abstract. Battery energy storage technology, with its fast and accurate power response, has become the focus of the auxiliary means of power system frequency modulation. However, the traditional simulation software lacks an accurate battery energy storage system component model, which affects the accuracy of analyzing the response characteristics of the energy storage system to a certain extent. This paper presents an electromechanical transient model of battery energy storage system without time delay, which considers the participation of energy storage system in frequency modulation dead zone and battery charging and discharging power. The control model is built based on the node current injection method by using the power system analysis integrated program, and the EPRI-7 standard system is selected for simulation analysis. The simulation results show that the model has good implant ability. And when the load is cut off in the power system, the energy storage system participating in frequency modulation can effectively maintain the frequency stability. Keywords: BESS · Frequency modulation · Electromechanical transient simulation · User-defined modeling

1 Introduction China’s installed wind power capacity is the largest in the world, but a high percentage of wind power cannot be absorbed, and nearly 30% of the abandoned wind power is caused by the frequency modulation problem [1]. In recent years, many commercial © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_1

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energy storage projects have played an important role in power system frequency modulation. The installed scale of the energy storage frequency modulation system in the US PJM market has exceeded 200 MW. After the first domestic demonstration, project of joint frequency regulation of energy storage and thermal power has been implemented in Jingneng Shijingshan Thermal Power Plant in China, Shanxi Province, and has successively carried out a number of similar energy storage and frequency regulation projects, which made energy storage frequency modulation requirements and control strategies a current research hot spot [2, 3]. Now there are several battery energy storage models in power system frequency modulation. A multi-scale simulation model of the battery energy storage system was established in [4, 5], and each component was modeled in detail, but the charging and discharging power limitations were ignored, which caused the simulation results to be biased. The power flow model and electromechanical transient model of energy storage system are proposed in reference [6], and the correctness of the model is proved by simulation analysis. However, no dead zone is set in the model, which will lead to frequent charging and discharging of energy storage system and affect battery life. Existing researches mostly focus on mature simulation software such as DIgSILENT, but have less research on PSASP. PSASP7.0 is self-developed and powerful power system simulation software in China, which can conveniently and quickly complete a variety of traditional operations such as power flow operation, temporary stability operation, and short-circuit operation. However, with the continuous emergence of new components, PSASP7.0 model base cannot be updated in actual time, in particular, the lack of energy storage components, which hinders the application of PSASP7.0 in power system simulation analysis. Based on the analysis of energy storage system structure and converter control system, this paper proposes a storage energy that takes into account the frequency modulation dead zone of the energy storage, the charge and discharge rate limitation, the energy storage capacity limitation, and the reactive power limitation of the energy storage system. The model interface is developed by the method of node current injection. Combined with the theoretical analysis, the model is simulated and analyzed by PSASP.

2 Battery Energy Storage System Model 2.1 Mathematical Model of BESS Taking the battery energy storage system (BESS) as an example, the BESS structure is mainly composed of three parts: an energy storage battery pack, a power conversion system (PCS), and a monitoring and control system, as shown in Fig. 1. The energy storage battery pack interacts with the grid through the PCS. In this paper, the electromechanical transient model of BESS is built according to Fig. 1, and its structure is shown in Fig. 2. The structure consists of three parts: energy storage battery model, grid-connected converter and control system model, and model interface. The port characteristics of the grid-connected energy storage system are closely related to the control strategy of the converter. The control strategy of the converter can be divided into outer-loop control and inner-loop control. The outer-loop control is active

Modeling and Simulation for Battery Energy Storage System … Energy storage battery

PCS Power Grid

3

Transformer

Monitoring and control system

Fig. 1. Structure of BESS

PCS and control system Outer loop control

ΔU

Pset Qset

Inner loop control

P

Q

Δω

Charge power limitation

Model interface

Battery model

IR U

Sampling Calculation

II

AC grid

ω

Fig. 2. Structure diagram of BESS

and reactive power control, which generates the given value of power; the inner-loop control is the voltage and current control to control the PWM-modulated switch signal to achieve the active power and reactive power output control of the energy storage system, as shown in Fig. 3. Δω

ΔU

Active controller

Reactive controller

Pset Qset

Current inner loop control

m

δ

Outer loop control

Fig. 3. Block diagram of converter’s control strategy

2.1.1 Design of Outer-Loop Controller The frequency change of power system mainly has to do with active power, and the voltage change mainly has to do with reactive power. Thus, in the design of converter

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outer-loop controller, frequency/active control and voltage/reactive control are adopted. The difference between the measured value of generator power angle and bus voltage and the set value of control target is taken as the input value of outer-loop controller, so as to get the input value of inner-loop controller active control quantity and reactive control quantity. The internal structure of the outer-loop controller is shown in Fig. 4.

Δω

ΔU

Kω p +

Kωi s

Pset

KVp +

KVi s

Qset

Fig. 4. Block diagram of outer control

It can be seen from Fig. 4.   Kωi ω Pset = Kωp + s   Kvi Qset = Kvp + ω s

(1) (2)

2.1.2 Inner-Loop Controller Design In the fast real-time control of the energy storage system, if the dynamic adjustment process of the inner-loop controller is simplified, the power regulation characteristics of the inner-loop of the energy storage system can be simplified into two independent first-order dynamic links [6]. Its power characteristics are as follows:  P˙ = −P/T + Pset /T (3) ˙ = −Q/T + Qset /T Q Therefore, the structure of the inner-loop controller can be obtained as shown in Fig. 5.

Pset

1 1+Ts

P

Qset

1 1+Ts

Q

Fig. 5. Block diagram of inner control

Modeling and Simulation for Battery Energy Storage System …

5

2.2 PSASP Model of Energy Storage System 2.2.1 Model Interface Design The PSASP transient stability calculation program uses time-domain simulation, uses the network node admittance matrix to establish differential equations, and uses the implicit trapezoidal integration algorithm to solve. Both the UD model and its inherent model participate in the implicit integration iteration [7, 8]. In PSASP, generators, loads, and energy storage devices are all injected as node current, so the user-defined model interface for transient stability calculation is current injection form [9–13]. Therefore, the active power of energy storage system should be injected into the grid in the form of current source, and the port voltage follows the grid voltage. The active power and reactive power injected by the energy storage device into the power grid are transformed into the real part and the virtual part of the current injected into the node. Let P and Q be the active power and reactive power of the injection system of the energy storage device, U e , U f are the real part and virtual part of the bus voltage U at the installation point, I e and I f are the real part and virtual part of the injection current I. S = UI ∗ = (Ue + jUf )(Ie − jIf )

(4)

Expand Eq. (5) to obtain the following equations: Ue Ie + Uf If = P

(5)

Uf Ie − Ue If = Q

(6)

By solving the above equations, Ie = (Ue P + Uf Q)/(Ue2 + Uf2 )

(7)

If = (Uf P − Ue Q)/(Ue2 + Uf2 )

(8)

In this paper, the user-defined modeling program in PSASP7.0 is used to build the transient simulation model of the energy storage system. The model structure of each module is shown in Fig. 6.

3 Example Analysis 3.1 Model Validity Verification Relying on the PSASP simulation platform, this paper selects a seven-node (EPRI-7) standard calculation example from the China Electric Power Research Institute, sets up corresponding simulation experiments, and verifies the validity of the energy storage system model constructed in Sect. 2. The load flow calculation result of this example is shown in Fig. 7. S1 is the balance node, and the system reference capacity is 100 MV A.

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+

COMP

X2

DB

Y=A

Y=A

TM3

Y=A VT

K DS

K 1 + DS

+

VT1I

×

TM2 VT1I TM1

×

X1 X2

X1 X2

ITR

+

X1 X2

X1 X2

×

×

1

1

×

Ramp

K 1 + DS

ITI

TM5

AND COMP

OR

COMP

TM4

COMP

AND

Y=A TM5

K DS

TM1

Y=A

X1 X2

TM4

TM2

COMP

Y=A

TM3

+

TM3

TM1

Y=A

TM5

X2

VT

×

K 1 + DS

+

1− X 2

TM3

+

× ×

VT1R

Y=A

Y=A

1

TM2

K DS

K 1 + DS

×

Y=A

VT1R TM1

×

COMP

Y=A

COMP

Y=A

Fig. 6. Model of the power storage system on PSASP

Fig. 7. Distribution of power flow in EPRI-7

3.2 Simulation of Power System Frequency Modulation Based on Energy Storage Model The increase or decrease of load will deviate from the rated frequency of the system, even exceed the allowable frequency deviation of the system, which will bring huge economic losses to various industries, even cause casualties. In general, when the load changes, the unit with primary or secondary frequency regulation function in the system will change the output of the generator accordingly, and the load will change the absorbed power

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due to its frequency regulation effect. Together, the system frequency can be adjusted to the allowable value. Firstly, set the system to cut off the load connected to b3-220 bus when the system is 0.5 s, and the cutoff ratio is 50%. When there is no energy storage involved, only the frequency response curve of the unit is observed. Then add the energy storage system, set the energy storage active power output as 1.5 pu, and observe the frequency response curve of energy storage participating in frequency modulation as shown in Fig. 8.

Fig. 8. Comparison of the frequency change curve of the system with or without energy storage after load shedding

It can be seen from the data and graphics, under the regulation of frequency by energy storage system, of the same size load disturbance, the system frequency fluctuation maximum deviation is reduced, and the frequency is stable to close to 50 Hz.

4 Conclusion In this paper, an energy storage model is established in PSASP7.0, which can reflect the characteristics of energy storage, such as the limitation of frequency modulation dead zone, the limitation of charge–discharge rate, and the limitation of reactive power of energy storage system. The validity of the model is verified by PSASP7.0 user integration platform. The simulation results show that the energy storage system can improve the transient operation of the system when the load is reduced, reduce the frequency fluctuation amplitude, and narrow the gap between the frequency stability value and the rated value.

References 1. Tian, S., Cheng, H., Zeng, P., et al.: Analysis on wind power curtailment at frequency adjustment level. J. Trans. China Electro Tech. Soc. 30, 18 (2015) 2. Hu, Z., Xie, X., Zhang, F., et al.: Research on automatic generation control strategy incorporating energy storage resources. J. Proc. CSEE 34, 5080 (2014) 3. Sun, B., Yang, S., Liu, Z., et al.: Analysis on present application of megawatt-scale energy storage in frequency regulation and its enlightenment. J Autom. Electr. Power Syst. 41, 8 (2017)

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4. Ye, X., Liu, T., Wu, G., et al.: Multi-time scale simulation modeling and characteristic analysis of large-scale grid-connected battery energy storage system. J. Proc. CSEE 35, 2635 (2015) 5. Zhang, L., et al.: Modeling and analysis of battery energy storage systems in multitime scales application. J. Proc. CSEE 33, 86 (2013) 6. Wu, J., Wen, J., Sun, H.: Study of control method for improving AC interconnected grid stability based on energy storage technology. J. Trans. China Electro Tech. Soc. 27, 261 (2012) 7. Li, Y., Wu, Z.: A method to interface matlab model with power flow module of PSASP software. J. Power Syst. Technol. 32, 20 (2008) 8. Li, Y., Wu, Z.: An approach to interface matlab model with PSASP transient stability module. J. Power Syst. Technol. 32, 31 (2008) 9. Liu, Q., Li, X., Sun, Y.: Power flow modeling of facts based on PSASP. J. Power Syst. Technol. 24(6) (2000) 10. Meng, Z., Pan, J.-S.: Monkey king evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization. Knowl. Based Syst. 97, 144–157 (2016) 11. Zhang, Y., Mao, X., Xu, Z.: UPFC models for power system steady-state and dynamic analysis. J. Power Syst. Technol. 24, 30 (2002) 12. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006 13. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020)

CNN Character Recognition Model for 3D Integral Image Character Zorig Badarch , Battogtokh Jigjidsuren, Nomin-Erdene Dalkhaa(B) and Ganbat Baasantseren

,

National University of Mongolia, Ulaanbaatar 14201, Mongolia {zorig,battogtokh,nomin-erdene,ganbat}@seas.num.edu.mn

Abstract. Integral image is one of methods to show 3D image. To research more, we need to find the position of integral images or in our case characters. It will be useful to recognize or reconstruct the 3D object, like 2D to 3D content conversion. To make it possible, we must have a character recognition system (ABBYY FineReader Engine [1]; Smith in An overview of the Tesseract OCR engine, pp. 629–633, 2007 [2]). First, we recognize the Mongolian character image by deep learning, a convolutional neural network (CNN). In the dataset for deep learning, we collected 550 fonts, and 80 characters from one font, and the sum of characters in the dataset is 44,000. Split dataset into two sections, training and validation sets. Validation result is 94%. Then, we tested on 3D character integral images. Recognition percentage was 34%. Thus, we trained the machine learning model in a different font and then tested the 3D integral image. Keywords: Integral image · Deep learning · Convolutional neural network · Elemental image

1 Introduction 3D image technology or 3D space is general name of many research field like 3D computer graphics, 3D film, 3D modeling, 3D television, etc. One of those is constructing 3D integral image by elemental images. Elemental image—this is a calculated image to create a 3D image after passing through lens array. An integral image is the generated 3D image described above. We need to recognize automatically the 3D character position showing integral images. This research is preliminary study of recognition of overlapped characters structured by integral image. We divide this problem into two section. First step, make model that is able to recognize characters structured by integral images, which is study of this paper. Second step, recognize overlapped 3D characters, to determine which character is placed in front and which is placed back, which will be study in future. Optical character recognition is long before reached 100%, such as ABBYY FineReader engine [1] or Tesseract [2]. ABBYY FineReader engine is commercial OCR © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_2

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able to recognize Mongolian text. Tesseract [2] is open course OCR engine which not supports Mongolian character. In the end, our study is to create model that is able to recognize almost all type of Mongolian fonts. After that, evaluate that model on generated 3D characters. We designed deep learning convolutional neural network (CNN), to learn Mongolian Cyrillic fonts recognition. Deep learning method uses many data for training, and for that, we collected 565 Mongolian fonts by crawling through Internet. 15 fonts are rejected because of characters that are too abstract. We have collected 35 big letters, 35 small letters, and 10 numbers from one font file. After many attempts by configuring CNN structure, we recognize font characters with good result. By this CNN model, we recognize Integral Image characters.

2 Method and Dataset This chapter explains method and dataset collection. How we generate characters from font files, and the purpose of our datasets. To create a machine learning model that can recognize the characters from all types of fonts, we need to find a method to extract common features [2]. One-character’s features are from 60 to 120 on Tesseract engine. Tesseract engine character feature extraction have typical feature (x, y position, angle) and prototype feature (x, y position, angle). We just used CNN model for feature extraction. The convolution neural network (CNN) [3] is the basis of the latest machine learning structure used to recognize objects in images with high efficiency. A simple CNN model was taken and used in machine learning. The first model was designed to avoid overfitting, which has more variables than a dataset. First model consists one convolution layer and other three fully connected layers. Total parameter of this model is 13,001,605. After some calibration, we add some convolution and Maxpooling layers. Second model is given in Table 1. Total parameter of this model is 25,475,589. From this method, we can see that one-character feature number is 900, and classification method is softmax. 2.1 Fonts and Characters Collection We searched and collected 565 Mongolian fonts from the Internet. We collected 80 characters from each font file, 35 uppercase letters, 35 lowercase letters, 10 digits which in total 80 pieces. After careful filtering, 15 font files were deleted from the database. With a total of 80 characters from each of the 550 font files, the database grew to 44,000. The dimensions of one character are 200 × 200, 30 × 30 pixels. The character was in two colors, written in white on a black background. Figure 1 shows the number “0”. Characters with a pixel size of 30 × 30 were used in machine learning to teach character recognition. The remaining largest characters were used to create an elemental image. In the next section, we describe how we generated elemental images. These images were to validate character recognition model. All character datasets from font files are given in Table 2.

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Table 1. Second machine learning model for character recognition Type of layer

Size

Kernel size

Stride

Activation

Parameter

Conv

30 × 30 × 16

3×3

1

Relu

160

Maxpooling

29 × 29 × 16

2×2

1

–

0

Conv

27 × 27 × 32

3×3

1

Relu

4640

Maxpooling

26 × 26 × 32

2×2

1

–

0

Conv

24 × 24 × 32

3×3

1

Relu

9248

Maxpooling

23 × 23 × 32

2×2

1

–

0

Conv

21 × 21 × 64

3×3

1

Relu

18,496

Flat

14,400

–

–

–

0

Dropout

0.5

–

–

–

0

Fully connected

900

–

–

Relu

25,402,500

Output

45

–

–

–

40,454

Fig. 1. Number “0” is measured from left to right in the sizes 200 × 200, 30 × 30 pixels Table 2. Character datasets from font files Size of characters [pixel]

Number of characters

Purpose

200 × 200

112

For elemental images

30 × 30

44,000

For training model

2.2 Generate Elemental Images To create an elemental image, we extracted it from a 2D image using a program written in MATLAB. Example of elemental image character “B” is shown in Fig. 2.

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Fig. 2. An elemental image created by the MATLAB simulation

2.3 Generate Integral Images The elemental image shown in Fig. 2 was displayed on the 2D screen of the integral imaging display setup as shown in Fig. 3, with the parameters given in Table 3.

Fig. 3. Integral imaging display setup. a Structure of the integral image display, b front view of structure of the integral image display

Figure 3a shows the structure of the integral image display. Consist of 2D display, lens array. Figure 3b shows an experimental setup view, and the 2D screen is located in the background, as it is seen from the front view of the integral imaging screen. The reflection of different points in the elemental image creates a three-dimensional image when they merge into a single point through the lens array. This image is called an integral image, see Fig. 3 [4, 5]. The EI1 , EI2 , EI3 , and EI4 shown in Fig. 4 are light source from elemental image, which, after penetrating the L2 , L3 , L4 , L5 lens array, combine to form a single integral

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Table 3. Integral imaging display setup parameter No.

Parameter name

Volume

1

2D display size

399 mm (vertical) × 709 mm (horizontal)

2

2D display one-pixel size (PD )

0.6 mm

3

Unit lens size of lens array (PL )

50 mm

4

Number of lens

12 (vertical) × 16 (horizontal)

5

Focal distance of lens array

90 mm

6

Distance from lens array to 2D display (g)

120 mm

image, which constructed by B. By this method, we created integral image, then capture them by camera with resolution 5184 × 3456. Total number of integral images are 112. The size of each of them was manually resized to a resolution of 30 × 30. Please see Fig. 5 showing elemental image manually cropped.

Fig. 4. Process of creating an integral image with an elemental image

3 Result 3.1 Training Result Above, two modules training results are shown in Fig. 6. Of course, we will choose the second model, and the result of recognizing font characters is 94% with a loss of 0.15.

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1

0.9

0.95

0.7

First model Loss Second model Loss

0.9 0.85 0.8

Loss

Accuricy

Fig. 5. An integral image created by the elemental image

First model Accuricy Second model Accuricy

0.75 5

10

15

Number of Epochs

0.3 0.1

0.7 0

0.5

20

-0.1

0

5

10

15

20

Number of Epochs

Fig. 6. From left to right, first model accuracy, second model accuracy in first graph and first model loss, second model loss in first graph for 20 epochs

3.2 Evaluation Result Last step was to evaluate the model by dataset which consist of 3D characters. The evaluation dataset has 112 images, which means that each class has 4 elements and 45 classes in total. The result of the accuracy of the evaluation of integral image character recognition is 38%, and the loss is 3.71.

4 Conclusion Training of machine learning models on the basis of font’s character and their evaluation of integral images can be completed as a successful implementation of this study. But 38% of the accuracy is insufficient. The reason for the shortage was the difference between the two types of datasets. One is a font’s character, and the other is a character of integral images. Although the model was not over fitting, the results were poor due to database differences. Therefore, the following four are suggested to improve our study. • Feature must be generalized by common lines or use Tesseract engine character feature extraction method. • Evaluate the preprocessing of the integral images. • Continue training the model that we have trained with 1000 integral images, as it takes a lot of time to create an integral images. This step is called transfer learning.

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• The camera position must not be moved when taking an integral image. Also, changing the image resolution to 30 × 30 pixel is done manually, which increases the risk of errors. Therefore, next suggestion is to train the model by replacing all datasets with simulated integrated images. • Create all font’s datasets of integral images. It will be very time consuming. Train a new model only by integral images. If above all mentioned are done, we can further calibrate parameter of model, etc. These are the next steps in our study to improve accuracy.

References 1. ABBYY FineReader Engine (July 2013). https://en.wikipedia.org/wiki/ABBYY_FineReader. Accessed 15 Aug 2020 2. Smith, R.: An overview of the Tesseract OCR engine. In: ICDAR ‘07: Proceedings of the Ninth International Conference on Document Analysis and Recognition, vol. 02, pp. 629–633 (2007) 3. Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455–5516 (2020) 4. Dalkhaa, N.E., Densmaa, B., Baasantseren, G.: Nonuniform viewing angle of integral imaging display. J. Soc. Inf. Disp. 457–463 (2015) 5. Batbayar, D., Dalkhaa, N.E., Erdenebat, M.U., Kim, N., Baasantseren, G.: Point light source display with a large viewing angle using multiple illumination sources. Opt. Eng. 56(5), 053113 (2017)

Using Virtual Machines in Computer Networking Class S. Batbayar(B)

, Oyun-Erdene Namsrai, Sh. Bat-Ulzii, and N. Munkhtsetseg

School of Engineering and Applied Sciences, NUM, Ulaanbaatar, Mongolia [email protected]

Abstract. The article covers some experiences of using and testing virtual machines in computer networking classes. The experiments include building local network topology and architecture, configuration of operating systems, working with virtual network devices. There is a difficulty of implementing these experiments in laboratories at universities and colleges. Therefore, laboratories are shared by different field students at higher education institutions. This problem can be solved by the use of virtual machines and virtual networks that mean students to work on their own virtual machines in own virtual networks. The use of virtual machines and network enables the other everyday activities and processes at the institutions run continuously. The article presents some experiments in the use virtual machines at laboratory works. Keywords: Virtualization · Laboratory design · Virtual network · Virtual machine

1 Introduction It is vital that students are able to use their theoretical knowledge through practice at computing networking course. Network configuration of physical computers at laboratory requires modification for laboratory tasks. University laboratory computing networking modification, adding new network card and networking devices are limited by the administrator’s access. Students need both server and client computers to complete laboratory tasks after the class for an assignment. In addition, it needs a certain amount of funds to build and maintain networking hardware and software and physical space, airconditioned space for servers and desktop computers and secure network environment for other services accessed on the same machines or network [1]. These problems can be solved by use of the virtual machines, a virtual network for each student’s access. A virtual machine reduces group size, desired space, and cost in the teaching of networking classes [2]. A virtual network enables to organize local network topology as much as want, to install additional network interface on the virtual machine, to configure a network rooting table, and to address different types of virtual networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_3

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2 Research Methodology 2.1 Network Laboratory Design A network laboratory is a necessary component for a university that teaches computer networking. A well-designed laboratory design allows for easy management and flexibility of the laboratory works to be performed in the laboratory [3]. The computer network laboratory infrastructure consists of several components: • Switches, routers, firewalls, wireless access points; • Network cabling, network connectors, patch panels, equipment racks; • Personal computer, laptops, servers. In order to perform laboratory works, it is necessary to design an infrastructure in which these network components are properly configured. Network laboratory design can be implemented in the following three ways (see Fig. 1):

Fig. 1. Network laboratory design

• Physical • Simulated • Virtualized. Simulator software is used when laboratory work is difficult or expensive to perform on a real device. One of the most popular network simulation tools is the Cisco Packet Tracer [4]. Virtual infrastructure is based on the concept of a virtual machine, which is used to create the same number of servers, clients, and routing systems within a physical machine. VMware [5] virtualization software is used to represent network equipment and hosts in a virtual network environment [6]. The physical network infrastructure uses real-network laboratory equipment that can be deployed in a centralized or distributed topology. The physical network infrastructure is used by students to study online lessons, download the necessary software and laboratory manuals, guidelines, and instructions from the teacher’s server computer. The various approaches to computer laboratory design are illustrated in Fig. 1.

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2.2 An Implemented Laboratory Design In computer laboratory classes, the physical network infrastructure is used to study online classes, download the necessary software and laboratory manuals from the server computer, and so on. The physical network infrastructure is responsible for supporting the student laboratory work performance. Unfortunately, administrator rights are limited, such as changing the network settings of the university laboratory computer and adding network devices. In the fall term of the 2019–2020 academic year, a total of 12 laboratory tasks were performed, 4 laboratory tasks were performed in a virtualization environment, 7 laboratory works were performed in a Cisco Packet Tracer simulator program, and 1 laboratory task was performed in a physical network environment at National University of Mongolia. In the past, only simulator software and physical networking were performed in the laboratory at the university. Performing laboratory work in a virtual environment has the advantage of fulfilling the limitations of the physical and simulation environments. 2.3 Virtual Machine A virtual machine is a copy of a physical computer isolated by a special container [7]. A virtual machine is capable of virtualize CPU, all types of hardware resources, memories, and hardware devices. Technology that adds a layer of abstraction to the hardware resources of a physical computer is a pool of virtual resources. This allows several operating systems to run simultaneously on single physical machine hardware [8]. A virtual machine monitor (referred as hypervisor) is a program which enables a virtual machine to be applied as an abstract layer. A virtual machine monitor creates one or more virtual machines on a physical machine and a host computer. A virtual machine provides a hardware resource that enables operating system to run. Thus, applications run in an operating system environment [9, 10]. 2.4 Virtualization Virtualization refers to a technology that enables software abstraction layer between the hardware and the operating system, and applications run on topmost of it. Full virtualization, OS-layer virtualization, and para-virtualization will be covered in the article. 2.5 A Full Virtualization Full virtualization enables guest operating system to run on the host operating system. Also, it creates a hardware simulation of a physical computer and enables a guest computer to run. A guest computer’s operating system which run in the virtual environment provided by a host computer hardware and it’s all types of applications must run smoothly without any alteration [11].

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2.6 Para-Virtualization Para-virtualization requires a guest operating system to be modified and ported for para-application programming interface of the VMM [12]. The main disadvantage of this approach is modifying a guest operating system for virtual machine monitor. Therefore, it is possible to make this modification with a license in the condition a guest operating systems source code which is open [13]. 2.7 OS-Layer Virtualization OS-layer virtualization also so known as Single Kernel Image (SKI) or container-based virtualization is carried out by running more instances of the same OS in parallel [14]. A physical computer hardware is not virtualized but its operating system is virtualized. As a result, same virtualized OS image is being applied by all virtual machines. The image of the virtualized operating system is called the virtualization layer. 2.8 Virtual Network Infrastructure The VMware infrastructure is a solution that simplifies the organization of a network in a physical environment by providing the virtual machine with various elements of a virtual network. Virtualization enables guest operating system to run on the host operating system. The virtual environment also gives us limited capabilities in the physical environment. A virtual machine has its own virtual network card (vNIC) like a physical computer. The operating system and application software interact with the network card using standard network device driver program. A physical network interacts with a virtual network card as a physical network card. A virtual network card has its own MAC address and IP address and responds to standard Ethernet protocols like a physical network card. A vSwitch works like a layer 2 physical switch. Each physical computer owns its vSwitch. Virtual machines are connected to one side of the vSwitch while are other side of the vSwitch is connected to physical network card [15].

3 Experimental Result 3.1 Classroom Environment In the fall term of the 2019–2020 academic year, a total of 99 students studied the basics of computer networking. Computer networking course is conducted in three laboratories with 88 computers connected to the local area network and Internet. The computer laboratory is connected to network devices such as switches, routers, and wireless routers, which are kept inside a locked patch panel. Students are not allowed to change the settings of these network devices and computer operating systems. The followings are main reasons to work on virtual machines and virtual networks: • Computer laboratory network devices and computer settings cannot be changed.

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• It is time-consuming to complete a computer laboratory tasks, even if you cannot find vacant computer in the university laboratory, you can continue to do the laboratory work at home. • Since university laboratories are shared by not only students majoring in computing science but also students majoring in other fields. 3.2 Computer Used in Laboratory Work Laboratory work was performed on a computer with the following specifications. The host computer specifications are shown below: 1. 2. 3. 4. 5. 6. 7. 8. 9.

System manufacturer: Dell Inc. System model: Inspiron 3576 Processor: Intel Core i7-8550U CPU @ 1.8 GHz (8CPUs) Memory: 8 GB RAM Video card: AMD Radeon 520 2 GB GDDR5 Graphics Hard disk: 2 TB 5400RPM HDD USB controller: 2x USB 3.1 Gen 1, 1x USB 2.0, 1x HDMI 1.4a CD/DVD: Tray load DVD drive Network adapter: 802.11ac dual band 2.4/5 GHz Wi-Fi + Bluetooth v4.1.

Guest virtual computer specifications: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

System manufacturer: VMware Inc. System model: VMware virtual platform Memory: 256 MB RAM Processor: Intel Core i7-8550U CPU @ 1.8 GHz (8CPUs) Hard disk (IDE): 40 GB CD/DVD (IDE): C:\Users\…\GRTMPVOL_EN.iso Network adapter: Custom (VMNet1) USB controller: Present Sound card: Auto detect Printer: Present Display: Auto detect.

3.3 Software Used in Laboratory Work The following software and operating systems were used: 1. Host computer’s operating systems: Microsoft Windows 10 Pro x64 bit. 2. Guest computer’s operating systems: Microsoft Windows XP Professional SP3 x32 bit. 3. Virtual machine: VMware workstation 15 Pro x64 bit. 4. Apache Web server. 5. Filezilla server. 6. Mercury mail server.

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3.4 Laboratory Practice 1 Instructions for laboratory practice 1: 1. Install the VMware workstation 15 Pro virtual machine on the host computer. 2. Create a virtual machine named PC1 and PC2 and install Windows XP SP3 operating system on it. 3. Set the IP address of PC1 to 192.168.1.100 and the IP address of PC2 to 192.168.1.101. 4. Test the IPCONFIG and PING commands. 5. Install Winsent software on PC1 and PC2 and test by sending a message between two computers. 6. Test the NET SEND command. At the end of the experiment, students are expected to learn how to create a virtual network and work with virtual network cards, switches, and routers, how to set the IP address of a virtual computer in dynamic and static form, and more about the IP and MAC addresses of computers using the IPCONFIG and PING commands. 3.5 Laboratory Practice 2 Instructions for laboratory practice 2: 1. On PC1, set the folder to share. Configure PC2 to allow access to this folder. 2. Install a new printer on PC1 and configure it to be shared. Make PC2 accessible to the printer. 3. Install Apache Web server and Filezilla server on PC1. 4. Download and upload files from PC2 using the Filezilla client program. 5. Access the Web server using the Internet explorer browser. Also, students will be able to work with files and printers in the Intranet environment, to use client–server network service applications and understand how HTTP and FTP protocols work and how to use them at the application level. 3.6 Laboratory Practice 3 Instructions for laboratory practice 3: Assignment 1—Set up a mail server. 1. Run the mercury mail server on the PC1 virtual machine. 2. Register two users named User 1 and User 2 on the localhost of the mercury mail server. 3. Install thunderbird mail client software on PC1. Create a mailbox for two users. Set the mailbox to localhost. 4. Send e-mails between two users and test the mail server.

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Assignment 2—Configure the SSH server. 1. Install the SSH server software on PC1. 2. Install the PuTTY client software on PC2 and use the system user account to access PC1. 3. Access the PC1 computer’s terminal using the public and private keys. Therefore, we imply that students will learn to configure SMTP, IMAP, POP3 protocols at the application layer, to test network applications on localhost, configure TCP/IP protocol, to work with firewalls, to make secure connections using the SSH protocol and gain a basic knowledge of cryptosystems. 3.7 Result Virtual machines and virtual networks provide the opportunity to test a model developed in the Cisco Packet Tracer simulator program in a fully virtualized environment. This article discusses client–server service applications, the sharing of network resources provided by an Intranet server computer, and the configuration of application layer protocols. In addition to exchange data between host and guest computers, the virtual network interface connected to the physical network allows the student to use network drives, network printers, and the Internet. Students did not encounter any difficulties with using the virtualization method in computer networking course. Students created own virtual network and worked with additional interfaces of network devices using a virtual machine, learned how a client–server application works in reality by using a fully virtualized environment and application layer protocols of TCP/IP stack, addressing and configuring them (Table 1). Table 1. Comparison of laboratory design approaches Initial costs

Reoccurring cost items

Operational requirements

Simulation

Low

Software licenses

Steep learning High curve

Low

Virtualization

Moderate

Software licenses, hardware upgrades

Lots of setup

Moderate

Moderate

Physical

High

Hardware upgrades

Easy maintenance

Low

High

Source https://www.researchgate.net/publication/52011704

Physical availability

Space/power

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4 Conclusion This article introduces some of the experiences of using virtual machines and virtualization methods to accomplish networking laboratory tasks. The virtualization approach was implemented due to the difficulty of performing the laboratory tasks in a simulator program. A new approach to network laboratory design was introduced to students. Previously, laboratory tasks performed only in a simulation environment but now, they will be able to be performed in a virtualized environment. This allowed us to configure network settings that were limited by the simulator program and the physical network environment. It is also advisable to develop and add laboratory tasks to be performed in the physical environment using the implemented network laboratory design.

References 1. Steffen, G.D., Abu-Mulaweh, H.I.: Teaching local area networking in a secure virtual environment. Comput. Appl. Eng. Educ. 18, 547–554 (2010) 2. Kneale, B., Box, I.: A virtual learning environment for real-world networking. In: Proceedings of 2003 InSITE Conference (2003) 3. Caicedo, C.E., Cerroni, W.: Design of a computer networking laboratory for efficient manageability and effective teaching. In: Proceedings—Frontiers in Education Conference, FIE (2009) 4. Cisco Systems, Inc.: Cisco packet tracer. https://www.netacad.com/courses/packet-tracer 5. VMware, Inc.: VMware. https://www.vmware.com 6. Galá, F., Fernández, D., Ruiz, J., Walid, O., De Miguel, T.: Use of virtualization tools in computer network laboratories. In: Proceedings of the Fifth International Conference on Information Technology Based Higher Education and Training, ITHET 2004 (2004) 7. Popek, G.J., Goldberg, R.P.: Formal requirements for virtualizable third generation architectures. ACM SIGOPS Oper. Syst. Rev. 7, 121 (1973) 8. Dittner, R., Rule, D.: Virtualization technologies. In: The Best Damn Server Virtualization Book Period, 1st edn. Syngress (2007) 9. Nabhen, R., Maziero, C.: Some experiences in using virtual machines for teaching computer networks. IFIP Int. Fed. Inf. Process. 210, 93–104 (2006) 10. Kelem, N.L., Feiertag, R.J.: A separation model for virtual machine monitors. In: Proceedings of the Symposium on Security and Privacy (1991) 11. Rehman, A., Alqahtani, S., Altameem, A., Saba, T.: Virtual machine security challenges: case studies. Int. J. Mach. Learn. Cybern. 5(5), 729–742 (2014) 12. Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization categories and subject descriptors. In: 19th ACM Symposium on Operating Systems Principles (2003) 13. Gilles, K., Groesbrink, S., Baldin, D., Kerstan, T.: Proteus hypervisor: full virtualization and paravirtualization for multi-core embedded systems. In: IFIP Advances in Information and Communication Technology (2013) 14. Marinescu, D.: State of the art in autonomic computing and virtualization. Technical report. Wiesbaden University of Applied Sciences (2007) 15. VMware, Inc.: VMware infrastructure architecture overview. VMware white paper (2006)

Usability Evaluation of Mobile Luxury Brand Websites Based on the Analytic Hierarchy Process Wen Qi(B) and Paite Yang Donghua University, Shanghai 200051, China design [email protected]

Abstract. The purpose of this paper is to combine the analytic hierarchy process (AHP) with usability test to understand the goals, needs, views and attitudes of young users while shopping on the official website of luxury brands on mobile phones, and provide design guidelines for website construction. In this study, the authors establish an evaluation index system that consists of six dimensions: learnability, ease of use, interface design, attractiveness, consistency, and security and fault tolerance. A total of 34 secondary indicators are identified. Corresponding weight values were calculated and assigned to them to establish a usability evaluation system of mobile websites. These research results are of great theoretical and practical importance to the evaluation of the usability of luxury official websites and the further improvement of luxury websites. Keywords: Analytic hierarchy process · Usability studies · Mobile phone · Official luxury website · Interaction design · Online shopping

1

Introduction

In the book “Luxury Study,” Liu Xiaogang et al. stated that luxury product is a product with a high brand premium and a symbol of status, which has characteristics that are different from ordinary products in terms of design, technology, materials, quality and marketing [1]. Even with the slowdown of China’s economy, the luxury market shows no signs of becoming weak and continues to show a momentum of prosperity. In 2018, Chinese consumption of luxury goods at home and abroad reached 77 billion RMB, accounting for one-third of the global total. Meanwhile, by 2025, the McKinsey team predicts that online sales of luxury goods will grow two to three times as much as they do now, equivalent to one-eighth of China’s 1.2 trillion RMB luxury market [2]. Moreover, the consumers of luxury products in China are getting younger, with the proportion of luxury consumers under 35 reaching 78% and the contribution of retail sales reaching 74% [3]. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021  J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_4

Usability Evaluation of Mobile Luxury Brands Website . . .

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The theoretical research on luxury brands has a long history at domestic and overseas. But luxury e-commerce is a new force in the field of e-commerce in recent years. And online shopping of luxury products on mobile platform still has a huge space for further development, especially for the post-85 generation (about under 35 years old) (see Fig. 1). The design of shopping website should not only meet the functional needs of users, but also pay attention to its usability so as to improve users’ browsing rate and duration of browsing, and finally arouse users’ desire for purchasing [4].

Fig. 1. Luxury, millennials and digital networks [3]

Despite the rapid development of online shopping, there are not many studies on how to improve the website design of luxury brands for the purpose of mobile shopping. Therefore, in this study, the authors use Nielsen’s usability evaluation design principle as the foundation and ask the participants go shopping on the selected luxury official website. The usability research method in this study is based on the analytic hierarchy process to establish a new usability evaluation index system. Furthermore, the authors propose some suggestions which are helpful to conduct website usability testing.

2 2.1

Related Work Research on Usability Evaluation

In ISO/DIS 9241-11 standard (Guidance on Usability, 1997), it states that usability refers to the effectiveness, efficiency and user satisfaction of the interaction process when a specific user completes a specific task in a specific environment

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[5]. Foreign researchers have proposed many different definitions on the concept of usability. From the perspective of user psychology, Nielsen gave a relatively complete explanation of usability: It refers to the experience whether users can use system functions well, and usability can be divided into five factors: learnability, efficiency, memorability, error and satisfaction [6]. At the same time, ten usability experience guidelines, which are visible state, appropriate environment, user control, consistency, error prevention, ease of access, flexibility, efficiency, elegance and simplicity, fault tolerance and humanization, were proposed by other researchers [7]. After summarizing the existing studies, the authors conclude that usability evaluation methods mainly consist of user survey method, expert review method and behavior observation method. The user survey method includes the questionnaire survey method and the user interview method. The expert review method is used by human factor experts to evaluate the usability of the website, including heuristic evaluation method and progressive evaluation method. Observation method is a method to observe users’ action when using the website, usually including user test method and user record method [8]. 2.2

Analytic Hierarchy Process

In order to establish a usability evaluation index system for the website of luxury products, it is necessary to select certain data analysis methods to determine the weight of usability factors based on the contribution and importance of each factor. Analytic hierarchy process (AHP) was proposed by American professor Thomas from Pittsburgh University. It was formally proposed in the mid-1970s and is suitable for solving qualitative and quantitative decision problem. It emphasizes the importance of human judgment during a decision-making process and makes decision thinking process standardization using certain patterns. It aims at making a complex problem-solving process to be systematic and hierarchical [9]. AHP is not only conducive to improving the reliability and validity of weight determination and the accuracy of index weight, but also can be applied to data processing task with computers to reduce the difficulty of work and improve operability [10]. Due to its effectiveness in solving complex issues, some researchers use AHP to study the usability of land and resources system user interface [11]. In this paper, the authors adopt AHP to decide weight factors in order to establish an evaluation index system of the official website of luxury goods. The purpose of using AHP is used to determine the degree of influence of factors at the lower level on the factors at the upper level. In other words, it is used to determine the ranking of relative importance of indicators at one level for certain indicators at its upper level. The calculation of hierarchical single sorting can be regarded as the problem of calculating the maximum eigenvalue and eigenvector of a judgment matrix. The detailed operation process of AHP is described as the following:

Usability Evaluation of Mobile Luxury Brands Website . . .

27

1. Calculate the product of elements in each row of a judgment matrix (i, j = 1, 2, . . . , n), as shown in Eq. (1) below. Mi =

n 

aij

(1)

j=1

2. Determine the nth root of the product of the elements of each row of the matrix, as seen in Eq. (2).  Ni = n Mi (2) 3. Normalize the vector, and Wi is the weight of the index, as seen in Eq. (3). ¯ i = nNi W i=1

Ni

(3)

4. To find the maximum characteristic root of the matrix, as seen in Eq. (4). Λmax =

n ¯i 1  A(i)W ¯i n i=1 W

(4)

5. To conduct the consistency test, as seen in Eq. (5), where n is the order of judgment matrix. Λmax − n (5) CI = n−1 2.3

Research Model of Usability Evaluation

In his usability book “usability engineering”, Jacob Nielsen introduced ten empirical criteria for usability empirical evaluation in detail. He believed that all interface designers should follow these usability principles and an excellent website design will need them as well [6]. These ten principles are visibility of system status, environment appropriate, user control, consistency, mistake proofing, easy to take, flexible and efficient, elegant and simple, fault tolerance and humanized help [6], which are basic elements to refine and build luxury brand website. The usability of luxury brand websites is taken as the overall study goal of evaluation in this paper. The index system of luxury website constructed in this paper includes three levels of indicators. There are six primary indicators: learnability, ease of use, interface design, attractiveness, consistency, and security and fault tolerance. Then, from these six aspects, it is decomposed into more specific secondary indicators, as shown in Table 1. There are also other studies about mobile website usability [12].

3 3.1

Experiment Design Experimental Subject

The actual selected brands in this study are Gucci, Burberry, Balenciaga and FENTY, whose core products are clothing. The brand’s official websites are

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W. Qi and P. Yang Table 1. Indexes of usability evaluation system of official luxury websites First-level indicators

Second-level indicators

Learnability

Easy to understand information Easy to operate The degree of localization of the text and video language

Ease of use

Convenient and controllable operation Prompt clarity Help usefulness Accurate and rapid feedback Real-time and comprehensive information A reasonable navigation system Functional utility Convenient mode of payment Website stability Web link response speed

Interface design

Operation habit Element and control design Rationality of interface layout Interface color matching

Attractiveness

Image and video quality Personalized design Content presentation interaction Online interactive sharing service Service information is comprehensive and efficient Cultural penetration and recommendation desire

Consistency

Consistent interface structure Color consistency and tone unity Feedback consistency Text consistency (size, style, color, layout)

Security and fault tolerance Error message indication Illegal operation tip Account security Privacy protection User permission setting and change Link reliability Element and control reliability

active, and their interaction designs are different. The length of this experiment was 30–40 min. Twenty-five subjects were invited to participate in this experiment. 6 people who were familiar with the luxury brands of the experiments had more than 1-year online shopping experience for luxury goods. And the shopping experience for luxury goods of 19 people were less than or equal to once but have 3 years’ experiences in online shopping. The ages of all subjects were equal to or less than 35 years old. The data from 21 subjects (8 males and 13 females, with an average age of 23) were valid in the end. The subjects had a wide range of occupational backgrounds with normal vision.

Usability Evaluation of Mobile Luxury Brands Website . . .

3.2

29

Experimental Methods

In order to ensure the reliability of the experimental samples and the accuracy of the experimental results, the experiment was carried out on the same android mobile phone with good network connection. The instructor explained to the subjects the tasks before the experiment. The subjects were asked to purchase specified goods from the official website of 4 luxury brands. The experiment process starts from browsing new fashion information and the brand story to find all four designated goods of a shopping list as shown in Table 2. The task is ended, while the subjects finished order, added the goods to the shopping cart and successfully shared the purchases. At the same time, the whole shopping process of the tested person is recorded with the screen recording device of the mobile phone. In addition, the search record is cleared before each experimental operation. The URLs of the four websites are as follows: 1. 2. 3. 4.

Prada http://www.prada.com Bottega Veneta http://store.bottegaveneta.cn Balenciaga http://www.balenciaga.com FENTY http://www.fenty.com. Table 2. List of experimental tasks The brand

The task

Prada

Browse brand news and brand stories Find the “black suede dress” (size =36, color =black ) and buy one by browsing and searching separately Share the outfit through the sharing method provided by the website

Bottega Veneta

Browse brand news and brand stories Find and buy one “stretch wool jumpsuits” (size =36, color =deep camel) by browsing and searching separately Share the outfit through the sharing method provided by the website

Balenciaga

Browse brand news and brand stories Find and buy one “zipper logo jacket” (size =36, color =black green)by browsing and searching respectively and buy it Share the outfit through the sharing method provided by browsing and searching separately

FENTY

Browse brand news and brand stories Find and buy one “drawstring midi satin dress” (size = 36, color = black tapioca)by browsing and searching separately Share the outfit through the sharing method provided by the website

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W. Qi and P. Yang

At the end of the experiment, subjects were asked to fill out the questionnaire in order to determine the index weight and evaluate their browsing and purchasing process while using four websites, then the usability evaluation will be conducted based on the data obtained from the questionnaire, and the final conclusion will be drawn.

4

Results and Discussion

The subjects were given to a questionnaire about the determination of index weight. The table used in the questionnaire is based on the sorting method to establish a comparative matrix. The subjects were only required to rank the importance of each indicator and then determine the relative importance of the two indicators. The relative importance is indexed by the number of 1, 2, 3. . . 9, and their reciprocals are 1/2, 1/3. . . 1/9. After collecting the opinions of each subject, their indexes given were calculated, and then the maximum eigenvalue of each judgment matrix and its corresponding eigenvector was calculated. The weight values of indicators at all levels are calculated based on the questionnaire survey. When a weight value is high, it means that the proportion of this index is big at this level, indicating that a subject thinks this index is relatively more important. As the results show, in terms of synthesis weights, site element distribution rationality (0.061), the operating habits (0.048), simple operation and easy to understand (0.055), information easier to understand (0.049) and convenient payment method (0.027), the values of these five indicators are identified to be relatively high. It explains the differences of preference between Chinese and Western users caused by the differences of culture and geography, etc. And the website design of luxury brands evaluated in this study cannot fully meet the requirements of the Chinese consumer. The questions like how to communicate with those post-85 users in terms of the brand operation and information transmission, how to improve customer loyalty and how to promote business growth with the help of data network assets are all needed to be carefully solved.

5

Conclusion

In this study, synthesis weight indicators of usability model based on analytic hierarchy process are proposed for future usability evaluation of luxury official website. A questionnaire survey is conducted out to evaluate the four selected sites of luxury brands for scoring and analysis. Based on the indicators established in the evaluation index system of luxury websites, this paper tries to provide some guidelines for researchers who focus on evaluating the usability of luxury websites and work on the improvement of luxury websites.

References 1. Xiaogang, L., Zehui, Z., Weijia, L.: Luxury Studies. Donghua University Press, Shanghai (2009)

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2. McKinsey clothing, fashion and luxury consulting team in China. China Luxury Report 2019 2019.4 3. Tencent advertising & Boston consulting group: Report of Chinese luxury consumers’ digital behavior in 2019(6), 18 (2019) 4. Lee, Y., Kozar, K.A.: Understanding of website usability: specifying and measuring constructs and their relationships. Dec. Support Syst. 52(2), 450–463 (2012) 5. ISO Guidance on usability. Ergonomic requirements for office work with visual display terminals (VDTs), ISO 9241-11:1998 6. Nielsen, J.: Usability Engineering. In: Zhengjie, L. et al. (eds.) China Machine Press, Beijing (2004) 7. Nielsen, J.: 10 Usability Heuristics for User Interface Design. Nielsen Norman Group 8. Liqing, H.: Study on Usability Evaluation System of C2C E-commerce Websites. Jiangnan university (2008) 9. Saaty, T.l.: Analytic Hierarchy Process: Application in Resource Allocation, Management and Conflict Analysis. Translated by Xu Shubai et al. Coal Industry Press, pp. 1–41 (1988) 10. Hui, Q.: Construction and Application of Luxury Website Evaluation System. Beijing Institute of Fashion Technology (2002) 11. Yue, Q.: Research on the Usability of Land and Resources System User Interface Based on AHP. Southwest Jiaotong University (2014) 12. Cooper, A.: Design for Mobile Devices and Other Devices (Chapter 19), in About Face 4: The Essentials of Interaction Design. Electronic Industry Press (2015)

Study of Low-Cost-Based Smart Home Control Using IoT Powered by Photovoltaic Cells Shoaib Ahmad1,2 , Kuo-Chi Chang1,2,4,5(B) , Tien-Wen Sung1,2 , Kai-Chun Chu3 , Yu-Wen Zhou1,2 , Abdalaziz Altayeb Ibrahim Omer1,2 , Governor David Kwabena Amesimenu1,2 , and Fu-Hsiang Chang6 1 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of

Technology, Fuzhou, China [email protected] 2 School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China 3 Department of Business Management, Fujian University of Technology, Fuzhou, China 4 College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 5 Department of Business Administration, North Borneo University College, Kota Kinabalu, Sabah, Malaysia 6 Department of Tourism, Shih-Hsin University, Taipei, Taiwan

Abstract. This research mainly uses the Wi-Fi module (ESP8266) microcontroller to implement the IoT-based smart home system. In this study of smart home system is able to power the circuit by the photovoltaic cell itself and then wirelessly switch to the required load. As technology is expanding every day, mobile, robotics, machines learning are advancing their technology then why our house is an exception. Today’s house moving slowly based on common/human input controlling to smart/IoT-enabled devices to be controlled remotely. Currently, use the existing smart home system technology that is limited to this device only. Because of this, IoT devices are now being applied in various fields, but unfortunately, low cost and simple IoT applications are still insufficient in smart home. However, this study is concerned with the Node MCU (ESP8266) microcontroller used to control the power switches remotely. The users can control witches using web application after authenticating based on low cost and simple IoT application systems; this study has important contributions to the development of smart homes with high price–performance ratio in future. Keywords: Smart home system · Node MCU (ESP8266) · IOT · Solar cells · Smart switches

1 Introduction We all dream of completing the whole task of serving us automatically or more intelligently. In this study, the smart home system is one of the suitable systems. It can be said © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_5

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that smart home or home automation is a second-hand technology in the home environment, peaceful, safe, and secure, bringing convenience to users or residents and saving energy. A smart home is a concept that involves controlling and monitoring various household appliances in real time its typical diagram shown in Fig. 1. Smart home is a concept that involves controlling and monitoring various household appliances in real time. The home energy management system (HEMS) is the most important thing for smart homes to consider many factors [1], which considers fir decrease energy consumption by simultaneously managing energy production and consumption. The Internet of Things is an integrated physical environment, projects for which IP is assigned, and functions that can be connected to the network without interruption. This does not require human-to-human or human–computer interaction to directly transmit data through the network. Previous ZigBee and PLC-based surveillance systems can be expensive. A conservative smart home solution can be used, but only requires complex and timeconsuming installations [2]. Sustainable energy is used to generate electricity for all smart devices to work properly [3–5]. Currently, there are different installation methods, and the use of sensors with GSM technology is prominent [6]. The consideration of maximum power point tracking of solar photovoltaic systems is one of the key factors [7]. However, this research purpose is to provide a smart home based on a low-cost Internet of Things, which is powered based on dual-axis solar tracking system for maximize photovoltaic energy production. The solar panel moves according to the revolution of the sun. Then get used to the photovoltaic cell voltage automatic trigger (ESP8266) microcontroller. Automation is performed via wireless feed and can control A’s singlephase AC power supply. Based on the IoT smart home system combined with other suggestion systems, users can use mobile devices or computers to remotely control all home appliances. This research clearly explains the modules involved in the realization of the system, explains the test cases, and finally introduces how the research system works on the test cases.

2 Proposed Smart Home System The smart home system block diagram of this study proposed in this study is shown in Fig. 2. 2.1 Photovoltaic Cell Design The microcontroller (ESP8266) in this study provided an independent resource. When the tracker was initially placed, where the sun was rising, the first panel powered the microcontroller and the sun followed. The charge regulator is a voltage and current regulating that prevents the battery from overcharging. It can regulate the solar panel to the battery of the voltage and current. In this study, the output voltage of most 12 V panels was about 16–20 V, and if there was no specification, the battery would be damaged by overcharging [8].

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Fig. 1. Typical smart home system

Fig. 2. Smart home system block diagram of this study

2.2 Servomotor Design Servomotors are mainly used to move photovoltaic cells following the direction of the sun. It is controlled by ESP8266, tracks the position of the sun and the panel, and automatically moves the tracking angle.

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2.3 Microcontroller Design ESP8266 is used to as microcontroller usually an open source IoT platform. ESP8266 is the popular open source community chip. It gets a lot of attention because of its low price; this is the main reason why this study chose it. 2.4 GHz transceiver with rich features, including power amplifier, CPU, ADC, timer, and GPIO. LUA is a program that is programmed with script language for ESP8266. Lava script language is used by firmware. We are using node MCU to control relays which are responsible for controlling our AC supply. The server ejected events node MCU can send and receive data in response to the CU goods, if needed, for service [9]. 2.4 Relay Design The main function of the relay is to protect electrical equipment using electrical switches. 5 V/12 V specifications are usually used for relay modules. This is often used for electromagnets to switch robots. The main function of the relay is to control the high voltage and low performance voltage of the appliance. And because there is no direct connection between the node MCU and the device, it is considered that it can be used safely in this study. This relay can be used with 10 A switching capacity and has a small size design for high-density P.C. In this research, the simplified relay magnetic circuit and circuit board mounting technology are used to meet the mass production target [10–14]. 2.5 Battery Design When the environment is free of sunlight, the battery will become a secondary source, which will allow the automation system to work normally on cloudy days and at night. 2.6 Load Design The load can be fan, lights, AC, television, etc.

3 Efficiency of Photovoltaic Cell The output of this unit is used to power the ESP8266. The efficiency part is the most common parameter used to compare solar cells. The ratio of the output energy obtained by the sun to the energy absorbed by the sun is defined as efficiency. And the energy is actually reflected back by the panel, so the efficiency level is determined by the incident solar cell spectrum, temperature, and sunlight intensity. Therefore, in order to compare the performance of two devices with each other, the conditions for measuring efficiency must be precisely controlled. In addition, the research results point out that when the sunlight is perpendicular to the solar panel, we can obtain the maximum efficiency from the solar cell. In this way, a solar panel will be installed on the servo motor to track the sun’s position and the maximum power generation. The effective tracking of the solar axis can be accomplished in two different methods. The first method is to use time as a variable to consider a specific weather or geographic location, and then, based on the

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predicted position of the sun, let the motor make a circular rotation and synchronize the sun’s rays to be parallel to the solar panel. The second method uses a photosensitive sensor, which can simply use LDR as a sensor. This is done by aligning the semicircles of the LDR placed at a specific angle. Because LDRs are very sensitive, they can be protected from unwanted light noise. Shielding is achieved by covering the sensing element with a long hollow black cylinder. This system can only allow vertical light to pass through, and the black coating will absorb light from other angles to achieve effective sensing. To ensure the best performance of the panel, the system will project the photovoltaic cell to face the sun according to the angle obtained by the corresponding LDR [15–19]. 3.1 Microcontroller (ESP-8266) Setting This study uses ESP8266 as a microcontroller to control the prototype smart home. The photovoltaic battery output in the system provides power to the ESP8266 and acts as a switch on the smart home controller relay, so the selector microcontroller selects the required relay and activates it when the wireless feed sends data to the microcontroller, it activates the switch to allow 230 V power to flow through. 3.2 Relay Setting This study uses relays to power AC power electronically. The relay selected in this study can be used in high-power electronic circuits. There are a series of relays that can trigger different devices based on the user. The microcontroller triggers the channel switch required for the bending of the relay. At present, there are mainly two triggering methods for relay ratings and trigger voltages on the market. For a 5 V dynamic relay, the output directly value of the ESP8266 is sufficient to trigger relay, but for heavy duty equipment, such as televisions (such as high-voltage relays) and air conditioners require a separate DC power supply based on the rating of the relay. A battery (given a 12 V SLA prototype) was used for this purpose, with its negative pole connected to one relay trigger pin and the other to the pin. In order to be conducted through this contact, another was used which worked according to the SPST switching principle servo motor. Basically, the positive pole of the battery is connected to the metal wire contact of the servo motor shaft, where the same microcontroller will control the servo motor instead of directly driving the relay. Initially, the servo system with the contacts installed was in a non-contact position. Once a signal was received from the esp8266, the servo system would move at an angle, so that the contacts between the relay and the servo system would contact each other, thereby conducting current and triggering the relay. On cloudy days or at night, the microcontroller will be powered by the battery to keep the system active. If more power is generated, the same battery can be used to charge the battery. During the day, the battery is charged by a separate DC power supply and used at night. The work of the relay-based smart home system is shown in Fig. 3. This architecture illustrates the mechanism of integrating solar panels and automated microcontrollers, so that the power control is independent, so that no other power source is needed to control the household appliances which we want to control.

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Fig. 3. Experimentation period

3.3 Control Setting Home automation systems are controlled manually via wireless communication via a smartphone method, for example, Bluetooth or Wi-Fi is automatic devices. Smartphone’s are controlled by applications to make them dynamic anytime, anywhere in the wireless signal range. The prototype in this study of Wi-Fi module and ESP8266 is used, and the wireless smartphone “Blynk application” is used for the interface. After clicking a specific button in the application, the signal will be sent to the ESP8266 and then activate specific home appliances such as fans, lights, TVs, etc. In this way, a centralized system is formed, and all controls are handed over to the smartphone to switch. Eliminate manual switching and equipment that saves space on the switchboard. 3.4 System Test Results and Analysis In this study, Wi-Fi was used to test fan, lighting of living room and master room under 2.4G, channel 1, 6, 11, and 11n HT40 conditions, and it was found that the control function was as expected. In addition, this study using Wi-Fi under the conditions of 5G, channel 149, 161, 11ac HT80, fan, lighting of guest room and children room were tested, also the same as expected control function. From the test results, it can be verified that this research is suitable for low-cost smart home applications. Table 1 shows the summary of system test results and analysis.

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S. Ahmad et al. Table 1. Summary of system test results and analysis

Parameter Test value

Test value

Wi-Fi

5G Channel 149, 161 11ac HT80

2.4G Channel 1, 6, 11 11n HT40

Test items Fan, lighting of living room and master Fan, lighting of guest room and children room room

4 Conclusion The research prototype successfully demonstrated an independent smart home using solar energy. Facing the new demands of modern energy and automation, this research proposes to focus on the application of photovoltaic cells in home automation. The system prototype used in this study can normally control lighting fixtures, household fans, or other household equipment. Because solar energy is non-polluting and renewable, the results of this research can fully meet environmental protection requirements, but the installation cost must still be measured. After all, cost is an important factor affecting the family. In this research prototype system, we assume that humidity, temperature, and various household environmental changes are ignored. The efficiency system and the independent smart home system have been successfully implemented, tested, and verified and were very successful in this study. This study establishes a set of low-cost smart home hardware architecture with control functions; if it can be widely used in the overall smart home in future, it will effectively reduce the promotion cost and achieve the popularization of smart home. The important goal of the extension is shown in Fig. 4.

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Fig. 4. Low-cost smart home hardware architecture

References 1. Zhou, B., Li, W., Chan, K.W., Cao, Y., Kaung, Y., Liu, X., Wang, X.: Smart home energy management systems: concept, configurations, and scheduling strategies. Renew. Sustain. Energy Rev. 61, 30–40 (2016) 2. Ethane, J., Aragorn, G.: Bluetooth low power modes applied to the data transportation network in home automation system. Sensors 17(5), 997 (2017) 3. Chu, K.C., Horng, D.J., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. J. IEEE Access 7, 105562–105571 (2019) 4. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006 5. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 6. Al-Mohammed, A.: Efficiency improvements of photovoltaic panels using a sun-tracking system. Appl. Energy 79(3) (2004) 7. Das, A., Gnana Swathika, O.V., Hemamalini, S.: Arduino Based Dual Axis Sun Tracking System. American Scientific Publishers, USA (2015) 8. Zhao, Z., Agbossou, K., Cardenas, A.: Connectivity for home energy management applications. In: Power and Energy Engineering Conference (APPEEC), 2016 IEEE PES Asia-Pacific, pp. 2175–2180. IEEE (2016) 9. Kaiser, I., Ernst, K., Fischer, Ch.-H., Könenkamp, R., Rost, C., Sieber, I., Lux-Steiner, M.Ch.: The eta-solar cell with CuInS2: a photovoltaic cell concept using an extremely thin absorber (eta). Solar Energy Mater. Solar Cells 67(1–4), 89–96 (2001). https://doi.org/10.1016/S09270248(00)00267-1 10. Kodali, R.K., Soratkal, S.: MQTT based home automation system using ESP8266. In: 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Agra, pp. 1–5 (2016). https://doi.org/10.1109/R10-HTC.2016.7906845

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11. Lu, C.C., Hang, K.C., Chen, C.Y.: Study of high-tech process furnace using inherently safer design strategies (IV). The advanced thin film manufacturing process design and adjustment. J. Loss Prev. Process Ind. 40, 378–395 (2016) 12. Chang, K.C., Pan, J.S., Chu, K.C., Horng, D.J., Jing, H.: Study on information and integrated of MES big data and semiconductor process furnace automation. In: Conference of Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol. 834 (2019) 13. Chen, C.Y., Chang, K.C., Wang, G.B.: Study of high-tech process furnace using inherently safer design strategies (I) temperature distribution model and process effect. J. Loss Prev. Process Ind. 26, 1198–1211 (2013) 14. Chen, C.Y., Chang, K.C., Lu, C.C., Wang, G.B.: Study of high-tech process furnace using inherently safer design strategies (II) deposited film thickness model. J. Loss Prev. Process Ind. 26, 225–235 (2013) 15. Meng, Z., Pan, J.-S., Tseng, K.-K.: PaDE: an enhanced differential evolution algorithm with novel control parameter adaptation schemes for numerical optimization. Knowl.-Based Syst. 168, 80–99 (2019). https://doi.org/10.1016/j.knosys.2019.01.006 16. Meng, Z., Pan, J.-S.: Monkey king evolution: a new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization. Knowl.-Based Syst. 97, 144–157 (2016) 17. Chang, K.C., et al.: Study on health protection behavior based on the big data of high-tech factory production line. In: Pan, J.S., Lin, J.W., Liang, Y., Chu, S.C. (eds.) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol. 1107. Springer, Singapore (2020) 18. Chang, K.C., et al.: Study on hazardous scenario analysis of high-tech facilities and emergency response mechanism of science and technology parks based on IoT. In: Pan, J.S., Lin, J.W., Liang, Y., Chu, S.C. (eds.) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol. 1107. Springer, Singapore (2020) 19. Chang, K.C., Chu, K.C., Chen, T., Lee, Y.W., Lin, Y., Nguyen, T.: Study of the high-tech process mechanical integrity and electrical safety. In: 2019 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), Taipei, Taiwan, pp. 162–165 (2019). https://doi.org/10.1109/IMPACT47228.2019.9024999

Design of Combined Cycle Gas Turbine and IoT in Production Electricity from KIVU Lake Methane Gas Joram Gakiza1,2 , Kuo-Chi Chang1,2,5,6(B) , Kai-Chun Chu3 , Hsiao-Chuan Wang4 , Governor David Kwabena Amesimenu1,2 , Shoaib Ahmad1,2 , Shamim MdObaydul Haque1,2 , and Fu-Hsiang Chang7 1 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of

Technology, Fuzhou, China [email protected] 2 School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China 3 Department of Business Management, Fujian University of Technology, Fuzhou, China 4 Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan 5 College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 6 Department of Business Administration, North Borneo University College, Sabah, Malaysia 7 Department of Tourism, Shih-Hsin University, Taipei, Taiwan

Abstract. In recent years, Lake Kivu, which has high levels of methane and carbon dioxide in Rwanda, has become a danger due to the release of toxins. The research process is that after the working cycle of the first starter ends, the work of the exhaust gas continues to generate heat, and then, the second heat engine can directly extract energy from the exhaust gas thermal energy. The engine can use different working gases. By transmitting multiple workflows, the system overall efficiency can be increased to 50–60%. The overall efficiency of a simple cycle can reach 34%, while the synthetic cycle can be as high as 64%. The theoretical efficiency of Carnot cycle reaches more than 84%. Similar to single-cycle thermal energy, synthetic cycles can be developed for industry and can provide lower temperature thermal energy in specific fields and other applications. This electricity from offshore power plants is new. It will solve the power problem in Rwanda and become a new energy source worldwide. However, the purpose of this study was to design for a cycle gas turbine facility that uses a power plant of combined cycle gas turbine system and the Internet of Things to convert chemical energy in methane gas into electricity throughout the process Keywords: Combined cycle · Gas turbine · Methane gas · IoT · Overall efficiency

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_6

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1 Introduction Lake Kivu is 1460m above sea level and is located near the border between the Democratic Republic of Congo and Rwanda. It is the smaller lake in the Great Valley of Africa, and the western valley system of Eastern Africa forms the western branch of volcanic dynamics. The first report on the characteristics of the lake was that it can survive in large gas fields with depths greater than 50–80 m and is rich in CO2 (CH4 ) components [1]. In 2004, it was estimated that all dissolved gaseous CO2 and CH4 were stored in the permanent deep water of Lake Kivu in the form of a dissolved phase, with an estimated 300 m and 55 km3 STP (0 °C and 1 atm gas volume). Merged into the open source development of the gas field, the lava generated by the two active volcanoes Nyamulagira and Nyiragongo during the two eruptions was sometimes flowed by the lake to Goma (2002), and the surrounding villages were destroyed (1977) and poured into the lake [2, 3]. 2002 on January 17, 2014, potential concerns were raised about the catastrophic thermal energy and volatile gases on the lake, similar to Monounand Nyos Lake (Cameroon) in 1984 and 1986, respectively [4, 5]. Currently, the project managed by contour global is the world’s first large-scale methane conversion power generation project. The project extracts methane from Lake Kivu to generate electricity, expands the household’s electricity use channels, reduces costs, and reduces environmental hazards. Kivu Watt extracts natural gas from 350 m below the lake and returns carbon dioxide to the lake to ensure the balance and continuity of the ecosystem. The methane gas is separated and used to propel the turbine, which then generates electricity. Therefore, the purpose of this study is to design a turbine power plant of combined cycle gas that uses combined different IoT technology to convert chemical energy in methane gas into electricity to provide remote production control to protect workers from risk.

2 Methodology 1. Nomenclature of this study 1. 2. 3.

O: no extraction IZ1 intake: low RZ; re-enter: middle IZ; take dilution water IZ2 intake: upper RZ; re-enter: upper IZ; use less CH4 to remove, use dilution water 4. IZ3 intake: low RZ; re-enter: IZ upper stage; take dilution water 5. IZ4 intake: low RZ; re-entry: middle IZ; no use of dilution water 6. IZ5 intake: upper RZ; re-enter: lower IZ; use less CH4 to remove, no use of dilution waterPR1 intake: RZ; re-enter PRZ 7. RZ1 intake: lower RZ; re-entry: upper RZ 8. RZ2 RZ has two delivery inlets (upper and lower); re-enter RZ (upper and lower) twice; exit and re-enter the entire RZ 9. RZ3 is the same as RZ2, which has a higher removal rate of gas 10. RZ4 and RZ have two delivery inlets (upper and lower); re-enter RZ (upper and lower) twice; RZ5 preparation process 11. RZ5 intake: all lower RZ; re-enter: upper RZ

Design of Combined Cycle Gas Turbine and IoT …

43

12. RZ6 priority is RZ4, use until the upper RZ operation ends; then, RZ5 execution completes. 13. CHP: combined heat and power plant. 14. HRSG: heat recovery steam generator. 15. IOT:Internet of Things 16. (abbreviations: BZ = biozone; RZ = resource zone; IZ = intermediate; Zone PRZ = potential resource zone) 17. STP: standard temperature and pressure 2. Sustainable Methane gas Extraction is carried out in six stages below This system uses self-siphoning to extract deep water rich in CH4 from RZ. If CH4 is poor, water will be re-injected into the lake after removing the target gas. The extracted mixed gas will be washed with washing water, the purpose is to dissolve H2 S and CO2 back into the lake water, and the remaining CH4 is piped to shore to generate electricity (Fig. 1) [6, 7].

Fig. 1. Sustainable methane gas extraction

Sustainable methane gas extraction is carried out in six stages below: 1. Extraction depth: In this study, the water intake depth refers to the center of the water inlet, and the outlet range refers to the vertical range of the water inlet. The main purpose of this study is to take water in the vertical range, because the cost is the lowest. The vertical range includes the extraction of CH4 water from resource areas below 260 m. 2. Re-injection depth: After extracting the deep water and stripping out CH4 and other gases from the previous stage, we will return the depleted deep water to the lake. When this release is in the vertical reinjection range, the reinjection depth represents the vertical center.

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3. Refill water also includes dilution water: If the density of the refill water at the depth of the refill is different from the density of the lake water, the remaining water released will be above or below the refill depth. 4. Dilution water: We define that if it is necessary to reinject water within a narrow or precise vertical range, it is generally considered necessary to add less dense surface water to deep water consuming CH4 in order to adjust the density before it enters the lake for release. Since the pumping depth will determine the density of the dilution water, the inflow depth of the dilution water must be specified. The supplemental water is the sum of the extracted deep water plus the dilution water, which is obtained because the dilution equipment divides the dilution water flow by the extraction water flow. 5. CO2 removal: We found that after the initial stripping, most of the CO2 in the washing water was washed back. However, the gas scrubbing process is not ideal. Part of the CH4 or CO2 collected in deep water will be removed and released into the atmosphere through power generation. Removal of carbon dioxide yields 40–90% of the carbon dioxide contained in deep water. The rest of the carbon dioxide is returned to the lake along with the injected and washed water. 3. Design of power plant of combined cycle gas turbine The preheated gas efficiency (which can be converted to use the power input heat energy) is limited by the temperature difference between the input engine heat energy and the separated engine waste heat energy. For thermal power plants, the water source is the working interface medium. High-pressure water vapor must be a combination of solid weight ingredients. High temperatures must be made of expensive synthetic gold, nickel, and cobalt, rather than cheap steel, which set the actual water vapor temperature to a fixed limit of 655 °C. The steam lower temperature is determined by the extraction temperature at the cooling water temperature. Within this range, the maximum efficiency of steam equipment is 35–42%. In the air cycle of an open gas turbine, there will be air compressors, burners, and turbines. The returned gas turbine must withstand a small amount of high temperature and high-pressure metals, but trace amounts of expensive materials can be used. In such a cycle, the turbine input temperature (ignition temperature) is relatively high (900–1400 °C). Smoke is emitted at higher temperatures (450–650 °C). This is sufficient to provide thermal energy for use in a second cycle using steam as the working fluid (rank cycle). In power plants of combined cycle, the thermal energy of the exhaust gas from the gas turbine passes through a stream of hot steam at 420 to 580 °C. The manufacturing output team will generate thermal energy steam. The staged circulation condenser is cooled by lake water, river, sea water, or cooling water tower. This temperature can be set to approximately 15 °C (Fig. 2) [5]. 4. IoT remote production control When IoT control cooperates with traditional controllers, it provides many benefits in handling dynamic systems. Smart IoT devices, including sensors and infrastructure, can determine local information outside the dynamic system to predict the object and situation behavior. There are many external disturbances in the trajectory of a dynamic system, which will affect the performance of traditional controllers designed for this

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45

Fig. 2. Clean natural gas combined cycle power plant

situation. Robust adaptive control strategies can solve this problem; however, due to the uncertainty and uncertainty of interference behavior, the performance of dynamic systems may fail even with accurate robust and adaptive controllers. Therefore, edge controllers supported by advanced data analysis algorithms and cloud computing environments can help dynamic systems understand their own local information and provide appropriate control commands to eliminate interference, as shown in Fig. 3.

Fig. 3. IoT control cycle in this study

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The feedback control strategy provides information on dynamic system updates. This control method will understand how to build command signals based on the information assigned to the desired trajectory. In most cases, the behavior of the disturbance is so fast that the feedback signal released by the dynamic system state cannot understand and identify its behavior. However, the dynamic system’s response direction may be wrong and lead to the wrong time. Cloud–fog machine learning uses deep feature extraction of local information to understand objects and situations around dynamic systems, and a variety of information on cloud and fog servers analyzes, matches and fuses sources, and sends the results to the controller. The IoT controller attempts to combine traditional signals with the results of cloud-based machine learning. The introduced IoT controller has the ability to predict the environment and sense dynamic systems and can send compatible control signals to respond to fast and complex external interference behaviors [5, 8–11].

3 Results and Discussion 1. The simulated extraction results After previous analysis, an introductory simulation will be performed to provide results and quantify the evaluation principle. Table 1 summarizes all extraction results obtained in this study simulation. 2. Efficiency of CCGT plants Through the cycle of synthetic combustion gas and water vapor, high-temperature input and low-temperature output can be performed. To improve cycle efficiency from the same source of combustion materials, the combined cycle power plant is equipped with a thermal energy cycle that operates when the gas turbine burns at high temperatures and the waste heat temperature of the water vapor cycle water condenser. The improvement of circular Carnot efficiency is represented. The actual efficiency is lower than that of Carnot, and the actual efficiency of any plant has been improved. If the estimated power efficiency of the combined cycle power station is a percentage of the lower heating value of the total consumption of combustion materials, it is ideal to perform a new operation. When sending continuous transmission, the power efficiency of the system can reach more than 60%. Like a single-cycle thermal unit, a combined cycle unit can also become an industrial process that provides thermal energy for this area and low-temperature thermal energy for other areas. This is called combined heat and power, and the power plant is called CHP. Due to its low calorific value and the cornerstone of total production, the efficiency of the service combined cycle has exceeded 50% [12–16].

4 Conclusion and Suggestion In this study, we found that combined cycle power plants can meet the increasing thermal energy requirements and must pay attention to the optimal optimization of the entire system. The development of methane gas for gas turbines is a late stage. If this view is confirmed, the use of biogas will become the main combustion material and meet the

%

m

CO2 re-injected

Dilution water withdrawal depth

0

Scenario

IZ1

IZ2

IZ3

IZ 4

IZ5

PR1

RZ1

30

160 160

400

R22

30

290

30

m

60

10 290

Re-injection range (2)

10

10 240

400

52.5

78

5

RZ2

m

100

10 190

475

70

78

5

RZ1

m

20

10 150

475

70

60

5.9

PR1

Re-injection depth (2)

40

20 90

320

70

60

30

IZ5

Withdrawal range (2)

100

10 90

475

70

60

5.9

1Z4

17.5

m

Re-injection range (1)

10 150

350

70

1

50

90

15

1Z3

290

m

Re-injection depth (1)

320

70

1.1

10

60

30

1Z2

m

m

Withdrawal range (1)

475

70

2

40

60

5.9

1Z1

Withdrawal depth (2)

m

Withdrawal depth (1)

0

Withdrawal (2)

m3 s−1

Withdrawal (1)

Dilution factor

%

CH4 re-injected

Design parameters

Scenario

Table 1. Summary of system test results and analysis

RZ3

30

290

30

290

17.5

160

400

160

400

52.5

46

3.4

RZ3

RZ4

10

270

10

310

47

5

325

60

410

23

60 (1) 55 (2)

6

RZ4

RZ5

10

270

160

400

70

50

3.4

RZ5

R26

RZ4->5

RZ4->5

RZ4->5

RZ4->5

RZ4->5

R24->5

R24->5

RZ4->5

R24->5

RZ4->5

R24->5

RZ4-> 5

RZ6

Design of Combined Cycle Gas Turbine and IoT … 47

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thermal energy demand of combined cycle power plants, which will be used for combined cycle power plants. Advances in combined heat and power—It is possible to produce heat and electricity in parallel from the same type of fuel source fuel combustion efficiency will be improved from 30% to 90%, reducing environmental damage and increasing economic output through more efficient use of resources. However, IoT technology extracts the supply process control at a certain distance from the production point to achieve remote production control purposes, which can prevent risks to workers; this is an important contribution of this study.

References 1. Tassi, F., Vaselli, O., Tedesco, D., Montegrossi, G., Darrah, T., Cuoco, E., Delgado Huertas, A.: Water and gas chemistry at Lake Kivu (DRC): geochemical evidence of vertical and horizontal heterogeneities in a multibasin structure. Geochem. Geophys. Geosys. 10(2) (2009) 2. Cabassi, J., Capecchiacci, F., Magi, F., Vaselli, O., Tassi, F., Montalvo, F., Esquivel, I., Grassa, F., Caprai, A.: Water and dissolved gas geochemistry at Coatepeque, Ilopango and Chanmico volcanic lakes (El Salvador, Central America). J. Volcanology Geoth. Res. (2019). https:// doi.org/10.1016/j.jvolgeores.2019.04.009 3. Chu, K.C., Horng, D.J., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. J. IEEE Access 7, 105562–105571 (2019) 4. Ethane, J., Aragorn, G.: Bluetooth low power modes applied to the data transportation network in home automation system 17(5), 997 (2017) 5. Evans, W.C., Kling, G.W., Tuttle, M.L., Tanyileke, G., White, L.D.: Gas buildup in Lake Nyos, Cameroon: the recharge process and its consequences. Appl. Geochem. 8(3), 207–221 (1993) 6. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 7. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Generat. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006 8. Roland, F.A.E., Darchambeau, F., Morana, C., Borges, A.V.: Nitrous oxide and methane seasonal variability in the epilimnion of a large tropical meromictic lake (Lake Kivu, EastAfrica). Aquat. Sci. 79(2), 209–218 (2016). https://doi.org/10.1007/s00027-016-0491-2 9. Etiope, G., Zwahlen, C., Anselmetti, F.S., Kipfer, R., Schubert, C.J.: Origin and flux of a gas seep in the Northern Alps (Giswil, Switzerland). Geofluids 10(4), 476–485 (2010) 10. Weiland, N.T., Dennis, R.A., Ames, R., Lawson, S., Strakey, P.: Fossil energy. In Fundamentals and Applications of Supercritical Carbon Dioxide (sCO2 ) Based Power Cycles, pp. 293–338. Woodhead Publishing (2017) 11. Cuoco, E., Minissale, A., “Magda” Di Leo, A., Tamburrino, S., Iorio, M., Tedesco, D., Fluid geochemistry of the Mondragone hydrothermal systems (southern Italy): water and gas compositions versus geostructural setting, Int. J. Earth Sci. 106(7), 2429–2444 (2017). https://doi. org/10.1007/s00531-016-1439-4 12. Lu, C.C., Chang, K., Chen, C.Y.: Study of high-tech process furnace using inherently safer design strategies (IV). The advanced thin film manufacturing process design and adjustment. J. Loss Prev. Proc. Ind, vol. 40, 378–395 (2016)

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13. Chang, K.C., Pan, J.S., Chu, K.C., Horng, D.J., Jing, H.: Study on information and integrated of MES big data and semiconductor process furnace automation. In: Conference of Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834, 2019 14. Chen, C.Y., Chang, K.C., Wang, G.B.: Study of high-tech process furnace using inherently safer design strategies (I) temperature distribution model and process effect. J. Loss Prev. Process Ind. 26, 1198–1211 (2013) 15. Chen, C.Y., Chang, K.C., Lu, C.C., Wang, G.B.: Study of high-tech process furnace using inherently safer design strategies (II) deposited film thickness model. J. Loss Prev. Proc. Ind. 26, 225–235 (2013) 16. Meng, Z., Pan, J.-S., Tseng, K.-K.: PaDE: An enhanced Differential Evolution algorithm with novel control parameter adaptation schemes for numerical optimization. Knowl.-Based Syst. 168, 80–99 (2019). https://doi.org/10.1016/j.knosys.2019.01.006

An Implementation of Ionic-Based Hybrid Mobile Application for Controlling Bluetooth Low-Energy-Based Humidifier Device Dongoh Jung1

, Khongorzul Munkhbat1 and Keun Ho Ryu2(B)

, Jong Yun Lee1

,

1 Department of Computer Science, School of Electrical and Computer Engineering, Chungbuk

National University, Cheongju 28644, Korea [email protected], [email protected], [email protected] 2 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam [email protected]

Abstract. Since the Bluetooth Low Energy (BLE) with version 4.0 of the Bluetooth Core Specification was introduced by Bluetooth Special Interest Group in 2010, the BLE technology has been developing in a wide variety of applications such as the Internet of Things, sports and fitness equipment, home automation, health care, and mobile payment for bringing a comfortable life to the human. In this work, we propose the hybrid mobile application which can remotely control the BLE-based humidifier device. A free and open source ionic framework is used for building beautiful cross-platform hybrid applications. Our proposed application needs to work on both Android and iOS mobile operating systems, and the latter requirement of the application is to control the functions of humidifier device such as turn on and off, adjust the operating time and power of humidity, and receive current humidity percentage, temperature level, and water indicator status from the humidifier. Keywords: Remotely controllable humidifier · Hybrid mobile application · Ionic framework · Bluetooth low energy

1 Introduction Proper humidity and temperature level affect positively to human activity, productivity, and health. Unfortunately, most of environment around us is often dry. Especially, dryness increases more and more due to the heating system during the cold days. The electronic devices such as personal computers, scanners, and printers in the workplace are also one of the factors of dryness. Clearly, the low humidity level has a negative effect on our health [1–3]. It is a cause of many diseases such as asthma, bronchitis, sinusitis, and nosebleeds because our body gradually dehydrates when we breathe dry © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_7

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air [4]. Besides that, moisture evaporation is the main cause of skin disorder, eye itching, and dehydration. As the research of the Canadian Center for Occupational Health and Safety in 2018, when the humidity level drops below 20%, it can dry out the mucous membrane and skin leading to discomfort. Hence, it is best to put some water, plants, and wet towels nearby the computer or to use a humidifier to prevent dryness. Lately, with the fast development of technologies, remotely controllable devices based on Bluetooth or wireless are widely using in daily life such as sports and fitness equipment, home automation, and mobile payment. Therefore, some applications based on Bluetooth Low Energy (BLE) are creating in the healthcare domain [5–10]. These BLE-based healthcare devices like blood pressure, heart rate, and glucose monitors and thermometers have the capability of sending the data to the smartphone, laptop, or smartwatch and receiving data from them. One of the remotely controllable applications used in the healthcare industry is humidifiers. In this study, we propose the hybrid mobile application which can remotely control humidifier device based on BLE technology. Our proposed application needs to work on both Android and iOS mobile operating systems, and the latter requirement of the application is to control the functions of the device such as turn on and off, adjust the working time and humidity power, and receive current humidity percent, temperature level, and water indicator status from the humidifier. The paper is organized as follows: Sect. 2 contains the used methods including BLE technology and ionic framework. Our proposed solution is described in Sect. 3, and its implementation is presented in Sect. 4. We provide conclusions and future work in Sect. 5.

2 Methods 2.1 Bluetooth Low Energy Since the BLE [7] with version 4.0 of the Bluetooth Core Specification was introduced by Bluetooth Special Interest Group in 2010, the BLE technology has been developing in a wide variety of applications such as the Internet of Things, sports and fitness equipment, home automation, and mobile payment to give a comfortable life to the human. The key difference of traditional Bluetooth and BLE technology is power consumption. Bluetooth is originally devoted to continuous, streaming data applications which means exchanging a lot of data at a close range, e.g., receiving data from one device to another and listening to music by wireless speaker. Unfortunately, the classic Bluetooth consumes battery life quickly and has high cost. For the BLE, it is one of the IoT communication protocol. The main advantage of the BLE is low power consumption [11, 12], i.e., applications can run on a small battery for four to five years by exchanging small amounts of data periodically. Unlike traditional Bluetooth, however, BLE remains a sleep mode regularly except for when the connection is initiated. The actual connection times of the BLE are only a few milliseconds, while Bluetooth takes over 100 ms. Figure 1 shows the BLE architecture which consists of three different parts: application, host, and controller. Each of them contains several layers that provide the functionality required to operate.

D. Jung et al.

ApplicaƟon (App)

APPS

52

Generic Access Profile (GAP)

AƩribute Protocol (ATT)

Security Manager Protocol (SMP)

HOST

Generic AƩribute Profile (GATT)

Logical Link Control and AdaptaƟon Protocol (L2CAP)

Link Layer (LL)

Physical Layer (PL)

CONTROLLER

Host Controller Interface (HCI)

Fig. 1. Architecture of BLE

Application. It is the top layer which is responsible for including the logic, user interface, and data handling of everything related to the actual use case that the application implements. Host. The upper layer is consisted of Logical Link Control and Adaptation Protocol (L2CAP), Generic Attribute Profile (GATT), and Generic Access Profile (GAP). L2CAP can multiplex the data channels from the above layers and contain fragmentation and reassembly for large data packets. While the GAP provides a framework that defines how BLE devices connect with each other, the GATT defines the format of the data revealed by a BLE device and the procedures needed to access the data exposed by a device. Controller. The lowest layers are Physical Layer (PL) and Link Layer (LL). PL takes care of transferring and receiving bits [13], and the LL provides medium access, connection establishment, error control, and flow control. 2.2 Ionic Framework The mobile applications developed by hybrid technologies such as Ionic, React [14], Xamarin [15], and PhoneGap [16] have been releasing since last decade [17]. Because native applications should be developed by native programming languages, e.g., Objective-C or swift language for iOS, JAVA language for Android and .NET language for Windows [18]. Developing the application by the native language is great; however, it requires high cost and time. The developer needs to have a broad knowledge of the

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53

native language for operating systems like Android or iOS as well. For solving these problems, one of the easy, simple, and powerful application development platforms is the ionic framework. The main advantage of this framework is to build Android and iOS applications using fundamental web technology languages such as HTML5, CSS3, and JavaScript. Features in native applications like modals, gestures, and pop-ups can be customized to fit the needs. The architecture of hybrid mobile application is indicated in Fig. 2. Mobile Applicaon Ionic Framework Framework-Compable Libraries WebView API iOS SDKs

Android SDKs

Mobile OS (iOS, Android, etc..)

Fig. 2. Architecture of hybrid mobile application

.

3 Proposed Solution BLE technology is the key element of connection between the mobile application and the humidifier device which are connected as shown in Fig. 3. We aim to build the application, controlling BLE humidifier device by remote. Figure 4 displays workflow diagram of the application. Firstly, the user needs to access to the application, and then, the application initial screen appears. Once click the power button on the screen, the application searches the BLE devices and tries to connect the humidifier until a connection is successful. After that, main screen which contains temperature and humidifier information and water indicator status shows in the application. From this screen, user can move to the temperature setting screen and the humidity setting screen.

4 Implementation The ionic framework is used for building the hybrid mobile application. The BLE plugin of this framework enables the communication between a phone and BLE peripherals

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D. Jung et al. Humidifier Device

Ionic Hybrid Mobile App Android App

Power

Timer

Humidity

Water indicator

iOS App BLE

Temperature

Fig. 3. Proposed architecture of this research

Temperature seƫng screen

No device Main screen Searching device

access

Temperature connect

Humidity Water indicator status

User

ConƟnuously 2 hours 4 hours 6 hours

IniƟal screen

Humidity seƫng screen AutomaƟc Low Medium High

Fig. 4. Workflow of the application

and connects with Bluetooth by providing scan(), connect(), disconnect(), read(), and write() functions. Figure 5 indicates the real screens of the humidifier controlling application. The background is bright, and text and buttons are bold, big, and colorful because the purpose of the application is to give simple and clear information to the user. The initial screen which contains the power on button is shown in Fig. 5a. Clicking the power on button, the application starts to search BLE-based humidifier device. If it finds successfully, the main screen in Fig. 5b will be appeared on the screen. This page provides the name of the connected device, power off button, current humidity and temperature, water indicator status, and menus. Selecting the menu positioned at the bottom of the application, it is possible to move to the “Humidity adjustment screen” and “Time adjustment screen” which are shown in Fig. 5c, d, respectively. From these screens, the user adjusts the humidity power and the time to run the device. The humidity adjustment has automatic, low, medium, and high options while the time adjustment has continuously, 2 h, 4 h, and 6 h options. When clicks the power off button, the humidifier device will be shut down.

5 Conclusion In this study, we proposed and implemented a mobile application which can remotely control the humidifier device based on ionic hybrid framework. This application can power on and off the device, receive current room temperature, humidity percent, and water indicator status, and adjust the power of humidity and working time. Also, it works on both Android and iOS mobile operating systems. BLE technology is the key element of the connection between the mobile application and the humidifier device.

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Fig. 5. Screen captures of real mobile application. a Initial screen, b main screen, c humidity adjustment screen, and d time adjustment screen

Once the application has been created, it can be expanded by developing more functions and connecting with other devices in the further. Acknowledgements. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (No.2017R1A2B4010826) and by Business for Cooperative R and D between Industrial, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2017 (No. C0541451).

References 1. Egawa, M., Oguri, M., Kuwahara, T., Takahashi, M.: Effect of exposure of human skin to a dry environment. Skin Res. Technol. 8(4), 212–218 (2002) 2. Berglund, L.G.: Comfort and humidity. ASHRAE J. 40(8), 35 (1998) 3. Mäkinen, T.M., Juvonen, R., Jokelainen, J., Harju, T.H., Peitso, A., Bloigu, A., SilvennoinenKassinen, S., Leinonen, M., Hassi, J.: Cold temperature and low humidity are associated with increased occurrence of respiratory tract infections. Respir. Med. 103(3), 456–462 (2009) 4. Wolkoff, P.: Indoor air humidity, air quality, and health—an overview. Int. J. Hyg. Environ. Health 221(3), 376–390 (2018) 5. Omre, A.H., Keeping, S.: Bluetooth low energy: wireless connectivity for medical monitoring. J. Diabetes Sci. Technol. 4(2), 457–463 (2010) 6. Yu, B., Xu, L., Li, Y.: Bluetooth low energy (BLE) based mobile electrocardiogram monitoring system. In: 2012 IEEE International Conference on Information and Automation, pp. 763– 767. IEEE (2012, June) 7. Gupta, N.K.: Inside Bluetooth Low Energy. Artech House, USA (2016) 8. Kang, H.W., Kim, C.M., Koh, S.J.: ISO/IEEE 11073-based healthcare services over IoT platform using 6LoWPAN and BLE: architecture and experimentation. In: 2016 International Conference on Networking and Network Applications (NaNA), pp. 313–318. IEEE (2016, July)

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9. Zhang, T., Lu, J., Hu, F., Hao, Q.: Bluetooth low energy for wearable sensor-based healthcare systems. In: 2014 IEEE Healthcare Innovation Conference (HIC), pp. 251–254. IEEE (2014, October) 10. Lin, Z.M., Chang, C.H., Chou, N.K., Lin, Y.H.: Bluetooth low energy (BLE) based blood pressure monitoring system. In: 2014 International Conference on Intelligent Green Building and Smart Grid (IGBSG), pp. 1–4. IEEE (2014, April) 11. Dementyev, A., Hodges, S., Taylor, S., Smith, J.: Power consumption analysis of bluetooth low energy, zigbee and ANT sensor nodes in a cyclic sleep scenario. In: 2013 IEEE International Wireless Symposium (IWS), pp. 1–4. IEEE (2013, April) 12. Heydon, R., Nick, H.: “Bluetooth low energy.” CSR Presentation, Bluetooth SIG (2012). https://www.bluetooth.org/DocMan/handlers/DownloadDoc.ashx 13. Siekkinen, M., Hiienkari, M., Nurminen, J.K., Nieminen, J.: How low energy is bluetooth low energy? Comparative measurements with zigbee/802.15.4. In: 2012 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 232–237. IEEE (2012) 14. Eisenman, B.: Learning React Native: Building Native Mobile Apps with JavaScript. O’Reilly Media, Inc., USA (2015) 15. Hermes, D.: Xamarin Mobile Application Development: Cross-Platform c# and Xamarin. Forms Fundamentals. Apress, New York (2015) 16. Wargo, J.M.: PhoneGap Essentials: Building Cross-Platform Mobile Apps. Addison-Wesley, Boston (2012) 17. Rakesh, P.K., Kannan, M.: Online mobile application development using ionic framework for educational institutions. Int. J. Adv. Res. Methodol. Eng. Technol 1 (2017) 18. Yusuf, S.: Ionic Framework by Example. Packt Publishing Ltd, UK (2016)

Study of Assessing the Stability of Rwanda’s Power System from Big Data Based on Power Generation Gilbert Shyirambere1,2 , Kuo-Chi Chang1,2,5,6(B) , Kai-Chun Chu3 , Hsiao-Chuan Wang4 , AbdalazizAltayeb Ibrahim Omer1,2 , Governor David Kwabena Amesimenu2 , and Fu-Hsiang Chang7 1 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of

Technology, Fuzhou, China [email protected] 2 School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China 3 Department of Business Management, Fujian University of Technology, Fuzhou, China 4 Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan 5 College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 6 Department of Business Administration, North Borneo University College, Sabah, Malaysia 7 Department of Tourism, Shih-Hsin University, Taipei, Taiwan

Abstract. Rwanda has insufficient of electricity in which the electricity access among its citizens is at 53% equals to 224 MW of power generation. However, even among 53% of electrification, there are still some blackouts and erratic outages in cities during the night when many devices are connected on the system. This blackout and sudden shutdown could make the power system stability of Rwanda to be questionable and it can be so-called unstable. Meanwhile, the Rwanda Government is very ambitious targeting to achieve universal access by 2023/24 in which 52% will be on-grid connected and 48% off-grid. In this study, the current and proposed Rwanda power generation sector, transmission sector, and distribution sector are discussed; general Rwanda energy sources like, hydro, solar, diesel thermal, methane gas, peat are also discussed based on big data. In the end, conclusion and some recommendations have been proposed to be done in order to make stable the power system. For instance, Kalman-filtered solar forecasting approach could be used to predict weather condition every hour so that blackout coming from bad atmosphere could be prevented. Keywords: Generation · Transmission · Distribution · Methane gas to power · Peat to power · Blackout in rwanda · Big data

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_8

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1 Introduction Stability in power systems is a worldwide problem that directly relates to the national economy as well as people’s livelihood; therefore, it has always been a great concern of governments and electric power enterprises [1]. However, to achieve the power generation, stability is extremely complex. There are many influencing factors, including the equipment quality, maintenance level, level of relevant personnel technical capabilities, as well as meteorological, geological conditions, and various other external factors, etc. Hence, it is an arduous task to achieve universal access and avoid erratic outages. Despite the hindrances and budget limitations of Rwanda, the country is targeting to achieve a universal access (100%) by 2023/24 [2]. Rwanda contains several natural energy resources like hydro, solar, peat, gas, as well as biomass [3, 4]; the whole nation is currently being supplied only about 224 MW [5]. In October 2019, the total connectivity of Rwandan households was 53% including 38% of the national grid and 15% accessing through off-grid systems (mainly solar) [5, 6]. In order to achieve the goal, the country through Rwanda Energy Group (REG) has chosen to increase the capacity of generation, transmission, and to diversify the energy sources, as well as reducing costs, with elaborating conducive legal and regulatory frameworks [6, 7]. Meanwhile, Rwanda is a landlocked country with total area equals to 26,338 km2 located in East Africa with 12,089,721 people, where 94.7% of it is a land, and the 5.3% remaining is water. The country has two rainy seasons within a year, which feed the rivers systems utilized to run alternator’s turbines of power plants. The economic growth of Rwanda is on rapid pace within these decades, and the country has numerous energy resources which need to be fully exploited [2, 4, 8, 9]. The electricity tariff in Rwanda of US $ 0.22/kWh is more expensive about 22.2% than other five countries of East Africa Community (EAC) [10]. Rwanda policy makers would follow the recommendations drawn by this paper to enhance the power system in terms of generation stability in order to avoid sudden outages. This study is organized into: Introduction, methodology, current power system of the country, expected future status of Rwanda Power System Sector, Rwanda General Energy Sources Sector, discussion and recommendations, and conclusion in the end.

2 Methodology The objective of this study is to make an assessment of the power energy sector of Rwanda particularly in terms of power generation based on big data. This creates the need of deep reading of literature review talking about it and exploring some websites like REG, Rwanda Development Board (RDB), etc. After understanding the literature and collecting related data, discussion takes places and drawing some recommendations. This study explores the big data processing flow of Rwanda’s power system on power generation as shown in Fig. 1.

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Fig. 1. The big data processing flow of Rwanda’s power system on power generation

3 Current Situation of Rwanda Power System Sector

1. Generation Sector According to the reports from REG, hydropower generation is dominant with 46.8%. The factors behind this dominancy are: its abundancy sources, its longer lifespan, higher capacity factor, and particularly the government plans of expanding this sector. The government is also doing core maintenance of the oldest hydropower plants built long time ago during colonialism period. The work mainly is studying on how other many mini hydropower plants could be constructed to bolster the energy sector of the country. Additionally, great focus is on exploitation and development of other sources of electrical energy such as solar, gas methane, peat power, geothermal, etc. The table below illustrates both the installed and potential electric energy supply resources for Rwanda in 2017. Uptodate, Rwanda is generating 224 MW from its installed electricity generation capacity without accounting the off-grids energy solution contributing more to power generation sector of the country [11–14] (Table 1). 2. Transmission Sector There are mainly three voltage levels in Rwanda’s transmission network which are 70,110, and 220 kV. In March 2017, about 744.7 km of high voltage had been laid, transmitting the power in the country, and facilitating the regional interconnection as well [6, 11]. So far, 64.5% equals to 480.4 km are 110 kV, and 35.5% of 264.3 km are 220 kV transmission lines, both on the total network. The rest 70 kV was upgraded to 110 kV to enhance electric network reliability and power supply stability among the country’s fickle power demand profile [11, 15–17].

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G. Shyirambere et al. Table 1. Different installed electricity supply resources

Power supply source

Installed capacity (MW) in 2007

Total potential (MW)

Hydropower

100.34

313

Methane

30.00

480

Solar

12.08

N/A

Thermal

57.80

N/A

Peat deposits

15.00

300

Geothermal

N/A

520

Biomass (Rice husk)

0.07

N/A

Total

216

1613

3. Distribution Sector Rwanda’s distribution voltage is 33 kV. There are 15 sub-stations, including: Rulindo, Jabana, Ntaruka, Gikondo, Mukungwa, Gasogi, Gifurwe, Kibogora, Mont Kigali, Karongi, Musha, Kilinda, Kabarondo, Mururu 1, Rwinkwavu, and Mururu 2.

4 Expected Future Status of Rwanda Power System Sector Nowadays, different projects are under process in order to increase the electricity generation in Rwanda. Among those projects, we can mention, Gishoma and Hakan peat power plant, Rusumo Hydropower plant. Other projects under developments are KivuWatt power plant and Hydropower plant of Nyabarongo 2. Apart of these generation projects, there are other sub-stations and transmission lines which are under construction like: the interconnector between Rwanda and Democratic Republic of Congo (DRC) of 220 kV on the length of 180 km, a transmission line of 110 kV 119 km between Rulindo—Gicumbi—Gabiro—Musha, Kigali Ring transmission line of 110 kV with length of 27 km. In addition, in order the country to have universal access, there are also under procurement projects such as: Rwanda—Burundi interconnector of 220 kV with 64 km, the transmission line of 220 kV with length of 74 km between kibuye— Kilinda—Kigoma—Rwabusoro, a transmission line of 220 kV 53 km between Mamba— Rwabusoro—Bugesera, and Gahanga—Rilima transmission line of 110 kV on 29 km length.

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5 Rwanda General Energy Source Sector Even though it seems that Rwanda is at low rate of electrification with 53% electricity access based to the reports from REG [5]. The country has many resources to be exploited in order to meet the demands. A thoroughly discussion of technologies which are being used as well as future ones to extract and exploiting different kind of energy sources is below. 1. Solar Energy Rwanda average solar radiation is around 5.2 kWh/m2 /day, but only the 250 kW solar PV plant built on Mount Kigali and Agahozo solar pv of 2.1 MW has been built and injected to the national grid. Off- grids companies are on the forefront of harvesting the energy from the sun; currently, there more than ten due to the opportunities the Government has offered them in order to contribute to electrification of the country. Among them, in 2017, there are Mobisol Rwanda which sold 8%, Ignite sold 11%, One Acre Fund sold 12%, and Bboxx sold 32% equal to 94,741 systems without tenders [5, 10]. 2. Methane Gas Power Plants Rwanda and DRC share lake Kivu which contains CH4 (Methane gas) and carbon dioxide (CO2 ), having around 60 and 300 billion of cubic meters, respectively. The lake Kivu is located in volcanic area where volcanic eruptions and other volcanic activities, as well as bacterial action on decomposed materials are responsible for accumulations of those gases where like methane gas is used to generate the electricity for both countries, Rwanda and DRC. Therefore, extracting those gases from lake Kivu will not only provide electricity but also would avoid catastrophes problems which could emanate from those gases. In the extraction of methane gas, the gas and nutrient-rich which are in deep water are lifted up, which separates/degasses automatically the constituents in a separator near the surface. Then, the CO2 is removed in a scrubber by passing the gas through washing water. Finally, the purified could then be used to generate electricity, and the degassed as well as washing water are reinjected back into the lake. 3. Diesel Thermal Power Plants In the range of 2000–2007, there were droughts in Rwanda in which the hydropower plants did not get enough water in order to operate. As result, in 2005, the country introduced petroleum diesel thermal power plants into its electricity mix to back up the hydropower generation. Even if those thermal power plants contributed a lot in the energy generation of the country that time, their operations have been expensive which resulted the electricity tariffs to be increased by more than 100%, and this affected negatively the electricity pricing advantages and competitiveness of Rwanda in EAC. On the other

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hand, it was expected to phase-out those thermal power plants by 2017, but still they are being used because other energy resources with relatively much cheaper generation costs like renewable energy, peat-to-power plants which can replace them, are not yet available exploited. 4. Peat Power Plants Peat layers which are found in marshes, bogs, fens, and moors, are extracted by using machine dredgers, hand cutting, or water jets on high pressure. Due to very high moisture content, the preparation of peat is done on site. Lignite, black, and brown coals have layers within rocks in stratified to tertiary age, but lignite brown coals are mostly tertiary or cretaceous. The moisture content of peat is very high and reduced by drying over 90%, then is briquetted and used locally. When peat is dried, it can be pulverized or briquetted, and there will be carbonization products such as tar, pat Coker, and liquor, or gas. The gas is obtained from gasification of the peat, some residues products like wax, and fuel. Rwanda has a single peat power plant which is the power plant of Gishoma.

6 Rwanda Power Blackout Experience In 2007, hundreds of electrical energy consumers in the country spent nights in darkness as they cannot load units of cash-power into their meters. This was due to the computers containing vital information have crashed. In 2018, it has been normal trend that whenever it rains either in Rwanda’s capital city, Kigali or upcountry, Rwandans expect power outage and, in some places, power goes off for as long as an hour or more. Rwanda has experienced more different circumstances of power outages some due to fault on the lines, upgrading, extensions and maintenances of some power transmissions, distributions, and stations. Despite, blackouts caused by technical works and faults, there some power outages come from weather and thieves who steal some materials of transmission towels and these can be so-called unexpected blackouts because they are coming from currently uncontrollable sources by REG. Because these unexpected blackouts cause big losses to citizens, REG wants to establish an electronic monitoring system so that whoever wants to sabotage the power infrastructure can be automatically identified.

7 Discussion and Recommendations Currently, the consumption of electricity in Rwanda is the lowest in East Africa Community with 30 kwh compared to 140 kwh of Kenya, 85 kwh of Tanzania, and 66 kwh which belongs to Uganda. The highest electricity price is behind this small accessibility of electricity in the country. Some factors which boost electricity price are the importation of petroleum products. Therefore, different researches and other alternatives must be done in order to mitigate this issue. Furthermore, there are some local resources which

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could be used to generate electricity economically like peat, methane gas in lake kivu; these resources are estimated to be around 1613 MW. Unfortunately, only less than 10% is being exploited. Therefore, the study recommends the exploitation of all available resources, promoting sustainable energy resources, in order to achieve universal access and reduce losses in the power electrification system. Generator grid, transmission lines, and others infrastructures should be also upgraded, and if possible, switch to smart grid which will help to fight against blackout and sudden outages occur during the night particularly in capital city Kigali. The investment in research and innovation is mainly recommended to the government; for instance, by using Kalman-filtered solar forecasting approach, REG would be able to predict the weather conditions every hour ahead. So, the outages caused by unexpected hazards will be prevented due to different precautions that will be taken based on the weather conditions at that time. Even countries which have a universal access of electricity like China, USA, and Europe countries prioritize research and innovation in everything; so, Rwanda could learn from them. The country has many energy resources to be exploited to the fullest. However, in existing distribution systems, there are still some blackouts and erratic outages in cities and load shedding during the night when many devices are connected on the system. This blackout and sudden shutdown could make the power system stability and reliability of Rwanda to be questionable and it can be so-called unstable. In order to fight against this instability and achieve universal access, the country would have to follow the recommendations provided in this study in which investment in research and innovation is strongly advised to the government (Fig. 2).

Fig. 2. The flow chart of recommendations in this study

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8 Conclusion Rwanda has insufficient of electricity in which the electricity access among its citizens is at 53% equals to 224 MW of power generation. However, even among 53% of electrification, there are still some blackouts and erratic outages in cities during the night when many devices are connected on the system. This blackout and sudden shutdown could make the power system stability of Rwanda to be questionable and it can be socalled unstable. Rwanda’s electric power generation capacity is low which yields the electricity access to be low as a result low, where in October 2019, the total connectivity of Rwandan households is 53% including 38% of the national grid and 15% accessing through off-grid systems (mainly solar).

References 1. Shu, Y.B., Zhang,W.L., Zhou, X.X., Tang, Y., Guo,Q.: Security evaluation of UHV synchronized power grid. Proc. CSEE, 27(34), 1–6 (Dec, 2007) 2. Hakizimana, J.D.K., Yoon, S.P., Kang, T.J., Kim, H.T., Jeon, Y.S., Choi, Y.C.: Potential for peat-to-power usage in Rwanda and associated implications. Energy Strateg. Rev. 13–14, 222–235 (2016). https://doi.org/10.1016/j.esr.2016.04.001 3. Bimenyimana, S., Asemota, G.N., Li, L.: The state of the power sector in rwanda: a progressive sector with ambitious targets. Front. Energy Res. 6, 68 (2018) 4. Museruka, C., Mutabazi, A.: Assessment of global solar radiation over rwanda. In: 2007 International Conference on Clean Electrical Power, pp. 670–676. IEEE (May, 2007) 5. Rashed, G.I., Shyirambere, G., Gasore, G., Yuanzhang, S., Shafik, M.B.: Applicability study of battery charging stations in off-grid for rural electrification–the case of Rwanda. In: 2019 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET), pp. 1–6. IEEE (August, 2019) 6. Chu, K.C., Horng, D.J., Chang, K.C.: Numerical Optimization of the Energy Consumption for Wireless Sensor Networks Based on an Improved Ant Colony Algorithm. J. IEEE Access 7, 105562–105571 (2019) 7. Chih-Cheng, L., Kuo-Chi, C., Chun-Yu, C.: Study of high-tech process furnace using inherently safer design strategies (IV). The advanced thin film manufacturing process design and adjustment. J. Loss Prev. Process Ind. 43, 280–291 (2016) 8. Chih-Cheng, L., Kuo-Chi, C., Chun-Yu, C.: Study of high-tech process furnace using inherently safer design strategies (III) advanced thin film process and reduction of power consumption control. J. Loss Prev. Process Ind. 43, 280–291 (2015) 9. Pasche, N.T., Mugisha, A., Rwandekwe, L., Umutoni, A.: Monitoring the Effects of Methane Extraction in Lake Kivu. MININFRA Report, Kigali, Rwanda (2010) 10. Miley, G.H., Clapp, A.L. “Generation,”. In: Fink, G.D., Beaty, H.W. (eds.) Standard handbook for electrical engineers, 14th edn. McGraw-Hill. 5–2-5-17 (2000) 11. Yang, D.: On post-processing day-ahead NWP forecasts using Kalman filtering. Sol. Energy 182, 179–181 (2019) 12. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 13. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006

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14. Lu, C., Hang,K.C., Chen, C.Y.: Study of high-tech process furnace using inherently safer design strategies (IV). The advanced thin film manufacturing process design and adjustment. J. Loss Prev. Proc. Ind. 40, 378–395 (2016) 15. Chang, K.C., Pan, J.S., Chu, K.C., Horng, D.J., Jing, H.: Study on information and integrated of MES big data and semiconductor process furnace automation. In: Conference of Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol. 834 (2019) 16. Chen, C.Y., Chang, K.C., Lu, C.C., Wang, G.B.: Study of high-tech process furnace using inherently safer design strategies (II) deposited film thickness model. J. Loss Prev. Proc. Ind. 26, 225–235 (2013) 17. Meng, Z., Pan, J.-S., Tseng, K.-K.: PaDE: an enhanced differential evolution algorithm with novel control parameter adaptation schemes for numerical optimization. Knowl.-Based Syst. 168, 80–99 (2019). https://doi.org/10.1016/j.knosys.2019.01.006

Image Feature Detection and Clustering for UAV Multiple Obstacles Avoidance Baohua Zhao, Tien-Wen Sung(B) , and Xin Zhang Fujian Provincial Key Laboratory of Big Data Mining and Applications, College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, China [email protected], [email protected], [email protected]

Abstract. In the past decade, a lot of researches have been done on the topic of real-time obstacle avoidance of Unmanned Aerial Vehicle (UAV). For example, the use of Light Detection and Ranging (LIDAR), ultrasonic sensors, and now more popular visual sensors can effectively avoid obstacles. Although these methods are simple and effective, there are few of UAV real-time obstacle avoidance for complex environments, and most of them are used to avoid a single obstacle. When facing multiple obstacles, they cannot choose the optimal strategy, which inevitably consumes more power and time. In view of the complex environment that UAV may face multiple obstacles, in order to avoid multiple obstacles with minimum efforts in the process of real obstacle avoidance, this paper proposes a new method to detect multiple obstacles. Firstly, it extracts obstacle feature points, and then uses unsupervised clustering algorithm in machine learning to divide the feature points of different obstacles into different categories, and finally detects the obstacles represented by convex hull of same category feature points. The method is simple and effective. This paper compares the time of several popular feature point detection and unsupervised clustering algorithms through experiments and estimates the approximate relationship between the number of feature points detected and the distance when the UAV approaches the obstacle, which is a priori study for the future practical obstacle avoidance link. It summarizes which algorithm can save more time and is more suitable for real-time obstacle avoidance of multiple obstacles. Keywords: UAV · Obstacle detection · Clustering · Multiple obstacles avoidance

1 Introduction With the development and popularization of artificial intelligence in the twenty-first century, robots have become more intelligent, especially in the last decade, and UAV has become a new research hotspot. UAV is widely used in agriculture, exploration, search, and so on. Its autonomous navigation [1] technology is the basis of these tasks. But in some complex environments, especially in the area without Global Positioning System (GPS) signal, it is a great challenge to realize autonomous navigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_9

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In the process of autonomous navigation, autonomous obstacle avoidance [2, 3] of UAV is considered as an indispensable ability. In early studies, obstacle avoidance mostly depends on a high-cost laser or radar. In recent years, with the rapid development of computer vision, vision sensors [4] are more and more used to avoid obstacles for UAV because of their advantages of light weight and low cost. Vision-based UAV obstacle avoidance is likely to become the mainstream in future. This study aims at the complex environment, where UAV may face multiple obstacles. In order to avoid multiple obstacles with minimum efforts in the process of real obstacle avoidance, this paper proposes an obstacle detection method based on the combination of feature points extraction and clustering [5–7] and analyzes and compares the experimental results of various algorithms. The rest of this paper is arranged as follows: The second section introduces the related work of UAV obstacle avoidance, the third section introduces the obstacle detection method proposed in this study in detail, the fourth section introduces the experimental process in detail and discusses and analyzes the experimental results, and the fifth section is the summary of this study.

2 Related Works In recent years, a lot of research has been done on autonomous obstacle avoidance of UAV, and many different technologies have been used to solve this problem. In the past real-time obstacle avoidance process, UAV sensors are used to measure the geometric data of the surrounding environment. Common sensors such as LIDAR, ultrasonic, and depth sensors can directly measure distance. For example, Yan Peng, Dong Qu, and others proposed a method to cluster the laser point cloud data to avoid obstacles, which has good real-time performance [8]. Some people use RGB-D camera to measure the depth of the surrounding environment and design a method to generate a safe flight path of UAV by intersection of two three-dimensional surfaces to avoid obstacles [9]. However, due to the load limitation of UAVs, especially small UAVs usually use ultrasonic rangefinder or visual sensor to avoid obstacles. For example, Meng Guanglei and Pan Haibing install four ultrasonic sensors on the UAV, which are, respectively, installed in front, back, left, and right directions. When the obstacles are at different angles, they can avoid obstacles [10]. However, compared with vision sensor or LIDAR, ultrasonic sensor has inherent time delay defect. Vision-based obstacle avoidance has become the main trend in recent years, for example, the method of size expansion based on monocular vision proposed by Abdulla et al., which simulates the concept of human behavior to avoid obstacles, avoids obstacles by matching feature points and extracting convex hulls in two adjacent frames [11, 12], Tien-Wen Sung and others also analyzed and compared this method [13]. In order to prevent the failure of single-sensor obstacle avoidance, there are also methods that use binocular vision and ultrasonic sensors to avoid obstacles to improve the accuracy of obstacle avoidance [14]. In the face of a complex environment with multiple obstacles, some people have proposed a machine learning clustering algorithm to avoid obstacles, but this method has not actually been verified [15]. This study proposes a new method to detect multiple obstacles, which combines feature point detection algorithm and unsupervised clustering algorithm of machine learning to avoid obstacles.

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3 Method This method is carried out in three steps. The first step is to detect the feature points, using different feature point detection algorithms to detect obstacles. The second step is the feature points clustering part, which uses the machine learning unsupervised clustering method to cluster the detected feature points. The purpose is to classify the feature points of the same obstacle into the same category. The third step is the convex hull detection part, which is used to detect the feature points of the same kind of obstacle (enclose the feature points of the most periphery of the obstacle into a convex hull), and use the convex hull to represent the location of real obstacles in the picture. The flowchart of this method is shown in Fig. 1.

Fig. 1. Flowchart of the method

Initially, take a picture one meter away from the obstacle. There are two obstacles on the left and right sides of the picture (this is to simulate the complex obstacle avoidance environment that may be encountered in the actual obstacle avoidance). We use Scale-invariant feature transform (SIFT) feature detection algorithm, Oriented FAST and Rotated BRIEF (ORB) feature detection algorithm, or SURF feature detection algorithm to detect the feature points in the graph. As shown in Fig. 2, it is the effect diagram of Speeded Up Robust Features (SURF) feature point detection.

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Fig. 2. Feature points detection

Secondly, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm or K-means clustering algorithm can be used to cluster the detected feature points for the purpose of classifying the feature points of the same obstacle into the same category. As shown in Fig. 3, it is the effect diagram of DBSCAN clustering, in which the same type of feature points is of one color, and two colors represent that two obstacles are detected. Eventually, the convex hull of the same kind of feature points is extracted; that is, the convex hull is surrounded by the outermost feature points of the same kind. The purpose is to show the location and size relationship of different obstacles in space, so as to prepare for further practical obstacle avoidance. As shown in Fig. 4, the effect picture of convex hull detection is shown.

4 Experimental Results In this experiment, pictures with resolution of 960 * 720 pixels are used. These pictures were taken indoors during the day. In order to show the characteristics and effectiveness of the algorithm, we choose the white wall as the background to take photos. The experimental platform is a 2.60 GHz Intel i5-4210m processor. This experiment is divided into two parts. The first part is about the time spent in detecting multiple obstacles after the combination of different feature detection algorithms and different machine learning algorithms. The obstacles in the detection pictures are desk (left) and coat hanger (right). There are six combination algorithms in this

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Fig. 3. Clustering of feature points

experiment, which are DBSCAN clustering algorithm and K-means clustering algorithm under SIFT feature detection algorithm, DBSCAN clustering algorithm and K-means clustering algorithm under SURF feature detection algorithm, and DBSCAN clustering algorithm and K-means clustering algorithm under ORB feature detection algorithm. As shown in Fig. 5, the time spent in detecting obstacles by different algorithms is compared. It can be seen from the figure that DBSCAN algorithm takes far less time than K-means algorithm because it does not need an iterative update process. And ORB runs shorter than SURF algorithm and SIFT algorithm in K-means algorithm or DBSCAN algorithm, and other detection algorithms run twice or more than ORB. In the second part, the number of feature points in the convex hull is detected with different distances between the UAV and the obstacles. As shown in Fig. 6, it is a curve drawn by the number of feature points detected by the UAV 0.6 m, 0.9 m, 1.2 m, and 1.5 m away from the obstacles. The figure shows that once the UAV is approaching the obstacle, more and more obstacle feature points will be detected. The curve is only the presentation of preliminary experimental results, and the more accurate relationship between them will be studied in future work.

5 Conclusion This study proposes a new method to detect multiple obstacles, and the effectiveness of this method is proved by experiments. In the experiment, six methods are used to detect

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Fig. 4. Convex hull of clustered feature points

orb

0.1441

surf

0.82

2.8743

0.2931

siŌ

2.3509

0.2218 0

0.5

1

1.5

2

2.5

3

3.5

Running Ɵme (sec) Kmeans

DBSCAN

Fig. 5. Running time of combined algorithm

obstacles, which algorithm has better real-time performance is compared and analyzed, and the relationship between feature points and distance is detected preliminarily. The main work in future is to use the detection method with the shortest running time, construct different complex obstacle avoidance environments, and try to avoid multiple obstacles with minimum effort.

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Number of feature points

1400 1303 1249

1200 1000

980 840

800

813 707

600

704 594

400 200 0 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Distance from obstacles (m) SIFT

SURF

Fig. 6. Relationship between the number of feature points and the distance

Acknowledgements. This work is supported by Fujian Provincial Natural Science Foundation in China (Project Number: 2017J01730) and Fujian University of Technology (Project Number: GY-Z20016 and GY-Z18183).

References 1. Wang, C., Wang, J., Shen, Y., Zhang, X.: Autonomous navigation of UAVs in large-scale complex environments: a deep reinforcement learning approach. IEEE Trans. Veh. Technol. 68(3), 2124–2136 (2019) 2. Radmanesh, M., Kumar, M., Guentert, P.H., Sarim, M.: Overview of path-planning and obstacle avoidance algorithms for UAVs: a comparative study. Unmanned Syst. 6(2), 95–118 (2018) 3. Sung, T.W., Sun, L., Chang, K.C.: Multi-hop route planning based on environment information for path-following UAVs. In: Proceedings of the 2020 International Conference on Artificial Intelligence and Computer Vision (AICV), Cairo, Egypt, 8–10 Apr 2020, pp. 831–839 (2020) 4. Sung, T.W., Yang, C.S.: A Voronoi-based sensor handover protocol for target tracking in distributed visual sensor networks. Int. J. Distrib. Sens. Netw. 2014(AID 586210), 1–14 (2014) 5. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD), Portland, Oregon, USA, Aug 1996, pp. 226–231 (1996) 6. Pham, D.T., Dimov, S.S., Nguyen, C.D.: Selection of K in K-means clustering. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 219(1), 103–119 (2005) 7. Pan, J.S., Kong, L., Sung, T.W., Tsai, P.W., Snasel, V.: A clustering scheme for wireless sensor networks based on genetic algorithm and dominating set. J. Internet Technol. 19(4), 1111–1118 (2018)

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8. Peng, Y., Qu, D., Zhong, Y., Xie, S., Luo, J. Gu, J.: The obstacle detection and obstacle avoidance algorithm based on 2-D lidar. In: Proceedings of IEEE International Conference on Information and Automation (ICIA), Lijiang, China, 8–9 Aug 2015, pp. 1648–1653 (2015) 9. Iacono, M., Sgorbissa, A.: Path following and obstacle avoidance for an autonomous UAV using a depth camera. Robot. Auton. Syst. 106, 38–46 (2018) 10. Guanglei, M., Haibing, P.: The application of ultrasonic sensor in the obstacle avoidance of quad-rotor UAV. In: Proceedings of IEEE Chinese Guidance, Navigation and Control Conference (CGNCC), Nanjing, China, 12–14 Aug 2016, pp. 976–981 (2016) 11. Al-Kaff, A., Meng, Q., Martín, D. , De La Escalera, A., Armingol, J.M.: Monocular visionbased obstacle detection/avoidance for unmanned aerial vehicles. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, 19–22 June 2016, pp. 92–97 (2016) 12. Al-Kaff, A., García, F., Martín, D., De La Escalera, A., Armingol, J.M.: Obstacle detection and avoidance system based on monocular camera and size expansion algorithm for UAVs. Sensors 17(5), 1061, 1–22 (2017) 13. Sung, T.W., Zhao, B., Chang, K.C.: Experimental comparison of different feature detection algorithms for UAV obstacle avoidance. In: Proceedings of the 2020 International Conference on Artificial Intelligence and Computer Vision (AICV), Cairo, Egypt, 8–10 Apr 2020, pp. 840– 849 (2020) 14. Yang, Y., Wang, T., Chen, L., Zhang, W.: Stereo vision based obstacle avoidance strategy for quadcopter UAV. In: Proceedings of Chinese Control and Decision Conference (CCDC), Shenyang, China, 9–11 June 2018, pp. 490–494 (2018) 15. Choi, Y., Jimenez, H., Mavris, D.N.: Two-layer obstacle collision avoidance with machine learning for more energy-efficient unmanned aircraft trajectories. Robot. Auton. Syst. 2017(98), 158–173 (2017)

Sentiment Analysis for Mongolian Tweets with RNN Orgilbat Ariunaa1 and Zoljargal Munkhjargal2(B) 1 Unimedia Solutions, Ulaanbaatar, Mongolia

[email protected]

2 Department of Information and Computer Science, National University of Mongolia,

Ulaanbaatar, Mongolia [email protected]

Abstract. As information flow in the electronic online environment is increasing, the need for automated processing is increasing. Every minute, 456,000 tweets were written on Twitter. The data in the Mongolian language is already joined in that stream and created their own space since a long time ago. However, in the Mongolian language, there is a lack of research, language resources, and an implemented system by now. In this work, we tried to classify the document level sentiment polarity of Mongolian tweets based on the RNN method of deep learning. The model was tested at the 62,917 SemEval English data, the highest F1 score of 64.7. Keywords: Sentiment analysis · Natural language processing · Machine learning

1 Introduction Sentiment Analysis is a task to process a text and identify sentiments from the text. Textual data can be divided into two categories: facts and sentiments. Facts are nonchangeable an objective expression. Sentiments are variable-subjective expression [1]. Consider the following example: (A) “Xipn dp mini kamep bana.” (There is my camera on the table.) (B) “n kamepyn batape tyn taapyyxan m.” (The battery of this camera is very bad.) (C) “Xapin tp kamepyn linz n max can tatdag.” (But lenses of the camera are very good.) Example A is the fact about “kamep” (camera) and is a neutral sentiment. Example B is the negative sentiment for “batape” (battery) feature of “kamep”. Example C is the positive sentiment for “linz” (lens) feature of “kamep”. Unlike traditional text, tweet consists of short messages expressed within the limit of 280 characters. Due to the length limitation, the language used to express opinions © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_10

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in tweets differs from that found in longer documents. Twitter language is the classic example of an incorrect, non-rule and unofficial language that we do use [2]. In Twitter, misspelling cases are common like making abbreviations, spelling error, write in multiple ) [3]. It affects the languages, uses emoticons and emoji instead of words ( :) :| meaning of the sentiments. But, the features of the system such as re-tweet (Retweet = RT), write to someone (@billgates) and using hashtags (#hashtags), will be counted as text, but will not affect the meaning of the sentiment. This research work will detect and evaluate the sentiment polarity of Mongolian tweets in Cyrillic by the RNN method with positive, negative and neutral values and will be a document-level study [4].

2 Related Work For the Mongolian language, we currently don’t have sentiment analysis researches in other data besides Twitter. For English, the SST (Stanford Sentiment Treebank) dataset collected from Rotten Tomatoes (which viewers write their opinions about the movie they watched) is being a test baseline data for sentiment analysis models. Originally published by Pan and Lee in 2005, Socher et al. were expanded in 2013. Sentiment analysis researches for tweets in Mongolian, F1 score was 59.0 when defining sentiment analysis for tweets with DNN to evaluate data which created during Zoljargal et al. [5]. The results (have three sentiment classification of positive, negative and neutral, with 2509 rows of data for training and testing) data of this research we used for in our research work. For English, we can compare the 4A task of Sentiment Analysis in Twitter which was one of the main tasks of the SemEval (Semantic Evaluation) International Research Conference in 2013–2017. Under this assignment, Dovdon and Saias [6] defined with BoW + MaxEnt method, and the F1 score was 53.9. In that data, Baziotis et al. [7] also got the F1 score of 67.5 to determine sentiments by the LSTM model with an Attention mechanism. Also in that data, Cliche [8] had the best result reaching F1 score 68.1 for this assignment when they determined sentiment analysis with the LSTM + CNN method. From the above three results, the RNN method of deep learning for sentiment analysis has been more successful than the traditional machine learning method [9].

3 Methodology 3.1 Deep Learning Deep Learning is a topic of machine learning that especially studies algorithms of the artificial neural network derived from the human brain structure. Supervised Learning of Deep Learning is creating the main success, it has some common structures such as MLP, CNN, RNN and algorithms based on them.

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3.2 Multilayer Perceptron The neuron is the basic unit of computation in a neural network. It receives input from other nodes, or from external input sources and produces an output. An input has an weight (w), which is assigned on the basis of its relative importance to other inputs. The neuron applies an activation function f to the weighted sum of its. The function f is non-linear that aims to introduce non-linearity into the output of a neuron. There are several activation functions you may encounter in practice such as Sigmoid, Tanh, ReLU and Softmax. An Artificial Neural Network contains one or more hidden layers and dozens of nodes (neurons) in each layer (each node is connected together) is called Multilayer Perceptron (MLP) or Deep Neural Network (DNN). In terms of the assignment for our study, the established output layer has three nodes representing positive, negative and neutral classes. Use the Softmax classifier function for multiple categories because the classification number is greater than two. The value of the real numerical output on each node represents the probability of the corresponding class. 3.3 Recurrent Neural Network The time or space in the previous or next position affects the quality and meaning of the sequence type of data. Text is the sequence data. Recurrent Neural Network (RNN) is the best deep learning method for sequence data by now [10]. 3.3.1 LSTM Structure LSTM network has the ability to transmit necessary data to the next node, forgetting the unnecessary, and maintaining the ultimate result [11]. The first step of LSTM is to decide what information we’re going to throw away from the cell state. This decision or activation function is sigmoid that called the “forget gate layer.” ht−1 and x t , and outputs a number between 0 and 1 for each number in the cell state C t−1 . An 1 represents “completely keep this” while a 0 represents “completely get rid of this.”     (1) ft = σ Wf ht−1 , xt + bf The second step is to decide what new information we’re going to store in the cell state. This has two sub steps: (a) A sigmoid layer (input gate layer) decides which values are updated. (b) A tanh layer creates a vector of new candidate values, that could be input to the state. In the next step, we’ll combine these two.     (2.1) it = σ Wi ht−1 , xt + bi     Ct = tanh Wc ht−1 , xt + bC

(2.2)

Now update the old state of the cell, C t−1 , into the new state C t . Ct = ft ∗ Ct−1 + it ∗ Ct

(3)

Sentiment Analysis for Mongolian Tweets with RNN

Finally, we need to decide what we’re going to output.     ot = σ Wo ht−1 , xt + bo ht = ot ∗ tanh(Ct )

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(4.1) (4.2)

We can make any changes to the structure [12] without losing the ability to forget the unnecessary information, save the necessary. 3.3.2 GRU Structure Gated recurrent units (GRU) are simpler variant of LSTM, it combines the forget and input gates into a single “updateable gate”. And it also merges the cell state and hidden state and makes some other changes [13]. The resulting model is performance comparable to LSTM on sequence modeling, but fewer parameters and easier to train. 3.4 Word Embedding Word Embedding is a common way of representing texts as multi-dimensional real numerical vector values. Any words in the text have their own vector value in which the text is entered and where they are entered [14, 15]. 3.5 Preprocessing The base data used in the experiments is the preprocessed, and the following processing was done before we use [5]: The sentences are separated, tokenized, lowercased, removed web links, removed special characters and replaced numbers by character. Also the following processing was added to the data: removed addressed strings— @username, removed # character, removed “.,‘ characters, removed Unicode characters and replaced more than 2 consecutive letters with 2 letters. Classification −1 is negative, 0 is neutral, 1 is positive sentiments accordingly. But the data did not process with Lemmatization and Stemming functions. We can’t able to extract linguistic features from a tweet.

4 Experiments and Results 4.1 Environment Setup Zoljargal et al. created a labeled tweets data for sentiment analysis using crowdsource [5]. We extended it. The data statistics are shown in Table 1. We have used 3,010 rows of data for training and test. The original base data extended by 501 rows because of classification distribution was uneven. Word Embedding vector representation: We collect data from Mongolian news sites and generate 300-dimensional vectors—Word Embedding with 431,674 unique words. And used it for the first layer’s initial weight of the model.

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Neutral

Positive

Total

787 (33%)

831 (34%)

794 (33%)

2412

Test data 196 (33%)

205 (34%)

197 (33%)

598

Total

1036

991

3010

Training data

983

Fig. 1. Network with RNN layer for sentiment analysis

4.2 Model Implementation Using the above network we created a four-layer deep learning model. The deep learning model is illustrated in Figs. 1 and 2. We have created a network with LSTM/GRU nodes to define sentiment polarity of text. The following is an illustration of the general structure of the network.

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Fig. 2. Deep learning model with LSTM (RNN) for sentiment analysis

4.3 Experiments and Results The highest F1 score was at 55.5 when the experiment was performed without the first Embedding layer (without using Word Embedding). We assume that this is our baseline score. Next, the Embedding layer added to the model. And the F1 score was reached to 60.0 which confirmed the use of the Embedding layer to increase the performance of the model. After that, we experimented on the modify of the LSTM and GRU unit count of the model. The results are shown in Table 2. As a result, the highest F1 score was 67.1 when the LSTM unit was 32. The highest F1 score was 67.0 when the GRU unit was 300.

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F1 score

GRU unit

F1 score

10

60

10

60.1

32

67.1

32

64

100

66.1

100

64.5

128

64.6

128

66.6

200

62

200

64.7

300

65.7

300

67

We created the model for the Mongolian language and compared the results of the English test data to verify the model was correct or not. For this purpose, SemEval’s 62,617 English training and test data were used and the results are shown in Table 3. Table 3. Experiment results of the RNN models on English tweets LSTM unit

F1 score

GRU unit

F1 score

10

61.2

10

62.2

32

63.5

32

63.9

100

62.2

100

64.7

128

60.6

128

63.7

200

62.3

200

63.4

300

64.1

300

64.3

As a result, in English, the LSTM unit is 300, with the highest F1 score of 64.1. The GRU unit is 100, while the highest F1 is 64.7. In comparison, Enkhzol’s results on this data were F1 score of 53.9, the results of Christos Baziotis F1 score was 67.5 and the result of Mathieu Cliche F1 score was 68.1 and highest on this data.

5 Conclusion In this work, we tried to classify the document level sentiment polarity of Mongolian tweets on the Twitter social network based on the RNN method of deep learning. LSTM and GRU methods (versions of RNN network) are implemented and the results showed that RNN deep learning method can be used in the Mongolian language. The model was tested on 3010 pre-labeled Mongolian tweets data. The highest F1 score reached to 67.1 for new data. It is a comparable result with similar international studies in English [6–8].

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The experiments show clearly the effectiveness of the addition of the Word Embedding layer to the model. To verify the model created for the Mongolian language, the model was tested at the 62,917 SemEval English data, the highest F1 score of 64.7. This means that the model has become a quite well model for the Mongolian language. In further studies, the RNN network should not be used alone. If someone uses the model that RNN mixed with other architecture (hybrid), the results may improve.

References 1. Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Comput. Linguist. 30(3), 277–308 (2004) 2. Eisenstein, J.: What to do about bad language on the internet (2013) 3. Yan, J.L.S., Turtle, H.R.: Exploring fine-grained emotion detection in tweets (2011) 4. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of EMNLP-02 (2002) 5. Zolapgal, M., Hanzadpagqaa, D., Altangpl, Q., Amapcanaa, G.: ipgni˘i ctggdli˘ig olny xyqp tmdglx, ynlx acyydal (2018) 6. Dovdon, E., Saias, J.: ej-sa-2017 at SemEval-2017 task 4: experiments for target oriented sentiment analysis in twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) (2017) 7. Baziotis, C., Pelekis, N., Doulkeridis, C.: DataStories at SemEval-2017 task 4: deep LSTM with attention for message-level and topic-based sentiment analysis (2017). https://aclweb. org/anthology/S17-2126 8. Cliche, M.: BB twtr at SemEval-2017 task 4: twitter sentiment analysis with CNNs and LSTMs (2017). https://aclweb.org/anthology/S17-2094 9. Hussein, D.M.E.D.M.: A survey on sentiment analysis challenges (2016) 10. Chilakapati, A.: Word bags vs word sequences for text classification (2019). https://xplordat. com/2019/01/13/word-bags-vs-word-sequences-for-text-classification/ 11. Hochreiter, S., Schmidhuber, J.: Long short-term memory (1997). https://www.bioinf.jku.at/ publications/older/2604.pdf 12. Olah, C.: Understanding LSTM networks (2015). https://colah.github.io/posts/2015-08-Und erstanding-LSTMs/ 13. Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing (2018). https://arxiv.org/abs/1708.02709 14. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information (2016) 15. Yin, Z., Shen, Y.: On the dimensionality of word embedding (2018)

Thin Point Light Source Display Nomin-Erdene Dalkhaa(B) , Bymba-Ochir Chagnaadorj , Choijamts Namsraijaw , and Ganbat Baasantseren National University of Mongolia, Ulaanbaatar 14201, Mongolia {nomin-erdene,ganbat}@seas.num.edu.mn

Abstract. Three-dimensional (3D) point light source (PLS) display have the disadvantage of being thick and bulky when enlarged. In this paper, modified the conventional 3D PLS display structure. A proposed method of thin PLS display has been developed by placing the SLM between the collimating lens and the lens array. An experiment was performed to create an elemental image of the proposed method that changed the structure of the conventional PLS display. As a result of the experiment, the proposed method created a 3D image similar to the 3D image created by the conventional PLS display in terms of viewing angle and resolution, which are the parameters of the PLS display. The proposed method that we are proposing changes the structure of the conventional PLS display, but the result is the same, with the advantage of having a thinner PLS display that can be made larger by reducing the thickness of the PLS display. Keywords: Point light source display · Integral imaging display · Three-dimensional display

1 Introduction There are many 3D display technologies available, such as stereoscopic display [1, 2], display [3, 4], lenticular display, and integral imaging display [5, 6], which can display three-dimensional images. From these displays, integral imaging display is advantages what full parallax, the actual depth of the 3D image choose freely but also has disadvantages. For example, narrow viewing angle (VA) and low resolution, when the larger the PLS display, the thicker it becomes. Conventional PLS display SLMs are placed at a constant distance, which makes them unusable due to the disadvantage of display thickness and high cost [7–13]. Y. Kim replaced the lens matrix and collimating lens of PLS display by two pieces of spatial light modulator (SLM) that appears pinhole array. This makes the PLS display thinner, but it is costly and changes the elemental images (EIs) with each change in the pinhole array [14]. In this paper, we will process the proposed method of thin PLS display that will change the structure of the conventional 3D PLS display and will allocate between SLM display collimating lens and the lens array.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_11

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2 Conventional Point Light Source Display 2.1 Point Light Source Display PLS display consists of a light source, the collimating lens, 2D transparent display or SLM, and lens array. From Fig. 1, the PLS display is shown from the side, according to the y- and z-axis. Rays from the light source that is located at a focal length f 1 on the optical axis of the collimating lens pass through the collimating lens. These rays emitted by the collimating lens generate an infinite number of parallel rays. Through the lens array, each small lens in the lens array or height of the elemental lens intersects at a point f of its focal point and propagates further. These points of intersection are called PLSs. Rays from the PLS penetrate the elemental images (EIs) of the SLM at a distance of g = 2f from the lens array, creating the 3D image or integral image (II). The thickness of this conventional PLS display is determined by the distance d from the light source to the SLM, as shown in Fig. 1.

Fig. 1. Technology of conventional PLS display

2.2 Calculation to Create an Elemental Image of the PLS Display The geometric structure of the 3D “P” point created by the conventional PLS display is shown in Fig. 1. The 3D point is generated at the intersection of the rays passing through the five EIs in the SLM at distances y1 and z1 along the y- and z-axis from the SLM. The Y 1 , Y 2 , …, Y i rays propagating from the PLS penetrate the EIs of the SLM and converge at the 3D “P” point to form II. The distance Y i along the y-axis is given, Yi = pL · (i − 0.5) + (y − pL · (i − 0.5)) · f /(z + f ),

(1)

where pL is the elemental lens size of the lens array (units in mm), i is the number of elemental lenses, y is the distance from the apex of the SLM along the y-axis to II (units in mm), f is the focal length of the elemental lens of the lens array (units in mm), z is the distance from SLM along the z-axis to 3D image or II (units in mm). Calculate the

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distance Y i by Eq. (1) to form a EIs based on the principle that the point of the SLM is transparent if the lens array is within the corresponding elemental lens. If the calculated Y i distance is less than the pL size of the corresponding lens of the lens array, the SLM unit becomes transparent. 2.3 The Viewing Angle and Resolution of a Conventional PLS Display Conventional PLS display has a higher resolution and viewing angle, and the depth of the 3D image is freely visible. Figure 1 shows the conventional PLS display viewing angle and resolution. The field of ray that scattered further of the PLS determines the viewing angle of a conventional PLS display. The viewing angle is given, VA = α  + α  = 2 · arctan ·(pL /(2 · f )),

(2)

where pL is the elemental lens size of the lens array (units in mm) and f is the focal length of the elemental lens of the lens array (units in mm). The viewing angle depends on the size of the elemental lens and the focal length of the elemental lens. The resolution of a conventional PLS display is determined by the inverse of the distance between neighboring PLSs. The distance between adjacent PLS display is equal to the size of the elemental lens. The resolution of PLS is given, R = 1/pL ,

(3)

where pL is the elemental lens size of the lens array (units in mm).

3 Proposed Method 3.1 The Proposed Method of PLS Display The proposed method is to place the SLM at any distance between the collimating lens and the lens array, as shown in Fig. 2, regardless of the focal length of the lens array. The proposed PLS display will be able to create a 3D image without having to place the SLM at a distance g = 2f from the lens array. This allows the SLM to be placed at any distance between the collimating lens and the lens array, as shown in Fig. 2, in the same way as the conventional method. Field from the light source passes through the collimating lens, and an infinite number of parallel beams pass through the EIs on the SLM, converge at the focal point of the lens array, and propagate. The thickness of the proposed PLS display is determined by the distance d 1 from the light source to the lens array, as shown in Fig. 2. Thick of proposed PLS display is given, d1 = d − g.

(4)

The thickness of this PLS display is a thin PLS display with a thickness d to g of the thickness of a conventional PLS display.

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Fig. 2. Technology of the proposed PLS display

3.2 Calculation to Create an Elemental Image of the Proposed PLS Display The geometry of the 3D “P” point generated by the PLS display is shown in Fig. 2, assuming that the proposed PLS display creates a 3D “P” point at a distance y1 and z1 from the SLM along the y- and z-axis. The parallel beams generated by the collimating lens on the proposed PLS display pass through the SLM’s EIs and are collected by each elemental lens in the lens array to form a 3D “P” point. As shown in Fig. 2, the Y 1 , Y 2 , …, Y i rays penetrated by the EIs in the SLM pass through the lens array and are collected by each lens array to form II. The distance Y i along the y-axis is given, Yi = pL · (i − 0.5) − (y − pL · (i − 0.5)) · f /(z − f ).

(5)

The proposed PLS display EIs generation formula, in which SLM is placed between the lens array and the collimating lens, appears to be changing from Eq. (1), which generates the EIs of the conventional PLS display. 3.3 The Viewing Angle and Resolution of the Proposed PLS Display As shown in Fig. 2, the viewing angle of the proposed PLS display is determined by the ray field collected by the elemental lens. The proposed PLS display, which has been repositioned by SLM, appears to have the same viewing angle as the conventional PLS display. Figure 2 shows that the VA1 appearance angle of proposed PLC display will express by Eq. (2) like appearance viewing angle of conventional PLS display. Proposed PLS displays resolution determines by gap inverse between adjacent proposed PLS and regardless of SLMs position. Thus, Fig. 2 shows that proposed PLS display resolution R1 will express by Eq. (3) like conventional PLC displays resolution.

4 Experimental Result In this experimental result, we have compared and proposed the conventional method, and specification used in the experiment is given in Table 1.

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№

Specification

Characteristic

1

Light source (SMD 3014)

3 mm × 1.4 mm

2

Diameter of collimating lens (D)

50.8 mm

3

Focal length of the collimating lens (f 1 )

61.8 mm

4

Number of elemental lens (i)

50 × 50

5

Size of elemental lens (pL )

1 mm × 1 mm

6

The focal length of the lens array (f )

3.3 mm

7

Size of SLM

27 mm × 36 mm

8

Pitch of SLM (PD )

0.036 mm

9

Pixel of SLM

768 × 1024

10

A distance of SLM from the lens array (conventional method) (g = 2f )

6.6 mm

11

A distance of SLM from the lens array (proposed method)

Any position between the collimating lens and the lens array

When experimenting with the conventional, and proposed PLS displays to configured each tool. The light source and collimating lens are first positioned, and the collecting generates parallel rays, and the lens matrix is positioned and checked to ensure that the lens array generates the PLS. 4.1 Experimental Result of Parallel Ray and PLSs A light source is placed at a focal length on the optical axis of the collimating lens, the rays of the light source pass through the collimating lens and produce a parallel ray, and the results of an experiment is shown in Fig. 3. Fig. 3 shows the result of ray with a ruler at a distance of 50 and 75 mm from the collimating lens along the z-axis. The experimental results show that the collimating lens generate parallel rays because the collimating lens is equal to the diameter of the collimating lens given in Table 1. Test in which a diffuser is placed at a distance of 0 mm, 3.3 mm, and 6.6 mm from the lens array along the z-axis to verify that the lens array is placed in front of the collimating lens and PLS is generated after the parallel ray test result as shown in Fig. 3. The experimental results are shown in Fig. 3. which show that the lens array generates PLS at a focal length of 3.3 mm or the lens array given in Table 1. For a conventional PLS display, the SLM is placed at a distance of g = 6.6 mm from the lens array, as shown in Fig. 3, and for the proposed PLS display, the SLM is placed at any distance between the collimating lens and the lens array. 4.2 Experimental Result of Conventional PLS Display In the experiment, we used two objects “B” and “D” that are inside the image volume and far from the SLM 40 mm and 20 mm, respectively, as shown in Fig. 4a. The geometric

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Fig. 3. Experiment result of distance from ruler to the collimating lens 50 mm, 75 mm, and distance from the diffuser to the lens array 0 mm, 3.3 mm, and 6.6 mm

structure of the experimental of a conventional PSL display is shown in Fig. 4a. A conventional PLS display needs one set of EIs for SLM. The created EIs are shown in Fig. 4b.

Fig. 4. Geometric structure of 3D “B”, “D” image to create a a conventional PLS display, and b elemental image

Experiments were performed on a conventional PLS display to create two 3D images in different positions along the z-axis. The experimental setup of the conventional PLS display is shown in Fig. 5. The EIs of the Fig. 4b is being shown on the SLM of the Fig. 5. As a result of the experimental, the 3D “B” and “D” images left (−8°), center, and right (+8°) which is generated by the conventional PLS showed in Fig. 6a–c. The viewing angle of a conventional PLS display is 17.2° according to Table 1, calculated by Eq. (2), and the experimental results show that the view of the 3D “B” and “D” image by a conventional PLS display is 16°.

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Fig. 5. Configured setup of the conventional PLS display

Fig. 6. Experimental result of 3D “B”, “D” images, a left (−8°), b center, and c right (+8°) view by the conventional PLS display

4.3 Experimental Result of the Proposed Thin PLS Display Before testing the proposed method, parallel rays and PLS tests were performed as in the conventional method, and the results were the same. The adjustment has been made, the PLS is placed at any distance between the collimating lens and the lens array. In the experiment, we used two objects “B” and “D” that are inside the image volume and far from the lens array 40 mm, and 20 mm, respectively, as shown in Fig. 7a. The geometric structure of the experimental of the proposed PSL display is shown in Fig. 7a. The proposed PLS display needs one set of EIs for SLM. The created EIs are shown in Fig. 7b. Experiments were performed on a proposed PLS display to create like a conventional PLS display that creates two 3D images in different positions along the z-axis. The experimental setup of the proposed PLS display is shown in Fig. 8. The EIs of the Fig. 7b is being shown on the SLM of the Fig. 8. As a result of the experimental, the 3D “B” and “D” images left (–8°), center, and right (+8°) which is generated by the proposed PLS showed in Fig. 9a–c. The viewing angle of the proposed PLS display is equal to the viewing angle of the conventional PLS display, and the experiment results show that the viewing angle of the 3D image is 16°.

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Fig. 7. Geometric structure of 3D “B”, “D” image to create, a a proposed PLS display, and b elemental image

Fig. 8. Configured setup of the proposed PLS display

Fig. 9. Experimental result of 3D “B”, “D” images, a left (−8°), b center, and c right (+8°) view by proposed PLS display

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The experimental results show that the viewing angles of the 3D “B” and “D” images created by the proposed PLS display are 16° and are the same as the experimental results of a conventional PLS display.

5 Conclusion In this experiment, we changed the structure of the conventional PLS display or the position of the SLM, the display thickness was reduced, and a proposed thin PLS display was tested. The 3D image generated by the proposed PLS display has the same results as the 3D image created by the conventional PLS display. The fact that the collimating lens can be placed at any position between the lens array without placing the SLM at a distance g = 2f , which is the non-overlapping position of the rays propagating beyond the lens array, makes it easy to experiment with in future research. Experiments have shown that our proposed method of modifying the conventional PLS display structure has shown the same results. This proposed method can be used for an actual PLS display in the manufacturing environment. We will enhance the resolution and viewing angle of the thin PLS display in future work. Acknowledgements. The research has received funding from the National University of Mongolia under grant agreement P2019-3730.

References 1. Lipton, L.: Selection devices for field-sequential stereoscopic displays a brief history. SPIE 1457, 274–282 (1991) 2. Hodges, L.F.: Time-multiplexed stereoscopic computer graphics. IEEE Comput. Graphics Appl. 12(2), 20–30 (1992) 3. Sandin, D.J., Margolis, T., Dawe, G., Leigh, J., DeFanti, T.A.: Varrier autostereographic display. SPIE 4297(5), 204–211 (2001) 4. Dodgson, N.A.: Autostereoscopic 3D displays. Computer 38(8), 31–36 (2005) 5. Lippmann, G.: La Photographie Integrale. C. R. Academie Sci. 146, 446–451 (1908) 6. Lee, B., Park, J.H., Min, S.W.: Three-dimensional display and information processing based on integral imaging. In: Poon, T. (eds.) Digital Holography and Three-Dimensional Display. Springer, New York (2006) 7. Park, J.H., Kim, J., Bae, J.P., Kim, Y., Lee, B.: Viewing angle enhancement of threedimension/two-dimension convertible integral imaging display using double collimated or noncollimated illumination. Jpn. J. Appl. Phys. 44, 991–994 (2005) 8. Park, J.H., Min, S.W., Jung, S., Lee, B.: Analysis of viewing parameters for two display methods based on integral photography. Appl. Opt. 40, 5217–5232 (2001) 9. Min, S.W., Javidi, B., Lee, B.: Enhanced three-dimensional integral imaging system by use of double display devices. Appl. Opt. 42, 4186–4195 (2003) 10. Alam, A., Baasantseren, G., Erdenebat, M.U., Kim, N., Park, J.H.: Resolution enhancement of integral imaging three-dimensional display using multi-directional elemental images. J. Soc. Inform. Display 20(4), 175–234 (2012) 11. Choi, H., Min, S.W., Jung, S., Park, J.H., Lee, B.: Multiple-viewing-zone integral imaging using a dynamic barrier array for threedimensional displays. Appl. Opt. 11, 927–932 (2003)

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12. Dalkhaa, N.E., Densmaa, B., Baasantseren, G.: Nonuniform viewing angle of integral imaging display. J. Soc. Inform. Display 23, 457–463 (2015) 13. Batbayar, D., Dalkhaa, N.E., Erdenebat, M.U., Kim, N., Baasantseren, G.: Point light source display with a large viewing angle using multiple illumination sources. Opt. Eng. 56(5), 053113(1–6) (2017) 14. Kim, Y., Kim, J., Kang, J.M., Jung, J.H., Choi, H., Lee, B.: Point light source integral imaging with improved resolution and viewing angle by the use of electrically movable pinhole array. Opt. Express 15, 18253–18267 (2007)

Dynamic Token Distribution Model for Privacy Protection of Mobile Users Tie Hua Zhou, Kai Tai Gao, Yu Lu, and Ling Wang(B) Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin, China {thzhou,smile2867ling}@neepu.edu.cn, [email protected], [email protected]

Abstract. With the growing maturity of cloud computing and the wider application range, deploying applications on cloud computing platform has become a common way in the Internet industry. The user privacy of cloud server has also become the focus of attention. It is difficult for application providers to fully control the data stored on the cloud server, which increases the possibility of user privacy disclosure. In this paper, we propose a dynamic Token-D model to distribute authentication token of client and cloud server, which uses a third-party server to store user information and generate dynamic tokens. The client and cloud server achieve dynamic tokens to manage login authentication and information interaction. The experimental results verify the feasibility of the model, which can effectively protect the privacy of users a degree. Keywords: Cloud computing · Privacy protection · Mobile privacy · Dynamic tokens

1 Introduction Cloud computing is a solution to large-scale computing using distributed resources, which has high availability, good elasticity, more flexibility and convenience, and more economy [1]. However, using the services provided by the third-party cloud service providers, many users are configured in the shared environment, and the storage of users’ personal information exceeds the management control, increasing the risk of data disclosure [2, 3]. Therefore, the information stored on the cloud server need to be processed by privacy protection. Using cryptography method to encrypt the data and send it to the cloud server, and decrypt it after obtaining the data from the cloud server is a feasible solution, such as homomorphic encryption [4] and secret sharing [5], which can effectively protect user privacy. However, these methods are too expensive to use on mobile devices with limited computing power [6]. The purpose of traditional privacy protection is to hide the information of individual users when data is on public, and also to ensure the availability of data as far as possible. Common models include k-anonymity [7], l-diversity [8], t-closeness [9], differential © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_12

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privacy [10], and so on. Most of these models are implemented by breaking index or adding noise. However, we hope that the information stored in cloud computing is complete and accurate, which makes most of the traditional privacy protection models unable to use. To sum up, in order to realize personal privacy protection on mobile devices, it is necessary to have sufficient protection ability and meet the requirements of low cost and data accuracy [11]. In this paper, we propose a model for privacy protection of mobile users named Token-D (dynamic token distribution), which replaces the identifier of the information with a random string token (It can be equivalent to deleting the identifier of information completely), and the corresponding relationship between token and identifier is stored in the third-party server (local), which satisfies the privacy of information stored on the cloud server and greatly reduces the computing overhead of the mobile end.

2 The Principle of Token-D Model 2.1 Privacy Protection Generally, recording the information produced by the users can be identified by the user’s unique login information, such as username. The identifier can be recorded as u. The information produced by users can be recorded as m. The complete information is recorded as: Inf = {u, m} For each u, a random string token is generated. The Inf is divided into two parts: D and Inf  , and these parts establish connection through token. Then, store D to the third-party server and store Inf  to the cloud server: D = {u, token} Inf  = {token, m} Because token is randomly generated, the attacker cannot obtain information of the specific user through Inf  without any prior information. But if the attacker has prior information, such as known inf 1 = {u1 ,m1 }, then as long as find inf  1 = {token1 , m1 } from Inf  , he can know that u1 corresponds to token1 . Therefore, token needs to be changed dynamically, so that even if the attacker knows some prior information, he can only obtain limited personal privacy data. According to the importance of information, the replacement frequency of token can be adjusted dynamically. So the user privacy is protected as long as D is not disclosed. 2.2 Token-D Model Architecture As shown in Fig. 1, the roles in the model are cloud server, third-party server and client. The application service is provided by the cloud server to the client, and the user’s personal information (such as user name, password, and email) is stored in the thirdparty server. The third-party server generates a dynamic token, which is sent to the cloud server and the client at the same time.

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Fig. 1. Token-D model overall architecture

3 Token Distribution Process The generation and distribution of tokens are actively carried out by the third-party server. First, the third-party server reads a fixed number of user information from the database to get the user information list and to generate a random number for each user to verify the personally identifiable information in the following process and then uses username as the key of ABE encryption to encrypt a piece of test information and send it to all clients. Only the client corresponding to username and key can decrypt and return the message [12]. The third-party server waits for a period of time to determine which clients are in a normal communication state and delete the abnormal communication users from the user list. Generate a random string token of fixed length for each user in the user list, send all tokens to the cloud server, and send the tokens of each user to the corresponding client. After receiving the token, the cloud server generates new login information about these tokens (such as session) and the client reset local token. Then, the third-party server records the user-token relationship and records the time when the tokens update is complete. The detailed consistecy analysis of token update time as shown in the sect. 3.1. Under the condition that the token update time is consistent, the updated client will use a new token to connect to the cloud server, and the cloud server’s record of user information will also depend on the new token. 3.1 Synchronous Update When the client receives the token, it may be in a state of frequent interaction with the cloud server. Due to network delay and other reasons, if not limited, it is likely to lead to inconsistent token replacement time between cloud server and client. Therefore, to ensure consistency, we propose the following strategies: First, the third-party server sends tokens to both the cloud server and the client. When the client receives the new token, it enters the “silent” state and can only accept

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the message, cannot send it. When the third-party server receives a successful message from the cloud server, it will send a message to the client. The client judges whether there is currently a waiting for the message returned by the cloud server (the message is sent before the silent state). If not, send the current time to the third-party server; if yes, wait for the message to arrive and then send the time to the third-party server. The third-party server takes the return time as the completion time of token replacement. Finally, the third-part server should tell the client that the updating token has been success and client can stop the silent state. This method ensure that the tasks that use old token already have been finished before the new token is using. 3.2 Validation Mechanism and Contingency Plan Token-D model has a relatively complex communication process, and it may risk service deny and critical information loss, so we must consider the validation mechanism and contingency plan in the communication process. And the tokens of the cloud server and the client must be updated synchronously, and errors in the communication process will break the consistency of tokens. Cloud server. Considering that the cloud server generates a new session during token updating, but does not delete the old session, even if an error occurs, the cloud server still retains the old login information. Therefore, the cloud server have to send the rollback message to the third-party server when an error occurs. Client. The client keeps the old token data when entering the silence, triggers rollback when the silence time exceeds the specified value, changes the current token to the old token, and sends the rollback information to the third-party server. The third-party server deletes all relevant information of the user in this update process and puts the user in the next update queue. Third-party server. When the waiting time for the cloud server or client to return a message exceeds the specified time threshold value (Fig. 1(3)(5)), a rollback will be triggered. The token change records of all users in this update process will be deleted directly, and all clients will be notified that the token update is invalid. The specific operation and trigger bar of rollback are given above. In order to ensure the token consistency, in Fig. 2(5)(6), both parties carry the new token for verification. 3.3 Three-Party Interaction Mode It can be seen from the above process that the data table on the cloud server is: T 1 = {Token, M , Time} where M represents the target information, the data table on the third-party server is: T 2 = {User, Token, Time}

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Fig. 2. Token-D model sequence diagram

For any user user1, if application providers want to obtain his information produced, you must first obtain the user’s information recorded as U1 from T 2: U 1 = {(token, time)|User = user1 } Then, obtain the information recorded as U2 from T 1: U 2 = {m|Token = tokeni and Time < timei , (tokeni , timei ) ∈ U 1} Then, U2 is the target information of the user user1 that application providers want to obtain.

4 Evaluation Analysis When the token is distributed, the client will have a silent state for a certain time. In this state, the client cannot send messages to the cloud service. If the silent time is too long, the service availability will be affected. In the section 2.4, in order to prevent longterm silence, the model increases the limit of the longest waiting time, so the service availability in special cases is guaranteed. In order to analyze the length of silence time in general, a group of comparative experiments have been made. The first group records the length of communication delay between the client and the cloud server, and the second group records the silence time of the client in the same environment. In the same network environment, 300 test of token distribution were carried out, and the results were distributed as Tables 1, and 2. The experiment results that the network communication delay is mainly concentrated in 90–100 ms, and the silence time is mainly concentrated in 190–210 ms, with an average difference of about 100 ms. It has little impact on using for users. Therefore, the Token-D model has good usability.

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Table 1. Client/cloud server network delay Delay time (ms) Frequency Proportion ≤90

35

0.12

91–100

203

0.68

101–110

41

0.14

>111

21

0.07

Total

300

1.00

Table 2. Client silence time Silent time(ms) Frequency Proportion ≤190

31

0.10

191–200

94

0.31

201–210

127

0.42

>211

48

0.16

Total

300

1.00

5 Conclusion This paper proposed the Token-D model which using a third-party server to protect the privacy of mobile users in the cloud computing environment. Through analysis, this model can well protect the privacy of users and ensure the integrity and availability of information. In the practical application of the Token-D model, an experimental analysis of the client’s silence time in the token distribution process, which shows the process, will not have a significant impact on the user’s actual experience. In the future, our goal is to protect the data on the cloud server from tampering on the basis of existing privacy protection. Acknowledgements. This work was supported by the Science and Technology Development Plan of Jilin Province, China (No.20190201194JC, and No.20200403039SF).

References 1. Zissis, D., Lekkas, D.: Addressing cloud computing security issues. Future Gener. Comput. Syst. 28(3), 583–592 (2012) 2. Varghese, B., Buyya, R.: Next generation cloud computing: new trends and research directions. Future Gener. Comput. Syst. 79, 849–861 (2018) 3. Ren, K., Wang, C., Wang, Q.: Security challenges for the public cloud. IEEE Internet Comput. 16(1), 69–73 (2012)

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4. Zhao, F., Li, C., Liu, C.: A cloud computing security solution based on fully homomorphic encryption. In: 16th International Conference on Advanced Communication Technology (ICACT), pp. 16–19 (2014) 5. Lai, C., Ding, C.: Several generalizations of Shamir’s secret sharing scheme. Int. J. Found. Comput. Sci. 15(2), 445–458 (2004) 6. Yu, S.: Big privacy: challenges and opportunities of privacy study in the age of big data. IEEE Access 4, 2751–2763 (2016) 7. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(5), 557–570 (2002) 8. Zhou, B., Pei, J.: The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowl. Inf. Syst. 28(1), 47–77 (2011) 9. Li, N., Li, T., Venkatasubramanian, S.: Closeness: a new privacy measure for data publishing. IEEE Trans. Knowl. Data Eng. 22(7), 943–956 (2010) 10. Dwork, C.: Differential privacy. In: 33rd International Colloquium on Automata, Languages and Programming, pp. 10–14 (2006) 11. Li, J., Zhang, Y., Chen, X., Xiang, Y.: Secure attribute-based data sharing for resource-limited users in cloud computing. Comput. Secur. 72, 1–12 (2018) 12. Bethencourt, J., Sahai, A., Waters, B.: Ciphertext-policy attribute-based encryption. In: 2007 IEEE Symposium on Security and Privacy (SP ‘07), pp. 321–334 (2007)

Cascading Fault Prevention of Power Grid Based on Key Power Generation Nodes Kuo-Chi Chang1,3,4,5(B) , Jie Luo1,2 , Hui-Qiong Deng1,2 , Qin-Bin Li2 , Rong-Jin Zheng2 , and Pei-Qiang Li2 1 School of Information Science and Engineering, Fujian University of Technology, Fuzhou

350108, China [email protected], [email protected] 2 Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control, Fuzhou 350108, China 3 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China 4 College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 5 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. In the case of power grid cascading trip, this study puts forward a strategy to prevent cascading trip by changing the output of generator set. Firstly, according to the specific performance of the power grid under the cascade tripping, combined with the action behavior of the line current-type backup protection, and an expression equation for measuring the safety level index between the power grid and the cascading trip failure is given. Secondly, the vulnerable branch is found out through the safety level index, the output of the key power generation nodes on the branch is taken as the optimization variable, and the optimization model is established to improve the safety level index under the chain trip of the power grid. Finally, a combination of sensitivity analysis and particle swarm optimization(PSO) was used to analyze an example on the IEEE-39 node system. The results demonstrate the feasibility and effectiveness of the model and method, and provide a reference for preventing chain trip accidents. Keywords: Power system · Cascade tripping · Safety level index · Sensitivity analysis · particle swarm optimization (PSO)

1 Introduction Cascading failures are the direct cause of large-scale power outages; preventive control is a prior control for potential faults. Therefore, for the research of power grid cascading faults, researchers have continued to deepen in several directions such as cascading fault simulation and the impact of network structure on cascading faults. For example, reference [1], the key nodes are selected by calculating the sensitivity, and then the transmission cross-section is adjusted by controlling the key nodes. However, this method © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_13

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cannot control large-scale power flow. In reference [2], a preventive control strategy based on PMU is proposed for the cascading fault caused by branch power flow transfer. Reference [3, 4] proposes a strategy to prevent chain trip by adjusting the output distribution of the generator set to change the system safety level index. In reference [5, 6], based on the transmission line vulnerability assessment method of cascading fault and the propagation characteristics of power grid cascading fault, a cascading fault suppression strategy is proposed by improving the relationship between node and branch current. Reference [7, 8], based on the assumptions of the DC power flow method, gives the relationship between the transmitted active power and the branch current after the branch L is opened. Reference [9] proposed an algorithm for screening severely interlocked disturbed branches and dividing the associated nodes of the branches under the assumption of an initial failure branch. This study mainly analyzes the power grid for cascading trip events, and combined with the action behavior of relay protection in the power grid, a kind of safety level index to measure the power grid under the chain accident is introduced. An optimization model with the optimal safety level index as the goal is proposed, which is analyzed and verified by calculation examples.

2 The Safety Level Index of Power Grid Cascading Trip This study only studies the current-type protection of the line. Referring to the method of reference, suppose that at a certain time, a certain branch L j in the power grid has an initial fault, when the branch L j is cut off and the power flow of the power grid is redistributed. For the remaining branches in the power grid, formula (1) can be used to judge whether cascading trip will occur [10]: Iji.ds = |Iji.s | − |Iji |

(1)

In the formula, I ji is the current on branch L ji after power flow redistribution, I ji.s is the setting value of protection current on branch L ji , and I ji.ds is the difference of electric quantity between I ji.s and I ji . It can be seen from the concept of cascading trip and Eq. (1) that when I ji.ds > 0, the branch L ji will not cause cascading trip, and when I ji.ds ≤ 0, the branch L ji is cut off by the backup protection, that is, L ji may have interlocking fault. It is assumed that after the initial fault branch is cut off, the number of remaining branches in the power grid is n. According to Eq. (1), taking into account all the remaining branches, and the index shown in Eq. (2) can be given.   (2) m = min Iji.ds Among them, special attention should be paid to the case where m = 0. In this case, at least one of the remaining branches, except the initial fault branch, is at the boundary of chain trip. According to the power flow knowledge of the power system, when the branch stops operation due to the initial fault, the power flow equation of the power grid is shown in the following formula.   ∗ ˙ = S˜ U ˙ (3) YU

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It can be seen from reference [6] that when an initial fault occurs in branch L ji , the I ji in Eq. (1) is mainly determined by the node voltage. Therefore, the expressions of the branch currents can be obtained as follows: Iji =

Uj − Ui Zji

(4)

where, I ji represents the branch current between node j and node i. When Zji is constant, it depends on the voltage difference between node j and node i. In the operation of power grid, I ji.ds is mainly determined by the output distribution of power grid units before the initial fault. If the generator output can be adjusted optimally, and on the basis of ensuring that the load requirements are met. If m > 0 in Eq. (2) and the value of m should be as large as possible, it will be beneficial to better prevent grid cascading trip, and it can be written as the objective function shown in Eq. (5). F = max(m)

(5)

3 Solution Process of the Model This study uses PSO to solve the problem. The reason for adopting this algorithm is that it is simple to program, easy to implement, and fast to converge. In the actual power grid, the current of the branch is mainly determined by the injection power of a few nodes. Therefore, when preventing the chain trip, we can start from the branches and key nodes which are most vulnerable to the initial fault in the power grid. When the branch is cut off and the power flow is redistributed, an correlation matrix Rp between branch current and nodal injection power can be obtained by referring to the literature [11], and its expression is shown as follows: Rp = SB0 + bEj SB0

(6)

S is the correlation matrix between the branch and the node, B0 is the n-order node susceptance matrix, E j is the j-th row of the l × l-order unit matrix, b is a quantity jointly determined by the network structure and the branch reactance, each element in Rp is denoted as rji (j = 1,2,…,l;i = 1,2,…,n), rji.s represents the sensitivity of node s to branch L ji [12, 13].

4 Example Analysis Taking IEEE-39 node system as an example, this study gives a further explanation of the algorithm. The wiring diagram of the example system is shown in Fig. 1. According to the algorithm steps given above and the given example system, this study has compiled the MATLAB calculation program. In the analysis, the parameters of each component in the system are expressed by the per-unit.

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Fig. 1. IEEE-39 node system wiring diagram

In order to avoid premature convergence, falling into local optimum. This example uses the algorithm combining sensitivity analysis and PSO. That is, iteratively calculates 20 times according to the PSO, and then the sensitivity method is used for PSO iterations. Because this study studies the influence of PV node’s output distribution on safety level index F, so it is not selected to cut off the generator set in the initial fault. So branches 1 to 36 are respectively used as the initial fault branch, and the safety level index F is calculated. The calculation shows that when the initial fault is branch 27, the safety level index F is the lowest, only 0.4001. Therefore, branch 27 is the vulnerable branch of the system, which means that when the initial fault occurs in branch 27, it is likely to cause a chain trip accident. Based on the above analysis, the calculation example in this study is for the initial fault branch L 27 , when L 27 is removed and the power grid readjusts the power flow. Firstly, the basic PSO is used for 20 iterations. Secondly, calculate I j.ds according to Eq. (1), and find out that the 29th branch L 29 corresponds to the minimum value of I ji.ds > 0. the data of roi.s corresponding to Rp matrix in L 29 are extracted. Only the PV node data of the branch is clustered, and taking roi.s as the characteristic inputs. After clustering analysis, find the PV nodes that have a greater sensitivity to the branch, and form the node-set G1 . Finally, in the PSO, only the nodes corresponding to the node-set G1 in the particle are updated with particle velocity and position, and the change of its security level index F value is recorded. Figure 2 for the initial fault branch 27, the F value in Eq. (5) is obtained by using the basic PSO. Figure 3 for the initial fault branch 27, the F value is obtained by using the PSO based on sensitivity analysis, that is, the algorithm only considers the key PV nodes.

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Fig. 2. F value of basic PSO

Fig. 3. F value based on sensitive PSO

As can be seen from Figs. 2 and 3, the algorithm combining sensitivity analysis and PSO has obvious advantages in calculation results and speed compared with the basic PSO. The optimal F value of the model with the initial fault of branch 27 is increased from the original maximum level of 1.0649 to 1.5354. It shows that the optimization algorithm improves the optimization level of basic PSO, increases the search range,

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and improves the calculation accuracy. The model with the initial fault of branch 27 is repeatedly calculated by using the optimization algorithm, and the F value converges to about 1.54. It can be seen that after the improved algorithm, the defect that the particle is easy to fall into the local optimum is obviously improved. An example shows the effectiveness of the improved algorithm.

5 Conclusion In this study, the safety level of the system is changed by adjusting the output of the generator set, and the relevant optimization model and algorithm are given. The main conclusions are as follows: 1. On the basis of cascading trip, this study gives the safety level index and calculation method of the system for cascading trip. The safety level index represents the electrical distance between the system and the cascading trip fault. The larger the safety level index value is, the safer the system is. 2. Combined with the action of relay protection, this study considers the relationship between the electric quantity of cascading disturbed branch and the nodal injection power. According to the sensitive relationship of PV nodes to the disturbed branch,it is beneficial to master the key PV nodes in the cascading trip and take preventive measures in urgent situations. 3. Improve the solution algorithm, based on the combination of sensitivity analysis and basic PSO, it is found that the improved algorithm improves the accuracy of model solution, and can more quickly and accurately find the unit output distribution most suitable for the current system.

References 1. Cheng, L.-Y., Zhang, B.-H., Hao, Z.-G., Li, P., Wang, C.-G., Shu, J.: Research on fast control algorithm based on comprehensive sensitivity analysis. Electric Power Autom. Equip. 29(04), 46–49 (2009) 2. Hui-Ming, X., Bi, T.-S., Huang, S.-F., Yang, Q.-X., Ma, R.: Control strategy for preventing chain trip based on wide area synchronous measurement system. Chin. J. Electr. Eng. 19, 32–38 (2007) 3. Wang, H.: Prevention Strategies to Improve the Safety Level of Power Grid for Chain Trip. Fujian University of Technology (2018) 4. Deng, H.-Q., Wu, P.-P., Lin, X.-Y., Lin, Q.-B., Li, C.-G.: A method to prevent cascading trip in power network based on nodal power. Genet. Evol. Comput. (2020) 5. Wang, X.-Y.: Analysis of Topological Parameters of Complex Power Grid and Research on Prevention and Control of Cascading Faults. Northeastern University (2014) 6. Wei, X.-G., Gao, S.-B., Li, D., Huang, T., PI, R.-J., Wang, T.: Transmission line vulnerability analysis based on chain fault network diagram and different attack modes. Chin. J. Elect. Eng. 38(02), 465–474 (2018) 7. Zhu, J.-W.: Power System Analysis. China Electric Power Press, Beijing (1995)

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8. Xu, G.: Research on Comprehensive Evaluation Method of Transmission Network Vulnerability Caused by Tidal Current. Shijiazhuang: Hebei University of Science and Technology (2014) 9. Deng, H.-Q., Li, P.-Q., Zheng, R.-J.: Analysis of disrupted branches and their associated nodes in power system cascading failures. J. Fujian Inst. Eng. 13(3), 223–228 (2015) 10. Wang, B., Ten, H.: Particle swarm reactive power optimization algorithm based on sensitivity analysis. Sichuan Electr. Power Technol. 2007(01), 15–18 11. Yan, B.-L.: Research on Grid Chain Trip Hazard Analysis Method Based on Node Injected Power. Fujian Institute of Technology (2019) 12. Deng, H., Li, C., Yang, B., Alaini E., Ikramullah K., Yan R.: A method of calculating the safety margin of the power network considering cascading trip events. In: Pan, J.S., Li, J., Tsai, P.W., Jain, L. (eds.) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol. 157. Springer, Singapore (2020) 13. Deng, H.Q., Lin, X.Y., Wu, P.P., Li, Q.B., Li, C.G.: A method of power network security analysis considering cascading trip. In: Pan, J.S., Lin, J.W., Liang, Y., Chu, S.C. (eds.) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore (2020)

Optimal Power Generation Output Considering Cascading Failure Hui-Qiong Deng1,2 , Jie Luo1,2(B) , Qin-Bin Li2 , Rong-Jin Zheng2 , Pei-Qiang Li2 , and Kuo-Chi Chang1,3,4,5 1 School of Information Science and Engineering, Fujian University of Technology, Fuzhou

350108, China [email protected] 2 Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control, Fuzhou 350108, China 3 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China 4 College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 5 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. Aiming at the cascading fault phenomenon of power system, this paper analyzes the disturbed branches involved in the initial fault and their relationship with the power generating nodes. Firstly, according to the performance of the initial fault branch in the cascading fault phenomenon and the action of relay protection, an expression form of evaluating the severity of the initial fault branch is given. Secondly, the relationship between the current of the residual system branch and the injection power of the nodes after the initial fault removal is analyzed. Then, on the basis of synthesizing the severity of the disturbed branch and its correlation with the power generating nodes, the key power generating nodes on the seriously disturbed branch are selected by clustering analysis. Finally, based on the key power generation nodes, the optimal power generation output model considering the power generation operation cost was established, the model was solved by particle swarm optimization (PSO), the model was simulated by IEEE39 bus system in MATLAB environment, and the correctness of the established model and method was verified. Keywords: Power system · Cascading fault · Power generation nodes · Optimal power generation output · Particle swarm optimization (PSO)

1 Introduction With the increasing scale of the interconnected power grid, the interaction between the internal components of the power grid is more and more complex. The major power blackouts caused by the grid chain reaction caused by the initial failure occur frequently at home and abroad. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_14

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In recent years, some scholars have made very fruitful research on cascading failures in large power grids. In reference [1], the evolution model of artificial power system including short-term and long-term dynamic process (OPA model) is established to simulate the catastrophic process of large power grid, and the self-organized critical characteristics of cascading faults are analyzed through the simulation results. In reference [2], a risk assessment model for cascading faults considering the frequency regulation characteristics of the system is established. In reference [3], the dynamic simulation method is used to simulate the transient process of power grid, and the influence of DC control mode on the characteristics of cascading fault is studied. In the aspect of cascading fault control measures, the academia has also carried out fruitful research. Reference [4] establishes the framework of wide area cooperative control system for cascading faults to avoid the occurrence of cascading faults. In reference [5], a line protection blocking strategy based on online identification of power flow transfer is proposed to block the chain reaction of power grid. In reference [6], an expression for evaluating the severity of the cascaded disturbed branches is proposed. Based on the synthesis of the severity of the cascaded disturbed branches and its correlation with the nodes, an algorithm for selecting the associated nodes of the seriously disturbed branches is formed to divide them. In reference [7, 8], the relationship between residual system current and nodal injection power after initial fault removal is analyzed.

2 Severity Evaluation Index of Interlocking Disturbed Branch When a branch L ij (the branch between node i and node j) of the power grid has an initial failure and exits operation, and the power flow of the power grid is redistributed, for any remaining branch L ok in the power grid, the form given in formula (1) can be used to measure whether cascading trip occurs. Hok.dt = |Hok.st | − |Hok |

(1)

In the formula, H ok and H ok.st are the quantities related to the configuration of branch L ok backup protection. The specific form can be set according to the action equation of backup protection. If the backup protection configured by the branch L ok is current protection, H ok.st is the current setting value of the branch L ok , and H ok is the current value measured by the branch L ok . H ok.dt in formula (1) reflects the distance between H ok and H ok.st . Obviously, when H ok.dt ≤ 0, the branch L ok will be cut off due to cascading fault, when H ok.dt > 0, the branch L ok will not be cut off due to cascading trip. In this study, the backup protection is current protection. Since formula (1) is analyzed by DC method, its error can reach 10%. In order to reduce the error, formula (2) is rewritten as follows: aok = |Iok.st | − |(Iok /0.9)|

(2)

In the formula, aok reflects the severity of the chain reaction disturbance to the branch L ok after the initial fault removal. According to DC method, when the initial fault of branch L ij is removed and the power flow is redistributed, the relationship between branch current vector I and nodal

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injection power vector P can be deduced [6]. I = Sp Pn

(3)

In the formula, S p represents the correlation matrix between the injected power vector of the grid node and the branch current vector. According to formula (3), after the initial fault branch is removed, the current of any branch can be expressed as: I ok =

n 

Sok.mPm

(4)

m=1

In the formula, S ok.m represents the sensitivity of node m to branch L ok , and S okm Pm represents the actual impact of node m on the severity of cascading trip of branch L ok .

3 Optimization Model Solution Based on PSO In the algorithm, considering that the disturbed branch first involves the initial fault branch. Therefore, considering all the initial fault branches, the main idea of the algorithm is as follows: Step 1: For the selected initial fault, formula (2) is used for calculation, and according to the calculation results, it is determined whether the expected initial fault satisfies aok ≤ 0. If it does, the analyzed expected initial fault is selected into the initial fault set G1, so as to preliminarily screen out the severe initial fault branch. Step 2: According to the initial fault set G1 selected in Step 1, for each initial fault in the set, its corresponding severely chained disturbed branch is recorded according to aok ≤ 0, and set G2(h) is formed, where h represents the initial fault branch corresponding to h. Step 3: For the disturbed branches in G2 (h) corresponding to each initial fault branch in G2, S ok.m Pm (m = 1, 2,…,n) data in formula (4) is extracted, and then take S ok.m Pm as the characteristic input to conduct clustering division. After the division, the power generation nodes which play a key role in the disturbed branch can be screened out, and the node-set G3 can be formed. Step 4: After finding out the key power generation nodes of the disturbed branch, taking the nodal injection power as the particles, the PSO is used to calculate the minimum economic cost of power generation, that is, the objective function is minimized. After the iterative stop condition is reached, the active power of the generator is output, and the optimization is finished.

4 Example Analysis This paper is based on the simulation of IEEE39 bus system. The parameters of system components are in unit value, and the power reference value is 100 MVA. The system wiring diagram is as follows (Fig. 1):

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Fig. 1. IEEE39 bus system wiring diagram

1. The expected initial fault is set as all the branches in the example system, for each initial fault branch, first calculate the current of each branch in the remaining system according to formula (4), then calculate the aok value of each branch according to formula (2), and judge whether each branch meets the cascading trip condition according to the value of aok . For the current fixed value of the backup protection, it is assumed to be 2.6 times of the current in the ground state power flow state. This value is mainly for the demonstration of the calculation example, and it is an assumed data. In the subsequent analysis, for each initial fault, as long as any branch in the remaining system satisfies aok ≤ 0, the initial fault branch is sent into set G1. After analysis, a total of 4 branches enter the set G1, which are L 1-2 , L 2-3 , L 21-22 , L 26-27 , where L 1-2 represents the branch between nodes 1 and 2, and so on. 2. In set G1, for each initial fault branch, the remaining disturbed branch is analyzed in detail, and whether it meets aok ≤ 0 is analyzed, then the branch satisfying the condition is recorded, and set G2 is given. As an example, here is given that the most severe disturbed branch after the initial failure of branch L 21-22 is branch L 22-23 . 3. Taking S ok.m Pm of branch L 22-23 as characteristic quantity then realize the division of all power generating nodes to evaluate the effect of different generating nodes on branch L 22-23 .

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In IEEE 39 bus system, node 31 is the balance node and the power generation nodes are the nodes {30, 32, 33, 34, 35, 36, 37, 38, 39}. Figure 2 shows the S ok.m Pm data corresponding to L 22-23 of the disturbed branch, in which the abscissa represents the node number.

Fig. 2. S ok.m Pm data

It can be seen from the figure that node 35 and node 36 have a great influence on branch L 22-23 . Besides node 35 and node 36, other power generation nodes have zero effect on branch L 22-23 . Therefore, node 35 and node 36 are regarded as the key power generation nodes of the branch. 4. For the key power generation nodes of the disturbed branch, an optimization model with the lowest operating cost of the system as the objective function was established, and the PSO was used to solve it, so as to obtain the optimal generator output. This paper refers to the operation cost coefficient data of generator node in literature [8], and the data table is as follows (Table 1): According to the data in the table. Figure 3 is the objective function in formula (9) calculated by PSO, i.e., generation operation cost. The abscissa in the figure represents the number of iterations, and the ordinate is generation operation cost. It can be seen from the figure that the final generation operation cost is 25066. Table 2 shows the active power of each power generation nodes with the minimum operating cost of power generation calculated by PSO. The unit of active power is MW, and the active power of the power generating nodes obtained is the optimal generating output of the current system. According to the analysis of the above results, the power operation cost is reduced from 38,207 to 25,066 by PSO, and the final active power of the generator is also reduced accordingly. By setting different disturbed branches, the corresponding key power generation nodes are screened out, and the generation operation cost is reduced to a certain extent. Thus, the most suitable output power of the generator for the system is obtained, which is helpful to take further measures to prevent the cascading trip.

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Table 1. Node operation cost coefficient Node ai

bi

ci

30

0.0128 17.82 10.15

32

0.0109 12.89 6.78

33

0.0044 13.29 39

34

0.0459 15.47 74.33

35

0.03

36

0.0098 22.94 58.81

37

0.0109 12.89 6.78

38

0.0106

39

0.011

10.76 32.96

8.34 64.16 26.7

17.95

Fig. 3. Operation cost of power generation

5 Conclusions The phenomenon of cascading trip is closely related to the action of relay protection of disturbed branches. In this paper, combined with the action of relay protection, firstly, according to the action equation of backup protection of disturbed branches, the evaluation of disturbed branches is given, and the serious initial fault branches and corresponding disturbed branches are screened out. Then, by studying the relationship between the electrical quantity of the disturbed branch and the nodal injection power and grouping different power generating nodes according to their actual functions to the disturbed branch, find out the key power generating nodes. Finally, an optimal generation output model considering the generation operation cost is established, and PSO is used

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Before adjustment

After adjustment

30

250

237.13

32

650

493.16

33

632

474.28

34

508

372.39

35

650

492.46

36

560

414.49

37

540

498.87

38

830

672.35

39

1000

964.98

to analyze the simulation examples on IEEE39 bus system, and the generation operation cost is minimized by adjusting the generator output power. The example shows that the optimization model and method proposed in this paper have certain effectiveness and practicability, ensure the security of power system, and make the system reach the optimal economic output, which is conducive to further take defensive measures in emergency. Acknowledgements. This research was financially supported by Scientific Research Development Foundation of Fujian University of Technology under the grant GY-Z17149 and Scietific and Technogical Research Project of Fuzhou under the grant GY-Z18058.

References 1. Carreras, B.A., Lynch, V.E., Dobson, I., et al.: Complex dynamics of blackouts in power transmission systems. Chaos, 14(3), 643–652 (2004) 2. Alizadeh, O.M., Cherkaoui, R.: Blackouts risk evaluation by Monte Carlo simulation regarding cascading outages and system frequency deviation. Electr. Power Syst. Res. 89(4), 157–164 (2012) 3. Tu, J.Z., Xin, H.H.: On self-organized criticality of the east china AC-DC power system—the role of DC transmission. IEEE Trans. Power Syst. 28(3), 3204–3214 (2013) 4. Jing, X, Xiaomin B., Bin, H.: Research on wide area cooperative pre-control system for cascading failure. Power Syst. Technol. 37(1): 131–136 (2013) 5. Yan, S., Wuneng, L., Ruqi, L., et al.: Power flow control of electromagnetic loop network based on distribution coefficient and phase angle matching analysis. Power Syst. Prot. Cont. 43(17), 22–28 (2015) 6. Deng, H.-Q., Li, P.-Q., Zheng, R.-J.: Analysis of disturbed branches and their related nodes in the cascading failures scenario in power network. J. Fujian Inst. Eng. 13(3), 223–228 (2015) 7. Fan, W.-D., Deng, H.-Q.: Analysis of key nodes in the cascading tripping event in power network. J. Fujian Inst. Eng. 13(06), 578–658 (2015)

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8. Luo, C., Lin, F., Li, Zi., Yang, J.: Optimal power flow model considering operation risk of cascading failure. Smart Power 46(01), 57–62 (2018) 9. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 10. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006 11. Chih, L., Kuo, C., Chun-Yu, C.: Study of high-tech process furnace using inherently safer design strategies (III) advanced thin film process and reduction of power consumption control. J. Loss Prev. Process Ind. 43, 280–291 (2015)

A Survey of Common IOT Communication Protocols and IOT Smart-X Applications of 5G Cellular Kai-Chun Chu1 , Elias Turatsinze2,3 , Kuo-Chi Chang2,3,6,7(B) , Yu-Wen Zhou2,3 , Fu-Hsiang Chang4 , and Ming-Tsung Wang5 1 Department of Business Management, Fujian University of Technology, Fuzhou, China 2 School of Information and Engineering, Fujian University of Technology, Fuzhou, China

[email protected]

3 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of

Technology, Fuzhou, China 4 Department of Tourism, Shih-Hsin University, Taipei, Taiwan 5 Institute of Communications Engineering, National Yunlin University of Science and

Technology, Yunlin, Taiwan 6 College of Mechanical and Electrical Engineering, National Taipei University of Technology,

Taipei, Taiwan 7 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. New technologies and the potential development of Internet-connected devices have made the IoT one of the main concerns in the areas of computing. In this review paper, Bluetooth, Zigbee, Z-Wave, Wi-Fi, 5G cellular, NFC, and LoRaWAN were described. Furthermore, IOT applications like smart city, smart energy and grids, smart transport, smart buildings, smart home, smart industrial manufacturing, smart Health, and smart farming were also described. However, every communication technology has its own merits and demerits when we consider scalability, cost, and network requirements. The survey provided sufficient knowledge to select the best communication protocol suitable for the application based on network reliability, roaming capabilities, recovery mechanisms, and costs. Keywords: IOT · IOT communication protocols · Bluetooth · Zigbee · 5G cellular

1 Introduction The IOT enables us to connect different devices for communication and data sharing. The objectives of this paper are to make a survey on the commonly used IOT communication protocols and see the application of IOT in different aspects to change and improve the welfare and the standards of the people. IoT technology facilitates the connection between the real environment and computer-based systems. Today’s modern communities promote the significant development of IoT technology by virtually integrating IoT © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_15

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and people-to-people communication. Due to the current research interest in the Internet of Things, each year new protocols are being standardized. Therefore, survey reports are continuously written to highlight different aspects of standardization associated to the Internet of Things. This paper is composed of the IOT ecosystem, IOT communication protocols, IOT Smart-X applications, discussion, conclusion, and references [1–4].

2 Internet of Things Communication Protocols 2.1 Bluetooth LE BLE is among the best wireless technology for monitoring and using short-term services and is anticipated to be combined into millions of devices (Fig. 1). BLE is a low voltage solution to control power consumption compared to previous Bluetooth versions. Other wireless network solutions (such as Zigbee or Z-Wave) are also very common, but BLE is the best option when you enter an application field that requires a multi-hop network [2–5].

Fig. 1. Bluetooth low energy architecture

2.2 Zigbee Zigbee is designed to be used for devices of low power consumption, low rate, and low duty cycle. Although the utmost transmission speed of Zigbee technology is 250 kbps which is very small relative to Bluetooth 4.0 and BLE, Zigbee is appropriate for the applications requiring an oversized number of devices and little data traffic (Fig. 2) [5–7]. 2.3 Z-Wave Z-Wave with the Zigbee wireless can be a mesh network technology of low-power where any node on the network can send and receive control orders through walls or floors and

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Fig. 2. Zigbee topology

use intermediate nodes to bypass potential obstacles in the home or clogged radio. ZWave operates in a frequency range of fewer than 900 MHz. It is addressed to relatively few nodes with a maximum of 232 devices, but the manufacturer recommends no more than 30–50 devices (Fig. 3) [4–8].

Fig. 3. Z-Wave wireless network

2.4 Wi-Fi Recent advances in network technology made various devices communicate with one another by employing a style of light emission and wave emission technologies. Wi-Fi could be the best instance of those modern technologies that permit computers to communicate and foremost popular IoT communication. It provides a variety of many megabits per second, which could be a sensible choice for file transfer,

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but maybe an excessive amount of power could be consumed for several IoT applications. Wi-Fi allows people to access the net from anywhere within the area provided (Fig. 4) [6–8].

Fig. 4. Wireless router network diagram

3 5G Cellular 5G cellular is the fifth generation of wireless technology in digital cellular networks and has been widely employed from china since 2019. By the continual development of mobile communication technology, the data rate is getting faster and faster compared to old technologies like 4G, 3G, etc., like previous standards; the coverage range is split into zones named cells served by separate antennas. 5G speeds are ranging from about 50 Mbit/s to tens of Gbit/s, which is almost 100 times faster than 4G. The frequency is more than 24 GHz till 72 GHz (Fig. 5) [9–12].

4 Near-Field Communication (NFC) It is among new technology of emerging short-range wireless connection which utilizes flux induction to allow short-range communication between electronic devices. NFC is predicated on RFID technology and provides a medium for identification protocols that verify secure data transmission. Tagged RFID technology objects contain transponders that carry messages within the sort of signals. RFID readers are accustomed to reading these messages. NFC allows to perform intuitive, secure, wireless transactions by obviously touching or bringing the device closer, accessing digital content, and connecting electronic devices. NFC supports reciprocal communication inside devices and the

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Fig. 5. 5G cellular network architecture

ability to inscribe RFID chips. This technology leads to the possibility of developing high-level applications like payments, secure data exchange, and authentication [6–14].

5 LoRaWAN Once the IoT monitoring system is undertaken, LoRaWAN fortified IoT devices are becoming a reliable answer due to its long life, low power consumption, and long distance. It specifically addresses the need for low energy consumption and supporting the large number of networks handling lots of IOT devices with data rates in the interval of 0.3–50 kbps. The LoRaWAN architecture has three main components which are the Internet server, the gateway (GW), and the terminal nodes (Fig. 6) [9–16].

6 IOT-X Applications 6.1 Smart Cities IOT technology is a new communication paradigm that foresees the coming future where human social and economic activities continue to shift to urban centers where everyone wishes to measure within the city, Smart digital and telecommunications technologies are being deployed in cities to boost management efficiency and improve the well-being of the residents. To attain this, IOT deployment will make the evolution of buildings, energy, planning, mobility, and ICT, smart governance, and smart citizens. The Web and broadband network technology are the promoters of electronics services which is becoming increasingly for cities development, and cities are playing the key role as driving of an innovation in several fields like health, etc. [12–17].

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Fig. 6. LoRaWAN architecture

6.2 Smart Energy and The Smart Grid By the exponential growth of population throughout the planet and therefore the augmentation of the general public awareness about exchanging the paradigm of the policies in power consumption, the energy generation should not only be supported by fossil re-sources or other harmful energy sources. The evolution of the smart grid is anticipated to appreciate a replacement transmission network concept that will efficiently route the energy generated by centralized and distributed generation to end-users with high security and high-quality standards. 5G network and 5G based IOT will undoubtedly provide the infrastructures for disaster recovery due to 5G transfer speed, high flexibility, strong security, and low energy consumption. However, no smart grid has started applying 5G technology [3–17]. 6.3 Smart Home, Smart Buildings, and Infrastructure The role of Wi-Fi in home automation is increasing mostly owing to the nature of the network of electronic devices deployed, where the electronic devices (televisions, audio and video receivers, mobile devices, etc.) are being parts of the home IP networks. Therefore, continual of the penetration rate of wireless networks increases the adoption of mobile computing; such devices can be tablets, cell phones, TV, etc. Some organizations are working for equipping homes with technology that allows people to utilize one device to regulate and monitor all electronic devices. This technology targets to environmental monitoring, power management, subsidized living, comfort and convenience, and compatible platforms that use intelligent sensor networks to produce status information of the house [1–17]. 6.4 Smart Industrial Manufacturing The Internet of Things not only affects transportation, healthcare, or smart homes but also the IOT works to the economic environment. The role of the IOT is prominently

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increasing to allow people to access the equipment and machines hidden in well-designed silos in manufacturing systems like monitoring of gas, water, and oil levels in storage tanks. This development will allow IT departments to further penetrate digital manufacturing systems. The IOT is connecting the industry or factory to a full new range of applications throughout the assembly process. It ranges from connecting factories and industries to the smart grid, machine auto-diagnosis, and assets control, sharing production facilities as services, or enabling greater flexibility and within the assembly system itself and monitoring of temperature or air quality indoor or outdoor [5–15]. 6.5 Smart Health The Internet of Things (IoT) includes three core processes, full spectrum sensing, reliable transmission, and intelligent processing. By combining sensors, information technology, computing, and available dynamic IoT devices, IoT can do remote communication between hospitals and patients, and medical equipment can ultimately improve this medical condition. Based on advanced information technology (IT) and electronics medicine, the medical Internet of Things (mIoT) has undergone four major developments, including the evolution of wireless sensing technology, the use of Internet technology for clinical medicine, using RFID and AI applications to implement IoT medical models. New 2019 Coronavirus, “2019-nCoV,” was discovered in Wuhan and was given this name by the WHO on January 12, 2020, and medical IoT (mIoT) can be used to fight against this type of virus. Information technologies like IoT, mobile Internet, big data, cloud computing, microelectronics, 5G, computing, and biotechnologies are the foundations of smart health care. Those new emerging technologies are often employed in all aspects of intelligent healthcare [8–17]. 6.6 Smart Farming The Internet of Things makes agriculture becomes smart one by helping the farmers monitor and access the information about the status of the fields by using wireless sensor network (WSN) where sensors are installed in everything needed to be monitored and also since the farmers want to know the best seasons of cultivating, the use of this technology in forecasting the future climate to determine whether it will rain or not and all geographical information. Autonomous robotic vehicles are being deployed for agricultural purposes to facilitate the farmers to fertilize, harvest, irrigation, and development of drones with automatic control for surveillance and monitoring purposes [3–16].

7 Discussion The BLE technology is designed for short-range applications specifically at the connectivity of mobile devices near relatively higher data rates. Zigbee aims at automation where its devices are capable of sending data to a long distance using a mesh network. Z-Wave devices will be used for remote regions and management in an exceeding sort of smart homes and IoT devices and applications. 5G is very important in IOT because

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faster networks and higher capacity are needed to meet connection needs but expensive compared to other cellular networks. NFC devices are utilized in wireless payment systems, just like those utilized in electronic ticket smart cards, and credit cards and permit mobile payments to replace these systems with relatively higher data rates and use much power. LoRaWAN network is the best suited to outdoor Internet of Things solutions, like airports, smart cities, agriculture, etc. The key to picking a wireless communication technology is to narrow down the requirements to able to specialize in what works or application fields you are visiting work on where it may be a smart grid, smart cities, smart health, etc., and the desired power consumption, operating range, data transmission speed, and value are the most criteria for choosing wireless communication. The future work should mainly focus on the use of LoRaWAN protocol and 5G cellular network in Smart-X application.

8 Conclusion In this paper, most used IOT communication protocols such as Bluetooth LE, Zigbee, Z-Wave, Wi-Fi, 5G cellular, NFC, and LoRaWAN were described with their applications in smart energy, smart home, smart cities, smart grid, smart transport, smart buildings, and infrastructures, smart industrial manufacturing, smart health, and smart farming. Today, the IOT supports dozens of different IOT protocols. With that in mind, many IOT experts have begun calling for standardization of global protocols. However, the selection of communication protocol depends on different criteria such as coverage, power consumption, cost, and data rate together with the condition of the scenario and application domain.

References 1. Hiwale, P., et al.: IOT based smart energy monitoring. Int. Res. J. Eng. Technol. (IRJET), 05 (03), 1–5 (2018) 2. Rani, D., Gill, N.S.: Review of Various IoT Standards and Communication Protocols. Int. J. Eng. Res. Technol. 12(5), 647–657 (2019) 3. Aswini, D., et al.: Power consumption alert system. Int. Res. J. Eng. Technol. (IRJET), 04(03), 1–6 (2017) 4. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006 5. Singh, S.: Eight Reasons Why 5G Is Better Than 4G. Altran. Archived from the original on May 25, 2019. Retrieved May 25, 2019 (March 16,2018) 6. Forum, C.L.X.: 1 Million IoT Devices per Square Km- Are We Ready for the 5G Transformation?”. Medium. Archived from the original on July 12, 2019. Retrieved July 12,2019 (June 13, 2019) 7. Industry Proposal for a Public-Private Partnership (PPP) in Horizon 2020 (Draft Version 2.1), Horizon 2020 Advanced 5GNetwork Infrastructure for the Future Internet PPP. [Online].Available: http://www.networks-etp-eu/fileadmin/user_upload/Home/draftPPP-proposal.pdf

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8. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 9. Ertürk, M.A., et al.: A Survey on LoRaWAN Architecture, Protocol and Technologies. Future internet. Received: 7 September 2019; Accepted: 11 October 2019; Published: 17 October 2019 10. LoRa Alliance. LoRaWAN v1.0 Specification; LoRa Alliance: Fremont, CA, USA, 2015. Available online: https://lora-alliance.org/resource-hub/lorawanr-specification-v10. Accessed on 15 Sept 2019 11. Chen, C.-H., et al.: Introduction to the special issue: applications of internet of things. Symmetry. Received: 19 August 2018; Accepted: 20 August 2018; Published: 1 September 2018 12. Kakran, S., Chanana, S.: Smart operations of smart grids integrated with distributed generation: a review. Renew. Sustain. Energy Rev. 81, 524–535 (2018) 13. Hui, H.: et al.: 5G network-based Internet of things for demand response in smart grid: A survey on application potential. Appl. Energy. (October 2019) 14. Zheng, P., et al.: Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Front. Mech. Eng. 13(2) (2018): 137–150 15. Chu, K.C., Horng, D.J., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. J. IEEE Access 7, 105562–105571 (2019) 16. Tian, S., et al.: Smart healthcare: making medical care more intelligent. Global Health J. (2019) 17. Villa-Henriksen, A., et al.: Internet of Things in arable farming: implementation, applications, challenges, and potential. Sci. Direct. (2020)

Study of Thermal Power Plant’s Intelligent Fire Detection and Suppression System Via Wireless Sensor Network and Carbon Capture and Storage Technology Kuo-Chi Chang1,2,8,9(B) , Governor David Kwabena Amesimenu1,2 , Tien-Wen Sung1,2 , Kai-Chun Chu3 , Fu-Hsiang Chang4 , Hsiao-Chuan Wang5 , Tsui-Lien Hsu6 , and Ming-Tsung Wang7 1 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of

Technology, Fuzhou, China [email protected] 2 School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China 3 Department of Business Management, Fujian University of Technology, Fuzhou, China 4 Department of Tourism, Shih-Hsin University, Taipei, Taiwan 5 Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan 6 Institute of Construction Engineering and Management, National Central University, Taoyuan, Taiwan 7 Institute of Communications Engineering, National Yunlin University of Science & Technology, Yunlin, Taiwan 8 College of Mechanical & Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 9 Department of Business Administration, North Borneo University College, Sabah, Malaysia Abstract. In terms of fire outbreak, the nature of some big industries and thermal plants seems extremely volatile. The existence of oxygen and fuel cannot be taken out in thermal power plant, and many actions may be originated from heat source. Many researchers have developed more interest in the research field of fire detection in some big industries and thermal plants, especially those who use hydrocarbon-based product as fuel. The fire that normally occurs at these places is normally class B type of fire, and carbon dioxide is used to put them off. This paper proposes a design of an automated fire alarm system which utilizes already existing technologies such as the wireless sensor network technology and carbon capture and storage system to help alleviate the problem of global warming and reduce the cost incurred in existing ineffective fire alarm system. During combustion process in the thermal plant, the carbon dioxide emitted is captured and stored to be used to put off or suppressing fire after early detection by the wireless sensor network with sensors such as optical flame detectors, heat detector, and hydrogen detector. The simulations done on the final designs proved that this system is feasible and can be successfully deployed on a larger scale. Keywords: FPGA · Intelligent fire detection · WSN · Local area network (LAN) · Carbon capture and storage technology (CCS) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_16

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1 Introduction In few decades, global power demands have continued to increase rapidly. This is caused by many factors including: industrialization, population increase, trends in using technology, electricity price, and some economic indicators. However, some operational conditions may impose huge fire risks. Fire is an undesirable and unexpected occurrence. Most thermal power plant fires are unprecedentedly caused by flammable fuel leaks, hydraulic and bearing oil leaks, unexpected system failure, electrical shortages, etc. The ignition of combustible fuel can as well produce explosion with high temperature. Fire incidents caused by flammable liquids and gas are characterized by hydrocarbon properties. According to National Fire Association Standard, hydrocarbon characterized fire are best suppressed by carbon dioxide. Producing electric power by driving steam turbine with burning fossil fuel and biomass is about 84–90% worldwide [1]. In this study, the method to be used is post-combustion capture technique because it can be applied to present power plant. About 90% carbon dioxide can be effectively trapped from the flue gas. The stored carbon dioxide can be discharged to suppress fire in case of unexpected events [1–4]. For the reason of safety and protection of properties, greater efforts should be made to detect fire at its early stage. Automated fire detection equipped with sophisticated sensors to detect fire, and abnormal increase in the temperature is needful in power plants to ensure the safe operation system and protection of equipment. In some autonomous fire alarm system, wireless sensors are employed due to its flexibility and capacity to operate in cumbersome and dangerous areas of interest. The integration of the communication aspect using radio signals, the sensing aspect, and also the limited computation of WSNs to make it a low-cost device with low power and into a small size to detect early situations to be solved was proposed here [3–6]. A base station receives data from each node and is made available to the control panel via LAN. Actions are taken by the control panel upon information received from the base stations; manual release and manual abort call points. The actions could be to sound alarms, activate strobes, and discharge CO2 and emergency shutdown of units. The FPGA is implemented as three subsystems interconnected under a single network. In view of this, the proposed design of this automated fire alarm system has been broken down into three modules namely the sensing unit, networking and controlling unit, and the carbon capture and storage unit. The system will have a huge effect on the amounts of carbon dioxide released into the atmosphere from such plants and ultimately save the power plants a lot of money and protect the planet from a lot of damage. The system will be made up of wireless sensor nodes, manual release call points, manual abort call points, buzzers, strobe, a suppression system, and the Xilinx Vivado Artix-7 FPGA circuit fire control unit.

2 Proposed Fire Alarm System Design Figure 1 shows the block diagram of the suppression subsystem where carbon capture and storage technology will be employed to capture carbon from byproducts of the combustion process in the thermal power plants and store them in storage tanks after which they would be released through ducts or pipes connected from each tank to every

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location with a sensing unit for the suppression of the fire. The post-combustion capture is a type of CCS technique which will be employed in this project, since it is the only technique that is easily integrated with already existing plants and is the cheapest of all currently available techniques.

Fig. 1. Block diagram of carbon capture and storage process

Figure 2 shows the overall block diagram of the system in general. The entire project was conducted in a step-by-step fashion in order to give equal and delicate attention to each module of the system.

Fig. 2. Block diagram of automated alarm system

3 Experimental Design and Verification The proposed design of an automated fire alarm system utilizes already existing technologies such as the wireless sensor network technology and CCS system to help diminish global warming problems and reduce the cost incurred by thermal power stations in the ineffective and partly efficient fire alarm system already in use. In view of this, the proposed design of this automated fire alarm system has been broken down into three

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modules namely the sensing unit, networking and controlling unit, and the carbon capture and storage unit. Each module is looked at in-depth and adapted with the finest of detail with the aim of achieving a comprehensive and practical system design. The system flow diagram is shown in Fig. 3.

Fig. 3. Overall flow diagram of the system

3.1 Wireless Sensor Network An embedded device of tiny sizes and low power battery operated which has limited processing and computing ability that is spatially dispersed and interconnected together to adequately collect data, process the data, and send the data is WSN. The WSNs measure temperature using heat detector, optical flame sensor, hydrogen, etc. The base station acts a gateway between the user and nodes network. It receives packet from the nodes and forward it to the control panel via the LAN. The sink station of the ad-hoc fashion receives all data being collected by the sensor, and the data is transmitted through the LAN to the control center. Figure 4 shows sensor node for sense, processing, data received and communication of the decoded data to be executed. 1. Sensing Unit: This is made up of sensors, analog converter, and digital converter. The unit for processing is fed with digital signals converted from the analog signals

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Fig. 4. A sensor nodes

of the sensors by the ADC [7–11]. Data sent from the sensors when fire is detected are processed and evaluated at the control center, and then, according to the preset rules, the signal is transfigured to suitable alarm indicator. Some sensors to be employed in this project includes: • Ultraviolet/Infrared (UV/IR) Optical Flame Detector: This optical flame detector combines both ultraviolet and infrared wavelength in the analysis of fire. The infrared wavelength from the narrow CO2 is a low-level ultraviolet wavelength between 0.185µ to 0.245µ. A simple voting system sends an output when it receives infrared and ultraviolet signals. The combination of ultraviolet and infrared detection makes the alarm both to solar radiation, artificial lighting, and welding arcs. Hydrocarbon fire of wide range is quickly responded by the sensor. • Heat or Thermal Detector: Heat energy emitted from fire is detected by thermal or heat detectors. When there is heat air or combustion product, the detector is activated. Heat detector compared to thermal detector has higher reliability factor and is quick in detection [12–15]. • Smoke detector: The conditions for a smoke detector to be activated are how the smoke spreads, how it rises, the rate at which the fire burns, and the movement of the air from smoke [13–16]. The ionization type smoke detector is effective on class A and class C types of fire. 2. Processing Unit: The course of action combines data from assigned sensor nodes which performs sensing operation based on a small storage unit [15]. The microcontrollers mostly used in nodes are ATMEGA 16, 128L, MSP 430, and integrated ROM of 8 or 16-bit with corresponding background debug interface, and internal clock module can be used [17, 18]. 3. Power Supply Unit: This has a number of batteries as its power supply to feed the required energy or power to the node components in the system. 4. Communicating Unit: Transceivers made up of transmitter and receiver with shortradio frequency are found in every node. The transmitter produces radio waves using the antenna. The antenna size is influenced by using shorter-range radio frequency,

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the shorter the radio wave range, the higher the frequency and shorter wavelength [19]. The control panel (FPGA) architecture, FPGA, is an integrated circuit which can be reconfigured again and again to perform different tasks as desired. FPGA can theoretically be programmed to function as any other digital integrated circuit. They are arrays composed of high-speed memories and programmable logic blocs with high synchronization acquisition semiconductor modules. The responsibility of the FPGA programming is not to collect instruction but to carry out logic functions and to interconnect. The FPGA circuit on which the proposed alarm system controller is based on is Xilinx Artix-7 XC7A35T and because of its low cost and low power consumption, WSNs are able to be optimized for higher performance. 3.2 Manual Initiating Device This section covers manual activation and manual abort switches, that serves as input devices to the main fire controller. Table 1 shows some of the processes that take place during manual initiative of device. Table 1. Some processes during manual initiative Process

Action

Start CO2 discharge The discharge of the carbon dioxide is initiated by the detection of fire. This can be either activated manually by the manual activation call point or automatically by the sensor nodes Initiate plant alarm

Activation of strobe light and buzzer (horns) are needed to alert staff on the discharge of CO2 as well as to prompt on the status of the current occurrence. The alarms are activated 15 s ahead for carbon dioxide discharge

Local indication

A form of local display can be made on the main controller panel to update on the status of the process area

Unit or plant trips

Detection of fire in a particular zone can lead to a shutdown of the affected unit by the controller or manually based on signal sent to the plant control unit from the fire control unit to prevent the plant from shutdown by itself during major operations

1. Manual Activation Call points / Manual Release Call Point: The manual activation call points are simple switches that are manually activated in case of emergency or failure of the sensor nodes. Most devices employ the use of positional force to activate the alarm system. This technique is used to avoid inadvertent and fraudulent trips. The alarm can be raised by any personnel on the field. Manual activation call points are mostly placed in the main exit way of the compartment.

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2. Manual Abort Call Point: These are simple switches that are manually activated to intercept an already activated alarm system. This is needed to intercept false alarm or situations where the activation of the alarm system is not required. They are mostly placed at vintage point such as the main egress from the process area. 3. Mitigating Actions: Action ranges from the alerting of the main fire controller, control room operator, release of extinguishers, activation of strobe lights and horns, and a complete unit or plant shut down. A close loop system by the control room operator is needed, perhaps to decide on whether to shut down the unit or the plant. 3.3 Technology of Carbon Storage and Capture Carbon dioxide emission into the atmosphere is greatly caused by fossil fuel power plants. The technology suitable for reducing the dangers caused to the atmosphere from power plants and other industries is CCS technology [12–19]. CCS technology employed here is the post- combustion, and in this process, fossil fuel is burnt normal during combustion, and ford of fuel flue gas goes through a channel that has an absorber column. Figure 5 shows schematic for the controller.

Fig. 5. Schematic for the controller

3.4 Codes, Standards, and Regulations of Fire The most recognized authority in protection fire is NFPA, and in so many areas of fire protection, they are responsible for codes publication and their standards (see Table 2) based on NFPA 850 code. Recommendation for protection of fire for fossil fuel plant is provided by the NFPA which contains special installation and design standards.

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Location or facility

Hazard

Class

Fixed detector type

NFPA reference

Office

Ordinary Electric fire

A E

MPS Heat, Smoke

NFPA 101

Control room

Ordinary Electric fire

A E

MPS Smoke

NFPA 75

Turbine compartment

Electric fire Hydrocarbon fire

E B

Heat Optical flame Sensor, MPS

NFPA 850 NFPA 850

Tank or vessel storage

Hydrocarbon fire

B

MPS Heat Optical

NFPA 30

4 Result Analysis and Discussion The process of capturing the CO2 was done successfully with the help of the amine, and there were some equilibrium and kinetic reactions that occurred physically and chemically for the reaction of extraction to be possible. After the action, the extracted CO2 was the channeled into a compressor for it to be compressed and from there to the storage site ready to be used in case of any fire. The sensing part and all cases of VHDL test bench was simulated, and the results of the simulation using Proteus and Xilinx Vivado were successful and showed that the system would work satisfactorily when deployed in the field. The hardware and the software system are given realization in this proposed system. 1. Simulation: Figure 6 shows the wireless sensor node setup. The microcontroller (Arduino UNO) is simulated using Proteus 8 software. 2. Testbench: Figure 7 shows the Testbench results for FPGA controller in VHDL using Xilinx Vivado. The results show that the microcontroller functions well and will perform its required function when deployed in real life to transmit useful information. It will be able to determine whether the alarm input comes from a manual activity such as pulling the manual release and manual abort switches or from an automatic activity such as the sensors in each zone sensing the presence of fire. When this is done, it is able to deploy carbon dioxide to the zone where the fire has been detected in order to fight it. The controller has shown that it can handle multiple inputs and outputs and still function favorably fast.

5 Conclusion and Suggestion The design of the carbon dioxide capture system was done with the use of amine to trap the CO2 , and the amine will be superheated to take the CO2 out and to be compressed and

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Fig. 6. Wireless sensor node setup in Proteus 8

Fig. 7. Simulation results of VHDL Testbench for FPGA controller

sent to the storage site. This compressed CO2 will be used to suppress fire. This study system design for fire alarm detects fire or any other sensitive material that can cause fire damage to individuals or properties and follows a specific step to put the danger off. This system unlike the other systems can give an early indication to the fire as soon as possible even before it worsens. This fire alarm system is able to sense fire at the early stage at the area it covers and to send notification to the control system for immediate action to be taken for the increment of human safety. The implementing of Internet of things so that the system can be monitored wirelessly and controlled via Internet at any place is suggested in the future work.

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References 1. Keith, F., Norton, P., Brown, D.: CO2 emissions from coal-fired and solar electric power plants. Sol. Energy Res. Inst. (2014) 2. Raugei, M., Leccisi, E.: A comprehensive assessment of the energy performance of the full range of electricity generation technologies deployed in the United Kingdom. Energy Policy 90 4, 46–59 (2016) 3. Cebrucean, V., Ionel, I., Dumitru, C.: Post-combustion CO2 -capture from coal-fired power plants: Preliminary evaluation of an integrated chemical absorption process with piperazinepromoted potassium carbonate. Int. J. Greenh Gas Control, 2(4), 539–52 (2008) 4. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 5. Chaamwe, N., Liu, W., Jiang, H.: Wireless Sensor Networks in the Context of Zambia: A Developing Country. In: International Conference on Computer Science and Information Technology, pp. 474–478 (2010) 6. Tanenbaum, A.S., Gamage, C., Crispo, B.: Taking sensor networks from the lab to the jungle. Computer (Long. Beach. Calif) 39(8), 98–100 (2006) 7. Zennaro, M., Pehrson, B., Bagula, A.:Wireless Sensor Networks, a Gt. Oppor. Res. Dev. Countries. In: 2nd IFIP International Symposium Information and Communication Technologies for Development Ctries. Pretoria, no. Oct 2008 (2008) 8. Demirbas, M., Chow, K.Y. , Wan, C.S.: INSIGHT: Internet-sensor integration for habitat monitoring. Workshop Buffalo, no. June 2006, (2006) 9. Kambezidis, H.D., et al.: An investigation on forest-fire risk assessment in selected areas in Greece and Turkey. For. Ecol. Manage., 234(supp-S) (2006) 10. Matin, M.A., IsIam, M.: Overview of wireless sensor network. Wirel. Sens. Networks. Technol. Protoc., 3–25 (2012) 11. Nolan, D.P.: Fire and gas detection and alarm system. In: Handbook of fire and explosion protection engineering principles for oil, Gas, Chemical, and Related Facilities, pp. 303–329 (2019) 12. Chu, K.C., Horng, D.J., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. J. IEEE Access, 7, 105562–105571 (2019) 13. Shu-Guang, M.A.: Construction of wireless fire alarm system based on ZigBee technology. Procedia Eng. 11(none), 308–313 (2011) 14. Vijayalakshmi, S.R., Muruganand, S.: Real time monitoring of wireless fire detection node. Procedia Technol. 24, 1113–1119 (2016) 15. Salahudddin, M., Kamal, Z.H.: Introduction to wireless sensor networks. wirel. Sens. Mob. Ad-Hoc Networks Veh. Sp. Appl. New York, 3–32 (2015) 16. “[Online]. Available: https://blog.pasternack.com/uncategorized/what-are-the-ism-bandsand-what-are-they-used-for/. Pasternack The Engineer’s rf sources, 22 March 2018 (2018) 17. E. Martinez, “[Online]. Available: https://numato.com/blog/fpga-faq.[30],” E. Martinez, 13 August 2018 (2018) 18. Cebrucean, D., Cebrucean, V., Ionel, I.: CO2 Capture and storage from fossil fuel power plants. Energ. Procedia 63, 18–26 (2014) 19. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comp. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006

Game-Theoretic Decision-Making Analysis on Antivirus Sang-Hoon Lee(B)

and Tae-Sung Kim

Chungbuk National University, Cheongju, Republic of Korea {sanghoon,kimts}@chungbuk.ac.kr

Abstract. Information security decision making is important to an organization. Moreover, the methodology of decision making about information security is insufficient. In this paper, we propose game model to analyze antivirus decision making in information security. We conduct empirical analysis using an antivirus report with real data. In order to proceed with game theory, we assume a twoplayer game and set up the payoffs to calculate the expected payoffs and the performance of antivirus. Finally, we find an equilibrium and the optimal proportion for each information security strategy. With an optimal strategy for antivirus selection game, companies can effectively plan information security investments within a limited budget. Keywords: Game theory · Information security investment · Antivirus

1 Introduction More than 50 nations have already put an information security strategy into practice [11]. According to a survey from the computer security institute [8], 60.4% of organizations had security policy, and 31% had formal security software. However, investment in security is disproportionate, because 43.4% of organizations investing in IT infrastructure spent below 5% of their total budgets on information security. There is a lack of research on information security decision making and control. The methodology of past research has been insufficient to measure cyber security investment; thus, quantitative analysis and objective prediction are difficult for cyber security investment decisionmaking [6]. According to a computer security institute survey [8], antivirus is the most used information security countermeasure, and 97% of the responding organizations deploy antivirus software. All antivirus vendors announce that their products guarantee security, but investors cannot always understand the differences among these products to choose one. Antivirus, one information security countermeasure, has many security defense options, such as offline detection, online detection, online protection, and URL protection. When evaluating the performance of each type, it is easy to rank them, but it is difficult to make comparisons. For example, the common vulnerability scoring system (CVSS), one of the standards for assessing the computer system security vulnerabilities, provides a framework for analyzing the impact of IT vulnerabilities. Users can use the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_17

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base score to evaluate the vulnerabilities, but the base score is fixed and unchanged. Thus, we conducted the analysis on antivirus in a more complex environment and with more considerations using antivirus report. In this study, we conducted a two-person game using performance and antivirus as players and examined the results of antivirus products. Through the game-theoretic methodology suggested in this study, it is expected that information security officers and managers can make optimal decisions. The contributions of this article are summarized as follows: • We use real antivirus data for antivirus selection decision-making. We also propose a game model to analyze the product according to each protection type. • We consider the proportion of antivirus software within a two-player game rather than considering simply whether to invest or not in information security countermeasures. • We provide examples of decision making based on numerical results. The rest of the paper is organized as follows. Section 2 reviews the studies about information security investment using game theory. Section 3 introduces the background of the game theory. Section 3 analyzes the data and proposes the model for calculating examples. Section5 discusses the implications of the research and conclusion.

2 Literature Review Alpcan and Basar [1] suggested a system for decision making and control of information security, and game theory provides a framework for analysis, modeling, and decision making in information security. These authors explained the relationship between an intrusion detection system and an attacker and derived a Nash equilibrium by using game theory. To define the value of investment in information security is quite difficult, and information security investment analysis requires a high level of data collection. Therefore, Cavusoglu et al. [2] proposed a return on investment (ROI) methodology based on game theory, overcoming these limitations. In addition, they divided information security into three stages of prevention, detection, and response and analyzed optimal investment decisions using the variables of a firewall, an intrusion detection system, and monitoring at each stage. Cavusoglu et al. [3] analyzed the value of the intrusion detection system with game theory and found a Nash equilibrium by setting payoffs according to the strategy of the company and the attacker. However, they assumed several variables, and therefore, empirical analysis using real data is needed. Cavusoglu et al. [4] analyzed the investment level of information security and vulnerability using game theory. Fielder et al. [5] found a Nash equilibrium based on multiple levels of security policy from the perspective of the organization and the attacker. They also conducted an optimization analysis of a budget related to information security. In the present study on information security investment using game theory, analysis has been carried out using various variables, and equilibrium has been found. Previous research used virtual variables to test hypotheses.

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3 Game Theory and Mixed Strategy Nash Equilibrium Game theory is a theoretical framework in which competitors make strategic decisions based on expectations or predictions of their opponents in an interdependent situation [10]. A strategic game consists of a set of players in the game situation, a set of actions for each player, a strategy used by a player, and a payoff for each strategy. The players of a game have an equilibrium, a solution that maximized utility. In all games where the number of players and strategies is finite, there is at least one equilibrium, which is the Nash equilibrium [7]. It is a pair of strategies such that no player can increase his payoff by unilaterally changing his strategy. The game is divided into pure strategy and mixed strategy [9]. A pure strategy determines all your moves during the game. Thus, the player’s strategy set is a set of pure strategy. A mixed strategy is a probability distribution of possible pure strategies. Each player may play a mixed strategy by randomly choosing from his pure strategies according to a probability distribution. We derive the mixed strategy Nash equilibrium. Suppose that there are two players, P1 and P2 . Each player has two strategies, I and J . The proportion of I is u(0 < u < 1), and the proportion of J is 1 − u. The expected payoff of P1 ’s strategy I is E(I ), and the expected payoff of P2 ’s strategy J is E(J ). EI (I ) and EI (J ) are strategies using I against its opponent player P2 ’s strategies I and J , respectively. EJ (I ) and EJ (J ) are strategies using J against its opponent player P2 ’s strategies I and J , respectively. Then, P1 ’s payoffs are as follows: PI (I ) = u · EI (I ) + (1 − u) · EI (J )

(1)

PI (J ) = u · EJ (I ) + (1 − u) · EJ (J )

(2)

4 Antivirus Report with Performance Test In this study, we used the data of AV-Comparatives Antivirus Report 2017 (https://www. av-comparatives.org) to conduct empirical analysis. AV-Comparatives tests security software such as PC and MAC-based antivirus products and security solutions. The antivirus report is divided into online and offline tests. All the products were installed on a fully up-to-date 64-Bit Microsoft Windows 10 environment. We analyzed AV-Comparatives malware protection test and real-world protection test. The malware protection test was conducted for 21 products, and the tests generally examined the home version. The products of CrowdStrike, eScan, Fortinet, and Seqrite used the business version. The tests were conducted with 20,011 malware samples and evaluated malware intrusion detection rates in both offline and online situations. In the real-world protection test, security products used signature-based detection, heuristic file scanning, and all the product features to prevent intrusions. The real-world protection test was conducted on 1769 URLs infected with malware. Table 1 summarizes the performance of the antivirus products for each test.

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No

Product

Type

Malware protection test

Real-world protection test

Offline detection rate (%)

Online detection rate (%)

URL protection rate (%)

1

Adaware

Home

98.60

98.60

95.36

2

Avast

Home

97.50

99.30

99.60

3

AVG

Home

97.50

99.30

99.60

4

AVIRA

Home

97.00

99.40

99.72

5

Bitdefender

Home

98.60

98.60

99.94

6

BullGuard

Home

98.60

98.60

99.66

7

GrowdStrike

Business

80.10

80.10

97.63

8

Emsisoft

Home

98.60

98.60

98.76

9

eScan

Business

98.60

98.60

96.66

10

ESET

Home

97.20

97.20

99.04

11

F-Secure

Home

98.60

98.90

99.94

12

Fortinet

Business

98.60

98.60

98.47

13

Kaspersky

Home

94.10

97.80

99.72

14

McAfee

Home

47.90

97.70

98.87

15

Microsoft

Home

84.90

88.80

99.15

16

Panda

Home

51.30

79.70

100.00

17

Seqrite

Business

98.60

98.60

96.78

18

Symantec

Home

78.70

99.90

99.77

19

Tencent

Home

98.60

98.60

99.94

20

Trend Micro

Home

49.80

98.60

99.94

21

VIPRE

Home

98.60

98.60

99.38

4.1 Game-Theoretic Model and Analysis In order to analyze antivirus using game theory, we set up a payoff matrix of performance strategy and antivirus strategy. We set up the products strategy for all the antivirus products in the report. This model assumes that each product has the same price P. The performance strategies were set up for three types: offline detection rate, online detection rate, and URL protection rate. In order to proceed with the game theory analysis, we assumed a two-player game in which the performance (test rate) and the antivirus (price) are the players. Two performance strategies and two antivirus strategies are selected, and each player has limited rationality. We assumed that x was the proportion of the strategy 1 used by the performance, with 1 − x as the proportion of strategy 2. We also assumed that y was the

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proportion of the antivirus strategy 1, with 1 − y as strategy 2. We used a mixed strategy to investigate the proportion between the products. Thus, dominant strategies were not considered because the dominant strategy is Nash equilibrium; if a comparison between the two products and the two strategies is superior to another, these pairs of strategies are excluded because the decision can be made without conducting the analysis. We analyzed two examples. A Case Study of Antivirus Game 1 We calculated the expected payoff of the performance strategy to obtain Nash equilibrium of Table 2. For Adaware and CrowdStrike, the payoff of the performance strategy 1 (online detection rate) is 98.6% and 80.1%, respectively. The payoff for the performance strategy 2 (URL protection rate) is 95.36% and 97.63%, respectively. By performance perspective, mixed strategy Nash equilibrium of Adaware is 0.84 and CrowdStrike is 0.16, respectively. Table 2 Adaware-CrowdStrike payoff matrix Performance

Antivirus Adaware (%) CrowdStrike (%)

Online detection rate 98.6

80.1

URL protection rate

97.63

95.36

A Case Study of Antivirus Game 2 A case study of antivirus game 2: We calculated the expected payoff of the performance strategy to obtain Nash equilibrium of Table 3. For Kaspersky and Seqrite, the payoff of the performance strategy 1 (offline detection rate) is 94.1% and 98.6%, respectively. The payoff for the performance strategy 2 (URL protection rate) is 99.72% and 96.78%, respectively. By performance perspective, mixed strategy Nash equilibrium of Kaspersky is 0.24 and Seqrite is 0.76, respectively. Table 3 Kaspersky-Seqrite payoff matrix Performance

Antivirus Kaspersky (%) Seqrite (%)

Offline detection rate 94.1

98.6

URL protection rate

96.78

99.72

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5 Results and Conclusion We suggest game theory to analyze antivirus selection strategy. We used the offline detection rate, online detection rate, and URL protection rate in the antivirus report to analyze the performance of products. We calculated the expected payoffs in order to find Nash equilibrium of the performance. In Table 2, mixed strategy Nash equilibrium was 0.84. Assuming that a company has budget C for information security investment, the proportion of optimal investment strategy would be 0.84 C for Adaware and 0.16 C for CrowdStrike. The result shows that performance of Adaware is better than that of CrowdStrike despite the URL protection rate of Adaware is lower than that of CrowdStrike. In Table 3, mixed strategy Nash equilibrium was 0.24. Assuming that a company has budget C for security investment, the proportion of optimal investment strategy would be 0.24 C for Kaspersky and 0.76 C for Seqrite. The result shows that performance of Seqrite is better than that of Kaspersky despite the URL protection rate of Seqrite is lower than that of Kaspersky. In this paper, we conducted empirical analysis using an antivirus report with real data. We found an equilibrium for the optimal solution using game theory. In previous research, there had only been considerations of whether to invest or not. We analyzed various conditions and considered the investment proportion for information security countermeasures. Finally, we found the optimal proportion for each information security strategy. Thus, companies can make an optimal investment decision with their limited budgets. Further studies consider as follows. First, there are several information security countermeasures that companies can consider, such as firewalls, intrusion detection systems, intrusion protection systems, however, we considered only antivirus software. If several information security countermeasures are collected and analyzed in addition to this study of antivirus software, the ability to make successful information security decision making will be further enhanced. Second, this paper assumed that the price of the antivirus software is the same; if we collect and apply actual prices, we can make a more realistic analysis. Third, game model is supposed to be a two-player game, so only the relationship between two players is analyzed. Further analysis of the assumptions of n-player and strategies will aid decision making in situations with multiple choices.

References 1. Alpcan, T., Basar, T.: A game theoretic approach to decision and analysis in network intrusion detection. In: 42nd IEEE International Conference on Decision and Control, pp. 2595–2600 (2003) 2. Cavusoglu, H., Mishra, B., Raghunathan, S.: A model for evaluating IT security investments. Commun. ACM 47(7), 87–92 (2004) 3. Cavusoglu, H., Mishra, B., Raghunathan, S.: The value of intrusion detection systems in information technology security architecture. Inf. Syst. Res. 16(1), 28–46 (2005) 4. Cavusoglu, H., Raghunathan, S., Yue, W.T.: Decision-theoretic and game-theoretic approaches to IT security investment. J. Manage. Inf. Syst. 25(2), 281–304 (2008) 5. Fielder, A., Panaousis, E., Malacaria, P., Hankin, C., Smeraldi, F.: Decision support approaches for cyber security investment. Decis. Support Syst. 86, 13–23 (2016)

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6. Kong, H.K., Kim, T.S., Kim, J.: An analysis on effects of information security investments: a BSC perspective. J. Intell. Manuf. 23(4), 941–953 (2012) 7. Nash, J.: Non-cooperative games. Ann. Math. 54(2), 286–295 (1951) 8. Richardson, R.: 2010/2011 CSI computer crime and security survey. Comput. Secur. Inst. (2011) 9. Smith, J.M.: The theory of games and the evolution of animal conflicts. J. Theor. Biol. 47(1), 209–221 (1974) 10. Von Neumann, J., Morgenstern, O.: Game Theory and Economic Behavior. John Wiley and Sons, New York (1944) 11. Von Solms, R., Van Niekerk, J.: From information security to cyber security. Comput. & Secur. 38, 97–102 (2013)

A New JPEG Encryption Scheme Using Adaptive Block Size Peiya Li(B) , Jiale Meng, and Zefan Sun College of Cyber Security, Jinan University, Guangzhou 510632, China [email protected], [email protected], [email protected] Abstract. An encryption scheme based on adaptive block size is presented in this paper through three different levels of encryption techniques, and all of them are JPEG friendly. First, according to the plain image’s local intensity information, the image is segmented into different sizes of blocks, and then we apply corresponding discrete cosine transform (DCT) to each block. Second, we convert different sizes of blocks to 8×8 blocks using coefficients distribution followed by block permutation at the quantization stage. Finally, the segmentation information is embedded in AC coefficients at JPEG’s entropy coding stage for the third-level protection. BLAKE-256 hashing function is employed to generate sensitive keys by inputting the plain image, which has a good effect on resisting known plaintext attack. In our proposed scheme, the biggest DCT block size we apply reaches 64. Experimental results have shown that the proposed method is secure enough and has a better compression ratio and peak signal-to-noise ratio (PSNR) compared with other approaches from literature. Keywords: Image encryption · JPEG compression distribution · Security analysis

1

· Coefficients

Introduction

In line with the need for growing data storage and data transmission in a safe and quick way, encryption techniques implemented in JPEG compression process received increasing attention. There are various types of methods being proposed. He et al. [1] developed encryption techniques toward DC and AC directly in a bitstream-based image encryption, which had good file size preservation. Chaudhary et al. [2] replaced zigzag scanning with column-wise scanning, and it provided good compression ratio and secure against attacks. Qian et al. proposed a JPEG encryption algorithm which kept the format compliant to JPEG decoder, but the RC4 used to generate keysteams is not safe enough [3]. A new orthogonal order-8 transform was developed in one of our previous works [4], but we found that 8×8 block unit-based encryption operations could not achieve perfect correlation removal effect. Thus, in [5], we proposed an encryption and compression scheme based on 16×16 DCT to solve the problem of low correlation remove ability. However, applying 16 × 16 DCT for all blocks may destroy detailed information. Hence, an adaptive block size transc The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021  J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_18

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form coding is proposed in this paper, and the processed block size is changed adaptively by splitting according to the internal characteristics of the image, which can better protect the details. Simultaneously, it is noted that there are many large uniform regions that can be coded as a single large unit. Thus, we try to explore the possibility of conducting encryption operations on larger block sizes. An evaluation of the proposed encryption scheme has shown that it does not compromise JPEG’s compression ability too much and can offer sufficient security. Moreover, we employ reversible data hiding (RDH) to share the segmentation way with the decoder. There are many existing methods of RDH in JPEG images [6–8], but they may result in low visual quality and limited embedding capacity, or a significant increase in file size. Fortunately, the novel RDH scheme developed by Huang et al. [9] has proved to have a good trade-off on these issues. The rest of the paper is organized as follows. Section 2 explains our encryption operations. Experimental results and analysis are presented in Sect. 3. Section 4 concludes our work.

2

Proposed Scheme

In this section, we mainly introduce the implementation details of our proposed scheme which contains transformation stage encryption, quantization stage encryption and entropy coding stage encryption. The encryption framework of the proposed scheme is presented in Fig. 1. For convenience, in this paper, we let the block of S ×S pixels be denoted as B S , and S ∈ {8, 16, 32, 64}.

Fig. 1. Proposed scheme framework

To resist known plaintext attack, we generate a 256-bit random hash value key 1 by using plain image as the input of BLAKE-256 hashing function [10].

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The reason why we choose it is also because of its high speed [11] and recognized safety [12]. Simultaneously, we predefine a 256-bit random value key 2 , which is only known to the two sides of secret sharing. We also use BLAKE-256 as the pseudorandom keystream generator to produce two keystreams, K enc and K emb from key 1 and key 2 , respectively. 2.1

Transformation Stage Encryption

In this stage, an novel adaptive block-based segmentation way is developed. The plain image is initially raster scanned into non-overlapping B 64 s. Then each B 64 generates sixty-four B 8 s through two methods: segmentation controlled by K emb and coefficients distribution controlled by K enc . For ease of illustration, let V D−S denote the difference value between the biggest pixel and the smallest pixel of each block whose block size is S ×S and let the average of all pixel values of the same block be denoted as V T −S . In a B 64 , if the values of all pixels are very close, then the coefficient’s distribution is a better choice for this B 64 , and V D−S must be less than V T −S at this time. On the contrary, if the difference between pixels is large, then it means there may exist some details of the plain image. Thus, it is better for this B 64 to be segmented. Then the sub-blocks of this B 64 do the same judge as B 64 until sixty-four B 8 s are generated. Figure 1 can be employed to illustrate the whole process. The method of coefficients distribution we adopted was proposed in [5], which can ensure that the coefficients in each B 8 can still obey the energy decreasing principle. We optimize this method in two aspects: running speed and scope of application. First, the speed of the original algorithm in [5] was limited by finding available blocks. Consequently, we optimize this method by preventing finished B 8 s from being selected repeatedly for invalid coefficients distribution, which allows the remaining B 8 s with coefficients less than 64 to be found more quickly. Additionally, the original algorithm is only suitable for B 16 . We lift this restriction, and the maximum block size allowed is 64. Let n denote (S ×S )/8×8, and each B S can generate n B 8 s. The developed strategy to prevent finished B 8 s being selected repeatedly can be explained as follows: 1. i ← pick N l bits from K enc , and convert to decimal; 2. i ← mod(i, length(EmptyBlock ))+1; 3. index ←EmptyBlock(i); where N l is the bits number of n, EmptyBlock denotes a vector with elements [1,2,. . ., n], and index represents which block will be distributed coefficients next. 2.2

Block Permutation

After quantization, we permute all B 8 s randomly controlled by K enc using the two key-driven cyclical shifts [13]. It is noted that JPEG’s Huffman tables can only process coefficients ranged in [-1024, 1024), but the range of coefficients may exceed if the DCT block size is

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larger than 8. Table 1 listed the range of coefficients under three kinds of DCT, the corresponding maximum quality factor (QF) and the minimum quantization table (QT) values. When current QF value exceeds respective maximum values, we replace the current quantization table with the acceptable quantization table, which is the one corresponding to the maximum QF value. Table 1. Different kinds of DCT and their corresponding maximum QF values and the minimum QT values DCT Order-64 DCT Order-32 DCT Order-16 DCT

2.3

Maximum QF Minimum QT value Coefficients range 63 83 93

8 4 2

[-8192, 8192) [-4096, 4096) [-2048, 2048)

Entropy Coding Stage Encryption

To correctly recover the cipher image, decoder needs to know plain image’s segmentation way, denoted as Δ. We use ‘1’ to show no block splitting, while ‘0’ means block splitting. Δ consists of the segmentation information of each B 64 in plain image. A B 64 needs to split once to generate four B 32 s, four B 32 s need to split four times to sixteen B 16 s, and sixteen B 16 s converting to sixtyfour B 8 s need sixteen splits. If we use one bit to represent one split, it means a B 64 needs 21 bits to totally represent its segmentation way. However, we can utilize fewer bits in most of B 64 s because coefficient’s distribution is the other way to generate B 8 s. An example of a randomly selected B 64 in Lena is given in Fig. 2. The segmentation information arranged in this order is to make it easier for decoder to understand the segmentation way. As shown, M 32−2 and M 32−4 are not segmented, so every four zeros after ‘1’ can be ignored when decoding. Therefore, only thirteen bits which is ‘0001011000101’ can completely represent the segmentation way of this B 64 . i ) with values 1 and After segmentation, we embed Δ in AC coefficients (AC -1 using RDH developed in [14]. The embedding algorithm of the RDH scheme is described as  ACi + sign(ACi ) ∗ b, if |ACi | = 1 i = (1) AC ACi + sign(ACi ), if |ACi | > 1 where b ∈ {0, 1} denotes the data bit in Δ, sign(ACi ) equals 1/-1 if ACi is positive/negative. And if ACi is 0, sign(ACi ) equals 0. After all bits in Δ are embedded, we use the same run shuffle method developed in[1] to realize AC coefficients encryption controlled by K emb to conceal the embedded segmentation data.

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Fig. 2. Map of segmentation information of a B 64 , M32−1 represents the segmentation information for the first B 32 of B 64 , and M16(1)−1 means the segmentation information for the first B 16 of the first B 32

3

Experimental Result and Analysis

In this section, the performance of the proposed scheme is illustrated. All test images are taken from USC-SIPI and UCID image databases. 3.1

Perceptual Security

Perceptual security refers to the perceptual distortion of cipher image with respect to the plain image, and the PSNR criterion is adopted to measure it. In Fig. 3, five plain images are taken as examples to show their cipher images encrypted by the proposed scheme when QF=60 and the corresponding PSNR values. It can be observed that these cipher images are totally randomized and no outline information about their plain images can be seen. Moreover, the PSNR values of these cipher images are low enough to prove the good perceptual security of the proposed scheme.

Fig. 3. Test images and the corresponding encrypted images

3.2

Brute-Force Attack

As a typical attacking method of cipher text only attack, brute-force attack tries to recover encrypted data by guessing all possible keys in a brute-force manner. As we mentioned in Sect. 2, the encryption keys are two 256-bit random hash values generated from BLAKE-256. Thus, the total size of key space is 2512 , which is impossible for attackers to crack.

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145

Key Sensitivity Analysis

A good cryptosystem should be extremely sensitive with respect to the key used in the algorithm. [12,15] have proved that BLAKE-256 has a very high security margin against all known attacks. Simultaneously, since the BLAKE256 is very sensitive, the correct decryption cannot be realized if there is any minor modification of the encryption key under our encryption scheme. 3.4

Statistical Attack

In this kind of attack, we use correlation diagrams of the original image and the encrypted image to indicate the degree of relationship between the plain image and cipher image. The correlation charts are plotted by randomly selecting 1000 pairs of two horizontal adjacent pixels, two vertical adjacent pixels and two diagonal adjacent pixels from the plain ‘Grass’ image and the corresponding cipher image, which are given in Fig. 4. We can see that the linear property of the encrypted ‘Grass’ image, which denotes the correlation degree between pixels, is decreased when compared with that of the plain image.

Fig. 4. Correlation charts of plain ‘Grass’ image and the encrypted image

3.5

Compression Performance

We use the bit per pixel (BPP) value and the PSNR value of decrypted images to evaluate compression performance. Thirty images are randomly selected for encryption and decryption under various QF values. Their average BPP-PSNR curve is plotted in Fig. 5, compared with those of JPEG and method in [5]. From the BPP-PSNR curves, we can observe that when the key is available to the decoder, the proposed scheme is closer to JPEG, compared with [5]’s method, which indicates a better compression performance. Average compression ratio (CR)-QF curve is also given in Fig. 5, and it is obvious that our proposed scheme has higher compression ratio compared with [5]’s method.

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Fig. 5. Compression performance of the proposed scheme

4

Conclusion

In this paper, we propose a JPEG image encryption scheme using adaptive block size. The whole encryption is realized during the intermediate stages of JPEG compression, which can achieve relatively good security and compression efficiency simultaneously. In the proposed scheme, we initially segment the plain image into various block sizes according to the internal property of image. Corresponding sizes of DCTs are applied for these blocks’ transformation, followed by coefficients distribution process. To solve the overflow problem of transformed DCT coefficients, we use different quantization tables for quantizing coefficients belonging to different sizes of blocks. 8×8 blocks’ permutation and same run RSV pairs’ shuffling are adopted to further improve the whole scheme’s security. The segmentation data is embedded in AC coefficients with values 1 and -1 to realize reversible information hiding and suppress bit stream size increment. In our next work, we will try to integrate current encryption techniques with other kinds of compression standards, such as H.264 and H.265, so as to extend its application. Acknowledgments. This work is supported by the Fundamental Research Funds for the Central Universities, project no. 21619314.

References 1. He, J., Huang, S., Tang, S., Huang, J.: Jpeg image encryption with improved format compatibility and file size preservation. IEEE Trans. Multimedia, 1-1 (2018) 2. Pratibha Chaudhary, A., Ritu Gupta, B., Abhilasha Singh, C., Pramathesh Majumder, D., Ayushi, Pandey E.: Joint image compression and encryption using

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a novel column-wise scanning and optimization algorithm. Proc. Comput. Ence 167, 244–253 (2020) Jindal, P., Singh, B.: A Survey on rc4 Stream Cipher (2015) Li, P., Lo, K.T.: A content-adaptive joint image compression and encryption scheme. IEEE Trans. Multimedia, 1-1 (2017) Li, P., Lo, K.T.: Joint image encryption and compression schemes based on 16 16 dct. J. Visual Commun. Image Representation 58 (2018) Qian, Z., Zhou, H., Zhang, X., Zhang, W.: Separable reversible data hiding in encrypted jpeg bitstreams. IEEE Trans. Dependable Secure Comput. (2016) Chang, J.C., Lu, Y.Z., Wu, H.L.: A separable reversible data hiding scheme for encrypted jpeg bitstreams. Signal Process. 133, 135–143 (2017) Qian, Z., Xu, H., Luo, X., Zhang, X.: New framework of reversible data hiding in encrypted jpeg bitstreams. IEEE Trans. Circuits Syst. Video Technol. 1-1 (2018) Huang, F., Qu, X., Kim, H.J., Huang, J.: Reversible data hiding in jpeg images. IEEE Trans. Circuits Syst. Video Technol. 26(9), 1610–1621 (2016) Saarinen, M., Aumasson, J.: The blake2 cryptographic hash and message authentication code (mac) (2015) Aumasson, J.P., Neves, S., Wilcox-O’Hearn, Z., Winnerlein, C.: Blake2: simpler, smaller, fast as md5. In: International Conference on Applied Cryptography & Network Security (2013) Luykx, A., Mennink, B., Neves, S.: Security analysis of blake2’s modes of operation. IACR Trans. Symmetric Cryptol., 158–176 (2016) Zhou, J., Liu, X., Au, O.C., Tang, Y.Y.: Designing an efficient image encryptionthen-compression system via prediction error clustering and random permutation. IEEE Trans. Information Forensics Security 9(1), 39–50 (2014) Huang, F., Qu, X., Kim, H.J., Huang, J.: Reversible data hiding in jpeg images. IEEE Trans. Circuits Syst. Video Technol. 26(9), 1-1 (2015) Guo, J., Karpman, P., Nikoli, I., Wang, L., Wu, S.: Analysis of blake2 (2014)

Study of Smart Decorating Machine on Cake Patten Shi-Jie Jiang1 , Kuo-Chi Chang1,2,6,7(B) , Hong-Jiang Wang1 , Kai-Chun Chu3 , Hsiao-Chuan Wang4 , and Fu-Hsiang Chang5 1 School of Information Science and Engineering, Fujian University of Technology, Fuzhou,

China [email protected] 2 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China 3 Department of Business Management, Fujian University of Technology, Fuzhou, China 4 Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan 5 Department of Tourism, Shih-Hsin University, Taipei, Taiwan 6 College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 7 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. Adding some shapes to food in the production process can greatly increase the added value of food and make the same food. The market is more competitive. At present, cake decorating is mainly done manually, which requires high operating skills of personnel, and there are fewer optional decorating styles. Aiming at these shortcomings, a design concept of a smart cake spotting machine is proposed. This design uses a three-axis linkage device to cooperate with the cream delivery mechanism; the main control system used STM32F103ZET6 chip. It stores the G-code files of the flower pattern through the SD card and externally stores the SD card in the SD card data, external stepper motor drive circuit to control the operation of the mechanical structure. Finally, the stepper motor controls the displacement of the mechanism and conveys the cream. The movement of the mechanism makes it possible to finish the work of decorating the cake. In this article, the specific design and implementation of the cake automatic flowering machine are introduced in detail. This study has undergone experimental testing and the results show that the cake embossing machine designed by this study institute can automatically complete embossing according to the set embossing pattern. Keywords: Smart decorating machine · Cake pattern · Stepper motor · SRAM module · G-code

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_19

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1 Introduction For improvement of people’s living and fashion requirements, food is becoming higher and higher, so adding some shapes to food during the production process can greatly enhance the added value of food and make the same food as it is more competitive in the market [1, 2]. Due to the particularity of food, most of the cake decorating currently on the market is artificial decorating, which has low efficiency and high cost, and the decorative patterns are not beautiful enough [3]. Therefore, it is of great significance to design the smart decorating machine on cake patterns. The application of mechanical technology to cake decorating not only reduces the contact between people and food during the production process, and prevents the invasion of bacteria, guarantees the hygienic requirements of the finished products, but also reduces the intermediate links such as transportation and storage to avoid unnecessary pollution. In this design, the mechanical structure of the system uses a three-axis linkage device with a discharge device. This combination can flexibly move and discharge cream in a certain space. In addition, this design uses the linear interpolation algorithm to realize the automatic function of the decorating machine [4–9].

2 System Hardware and Software Design Three-axis in this design, the main control chip of the decorating machine adopts STM32F103ZET6 microcontroller. Use the SD card reader module to read the G-code file. The IS62WV51216 model SRAM chip is used as an external SRAM to store data after parsing G-code files. 42 stepper motor drive uses DRV8825 motor drive module. Figure 1 shows the block diagram of the system [10, 11].

Fig. 1. Block diagram of system composition

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2.1 Linkage Mechanism The project uses a three-axis linkage device with a discharge device. The three-axis linkage is composed of X-axis mechanism, Y-axis mechanism, and Z-axis mechanism. The discharge device is placed on the Z-axis mechanism. Figure 2 shows the three-axis linkage mechanism.

Fig. 2. Three-axis linkage mechanism

2.2 G-Code Introduction and Analysis The G-code is a character string with a specific structure, and the analysis of the G-code is essentially the processing of the character string. Its main features are as follows: take the semicolon as the line comment character, use G, S, M as the starting character of a line of code, and may carry parameters after the command. Commands and parameters are separated by spaces, so the commands and parameters can be parsed by positioning search and format matching. For each G-code, it can be divided into three parts: command type, command value, and parameter. It is embodied as a structure in the program. Each structure contains the basic elements of G-codes. The storage of multiple G-codes is equivalent to an array of structures. Because the memory space of the single chip microcomputer is small, it is impossible to store more G-code structures, so the G-code structures are stored in the external SRAM to ensure that the commands will not be lost when the amount of input codes is large [11, 12].

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The analysis of the G-code first copies a line to the line buffer, and then judges whether the line is a comment line. If it is a comment line, skip this line, then clear the line buffer and read the next line; if the line is not a comment line, then extract its command type, command value, and parameters into the current structure. Since the first addresses of all structures are located in SRAM, the data will be written directly to the external SRAM after analysis. If the extracted command type, command value, or parameter is incomplete, it means that the input data is incomplete. The program will calculate the remaining amount, retain the contents of the line buffer, and merge with the next input into a complete command to participate in the next parsing. Figure 3 shows the flowchart of the data analysis and storage program.

Fig. 3. Flowchart of data analysis and storage program

2.3 Linear Interpolation Algorithm Straight-line interpolation means that interpolation between two points is approached along a point group of straight lines, that is, a straight-line trajectory is used to approximate the curve to be walked. Assign (X1, Y 1) and (X2, Y 2). (X1, Y 1) is the current

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position of the discharge device. (X2, Y 2) is the position to be moved by the discharge device. This study compares the X and Y coordinates of the two points and finds that the corresponding motor can be controlled to allow the emission device to travel. Figure 4 shows the flow chart of the replacement procedure.

Fig. 4. Flowchart of displacement program

2.4 System Main Program The main program flowchart of the system is shown in Fig. 5.

3 System Integration and Testing Through the selection of the system plan, the selection of the module, the construction of the frame, the design of the hardware circuit, the preparation of the program, the welding of the circuit, and the adjustment of the system, the cake automatic decorating machine is finally completed. Decorating machine is shown as in Fig. 6. 3.1 Accuracy Test of Three-Axis Linkage Device When running the decorating machine, the data stored in the external SRAM is used to move the X and Y axes to test the accuracy of the moving distance. The test distances are 2, 15, 30, 70, 130, and 200 mm. Tables 1, 2, 3, and 4 show the accuracy test of the three-axis linkage.

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Fig. 5. Flowchart of main program

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Fig. 6. Cake decorating machine

Table 1 X-axis travel accuracy test table Test content

Test distance (mm)

The X-axis actually travels to the right (mm)

The X-axis actually travels to the left (mm)

X-axis travel accuracy test

2

2

2

15

15

15

30

30

30

70

70

70

130

130

130

200

200

200

3.2 Discharge Device Status Test By controlling the discharge device, the cream can be discharged when the decoration is needed, and the cream can be stopped when the decoration is not needed. Through the data commands stored in the external SRAM in advance, the peristaltic pump of the decorating machine is rotated to control the start and stop of the cream of the decorating machine, and watch whether the action is normal. Table 5 shows the test of the state of starting discharging and stopping discharging.

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Table 2 Y-axis travel accuracy test table Test content

Test distance (mm)

The Y-axis actually travels forward (mm)

The Y-axis actually travels backward (mm)

Y-axis travel accuracy test

2

2

2

15

15

15

30

30

30

70

70

70

130 mm

130 mm

130 mm

200 mm

200 mm

200 mm

Table 3 X-axis to the right and Y-axis forward simultaneously travel accuracy test table Test content

Test distance (mm)

The X-axis actually travels to the right (mm)

The Y-axis actually travels forward (mm)

X-axis to the right and Y-axis forward simultaneously travel accuracy test

2

2

2

15

15

15

30

30

30

70

70

70

130

130

130

200

200

200

Table 4 X-axis to the right and Y-axis forward simultaneously travel accuracy test table Test content

Test distance (mm)

The X-axis actually travels to the left (mm)

The Y-axis actually travels backwards (mm)

X-axis to the left and Y-axis backward simultaneously travel accuracy test

2

2

2

15

15

15

30

30

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Action Times form 1

2

3

4

5

6

7

8

9

10

Start

Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal

Stop

Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal

3.3 Decorating Function Test After editing the text and pictures through the host computer, the data is sent to the machine to realize the decoration. Figures 7 and 9 are preview effect diagrams, and Figs. 8 and 10 are actual effect diagrams.

Fig. 7. Preview effect diagram

3.4 Data Analysis and Discussion Through the above test data and charts, the displacement accuracy of the three-axis linkage device meets the actual working error during operation. The discharge device can work normally during operation. By comparing the preview effect picture generated by the host computer with the actual effect picture, the size of the two is close to meet the expected effect.

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Fig. 8. Actual effect diagram

Fig. 9. Preview effect diagram

4 Conclusion and Suggestion In this study, the design and production of a cake automatic decorating machine are completed, which satisfies the function of replacing manual cake decorating. The cake decorating machine can automatically discharge materials and can complete functions such as automatic character drawing and drawing according to preset settings. The automatic cake decorator will facilitate the implementation of a low-cost build-to-order strategy.

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Fig. 10 Actual effect diagram

Restricted by factors such as modules and mechanisms, the cake automatic decorator currently designed has room for improvement, such as the following aspects: 1. The decorating machine is equipped with a screen for control; 2. Add multiple nozzle heads to the decorator to change the color.

References 1. Vassallo, A.J., Kelly, B., Zhang, L., Wang, Z., Young, S., Freeman, B.: Junk food marketing on instagram: content analysis. JMIR Public Health Surveill 4(2), e54 (2018). https://doi.org/ 10.2196/publichealth.9594 2. Mehta, K., Phillips, C., Ward, P., et al.: Marketing foods to children through product packaging: prolific, unhealthy and misleading. Public Health Nutr. 15, 1763–1770 (2012) 3. Ahmad, M., Rashid, K., Naz, N.: Study of the ornamentation of Bhong Mosque for the survival of decorative patterns in Islamic architecture. Front. Architectural Res. 7(2), 122–134, ISSN 2095–2635. https://doi.org/10.1016/j.foar.2018.03.004 4. Bramerdorfer, G., Tapia, J.A., Pyrhönen, J.J., Cavagnino, A.: Modern electrical machine design optimization: techniques, trends, and best practices. IEEE Trans. Industr. Electron. 65(10), 7672–7684 (2018). https://doi.org/10.1109/TIE.2018.2801805 5. Chu, K.C., Horng, D.C., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. IEEE Access 7, 105562–105571 (2019). https://doi.org/10.1109/ACCESS.2019.2930408 6. Cheng, Q., Zhao, H., Zhao, Y., et al.: Machining accuracy reliability analysis of multi-axis machine tool based on monte carlo simulation. J. Intell. Manuf. 29, 191–209 (2018). https:// doi.org/10.1007/s10845-015-1101-1 7. Tian, W., Yang, G., Wang, L., et al.: The application of a regularization method to the estimation of geometric errors of a three-axis machine tool using a double ball bar. J. Mech. Sci. Technol. 32, 4871–4881 (2018). https://doi.org/10.1007/s12206-018-0935-9 8. Xiaodong, Z., Jie, Z.: Design and implementation of smart home control system based on STM32. In: 2018 Chinese Control And Decision Conference (CCDC), Shenyang, pp. 3023– 3027. (2018). https://doi.org/10.1109/CCDC.2018.8407643

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9. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Generation Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006 10. Wang, G., Shi, F., Xiang, X.: Unmanned boat design for Challenges and verification of unmanned surface ship intelligent navigation. In: 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS), Wuhan, China, pp. 1–5 (2018). https://doi.org/10.1109/USYS.2018.8778970. 11. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 12. Postalcıo˘glu, S., Ali¸siro˘glu, Y.Ç.: Real time DC motor speed/position control with bluetooth communication. Int. J. Eng. Comput. Sci. 7(02), 23663–23668 (2018). https://103.53.42.157/ index.php/ijecs/article/view/3976

Study of Advanced Low-Cost Smart Prepaid Electricity Meter Using Arduino and GSM Abdalaziz Altayeb Ibrahim Omer1,3 , Kuo-Chi Chang1,3,6,7(B) , Hui-Qiong Deng1,2 , Kai-Chun Chu4 , Governor David Kwabena Amesimenu1,3 , Yu-Wen Zhou1,3 , and Fu-Hsiang Chang5 1 School of Information Science and Engineering, Fujian University of Technology, Fuzhou,

China [email protected] 2 Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control, Fuzhou 350108, China 3 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China 4 Department of Business Management, Fujian University of Technology, Fuzhou, China 5 Department of Tourism, Shih-Hsin University, Taipei, Taiwan 6 College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 7 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. The use of prepaid electricity meters appeared in many countries a few years ago and has been widely recognized by consumers and managed to find solutions to too many problems faced by distribution companies. But for traditional systems, users must purchase electricity and manually charge the meter. For example, if he is in the office and needs to charge the house electricity meter, then he must go home to do this, which is considered a waste of time and energy. However, the advanced low-cost smart prepaid electricity meter is a solution to the problem mentioned earlier. By sending GSM text messages to the Arduino as a microcontroller through the GSM module, consumers can use their mobile phones to charge the electricity meter anywhere. This is the main reason for the microcontroller. The control process is also connected to an electricity meter and also to a relay module to provide power for consumers or consumers when the balance is equal to zero. Keywords: Low-cost smart energy meter · Arduino UNO · GSM module · Relay module · Electricity charge

1 Introduction Global energy measurement is currently focusing on electronic prepaid meters, which is one of the latest technologies in this field, because it enables companies to distribute electricity to overcome errors and wrong electricity usage. Moreover, the problem of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_20

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human error in collecting data from the electricity meter located at the user’s site is overcome, which always exhausts the power distribution company. Although electronic meters have solved these problems, they can still be charged manually after purchasing electricity from distribution companies. Therefore, the smart prepaid electricity meter is designed to be charged anytime and anywhere by sending SMS. Smart prepaid energy meters are implemented using Arduino, GSM, relays, and LCD and can provide solutions to the problems discussed. The project helps to manage the energy consumed automatically and in a controlled manner, which shows the results of efficient use of electrical energy. GSM modems facilitate the message alerts and notifications required to achieve these goals. The different components used are controlled by Arduino UNO.

2 Literature Reviews Electronic meters display the consumed power and reactive power. The power factor mostly uses LCD or LED displays. It can also send energy consumption readings to remote areas through various communication networks. The difference from oldfashioned electric meters is electromechanical induction meters and electronic meters. The total rotation of the aluminum disk in the electromechanical induction meter is proportional to the power consumed. Scratch card and smart card methods have two possible options for charging electric meters. The former is that after the payment is completed, the consumer automatically generates a number and prints it on a piece of paper based on the scratch card in the vending station, and the consumer uses the small keyboard on the front of the meter as shown in Fig. 1 to enter the encryption digital numbers, these numbers will be written into the firmware by the machine to decrypt, and the cost will be added to the previous balance after decoding. As a result, when the final balance is positive, it will continue to power the client load. Figure 1 shows the measurement of voltage, reactive power, instantaneous/maximum utilization requirements, power factor, and other energy. This electronic equipment can also display and record the power supply and load parameters required by other power companies or customers [1–3]. In this research, we use Arduino as the main control circuit board. This programmable microcontroller uses a language similar to C++ to write the control process in an integrated development environment (IDE) [4–6].

3 Proposed System Prepaid smart meters should use the GSM module to communicate with the wireless data protocol. This module contains a SIM card for sending and receiving data and messages from a specific server. Consumers can purchase power through their mobile phones and send charging messages to the GSM module, which passes the power to the Arduino as a controller to turn off the relay and connect the load line. Figure 2 shows the advanced prepaid smart meter system. Energy consumption is calculated by an analog electric meter interfaced with Arduino UNO. The proposed system can be divided into analog energy meters, LCD, Arduino, GSM module, and 1 channel relay module.

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Fig. 1. Electronic energy meter

Fig. 2. System block diagram

4 Hardware Implementation Prepaid smart meters should use the GSM module to communicate with the wireless data protocol. This module contains a SIM card for sending and receiving data and messages from a specific server. Consumers can purchase power through their mobile phones and send charging messages to the GSM module, which passes the power to the Arduino as a controller to turn off the relay and connect the load line.

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4.1 Controller of Arduino UNO At present, the most important open-source platform for low-cost electronic projects is Arduino. Compared with different types of controllers, Arduino has many ROM, RAM, analog and digital pins, and power supplies required for operation, and Arduino is easy to program electronic systems. Many researchers are just starting to use sub-products like the Arduino platform very much. Arduino does not require separate hardware, which is very different from most programmable circuit boards, and you can use a USB cable to load our completed code onto the board, and C language can be used in a simplified version of the Arduino IDE, making it easier for researchers to learn or programming, Arduino provides a software package for standard microcontroller functional decomposition to designers [7–9] (Fig. 3).

Fig. 3. Arduino UNO

4.2 Module of GSM Low-priced mobile communication modems currently, mostly, use GSM, which is a global mobile communication system that mainly uses open digital cellular technology. Its main functions can be used to transmit data and mobile voice services. GSM can operate on specific frequency bands. Arduino has a GSM card, we can receive digital commands from SMS through any mobile phone, and then use the serial communication method to send the data to the Arduino [10–13] (Fig. 4). 4.3 Relay Module This study uses the DC 5 V single-channel relay as an electronic switch to control the power on or off according to the balance of the meter. The electric coil and the induction unit constitute the relay. In this research, the electric coil is powered by DC. We set when the voltage or current exceeds the threshold, the electric coil will be activated, and the system will actively perform ON/OFF actions (Fig. 5).

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Fig. 4. GSM module

Fig. 5. 1-Channel relay module

4.4 Energy Meter The traditional mechanical energy meter is based on the “magnetic induction” phenomenon. It has a rotating aluminum wheel (called a Ferris wheel) and many gears. According to the current flow, the Ferris wheel rotates, causing the other wheels to rotate. This will be converted to the corresponding measured value in the display section. Because many mechanical parts are involved, mechanical defects and malfunctions are common. This study based on 3200 pulses calculates 1KWh for this meter, then it is 3200 imp/KWh of rated value, and each pulse signal will have an LED flashing. An optocoupler is already connected to the LED, so as soon as the LED blinks, the optocoupler will switch. We cannot directly connect the LED of the meter to the Arduino, because when we power the Arduino to the digital side, the LED has an analog signal. The Arduino pin number (D8) is connected for the optocoupler switch side and is used to detect pulses from the electricity meter. The number of LED flashes in the analog

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energy meter is synchronized with the energy consumed, which is used in the proposed system to calculate the unit through LED sensing [14–18] (Fig. 6).

Fig. 6. Conventional energy meter

5 Working System and Experimental Results When the switch button is pressed, 5 V DC is applied to the Arduino board, which is connected to the 1-channel relay, GSM module, meter, and LCD. After a few seconds, an SMS message sent to the phone via GSM indicates that the system is ready. Please charge the meter. In order to charge the meter, we sent an SMS charging message with the required balance. After GSM receives the message, the relay contact is directly connected and supplies power to the load. The advantage of GSM system is that it can monitor the placed energy meter anytime and anywhere and only need to send SMS. The project will help improve the energy situation manage, use energy wisely, and avoid disputes avoided by wrong bills. The prepaid electricity meter system can track electricity usage and, hardly, leaves space to avoid consumption and billing (Figs. 7 and 8).

6 Conclusion One of the obvious facts is that the electrical energy system has made great progress and uses the latest technology to generate, transmit, and distribute electrical energy,

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Fig. 7. Prototype in this study

Fig. 8. Result of LCD display and SMS notes on mobile phone

which is actively reflected in all aspects of daily life. The prepaid smart meter in this study is an extension of the new method for measuring power consumption. This new method uses Arduino because it has open source software, easy programming, and many advantages of connecting to multiple devices and devices. The equipment provides equivalent units. Recharge the Arduino and remind consumers about the insufficient balance. The proposed system saves the customer, the amount of work, and time wasted in the queue to purchase electricity, and there is no need to manually charge the meter. Customers only need to send text messages from their mobile phones to charge the meter. As long as the balance is insufficient, the system can also issue a warning message to monitor the meter, thereby rationalizing the use of electrical energy.

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References 1. Masudur Rahman, M., Noor-E-Jannat, Ohidul Islam, M. Serazus Salakin, M.: Arduino and GSM based smart energy meter for advanced metering and billing system. In: 2nd International Conference on electrical engineering and information & communication technology (ICEEICT) 2015 Jahangimagar University, Dhaka-I 342, Bangladesh, 21–23 May 2015. 2. Visalatchi, S., Kamal Sandeep, K.: Smart Energy Metering and Power Theft Control using Arduino & GSM. In: 2017 2nd international conference for convergence in technology (I2CT) Zeal College of Engineering & Research Narhe, Pune. 3. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006 4. Revanda, T.H., Ghofir, A., Roestam, R.: Smart Postpaid Electricity Meter Using Arduino. Faculty of ComputingPresident University Cikarang, Indonesia (2019) 5. Smith, A.G.: Introduction to Arduino. In: A piece of cake!, CreateSpace Independent Publishing Platform, pp. 1–3 (2011) 6. Chu, K.C., Horng, D.C., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. IEEE Access 7, 105562–105571 (2019). https://doi.org/10.1109/ACCESS.2019.2930408 7. Rahman, N.-E.-J., Islam, S. Masudur Rahman, M., Noor-E-Jannat; Ohidul Islam, M., Serazus Salakin, M.: Arduino and GSM based smart energy meter for advanced metering and billing system. In: 2nd International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) 2015 Jahangimagar University, Dhaka-I 342, Bangladesh, pp. 21–23 May 2015 8. Mejbaul Haque, M., Kamal Hossain, M., Mortuza Ali, M., Rafiqul Islam Sheikh, M.: Microcontroller based single phase digital prepaid energy meter for improved metering and billing system. Int. J. Power Electron. Drive Syst. (IJPEDS) I(2) (2011) 9. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5g base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 10. Chaudhari, S., Rathod, P., Shaikh, A. Vora, D., Ahir, J.: Smart energy meter using arduino and GSM” CGPIT, Uka Tarsadia UniversitySurat, GujaratICEI 2017 11. Amesimenu, D. K. et al.: Home appliances control using android and arduino via bluetooth and GSM Control. In: Hassanien, A. E., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds.) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol. 1153. Springer, Cham (2020) 12. Chang, K., Chu, K., Chen, T., Lee, Y.W., Lin, Y., Nguyen, T.: Study of the high-tech process mechanical integrity and electrical safety. In: 2019 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), Taipei, Taiwan, pp. 162–165 (2019). https://doi.org/10.1109/IMPACT47228.2019.9024999 13. Chang, K.C. et al.: Study on hazardous scenario analysis of high-tech facilities and emergency response mechanism of science and technology parks based on IoT. In: Pan, J.S., Lin, J.W., Liang, Y., Chu, S.C. (eds.) Genetic and evolutionary computing. ICGEC 2019. advances in intelligent systems and computing, vol. 1107. Springer, Singapore (2020) 14. Fallah, S.N., Deo, R.C., Shojafar, M., Conti, M., Shamshirband, S.: Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions. Energies 11, 596 (2018) 15. Upadhyaya, T.K., Desai, A., Patel, R.H.: Design of printed monopole antenna for wireless energy meter and smart applications. Prog. Electromagnet. Res. Lett. 77, 27–33 (2018). https:// doi.org/10.2528/PIERL18042203

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Study of Integrating the Data Fusion Method for Reducing and Preventing Road Accidents Occur at Blackspots Places in Third World Countries Kai-Chun Chu1 , Gilbert Shyirambere2,3 , Kuo-Chi Chang1,2,6,7(B) , Hsiao-Chuan Wang4 , Governor David Kwabena Amesimenu2,3 , Fu-Hsiang Chang5 , and Shoaib Ahmad2,3 1 Department of Business Management, Fujian University of Technology, Fuzhou, China

[email protected]

2 School of Information Science and Engineering, Fujian University of Technology, Fuzhou,

China 3 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of

Technology, Fuzhou, China 4 Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan 5 Department of Tourism, Shih-Hsin University, Taipei, Taiwan 6 College of Mechanical & Electrical Engineering, National Taipei University of Technology,

Taipei, Taiwan 7 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. Every year, nearly 1.3 million of people worldwide are losing life due to road accidents. Majority of 90 percent of the world’s accidents occur in developing countries. Many accidents are due to recklessness of drivers who take high speeds and the poor quality of roads in third world countries. The measures taken by some governments to increase road safety such as the use of speed governors failed to prevent road crashes and accidents at Blackspots places. Therefore, in this study, we proposed data fusion as a method for reducing and preventing the road accidents occur at Blackspots. However, in order to eliminate traffic accidents, the improvement of current hardware should be adopted to be more effective. Being pragmatic, GPS sensors and artificial landmarks were supposed to be used in the system. We describe overall architecture of the proposed data fusion system using Kalman filter algorithm. The results are able to filter out the data so that they will not be unwanted vehicle braking due to bad image of the landmark at Blackspots taken by the front camera of the vehicle, showing that the fusion is doable. Keywords: Data fusion · Kalman filter · Road accidents · Blackspots · Driving safety

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_21

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1 Introduction A minor error can ruin the whole paramount system, for instance, the system concerning with the life of people or weighting billions of dollars based on the purpose of that system. Therefore, the dependency on one source of data/information is not guaranteed and must be avoided if possible and needed. It is possible to combine different sources of data/information in the so-called: data/information fusion. Meanwhile, there is a little difference between data fusion and information fusion [1–3]. Smart prepaid energy meters are implemented using Arduino, GSM, relays, and LCD and can provide solutions to the problems discussed. The project helps to manage the energy consumed automatically and in a controlled manner, which shows the results of efficient use of electrical energy. GSM modems facilitate the message alerts and notifications required to achieve these goals. The different components used are controlled by Arduino UNO. In third world countries, highway roads are very few, there are very narrow roads. In addition, those roads are not flat; there are many ups and downs in the roads. And even some roads are very sloppy and have dangerous crosses. It is obvious that the movements of cars in those roads are not comfortable, and vehicles are prone to accidents [2–5].

2 Problem Description Globally, according to the 2018 World Health Organization (WHO), ranks road traffic accidents at eighth place among the leading cause of death, with an estimated of 1.3 million lose lives every year which means nearly 3700 people on the world’s roads every day, and more than tens of millions are injured or become impaired every year [4–6]. Majority of 90 percent of the world’s accidents occur in developing countries, and more than 60 percent of accidents occur in sub-Saharan Africa [6, 7]. In Rwanda, in 2018, about 5000 road crashes were registered, and about 700 were killed as well as other 2000 people were injured [6] (Fig. 1).

Fig. 1. A highway road in Rwanda with a dangerous cross where a landmark could be installed, source

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Even though, those measures have been taken, accidents are continuing to occur as the figures above have shown. Not only the CCTVs are in the capital city of Kigali, but also their intervention in preventing road accidents is limited. Speed governors are trying to prevent the accidents by limiting the speed, but they cannot guarantee us to prevent traffic crashes and accidents in road Blackspots dispersed in the whole country because the allowed speed of 60 km per hour can be seen as high when you see the number of people continue to lose lives at Blackspots locations. The chief inspector of Rwandan Police said: Drivers should be careful particularly at dangerous corners and in Blackspots corners [8]. The literature shows that in Rwanda, there are numerous notorious Blackspots like: Ku mukobwamwiza (at beautiful girl) in Huye District, Gacurabwenge, Musambira’s hill, and Gihanga located in Kmonyi District, all of them are in South province. There is also Blackspot in Eastern province called kukabuga ka musha (at musha’s place) in Rwamagana District, and at Buranga in Gakenye District in Northern Province; there is also dangerous corner in Nyungwe National Park in Western province [8, 9]. There are other many Blackspots and dangerous corners in narrowed roads in developing countries to be treated carefully while driving. Therefore, this paper aims to provide the real-time recognition of traffic road signs particularly in Blackspots by switching to data fusion technique. By using different sensors and camera, the vehicles could scan and recognize the road signs and/or other landmarks which will be put at Blackspots and dangerous corners on the road so that they will slow down automatically, and the sound will alert the driver and drive carefully before reaching in dangerous zones. Fortunately, these actions could be performed by the speed governor which is currently used in road accidents prevention. Depending on the types of sensors used, the vehicles can be tracked in the case it is needed either by police or by the owner of the automobiles.

3 Methodology This paper objective is to provide the way of reducing and preventing the traffic road crashes and accidents in the road through adopting data fusion. Thus, this creates the need of knowing in deep what is data fusion and how to fuse data. Therefore, the literature review of data fusion and others related is thoroughly done. After understanding the literature, data fusion was understood (as it has been explained in the introduction of this study) and the technique of fusing data named: State estimator method was found to be perfect to handle our problem as described in detail in problem description. Our goal is to locate/estimate Blackspots and dangerous cross that are in the road and filtering all noises in the system; so, Kalman filter presents all the potentials to be used in order to achieve the goal. 3.1 Filter of Kalman The potentials of Kalman filter related to this study are its powerfulness in controlling noisy systems [10]. It has been started to be studied and applied after being firstly proposed by Rudolf Emil Kalman in 1960. The mathematical models involved in Kalman filter are as follows: Xk+1 = MXk + NUk + Wk

(1)

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y = HXk + Vk

(2)

where the estimation equation is Eq. (1), and the measurements equation represents in Eq. (2). The discrete-time index is K, the state variable at time-step k represents in X k , the estimated/predicted state at time-step describes in k + 1X k+1 , the state transition matrix is M, the relationship between the next state X k+1 and the previous state X k which shows in M, the process noise/perturbation matrix with variance Qk , and zero mean is W k . Equation (2) is for the measurements, which shows the relationship between the observation and the state, where yk stands for the available measurements of the system at time-step k, H shapes/maps the measurements variables into the state variables, vk is also the measurements noise with again zero mean and some variance Rk , note that measurements noise, Rk and process noise, Qk are independent/uncorrelated. However, when we are predicting, there are some uncertainties because we are not sure a hundred percent about our prediction. That is why the uncertainty covariance matrix P is introduced in Kalman filter algorithm and is calculated as follows in Eq. (3): P = M ∗ P ∗ MT + Q

(3)

where M T is the transpose of state transition matrix. In general, in order to work with Kalman filter algorithm, the following steps should be gone through: Step 1. Initialization. Define: M, N, W, and H. Precise X, P, Q, and R. Step 2. Prediction of state in Eq. (4) and (5) for uncertainty covariance P at the arrival of input Uk : Xk+1 = MXk + NUk

(4)

Pk+1 = M ∗ P ∗ M T + Q

(5)

Step 3. Correction of state in Eqs. (6) and (7) for P at the arrival of input measurement yk : e = H ∗ Xk

(6)

E = H ∗ P ∗ HT

(7)

z =y−e

(8)

Z =R+E

(9)

Equation (8) is the innovation equation: difference from expectation from measurements. Equation (9) is the covariance of innovation.

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Step 4. Kalman gain calculation K = P ∗ H T ∗ Z −1

(10)

Step 5. Updating the state in Eq. (11) and error covariance in Eq. (12). Xk+1 = X ∗ K ∗ z

(11)

P = P − K ∗ H ∗ P = (I − K ∗ H )P

(12)

Step 6. Let k + 1 = k + 2, and return to step 2; then, continue the process until the end of the current time. Figure 2 illustrates how data are fused and filtered by Kalman filter. It is evident that the measurements output Zk is followed whenever it is good (fewer noise). And if it is bad, by the use of the timestamp, we trigger the input signal Uk to pass through the Kalman filter, and hence, we get the filtered signal and/or we follow the estimated/predicted signal Xk+1 .

Fig. 2. Kalman filter layout

The Kalman filter is limited to work only with linear functions and Gaussian distribution noise [1, 11], whereas there are many nonlinear systems in many real-life situations. Therefore, to overcome, the limitation of Kalman filter is modified to become the extended Kalman filter (EKF) [11, 12]. The EKF is most used in robotics applications like navigation. The prediction step EKF is the same as in KF; the difference is in other steps. It simply takes a point at the mean and then uses it in derivative of a nonlinear function that is being transformed/approximated to be linear function; this derivative technique is known as Taylor series refer to Eq. (13) [11, 13]. f (a) +

f (a) f (a) f (a) (x − a) + (x − a)2 + (x − a)3 + . . . 1! 2! 3!

(13)

The literatures [11, 12, 14] indicate that the derivative stops to the first derivative because the intention is to get the tangent slope on the function. Because the EKF takes only a point from the mean, this creates the problem of instability and the computations are expensive [1, 15–18].

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3.2 Data Fusion Flow Chart and Government Intervention Although speed governors are better suited to mitigate the traffic road crashes and accidents, but the maximum speed allowed is still high to make vehicles across the Blackspots without crashing accompanied with the loss of human lives. On the other hand, the integration of data fusion approach to the current way of preventing road accidents can boost the road safety particularly at Blackspots locations. Three parts of this data fusion includes: (1) sensors as input and landmarks as positions beacons to provide information about the dangerous zone; (2) information processing; (3) output, which are shown down in Fig. 3.

Fig. 3. Illustration of working system of data fusion for road accidents and crashes

4 Simulation and Discussion To demonstrate how data are fuse using Kalman filter algorithm as well as being around our case, we take an example in which a moving object is utilizing GPS sensor and beacon signal from Blackspot landmark. The literatures show that the GPS accuracy is about ±10meters which is not good for navigation. However, this is more concerning about robots and driverless cars. But for our situation, there will be a driver in a car, GPS will help in finding the location of the vehicle whenever it is needed, and the camera will be used to take the image of the landmark. From equations of Kalman filter presented in Sect. 3.1, with MATLAB 2016a, we assumed that, M = 1, N = dt = 1, w = 1, H = 1. dt means the time of taking measurement every one second. Initially, we make the values of process noise to be very small with 0.01. Measurement input to be constant U k = 1, covariance noise obviously equals to 100. But actually, U k will change with the time. At starting, we estimated the state x = 0 and covariance P = 104 . The time is updated from 0 to 100 s. Figure 4 shows the output of those values. It is obviously that the estimate was linear and follows the measurements from GPS. On the other hand, when we increase noise in estimation, Q = 10, if it is the case of the robot, it will follow GPS noise because it has fewer disturbance than prediction as shown in Fig. 5. No matter how noise is in the system, the objective of this paper will be accomplished because we are more interested in fusing data. After fusion, the signal will be sent to precaution measures board which

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is supposed to expect to be the current speed governor in order to decelerate because the vehicle is approaching Blackspots. Consequently, traffic accidents and crashes will be prevented and reduced.

Fig. 4. Estimate with small process noise Q

Fig. 5. Estimate with big process noise Q

5 Conclusion In this study, an approach of using data fusion to prevent and reducing traffic road crashes and accidents at Blackspots is proposed. Kalman filter algorithm was chosen to be a technique of fusing data and filtering out the noise. For illustrating the things, GPS

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sensors and artificial landmarks were used to providing the data. The GPS is for locating the vehicle whenever it is needed by the vehicle owners and/or other institution(s) such as police in the case of violating the laws, and artificial landmarks were selected as to locate where Blackspots and dangerous zones are in the road. The vehicle is supposed to have a front camera in order to capture the image of the landmark. The GPS signal and the captured image signal can be integrated to provide a warning sound and reduce speed through the ordinary speed limiter currently used to prevent traffic accidents. Locating the vehicle is not the primary purpose of this paper, but it is also providing the benefits of knowing where the vehicle is whenever it is needed. In the end, the simulation section was provided in order to show the possibility implementation of the approach, which gives rise of the future project. For future work, the approach presents better features needed for increasing the road safety. In addition, we have to believe enough in the images captured by the camera that are really representing the Blackspots locations in order to avoid early or unwanted speed reduction. Therefore, there is still a work for passionate students and researchers of putting the study into practice, particularly by using extended Kalman filter (EKF) because many systems in real-life are nonlinear. Other ideas for extending the effectiveness of the approach are also welcomed.

References 1. Castanedo, F.: A review of data fusion techniques. Sci. World J. (2013) 2. White, F.E.: JDL, data fusion lexicon. Tech. Panel C 3, 15 (1991) 3. Hall, D.L., Llinas, J.: An introduction to multisensor data fusion. Proc. IEEE 85(1), 6–23 (1997). https://doi.org/10.1109/5.554205 4. World Health Organization. Geneva, Switzerland: WHO: Global Status Report on Road Safety: Supporting a Decade of Action (2015) 5. International Bank for Reconstruction and Development/The World Bank. © 2014. Making Roads Safer. Available at: https://documents.worldbank.org/curated/en/277731468321 541488/pdf/955580WP00PUBL0afetyLearningProduct.pdf 6. Rwanda National Police: Technology: A Sustainable Solution to Road Safety (2019). Available at: https://www.police.gov.rw/media-archives/news-detail/?tx_news_pi1%5Bn ews%5D=14285&tx_news_pi1%5Bcontroller%5D=News&tx_news_pi1%5Baction%5D= detail&cHash=0323b03ff6805afec0c57d856c33f159 7. Rwanda National Police: Rwanda marks ‘Africa Road Safety Day’ (2019). Available at: https://www.police.gov.rw/media-archives/newsdetail/?tx_news_pi1%5Bnews%5D=3053& tx_news_pi1%5Bcontroller%5D=News&tx_news_pi1%5Baction%5D=detail&cHash=fd8 325ad720fc75bfe17bd3f057c8a99 8. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Generation Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006 9. African Development Bank Group. © 2013: Rwanda Transport Sector Review and Action Plan, available at: https://www.afdb.org/fileadmin/uploads/afdb/Documents/Project-and-Ope rations/Rwanda_-_Transport_Sector_Review_and_Action_Plan.pdf 10. Park, S., Gil, M.S., Im, H., Moon, Y.S.: Measurement noise recommendation for efficient kalman filtering over a large amount of sensor data. Sensors 19(5), 1168 (2019) 11. Jaiswal, P.: Sensor Fusion — Part 1: Kalman Filter basics (2018). Available at:https://toward sdatascience.com/sensor-fusion-part-1-kalman-filter-basics-4692a653a74c

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12. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5g base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 13. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960) 14. Wikipedia: Taylor series. Available at: https://en.wikipedia.org/wiki/Taylor_series 15. Julier, S.J., Uhlmann, J.K.: A new extension of the Kalman filter to nonlinear systems. In: Proceedings of the InternationalSymposium on Aerospace/Defense Sensing, Simulation and Controls, vol. 3 (1997) 16. Chadha, H.S.: The Unscented Kalman Filter: Anything EKF can do I can do it better! (2018). Available at: https://towardsdatascience.com/the-unscented-kalman-filter-anything-ekf-cando-i-can-do-it-better-ce7c773cf88d? 17. Fei, Z., Yang, E., Hu, H., Zhou, H.: Review of machine vision-based electronic travel aids. In: 2017 23rd International Conference on Automation and Computing (ICAC), pp. 1–7, IEEE (2017, September) 18. Chu, K.C., Horng, D.C., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. IEEE Access 7, 105562–105571 (2019). https://doi.org/10.1109/ACCESS.2019.2930408

Study of 2D SubMarine Tracking with Complete Worked Out Example Based on Kalman Filter Kuo-Chi Chang1,2,7,8 , Joram Gakiza1,2 , Kai-Chun Chu3(B) , Hsiao-Chuan Wang4 , Tsui-Lien Hsu5 , Governor David Kwabena Amesimenu1,2 , and Fu-Hsiang Chang6 1 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of

Technology, Fuzhou, China 2 School of Information Science and Engineering, Fujian University of Technology, Fuzhou,

China 3 Department of Business Management, Fujian University of Technology, Fuzhou, China

[email protected]

4 Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan 5 Institute of Construction Engineering and Management, National Central University, Taoyuan,

Taiwan 6 Department of Tourism, Shih-Hsin University, Taipei, Taiwan 7 College of Mechanical & Electrical Engineering, National Taipei University of Technology,

Taipei, Taiwan 8 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. In many applications such as maritime surveillance and autonomous derivation, specific target tracking has become extremely important, and the characteristics of the research target will come from various distributed sources in different spaces and their noise measurements. We provide a new idea based on mobile submarine prediction data and ion pair measurement in this study, using Kalman filter based on accurate two-dimensional to approximate the results. However, we proposed to minimize the error covariance to calculate the total uncertainty measurement using the gain matrix K of Kalman filter. The actual measurement ix(k) of Kalman filter will be incorporated into each previously estimated sampling time. According to the correction equation to further improves the estimated value. The research results can be ground, the method can quickly converge to the true value, and its accuracy is better than the current passive omnidirectional sonar buoy submarine. This is an important result of the research. Keywords: Motion control · Filter of kalman · Velocity control · Sonar Buoy measured data · Predicted data

1 Introduction Most of the earlier anti-submarine aircraft used other types of aircraft for maritime patrols or bombing people (since submarines have become an important role in maritime operations, many countries have begun to carry out aircraft search and attack © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_22

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submarine missions. Mission. Vietnam. More and more countries use modified bombers or seaplanes to perform anti-submarine missions. These aircraft can stay in the air continuously and have a wide range of patrols. At the same time, they avoid being in the open sea with the assistance of the navigator on board. Lost. The use of radar to detect submarines at sea is a major change in the confrontation between anti-submarine aircraft and submarines. Submarines that use night cover have lost their original protective effect, and the ability of anti-submarine aircraft to find submarines has been greatly improved. Before the release of sonar, it will set a number for each sonar buoy. After release, the current coordinates will be recorded. These data will be input to the on-board control system [1, 2]. When one of the buoys returns a signal, the anti-submarine personnel can understand the data sent back by the buoy through the display. If the buoy belonging to a passive positioning system, it can also display the approximate location of the sound signal. According to the information collected by the sonar buoy, the anti-submarine aircraft can release more buoys, subdivide the detection area, and further determine the location, moving speed, and heading of the suspicious target. The number of sonar buoys carried by anti-submarine aircraft is limited, and the power duration of sonar buoys is not long. Therefore, a good plan is required when launching, and the brakes gradually break through the monitoring range [3]. Mann filters can appropriately reduce the noise system and measurement of the evaluation value, especially for the system of motion control. The Kalman filter effectively changes the parameters and anti-interference, and effectively uses the wideband force sensing and the noise suppression ability is very strong. However, the target of this study is to complete Kalman filter for 2D submarine tracking. The logic of thinking is to use two Gaussian function occupants to form another Gaussian function, and then track the measurement results according to Table 1. When the function of resulting is not in number of terms in the case of increasing or complexity, the measurement result is an infinite Gaussian pdf with the growth of time, but after each time period, under the Kalman filter elegant recursive key characteristics. The new pdf will be completely Gaussian function [4–10]. Table 1. Measured observations No

Position I (m)

Velocities (mfm/s)

0

4000

280

1

4260

282

2

4550

285

3

4860

286

4

5110

290

2 Methodology Because the output value is a linear combination of the input values, the best linear filter is also called for Kalman filter. In order to implement a simple and pure time domain

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filter, in the implementation process, the relationship between all uncertainties (noise) will use the covariance matrix. The calculation is evaluated by the following formula [11–17]. iXi (ki +i 1)i = iAxi (k)i + iBui (k)i +i Wi (k)

(1)

Zi (k)i = H xi (k)i +i Vi (k)

(2)

Here, the state is x(k); the noise corrupted data is measured z(k); and the known data input of this system is u(k). The process noise represents v(k), and the random variable represents w(k). The matrix states of observation and control input are A, B and H. We assume that the irrelevant Gaussian white noise is both w(k) and v(k) at zero mean. In Eqs. (1) and (2), there are covariance matrix of measurement noise R and process noise Q. R and Q matrices are show as follows. T Q=i EWW

(3)

R=i EVV T

(4)

R and Q are the expected value expressed as E [], and definite matrices of nonnegative. We use the actual experimental data of the measurable signal to test the simulation, and calibrate Q and R under an alternative Kalman filter with constant noise characteristics. The variance matrix R and the covariance matrix of process noise Q determine the sensitivity of the Kalman filter to noise. This research proposes a feedback control form to estimate the state x, which belongs to the Kalman filter. In a given time distance state, the filter performs prediction, and finally gets feedback in the measurement result value. Therefore, Fig. 1 shows the entire operation process in the submarine tracking of 2D process.

Fig. 1. Entire operation process in the submarine tracking of 2D process

When the error value of the forward projection and the current state estimation can be seen through the Kalman filter in Eqs. (5) and (6), we find that the reaction time will be damaged by noise, which will cause prediction distortion. xi (ki k − l)i =i Axi (k − lii k − 1)i +i Bui (k − 1)

(5)

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Pi (kii k − 1)i =i APi (k − 1ii k − 1)i ati +I q

181

(6)

Using formula (7) to calculate the total uncertainty, the error covariance can be obtained under the condition of minimizing the gain matrix K of the Kalman filter. The P in the equation is the obtained invariance error covariance matrix after predicting and improving the estimated values of the two states.  −1 (7) Ki (k)i = i Pi (ki i k − 1)i HiT H Pi (ki i k − 1)i HiT + i R i

We merge the actual measurement value z(k) obtained at each sampling time point into the previous estimate, and the brake uses the following corrector formula to obtain an improved estimate. Then update the error covariance state estimation and the previous related calculation value, as shown in Eqs. (8) and (9). i Xi (ki i k)i

= i Xi (ki i k − 1)i + i Ki (k)i (zi (k)i − H xi (ki i k − 1))

Pi ( ki | i k)i =i Pi ( ki |i k − 1)i − Ki (k)i H Pi ( ki | i k − 1)

3 Complete Example Worked Out The example parameter settings for the complete example of this study are: (1) (2) (3) (4)

V 0x i = 280 m/s. V 0y = 120 m/s. Y 0 i = 3000 m. DATA: iX0i = 4000 m. Table 1 shows the measured results of this study. Where process errors in the process covariance matrix is set to:

(1) Px i = 20 m. (2) Pvx i = 5 m/s. And observation errors are set to: (1) X = 25 m. (2) Vxi = i6m/s. In addition, initial conditions are set to: (1) (2) (3) (4)

ti = 1 s. Ax = 2 m/s2 . m = 25 m. Vx = 280 m/s. The following study will be calculated item by item.

(8) (9)

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3.1 Predicted State The predicted state of this study can be obtained as shown in Table 2. Table 2. Predicted state of this study ix (kk − 1)i = iAx(k − 1k − 1)i + iBu(k − 1) ⎡ ⎤





 X0

⎢ ⎥ X0 0.5t2 ⎢ i iV 0x ⎥ = i 1 t + axo ⎣ ⎦ 0 1 i iV 0x t X0 i

X0





i iV 0x i

 X0 i

i iV 0x i

=i

=

11



01 4281

4000 i i280





+

0.5



2

1



i i282

3.2 The Initial Covariance Matrix In this study, the initial covariance matrix can be obtained as shown in Table 3. Table 3. Initial covariance matrix Pxi = 20 m Pvx = 5 m/s



 X0 400 100 i= i iV 0x 100 25 ⎡ ⎤

 X0 ⎢ ⎥ ⎢ i iV 0x ⎥i = 400 0 ⎣ ⎦ 0 25

3.3 The Covariance Matrix of Predicted Process In this study, the covariance matrix of predicted process can be obtained as shown in Table 4.

Study of 2-D Sub-Marine Tracking with Complete … Table 4. Covariance matrix of predicted process P(kk − 1)i = iAPi(k − 1k − 1)iAT i + iQ





 1 t 400 0 1 0 P(kk − 1)i = i +0 0 1 0 25 t 1





 11 400 0 10 P(kk − 1)i = i i 01 0 25 11

 425 25 P(kk − 1)i = i 25 25

 425 0 P(kk − 1)i = i 0 25

Table 5. Calculating the Kalman gain K(k)i = iP(k/k − 1)iHT i(HP(kk − 1)iHT i + iRi − 1

 425 0 P(kk − 1)i = i 0 25

 10 HTi = i 01

 10 H =i 01

 425 0 Ri = i 0 25

 0.405 0 K(k)ii = i 0 0.410

3.4 Calculating the Kalman Gain In this study, calculating the Kalman gain can be obtained as shown in Table 5. 3.5 The New Observation The new observation in this study can be obtained as shown in Table 6.

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K.-C. Chang et al. Table 6. New observation Hxi = H ∗ X 1 + X 1

 10 H =i 01

 4260 Xl = i ii282 X 1 = 0

Z(k) = i

4260



i i282

3.6 Calculating the Current State In this study, calculating the current state can be obtained as shown in Table 7. Table 7. Calculating the current x(kk)i = ix(kk − 1)i + iK(k)(z(k)i − iHx(kk − 1))

 4281 x(kk − 1)i = i i i282

 0.405 0 K(k) = i 0 0.410

 4260 z(k) = i i i282

 10 Hxi = 01

 4272.5 x(kk)i = i i i282

3.7 Update the Covariance Matrix with the Overlapping Process for the Next Stage This study updates the covariance matrix with the overlapping process for the next stage and can be obtained as shown in Table 8.

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Table 8. Calculating the current Pi(kk)i = iP(kk − 1)i − iK(k)iHP(kk − 1)

 425 0 P(kk − 1)i = i 0 25

 0.405 0 K(k) = i 0 0.410

 10 Hxi = 01

 253 0 Pi(kk) = i 0 14.8

4 Results and Discussions The Kalman filter can mainly help us provide more accurate values between the results of measurement and prediction, so that the system can perform the next tracking action, it is the same as the situation solved in this research, and the result is very good. In this study, the next positions and speeds are in Table 2. In this study, the first update becomes the second update of the previous indicators, and so on to continue optimization. The updated calculation of Table 2 is the same as that of the first update, so the application can be executed based on past experience. Table 2 results have successfully given that the process in this study can converge to the on-site value actual quickly, and its accuracy is higher than that of submarines tracking for passive sonar buoys of omnidirectional (Table 9). Table 9. 3 Consecutive calculations of measurement, prediction and Kalman conversion results No

Positions (meters)

Velocities n(meters/sec)

Measured

Kalman filler

Predicted

Measured

Kalman filter

Predicted

1

4260

4272.5

4281

282

282

282

2

4550

4553.8

4564

285

284.3

284

3

4860

4843.9

4841

286

2862

286

5 Study Conclusion According to the information collected by the sonar buoy, the anti-submarine aircraft can release more buoys, subdivide the detection area, and further determine the location,

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moving speed, and heading of the suspicious target. This is from the establishment of the standard approximation of the nonlinear Gaussian substitute, so the results in the Kalman filter Table 2 have successfully proved that the method can very quickly converge to the on-site value of the mathematical iterative idea, the Kalman filter Use grouping equations and Input data to achieve fast tracking purpose based on iterative calculation the bottom position and velocity values. And its accuracy is higher than that of submarines tracking with passive sonar buoys of omnidirectional above.

References 1. Nguyen, D., Vadaine, R., Hajduch, G., Garello, R., Fablet, R.: A multi-task deep learning architecture for maritime surveillance using AIS data streams. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, pp. 331–340 (2018). https://doi.org/10.1109/DSAA.2018.00044 2. Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: The European Conference on Computer Vision (ECCV), pp. 101–117 (2018) 3. Ma, Y., et al.: A quick deployment method for sonar buoy detection under the overview situation of underwater cluster targets. IEEE Access 8, 11–25 (2020). https://doi.org/10.1109/ ACCESS.2019.2961555 4. Liu, Y., Fan, X., Lv, C., Wu, J., Li, L., Ding, D.: An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles. In: Mechanical Systems and Signal Processing, vol. 100, pp. 605–616 (2018). ISSN 0888–3270. https://doi.org/10.1016/j.ymssp.2017.07.051 5. Chu, K.C., Horng, D.J., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. J. IEEE Access 7, pp. 105562–105571 (2019) 6. José de Jesús, R., et al.: Neural Network Updating via Argument Kalman Filter for Modeling of Takagi-Sugeno Fuzzy Models, 1 Jan. 2018, pp. 2585–2596 7. Cao, L., Qiao, D., Chen, X.: Laplace 1 Huber based cubature Kalman filter for attitude estimation of small satellite. Acta Astronautica 148, 48–56 (2018). ISSN 0094-5765. https:// doi.org/10.1016/j.actaastro.2018.04.020 8. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on Internet of Things collaborative control. IEEE Access 8, 32935–32946 (2020) 9. Chang, K-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Generation Comput. Syst. 108, 445–453 (2020). ISSN 0167-739X. https://doi.org/10.1016/j.future.2020. 03.006 10. Zheng, Y., Gao, W., Ouyang, M., Lu, L., Zhou, L., Han, X.: State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter. J. Power Sources 383, 50–58 (2018). ISSN 0378-7753. https://doi.org/10.1016/j.jpo wsour.2018.02.058 11. Garbuno-Inigo, A., Hoffmann, F., Li, W., Stuart, A.M.: Interacting Langevin diffusions: gradient structure and ensemble Kalman sampler. SIAM J. Appl. Dyn. Syst. 19(1), 412–441 (2020) 12. Chih-Cheng, Lu., Chang, K.-C., Chen, C.-Y.: Study of high-tech process furnace using inherently safer design strategies (III) advanced thin film process and reduction of power consumption control. J. Loss Prev. Process Ind. 43, 280–291 (2015)

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13. Li, S.E., Li, G., Yu, J., Liu, C., Cheng, B., Wang, J., Li, K.: Kalman filter-based tracking of moving objects using linear ultrasonic sensor array for road vehicles. Mech. Syst. Signal Process. 98, 173–189 (2018). ISSN 0888-3270. https://doi.org/10.1016/j.ymssp.2017.04.041 14. Chang, K.-C., Chu, K.-C., Lin, Y.-C., Nguyen, T.-T., Sung, T.-W., Zhou, Y.-W., Pan, J.-S.: Study on health protection behavior based on the big data of high-tech factory production line. In: Pan, J.S., Lin, J.W., Liang, Y., Chu, S.C. (eds.), Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore (2020) 15. Chang, K.-C., Chu, K.-C., Chen, T.-L., Ward Lee, Y.-L., Lin, Y.-C., Nguyen, T.-T., Zhou, Y.W.: Study of improvement and verification for fan wall of network rack server using six sigma. In: 2019 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), Taipei, Taiwan, 2019, pp. 169–172 16. Chang, K.C., Chu, K.C., Lin, Y.C., Nguyen, T., Pan, J.S.: Study of inherently safer design strategy application for IC process power supply system. In: 2019 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), Taipei, Taiwan, 2019, pp. 158–161 17. Chang, K.C., Chu, K.C., Chen, T., Lee, Y.W., Lin, Y.C., Nguyen, T.: Study of the high-tech process mechanical integrity and electrical safety. In: 2019 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), Taipei, Taiwan, 2019, pp. 162–165

Retina Macular Edema and Age-Related Macular Degeneration Feature Recognition Method Based on the OCT Images Ling Wang1 , Wen Ce Xie1 , Tong Li2 , Yi Min Liu1 , and Tie Hua Zhou1(B) 1 Department of Computer Science and Technology, School of Computer Science, Northeast

Electric Power University, Jilin, China {smile2867ling,thzhou}@neepu.edu.cn, [email protected], [email protected] 2 Jihua General Hospital, Jilin 132000, China [email protected]

Abstract. Diabetic Macular Edema (DME) and Age-related Macular Degeneration (AMD) are the main causes of vision loss in patients with retinal diseases. Computer-assisted, deep learning-based enables intelligent analysis of the layered structure of the retina, which promotes intelligent medical reform. In this paper, we proposed an OCC-DME algorithm to adaptively identify the types of retinal diseases based on OCT image analysis and detection. After preprocessing, the lesions features of pucker in the retinal macular area can be extracted, and then update the weights of features and further to calculate through back propagation. Experimental analysis shows that our algorithm has a good classification accuracy rate and high recognition rate. Keywords: Diabetic Macular Edema · Age-related Macular Degeneration · CNN · Feature extraction

1 Introduction Optical Coherence Tomography (OCT) is an imaging modality capable that captures structural composition of biological tissues. It is popular in ophthalmology for clinical diagnosis of retinal diseases [1]. The current application aims to clinical classify diabetic macular edema and age-related macular degeneration to achieve the role of auxiliary diagnosis. Diabetic Macular Edema (DME) [2] is common in type I and type II diabetes, and it is the most common cause of blindness in patients. The international clinical classification of the severity of DME, disease is divided into two main levels: absence and presence. Using intelligent analysis method to analyze OCT image data can well identify DME, thickening of the retina or rigid exudation near the posterior pole, and in severe cases it appears in the center of the macula. Before the onset, the patient had no obvious symptoms, so it was not easy to find. People of all ages may suffer from DME [3], and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_23

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early diagnosis can improve the treatment effect. Age-related Macular Degeneration (AMD) [4] is characterized by the growth of abnormal blood vessels in the choroid, and the resulting fluid leakage into the retina and retinal pigment epithelium, which is deposited as DRUSEN. In order to realize the diagnosis and treatment of DME and AMD, we focus on deep learning technology which identify and diagnose OCT images. The principle is to use nonlinear methods to quickly extract data from neural networks [5]. As deep learning has received great attention in medical imaging, especially the Convolutional Neural Network, which is most suitable for image processing, has rapidly developed in medical image analysis [5, 6]. Srinivasan et al. [7] proposed a method of combining Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM), using algorithms to analyze each diseased OCT image to improve the accuracy of recognition. Karri et al. [8] proposed a method of using fine-tuned CNN GoogLeNet to train disease data sets, learn pathological features, and distinguish the types of targets by rules or boundaries. Zeiler et al. [9] proposed a method of using a square patch to cover different parts of the image and monitor the classifier to identify the position of objects in the image. In this paper, we proposed the OCC-DME algorithm, which combines the Convolutional Neural Network and the occlusion sensitivity to identify and diagnose the retina diseased location and type. The algorithm mainly uses the weights obtained by training to identify and diagnose OCT images, which greatly increases the accuracy rate. By generating a sensitive heat map, which can illustrate the retinal disease level and location.

2 Data Preprocessing A typical OCT image contains a part of uninteresting regions (outside the retina layers), which will waste resources when extracting useful features. At the same time, the image data is taken from two different data sets, the height and width are inconsistent. Therefore, it is necessary to preprocess the image. As shown in Fig. 1a threshold calculation is performed on OCT image, the image is divided into black and white parts, and the processed image is eroded and then expanded. The dilation operation is to add pixels to the objects in the image, and loop through the pixels to get the index. The main purpose of this step is to extract the most interesting area that is the target object, and set the image pixel value outside the area to 0 to complete the cropping of the image. The cropped image is shown in Fig. 1b.

3 Convolutional Neural Networks and Occlusion Sensitivity 3.1 Overview OCC-DME algorithm has two steps. The first step, a large number of images are trained through improved CNN, the features contained in the images are learned, the weights are automatically adjusted, and the presence of DME and AMD was detected by OCT image recognition. The second step, the heat map is generated by applying the occlusion sensitivity to display the image of the lesion area.

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Fig. 1 a is the original image, b is the preprocessed image, the retina area is clearly clear, and the area of interest is reduced

3.2 OCC-DME Algorithm Process In the first step of OCC-DME, we improved traditional CNN algorithm to preprocess the original OCT images, and next to automatically extract the features of pucker in the retinal macular area, furthermore to update the weights in order to improve the recognition accuracy of our proposed model. The improved CNN architecture is composed of three different types of layers, namely convolution layer, pooling layer and fully connected layer. The main purpose of the convolution operation is using a convolution filter with a step size of 3 to convolve the processed image for generate a feature image, and extract important features in the OCT fundus image. The calculation result of the image width (similar to the height) is shown in formula 1: Wo =

Wi − Wk + 1 + 2 × Px Sx

(1)

where Wi represents the width of the OCT image, Wk represents the width of the convolution filter, Px represents padding, and Sx represents the length of stride. The pooling layer selects the max pooling layer with stride of 2 to obtain the maximum value in each kernel, saves the features of pucker in the retinal macular area, reduce the feature dimension. The fully connected layer connects all the neurons in the previous layer to each individual neuron in the current layer to generate global semantic information, the detailed notations as Table 1. Table 1 Frequently used notations Symbol Meaning I int

OCT image to be detected

O

Occlusion patch

Sp

Occlusion path striding



The superposition operator, A  B, Superimpose B on A

L

Class label of I int

f

Improved CNN function

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The second step of OCC-DME is to diagnose the condition and severity by observing the generated sensitivity heat map. Set a small square patch on the image to block some pixels, and re-run CNN. The probability of the predicted label of the blocked image will change. Move the square patch on the image to generate a sensitivity change heat map that will highlight highly sensitive areas in the image. The specific steps are as follows: W (Iint ) − W (o) + 1 Sp H (Iint ) − H (o) + 1 H= Sp

W =

(2) (3)

Steps (2) and (3) calculate the width and height of the heat map.

Step (4) superimposes o on I int .

I  = Iint o

(4)

  C = f I L

(5)

Step (5) uses f to re-convolve I  to obtain the predicted probability of L. The detailed steps of OCC-DME algorithm are as follows: Algorithm 1:OCC-DME algorithm Input DME and AMD dataset Output AMD and DME disease diagnosis results 1. for each data df in D do 2. grayscale and binary processing on ( ) < 70% 3. if and adjust the contrast of the image 4. then crop the image on 5. else 6. adjust the contrast of the fundus image 7. end if 8. end for 9. building a convolutional neural network 10. feature_vector_set ← obtain feature vectors from convolutional neural networks(df) 11. average_ feature_vector ← average(feature_vecotr_set) 12. for each data fv in feature_vector_set do 13. if the fv is too different from the average_ feature_vector 14. then delete fv 15. else 16. the coefficient k obtained by (b-a)/(max(fv)-min(fv)) 17. mean ← a+k(x-min(fv)) /*x [a,b]*/ 18. end if 19. end for 20. training model model 21. obtain the predicted probability by using model(Iing) 22. return OCT disease diagnosis results

4 Experimental Results and Discussion 4.1 Dataset Our datasets are come from 2 different research institutions. One is Shiley Eye Institute of the University of California San Diego and other institutions [10]. The dataset contains

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four categories, NORMAL, DME, DRUSE, CNV. There are more than 82,000 OCT images of the training set, and more than 950 OCT images of the training set. The other dataset contains 38,400 BScans of MD patients and normal testers, and the accuracy of all images has been confirmed by experts, with clinical relevance [11]. Figure 2 shows a comparison of three different retinal diseases with a normal retina.

Fig. 2 Compared with normal retina, the layer deformed in the affected area, a NORMAL, b CNV, c DME, d DRUSEN

4.2 OCC-DME and SVM Algorithm Accuracy Comparison In this experiment, we performed a series of analyses on the performance to verify the accuracy of the OCC-DME algorithm, and applied the Support Vector Machine (SVM) algorithm [12] as the comparison algorithm. Use confusion matrix and ROC curve to check model training accuracy. Figure 3a, b represents the number of images that are identified as correct and incorrect when the test set is used by OCC-DME and SVM. (c) (d) represents the accuracy of OCC-DME and SVM model, as show in formula (6): pred =

Tp Tp + Fp

(6)

where Tp is the number of images predicted correctly in Fig. 3 a, b, Fp is the number of images predicted incorrectly in Fig. 3 c, d, and the results are approximate. There is no significant difference between OCC-DME and SVM for DME recognition, as shown in Fig. 3. But, when the image indicates NORMAL, CNV or DRUSEN, OCC-DME is slightly better than SVM. The ROC curve of the model loss function is shown in Fig. 4a, b, and the ROC curve of the model accuracy rate is shown in Fig. 4c, d. In Fig. 4a, c, the OCC-DME algorithm accuracy rate is 93.5% and the loss function is close to 35%, while (b, d) the SVM accuracy rate is 88.2% and the loss function is close to 43%. Therefore, in this paper, the performance of OCC-DME is higher than that of SVM. 4.3 Discussion We mainly use OCC-DME for the auxiliary detection of OCT images, and obtain the diagnosis results by using the weight information. Experimental analysis shows that the accuracy of our model is 93.5%, and the comparison algorithm is SVM (Support

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Fig. 3 a, b the number of test images in each interval in OCC-DME and SVM, c, d the accuracy rate of the model OCC-DME and SVM

Vector Machine) algorithm, with the accuracy is 88.2%. The OCC-DME algorithm can basically meet our requirements. After the OCT image is preprocessed, the expected disease type can be obtained through CNN, as shown in Fig. 5.

5 Conclusion In this paper, we proposed an OCC-DME algorithm to detect the presence of DME and AMD in retina. This algorithm applies pre-trained model to diagnose diseases, and the generated sensitivity heat map to observe the retina lesion. The experimental results show that OCC-DME is suitable for the recognition of retinal diseases on OCT images as a method of computer-assisted diagnosis. In the future work, we will extend our research to other complex dynamic video analysis tasks and solve other problems in medical imaging processing.

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Fig. 4 a, b ROC curve for OCC-DME and SVM loss function. c, d ROC curve of OCC-DME and SVM accuracy rate comparison

Fig. 5 Classify images and verify the presence of DME

Acknowledgements. This work was supported by the National Natural Science Foundation of China (No. 61701104), and by the Science and Technology Development Plan of Jilin Province, China (No.20200403039SF).

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References 1. Fujimoto, J.G., Drexler, W.: State-of-the-art retinal optical coherence tomography. Prog. Retin. Eye Res. 27(1), 45–88 (2008) 2. Bhagat, N., Grigorian, R., Tutela A.C., et al.: Diabetic macular edema: pathogenesis and treatment. Surv. Ophthalmol. 54(1), 1–32 (2009) 3. Tapp, R.J., Shaw, J.E., Harper, C.A., et al.: The prevalence of and factors associated with diabetic retinopathy in the Australian population. Diabetes Care 26(6), 1731–1737 (2003) 4. Farsiu, F.J.S., Connell, R.V.O., Folgar, F.A., Yuan, E., Izatt, J.A., Toth, C.A.: Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology 121(1), 162–172 (2014) 5. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) 6. Gopinath, K., Gopinath, S.B., Sivaswamy, J.: A deep learning framework for segmentation of retinal layers from OCT images. In: IAPR Asian Conference on Pattern Recognition (ACPR), pp. 888–893 (2017) 7. Srinivasan, P.P., Kim, L.A., Mettu, P.S., et al.: Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed. Opt. Express 5(10), 3568–3577 (2014) 8. Karri, S.P.K., Chakraborty, D., Chatterjee, J.: Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed. Opt. Express 8, 579. 10.1364 (2017) 9. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional neural networks. ECCV 2014, Part I, LNCS 8689. 8689 (2013) 10. Kaggle. https://www.kaggle.com/c/diabetic-retinopathy-detection/data. Last accessed 2020/03/20 11. Kaggle. https://www.kaggle.com/paultimothymooney/farsiu-2014. Last accessed 2020/03/20 12. Carrera, E.V., González, A., Carrera, R.: Automated detection of diabetic retinopathy using SVM. In: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, pp. 1–4 (2017)

Gene Expression PPI Network Clustering Analysis Between Endometrial Cancer and Ovarian Cancer Tie Hua Zhou1 , Wei Jian Pu1 , Hua Xie2 , Li Yan Zhang2 , and Ling Wang1(B) 1 Department of Computer Science and Technology, School of Computer Science, Northeast

Electric Power University, Jilin City, China {thzhou,smile2867ling}@neepu.edu.cn, [email protected] 2 Jilin Central General Hospital, Jilin City, China [email protected], [email protected]

Abstract. Recently, computer intelligent analysis as a significant technical means to discuss the differential mutant genes expression with the PPI network is current hot issues. In this paper, we mainly focus on discussing the relationship between endometrial cancer (EC) and ovarian cancer (OC) based on the gene expression, which could be classified into typical 13 categories after clustering processing over PPI network. Especially for the further analysis of homologous and heterogeneous genes’ comparison based on the similar protein function expression, we present a categorical scale to calculate the probability of endometrial cancer metastasizes to ovarian cancer by statistical method. Keywords: Endometrial cancer · Ovarian cancer · PPI network · Clustering analysis

1 Introduction Synchronous endometrial and ovarian carcinomas represent 5% to 10% of endometrial or ovarian carcinomas [1]. Endometritis histopathological tumors are diagnosed in both the uterus and ovary; they often occur simultaneously, such as synchronizing primary tumors or metastases from the uterus to ovary [2]. Although describe the molecular differences of dual primary tumors, metastatic tumors are identical in cloning [3]. TCGA research results show a large number of whole-exome sequencing data of gynecological malignant tumors [4, 5]. In addition, studies on endometrial and ovarian cancer are identified in two different cohorts; one group includes endometrioid carcinoma and the other group contains ovarian endometrial cancer [6]. Both ovarian and endometrioid carcinomas are described as containing PTEN, PIK3CA, ARID1A, PPP2R1A, and CTNNB1 mutations[7, 8].

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_24

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Addressing the association between disease and disease at the gene-level includes: gene expression [9], genome-wide associations [10], heterogeneous networks [11], and genome clustering [12]. At present, the clustering function module of PPI network is a common method to analysis diseases. A typical concept of a PPI network is a modular or densely connected sub-net, which can analyze the strong correlation between genes and diseases. The interaction of homologous genes and heterogeneous genes explains the relationship between endometrial cancer and ovarian cancer, and then calculating its occurrence probability.

2 Gene Data Clustering Analysis 2.1 Motivation In PPI network studies, in order to discuss the correlation analysis of the different gene mutation expressions, clustering processing as a common method to find co-expressed genomes. It can be expressed the functional pathways and interactions among a large of different genes. Co-expressed genomes are helpful to discover the potential co-regulatory genes or related conditions. For the possibility of endometrial cancer metastasizing to ovarian cancer, we constructed a PPI network to discuss the gene-related protein functional modules by clustering processing, and calculated the probability of having disease by statistical analysis. In fact, it is helpful for cancer metastasis research to locate the gene type and binding site, and the interaction frequency among different categories to represent the degree of relevance. We transform the correlation results between OC and EC to a probability scale based on the strong, medium, weak, and none four grades, which can used for the probability of disease diagnosis. 2.2 Preprocessing In this section, we focus on 12,834 mutant genes to be extracted from 157 high mutation frequency genes in the cBioportal [13], and the endometrial cancer and ovarian cancer gene list is also based on Cancer Database from cBioportal platform. The high expression genes retrieved from the database include 106 synchronous mutations genes and 49 heterogeneous mutations genes with ovarian cancer. Other datasets are come from COSMIC database [14] and PUBMED database [15]. We use k-means algorithm to classify the homologous genes and heterogeneous genes based on the probability of gene occurrence, similarity, and confidence scores. The centroids are random selected 8 nodes as the initial nodes, and then the gene nodes could be classified into different typical groups by k-means algorithm, which is based on the confidence distance value is greater than 0.4 in the PPI network.

3 PPI Network Analysis 3.1 Homologous and Heterogeneous Genes PPI Network In this section, we construct the PPI network of endometrial cancer and ovarian cancer by the k-means algorithm, and the clusters of the expression functions of heterogeneous

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mutated genes are 13 categories based on the typical protein characteristics, which include eight homologous genes and five heterogeneous genes. Figure 1 shows theeigh homologous genes’ PPI network based on the gene mutation list from electronic medical records of each patient.

Fig. 1 Homologous gene PPI network

As shown in Fig. 1, different colors represent different gene classifications. For example, blue represents the mutant A1 gene, which mutation probability is 53.9%. Orange represents the mutant A8 gene, which mutation probability is 17.83%. The clustered mutation genes categories are shown in Table 1. 3.2 Dedicated Ovarian Cancer Gene Analysis Adopting the expression function of 49 mutated genes, five characteristic categories are calculated. We calculate the probability of each category based on different protein functions according to the electronic medical record. The heterogeneous PPI network is shown in Fig. 2. As shown in Fig. 2, red represents the mutant B1 gene, which mutation probability is 37.57%. Purple represents the mutant B5 gene, which mutation probability is 22.84%. The detailed heterogeneous mutation genes classifications are shown in Table 2.

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Table 1 Homologous genes classification analysis Class No

Cluster color

Class name

Gene name

Mutation probability (%)

A1

Blue

Tumor suppressor TP53 PTEN BRCA1 53.9 CDK12 BRCA2 BLM SETD2 …etc

A2

Red

Transcription AR SPEN RICTOR regulatory activity BAP1 PIK3C2G APC KDM5A…etc

35.68

A3

Pink

Signal transaction ERBB3 KRAS of protein ERBB2 GRIN2A phosphorylation NF1 NRAS…etc

34.39

A4

Purple

Gene expression regulation

ARID1A ARID2 ARID1B DNMT1 DNMT3A XIAP…etc

26.75

A5

Yellow

Trans-membrane receptor protein tyro-sine kinase signal pathway

BTK ALK EPHB1 FGFR1 INSR IRS1 AXL KIT FGFR2 CDH1 FLT1 KDR

24.2

A6

Green

Regulate cell proliferation

STAT3 PDGFRB MTOR MET PTPRD EGFR…etc

22.29

A7

Pale pink

Immune response regulation

XIAP BIRC3 MALT1 IKBKE TNFAIP3

19.74

A8

Orange

RTK signal transaction

EPHA7 EPHA5 EPHA3 ABL1 NTRK1

17.83

4 Evaluation Analysis We use String software [16] and Cytoscape software and platform [17] to build a PPI network, and make use of k-means clustering algorithm to discover the different functional gene expressions and groups. The category module is integrated with the electronic medical record from cBioportal for cancer platform [13] to analysis the correlation among the categories. Adopting electronic medical records of endometrial cancer and ovarian cancer, 157 cases are selected for further analysis in order to calculate the probability of endometrial cancer and ovarian cancer simultaneously. Moreover, we deeply discussing the probability for a certain patient group, how many percent they have the same gene mutation expression. For example, there are 33.12% patients have the same gene mutation, which represents as A1 + A2 + A3 + B1 + B2 + B3. High probability of other mutation probability list is shown in Table 3.

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Fig. 2 Heterogeneous mutation genes’ PPI network

In order to calculate the degree of association among the different categories, we calculate the proportion of the maximal difference between the two categories. The probability values have four intervals, namely: strong (S), medium (M), weak (W), and none (N). The formula is as follows: Base =

max(mo, m1, . . . , mn ) − min(mo, m1, . . . , mn )  3 × n0 mi

Pace = {i × Base, (i + 1) × Base}(i = 0, 1, 2)

(1) (2)

In the study, we calculated the relationship among all of these 13 categories based on the formula 1 and formula 2, which presents concurrent probability for each mutation gene group, as shown in Table 4. This table as an auxiliary method can be used for the further analysis of endometrial cancer metastatic ovarian cancer.

5 Conclusion In this paper, we applied a gene concurrent probability categorical scale to calculate and evaluate the probability of endometrial cancer with ovarian cancer metastasis, which for deeply discussing the grade of endometrial cancer metastasis to ovarian cancer. The experimental results show that the probability of endometrial cancer metastasis to ovarian cancer is 8.91%–33.12%. The PPI network as a computer analysis method to study the correlation with genes and proteins becomes an important tool for computer assisted diagnosis.

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Table 2 Heterogeneous mutation genes Class No

Cluster color

Class name

Gene name

Mutation probability (%)

B1

Red

Cell adhesion

AHNAK2 FAT4 HMCN1 PKHD1 FCGBP PKHD1L1 CSMD1 FAT3 TENM1 USH2A…etc

37.57

B2

Yellow

Actin movement

MUC16 VWF RYR1 TTN DMD OBSCN NEB SYNE2 SYNE1 DYSF…etc

42.85

B3

Green

ATPase activity

DNAH3 DYNC1H1 35.05 MDN1 DNAH8 DNAH10 DNAH5 ANK2 DNAH17 DNAH9…etc

B4

Blue

Signaling receptor binding

APOB TG LRP1 LRP2

36.57

B5

Purple

Ubiquity-protein transferase activity

HUWE1 RNF213 MACF1 MYCBP2

22.84

Table 3 Pattern analysis of gene mutation probability Categories combination

Probability (%)

A1 + A2 + A3 + B1 + B2 + B3 33.12 A1 + A4 + A5 + A6 + B2 + B4 19.10 A1 + A5 + A6 + B4

14.60

A1 + A3 + A7 + A8

13.37

A2 + A4 + A5 + B2 + B5

15.28

A1 + A7 + A8 + B4

12.73

A2 + A5 + A6

14.64

A5 + A7 + A8

9.55

A2 + A4 + A5

12.10

A1 + A7 + B4

8.91

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T. H. Zhou et al. Table 4 Gene concurrent probability categorical scale A1 A2 A3 A4 A5 A6 A7 A8 B1 B2 B3 B4 B5 A1 A2 A3 A4

S

S

S

S

S

S

S

M S

M

S

M

W W W N

S

S

S

N

W

N

W W W W S

S

S

N

N

M S

N

S

W

M

S

W N

S

S

W N

N

M

W S

S

N

S

M W W S

S

A8

M W N

S

S

B1

S

S

N

W

S

S

W

A5 A6 A7

B2

S

S

S

W M M N

B3

Acknowledgements. This work was supported by the National Natural Science Foundation of China (No. 61701104), and by the Science and Technology Development Plan of Jilin Province, China (No.20190201194JC).

References 1. Sevimoglu, T., Arga, K.Y.: The role of protein interaction networks in systems biomedicine. Comput. Struct. Biotechnol. J. 11(18), 22–27 (2014) 2. van Niekerk, C.C., Bulten, J., Vooijs, G.P., Verbeek, A.L.: The Association between Primary Endometrioid Carcinoma of the Ovary and Synchronous Malignancy of the Endometrium. Obstet Gynecol Int (2010) 3. Ricci, R., Komminoth, P., Bannwart, F., Torhorst, J., Wight, E., et al.: PTEN as a molecular marker to distinguish metastatic from primary synchronous endometrioid carcinomas of the ovary and uterus. Diagn. Mol. Pathol. 12(2), 71–78 (2003) 4. Cancer Genome Atlas Research, N.: Integrated genomic analyses of ovarian carcinoma. Nature 10(11), 609–615 (2011) 5. Cancer Genome Atlas Research, N, et al.: Integrated genomic characterization of end endometrial carcinoma. Nature 5(1), 67–73 (2013) 6. McConechy, M.K., et al.: Ovarian and endometrial endometrioid carcinomas have distinct CTNNB1 and PTEN mutation profiles. ModPathol 27(1), 128–134 (2014) 7. Kurman, R.J., Shih, IeM.: Molecular pathogenesis and extra ovarian origin of epithelial ovarian cancer—shifting the paradigm. Hum. Pathol. 42(7), 918–931 (2011) 8. McConechy, M.K., Anglesio, M.S., Kalloger, S.E., et al.: Subtype-specific mutation of PP2R1A in endometrial and ovarian carcinomas. J. Pathol. 223(5), 567–573 (2011) 9. Menyhárt, O., Fekete, J.T., Gy˝orffy, B.: Gene expression indicates altered immune modulation and signaling pathway activation in ovarian cancer patients resistant to Topotecan. Int. J. Mol. Sci. 20(11) (2019)

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10. Reid, B.M., Permuth, J.B., Chen, Y.A., et al.: Genome-wide Analysis of Common Copy Number Variation and Epithelial Ovarian Cancer Risk 28(7), 1117–1126 (2019) 11. Xiong, Y., Guo, M., Ruan, L., et al.: Heterogeneous network embedding enabling accurate disease association predictions. BMC Med. Genomics 186(12) (2019) 12. Haque, M.M., Nilsson, E.E., Holder, L.B., et al.: Genomic clustering of differential DNA methylated regions associated with the epigenetic transgenerational inheritance of disease and phenotypic variation. BMC Genomics 418(17) (2016) 13. cBioportal For Cancer Homepage. https://www.cbioportal.org/. last accessed 2020/4/16 14. COSMIC Homepage. https://cancer.Sanger.ac.uk. last accessed 2020/4/22 15. PubMed Homepage. https://www.ncbi.nlm.nih.gov/pubmed. last accessed 2020/4/22 16. String Homepage. https://string-db.org/. last accessed 2020/4/21 17. Cytoscape Homepage. https://cytoscape.org/. last accessed 2020/4/18

Automatic Identification and Classification Method for Diabetic Retinopathy FFA Image Processing Tie Hua Zhou1 , Yi Min Liu1 , Wen Ce Xie1 , Hong Na Li2 , and Ling Wang1(B) 1 Department of Computer Science and Technology, School of Computer Science, Northeast

Electric Power University, Jilin City, China [email protected], [email protected], [email protected], [email protected] 2 Jihua General Hospital, Jilin City, China [email protected]

Abstract. Diabetic retinopathy (DR) is one of the most significant manifestations of diabetic microvascular lesions. In this paper, we proposed a sub-divisional algorithm of diabetic retinopathy degree (SADRD) algorithm to identify the severity of diabetic retinopathy based on the FFA images. It can accurately access the severity of DR and can identify complex features involved in classification tasks, such as microaneurysms, hyperplasia, and lesions on the retina. The experimental results demonstrate that this method has better DR recognition rate. Keywords: Diabetic retinopathy · Branch-SVM · Automatic detection · FFA image processing

1 Introduction According to the Global Diabetes Report published by the World Health Organization in 2016, the current trend of diabetic retinopathy is becoming increasingly serious. It also emphasizes the possibility of reversal of the current trend. Diabetic retinopathy (DR) [1] is one of the most common microvascular complications of diabetes, and blood circulation is the reason that blocks the blood vessels of retina. In addition, damaged blood vessels [2] are insufficient blood in the retina area which lead to permanent vision loss [3]. Therefore, regular screening of diabetic patients for retinopathy is crucial to the timing of treatment. Considering the general topic of the study, some outstanding studies from the literature can be explained slightly as follows: Vaishnavi et al. [4] proposed the development of an automated assessment system for diabetic retinopathy using a support vector machine. Venkatraman [5] proposed the automatic differentiation of retinal images from DR, which uses discrete wavelet transform (DWT) and wavelet coefficients compiled from various scales and apply them to the wavelet transform of the region for preprocessing. The results prove the performance of this system that gives the higher accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_25

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Labhade et al. [6] used Gaussian Naive Bayes, Adaboost, gradient boosting, random forest (RF), and SVM classifiers for DR detection comparison according to the texture changes in the retinal fundus image, texture analysis method. Shingade et al. [7] further proposed an algorithm for effectively detecting DR, which considered the gray level co-occurrence matrix (GLCM) characteristics of the color fundus images to evaluate the severity of the disease. In the case of real-time implementation, the accuracy of classification is about 84%. In addition, some existing CAD external systems [8, 9] have realized automatic DR detection. To detect eye diseases of retinopathy, the CAD system uses fundus image models [10, 11], which is essential for the diagnosis of retinopathy. Mansour [12] discussed and that DR’s CAD system, ant colony optimization (ACO), particle swarm optimization (PSO), and genetic algorithm (GA) are optimizing DR-CAD functions and the efficiency of components plays a vital role. According to the severity classification of international clinical diabetic retinopathy, we proposed a SARDD method based on HOG and SVM to automatically identify the severity classifications of FFA images. This method can be used to assess the development of different stages of the disease and the analysis of the severity of the lesion.

2 DR Classification Method Diabetic retinopathy is classified into proliferative and non-proliferative diabetic retinopathy according to the international clinical severity level, which includes nonproliferative and four grades: no obvious diabetic retinopathy, mild non-proliferative diabetic retinopathy, poisoning non-proliferative diabetes retinopathy, and severe nonproliferative diabetic retinopathy. According to international standards, the degree of lesion is related to the degree of micro-hemangioma, bleeding point, and lesion. DR classification process have two main steps: Firstly, we capture the feature vector of the original image, and determine the baseline feature vectors of different DR severity according to the international diabetic retinopathy severity standard. Secondly, making use of HOG method to initialize and calculate the feature vectors as a preprocessing, then we apply the multi-class support vector machine method to obtain each classification result and take them into the subdivision formula for further identifying the DR severity. The whole process is shown in Fig. 1.

3 SADRD Algorithm 3.1 Preprocessing SADRD has three main processes, which are avoiding image noise, unfirming image size, and complexity reducing process. Firstly, the SADRD adjusts the contrast of the entire data set for different labels based on the bio-morphologic characteristics (micro-hemangioma, intraretinal bleeding points, neovascularization, and vitreous hemorrhage) and reduces image noise. Secondly, since the particularity of the eye shape, setting the macular area of the retina as the point of interest and highlight the whole lesion area in order to reduces the zero-vector generated by HOG. Finally, the percentage of hemorrhagic point severity could be calculated after fundus images’ gray-scale processing.

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Fig. 1 Diabetic retinopathy degree sub-divisional algorithm flow

3.2 Standard Vectors Standard vectors are used for calculating the different diabetic retinopathy severity degree. The main purpose of generating the standard vector is to compare with the feature vector of the category predicted by the SVM to obtain the severity of the retinal disease. This standard formula is calculated from the generating formula, which is as follows.: − → − → − → − → C 0 + C1 + C 2 + · · · + Cn − → (1) Ai = n The original data images contain five categories according to the severity of inter− → national retinopathy. The category feature vector set is called set m. Cn belongs to the − → set m, and Ai is the calculated mean vector by 1.1. A set n is standard vectors for different severity level. After calculating the SVM, the corresponding standard vector is related to the severity of the clinical standard, whose value could be calculated by SVM method. The severity of the subdivided retinopathy can be obtained by comparing the difference between the standard vector and the feature vector. The subdivision formula of the diabetic retinopathy is as follows:  − → → α − Ai  i  − (2) r = +  4  Ai  − → → where − α is a feature vector could be calculated by SVM, Ai is the standard vector, and i is a certain degree.

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3.3 Multi-class Support Vector Machine The multi-class support vector machine training process is our proposed method, which is well classified the original FFA images to its respective classifications, namely PDR, NPDR, or normal. The SVM classifier is a two-class classifier, which is mostly used to solve multiclassification problems. A multi-level classifier structure is a branch tree classification structure, which enables to transform multi-classification problems into two-class problems. The main idea of SVM to solve multi-classification problems is to convert the original problem into a two-category problem, which can be directly solved by multiple SVM of combine multiple two-categories into multiple classifications. For example, n1 is the first category which is a cluster set will compare with other clusters such as n2 , n3 , n4 in a whole, as shown in Fig. 2. And next, calculating the cluster distance with each other, if n1 is the farthest cluster, n1 could be the left son in the second layer of the tree, otherwise continuously calculating the second cluster n2 or n3 , n4 . In this example, n2 is the farthest cluster to be the left son in the second level of the tree. After that, n1 will recursively calculate by the same criteria from the right son in the second level of the tree, until all of the categories classified completely. The multi-class SVM classification model based on branch tree is shown in Fig. 2.

Fig. 2 Multi-class SVM classification model branch tree structure diagram

3.4 Algorithm Description In this section, we use pseudocode to demonstrate the entire process of the SADRD algorithm to subdivide the retinal disease criteria. The detailed algorithm is as follows:

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Algorithm 1: Sub-divisional Algorithm of Diabetic Retinopathy Degree Input: FFA dataset(H), FFA catagory(K), FFA prediction data(d) Output: classification accuracy (ca) 1. for each data Hi in H do 2. perform noise processing and grayscale on Hi 3. If contrast_ratio (Hi) < 10% do 4. remove data Hi 5. end if 6. vectori ← perform graphic standardization and HOG on Hi 7. for each catagory ki in K do 8. baseline[ki] ← vectori / Hi ‘s size 9. end for 10. end for 11. construct multi-class SVM according to FFA categories(K) 12. train model by svm 13. hog process on FFA prediction data (d) 14. category_result ← prediction by svm(d) 15. for each catagory ki in K do 16. ca ← ki /4 + mold(baseline[ki]) 17. end for 18. return ca

4 Evaluation The data set is provided by non-profit medical organizations that contains more than 3662 fundus images of the training set, and more than 1000 fundus images of the training set [13]. The dataset is separated into five categories: normal, no obvious DR, mild nonproliferative DR, moderate non-proliferative DR, and severe non-proliferative DR; Fig. 3 is a comparison of four retinal diseases and normal retina.

(a)

(b)

(c)

(d)

(e)

Fig. 3 Compared with normal retina, the layer deformed in the affected area, a normal, b no obvious DR, c mild non-proliferative DR, d moderate non-proliferative DR, e severe non-proliferative DR

The accuracy comparison with different severity of diabetic retinopathy is shown in Table 1. The classification of retinal diseases has two types: diseased and normal. Sensitivity is the number of correctly tested diseases divided by the number of all tested diseases. Specificity is the number of correctly tested normal numbers divided by the number of actually tested. We compared our proposed SADRD with convolutional neural network (CNN) as shown in Table 2, which shows the results of the sensitivity, specificity,

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and accuracy for the classification of fundus images. In addition, the recognition speed of SDARD is 2.5 times faster than that of the CNN method. Table 1 SADRD classification results Heading level

No DR

Mild

Moderate

Severe

Proliferative

Accuracy

96.6

96.5

97.2

96.3

96.4

Table 2 Sensitivity, specificity, and accuracy comparison Models

Sensitivity Specificity Accuracy

SADRD 97.2

98.3

96.3

CNN

97.2

95.2

96.1

5 Conclusion In the paper, we proposed the sub-divisional algorithm of diabetic retinopathy degree (SADRD) for the diagnosis of DR. Firstly, mathematical morphological operations and HOG are used for extracting the feature vectors. Secondly, the extracted feature vector is applied as the training set in the classifier algorithm branch-SVM. Finally, an evaluation method is used to detect the severity of the disease. As a result, the sensitivity and specificity of SADRD are, respectively, 97.2% and 98%. It is helpful to provide an objective reference to the evaluation of patients’ diseases, and further to the treatment of diseases. Acknowledgements. This work was supported by the National Natural Science Foundation of China (No. 61701104), and by the Science and Technology Development Plan of Jilin Province, China (No.20190201194JC).

References 1. WHO. www.who.int/news-room/fact-sheets/detail/diabetes. Last accessed 2020/03/20 2. Balaji, G.N., Subashini, T.S., Chidambaram, N.: Detection of heart muscle damage from automated analysis of echocardiogram video. IETE J. Res. 61(3), 236–243 (2015) 3. Amin, J., Sharif, M., Yasmin, M., Ali, H., Fernandes, S.L.: A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions. J. Comput. Sci. 19, 153–164 (2017) 4. Vaishnavi, J., Ravi, S., Devi, M.A., Punitha, S.: Automatic diabetic assessment for diabetic retinopathy using support vector machines. IJCTA 9(7), 3135–3145 (2016)

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5. Venkatraman, K.: Programmed detection of diabetic retinopathy in fundus images utilizing wavelet features. J. Chem. Pharmaceut. Sci. (JCPS) 9(2), 59–63 (2016) 6. Labhade, J.D., Chouthmol, L.K., Deshmukh, S.: Diabetic retinopathy detection using soft computing techniques. Automatic Control and Dynamic Optimization Techniques (ICACDOT). Int. Conf., pp. 175–178, IEEE (2016) 7. Shingade, M.A., Hande, M.K., Mundada, M.R., Langar, M.H., Pachghare, M.A., Yavatmal, J.D.I.E.T., Yavatmal, J.D.I.E.T.: Real time implementation of an intelligent algorithm for effective detection of diabetic retinopathy. Int. J. Adv. Found. Res. Comput. (IJAFRC) 3(5), 14–23 (2016) 8. Sanchez, C.I., Niemeijer, M., Dumitrescu, A.V.: Evaluation of a computeraided diagnosis system for diabetic retinopathy screening on public data. Investig. Ophthalmol. Visual Sci. 52(7), 4866–4871 (2011) 9. Kumar, B.S.S., Manjunath, A.S.: Christopher S, Improved entropy encoding for high efficient video coding standard. Alex Eng. J. 57(1), 1–9 (2018) 10. Odstrcilik, J.: Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Proc. 7(4), 373–383 (2013) 11. Trucco, E.: Validating retinal fundus image analysis algorithms: issues and a proposal. Investig. Ophthalmol. Vis. Sci. 54(5), 3546–3559 (2013) 12. Mansour, R.: Evolutionary computing enriched computer aided diagnosis system for diabetic retinopathy: a survey. IEEE Rev. Biomed. Eng. 334–349 (2017) 13. Kaggle: www.kaggle.com/tanlikesmath/diabetic-retinopathy-resized. Last accessed 2020/03/20

Emotional Expression Analysis Based on Fine-Grained Emotion Quantification Model Via Social Media Ling Wang, Hang Yu Liu, Wen Long Liang, and Tie Hua Zhou(B) Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin, China {smile2867ling,2201900564,thzhou}@neepu.edu.cn, [email protected]

Abstract. Social media has gradually become an essential part of individuals, where unrestrained emotions are shared on the public platform. Emotion is the nature production of physiological reaction which can even affect the mental status of individual. Emotional expression is used to describe when the individuals experience emotions, the ability of expressing their emotions with verbal or nonverbal behaviors. Therefore, in this paper, we proposed a fine-grained emotion quantification (FGEQ) model to quantify emotional expression with two main factors as emotional dimension and emotional intensity to seek for the correlation between emotional expression and mental status. Keywords: Social media · Emotional expression · Data mining · Emotion qualification

1 Introduction A survey from World Health Organization [1] indicates that more than three hundred million population has been diagnosed with mental disorder, which can be caused by multidimensional factors such as the growing environment, personality, psychological trauma, poverty, abused drug or alcohol and so on [2]. A high suicide rate [3] due to mental disorder makes the thorough psychological intervention for diagnosed and potential patient extremely urgent. Generally, various types of questionnaires [4] are conducted to evaluate the status of mental health, which is aimed at quantizing the expressed emotions during the daily life. And emotions are the obvious sign and symptom of mental disorder, such as bipolar disorder [5] is usually shifting from emotion state depression to mania. However, questionnaire can be one-sided due to the low emotional granularity of some people who are unable to distinguish certain emotions properly which might compromise the accuracy and efficacy of the self-rating approach. A study conducted by Kimhy et al. [6] indicts the correlation between emotional granularity and schizophrenia, which points out that the patient with mental disorder is capable to distinguish negative emotions © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_26

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as mentally healthy ones but incapable to distinguish the overall emotions especially positive ones. Sentiment analysis in nature language processing is commonly applied in the emotion and opinion-related detection or identification. Zucco et al. [7] proposed a multimodal system based on sentiment analysis for monitoring of depression, and the facial expression is used to analyze the mood trend of individual. Zhou et al. [8] proposed an emotion perception model based on multidimensional sentiment polarities for the identification of mental disorder, an extended 11 polarities of sentiment is proposed to extend the range of detection, and as a result, the identification of mental disorder based on extended sentiment polarity outperformed than other sentiment analysis method.

2 Motivation The affection of mental disorder has rapidly increasing due to the lack of proper understanding of mental health and self-adjustment of spontaneous emotions. Questionnaire is commonly applied on the offline diagnosis of mental disorder; however, even with easy access to the various online mental questionnaires, due to lack of monitoring intervened by the specialist, the online self-rating questionnaire can be subjectively for the evaluation of mental status. Currently, sentiment analysis is the commonly used approach for detection and identification of mental illness; however, general sentiment analysis consists only with three polarities as “positive”, “negative”, and “neutral”, which will restrain the overall performance of identification. To be more precise in the detection process, we proposed a twenty-one-dimensional emotional lexicon for the extraction and detection of individual’s emotions from our previous study [9]. The concept of emotional expression is when individuals experience emotions, the ability of expressing their emotions with verbal or non-verbal behaviors, which can be generated with or without self-awareness. Also, emotional expression is related to emotional regulation where emotional dysregulation is commonly observed among mental disorders [10]. In this paper, a fine-grained emotion quantification (FGEQ) model is proposed based on the concept of emotional granularity to quantify emotional expression to seek for the correlation between emotional expression and mental disorder. The emotional dimension and emotional intensity are listed as two main factors in FGEQ model to thoroughly evaluate the ability of emotional expression. Firstly, the public daily information posted by individual during approximately one week from Twitter is collected based on keyword search of different labels, and which are subdivided as diagnosed label and occupation label for further control experiment. The diagnosed label contains four mental disorder as: “anxiety disorder”, “bipolar disorder”, “depression disorder”, and “obsessive–compulsive disorder (OCD)” and the occupation label contains six occupations as: “waiter”, “reporter”, “engineer”, “traveler”, “singer”, and “comedian”. Secondly, the emotions are extracted from the text based on the emotional corpus. Thirdly, the emotional expression is calculated based on two key factors: emotional dimension, which represents the ability of distinguishing differential emotions, and emotional intensity, which represents the strength of expressed feelings. Finally, the emotional expression among different labels is calculated to seek the correlation with different characteristic population. The process of fine-grained emotion quantification model is shown at Fig. 1.

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Fig. 1 Process of fine-grained emotion quantification model

3 Fine-Grained Emotion Quantification Model 3.1 Preprocessing The twitter data are combined with diagnosed set and occupation set; in diagnosed set, individuals are separately labeled as four mental disorders and six different occupations. Once datasets are collected completely, the twenty-one-dimensional emotional lexicon is applied to extract the various expressed emotions from each tweet and transform them into related sentiment polarity. The twenty-one -dimensional sentiment polarities are shown at Fig. 2.

Fig. 2 Twenty-one -dimensional sentiment polarities

3.2 Quantification of Emotional Expression The definition of emotional expression (EE) is to describe the ability of individual for expressing various emotions with different intensity. Therefore, two ingredients are needed as: emotional dimension and emotional intensity. The emotional dimension

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indicts the range of expressed emotions by individual, and the emotional intensity indicts the strength of expressed emotions by individual. To be more precise in the evaluation, the overall emotional expression (EE all ) and the negative emotional expression (EE neg ) are calculated for further analysis. The quantification process contains with three steps: Step1: Calculate the EE(all,1) and EE(neg,1) value of individual from one tweet with Eq. (1)–(2); EE(all,1) = EE(neg,1) =

d1 × n1 21

(1)

nd1 × nn1 11

(2)

The definition of d1 is the number of emotional dimensions detected in one posted tweet; n1 is the number of emotional words detected among d1 dimensions in one posted tweet; nd1 is the number of negative emotional dimensions detected in one posted tweet; nn1 is the number of negative emotional words detected among nd1 dimensions in one tweet. Step2: Calculate the EE(all,ind) and EE(neg,ind) value of individual from all tweets with Eq. (3)–(4); m d i × ni  v  dmax × 1− × (3) EE(all,ind) = i=1 21 m−v m 21 m ndi × nni  v  ndmax EE(neg,ind) = i=1 11 × 1− × (4) m−v m 11 The definition of m is the number of tweets; di is the number of emotional dimensions detected in ith posted tweets; ni is the number of emotional words detected inth posted tweets; v is the number of non-emotional posted tweets; dmax is the number of expressed dimensions contained in all posted tweets; ndi is the number of negative emotional dimensions detected in ith posted tweets; nni is the number of negative emotional words detected in ith posted tweets; ndmax is the number of expressed negative dimensions contained in all posted tweets. Step3: Calculate the EE(all,lab) and EE(neg,lab) value of each label with Eq. (5)–(6). k EE(all,lab) =

j=1 EE(all, j)

k EE(neg,lab) =

×

k × dmax

j=1 EE(neg, j)

×

m

i=1 ni

(5)

m

i=1 nni

k × ndmax

(6)

The definition of k is the number of individuals under each label. 3.3 Emotional Expression Analysis Algorithm The emotional expression analysis (EEA) algorithm can efficiently quantify the ability of individual to express their emotions with intensity. The preprocessed twitter data P

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which contains with emotion sequence as Es and label of individual as lab is inputted in EEA. On the other hand, the average value and standard deviation of overall emotional expression EE(all,lab ) and negative emotional expression EE(neg,lab ) under each label separately as Ave(all,lab ), Ave(neg,lab ), Sd(all,lab ) and Sd(neg,lab ) are obtained from EEA. The pseudo code of EEA is shown at Algorithm 1. Algorithm 1: EEA Input: preprocessed twitter data P Output: EE(all,lab), EE(neg,lab), Ave(all,lab), Ave(neg,lab), Sd(all,lab), Sd(neg,lab) 1: Begin 2: Calculate each EEall and EEneg value of individual from Es according to Equation (1-2); 3: Calculate the EE(all,ind), EE(neg,ind) value with calculated EEall and EEneg according to Equation (3-4); 4: Calculate the EE(all,lab), EE(neg,lab) from each lab with calculated EE(all,ind), EE(neg,ind) according to Equation (5-6); 5: Calculate the number of individuals under one lab as n; 6: for i = 0, i < n, i++ do 7: totalall += EE(all,ind); 8: totalneg += EE(neg,ind); 9. end for 10: Ave(all,lab) = totalall / count ; Ave(neg,lab) = totalneg / count ; 11: for i = 0, i < n, i++ do 12: difall += pow((Ave(all,lab)-EE(all,ind)), 2) / count; 13: difneg += pow((Ave(neg,lab)-EE(neg,ind)),2) / count; 14. end for 15. Sd(all,lab) = sqrt(difall / count); Sd(neg,lab) = sqrt(difneg / count) 16: return EE(all,lab), EE(neg,lab), Ave(all,lab), Ave(neg,lab), Sd(all,lab), Sd(neg,lab)

4 Experiment and Results Once the emotional expression values are separately calculated, the correlation of emotional expression with mental disorder or occupation can then be analyzed. The average value and standard deviation under each category are listed at Table 1. From Table 1, the average values Ave(all,lab ) and Ave(neg,lab ) of emotional expression among the diagnosed set are averagely above 0.5 and nearly twice the value of the occupation ones, which indict that individuals with mental disorder have a higher average ability to express their emotions, and as for traveler, the Ave(all,lab ) has reached 0.440 which might indict that individuals with more relax job content tend to share more emotions on social media. However, the standard deviation values Sd(all,lab ) and Sd(neg,lab ) among diagnosed ones are rather large than the occupation ones, which mean the group performance can be instable and the proportion of the distribution can be uneven. To further comprehend the standard deviation among each label, the proportions of distribution among each label are listed at Table 2. From Table 2, it can be observed that, for those who have a higher value of expressing emotions among mental disorder patients takes up nearly 45% of proportion which is two time of the occupation ones expect for travelers. And 54% of individuals with bipolar disorder have a greater sense of distinguish differentia emotions with higher intensity.

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Ave(all,lab ) Sd(all,lab ) Ave(neg,lab ) Sd(neg,lab )

Anxiety disorder

0.518

Bipolar disorder

0.663

0.558

1.640

0.745

1.152

0.852

2.338

Depression disorder 0.508

0.509

0.604

1.062

OCD

0.533

0.627

0.491

0.978

Traveler

0.440

0.367

0.147

0.263

Reporter

0.289

0.190

0.184

0.260

Comedian

0.323

0.263

0.272

0.386

Engineer

0.341

0.267

0.178

0.269

Singer

0.224

0.213

0.140

0.325

Waiter

0.290

0.197

0.238

0.278

Table 2 Proportions of distribution among each label Label

EE(all,lab )

EE(neg,lab )

High

Median

Low

High

Median

Low

Anxiety disorder

46.22%

33.14%

20.64%

36.02%

20.16%

43.82%

Bipolar disorder

54.65%

27.30%

18.05%

42.67%

16.36%

40.97%

Depression disorder

44.10%

31.76%

24.14%

38.23%

18.40%

43.37%

OCD

44.96%

31.40%

23.64%

31.27%

20.26%

48.47%

Traveler

44.43%

25.81%

29.76%

8.43%

15.71%

75.86%

Reporter

17.92%

49.79%

32.29%

12.50%

18.65%

68.85%

Comedian

25.35%

40.44%

34.21%

20.08%

21.38%

58.54%

Engineer

29.55%

35.28%

35.17%

10.79%

16.52%

72.99%

Singer

10.88%

33.00%

56.12%

8.00%

12.10%

79.90%

Waiter

17.66%

50.08%

32.26%

17.15%

23.25%

59.60%

For the proportions of travelers indict that they tend to share more emotions especially positive ones on social media. To illustrate the ability on emotional dimension, the proportions of expressed overall and negative emotional dimension are listed at Table 3. From Table 3, it can be observed that those who suffered from mental disorder not only have a higher value of emotional expression, but also, they tend to express more emotional dimensions than the occupation ones especially with signification proportion of expressed negative emotions. Among the occupation set, the poor proportion of singers might be caused due to the influence of public figure that those accounts might be ran by their company and less private information are released on the social media. Another fact

Emotional Expression Analysis Based on Fine-Grained …

217

Table 3 Proportions of expressed dimension Label

Overall dimensions

Negative dimensions

High (%)

Median (%)

Low (%)

High (%)

Median (%)

Low (%)

Anxiety disorder

33.81

51.87

14.32

57.71

31.80

10.49

Bipolar disorder

36.60

48.93

14.47

59.70

29.79

10.51

Depression disorder

35.56

46.97

17.47

58.79

29.60

11.61

OCD

30.57

51.89

17.54

51.97

34.65

13.38

Traveler

14.78

46.30

38.92

23.31

37.15

39.54

Reporter

18.34

60.10

21.56

37.08

43.44

19.48

Comedian

23.48

55.70

20.82

46.23

38.46

15.31

Engineer

16.74

56.07

27.19

31.57

40.68

27.75

Singer

9.60

47.74

42.66

24.81

37.33

37.86

Waiter

19.19

63.32

17.49

48.90

38.11

12.99

Overall dimension: High [14, 21], Median [10, 14), Low [0, 10); Negative dimension: High [5, 11], Median [3, 5), Low [0, 3).

is that negative emotions are more constantly expressed among stressful occupations such as reporter, engineer and waiter. Interestingly, 46% of comedian has detected with higher expressed negative emotional dimension, which might be caused due to that comedians are generally posting jokes on social media to gain attention from other users, and sarcasm is the common approach to produce the bittersweet jokes, which appreciated by most individuals; therefore, a significant amount of emotions are detected among the comedian. This phenomenon clearly indicts that expressing emotion is different from distinguishing emotions.

5 Conclusion In this paper, a fine-grained emotion quantification (FGEQ) model is proposed to seek for the correlation of emotional expression with mental disorder and occupation, where the ability to express emotion is quantified with emotional dimension and emotional intensity. Analysis based on several indices can be concluded as: Firstly, individuals with mental disorder have a high score on the ability of expressing overall and negative emotions with significant emotional intensity and larger expressed emotional dimensions; however, the uneven distribution of emotional expression can indict that the differentia of their emotional expressions can be significant. On the other hand, from the occupation ones, individuals with stressful occupations have a higher emotional expression and more negative emotions are detected. Secondly, the emotions detected from diagnosed dataset are rather clustered around negative emotions, and the

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overall performance might be affected by the numerous amounts of negative emotions. Finally, from the results of comedian which might indict that the comedian can distinguish emotions of other individuals rather than themselves, and which brings up the concept of emotional granularity as the capability of individual to generate certain emotions toward difference incidents. Therefore, how to build up the connection between emotional expression and emotional granularity based on the ability to distinguish emotions separately from self and others, and also the ability to express the emotion along with the intensity of expressed emotion can be conducted in our future work. Acknowledgements. This work was supported by the Science and Technology Development Plan of Jilin Province, China (No.20200403039SF, and No.20190201194JC), and by the National Natural Science Foundation of China (No. 61701104).

References 1. World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates. World Health Organization (2017). 2. Chibanda, D., Weiss, H.A., Verhey, R., Simms, V., Munjoma, R., Rusakaniko, S., et al.: Effect of a Primary Care-Based Psychological Intervention on Symptoms of Common Mental Disorders in Zimbabwe: A Randomized Clinical Trial. JAMA 316, 2618–2626 (2016). https:// doi.org/10.1001/jama.2016.19102 3. Harris, E.C., Barraclough, B.: Suicide as an outcome for mental disorders. a Meta-Analysis. British Journal of Psychiatry. 170, 205–228 (1997). https://doi.org/10.1192/bjp.170.3.205 4. Cleary, P.D., Goldberg, I.D., Kessler, L.G., et al.: Screening for mental disorder among primary care patients: Usefulness of the General Health Questionnaire. Arch. Gen. Psychiatry 39(7), 837–840 (1982) 5. Kawa, I., Carter, J.D., Joyce, P.R., et al.: Gender differences in bipolar disorder: age of onset, course, comorbidity, and symptom presentation. Bipolar Disord. 7(2), 119–125 (2005) 6. Kimhy, D., Vakhrusheva, J., Khan, S., et al.: Emotional granularity and social functioning in individuals with schizophrenia: an experience sampling study. J. Psychiatr. Res. 53, 141–148 (2014) 7. Zucco, C., Calabrese, B., Cannataro, M.: Sentiment analysis and affective computing for depression monitoring. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1988–1995. IEEE (2017) 8. Zhou, T.H., Hu, G.L., Wang, L.: Psychological Disorder Identifying Method Based on Emotion Perception over Social Networks. International Journal of Environmental Research and Public Health. 16(6), 953 (2019). https://doi.org/10.3390/ijerph16060953 9. Wang, L., Liu, H., Zhou, T.: A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media. Applied Sciences. 10(5), 1647 (2020) 10. Sheppes, G., Suri, G., Gross, J.J.: Emotion regulation and psychopathology. Annual Review of Clinical Psychology. 11, 379–405 (2015). https://doi.org/10.1146/annurev-clinpsy-032814112739

Fuzhou PM2.5 Prediction and Related Factors Analysis Wen-Ji Zhang1 , Li-Wen Chen1 , Yao Zhou1 , Ri-Jing Zheng1 , and Kuo-Chi Chang1,2,3,4(B) 1 School of Information Science and Engineering, Fujian University of Technology, Fujian

350118, China [email protected] 2 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian 350118, China 3 College of Mechanical & Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 4 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. The prediction of urban air pollutant concentration requires processing of large amounts of meteorological data and complex changes of air pollutants. PM2.5 is affected by many factors, including meteorological factors and pollutant factors. However, most existing methods either do not consider the corresponding relationship between the surrounding detection sites or ignore the strength of the correlation between them. Without considering the spatial correlation or having too much weak correlation factors, the input will affect the accuracy of the prediction results. To solve this limitation, we use the Pearson correlation coefficient (PCC) to make a correlation analysis of PM2.5 influencing factors. According to PCC, we can extract highly relevant data and drop weaker data. Then, we use the long shortterm memory (LSTM) model to predict the PM2.5 of the monitoring station. The experimental results show that selecting input factors based on PCC can improve the prediction performance of the model. Keywords: Air pollution · Spatial–temporal correlation · Pearson correlation coefficient · Long short-term memory · PM2.5

1 Introduction In the past few decades, with the expansion of cities and the rapid development of industrial industries, the environment has become worse and worse, resulting in much environmental problems. In the air pollution incident, it affects human health and daily life [1]. Even worse, air pollution has increased the incidence of some diseases. It may also affect the normal development of the baby. Inhalation of air pollutants can cause harm to human respiratory tract. The harsh air environment is harmful to people’s physical and mental health. Improving the air environment is of great help in improving people’s living conditions and quality of life. Therefore, the air data can be used to predict the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_27

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concentration of urban air pollutants more accurately to help people early warning of air pollution and take necessary measures [2]. According to different prediction methods used in related studies [3–5], there are many ways to predict air quality. In air prediction, Masih used different methods to predict the concentration of sulfur dioxide in the atmosphere [6]. Araki et al. establish a spatiotemporal land use random forest (LURF) model to predict NO2 [7]. Ambika et al. used linear regression and other models to predict air quality [8]. Deep learning models have been widely used in various industries, including text recognition, image processing, speech recognition, etc., creating a lot of value in various industries and solving many problems that have not been solved for a long time in many industries. It is now also widely used in environmental governance, solving many environmental problems. In addition to traditional prediction models, deep learning has been widely used as a model for predicting air pollution. Compared with machine learning, deep learning is a more advanced non-linear modeling technology. It is based on the improvement of artificial neural networks and will further increase the number of neural-like computing units to make the model better. It has passed many research tests and is reported to have excellent predictive performance in air quality prediction. Freeman et al. used LSTM predict average surface ozone concentration [9]. Qi et al. used a general and effective approach to solve air prediction and feature analysis in one model called the deep air learning (DAL) [10]. Li et al. used a LSTM model to predict PM2.5 [11]. Li combining the convolutional neural network (CNN) with the LSTM for forecasting the next 24 h PM2.5 concentration [12].Wang and Song, design a data-driven method that predicts future air quality [13–15]. Urban air pollution is caused by a large number of pollutants and weather conditions. Meteorological elements have an important effect on air pollutants. Existing literature shows that urban air quality is closely related to atmospheric pressure, temperature, precipitation, and relative humidity. However, although the above methods can obtain more accurate prediction results, most methods only consider historical air pollutant data and meteorological data. Only a few consider data from surrounding sites. Still, these papers have a common limitation. These articles do not consider the ranking of the relevance of the influencing factors. The correlation of input factors is weak, and more noise may be added during the modeling process and affect the prediction effect of the model.

2 Materials and Methods 2.1 Study Sites Fuzhou is located at the mouth of the Minjiang River in the southeastern coast of China and in the middle and east of Fujian Province, across the sea from Taiwan Province. Located at 25°15 N latitude, 120°31 E. The main wind direction of Fuzhou in winter is northeast, and in summer, it is south. The weather is hot from July to September, which is the period of typhoon activity. Fuzhou has a monitoring network consisting of five monitoring stations, namely Shida station (SD), Gushan station (GS), Wusibei station (WSB), Yangqiaoxi station (YQX), and Ziyang station (ZY) is shown in Fig. 1. In this study, the real-time daily of PM2.5 and PM10 data from January 1, 2019 to June 30, 2019 at the five monitoring sites were collected from China city air quality

Fuzhou PM2.5 Prediction and Related Factors Analysis

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Fig. 1. Map of monitoring sites used in this study

real-time release platform. We obtain meteorological data from Fuzhou Meteorological Bureau, including wind, temperature, humidity, and precipitation. Due to the interference of various factors in the process of collecting information, it is necessary to pre-process the meteorological data and pollution data to ensure the validity of the data and improve its accuracy. In data preprocessing, the interpolation method is used to fill in the missing values and normalized, and the data range is (0, 1). The data set selected in this paper is a time series data set, sorted according to the time index, while the supervised learning data set consists of input X and output Y. Through the data translation operation, the processed data set is converted into a supervised learning problem, and the transformed data set is used as the input variable of the model. 2.2 LSTM LSTM introduces an improved strategy for traditional RNN to overcome the problem of vanishing gradient. After introducing the gate mechanism in the internal structure of LSTM, the gate can be used to control or retain information, so that the stored information in the time series can be controlled. As shown in Fig. 2, historical information passes through three gates. Forget gate can decide what information to discard, input gate can decide what information to update from the input, the output gate determines which information is to be output. Forget gate ft , input gate it , output gate ot , status of this unit ct , and the output of this unit ht as shown in Eqs. (1)–(6),     ft = σ Wf ht−1 , xt + bf (1)

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    it = σ Wf ht−1 , xt + bf

(2)

    ot = σ Wo ht−1 , xt + bo

(3)

    c˜ t = tanh Wc ht−1 , xt + bc

(4)

Ct = ft ∗ Ct−1 + it ∗ ct

(5)

ht = ot ∗ tanh(ct )

(6)

Fig. 2. LSTM unit

where wf , wi , wc , and wo are weight matrices. bf , bi , bci and bo are bias vectors. C˜ t is a new candidate state. σ () is the sigmoid activation function. 2.3 PCC The PCC for any two random variables A and B is defined as: AB − AB  A2 − A2 B2 − B2

CAB = 



(7)

whose values can lie between −1 and 1, and < > is an average value. This article uses PCC to calculate the correlation between the local site PM2.5 and other factors, and to investigate the impact of various factors on the local site.

3 Results and Discussions We use ZY as the target station to predict the PM2.5 of the site. PM2.5 affecting the ZY site includes other pollutants, meteorological conditions, and PM2.5 of surrounding sites. We use the PCC to make an analysis of the correlation of influencing factors as shown in Fig. 3.

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Fig. 3. Pearson correlation coefficient of factors affecting ZY site

We can see that the correlation coefficients of the PM2.5 of the surrounding sites, and the PM2.5 of the ZY site are relatively high. The PM2.5 concentration of YQX, SD, WSB, and GS in the surrounding monitoring stations is highly correlated with the PM2.5 concentration of the target station, all of which are above 0.8.The correlation coefficients of meteorological conditions are small, including humidity, wind, temperature, and precipitation, which are inversely related to the PM2.5 concentration of the target site. This is because the vertical temperature distribution will determine the vertical diffusion of PM2.5. The higher the temperature, the strong convective wind will bring the particulate matter to the upper altitude, causing PM2.5 to diffuse upward. On the other hand, it can be known that strong wind and precipitation can reduce PM2.5. Among other air pollutants, PM10 and PM2.5 have a certain correlation, the correlation is 0.51, followed by SO2 and NO2 , which shows that when PM2.5 is higher, the air pollutants in these three are also higher. According to PCC correlation analysis, we exclude the influencing factors of PCC less than 0.2, which are humidity, temperature, O3 . Other influencing factors as input for PM2.5 prediction. The optimization performance is evaluated using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The calculation formula of the three indicators is shown in Eqs. (8), (9), and (10), respectively.

 N  2 1 i i ytrue − ypred (8) RMSE = N i=1

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i i where N is the amount of observations, and ypred is the forecast value, ytrue is the actual value. N  1  i  i ytrue − ypred  N i=1   i  N y i − y 

true pred 1 MAPE = ∗ 100% i N ytrue i=1

MAE =

(9)

(10)

We chose LSTM as the prediction model, and selected RNN as our comparison model. One is through the PCC to select the correlation degree, the other is not through the PCC to select the input factor. The model effect is shown in Table 1. Table 1. Measurements for different method Method

Model MAPE RMSE MAE

PCC

RNN

20.575 19.954 15.362

LSTM 19.686 19.444 14.551 Non-PCC RNN

22.948 21.785 16.931

LSTM 21.518 21.022 15.909

The PM2.5 prediction results of ZY stations without PCC are shown in Fig. 4. We compare the real data with the data predicted by the LSTM model and RNN model. We found that both LSTM and RNN models can predict PM2.5 well. From the perspective of model effects, LSTM is more accurate than RNN prediction results. Figure 5 is the prediction result using the influence factors selected by the PCC as the input prediction result of the LSTM model. It can be seen that compared with the prediction result without PCC algorithm, the model has better prediction effect, can better track the change trend of PM2.5 concentration, and respond to its fluctuation to achieve relatively accurate prediction. Among them, MAPE decreased from 21.518 to 19.686, RMSE decreased from 21.022 to 19.444, MAE from 15.909 to 14.551.

4 Conclusion There are many factors involved in PM2.5 prediction. In this paper, the PCC method is used to analyze the index correlation of meteorological indicators, other pollutants, and PM2.5 of surrounding stations, and the three indicators with the smallest correlation are discarded. Other indicators serve as the input factors for predicting PM2.5. Results show that: (1) The LSTM model is better than the RNN model with better fit and higher prediction performance. (2) Compared with the general LSTM, we used PCC to evaluate the correlation degree of other pollutants including surrounding stations, weather data and target stations, and removed the influencing factors with small correlation degree.

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Fig. 4. The PM2.5 prediction results of the ZY station without PCC

(3) According to the PCC correlation analysis, we can judge the level of the PM2.5 concentration of the target station by the PM2.5 concentration of other stations. The main contribution of this research is that we have proposed a method of combining PCC and LSTM for prediction of spatiotemporal time series. In addition to predicting PM2.5 concentration, the prediction model processed by PCC can also be applied to other types of spatiotemporal time series problems, such as meteorological problems, typhoon problems, and prediction of other types of environmental issues. Of course, other problems need to be verified to prove their effectiveness.

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Fig. 5. The PM2.5 prediction results of the ZY station use PCC

References 1. Gu, H., Yan, W., Elahi, E., Cao, Y.: Air pollution risks human mental health: an implication of two-stages least squares estimation of interaction effects. Environ. Sci. Pollut. Res. 27(2), 2036–2043 (2020) 2. Zhang, L., et al.: Spatiotemporal variations and influencing factors of PM2.5 concentrations in Beijing, China. Environ. Pollut., 262 (2020) 3. Xayasouk, T., Lee, H.: Air pollution prediction system using deep learning. WIT Trans. Ecol. Environ. 230, 71–79 (2018) 4. Ma, J., et al.: Spatiotemporal prediction of PM2.5 concentrations at different time granularities using IDW-BLSTM. IEEE Access 7, 107897–107907 (2019) 5. Saeed, S., Hussain, L., Awan, I.A., Idris, A.: Comparative analysis of different statistical methods for prediction of PM 2.5 and PM10 concentrations in advance for several hours. Int. J. Comput. Sci. Netw. Secur. 17(11), 45–52 (2017) 6. Masih, A.: Application of ensemble learning techniques to model the atmospheric concentration of SO2 . Global J. Environ. Sci. Manage. 5(3), 309–318 (2019) 7. Araki, S., Shima, M., Yamamoto, K.: Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan. Sci. Total Environ. 634, 1269–1277 (2018) 8. Ambika, G.N., Singh, B.P., Sah, B., Tiwari, D.: Air quality index prediction using linear regression. Int. J. Recent Technol. Eng. 8(2), 4247–4252 (2019) 9. Freeman, B.S., Taylor, G., Gharabaghi, B., Thé, J.: Forecasting air quality time series using deep learning. J. Air Waste Manag. Assoc. 68(8), 866–886 (2018)

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10. Qi, Z., Wang, T., Song, G., Hu, W., Li, X., Zhang, Z.: Deep air learning: interpolation, prediction, and feature analysis of fine-grained air quality. IEEE Trans. Knowl. Data Eng. 30(12), 2285–2297 (2018) 11. Bai, Y., Zeng, B., Li, C., Zhang, J.: An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting. Chemosphere 222, 286–294 (2019) 12. Li, L.: A robust deep learning approach for spatiotemporal estimation of Satellite AOD and PM2.5. Remote Sens. 12(2) (2020) 13. Wang, J., Song, G.: A deep spatial-temporal ensemble model for air quality prediction. Neurocomputing 314, 198–206 (2018) 14. Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020) 15. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gener. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006

An Improved Whale Optimization Algorithm and Its Application to Power Generation in Cascade Reservoir Ji-Xiang Lü1 , Li-Jun Yan2 , Tien-Szu Pan3 , Shu-Chuan Chu4,5 , Jeng-Shyang Pan4(B) , Xian-Kang He1 , and Kuo-Chi Chang1,6,7,8 1 School of Information Science and Engineering, Fujian University of Technology, Fuzhou

350108, China 2 Shenzhen Institute of Information Technology, Shenzhen 518172, China 3 Department of Electronic Engineering, National Kaohsiung University of Science and

Technology, Kaohsiung 82445, Taiwan 4 College of Computer Science and Engineering, Shandong University of Science and

Technology, Qingdao 266590, China [email protected], [email protected] 5 College of Science and Engineering, Flinders University Sturt Rd, Bedford Park, SA 5042, Australia 6 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350108, China 7 College of Mechanical & Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan 8 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. Nowadays, there is a very popular artificial intelligence algorithm called whale optimization algorithm (WOA). WOA is obtained through the special bubble net foraging process of humpback whales. It has a special search mechanism, and is very helpful to solve some complex optimization problems and large scale. An improved WOA is implemented to optimize the cascade reservoir power generation. Firstly, by introducing nonlinear time-varying adaptive weights, the WOA performance in the local optimization and global exploration stages is improved; secondly, the differential mutation perturbation factor is introduced in the shrinking and surrounding stage of the whale algorithm to prevent premature convergence. In addition, the logarithmic spiral search method of whale individuals has been improved so that the ability to solve the algorithm traversal can be found. Experimental results show that it has a great improvement in accuracy and convergence compared with the original WOA in the optimal dispatch model of cascade reservoir power generation. Keywords: Whale optimization algorithm · Cascade reservoir power generation · Nonlinear time-varying adaptive weights · Logarithmic spiral · Differential mutation

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_28

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1 Introduction Artificial intelligence algorithms are very popular intelligent computing technologies in recent decades. The argument of intelligent optimization algorithm is extended [1]. It refers to the characteristics of swarm intelligence behavior of individuals who are not intelligent or have simple intelligence through group collaboration and organization. The swarm intelligence algorithm finds the optimal solution in the given solution space through probabilistic search, does not require much prior knowledge, and is very suitable for solving NP complete problems. The well-known swarm intelligence optimization algorithms include particle swarm optimization [4, 5], ant colony optimization [2, 3], artificial bee colony [6, 7], cat swarm optimization [8, 9], cuckoo search algorithm [10, 11], gray wolf optimizer [12, 13], pigeon-inspired optimization [14, 15], whale optimization algorithm [16, 17], etc. In 2016, Australian scholars Mirjalili and Lewis were inspired the humpback whales feeding action by observing, and proposed a new intelligent algorithm—Whale optimization algorithm [18]. The algorithm mainly solves the optimization problem by imitating the predatory behavior of whale swarms. Because of its simple principle and special advantages of search mechanism, it has been widely researched and applied. Recently, scholars gradually paid attention to WOA. An improved WOA [19] based on Levy’s flight trajectory is proposed. The Levy flight mode to increase the diversity in the population has been used; A CWOA algorithm based on chaos proposed by Sun et al. [20], which improved the accuracy of neural network prediction. Because the disadvantage of WOA is slow convergence speed and low optimization accuracy, adaptive perturbation WOA (APWOA) proposed in this study first designed a nonlinear time-varying adaptive weight to balance the performance of global optimization and local optimization. In the proposed algorithm, when the optimal perturbation factor based on individual differences is used, premature convergence can be prevented. In addition, the logarithmic spiral search method of the algorithm in the spiral update stage is improved. The APWOA has the higher ability to traverse the solution space. Finally, APWOA was tested with standard functions and optimal operation of reservoirs is a multi-constrained, nonlinear, multi-stage-combined optimization problem. The traditional solution methods faced with the problems of large amount of calculation and “dimensional disaster”. Intelligent optimization algorithms are widely used in reservoir optimization dispatching due to their simple concepts, easy implementation, no gradient information, and avoiding local optimal solutions. However, traditional intelligent optimization algorithms generally have converged prematurely in practical applications and get local extreme values. In this paper, APWOA is proposed for reservoir optimal dispatching problem.

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2 Reservoir Optimal Operation Model 2.1 Power Generation Formula In the dispatch optimization, the maximum of annual power generation E can be expressed as: E = max

Z R  

B ∗ q ∗ h ∗ t

(1)

e=1 f =1

where B is a coefficient, q means flow, h is the height difference between the upstream and downstream water levels, unit time is t, and the maximum annual power generation is E. The total number of hydropower stations is R and the time of year (Z = 12) is Z. 2.2 Restrictions There are complex hydraulic and electrical connections between cascade reservoirs. According to the economic operation requirements of hydropower stations, many items should be considered in the medium and long-term dispatch, such as the reservoir water balance constraints, water level constraints, flow constraints, output constraints and hydraulic connection constraints between cascade reservoir [21]: a1 + h1e,f − a2

Ve,f =

a3 − a4

∗ (a5 − a6 ) ∗ 108

(2)

In the formula (2), a1 , a2 , a3 , a4 , a5 , a6 are parameters. These parameters vary with the eth hydropower station during the f th interval when the water level difference. The reservoir capacity of the eth hydropower station during the f th period is Ve,f . qe,f = h0f −

Ve,f +1 − Ve,f tf ∗ 3600

(3)

The flow in the f th period of the eth hydropower station is qe,f , the time in the f th period is tf , and initial flow in the e−th hydropower station is h. h2e,f =

a7 + (qe,f − a8 ) ∗ (a11 − a12 ) a9 − a10

(4)

a7 , a8 , a9 , a10 , a11 , a12 are parameters. These parameters will be determined by the water level difference of h2e,f is the downstream water level of the eth hydropower station in time f th period, and the eth hydropower station during the f th interval. he =

h1e,f + h1e+1,f 2

− h2e,f

(5)

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Downstream water level difference of the eth hydropower station and he is the upstream. Because the difference between the downstream water levels and upstream is obtained, it will have a great impact on the upstream water level. This paper chooses to calculate the average water level difference between upstream and downstream. Other constraints are as follows: he,min ≤ he,f ≤ he,max

(6)

The minimum water level of the eth hydropower station is he,min , the maximum water level of the eth hydropower station represents he,max . qe,min ≤ qe,f ≤ qe,max

(7)

The eth hydropower station minimum flow represents qe,min , the eth hydropower station maximum flow represents qe,max . Ve,min ≤ Ve,f ≤ Ve,max

(8)

The minimum value of the reservoir capacity of the eth hydropower station is Ve,min , the minimum value of the reservoir capacity of the e − th hydropower station is Ve,max .

3 Adaptive Perturbation Whale Optimization Algorithm 3.1 Nonlinear Time-Varying Adaptive Weights In the WOA, if |Q| ≥ 1, the whale will enter the search for prey phase, and the search range needs to be expanded as much as possible. At this time, a large Q will lead to good exploration ability for whale individual. when |Q| < 1, WOA will try to achieve higher optimization accuracy according to the optimal solution position. A small Q will improve the local search performance of individual. In other words, the Q directly affects the local development ability and global exploration ability of the algorithm at different stages. Q is affected by the control parameter A. A weight factor that changes with the number of iterations can be introduced to calculate A. This paper proposes a nonlinear time-varying adaptive weight, which is defined as follows:   1 c ≤ T2 0.5 · [1 + cos πTc k (19) ϕ= 1   0.5 · [1 − cos π + πTc k c > T2 k is the adjustment coefficient in the equation. In the WOA, the factors M and M  control the distance between the target individual and the current individual. Changing the rate and size of M and M  can also adjust the accuracy and speed of the algorithm.Therefore, after introducing A, Eqs. (18) and (15) can be updated to: X (c + 1) = Xrand (c) − φ · Q · M

(20)

X (c + 1) = φ · M  · egh · cos(2π h) + Xbest (c)

(21)

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3.2 Differential Variation Perturbation Factor In the contraction and envelopment phase, the whale individuals mainly use the current best whale individual’s position information and approach it, and update their positions according to Eqs. (9) and (10). During the algorithm iteration process, new feasible solutions are constantly generated around the optimal solution. As the c increases, the diversity in the population will be lost, and the algorithm is also prone to converge prematurely. Due to the shortcomings of the basic whale algorithm, this article introduces the differential mutation perturbation factor in the contraction and closure stages, and draws on the idea of the differential evolution algorithm mutation operator, whose expression is: ψ = λ · (Xbest (c) − X (c))

(22)

where λ is the variation scale factor, and then Eq. (10) becomes: X (c + 1) = Xbest (c) − Q · M + ψ

(23)

3.3 Improved Spiral Update Method The WOA in the spiral update position stage, the whale individual adopts the logarithmic spiral update method when advancing to the current best whale individual position, the literature [22] pointed out that the logarithmic spiral search method is not necessarily the best, if the spiral If the stepping distance exceeds the search range, the algorithm will not be able to traverse the entire search space, thereby reducing the various ergonomics of algorithm optimization. In this paper, the logarithmic spiral search method is replaced by the Archimedes spiral update method. Equation (21) is further improved to: X (c + 1) = φ · M  (gh) · cos(2π h) + Xbest (c)

(24)

g and h in formula (24) are the same as formula (15). 3.4 APWOA Algorithm Flow See Fig. 1.

4 Experimental Results 4.1 Test on Standard Function Four test functions used in this experiments are shown in Table 1. These functions are used to WOA performance testing, APWOA, PSO, linearly decreasing weight PSO [23] (hereinafter referred to as DPSO).

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Fig. 1 APWOA algorithm flow chart

Table 1 Function of test Function Sphere

Expression  2 f (x) = D i=1 xi

Griewank f (x) = 1 D 4000





Dimension Ranges

optimal solution

30

[−100, 100]

min f (x) = 0

30

[−600, 600]

min f (x) = 0

30

[−5.12, 5.12] min f (x) = 0

xi 2 D j=1 xi − i=1 cos √i +1

Rastrigin

f (x) =

 D 2 i=1 xj − 10 cos(2π xi ) + 10

Ackley

30 f (x) = 

 D 1 2 −20 exp −0.2 D i=1 xi −

   D 1 exp D i=1 cos 2π xj + 20 + e

[−32, 32 ]

min f (x) = 0

30 is total number of particles in PSO. The inertia factor is 0.6. Both learning factors c1 and c2 are 2. The individual speed limit is [−1, 1]. The weights in DPSO are: ωmax = 0.9, ωmin = 0.4. Other parameters are the same as that in PSO. The number of WOA

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groups is 30.g = 1; The number of groups in APWOA is 30. g = 1. The adjustment coefficient of the weighting factor k = 2. The variation scale factor λ = 0.6. The four algorithms run 30 times on four functions. Each algorithm iterates 500 times. Results are displayed in Fig. 2.

(1)

(2)

(3)

(4)

Fig. 2 a Sphere; b Griewank; c Rastrigin; d Ackley; Comparison of four algorithms: PSO, DPSO, WOA, APWOA

We can see that APWOA obtains a better optimization performance than the other three algorithms under 500 iterations, whether it is a unimodal or multimodal function. 4.2 Test on Reservoir Power Generation Taking the one-year reservoir flow of Xin’an River and Fuchun River as an example, Table 2 is drawn. The monthly inflow data of the two hydropower stations in the wet year in normal and dry years are shown in Table 2. According to the two cascade reservoir power stations introduced above, they are optimally dispatched through Eqs. (1); we can calculate the sum of the maximum annual output of the two hydropower station. It can be easily found from Figs. 3 and 4 that

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Table 2 Water situation of cascade hydropower stations of Xin’an River and Fuchun River Month 1

2

Wet years

149.7 193

176.2 900.1 1077.1 1441.7 343.9 318.1 177.9 36.6

28.6

45.9

209

342

1514

143

151

82.9 526

Flat years Dry years

3

4 661

5

6

7

8

9

10

1489

1937 341

243.9 598.1 554

203.5

146.2

491.7 208.3 147.8 340.9 573.3 104.5

1364 780

1117

860

328

316

272

188.2 251

255.8 550.2 406.6

849.2

132.9 81

59

121.3 11.6

7.8

112

427

705

847

1 16

279

535

427

266

850

364

224

12

962

488

768

11

623

326 340

297

APWOA has the best dispatching performance of two cascaded hydropower stations at any time. Its total power generation is also the biggest. The maximum power generations of APWOA are 4.0559E + 17 kWH in wet water years, 3.269E + 17 kWH in flat water years, 2.5035E + 17 kWH in dry water years.

(a)

(b)

(c) Fig. 3 a Power generation in wet water years; b Power generation in flat wateryears; c Power generation in dry water years; Comparison of four algorithms: PSO, DPSO, WOA, APWOA

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(a)

(b)

(c) Fig. 4 a Changes in water volume in wet water years; b Changes in water volume in flat water years; c Changes in water volume in dry water years; APWOA water dispatch within one year

5 Conclusions In this study, an improved WOA algorithm with nonlinear time-varying adaptive weights and differential mutation perturbation factors (APWOA) is proposed to overcome the shortcomings of WOA algorithm, such as easy premature convergence and local optimization. The simulation results of four typical test functions show that APWOA has good development and exploration capabilities, and its optimization effect is better than WOA, PSO, DPSO with fast convergence speed, high optimization accuracy, and global extreme value optimization ability. The verification results of the optimized scheduling of engineering examples show that the convergence speed is better. For cascade reservoirs, the optimized scheduling results of the APWOA algorithm are the best, and the convergence speed is better. The feasibility and efficiency of APWOA are verified by typical test functions and actual engineering, which can be used to solve the complex, high-dimensional cascade reservoir group optimal dispatch model.

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References 1. Beni, G., Jing, W.: Swarm intelligence. In: Proceedings of the Seventh Annual Meeting of the Robotics Society of Japan, pp. 425–428 (1989) 2. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the 1st European Conference on Artificial Life, pp. 134–142 (1991) 3. Tang, L.L., Ma, K.Q., Li, Z.H.: A new scheduling algorithm based on ant colony algorithm and cloud load balancing. J. Inf. Hiding Multimedia Signal Process. 8(1), 191–199 (2017) 4. Kennedy, J., Eberhart. R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995) 5. Zhao, M., Pan, J.S., Chen, S.T.: Entropy-based audio watermarking via the point of view on the compact particle swarm optimization. J. Internet Technol. 16(3), 485–495 (2015) 6. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. TechnicalReport, TR-06, Erciyes University. Turkey (2005) 7. Tang, L.L., Li, Z.H., Pan, J.S., Wang, Z.F., Ma, K.Q., Zhao, H.N.: Novel artificial bee colony algorithm based load balance method in cloud computing. J. Inf. Hiding Multimedia Signal Process. 8(2), 460–467 (2017) 8. Snasel, V., Kong, L.P., Tsai, P.W., Pan, J.S.: Sink node placement strategies based on cat swarm optimization algorithm. J. Netw. Intell. 1(2), 52–60 (2016) 9. Kong, L.P., Pan, J.S., Tsai, P.W., Vaclav, S., Ho, J.H.: A balanced power consumption algorithm based on enhanced parallel cat swarm optimization for wireless sensor network. Int. J. Distrib. Sens. Netw. 11, 729680:1–729680 (2015) 10. Yang, X.S., Deb, S.: Cuckoo Search via Lévy flights. In: Proceedings of the IEEE 2009 World Congress on Nature Biologically Inspired Computing (NaBIC), Coimbatore, India, 9–11 December; pp. 210–214 (2009) 11. Song, P.C., Pan, J.S., Chu, S.C.: A parallel compact cuckoo search algorithm for threedimensional path planning. Appl. Soft Comput., 106443 12. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014) 13. Hu, P., Pan, J.S., Chu,S.C.: improved binary grey wolf optimizer and its application for feature selection. Knowl.-Based Syst., 105746 14. Duan, H., Qiao, P.: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int. J. Intell. Comput. Cybern. 7, 24–37 (2014) 15. Tian, A.Q., Chu, S.C., Pan, J.S., Liang, Y.: A novel pigeon-inspired optimization based MPPT TECHNIQUE for PV systems. Processes 8(3), 356 16. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016) 17. Chai, Q., Chu, S.C., Pan, J.S., Hu, P., Zheng, W.: A parallel WOA with two communication strategies applied in DV-Hop localization method. EURASIP J. Wirel. Commun. Network. 1, 1–10 (2020) 18. Mirjalili, S., Lewis, A.: The whale optimization algorithm. J. Adv. Eng. Softw. 95(5), 51–67 (2016) 19. Ling, Y., Zhou, Y., Luo, Q.: Levy flight trajectory based whale optimization algorithm for global optimization. J. IEEE Access 99(5), 6168–6186 (2017) 20. Sun, W.Z., Wang, J.S.: Elman neural network soft-sensor model of conversion velocity in polymerization process optimized by chaos whale optimization algorithm. J. IEEE Access 5, 13062–13076 (2017) 21. Qu, Y.Z., Ni, J.R., Meng, X.G.: Mechanism of vertical segregation of solid particles insediment-laden flow. J. Hydrodyn. Ser. A. 18(4), 483–488 (2003) 22. Sun, W.Z., Wang, J.S., Wei, X.: An improved whale optimization algorithm based on different searching paths and perceptual disturbance. Symmetry 210(10), 1–31 (2018) 23. Shi, Y., Eherhartr.: A modified particle swarm optimizer. In: Proceedings of IEEE World Congress on Computational Intelligence, pp. 69–73 . Indiana University, Indianapolis (1998)

Prediction of Hypertension Using Deep Autoencoder-Based Feature Representation Hyun Woo Park1

, Yul Hwangbo1

, and Keun Ho Ryu2(B)

1 Healthcare AI Team, National Cancer Center, Goyang, South Korea

{hwpark,yulhwangbo}@ncc.re.kr

2 Department of Computer Science, College of Electrical and Computer Engineering,

Chungbuk National University, Cheongju, South Korea [email protected]

Abstract. As the elderly population increases, the number of patients with chronic diseases, which usually occur in the elderly population, continues to increase. Many studies have been conducted to predict chronic disease using various medical data. Chronic diseases are mainly caused by complex factors rather than independent factors. In this study, the disease was predicted by considering KNHNAES data and air pollution as complex factors. However, when considering complex factors, the accuracy of disease detection is poor because high dimensions require high computational complexity. In order to overcome this high-dimensional problem many studies have been carried out feature representation method. The feature representation approach plays an important role in the success of classification, while ex-pressing high-dimensional features in a low dimension. This study used deep autoencoder to compress representative feature by applying variational autoencoder. In the experiment, the proposed feature representation method and conventional feature reduction method were compared. The results showed that the proposed method outperformed the conventional method. The compressed representative features to predict hypertension using xgboost method showed best performance. Keywords: Hypertension · KNHANES · Feature representation · Autoencoder · Prediction

1 Introduction The proportion of elderly people population is increasing, and the statistics show that it increased from 10.8 to 14.9% compared from 2010 to 2019. According to a recent report, the proportion of elderly people population will increase to 43.9% by 2060 [1]. The chronic diseases usually occur in elderly people population. As the elderly population increases, the number of patients with chronic diseases, which usually occur in the elderly population, continues to increase. Chronic diseases account for 81% of all deaths and account for seven of the top 10 deaths in Korea [2]. As the number of chronic patients and the death rate increase, the socioeconomic burden is increasing significantly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_29

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In general, chronic disease prevention is important because chronic diseases cannot be prevented with vaccines or treated with drugs [3]. Hypertension is one of the most common chronic disease. The prevalence rate of hypertension is 28.3% among over 30 aged population. Since symptoms are usually difficult to detect, it is easy to overlook the importance and seriousness of the disease. In particular, many studies have been carried out, finding the relationship between chronic disease with medical data and air pollution data [4–12]. Chronic diseases are mainly caused by multiple complex factors rather than by one independent factor. In this study, the disease was predicted by considering KNHNAES data and air pollution as complex factors. This study used the deep autoencoder to compact representative features and prediction the hypertension considering various factors.

2 Materials and Method 2.1 Data This study conducted Korea National Health and Nutrition Examination Survey (KNHANES) dataset from 2013 to 2015. This dataset conducted by the Korea Centers for Disease Control and Prevention (KCDC). The data is stratified by region, household, population ratio to better reflect the Korea population. The KNHANES dataset contains various factors such as health examination, health interview, and nutrition survey [13]. The air pollution data are available through AirKorea website [14], this website releases the PM10 , O3 , NO2 , SO, and CO every hour since 2014. The KNHANES dataset were provided each subjects’ residence city and integrated with air quality data through matching residence city and air quality observed city. The KNHANES data contains irrelevant and redundant features related to classification target. Those features can lead to low performance because high dimensions require high computational complexity. This study generates target population through eliminating a considerable number of hypertension. Figure 1 shows procedure of generating target population. 2.2 Method An overview of research framework is shown in Fig. 2. The first part is KNHANES and air pollution data collection. The second part is preprocessing, it consists of two phase. The first phase integrated KNHANES and air pollution data, and next phase is data cleaning for handling noise and outliers. Third part apply deep autoencoder-based feature representation method to compress features. The final part compares predicted results using representative features compressed with the proposed method with feature reduction using principal component analysis (PCA). Variational autoencoder (VAE) [15] consists of encoder, decoders, and latent layers. Assume X is an input data, output is reconstructed of input data X and latent variables, which means distribution of input data. By Jensen’s inequality [16], the Kull-backLeibler (KL) divergence is always has a greater than or equal to zero. Thus, minimizing the KL divergence meaning is equivalent to maximizing the evidence lower bound (ELBO).

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Fig. 1. Procedure of generating target population

Fig. 2. Framework of proposed model architecture

3 Experiment The proposed method is implemented MAC OS using python (version 3.7) and scikitlearn package with a 2.6 GHz, 6 cores, intel core i7, 16 GB RAM of memory running for extracting feature representation and disease detection algorithm. The experiments were repeated to obtain optimal parameters values of VAE with the maximum value of ELBO. In this, ELBO value is converted to negative ELBO as the final loss and minimize this value. Figure 3 shows VAE architecture. The experiments were repeated to find the optimal number of compressed features with a minimum negative ELBO loss. Initially, 45 features are entered into the encoding layer; the features are compressed to 5, 10, 15, and 20.

4 Results Patients with hypertension (61.6 ± 12.2 years) were significantly (p-value < 0.001) older than normal group (42.1 ± 13.7 years). Men (40.6%) more likely to related with

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Fig. 3. Variational autoencoder architecture

hypertension than women (28.3%). The income quartiles of the individuals were obtained annually according to the sex and age of the sample group, and it categorized into low, low-middle, middle-high, and high classes. Lower income of individual (p-value: 0.045) were significantly related with hypertension. The prevalence of hypertension has been significantly lower in recent years when drinking more than once a month (p-value < 0.000). The prevalence of hypertension is significantly reduced on people who never smoke than people who smoke and quick smoking (p-value < 0.001). In the normal group waist circumference (WC) are significantly lower than hypertensive patients group (Hypertension group: 83.6 ± 8.9, normal group: 76.1 ± 8.7, p-value < 0.001). Normal groups body mass index (BMI) are significantly lower than hypertensive patients group (Hypertension group: 24.6 ± 3.3, normal group: 22.3 ± 3.0, p-value < 0.001). This study redefines new feature as the number of days exceeds the national standards value in 24 h, 8 h, and 1 h of air pollution factors. The result showed that the annual average for O3 was associated with hypertension (p-value: 0.06), whereas the O3 exceeds warning days was not associated with hypertension (p-value > 0.05). As a result, annual average and exceed days of NO2 are related with hypertension, and SO2 was associated with hypertension only for annual average value (Table 1). Figure 4 shows that comparison of the negative ELBO loss values obtained repeatedly to find the optimal number compressed feature through VAE. The experimental results show that 15 features has the minimum negative ELBO loss value (Mean ± SD: 25.55 ± 0.20). Figure 5 shows that the negative ELBO loss according to epoch number. The

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Variables

Hypertension (%)

Normal (%)

p-value

Age (yr)

61.6 ± 12.2

42.1 ± 13.7

0 and xi = yi If xm = yn , the problem turns to find xm−1 of yn−1 and LCS(LCS(X , Y )): LCS(LCS(X , Y )) = LCS(xm−1 , yn−1 ) + 1

(2)

If xm = yn , then either zk ∈ LCS(xm−1 , y), or zk ∈ LCS(x, yn−1 ). If zk = xm , then z ∈ LCS(xm−1 , y), and similarly if zk = yn , then z ∈ LCS(x, yn−1 ). The length of LCS(x, y) is: max{LCS(xm−1 , y), LCS(x, yn−1 )}

(3)

3.3 Verification of the Switchgear Portrait Algorithm Suppose there are N kinds of event signal sequence labels in switchgear portrait. The characteristic texts of signal sequence u and v both contain N kind of labels. The similarity of the two kinds of signal sequence labels is expressed by Pu and Pv , respectively, as shown in Formula (4): SimD(u, v) =

|Pu ∩ Pv | |Pu ∪ Pv |

(4)

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where Pu ∩Pv represents the number of labels shared with u and v; Pu ∪Pv represents the number of total features. In order to evaluate the effectiveness of the clustering algorithm for the prediction of equipment portrait, we select three evaluation indexes: mean absolute error (MAE) [11], root mean squared error (RMSE) [12], and perplexity value [13].

4 Experimental Results and Conclusion 4.1 Experimental Results In the experiments, we compare our switchgear portrait algorithm results with the rule of expert knowledge [14], and with the other different clustering algorithms, such as the agglomerative clustering [15], the k-means clustering [16], and the clustering algorithm [17]. The results of the MAE, RMSE, and perplexity values are shown in Table 2. From the three results of our algorithm, we can see our algorithm’s MAE value is 0.5722, the RMSE value is 0.6904, which is the lowest among the three values, and the perplexity value reaches 90.35, which is also the best effect. The detailed settings of simulation environment are single GPU and 8 GB RAM. Table 2. Comparison table of equipment portrait accuracy Algorithm

MAE

RMSE P value

K-means

0.8319 0.8027 66.27

DBSCAN

0.7715 0.7932 79.28

Agglomerative 0.6295 0.7144 87.06 Our algorithm 0.5722 0.6904 90.35

4.2 Conclusion Firstly, the thesis collects the data of the distribution network switchgear, through serializing and labeling the switchgear signals, using the method of time serialization of the equipment signal, through cluster analysis [18, 19], matching the acquired signal data with the equipment event, and obtains the switchgear image. Finally, the experimental results show that the prediction model of this algorithm can achieve the best results.

References 1. Huang, L.J., Zhai, D.H., Li, R.S., et al.: Research and discussion on safety and protection technology of distribution network in rural areas in the future. Power Syst. Prot. Control 047(002), 167–174 (2019) 2. Wang, T., Guo, L., Song, W., et al.: Research on personas recommendation algorithm based on big data technology. Comput. Meas. Control 26(12), 225–229 (2018)

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3. Miao, T., Chen, W.H.: Research on smart distribution network equipment. In: 2019 International Conference on Advanced Electrical, Mechatronics and Computer Engineering, pp. 28–32 (2019) 4. Sun, B., Chen, L., Xia, D., Han, T.: Design and application of an intelligent operation and maintenance control platform of the power distribution network based on the big data platform. Power Syst. Autom. 40(6), 81–84 (2018) 5. Wang, P., Wang, H., Fu, M., Wu, S.: Research on semantic location prediction of indoor users. J. Geo-Inf. Sci. 20(12), 1689–1698 (2018) 6. Wang, F.F., Zhou, S.H., Han, Y.J.: Analysis on power user profile based on big data technology. Shanxi Electr. Power (04), 26–29 (2019) 7. Zhu, J., Yang, B., Wang, Y.J., et al.: New operation optimization method with time series based on Levenshtein distance hierarchical clustering. CIESC J. 70(02), 161–169 (2019) 8. He, Y.J., Du, J.P., Kou, F.F., et al.: Images auto-encoding algorithm based on deep convolution neural network. J. Shandong Univ. (Eng. Sci.) 49(2), 1–7 ( 2019) 9. Shi, J., Wu, X., Liu, T.: Bearing compound fault diagnosis based on HHT algorithm and convolution neural network. Trans. Chin. Soc. Agric. Eng. 36(4), 34–43 (2020) 10. Xin, L., Shen, J., Yuan, G.: Construction of RFID+ two dimensional code equipment label management system. Exp. Technol. Manag. 036(001), 278–282 (2019) 11. Wang, W., Lu, Y.: Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model. Iop Conf. 324, 1–10 (2018) 12. Omari, A.I.: New entropy estimators with smaller root mean squared error. J. Mod. Appl. Stat. Methods Jmasm 14(2), 88–109 (2015) 13. Frankenberg, C., Weiner, J., Schultz, T., et al.: Perplexity- a new predictor of cognitive changes in spoken language? Results of the interdisciplinary longitudinal study on adult development and aging (ILSE). Nephron Clin. Pract. 5(s2), 3727–3737 (2019) 14. Shi, M.H., Mao, X.M., Zhang, Z., et al.: Evaluation of emergency logistics scheme based on expert knowledge and reliability. Logist. Sci.-Tech 42(06), 72–78 (2019) 15. Zhang, L., Mei, H., Sun, J.S.: A functional requirement based hierarchical agglomerative approach to program clustering. J. Softw. 17(8), 1661–1668 (2006) 16. Zhang, G., Wu, G.: Improved K-means text clustering based on kernel function. Comput. Appl. Softw. 36(9), 281–301 (2019) 17. Lv, C.: Research and Application of Clustering by Fast Search and Find of Density Peaks. Hang Zhou: Zhejiang University of Technology (2019) 18. Hayashi, Y., Friedel, J.E., Foreman, A.M., et al.: A cluster analysis of text message users based on their demand for text messaging: a behavioral economic approach. J. Exp. Anal. Behav. 112(3), 273–289 (2019) 19. Huang, J., Liang, Y., Bian, H., et al.: Using cluster analysis and least square support vector machine to predicting power demand for the next-day. IEEE Access 7, 82682–82692 (2019)

Detection of False Data Injection Attack in Power Grid Based on Machine Learning Xiaoli Guo1 , Shiyuan Wang1 , Yuhan Sun1 , Tieli Sun2(B) , Li Feng3 , and Zhexing Jin4 1 School of Computer Science of Northeast, Electric Power University, Jilin City, China 2 College of Humanities and Sciences of Northeast Normal University, Changchun City 130117,

China [email protected] 3 State Grid Xin Yuan Fengman Training Center, Jilin City 132018, China 4 State Grid Tongliao Power Supply Company, Inner Mongolia 028000, China

Abstract. False data injection attack in power grid can bypass the traditional bad data detection module, which poses a great threat to the stable operation of power system. In order to detect such attack quickly and accurately, a detection method of false data injection attack based on gradient lifting decision tree GBDT is proposed by utilizing the advantage of machine learning in dealing with two-classification problems. Firstly, a feature extraction method of iForest-LLE power measurement data is designed by combining isolated forest and local linear embedding algorithm. Then, a high-precision attack detection model of GBDT is designed by using decision tree classification model and gradient lifting framework. Finally, simulation attacks are carried out, and an example is analyzed. The experimental results show that the proposed method can effectively detect false data injection attacks. Keywords: Attack detection · Smart grid · False data injection attack · GBDT

1 Introduction As one of the large national infrastructures with important strategic significance, power system is a high-value target of network attack [1]. At the beginning of 2019, Ubiquitous Electric Internet of Things State Grid Corporation of China will establish the construction and operation of “Ubiquitous Electric Internet of Things” in its strategic position [2, 3]. The intelligent level of the power system will be further improved, and power system is becoming a typical cyber-physical systems [4, 5]. The impact of network attack may exceed the normal expectation. Ukraine’s power failure accidents and Venezuela’s power failure accidents happened one after another [6]. The serious damage of power system caused by network attacks has aroused widespread concern. How to effectively recognize and detect all kinds of malicious network attacks is a hot research topic in the field of power system security in recent years [7–9].

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_45

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This paper proposes a false data injection attack detection method based on gradient lifting decision tree. The process of attack detection can be divided into two steps: model training and classification decision, which can effectively improve the detection progress, has good generalization ability, and is convenient for practical engineering.

2 Feature Extraction of Attack Detection Data The process of attack detection is to use machine learning classification method to detect the false data in the power measurement data. The initial collected measurement data has the characteristics of many attributes, many noises, and strong correlation, which cannot be directly applied to the detection method in this paper, so feature extraction is needed. 2.1 Outlier Score Extraction Based on Isolation Forest Isolation forest (iforest) is a new data mining algorithm for detecting abnormal data. It has an efficient and special detection strategy, which can directly identify abnormal data without building a model of normal data or analyzing a large number of historical data in advance. Therefore, compared with local outlier factor, clustering, and other traditional methods, it has higher stability and detection efficiency in the big data environment. For the electric power measurement data d containing N data samples x, a binary isolated tree iTree is established based on the sample data set, and the iforest is composed of multiple itrees. The establishment process of iTree is as follows: (1) Select randomly an attribute P from power data set D; (2) Select randomly a single value Q in attribute p; (3) According to the selected feature P, each record is divided into a binary tree. If any record R < Q in attribute P, the record is placed in the left child node, and if R ≥ Q the record is placed in the right child node; (4) The left and right child nodes are constructed recursively until each sample is isolated or the height l of the tree reaches the limited height. Through the multiple sampling of measurement data set D, many sub-data sets are obtained. According to the sub-data sets, multiple itrees are established to form the iforest. 2.2 Feature Extraction Method of Iforest-LLE Measurement Data For the detection of false data injection attack, we take the abnormal score iscore (x) after the quantification of outliers as an independent feature of attack detection. The power measurement data after the extraction of the abnormal score still has the problems of high dimension and strong noise, so further data dimensionality reduction is needed. Because most of the power measurement data are nonlinear structure, principal component analysis (PCA) and other linear dimensionality reduction methods are used, Although the implementation is simple, the training time of the processed data is long,

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and the detection effect is poor. For the power measurement data, in this paper, we further use the nonlinear local linear embedding (LLE) to reduce the dimension of data. Compared with the traditional PCA method, LLE can keep the nonlinear structure of high-dimensional space and find its low-dimensional mapping, which has better effect on classification decision making and is suitable for the needs of attack detection in this paper. The main steps of this method are as follows: (1) To find the distance between sample points and adjacent points in high-dimensional space; (2) Construct local weight matrix; (3) Find the mapping of high-dimensional space in low-dimensional space and output the feature data set of new attributes.

3 Attack Detection Model Based on GBDT Gradient boosting decision tree (GBDT) is a simple and effective boosting framework integration algorithm. The advantage of this model is compared with other single classification models, and it has higher classification accuracy and has better generalization ability and construction efficiency compared with artificial neural network. Now, the power measurement data set X with n samples is given: X = {xi }, i = (1, 2, , N )

(1)

There are the following classification tag values: Y = {yi }, i = (1, 2, , N ) yi ∈ {−1, 1}

(2)

Then there are the following training data sets: TraX = (xi , yi ) ∈ X × Y

(3)

Assuming that the classification result is ci , the new data to be judged is x, and the prediction calculation function is: Pre(xi ) = fp (xi )

(4)

Then, the detection problem of false data injection attack can be expressed as follow:  −1 if a = 0 Pre(xi ) = (5) 1 if a = 0 where a is the attack injection vector, if a = 0, it means that the ith data sample is not attacked; otherwise, it is judged to be attacked. The learning goal of the attack detection model is to obtain the classification model F boost (x) with the highest accuracy according to the given measurement data training set, so that the loss function L(y, F(x)) of the data sample x mapped to the classification result y can be minimized. The construction process is as follows:

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Using cart regression tree as the basic learning device of attack detection model, the selection of loss function plays an important role in affecting the effect of the model. The decision-making process of attack detection is essentially a binary classification process. Through different loss functions, the model can complete the learning task of binary classification. The following logarithmic loss functions are defined: L(y, F(x)) = log(1 + exp(−2yF(x)))

(6)

Initialize the base learner F 0 (x). Input the training set TraX of attack detection features, loss function: L(y, F(x)), set relevant parameters, estimate the value of minimizing loss function β: F0 (x) = argminβ

N 

L(yi , β)

(7)

1

where L(y, F(x)) represents the set logarithmic loss function. F 0 (x) is a kind of weak classifier based on cart. After getting the loss function and initializing the basic learning device through the above work, we will enter the iterative lifting process. Each lifting of the model is to reduce the residual in the direction of the minimum loss function of the previous generation model, and constantly establish a higher accuracy attack detection classification model. (1) Set the number of iterations be m and define the residual r im in the direction of the minimum value of the loss function of the previous generation model.   ∂L(yi , F(xi )) rim = − i = 1, 2, . . . , N (8) ∂F(xi ) Fm (x)=Fm−1 (x) (2) If the number of iterations is m, the estimated residuals obtained from the above formula are used as input to obtain the Rnm of the leaf node area of m decision trees, where i = 1, 2, …, N. Rnm = argmin

N 

[rim − F(xi )]2

(9)

i=1

(3) The optimal step length β nm of the gradient descent direction of the loss function is obtained to minimize the loss function:  βjm = argmin β L(yi , Fm−1 (x) + β) (10) x∈Rj,m

(4) A more accurate weak classifier model F boost (x) is constructed, and ν ∈ (0, 1] is defined as the learning rate of iterative improvement. Fm (x) = Fm−1 (x) +

N  n=1

vβnm I (x ∈ Rnm )

(11)

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(5) At the end of the iteration, the final gradient elevation decision tree model is obtained by combining m more accurate weak classifiers. Fboost (x) =

N M  

βnm I (x ∈ Rnm )

(12)

m=1 n=1

F boost (x) is the final GBDT attack detection model, which is composed of m weak classifiers. Through the thought of sigmoid function and the definition Formula (13), we calculate the probability P+ (x) that the data sample is attacked by data tampering, and the probability P− (x) that the data sample is not attacked. 1 1 + e−2Fboost(x) 1 P− (x) = 2F 1 + e boost(x) P+ (x) =

(13)

4 Simulation Experiment and Example Analysis 4.1 Experiment Preparation Matpower is used to generate network topology and measurement data of IEEE118-bus standard node system. It is assumed that all lines and equipment are equipped with two side units in the experiment, and it can generate respective measurement data; IEEE118bus system has 304 dimension measurement unit in total. Take the measurement data of 20,000 times as a positive sample, at the same time, 20,000 false measurement data based on standard nodes are used as negative samples, and there are 40,000 pieces of experimental data. Before the training and classification of attack detection model, data dimension reduction method is used to reduce the dimension to the specified dimension, and the attack detection experiment is carried out according to the ratio of 3:1 of training samples and test samples. There are 30,000 training samples and 10,000 test samples. 4.2 The Construction of False Data Injection Attack Vector The principle of false data injection attack makes use of the vulnerability of state estimation bad data detection module widely used in power system. Assuming a DC system, power measurement Z, topological Jacobian matrix H, state measurement x to be estimated, and measurement error e, the state evaluation model can be summarized as Z = Hx + e. To detect bad data, the following objective functions are established: min g(x) = (Z − Hx)T W (Z − Hx)T

(14)

In the formula, W is the diagonal matrix formed by the measurement error processing, and the weighted least square method is used to solve the objective function, with the following solutions xˆ : xˆ = (H T WH )−1 H T WZ

(15)

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Then the judgment quantity P of bad data is expressed as:    P = 0, g xˆ  ≤ δ P = 1, g xˆ > δ

(16)

In the formula, δ is the judgment threshold, when p = 0, it means there is no bad data; otherwise, it is necessary to eliminate the bad data and re-estimate the state. Set false data attack vector a, after injection attack, the measurement data becomes: Za = Z + a

(17)

The state estimator xˆ also deviates. Let C be the deviation value of the state estimator after the injection attack. At this time, the state estimator is xˆ a = xˆ + c. Obviously, if the injected false data satisfies a = Hc, then:     g xa = (Za − a − H xˆ )T W Za − a − H xˆ     g xˆ a = g xˆ (18) 

From the above analysis, when the injected false data a meets the specific condition, it is a = Hc; although the measurement data has been maliciously modified, which is inconsistent with the actual situation, the target function value of the bad detection remains unchanged. At this time, the false data injection attack can successfully bypass the bad detection module. The attacker uses the vulnerability of the state estimation detection module to destroy the stable operation of the power system and make the traditional detection method invalid. Through this method, the false data injection attack vectors are constructed based on 20,000 IEEE118-bus legitimate measurement data sets, and 20,000 data samples are generated after being attacked. 4.3 Experimental Results and Analysis (1) Comparison of feature extraction methods In order to verify the accuracy and efficiency of the feature extraction method, two other nonlinear and linear feature extraction methods, i.e., local linear embedding method (LLE) and principal component analysis method (PCA), are selected to compare with the feature extraction method of iForest-LLE proposed in this paper. The training set and data set are reduced to the specified dimension, respectively, and the 304 dimension measurement data samples generated in IEEE118 bus node system by GBDT are used for model training and attack detection. One of the purposes of feature extraction is to reduce the dimension of data; if the dimension is too large, it will not help the training of attack detection model. The specified dimensions discussed in this article are below 30. It can be seen in Fig. 1. With the increase of dimensions, the training time of PCA increases the fastest and basically linear. In this paper, the model training time of the iforest-LLE feature extraction method is faster than that of the basic LLE method, and the training time of the two methods is more stable with the increase of the specified dimension.

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Fig. 1. Model training time of different feature extraction methods

Figure 2 shows the attack detection accuracy (obfuscation matrix accuracy) of different dimensionality reduction methods. Compared with the basic LLE and PCA feature extraction methods, the feature extraction method of iForest-LLE proposed in this paper has more advantages in detection accuracy, when the specified dimension is 6 dimensions, the attack detection accuracy reaches 0.9523, when the specified dimension is 10 dimensions, the attack detection accuracy reaches the highest 0.953, when the LLE method is 10 dimensions, the detection accuracy reaches the highest 0.925, while when the PCA method is 15 dimensions, the detection accuracy reaches the highest 0.905.

Fig. 2. Attack detection accuracy of different feature extraction methods

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In conclusion, the iforest-LLE feature extraction method proposed in this paper has a great advantage in the training time and detection accuracy of attack detection model. Considering the training time and detection accuracy in a balanced way, the best training effect can be achieved when the specified dimension is 6 dimensions. (2) Classifier effect comparison In order to verify the detection accuracy of this method, ID3 decision tree, linear support vector machine (linear SVM), and random forest (RF) are used to compare with this classifier. The penalty coefficient of linear SVM is C = 1.0, the loss function is logarithmic loss function, the number of decision trees of RF is nTree = 100, and the depth of decision tree is Dtree = 10. It can be showed in Fig. 3, when the false detection rate is 0.15, the recall rate of this method is 0.951, the ID3 decision tree is 0.781, the linear support vector machine is 0.843, and the random forest is 0.901. It shows that the false data injection attack detection method based on GBDT proposed in this paper can detect the attacked measurement data accurately and has a low false detection rate. For the simulated attack of false data injection, the detection accuracy of this paper is significantly higher than the other three algorithms, and it is because GBDT is a high-strength classification model formed by the iterative accumulation of multiple weak classification models, which is better than the linear SVM and ID3 decision tree of a single classification model. At the same time, because the parameters of the training model are optimized dynamically and the decision result is the accumulation of many schemes, the detection effect is better than the RF which uses the majority voting principle to determine the result.

Fig. 3. ROC curves of various classifiers

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5 Conclusion Aiming at the false data injection attack of power grid, an attack detection method based on gradient lifting decision tree is proposed, and the feature extraction method of iforest-LLE measurement data is designed. This paper provides a new idea from the perspective of machine learning, through simulation experiments, it is verified that the feature extraction and attack detection methods in this paper have higher detection accuracy and efficiency and achieve the goal of high-precision detection. Because the theoretical method of machine learning has good generalization ability, the proposed attack detection method can be used for other kinds of attacks. However, the attack vector constructed in the simulation experiment is based on the condition that the attacker completely grasps the power grid topology. In the actual scenario, it is difficult for the attacker to master the complete grid topology matrix. The research shows that it is also feasible to form an effective false data injection attack by using incomplete topology information. The following work will study the detection method of false data injection attack under incomplete topology. Acknowledgements. This work are supported by the Science and Technology Development Projects of Jilin Province of China (grant numbers: 20180101335JC, 20180201092GX and 20170204002GX).

References 1. Zguo, Q., Xin, S., Sun, H., et al.: Power system cyber-physical modelling and security assessment: motivation and ideas. Proc. CSEE 36(6), 1481–1489 (2016). 2. Wang, X., Tian, M., Dong, Z., et al.: Survey of false data injection attacks in power transmission systems. Power Syst. Technol. 44(11), 3406–3414 (2016). 3. Hassan, B.C., Christo, M.S., Devi, T.: Detection of false data injection attacks in smart grid communication systems. TEST Eng. Manag. 82, 10393–10398 (2020) 4. Zhao, J., Liang, G., Wen, F., et al.: Lessons Learnt from Ukrainian Blackout: Protecting Power Grids Against False Data Injection Attacks[J]. Automation of Electric Power Systems, 2016, 40(7):149–151. 5. Huang, L., Zhu, Q.: A dynamic games approach to proactive defense strategies against advanced persistent threats in cyber-physical systems. Comput. Secur. 89(5), 101660 (2020) 6. Liu, F., Xia K., Niu, W., et al.: Improved reconstruction weight-based locally linear embedding algorithm. J. Image Graph. 23 (1) (2018). 7. Jia, K., Wang, Z., Fan, S., et al.: Data-centric approach: a novel systematic approach for cyber physical system heterogeneity in smart grid. IEEE Trans. Electr. Electroni. Eng. 14(5), 748–759 (2019) 8. Ismail, M., Shaaban, M.F., Naidu, M., et al.: Deep learning detection of electricity theft cyberattacks in renewable distributed generation. IEEE Trans. Smart Grid 3(8), 1–1 (2020) 9. Wang, L., Qu, Z.Y., Li, Y., et al.: Method for extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation. IEEE Access 8, 57260–57272 (2020)

Demand Response Strategy Model Based on User Satisfaction Xiaoli Guo1 , Yuhan Sun1 , Li Feng4 , Chaoyang Qu1,3 , and Tieli Sun2(B) 1 School of Computer Science of Northeast, Electric Power University, Jilin City 132012, China 2 College of Humanities and Sciences of Northeast Normal University, Changchun City 130117,

China [email protected] 3 Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing, Jilin City 132012, China 4 State Grid Xin Yuan Fengman Training Center, Jilin City 132018, China

Abstract. The demand response model of household microgrid based on satisfaction of customers is proposed after the users’ habit of using electricity which is found from the historical electricity data and aims at the problems of the load diversity of the household microgrid and the improvement of user satisfaction in response process, combined with the demand response theory of the users and the thought of data mining. The demand response strategy model based on users’ satisfaction is designed with the research of demand response behavior integrated with user’s side experience. The model takes the user’s demand responsiveness as the load constraint, takes user’s comprehensive satisfaction maximum as the demand response objective, and takes the bacterial colony chemotaxis hybrid algorithm to solve the model. Finally, the optimal load power consumption plan is obtained as the user’s demand response strategy. The experimental simulation results verify the energy-saving effect of the model and the effectiveness of the algorithm. Keywords: Consumption · Demand response · Satisfaction index · Microgrid load

1 Introduction With the development of renewable energy generation technology, household microgrid is a new type of energy organization form of family unit. Because family unit is considered, the management of intelligent grid and demand side becomes more difficult [1]. As an incentive means, demand response coordinates the balance of power supply and demand between power companies and users [2]. Demand response encourages the users to reduce electricity consumption in the peak period, which plays an important role in load transfer so as to help the grid operate more efficiently [3, 4]. For home users, their participation in the demand response is not only to respond to the grid, but may play an important role in improving the users’ comfort and energy utilizing efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_46

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Therefore, the household microgrid still faces the difficulty in considering the users’ demand and habits of using electricity [5–8]. In order to achieve the goals of saving electricity, reducing expenditure and improving customer satisfaction, the demand response model is built by taking the maximum index of the comprehensive satisfaction degree of the users as the objective function.

2 Intelligent Family Microgrid Load Analysis According to the load characteristics of power equipment on the user side, it can be generally classified into translational load and non-translational load. Due to the diversity of load in the micro grid, it divides into three types according to the equipment characteristics and the equipment classification. 2.1 Load Model The mathematical model of intelligent household microgrid load is expressed as the total load in the period and is the sum of the adjustable load, fixed load, and time-shifted load, which is showed as Lall (t) = Ladjust (t) + Lfixed (t) + Lshift (t)

(1)

where L all (t), L fixed (t), and L shift (t), respectively, indicate the total load, fixed load, and time-shifting load of the tth period of time. 2.2 Load Characteristic Constraints In the response process, the adjustable load and time-shifted load must meet the constraints of its power fluctuations and response duration, and the expression is as follows Min Max Padjust,i ≤ Padjust,i ≤ Padjust,i , i = 1, 2, . . . , L

(2)

Min Max Tshift,i ≤ Tshift,i ≤ Tshift,i

(3)

where PadjustMin,i and PadjustMax,i , respectively, indicate the allowable minimum power and maximum power of the ith adjustable load equipment to participate in the adjustment of the power after response; T shift,i represents the actual use of time-shifting equipment within 1 day, TshiftMin,i , and T shiftMax,i , respectively, indicate the minimum and maximum time allowed for a time-shifting load device within 1 day.

3 A Demand Response Strategy for Household Microgrid 3.1 The Key Technology of Demand Response Strategy Model Demand response strategy model is proposed by considering both the characteristics of residential power load and customer satisfaction. The objective function is shown in Eq. (4). Emax = αCcom + βCpri + γ Ceco

(4)

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When the resources of smart home participate in the demand response, the power balance constraint must be observed, that is, the power supply power and photovoltaic power generation power of the grid should be equal to the sum of the energy storage resource power and load resource, as shown in Eq. (5). Pgrid (t) + Ppv (t) = Pes (t) + Lall (t)

(5)

When pgrid (T ) > 0, it is the power supplied to the microgrid at any time, otherwise, it is the power sold to the microgrid; Ppv (T ) is the photovoltaic power at time t; Pes (T ) is the power of storage resources such as battery at time t. The objective function of the demand response model should not only meet the internal load constraints of the family, but also consider the user’s own demand response ability. The power consumption plan finally solved by the demand response strategy model is further constrained, as shown in Eq. (6). ⎧ ⎨ L0,t + Lpea,t λpea,val + Lsmo,t λpea,val , t ∈ Tpea (6) Lt = L0,t + Lpea,t λpea,smo − Lsmo,t λsmo,val , t ∈ Tsmo ⎩ L0,t − Lpea,t λpea,val − Lval,t λpea,smo , t ∈ Tval where L t —load of T period after demand response, kW; L 0,t —Load of period T before demand response, kW; Lpea,t , Lsmo,t , Lval,t —It represents the average value in peak period, normal period, and valley period before demand response, kW; T pea , T smo , T val —The peak, average, and valley periods of TOU price are, respectively, expressed; λpea,val —Demand responsiveness corresponding to price difference between peak and valley electricity in time period; λpea,smo —Demand responsiveness corresponding to price difference between peak and average electricity in time period; λsmo,val —Demand responsiveness corresponding to price difference between flat and valley electricity in time period. 3.2 Bacterial Colony Chemotaxis Hybrid Algorithm The main steps of the algorithm are as follows. Step 1: S air , S heater , and S washer are used to indicate the hourly operation status of air conditioner, water heater, and washing machine. Step 2: Set the number of iterations, convergence accuracy and initial population number, and initialize the bacterial position. Step 3: Determine the new bacterial site x i . Step 4: Select the better optimal location x better . Step 5: Judge algorithm precocity. Step 6: Design the taboo list. The solution in the previous search process is set as taboo object, and the first in first out rule is used to define the tabu table to avoid detour in

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the search process. When the fitness value of a tabu candidate solution is better than the current optimal solution, the current tabu candidate solution is set as the current optimal solution. The flowchart is shown in Fig. 1. Start Species initialization Calculate individual fitness values

convergence judgement N

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The ith bacteria's sensing process

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Position of bacteria xi’

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End Fig. 1. Bacterial colony chemotaxis hybrid algorithm

4 Instance Validations In this section, the demand response model and the solution algorithm proposed in the preceding article are simulated and verified on MATLAB2013a. The programs are implemented on a desktop computer with the master frequency of 3.20 GHz and main

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memory 4 GB. The convergence accuracy is set to 10–3 , and the initial group number is 50. This algorithm applies the actual electricity consumption data of an intelligent household user in the database of the University of California, Irvine (UCI) and chooses 7 devices which are very common in the household concentrated data, including four fixed loads, one adjustable load and three translatable loads. The simulation cases with four different demand response goals are analyzed to further analyze the effectiveness of the household microgrid demand response model and the solution algorithm based on users’ satisfaction. Case 1: the original electricity using plan where the users do not participate in the demand response; Case 2: users participate in the demand response and only consider the comfort; Case 3: users participate in the demand response and only consider the economy; Case 4: users participate in the demand response and only consider the overall satisfaction degree. Figure 2 shows the typical daily load curve of the different electricity price in different periods. Figure 3 shows two kinds of tuo prices experienced by consumers. 12

7

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time Fig. 2. Typical daily load curve at tuo price

4.1 User’s Satisfaction Verification The users’ satisfaction degree of the participation in the demand response and Table 1 show that during the implementation of the original electricity using plan where the users do not participate in the demand response, the user’s electricity behavior is not affected.

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Fig. 3. Two kinds of tuo prices experienced by consumers

When the users participate in the demand response and only consider the comfort, the economy index is lower than any other cases under the influence of the different electricity price in different periods, so the electricity fee in this case is the highest. When the users participate in the demand response and only consider the economy, they change their electricity behavior to achieve the “load shifting” of the electricity load and respond to the call of different electricity price in different periods, and the electricity fee in this case is the lowest. When the users participate in the demand response and only consider the overall satisfaction degree, their comfort and economy decline, but their overall satisfaction degree is in the best condition; they appropriately change their electricity behavior to reduce electricity fee. 4.2 Algorithm Verification Seen from the algorithm solution efficiency, this paper selects tabu search (TS) and bacterial colony chemotaxis (BCC) to verify the computational efficiency and adaptability of BCCTS algorithm. Different algorithms of the model number of iterations and timeconsuming are analyzed from three different demand response goals, and the results in Table 2 show that compared with TS algorithm and the improved BCC algorithm, BCCTS algorithm can show obvious computational efficiency. Compared with TS algorithm and the improved BCC algorithm, the number of iterations and the solution time of BCCTS algorithm in the simulated case 3 are reduced by 327 times and 3.4 s, so BCCTS algorithm has advantages in the aspects of adaptation and solution efficiency, and it is verified the effectiveness of this algorithm.

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The first stage demand response

The second stage demand response

Simulation cases

Comfort index

Economy index

Price/cents

Case 1

1

1

362.7

Case 2

1

0.81

416.2

Case 3

0.44

1.76

289.1

Case 4

0.85

1.53

329.4

Case 1

1

1

380.6

Case 2

1

0.69

441.8

Case 3

0.36

1.72

299.5

Case 4

0.81

1.48

342.3

Table 2. Comparison of three algorithms Case 2

Case 3

Case 4

TS

BCC

BCCTS

TS

BCC

BCCTS

TS

BCC

BCCTS

Iterations times

621

559

357

598

573

382

603

572

357

Solving time

5.4

4.6

3.7

5.2

4.9

3.8

4.1

4.7

3.4

4.3 Verification of Demand Response Model Seen from the overall energy efficiency level of the demand response model, the load curve changes after the user participates in the demand response are shown in Fig. 4. It can be seen that the effect of participating in demand response has more obvious optimization effect for the electricity load curve. The peak of electricity using in the high electricity price period (from 10: 00 to 13: 00) is transferred to the low electricity price periods from 01: 00 to 06: 00 and from 13: 00 to 15: 00. The typical daily load curve after the participation in the demand response is gentler. The changes in peak and valley values can be obtained by comparing the load changes before and after the demand response. It can be seen from Fig. 6 that the user’s difference value of the highest load is reduced by 18.3%, and the lowest load is increased by 2.7%. At the same time, the overall cost of electricity consumption for the demand response is 329.4 cents, and the cost of electricity consumption has reduced by 33.3 cents compared to the cost without the demand response. The demand response strategy from the demand response model of household microgrid proposed in this paper can reduce the electricity consumption by about 10%, which validates the availability of the model.

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Fig. 4. The changes in peak and valley values before and after the demand response

5 Conclusion This paper proposes a multi-attribute demand response strategy for household microgrid based on the users’ satisfaction including multi-attribute demand response model and solution algorithm. The tabu colony chemotaxis hybrid algorithm is proposed to solve the model according to the features of the model. The model is used to make electricity using plan that considers the users electricity consumption demand and habits during the process of household microgrid family participating in the demand response. Acknowledgements. This work are supported by the Science and Technology Development Projects of Jilin Province of China (grant numbers: 20180101335JC, 20180201092GX and 20170204002GX).

References 1. Zhang, Y., Rong, Z.P., Zhan, Y.X., Hu, H.Z., Zhao, J., Wei, M.: Study of grid demand response based on micro grid. J. Power Syst. Prot. Control 21, 20–26 (2015) 2. Yin, Z., Che, Y., Li, D., Liu, H., Yu, D.: Optimal scheduling strategy for domestic electric water heaters based on the temperature state priority list. J. Energies 10, 1425 (2017) 3. Zeng, B., Yang, Y.Q., Duan, J.H., Zeng, M., Ouyang, S.J., Li, C.: Key issues and research prospects for demand-side response in alternate electrical power systems with renewable energy sources. J. Autom. Electr. Power Syst. 17, 10–18 (2015) 4. Park, L., Jang, Y., Bae, H., Lee, J., Park, C.Y., Cho, S.: Automated energy scheduling algorithms for residential demand response systems. J. Energies 10, 1326 (2017)

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5. Yu, H., Liu, Z., Li, C., et al.: Study on pricing mechanism of cooling, heating, and electricity considering demand response in the stage of park integrated energy system planning. MDPI 10(5) (2020). 6. Vuelvas, J., Fredy, R.: A novel incentive-based demand response model for Cournot competition in electricity markets. Energy Syst. 10(1) (2019). 7. Wang, F., Zhang, Z., Liu, C., et al.: Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting. Energy Convers. Manag. 181 (2019). 8. Ghayekhloo, M., Azimi, R., Ghofrani, M., et al.: A combination approach based on a novel data clustering method and Bayesian recurrent neural network for day-ahead price forecasting of electricity markets. Electr. Power Syst. Res. 168 (2019).

Low-Complexity MMSE Precoding Based on SSOR Iteration for Large-Scale Massive MIMO Systems Jianpo Li1 , Saeed I. A. Saeed1(B) , Tao Yang2 , Yan Xie3 , and Guoge Zhang3 1 School of Computer Science, Northeast Electric Power University, Jilin 132012, China

[email protected]

2 State Grid Jiangxi Information & Telecommunication Company, Nanchang 330096, China 3 State Grid Jixi Electric Power Supply Company, Jixi 158100, China

Abstract. The scaling up of antenna and terminals in large-scale multiple-input multiple-output (massive MIMO) systems helps increasing the spectral efficiency at the penalty of prohibitive computational complexity. Linear precoders, such as the minimum mean square error (MMSE) precoding, can achieve the near-optimal performance in massive MIMO systems due to the asymptotic orthogonality channel matrix property, which makes them more attractive. But these precoders suffer from higher computational complexity due to the required matrix inversion. So, we propose a symmetric successive over-relaxation (SSOR) method-based MMSE precoding referred to as MSSR algorithm to avoid the complicated matrix inversion in an iterative way, and it can approach the performance of the classical MMSE. Simulation results show that the proposed algorithm can also approach the classical MMSE precoding performance with small number of iterations. Keywords: Massive MIMO precoding · SSOR-based precoding · MSSR algorithm

1 Introduction A massive multiple-input multiple-output (MIMO) is a type of wireless communication system, which can uses large number of antenna array, on a scale of few hundreds or thousands [1]. These antennas can simultaneously serve many tens or hundreds of terminals within the same frequency resource. Massive MIMO relies on the spatial multiplexing that in turn relies on the base station (BS) having sufficient channel state information (CSI). The CSI can be obtained by having the terminals sending pilots to the BS. The precoding is one of the ways to improve the transmission using this CSI [2]. This CSI at the transmitter can be exploited to enhance the system performance using various precoding techniques. The precoders adapt the transmission to the channel using CSI to improve the performance of the system. The performance of linear precoding gets close to the performance of dirty paper precoding (DPC) when the number of base station antennas tends to infinity [3], but it has higher complexity. However, the increase of antenna array makes © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_47

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massive MIMO and suffers from many problems such as computational complexity due to inverse of large matrix [4]. Recently, to reduce the complexity, Ref. [5] proposed Richardson method-based linear precoding, which to replace the matrix inversion and multiplication by Richardson method (RM). Reference [6] proposed Neumann-based precoding, which approximate matrix inversion in an iterative method. But Neumannbased precoding reveals the approximate equivalent complexity as the RZF precoding when much iteration is required. In this paper, we propose MSSR algorithm which is a symmetric successive over-relaxation (SSOR) method [7]-based MMSE linear precoding for massive MIMO systems that can reduce the complexity of matrix inversion, and approach MMSE precoding performance with only small number of iterations. To solve these questions, a way to determine the optimal relaxation parameter is proposed by utilizing the channel property of asymptotical orthogonality. The rest of the paper is organized as follows. Section 2 introduces the system model of massive MIMO. In Sect. 3, we present the proposed MSSR algorithm and discuss the method to select the relaxation parameter for the proposed method and the computational complexity is provided as well. Section 4 shows the simulation results. Finally, conclusions are drawn in Sect. 5.

2 System Model of Massive MIMO We consider massive MIMO system employing at the base station M transmit antenna serve K number of users each user equipped with single antenna and usually the number of transmit antenna is far greater than the number of users, e.g., M = 128 and K = 16. The received K × 1 single vector y, which includes the received signals from all K users, can be expressed as:  (1) y = Pd H H X + n H ∈ CM ×K is a flat Rayleigh fading channel matrix which its entries follow the disK×1 tribution CN (0, 1), x ∈ CM ×1 is the transmit signal after precoding,   and n ∈ C 2 denotes the additive white Gaussian noise vector (AWGN) CN 0, σ I , where Pd is the downlink transmit power. After we use MMSE linear precoding, we can express x as x = Fs

(2)

where s ∈ CM ×K denote the source signal vector for K different users.

3 MMSE Precoding Algorithm Based on SSOR Iteration In this section, firstly, we briefly introduce the classical MMSE precoding method; secondly, we propose SSOR-based precoding and discuss the optimal relaxation parameter for the proposed precoding to grantee its performance in practice.

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3.1 Classical MMSE Precoding The precoding matrix of MMSE is presented as [8]:  −1 FMMSE = βH H HH H + αIk = βWMMSE

(3)

 −1 , where β denotes the power control, and α So WMMSE = H H HH H + αIk is regularized parameter, which has a relation with signal-to-noise ratio (SNR) at the transmitter side, and it is been selected according to [9].  K   β= (4) tr WW H α=

K ρ

(5)

where ρ is signal-to-noise ratio at the transmitter, if we take a quick look at (3), we will notice that the equation needs an inversion of a matrix of K ×K size, so the computational complexity will be very high so we will use the iteration method to solve this equation. 3.2 SSOR-based Precoding Algorithm   Form (3), we can define A = H H H + αI K so FMMSE = HA−1 , then we can rewrite (2) as follow X = βH H A−1 s

(6)

We still suffer from matrix inversion in (6); let us give B = A−1 s, we have X = βH H B

(7)

s = AB

(8)

Assume that c is a random nonzero vector, its size is K × 1, and then we got   cH Ac = λH HH H + αIK c = cH HH H + cH αIK c = Hc(Hc)H + cH αIK c > 0 (9) AH = (HH H + αIK )H = HH H + αIKH = A

(10)

So that means A is a positive definite Hermitian matrix. Thus, we propose SSORbased MMSE precoding, so we are going through SSOR method, which will be accomplished under three steps [10]: Decomposition Which to decompose positive definite Hermitian matrix A as A=D+L+U

(11)

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where D is the diagonal component. L is the strictly lower triangular component, and U is the strictly upper triangular component of A [11, 12]. ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ B11 0 0 0 0 0 0 B12 B13 D = ⎣ 0 B22 0 ⎦, L = ⎣ B21 0 0 ⎦, U = ⎣ 0 0 B23 ⎦. 0 0 0 0 0 B33 B31 B32 0 Iteration in Forward Order It is going to be computed in forward order; we will compute the first half of successive over-relaxation (SOR) as follow (D + ωL)B(i+1/2) = (1 − ω)DB(i) − ωUB(i) + ωs

(12)

Iteration in Backward Order It is going to be computed in backward order; we will compute the second half of successive over-relaxation (SOR) as follow (D + ωU )B(i+1) = (1 − ω)DB(i+1/2) − ωLB(i+1/2) + ωs

(13)

where i denotes times of iteration, ω represents relaxation parameter that has a big impact in MSSR algorithm, especially in convergence rate. With several times of iterations using the above two Eqs. (12), (13). The obtained vector of B will be used in (7) to realize the signal X. 3.3 Relaxation Parameter The relaxation parameter ω has an important role in the convergence rate, and the optimal relaxation parameter ωopt was given by Young in [7] ωopt =

2 √ 1 + 2(1 − (PJ ))

(14)

where (PJ ) is spectral radius of Jacobi iteration matrix PJ , which can be presented as PJ = D−1 A − IK

(15)

For massive MIMO, each element of the diagonal component D will converge to a fixed value M [4], which means we have D−1 ≈

1 IK M

(16)

Furthermore, as A is a central Wishart matrix, at the time that the number of transmit and receive antenna M and K is big enough as K/M remains fixed, the largest eigenvalue λmax of the matrix A can be well approximated by [4]

2 K λmax = N 1 + (17) M

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Then, we exploit a simple quantified relaxation parameter to replace the optimal parameter ωopt (14) with negligible error as follows 2 1 + 2(1 − a)

2 K a = 1+ −1 M =

√

(18)

(19)

It simply indicates that the quantified relaxation is only determined by M and K. So this will tend to be constant if only the configuration of massive MIMO system is fixed; summarily, the convergence rate is not very sensitive to relaxation parameter ω. Therefore, the proposed quantified relaxation parameter can be replaced to achieve the satisfied performance, which will be verified by MATLAB simulation throughout the next section. 3.4 Complexity Analysis In this section, we evaluate the complexity with respect to the required number of multiplications in each iteration, and for proposed SSOR-based precoding, we can rewrite (12) as ⎛ ⎞ k−1 k   ω⎝ (i+1/2) (k+1/2) (i) (k) = Bk + Akj Bj − Akj Bj ⎠ si − (20) Bk Aii j=1

j=k

(i+1/2)

where Bk denotes the kth (k = 1, 2, . . . , K) element in B(i+1/2) . For the fair comparison of complexity, the channel coherence interval (Tc ) is to be considered. We can see that to solve (19) it needs K times of multiplication, on account of there are K elements need to be computed, so in the forward order need K 2 multiplication as well as backward order (13) need K 2 times multiplication, so the total complexity  also 2 of MSSR algorithm is TC i 2K + MK . Where the complexity of the classical MMSE   is 2K 3 + TC MK. Table 1 shows that the complexity between MSSR algorithm and Neumann-based precoding. Table 1 compares the complexity of Neumann-based precoding and MSSR algorithm, we can see clearly    that when the iteration i > 2, the complexity of Neumannbased precoding is O K 3 , while the complexity of the proposed algorithm is O K 2 for any iterative number i.

4 Simulation Results We use MATLAB R2014a to evaluate the performance of the proposed MSSR algorithm; we provide the simulation result in term of bet error rate (BER) performance compared to Neumann-based precoding [6] and conventional MMSE precoding. We also consider a large-scale MIMO system with M × K = 128 × 16 and with simulation scheme of 64 QAM is employed.

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J. Li et al. Table 1. Computational complexity comparison table Iteration number Neumann-based precoding MSSR algorithm   i=2 3K 2 − K + Tc MK TC 4K 2 + MK i=3

K 3 + K 2 + Tc MK

  TC 6K 2 + MK

i=4

2K 3 + Tc MK

  TC 8K 2 + MK

i=5

3K 3 − K 2 + Tc MK

  TC 10K 2 + MK

Figure 1 shows the BER performance comparison in Rayleigh fading channel, where i denotes the iteration number, from Figs. 1 and 2, we can see in spite of conjugate beamforming is been proposed in [4] to be near-optimal, but the BER of conjugate beamforming suffers from a higher performance loss when the number of antenna goes to infinity due to limit number of BS antenna in practice. In Fig. 1, we can see that the BER performance for proposed MSSR algorithm with i = 2 is defeating Neumann-based precoding even when i = 5. In general, the BER performance of both Neumann-based precoding and MSSR improves when the number of iteration increases, but the proposed MSSR algorithm improves faster. The proposed MSSR algorithm can approach the exact BER performance of MMSE precoding with small number of iterations, e.g., i = 4.

Fig. 1. BER performance comparison for the 128 × 16 massive MIMO system

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Fig. 2. BER performance comparison for the 128 × 16 massive MIMO system under special correlation channel

Figure 2 shows the performance of BER comparison under spatial correlation massive MIMO channel, which a crucial in realistic MIMO system configuration. We can observe the degradation for all the linear precoding scheme, the BER error floor for Neumann-based precoding is very severe, but meanwhile MSSR algorithm still performing well as that in Raleigh fading channel, since it approach MMSE precoding performance by increasing the number of iteration.

5 Conclusion The proposed low-complexity MSSR algorithm can avoid matrix inversion of large size by exploit an iterative method. It achieves near-optimal performance of conventional MMSE precoding to reduce the computational complexity. As we propose a simple way to appoint relaxation parameter, which only depends on the system   It  dimension. has been shown that MSSR algorithm reduces the complexity from O K 3 to O K 2 . Simulation results show that the algorithm with a few number of iteration can achieve the near-optimal performance of linear precoding. Acknowledgements. This work is supported by Science and Technology Foundation of Jilin Province (No. 20180101039JC), and Science and Technology Foundation of Jilin City (No. 201831775).

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References 1. Sun, Z., Yang, D.: D2D radio resource allocation algorithm based on global fairness. J. Northeast Electr. Power Univ. 39(1), 81–87 (2019) 2. Shi, B., Wang, Y., Li, W., Sun, G.: Study on communication network effect on system performance of wide area control. J. Northeast Electr. Power Univ. 38(6), 29–34 (2018) 3. Yang, H., Marzetta, T.L.: Performance of conjugate and zero-forcing beamforming in largescale antenna systems. IEEE J. Sel. Areas Commun. 31(2), 172–179 (2013) 4. Rusek, F., Persson, D., Lau, B.K., Larsson, E.G., Marzetta, T.L., Edfors, O., Tufvesson, F.: Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process. Mag. 30(1), 40–60 (2013) 5. Muller, A., Kammoun, A., Bjrnson, E., Debbah, M.: Linear precoding based on polynomial expansion: reducing complexity in massive mimo. EURASIP J. Wireless Commun. Netw. 63 (2016) 6. Prabhu, H., Rodrigues, J., Edfors, O., Rusek, F.: Approximative matrix inverse computations for very-large MIMO and applications to linear pre-coding systems. In: IEEE Wireless Communications and Networking Conference, Shanghai, pp. 2710–2715 (2013) 7. Bjorck, A.: Numerical methods in matrix computations. Texts Appl. Math. (2015) 8. Sun, Z., Li, Y.: Hybrid precoding algorithm based on sum-rate maximization for millimeterwave MIMO system. J. Northeast Electr. Power Univ. 37(6), 100–106 (2017) 9. Zhang, L., Hu, Y.: Low complexity WSSOR-based linear precoding for massive MIMO systems. In: 7th International Conference on Cloud Computing and Big Data, Macau, pp. 122– 126 (2016) 10. Xie, T., Dai, L., Gao, X., Dai, X., Zhao, Y.: Low-complexity SSOR-based precoding for massive MIMO systems. IEEE Commun. Lett. 20(4), 744–747 (2016) 11. Sun, Y., Li, Z., Zhang, C., Zhang, R., Yan, F., Shen, L.: Low complexity signal detector based on SSOR iteration for large-scale MIMO systems. In: 9th International Conference on Wireless Communications and Signal Processing, Nanjing, pp. 1–6 (2017) 12. Wu, C., Shang, H.: QoS-aware resource allocation for D2D communications. J. Northeast Electr. Power Univ. 40(2), 89–95 (2020)

Design of Intelligent Substation Communication Network Security Audit System Wenting Wang1 , Xin Liu1 , Xiaohong Zhao1 , Yang Zhao1 , Rui Wang1 , and Jianpo Li2(B) 1 State Grid Shandong Electric Power Company Electric Power Research Institute, Jinan

250003, China 2 School of Computer Science, Northeast Electric Power University, Jilin 132012, China

[email protected]

Abstract. As the security threats of industrial control networks such as smart grids continue to escalate, smart substations, as the core facilities of smart grids, are becoming an important target of malicious attacks. It is very necessary to strengthen the security audit of intelligent substation communication network, and it is an important way to improve the protection capability of intelligent substation. In this paper, the intelligent substation communication standard IEC61850 and its three main communication protocols are studied. The basic functions of GOOSE, SV, and MMS are introduced. By designing a system based on deep analysis of network traffic, the communication security audit of GOOSE network, SV network, and MMS network provides an intelligent means for intrusion detection, abnormal analysis, and monitoring audit. Firstly, the system is initially constructed from the functional architecture and the overall technology. Then, the different functional modules are designed in detail, and the GOOSE, SV, and MMS messages are parsed to determine the technical solutions for identifying threats and security audits. Keywords: Monitoring and auditing · Smart grid · Communication security · Protocol analysis

1 Introduction As a “transport hub” of smart grid, intelligent substation is becoming the focus of current smart grid construction based on its own advantages. In the intelligent substation, the monitoring and control of the electrical equipment can be realized by the computer, the parameters of the operation of the electrical equipment can be grasped in time, and the working information of the substation can be understood to ensure the operation of the substation. Although a large number of computer technologies are used in the substation, the accuracy of the monitoring is improved, and the automation of the grid operation is greatly improved, there are also many risks. Once any network invades, the protection unit in the substation will issue the wrong action, which will seriously threaten the normal operation of the grid. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_48

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In 2010, the “Suxnet” network virus broke out, “Duqu” and “Flame” network viruses also appeared one after another, forming a virus attack group. In 2015, the Ukrainian power sector was subjected to malicious code. Attacks, hacking and monitoring management systems, more than half of Ukraine’s power outages for many hours, the security audit protection for core facilities substation in the smart grid has become particularly important [1]. At present, research on communication safety of intelligent substation has been studied from various angles, such as building a communication network vulnerability assessment model and building a reliability model to improve the reliability of intelligent substation operation. This paper changes the way of constructing the model in the past, combined with the problem of abnormal traffic when attacked, designed a system that can be used for grid communication traffic audit, and designed and implemented the system in detail.

2 IEC61850 The IEC61850 protocol is an international standard for substation network communication established by the IEEE in 2004. The IEC61850 standard adopts an object-oriented, self-describing modeling method to establish a hierarchical structured information model for intelligent electronic devices in substation stations. From top to bottom, the server, logical device, logic nodes, data and data attributes, and use the well-defined substation automation semantics to standardize the various levels of the information model. The overall architecture of the intelligent substation network is shown in Fig. 1. It is divided into three layers, namely station control layer, bay level, and process layer. The station control layer includes automation system, station domain control, communication system. The interval layer mainly includes system monitoring and control devices, relay protection devices, monitoring function groups, IED, and other secondary devices. The process layer includes intelligent electronic devices and voltages. The IEC61850 standard operating on intelligent substations does not specify a security policy itself, nor does it enforce any authentication or encryption technology and thus is not resistant to various network attacks. Once the substation is attacked, it will cause substation automation system failure, circuit breakers to perform spurious operations, physical damage to field devices, or power outages throughout the control area. IEC61850, which works in the three-tier architecture of a smart substation, provides three communication models: The client–server model, the publisher–subscriber model, and the sampled value model. There are three main types of communication protocols: GOOSE, SV, and MMS. 2.1 Substation Event Message GOOSE GOOSE adopts the publisher–subscriber model. The main function is to send a trip signal from the IED to the circuit breaker, control the power supply in the specified area, and exchange information between devices. The protocol is mainly applied between the process layer and the interval layer in the intelligent substation. The GOOSE protocol data unit size is about 1500 bytes, and the time limit is less than 4 ms. It is visible to all Internet-connected IED (temporarily regardless of the virtual LAN VLAN problem).

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Fig. 1. Intelligent substation overall architecture

The GOOSE subscription will determine whether the information is needed according to the destination address in the GOOSE message. In order to ensure real time and reliability, GOOSE transmission adopts sequential retransmission mechanism and does not require receipt confirmation. A valid GOOSE packet spoofing attack is divided into four steps. The first step is to monitor the Ethernet packet and obtain the packet interval by intercepting the GOOSE packet identified by the protocol type field of the Ethernet frame as 0X88B8. The second step is to use ASN.1 encoding method decodes GOOSE message. The third step changes the value in the data set such as the Boolean value related to the control quantity in the flip message and maintains the sequence nature of stNum and sqNum. The fourth step uses the BER encoded data packet and clones. The physical port of the source MAC address inserts a spoofing packet within the observed interval to complete a spoofing attack. 2.2 Sample Value Message SV The SV protocol is similar to GOOSE and also uses multicast technology, which reflects the real situation of the operation of the intelligent power station system equipment, and is the main basis for SCADA and EMS state estimation. SV provides sample value

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model objects and services. The protocol data unit size is about 1500 bytes, and the time limit is less than 4 ms. This service is mainly used for digital substation interval. Attacks against SV messages are similar to attacks against GOOSE, so most studies put together attacks on these two types of message protocols. The first attack method is a GOOSE and SV-based spoofing attack proposed by Kush et al. [2], which is used by an attacker to modify the content of the captured network packet for damage to the IED device. The second attack method is to send a large number of SV message messages to “flood” the IED device and prevent the IED from serving legitimate SV messages. Of course, this method is a Dos attack and can also be used for attacks on GOOSE messages. For SV packets, an attacker can generate an analog value (current or voltage value) and send it to the control center in the substation to generate a malicious operation that will allow the attacker to gain control of the IED and caused an unplanned power outage or even damage to the substation field equipment. Another type of attack is a replay attack. An attacker can capture an SV packet containing a voltage value and then replay the SV packet of the phase to the IED in the substation, causing the IED to fail to receive the correct sampled value [3]. 2.3 Manufacturing Message Specification MMS MMS is a set of independent international message specifications for the exchange of real-time data and monitoring information between smart devices. The purpose is to standardize the communication behavior of intelligent electronic devices, smart sensors, and intelligent control devices. In the intelligent substation, the MMS protocol is mainly used for communication between the bay level and the station control layer equipment and does not specify the network type of communication and the format of the communication frame, but defines the functions of the communication mode and the communication frame for the stable operation of the substation. The protocol data unit is larger than 3000 bytes, no time limit requirement, and the transmission type is connection oriented. The importance and priority of the information are relatively low, and the transmission time should be within 1000 ms. Since the operation of the MMS is based on the TCP/IP protocol, and the latter is very vulnerable to man-in-the-middle attacks, and the MMS protocol messages are not encrypted. The MMS also faces a man-in-the-middle attack threat. For example, through ARP spoofing, an attacker can initiate a man-in-the-middle attack on the MMS communication of the intelligent substation device. The attack location is between the gateway and the SCADA system. The attacker cuts off the communication between the SCADA system and the physical device at the switch location and falsifies the identity for the man-in-the-middle attack. Khaled et al. [4] mentioned four types of man-in-the-middle attacks against MMS: eavesdropping, modification, injection, and DOS attacks. In addition, from the fusion of information domain and physical domain, the attack threats of the whole system composed of intelligent substation can be divided into four categories. The first is the attack against the information domain, the authenticity and integrity of the threat information. The second category refers to the attack from the information domain to the physical domain. The third type is the physical domain to physical domain attack. An attack on a device in the physical domain causes an

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abnormality in the related physical device. The fourth type is an attack from the physical domain to the information domain. 2.4 Summary of Intelligent Substation Communication Security Threats At present, the merging unit does not have any information security protection in the process of processing SV packets, and an attack method similar to false data injection will pose a serious threat [5]. The SV message with protection is vulnerable to malicious tampering, which will cause the relay protection system to respond incorrectly or even refuse to respond, resulting in serious consequences. The SV message that plays the role of measurement and control is an important reference for SCADA and EMS to estimate the system status. Malicious tampering will cause SCADA/EMS to make erroneous and dangerous decisions. MMS is a widely used communication service protocol in smart substations. Although MMS has authentication and access control mechanisms, it works on TCP/IP, which is vulnerable to man-in-the-middle attacks, making MMS equally vulnerable to man-in-the-middle attacks. In addition, the MMS has no encryption and no checksum field. Even if there is a checksum field, the attacker can use the man-in-the-middle attack method to read all the data packets, or read the related information through the unencrypted data packet. Forge the check field to implement the attack. Therefore, MMS is very fragile and security cannot be guaranteed. In summary, the multicast communication mechanism, the plaintext transmission of the message, and the non-secure authentication mechanism make the intelligent substation vulnerable to most network attacks.

3 Security Audit System Design 3.1 Overall Architecture Design This paper designs the core technologies of the intelligent substation communication network security audit system into three engines, namely the data communication engine, the decoding engine, and the business engine, which is shown in Fig. 2. The data communication engine is mainly captured by the message, which is used to capture the original data packet of the substation, process and filter the captured data packet, and select the data packet required by the system. The decoding engine mainly analyzes the packets in the data communication engine [6]. The service engine establishes a security baseline based on actual service conditions, compares the results with the decoding engine, and issues an alarm to the abnormal situation. 3.2 Data Communication Engine The network analyzer receives the GOOSE and SV messages or mirrors the GOOSE and SV through the mirroring function of the switch to the monitoring background. By using the port mirroring technology of the switch, the switch forwards the GOOSE and SV packets. The switch filters the certain GOOSE and SV packets to the CPU of the switch.

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Fig. 2. System design principle

Therefore, the acquisition of the specified packet is implemented quickly and efficiently without adding the device [7, 8]. As shown in Fig. 3, capture message configuration is as follows. 1. Configure a filtering rule, and the matching condition is the packet Ethernet class. The type is 0x88BA (SV message) and 0x88B8 (GOOSE message), and the corresponding behavior is the uplink switch CPU; 2. The switch CPU parses the packet and obtains the destination multicast address. 3. Configure a new filter rule with matching conditions as obtained. Multicast address, the corresponding action is discarded; that is, the group is not captured. 4. Repeat steps (2)–(3) to capture all GOOSE and SV packets with different destination multicast addresses, and each multicast address has only one packet. 5. Parsing the obtained GOOSE and SV messages, and obtaining the device name, source MAC address, and destination multicast address. The system is designed with a message queue, which will store the captured GOOSE, SV, and MMS messages into three message queues, and at the same time, read the messages in response by the process. This method can avoid the huge pressure caused by the simultaneous operation of various functional modules. 3.3 Decoding Engine In the decoding engine, the TCP/IP protocol is the key to implementing GOOSE and SV transmission mechanisms. On the basis of this, when the message parsing module

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Fig. 3. Packet capture process

performs the packet parsing work, the GOOSE and SV packets need only be parsed for the data link layer and the application layer in the system. Firstly, the packet data layer data is parsed, and the packet abnormality is judged according to the analysis result. If the parsing fails, the packet is determined to be an abnormal packet, and the packet is not parsed. If the parsing succeeds, the application layer data of the packet is further analyzed. If the application layer packet parsing fails, the packet is also abnormal. The packet parsing is successful only after the parsing succeeds. 3.4 Abnormal Detection For the attack behavior of GOOSE and SV messages, the intelligent substation multicast packet anomaly detection model (SMMAD), which consists of three parts, namely packet classification module, anomaly detection module, and evaluation module. The transmission rule estimates the number of GOOSE packets in the limited time as the threshold. When the number of packets captured in the unit time is greater than the threshold, it indicates that an injection attack or a Dos attack is very likely to occur. The SV abnormality detection is similar. There are some other ways: one of them is a false data detection algorithm to train probabilistic neural networks (PNNs) using various fault data in intelligent substation networks and used trained PNNs as a means of intrusion detection. Another one is a new intrusion detection system based on IEC61850,

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which includes access control detection, protocol whitelisting, model-based detection, and multi-parameter-based detection. For example, a network intrusion detection system based on physical attributes, which uses three steps to determine features and physical information. The first step identifies 24 key physical feature variables. The second step extracts information such as physical alarms and control commands based on the dialog. The third step determines the correlation between variables to enhance the information physical domain association. In the real-time communication environment of substation, the security communication mechanism focused on the TCP/IP security configuration of MMS, tested different encryption suites, and finally a series of cipher suite combinations are proposed. IEC62351 enhances the protocol and improves the message transmission security of the intelligent substation network through relevant encryption algorithms. IEC62351-4 specifies protocol extension algorithms to protect MMS messages. It is recommended to use TLS method to protect MMS communication. Intelligent substation networks are facing the risk of increasing cyber attacks. To cope with this risk, a reasonable development direction is to design and deploy an additional layer of defense for smart substations. Another one is a software-based smart grid test bench for evaluating the effectiveness, performance, and interoperability of security solutions for protecting substation remote control interfaces, demonstrating the functionality and practicality of SoftGrid.

4 Conclusion Based on the research and analysis of relevant work, it is believed that the intelligent substation network has the following development trends in security and defense detection. For the current existing and future various types of attack methods, it is urgent to design a smarter, reliable, and applicable intelligent substation communication system to ensure confidentiality, integrity, and controllability. For the real-time requirements of monitoring information transmission, it is necessary to propose a new lightweight encryption and authentication algorithm that meets the performance of the substation hardware. Due to the wide variety of hardware and software platforms used in smart substation systems, the research on general-purpose lightweight cryptography still faces challenges. In addition to considering detection, traditional means of attack should also consider human factors.

References 1. Wang, S.: Prediction and analysis of systemic network security model based on data mining. J. Northeast Electr. Power Univ. 39(6), 91–93 (2019) 2. Kush, N., Ahmed, E., Branagan, M.: Poisoned GOOSE: exploiting the GOOSE protocol. In: The Twelfth Australasian Information Security Conference, Auckland, New Zealand, pp.17–22 (2014) 3. Gunathilaka, P., Mashima, D., Chen, B.: SoftGrid: a software based smart grid testbed for evaluating substation cybersecurity solutions. In: 2nd ACM Workshop on Cyber Physical Systems Security and Privacy, Xi’an, pp. 113–124 (2016) 4. Khaled, O., Marfn, A., Almenares, F.: Analysis of secure TCP/IP profile in 61850 based substation automation system for smart grids. Int. J. Distrib. Sens. Netw. 2, 1–11 (2016)

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5. Shi, B., Wang, Y., Li, W., Sun, G.: Study on communication network effect on system performance of wide area control. J. Northeast Electr. Power Univ. 38(6), 29–34 (2018) 6. Wang, B., Wang, M., Zhang, S.: Research on secure message transmission of smart substation based on GCM algorithm. Lect. Notes Electr. Eng. 139, 533–538 (2012) 7. Chromik, J.J., Remke, A., Haverkort, B.R.: What’s under the hood Improving SCADA security with process awareness. In: Joint Workshop on Cyber-Physical Security and Resilience in Smart Grids, pp. 1–6 (2016) 8. Sun, Z., Yang, D.: D2D radio resource allocation algorithm based on global fairness. J. Northeast Electr. Power Univ. 39(1), 81–87 (2019)

Research on Security Auditing Scheme of Intelligent Substation Communication Network Wenting Wang1 , Guilin Huang1 , Xin Liu1 , Hao Zhang2 , Rui Wang1 , and Jianpo Li3(B) 1 State Grid Shandong Electric Power Company Electric Power Research Institute, Jinan

250003, China 2 Imperial College London, London SW72AZ, UK 3 School of Computer Science, Northeast Electric Power University, Jilin 132012, China

[email protected]

Abstract. This paper puts forward a security audit scheme that combines SCD configuration file parsing with network traffic identification. Obtain a pre-defined network topology and virtual link logic loop by parsing the SCD file and establish a security baseline for the service. Identify and capture generic object-oriented substation event (GOOSE) messages, sampled value (SV) messages, manufacturing message specification (MMS) messages, and the information of devices connected to the switch to obtain the actual network topology, and virtual link physical loop. When an unknown system IP, communication protocol, or message occurs, the device protocol auditing alarm can be triggered. And when the device polling data traffic is missing, the offline disconnection alarm of the auditing device can be triggered. The manipulation of industrial control command parameters caused by Trojans, computer viruses, and man-in-the-middle attack, and the abnormal state caused by operators’ misoperation also can trigger the legal abnormal alarm. In this way, real-time monitoring and security auditing of intelligent substation network threats can be realized. Keywords: Network monitoring · Security auditing · Network topology · Virtual link loop

1 Introduction Intelligent substations adopt advanced, reliable, integrated, low carbon, and environmentally friendly intelligent equipment, which can support advanced functions such as real-time automatic control of the power grid, intelligent adjustment, online analysis and decision-making, collaborative interaction. The malicious program Havex, which aims at the industrial control system of energy and power industry, has attacked more than 1000 energy companies around the world, causing serious social impact and huge economic losses. Hostile governments, hacker organizations, and extremists can exploit system vulnerabilities to steal enterprise sensitive information such as design drawings, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_49

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production plans, and process flows and even can manipulate the industrial control system to interfere and disrupt the normal production or operation activities of the industrial control system. In view of the current security status of intelligent substation, it is imperative to audit and analyze the network traffic of intelligent substation. By considering the intelligent substation business situation and actual security protection requirements, the required security auditing schemes are explored and researched in this paper, which realizes intelligent substation network traffic security auditing by using the latest packet deepparsing technology of industrial control system, threat analysis, and security monitoring methods.

2 Introduction to Traditional Security Audit Program When periodic anomaly detection to control network traffic based on intrusion detection designed, field devices typically use a polling mechanism to collect and upload data, so they are highly periodic [1]. Many attacks can cause changes in the frequency domain of network traffic, which include the occurrence or disappearance of periodic burst frequencies, changes in the duration of periodic bursts, and increase in the total amount of noise. The method fully utilizes the characteristics of controlling network traffic and has a high detection efficiency. However, there is a big problem in accuracy. Protocol-based security: As the connection between the control system and the external network increases, it is necessary to increase the authentication process of both parties. There are two ways to improve the protocol security. One is to directly modify the protocol and increase the authentication function. The other is to increase the information security layer without modifying the existing protocol. Reference [2] borrowed the concept of functional safety and proposed a method to increase the information security layer on the communication system. The functional safety adds a functional safety layer to the transmission system, which can safely disable the system without changing the underlying transmission system. Based on the security control algorithm, the attack factor is considered in the controller algorithm design, and a control algorithm that can resist the attack is designed. Reference [3] first established a typical model of a physical system, simplifying the physical system into a linear system. Then, for DoS attacks and spoofing attacks, the attack factors are added based on the original linear system model. Security-based defense strategy: In a network environment, the security of a single node depends not only on its own security measures, but also on the security defenses of other nodes in the network. Reference [4] studied the problem of the intrusion detection, because attackers will cause different degrees of damage to the nodes with different importance when attacking the network. Therefore, when formulating the security policy for the network nodes, the difference between the nodes need to be considered. At the same time, due to the network connections, there are also security cross-correlations between nodes. In summary, security audit and analysis technology of the industrial control system is an interdisciplinary research direction. The application of various technologies has led to a lack of systematic and standardized support for research results in this field. Research and application of intelligent substation network security auditing need to integrate

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these technologies and conduct research in conjunction with the power protocol and communication protocol.

3 IEC61850 IEC61850 is widely used worldwide because of its outstanding performance in improving equipment interoperability and the openness and scalability for future needs. IEC61850 proposes the concept of information layering. From the logical concept to the physical concept, the substation is divided into three levels: process layer, bay layer, and station control layer. GOOSE network and MMS network are used for data communication and integration [5, 6]. The intelligent substation can also form a large SCADA system network with a dedicated network or real-time subnet of power dispatching with other intelligent substations. The process layer includes primary devices such as transformers, circuit breakers, disconnectors, current/voltage transformers, and their associated intelligent components. By configuring intelligent primary equipment, it automatically completes information collection, measurement, control, protection, detection, and other functions [7]. The station control layer adopts the TCP/IP MMS protocol of the Ethernet medium to realize digital unified modeling and control of the bay layer. The substation configuration description (SCD) file is a full station system configuration file and should be unique to the entire station. This file describes the instance configuration and communication parameter information of all intelligent electronic device (IED) in the whole station [8, 9]. The contact information between the IEDs and the substation primary system structure is generated by the system integrator. The SCD file should contain version modification information, clearly describing the modification time, modifying the version number, and so on. Generic object-oriented substation event (GOOSE) is a mechanism used in the IEC61850 standard to meet the fast message requirements of substation automation systems. It is mainly used to realize information transmission between multiple IEDs, including transmitting a tripping signal (command) with a high probability of successful transmission. This protocol is used for communication between the bay layer and the process layer equipment in the intelligent substation. Figure 1 shows the file relationship of intelligent substation. Sampled value (SV), a sampled measurement value, is a communication service for digitally sampled information transmitted in real time. This protocol is used for communication between the bay layer and the process layer equipment in the intelligent substation. Manufacturing message specification (MMS) is a manufacturing message specification. MMS regulates the communication behavior of intelligent sensors [10, 11], intelligent electronic devices, and intelligent control devices with communication capabilities in the industrial field, so that devices from different manufacturers have mutual relations. This protocol is used for communication between the station control layer and the bay layer device in the intelligent substation.

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Fig. 1. File relationship of intelligent substation

4 Research Program This security auditing scheme adopts a three-tier architecture mode with loosely coupled system functions and can be disassembled, which is convenient to adjust and use according to the actual situation on site. The client is located at the front end of the overall architecture of the system, and its main function is to display the system interface and present the system functions to the operator in a visual form. The back-end is located in the middle of the system. Its function is to parse the SCD file and compare it with the obtained network traffic analysis result. When an abnormal situation is identified, providing it to the management reviewer through the client and an alert is issued in time. The underlying system uses the NIC driver to receive data packets in the system to implement traffic resolution and protocol auditing. 4.1 SCD File Parsing By analyzing the SCD file, not only the relationship between the station control layer and the bay layer device, but also it between the bay layer and the process layer device are obtained, and a communication logic topology map is formed, which is shown in Fig. 2. The intelligent substation is mostly a “three-layer two-network” structure, in which “three layers” include station control layer, bay layer, and process layer; “two networks”

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refers to the station control layer network between the station control layer and the bay layer and process layer network between the bay layer and the process layer. The station control layer network adopts the MMS protocol, and the process layer network adopts the GOOSE/SV protocol. The MMS protocol is the point-to-point transmission and reception, and the GOOSE/SV is the broadcast protocol. Therefore, the SCD file parsing process is also different for the “two networks.”

Fig. 2. System logic

The SCD file describes the complete substation configuration from the perspective of primary and secondary equipment objects for the substation. The SCD data structure model defined in the IEC61850 standard is shown in Fig. 3. According to the above manner, the information distribution and information subscription status of all IED devices are obtained, and the summary mapping is performed to obtain the device communication topology diagram specified in the SCD file. 4.2 Network Traffic Analysis The MMS protocol and the GOOSE/SV protocol are used for communication between the station control layer and the bay layer device of the intelligent substation, and between the bay layer and the process layer device. By obtaining traffic data by capturing packets on the mirroring port of the substation switch and performing protocol analysis on

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Fig. 3. Structure of SCD file

the data in the substation, we can obtain the time, source/destination MAC address, and source/destination IP address of each communication operation. The service logic relationship of the parsed message is shown in Fig. 4. The process is as follows: 1. After the message enters the device, the service is first identified by the FPGA. Looking up the packet header, if the service is identified as GOOSE or SV service (EtherType is 0x88BA/OX88B8), the APPID correctness is first checked according to the APPID division rule (SV service: APPID is greater than 0x4000; GOOSE service: APPID is less than or equal to 0x4000).And if it is detected that the service is not the GOOSE or SV service (EtherType is 0x88BA/0X88B8), the service is directly delivered to the switching processing engine, and the service is forwarded according to the forwarding mode of the conventional layer 2 industrial Ethernet switch. 2. Find the virtual secondary loop forwarding table of the device according to the detected APPID. If the APPID is not in the virtual secondary loop forwarding table, discard the packet and give an alarm prompt; if the APPID matches the APPID of the virtual secondary loop forwarding table, the FPGA forwards the service to the switching processing engine to complete the packet forwarding from the specified path.

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Fig. 4. Network traffic resolution process

4.3 Security Baseline Establishment By analyzing the actual traffic captured in the substation, the source MAC, destination MAC, source IP, and destination IP of communication between devices can be obtained, and the actual device communication topology map can also be formed to form a security baseline. Comparing the communication topology obtained by parsing the SCD file with the communication topology obtained by parsing the actual traffic, the active device, the newly added device, the legal operation, the unauthorized operation, the new relationship, and the like can be obtained. This method greatly improves the efficiency and accuracy of asset learning for the power industry and can detect abnormal events in the actual operation of the substation in time (Fig. 5). 4.4 Threat Analysis Alarm First, the system captures a piece of traffic, matches it with the blacklist, and determines whether it has a blacklist signature. If the situation occurs, it generates an alarm message triggered by the blacklist. If the situation does not occur, it continues to determine whether it is in the whitelist. In the baseline range, if it is in the whitelist, it will be successfully verified by the blacklist and whitelist rule, and no alarm will be generated; otherwise, the alarm information triggered by the whitelist will be generated. Blacklist: The built-in blacklist of the system is based on the vulnerability data of CVE, CNVD, CNNVD, and other network threat signature databases and security vulnerability sharing platforms. It covers a wide range of attack methods or malicious instructions in the industrial control network, which can detect or prevent. It is known that

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Fig. 5. Security baseline establishment

malicious programs run or enter unauthorized areas. The blacklist database is updated regularly and updated as the system is upgraded. Whitelist: The whitelist includes the behavioral baseline formed by the IP/MAC, type, and cmd information parsed by the mms protocol traffic in the mms layer intercepted over a period of time; LLC, STP, NTP, and BOOT are used in the mms layer intercepted over a period of time. Newly discovered asset warnings: (a) new assets (IP) are generated in the production environment; (b) new assets have security risks, external maintenance access, illegal intrusion access, and asset IP changes. New discovery relationship alarm: (a) new communication connection relationship establishment, new TCP relationship of new IP; (b) new TCP relationship of original IP, such as more SSH request; (c) known TCP new more func_code, such as the original only the read request, the current write request service exceeds the limit operation alarm: The system detects a fixed value modification, the fixed value cut area, the remote control message is generated; that is, an alarm is required. Path error alarm: The GOOSE operation is directly triggered by the console of the measurement and control device, and no MMS command is issued. Unknown protocol alarm: The protocol alarm outside the core service that the nonaudit product can analyze. In the alarm center, the user can view abnormal events that occur in the industrial control system audited by the analysis platform, such as unauthorized operations, illegal agreements, new assets, and new relationships.

5 Conclusion The intelligent substation communication network security audit scheme can quickly identify the business behavior model, sort out the relationship of assets, and intuitively

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understand the situation of on-site equipment. And it can demonstrate the safe operation status of the monitored network system, record the key operations of the system, alert the identified high-risk operations and risks to understand the current status of the system security. Through continuous monitoring of risk quantification indicators, risk alerts are discovered in time for business operations to guide safe operation and maintenance. Once a security incident occurs, it can be located in a timely and effective manner, which is convenient for tracking traceability and blaming the problem. Based on the comprehensive analysis of multiple on-site audits, the baseline of the same-level substation business model is extracted under the big data scenario, providing more accurate intelligence dependencies for security warnings.

References 1. Hong, J., Liu, C.C., Govindarasu, M.: Integrated anomaly detection for cyber security of the substations. IEEE Trans. Smart Grid 5(4), 1643–1653 (2014) 2. Hoyos, J., Dehus, M., Brown, T.X.: Exploiting the GOOSE protocol: a practical attack on cyber-infrastructure. In: IEEE Globecom Workshops, Anaheim, pp. 1508–1513 (2013) 3. Neuman, C., Tan, K.: Mediating cyber and physical threat propagation in secure smart grid architectures. In: IEEE International Conference on Smart Grid Communications, Brussels, pp. 238–243 (2011) 4. Sheng, S., Chan, W., Li, K.: Context information-based cyber security defense of protection system. IEEE Trans. Power Delivery 22(3), 1477–1481 (2007) 5. Yang, Y., Xu, H.Q., Gao, L.: Multidimensional intrusion detection system for IEC61850-based SCADA networks. IEEE Trans. Power Delivery 32(2), 1068–1078 (2017) 6. Wang, S.: Prediction and analysis of systemic network security model based on data mining. J. Northeast Electr. Power Univ. 39(6), 91–93 (2019) 7. Premaratne, U.K., Samarabandu, J.: An intrusion detection system for IEC61850 automated substations. IEEE Trans. Power Delivery 25(4), 2376–2383 (2010) 8. Kang, B., Maynard, P., Mclaughlin, K.: Investigating cyber-physical attacks against IEC61850 photovoltaic inverter installations. In: IEEE 20th Conference on Emerging Technologies & Factory Automation, Berlin, pp. 1–8 (2015) 9. Shi, B., Wang, Y., Li, W., Sun, G.: Study on communication network effect on system performance of wide area control. J. Northeast Electr. Power Univ. 38(6), 29–34 (2018) 10. Kwon, Y., Kim, H.K., Lim, Y.H., Lim, J.I.: A behavior-based intrusion detection technique for smart grid infrastructure. In: IEEE Eindhoven PowerTech, Eindhoven, pp. 1–6 (2015) 11. Sun, Z., Yang, D.: D2D radio resource allocation algorithm based on global fairness. J. Northeast Electr. Power Univ. 39(1), 81–87 (2019)

Design of Radio Frequency Energy Harvesting System Xing Liu1 , Jihai Yang1 , Tao Yang1 , Jun Gao2 , and Jianpo Li2(B) 1 State Grid Jiangxi Information & Telecommunication Company, Nanchang 330096, China 2 School of Computer Science, Northeast Electric Power University, Jilin 132012, China

[email protected]

Abstract. The power supply mode of microelectronic equipment is still mainly battery. The difficulty in replacing the batteries affects the life time of these devices. Radio frequency energy harvesting (RFEH) technology provides a new way for the power supply of low-power microelectronic equipment. This paper designs a RFEH system which harvests 2.4 GHz radio frequency signal. The system is mainly composed of miniature patch antenna, impedance matching circuit, rectifier booster circuit, and storage circuit. The ADS simulation tool is used to verify the feasibility of the system design. When the load resistance is 100 K and the input power is between −10 and 5 dBm, the energy conversion efficiency is above 30%. Continuous power supply for low-power microelectronic equipment can be realized. Keywords: Radio frequency energy harvesting · Harvesting antenna · Impedance matching circuit · Rectifier boost circuit

1 Introduction With the rapid development of wireless communication technology, the use of portable and distributed wireless devices is gaining popularity. How to provide stable and reliable power for these devices is becoming one of the problems that restrict the application and popularity of wireless devices. The development of radio frequency energy harvesting (RFEH) technology provides a new way to the power supply of low-power wireless equipment. RFEH system can harvest both radio frequency signals in the environment and those generated by a particular transmitter. A RFEH system based on ultra-wideband Archimedes spiral antenna and half wave multiplier circuit was designed [1]. The rectifying efficiency of 30% is obtained under the input power of 0 dBm, and the output voltage and power obtained can meet the working requirements of low-power equipment. But it still cannot avoid the problem that the energy harvesting system is too large. Reference [2] designed a RFEH system with 4-RF band antenna as the harvesting antenna. The system harvests energy from GSM900 (Global System for Mobile Communications), GSM1800, UMTS (Universal Mobile Telecommunications System) and WiFi bands simultaneously. RF-to-DC conversion efficiency is measured at 62% for a © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_50

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cumulative −10 dBm input power homogeneously widespread over the four RF bands and reaches 84% at 5.8 dBm. But the problem remains that the system’s antennas are too large. Now some RFEH systems are designed by CMOS [3–5]. Reference [5] achieved efficiencies of up to 74% for an input power of 1 mW. They externally feed the circuit in question and use the self-body biasing technique in a CMOS configuration to change the threshold voltage and turn on the transistor more quickly. Nevertheless, no schemes that have reached high efficiencies in the conditioning circuit make use of a true passive configuration. They externally bias the elements of the circuit. This paper starts with the miniaturization of RFEH system. An energy harvesting system is designed to harvest 2.4 GHz RF signal by using diode and capacitor combined rectifier circuit.

2 Design of RFEH System RFEH systems can efficiently convert radio frequency signals into direct current energy and store it to power loads. Its system structure is shown in Fig. 1. The harvesting antenna harvests RF signals from the environment. RF signals are converted into direct current for equipment operation by RF-DC rectifier. In order to realize the maximum power transmission between the harvesting antenna and the rectifier, an impedance matching circuit is designed between the antenna and the rectifier.

Fig. 1. RFEH system

This paper takes the RFEH of harvesting 2.4 GHz as an example. The energy harvesting system is shown in Fig. 2. This system uses a kind of miniature patch antenna with coaxial feeding at the frequency of 2.4 GHz. Impedance matching circuit is used to ensure the maximum power transmission of RF energy. The circuit is realized by an LC matching network. The rectifier booster circuit uses a third-order Villard circuit. It cannot only converts the AC signal into a stable DC signal, but also improves the output voltage. The storage circuit consists of a large capacity capacitor Cst , which can store the harvested energy and power the load. 2.1 Harvesting Antenna Design A suitable harvesting antenna can provide a high primary voltage to ensure the design of RFEH system [6]. Suppose that the distribution of current excited on the antenna by

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Fig. 2. RFEH system

the wave electric field arriving at the harvesting antenna is I. The power absorbed by the harvesting antenna from the incident electric field is:  P = Pt

λ · A · Gr 4π r

2 (1)

where Pt is the transmitting power, λ is the wavelength of the electrical signal, r is the transmission distance, A is the spatial attenuation factor, and Gr is the gain of the harvesting antenna [7]. Then the directional gain of the antenna is: Gmax =

120π 2 L2e λ2 Ra

(2)

where Le is the effective length of the antenna and Ra is the impedance of the antenna. The Friis free-space formula is an important formula for the overall design of wireless telecommunication system. Its simplified expression is as follows.  Pr =

λ 4π r

2 Gr Gt Pt

(3)

where Pr is the power density harvested by the harvesting antenna, Gr is the gain of the harvesting antenna, Gt is the gain of the transmitting antenna, and Pt is the transmitting power. According to the above formula, the RFEH system cannot be adjusted the transmission power Pt of the transmitter and the gain Gt of the transmitting antenna. At the same time, the selected antenna should reduce the reradiation power, wire and media loss power, to maximize the load absorption power. From Eq. (3), it can be seen that, to increase the power density Pr harvested by the harvesting antenna, increasing the gain Gr of the harvesting antenna is a method. Generally speaking, the gain of directional antenna is larger than that of omnidirectional antenna. But the direction angle is smaller

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than omnidirectional antenna. Therefore, there are strict requirements on the installation direction. For weak WiFi signals, it is necessary to select a broadband, high-gain harvesting antenna to harvest. Due to the different feeding modes have different effects on the performance of antenna [8, 9]. The two feeding modes of microstrip and coaxial are compared. When feeding with microstrip line, the microstrip line itself will produce radiation loss. This additional loss will adversely affect the directional parameters of the antenna. The antenna gain also decreases. In this paper, an improved patch antenna with coaxial feed is selected. It is modeled and simulated with ANSYS electronics desktop software. The established antenna geometric model is shown in Fig. 3. The geometric parameters of the antenna are shown in Table 1.

Fig. 3. Geometric model of patch antenna

Table 1. Table captions should be placed above the tables Param

L1

L2

L3

L4

L5

L6

L7

L8

H1

H2

H3

H4

H5

H6

Size (mm)

25.5

9.2

2.2

2.2

3.2

10

10.2

0.5

7

2.2

4.2

1.9

1.2

3

The S11 parameters of the antenna are shown in Fig. 4. The S11 parameters are the square root of the ratio of the reflected power to the incident power of the electromagnetic wave at the wave port. As can be seen from the S11 parameter diagram, the center frequency of the antenna is 2.4250 GHz. The upper cut-off frequency is 2.3350 GHz. The lower cut-off frequency is 2.5000 GHz. The absolute bandwidth is 0.1650 GHz. Voltage standing wave ratio (VSWR) is an important parameter to measure the antenna performance. When the antenna works normally, the voltage standing wave ratio of the antenna is generally required to be less than 2. As can be seen from Fig. 5, the voltage standing wave ratio is less than 1.5 in 2.39–2.45 GHz frequency, which meets the requirements of normal antenna operation. The result is consistent with that of S11 parameter graph.

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Fig. 4. Schematic diagram of antenna S11 parameters

Fig. 5. Schematic diagram of standing wave ratio of antenna voltage

2.2 Matching Circuit In order to effectively improve the voltage amplitude of the wireless signals harvested by the antenna, the system adopts the matching booster circuit as shown in Fig. 2. Rm1

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is the 50  antenna characteristic impedance. Lm is the adjustable matching inductance. Rm2 and Cm are the equivalent input resistance and capacitance of the input port of the back-stage rectifier circuit network, respectively. The matching booster circuit is realized by the LC matching network. The RF signal harvested by the antenna is matched boost and supplied to the subsequent rectifier boost circuit. The working principle of the matched booster circuit is as follows. Set Vo as the input signal of the matching booster circuit transmitted to the subsequent rectifier booster circuit. It can be obtained from the LC matching theory [10]. Vo = Vin

1 jωCm

+ Rm2

Rm1 + Rm2 + jωLm +

1 jωCm

(4)

According to Eq. (4), when jωLm = −1/jωCm , the matching network is pure resistive and the current value is the maximum. When Cm is reduced, you can get a sufficiently large Vo . 2.3 Design of Rectifier Booster Circuit This system adopts the Villard rectifier booster circuit to rectify and amplify the high frequency current harvested by the antenna. The output of signal the Villard circuit is DC. And the single-stage circuit can output twice the voltage of the input signal. Higher voltage can be generated by cascading multiple circuits. The third-order Villard circuit is shown in Fig. 2. Each stage of the rectifier booster circuit consists of two Schottky diodes and two capacitors. In Fig. 2, C1 , D1 , C2 and D2 is the first order of the Villard circuit. C1 and D1 generates the first voltage clamp. C2 and D2 achieve peak rectification. When the input signal Vin is negative half period, diode D1 is on and D2 is off. The current pass through D1 to store electrical energy in C1 . Because the current passing through D1 requires overcoming the diode threshold voltage Vth . Therefore, the voltage of C1 is: V1 = Vin − Vth

(5)

When the input signal is positive half cycle, the diode D1 is off and D2 is on. The current passes through D2 to charge capacitor C2 . Since the voltage of C1 is V1 and the threshold voltage of D2 is Vth . So in the whole circuit, the voltage at both ends of C2 is: V2 = 2(Vin − Vth )

(6)

According to the above analysis method, the output voltage of the Villard circuit after N class connection is the voltage value of the capacitor series with N terminal voltage of 2(Vin − Vth ). That is, the output voltage Vn is: Vn = 2N (Vin − Vth )

(7)

By Eq. (7), as far as the circuit itself is concerned, both voltage doubling series N and diode threshold voltage Vth can affect the output voltage of voltage doubling circuit. When the input signal amplitude Vin is less than the diode threshold voltage Vth , the

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circuit will have no voltage output. Because the RF signals harvested by the harvesting antenna may be weak, in order to ensure the system output ideal voltage value, ideal should make Vth as small as possible. Schottky diodes have a low threshold voltage and very fast conversion speed, which is very suitable for RFEH circuits. The diode used in this system is HSMS2852 with threshold voltage as low as 150 mV. In order to ensure that the circuit can output effective voltage under the condition of weak input signal, a third-order Villard circuit is used in this paper.

3 Experimental Results and Analysis For RFEH system, output voltage and energy conversion efficiency are two important parameters to measure system performance. The power conversion efficiency of the system can be expressed by the ratio of the output power to the incident power [11]. That is: PCE =

Vout P0 = Pr Pr RL

(8)

where P0 is the output power, Pr is the incident power, Vout is the output voltage, and RL is the load resistance. It can be seen from Eq. (8) that the PCE of the system is related to the output voltage, incident power, and load resistance. Figure 6 shows the test results of the

Fig. 6. Energy conversion efficiency under different input power and load resistance values

energy conversion efficiency of the RFEH system under different input powers and load resistors at 2.4 GHz. It can be seen from the figure that the energy conversion efficiency of the system increases with the larger input power. As the load resistance increases, the energy conversion efficiency decreases. When the load resistance is 100 K, the energy conversion efficiency reaches the maximum value of 55% when the input power is between −5 and 0 dBm. Figure 7 shows the output voltage at different input power when the load resistance is 100 K. It can be seen from the trend of the curve in the figure. With the increase of input power, the output voltage of the system becomes larger and larger. The maximum output voltage is 4.2 V. And when the input power is from −10 to 10 dBm, the output voltage is greater than 2 V.

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Fig. 7. Output voltages under different input powers

4 Conclusion RFEH technology is now becoming one of the effective power supply methods for low-power microelectronic equipment. This paper designs a RFEH system that harvests 2.4 GHz RF signals. The system is mainly composed of miniature patch antenna, impedance matching circuit, rectifier booster circuit, and storage circuit. The simulation results show that the continuous power supply of low-power microelectronic equipment can be realized in the room with sufficient 2.4 GHz signal.

References 1. Aex-Amor, A., Palomares-Caballero, A., Fernandez-Gonzalez, J.M., Padilla, P., Marcos, D., Sierra-Castaner, M., Esteban, J.: RF energy harvesting system based on an Archimedean spiral antenna for low-power sensor applications. Sensors 19(6), 1318 (2019) 2. Kuhn, V., Lahuec, C., Seguin, F., Person, C.: A multi-band stacked RF energy harvester with RF-to-DC efficiency up to 84%. IEEE Trans. Microwave Theory Techn. 63(5), 1768–1778 (2015) 3. Natarajan, A., Sadagopan, K.R., Ramadass, Y.: A cm-scale 2.4-GHz wireless energy harvester with NanoWatt boost converter and antenna-rectifier resonance for WiFi powering of sensor nodes. IEEE J. Solid-State Circ. 53(12), 3396–3406 (2018) 4. Karami, M.A., Moez, K.: Systematic co-design of matching networks and rectifiers for CMOS radio frequency energy harvesters. IEEE Trans. Circ. Syst. I Regul. 66(8), 3238–3251 (2019) 5. Moghaddam, A.K., Chuah, J.H., H. Ramiah, J. Ahmadian, P. I. Mak, and R. P. Martins.: A 73.9%-efficiency CMOS rectifier using a lower DC feeding (LDCF) self-body-biasing technique for far-field RF energy-harvesting systems. IEEE Trans. Circ. Syst. I Regul. 64(4), 992–1002 (2017) 6. Poornima, S., Dutta, K., Gajera, H., Chandrashekar, K.S., Chandramma, S.: Flexible and miniaturized design of microstrip patch antenna with improved cross-polarized radiation. AEUE Int. J. Electron. Commun. 2020, 116 (2020) 7. Li, W., Qin, J., Chen, J., Cui, M.: Selection and calculation for sub-module capacitance and inductance of bridge arm in MMC-HVDC system. J. Northeast Electr. Power Univ. 39(2), 47–53 (2019)

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8. Paz, H.P., Silva, V.S., Cambero, E.V.V., Araujo, H.X., Casella, I.R.S., Capovilla, C.E.: A survey on low power RF rectifiers efficiency for low cost energy harvesting applications. AEUE-Int. J. Electron. Commun. 2019, 112 (2019) 9. Li, G., Lou, J., Bian, J., Wang, H., Tan, L.: Analysis on malfunction of MMC-HVDC DC voltage protection consider DC circuit breaker protection. J. Northeast Electr. Power Univ. 38(2), 1–8 (2018) 10. Chen, T., Wei, B., Zhang, X., Xu, W., Duan, J.: A −30 dBm RF power harvester rectifier. Microelectronics 47(2), 212–216 (2017) 11. Liu, D., Yan, N., Min, H.: A wide dynamic range radio frequency energy harvester with the maximum power point tracking. J. Fudan Univ. (Nat. Sci.) 58(4), 433–440 (2019) 12. Zhang, B., Wen, X.: Analysis of positive and negative sequence impedance modeling of threephase LCL grid-connected matrix converter based on harmonic linearization. J. Northeast Electr. Power Univ. 39(5), 41–52 (2019)

An Encryption Method of Power Cloud Data Based on n-RSA Yong Wang1 , Qiang Ma1 , Lei Li1 , Ti Guan1 , Yujie Geng1 , Shuowang Yao2 , and Jianpo Li2(B) 1 State Grid Shandong Electric Power Company, Jinan 250000, China 2 School of Computer Science, Northeast Electric Power University, Jilin 132012, China

[email protected]

Abstract. With the development and progress of the Internet, the traditional grids are unable to meet the needs of the power industry. The development of the power industry requires the support of cloud computing technology. However, the storage and calculation of data are performed in the cloud, which threatens the privacy issues of power users and institutions. In this issue, this paper proposes a multi-level security model of the n-RSA encryption algorithm to solve the privacy problem of sensitive data transmitting in the cloud. Firstly, the power cloud security center conducts a security assessment of the data and determines the security level. Then, select the appropriate prime number for the n-RSA algorithm according to the security level of the data. The detailed steps of the proposed method for n-RSA encryption are given, and the advantages and disadvantages of this method for power cloud are analyzed. Experimental results show that the proposed method can effectively improve the security and flexibility of the secure transmission of power cloud data. Keywords: Cloud computing · RSA algorithm · Power cloud · Asymmetric encryption

1 Introduction Informatization of the power industry is moving in the direction of cloud computing. Cloud computing technology has been used to build “public service cloud” and “enterprise management cloud”. It has started to improve grid security and new business development with comprehensive support. The power cloud has become an important part of power critical information infrastructure [1, 2]. The power cloud has more precise requirements for real-time performance, a more complex network structure, and stricter data security levels [3]. This paper proposes an improved RSA encryption algorithm to solve the security problem of data leakage [4] in data transmission between cloud platform information internal network and external network. The RSA encryption algorithm is an asymmetric encryption algorithm, which was proposed by Ron Rivest, Adi Shamir, and Leonard Adleman of the Massachusetts Institute of Technology in 1977. Its basic theoretical is modulo operation, Fermat’s little theorem, Euler’s theorem, and Euler function. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_51

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RSA is a high-strength asymmetric encryption system. The larger the value of the large prime factor is, the more secure it is. So far, no one has been able to crack RSA algorithms with more than 1024 bits. However, the encryption and decryption of asymmetric encryption take a long time, and this algorithm can be cracked when an attacker knows one of the large prime factors. To solve this problem, many scholars have proposed some different improved algorithms. The enhanced RSA based on four prime numbers reduces direct attacks because the calculation of public and private keys depends on the value of N, where N is the product of four prime numbers [5]. Although it enhances the key generation time compared to traditional RSA, the speed of the encryption and decryption process is slower than that of traditional RSA algorithms. Reference [6] proposes an algorithm that adds new factors to increase the complexity of the encryption and decryption process. Although the algorithm is more secure than the traditional RSA algorithm and no attackers can directly attack it, many parameter participation overloads the system. Reference [7] proposes an algorithm to re-encrypt the key (d , N ) using a mathematical transformation method. Although its security is improved compared to traditional RSA, it is more computationally intensive. Reference [8] performs ASCII conversion of the generated large prime factors and performed XOR operation on the converted ASCII codes. This method improves security and calculation speed, but it cannot convert ASCII code when the prime number factor is too large. In view of the above problems, this paper focuses on an improved RSA algorithm. This algorithm is improved in security and solves the problem that it cannot be used when the prime factor is too large. And then this algorithm is used for power cloud security simulation.

2 Power Cloud Security Technology The three-layer model of the power cloud is divided into a cloud infrastructure layer, a cloud platform component layer, a business application layer, and a cloud security center [1, 9]. The cloud infrastructure layer corresponds to the IaaS layer for business applications layer and cloud platform component layer to prepare its required computing and storage resources. The cloud platform component layer corresponds to the PaaS layer, which provides the software development environment to users as a service and is the bridge between the business application layer and the cloud infrastructure layer. The business application layer corresponds to SaaS. The users only need to pay a certain fee to enjoy the corresponding services through the Internet. The cloud security center is a new main part added in consideration of network security issues. It runs through the above layers to ensure the security of each layer, which is shown in Fig. 1. According to the cloud security center in the three-layer model of the power cloud, the cloud security center architecture [10] can be given as Fig. 2. The cloud security center is used to protect the security of application software in the business application layer, cloud platform component layer, and cloud infrastructure layer. The password service module is a security guarantee that runs through these three layers. It can encrypt the user’s privacy and the key data of the organization to protect the power cloud’s information security. Combined with the cryptographic service module of power cloud, this paper proposes an improved RSA encryption algorithm to provide an effective information protection measure for power cloud.

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Fig. 1. Three-layer model of power cloud

Fig. 2. Cloud security center architecture

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3 RSA Encryption Algorithm Introduction The RSA encryption algorithm is an asymmetric encryption algorithm, which is mainly an algorithm composed of number theory knowledge such as Euler’s theorem, Fermat’s little theorem, and digital–analog operations. The specific steps of the algorithm are as follows. Randomly select two large prime numbers p with q, satisfying the condition of a large integer N = p × q. Calculated ϕ(N ) according to Euler’s formula. ϕ(N ) = ϕ(p) × ϕ(q) = (p − 1)(q − 1)

(1)

where ϕ(N ) is the number of positive integers less than or equal to N that form ϕ(N ) reciprocal relationship with N. in particular, when N is prime, then ϕ(N ) = N − 1, and ϕ(1) = 1. Both p and q are prime numbers, so ϕ(p) = p − 1, ϕ(q) = q − 1. Then, the smaller odd e is randomly selected. e does not have the same common factor as ϕ(N ), and it is part of the public key. That is, the public key is (e, N ), and then the data is encrypted into ciphertext by means of modular inverse operation. The specific process is: me mod N ≡ C

(2)

where m is data, C is ciphertext. Similarly, ciphertext C decrypts into data m. The formula is as follows. C d mod N ≡ m

(3)

where d is part of the private key, i.e., the private key is (d , N ). d = (k × ϕ(N ) + 1)/e

(4)

where k is not a fixed value, but a value guaranteed to be rounded to d. If an attacker wants to steal the data content after the ciphertext is generated, it is needed to obtain the key to decrypt the ciphertext. Or, if one of the large prime numbers p or q is known, the key can be deduced by using the inverse function of the modulus.

4 n-RSA Encryption Algorithm Introduction The security of the RSA encryption algorithm depends on a large number of decomposition. The larger the prime number is, the higher the security of the algorithm is. Considering the computing power of the computer, the prime number should be appropriately selected when using this algorithm. The encryption process of this algorithm uses a single to the function, the attacker cannot theoretically deduce the correct information without knowing the private key. Therefore, the decomposition of a large integer N is the most effective attack way. Assuming that the attacker knows one prime p of the decomposition of large integer N, another prime q can be deduced to crack the algorithm.

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4.1 Prime Selection The security of the RSA encryption algorithm depends on a large number of decomposition. The larger the prime number is, the higher the security of the algorithm is. Considering the computing power of the computer, the prime number should be appropriately selected when using this algorithm. The encryption process of this algorithm uses a single to the function, the attacker cannot theoretically deduce the correct information without knowing the private key. Therefore, the decomposition of a large integer N is the most effective attack way. Assuming that the attacker knows one prime p of the decomposition of large integer N, another prime q can be deduced to crack the algorithm. Random selection of n-RSA encryption algorithm prime numbers, set to p1 , p2 , ..., pn . N is a large integer, satisfying: N = p1 × p2 × · · · × pn

(5)

where p1 , p2 , . . . , pn can be partially equal, but not all equal. Calculate N extended Euler function ϕ(N ): ϕ(N ) =

n 

j−1

pi

× (pi − 1)

(6)

i=1

where ϕ(N ) is the number of positive integers less than or equal to N, the number of which forms a coprime relationship with N. j is a power of prime numbers, 1 ≤ i < n and pi are randomly selected prime numbers. Among which ϕ(N ) is confidential. 4.2 Key Generation Assume that the data m needs to be encrypted. A smaller odd number e is selected randomly as part of the public key. e satisfies no common factor with ϕ(N ). e and a large integer N forms a public key (e, N ). The data m is encrypted into the ciphertext C The formula is: me mod N ≡ C

(7)

formula (7) is a one-way function. Given C, N, e, it is difficult to calculate m. The decryption formula for decrypting ciphertext C into data m is: C d mod N ≡ m

(8)

where d and N constitute the private key (d , N ), and the private key is confidential. According to Euler’s theorem, the relationship between d and e is derived as: d = (k × ϕ(N ) + 1)/e

(9)

k has no fixed value in advance, and its role is to ensure that d is rounded. At this time, even if the attacker knows one of the prime numbers p1 , p2 , . . . , pn , it is still difficult to crack the ciphertext. Compared with the traditional RSA algorithm, security is significantly improved.

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5 Security Proof of n-RSA Algorithm in Power Cloud To verify the effectiveness of the algorithm, this paper simulates the use of the traditional RSA algorithm and n-RSA algorithm in an electric power cloud to conduct data encryption efficiency and security-related experiments. The test configuration is shown in Table 1. Table 1. Power configuration table Information

Power cloud server

RAM

Hard disk

Network card

Configuration parameter

Intel XeonE3-120v53.0 Hz quad-core 120v53.0 Hz

8GEEC

1 × Intel Enterprise SSD, 1 SATA1

2 × Gigabit Ethernet port

Here, we use the traditional RSA algorithm and the n-RSA algorithm in these servers. Because the number of prime numbers in the n-RSA algorithm is determined based on the current server configuration, and the n-RSA algorithm is divided into three here. In this case, the prime numbers are equal to 2, 3, and 4, respectively. There are two groups of data. The size of data 1 is 512 MB, and the size of data 2 is 1024 MB. The three cases of the n-RSA algorithm and the traditional RSA algorithm are used to encrypt data 1 and data 2 to generate ciphertext 1 and ciphertext 1. The time is shown in Table 2, and the ciphertext is decrypted into data, and the decryption time is shown in Table 3. When the prime number is two, the n-RSA algorithm generates the key time of 512 MB data. 3% less than the traditional RSA algorithm and 6% decryption time of ciphertext. Under the same conditions, the number of primes of the n-RSA algorithm is changed to 3, and the n-RSA key generation time is increased by 4% compared with the traditional RSA algorithm. The time is increased by 3.9%, and ciphertext decryption time is increased by 1.2%. Under the same conditions, the number of prime numbers of the n-RSA algorithm is changed to 4. The time is increased by 22%. It is concluded that the more the prime number is, the longer the calculation time is. Table 2. Key generation time of n-RSA algorithm and traditional RSA algorithm (ms) Electricity data

Traditional RSA

n-RSA (n = 2)

n-RSA (n = 3)

n-RSA (n = 4)

Data1

9303

9012

9668

10,235

Data2

75,074

71,265

75,231

82,565

Also, we compare the security of the n-RSA algorithm and the traditional RSA algorithm and calculate the traditional RSA cracking probability, which can be calculated from the probability of prediction to the correct large prime number. lim

x→∞

π(x) =1 x ln x

(10)

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Table 3. Ciphertext decryption time (ms) of n-RSA algorithm and traditional RSA algorithm Ciphertext

Traditional RSA

n-RSA (n = 2)

n-RSA (n = 3)

n-RSA (n = 4)

Ciphertext 1

3371

3165

3793

4136

Ciphertext 2

24,658

24,035

25,381

27,215

where π(x) is the number of prime numbers, x is the value of the prime number, the number of prime numbers less than x is approximately equal to lnxx , and among integers less than x, the probability of randomly selecting prime numbers is approximate ln1x . Therefore, the probability of traditional RSA being cracked is P1 . P1 =

1 1 × ln x1 ln(x2 − 1)

(11)

where x1 is the first prime number, x2 is the second prime number, and the probability of n-RSA being cracked is P2 : P2 =

n  k=1

1 ln xk

(12)

Comparing (11) and (12), when x1 = x2 , P1 > P2 . So theoretically, it can be proven that the n-RSA algorithm has a lower cracking rate than the traditional RSA algorithm. From the above all, it can be known that increasing the number of primes makes the algorithm more secure, but the calculation time becomes longer. To apply this algorithm to the power cloud more effectively, this paper designs a power data information security center. It divides the power data security levels, followed by setting different usage rights for different levels of power cloud users. The higher the permission level is, the higher the security level is, which is shown in Table 4 [11]. Table 4. Electricity data security center Security level

Level 0

Level 1

Level 2

Sensitive information

No

Have

Have

The number of n-RSA primes

No encryption required

2, normal encryption

4, strong encryption

Characteristics

Fast

Slower speed

Slow speed

Application scenario

Sensor and detection device information storage

Sensitive data

Extremely sensitive data

When the security level is 0, the data is determined to be public data and no encryption is required. When the security level is 1, the data is determined to be more sensitive data,

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and the prime number of the n-RSA algorithm is set to 2 as important data. When the security level is 2, the data is determined to be extremely sensitive, and the prime number of the n-RSA algorithm is set to 4. Establishing a power data security center can give full play to the security and flexibility of the n-RSA algorithm.

6 Conclusion This paper proposes the n-RSA algorithm for the security of power cloud data transmission. This algorithm can customize the number of primes, allow the primes to repeat, and use the extended Euler theorem to generate private and public keys. Simulation results show that increasing the number of primes makes the algorithm more secure, but the calculation time becomes longer. To better apply this algorithm to the power cloud, this paper proposes that a powerful data security center must be established to divide the power data into security levels. As the security level increases, the number of primes of the n-RSA algorithm increases. The experimental demonstration shows that the method designed in this paper is highly effective, with a view to providing a theoretical basis for research methods based on electric power cloud security.

References 1. Shen, L., Wang, D., Xuan, J.: Security risk analysis and evaluation techniques in power in-formation system cloud. Telecommun. Sci. 34(02), 153–160 (2018) 2. Li, P., Liu, X., Li, J.: Application study of self-organizing map network based on immune genetic algorithm. J. Northeast Electr. Power Univ. 38(06), 82–85 (2018) 3. Ren, X.: Research on the Task Scheduling Strategy of Power Cloud Data Center. North China Electric Power University (2016) 4. Ye, B.: Task scheduling algorithm for cloud computing based on load balance degree. J. Northeast Electr. Power Univ. 39(01), 88–95 (2019) 5. Thangavel, M., Varalakshmi, P., Murrali, M., Nithya, K.: An enhanced and secured RSA key generation scheme (ESRKGS). J. Inf. Secur. Appl. 20, 3–10 (2015) 6. Dhakar, R.S., Gupta, A.K., Sharma, P.: Modified RSA encryption algorithm (MREA). In: IEEE Second International Conference on Advanced Computing & Communication Technologies, Beijing, pp. 426–429 (2012) 7. Chen, C., Qi, N., Yu, H.: Research and improvement of the RSA algorithm. Comput. Technol. Dev. 26(8), 48–51 (2016) 8. Wang, H., Liu, P.: An improved security algorithm based on RSA. Meas. Control Technol. 38(10), 104–107 (2019) 9. Wang, S.: Prediction and analysis of systemic network security model based on data mining. J. Northeast Electr. Power Univ. 39(06), 91–93 (2019) 10. Yin, Y., Zhang, W., Wang, S.: Application model of cipher technology in cloud computing security. Commun. Technol. 47(09), 1075–1078 (2014) 11. Wang, Z.: Researches and applications of safety program for power Internet of Things relying on cloud computing. Autom. Technol. Appl. 38(12), 93–96 (2019)

K-Means-Based Method for Identifying Characteristics of Wireless Terminal Equipment in Power System Yueqin Yin1 , Zhantu Zhang2 , Huajian Zhang1 , Shengze Sun3 , and Jianpo Li3(B) 1 State Grid Yanan Electric Power Supply Company, Yanan 716000, China 2 Xi’an Shiyou University, Xian 710000, China 3 School of Computer Science, Northeast Electric Power University, Jilin 132012, China

[email protected]

Abstract. With the rapid development of the ubiquitous electric power Internet of things (IoT), a large number of terminal devices are connected to the power system. The problems of dynamic management and information security of ubiquitous IoT terminal devices are facing huge challenges. Aiming at this problem, a method based on k-means is proposed, which is for identifying characteristic behaviors of wireless terminal equipment in power system. At first, the improved k-means clustering algorithm and mobile terminal device fingerprint identification are used to construct a network terminal device identification access control model. Then, the general steps of using the proposed method for network terminal identification are given, and the advantages and disadvantages of the method are analyzed. Simulation results show that the proposed method can effectively improve the data processing capability and recognition accuracy of ubiquitous power IoT terminal equipment. Keywords: Ubiquitous electricity Internet of things · K-means algorithm · Device fingerprint · Characteristic behaviors recognition

1 Introduction With the continuous advance on the construction of the ubiquitous electric power Internet of things (IoT), more and more nodes (such as power terminals, user smart equipment, and other energy system equipment) are connected to the power system. The corresponding network data volume and data types have increased dramatically. Ubiquitous power IoT involves advanced application scenes such as current–time situation perception, the best operation schedule, etc. As the basis for the overall security situation perception and security system construction of the ubiquitous IoT, smart identification of the IoT terminals at the perception layer is particularly critical. Compared with the traditional Internet, the IoT has a great number of terminals and a wider range of physical deployments [1]. In addition to human–machine interconnection, it also includes lots of device interconnections. So, the new question is obviously present. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_52

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How to ensure the IoT is visible at current time and controllable in the whole process. The front-end equipment of the IoT is scattered and vulnerable to attacks [2], which affects the overall network security and causes the core business systems to fail to operate normally. According to the above problems, relevant researchers have carried out deep surveys, but most of them are still based on recognition methods based on device static identification and device fingerprints [3, 4]. For massive amounts of data and complex types of IoT terminal devices, the use of device static identification and device fingerprint methods are vulnerable to tampering and counterfeiting [5, 6], meanwhile, it has the disadvantages of emulation and poor scalability to the actual environment. Hackers often use illegally connected IoT terminals to attack or invade servers and other legal devices; therefore, challenge the entire IoT security system [7, 8]. Reference [9] proposed the device fingerprint idea and applied the product dynamic attribute set to establish a device fingerprint instance. From the perspective of IoT device protocol analysis, Hagen [10] and others extracted product attribute information from the process of device protocol search, constructed an information base containing static properties of IoT devices, and proposed hierarchical identification and calibration method of specific devices. From the perspective of Web protocol feature analysis, Mori [11] and other methods use information gain models to extract device identification characteristics of specific types of terminals, and use positive and unlabeled learning (PUL) to identify and classify devices. Reference [12] analyzed the phenomenon that the device identification in the existing security mechanism is too simple and easily stolen, and proposed the identification strategy and steps by using the device feature set to establish the IoT smart terminal feature set. Reference [13] applied gradient boosting machine (GBM), random forest (RF), extreme gradient boosting (XGBoost) through network data analysis and classifier training based on supervised learning. The machine learning model and other machine learning models are used to establish an IoT device classifier, which has achieved good classification and recognition results for IoT terminals. Reference [14] established the IoT terminal automatic identification system IoT Sentinel that achieved device identification with the combination of media access control (MAC) addresses and device IDs. Moreover, Miettinen verified the identification capability and performance overheads of IoT Sentinel as well. The identification method based on the static identification of the device has static identification characteristics and is vulnerable to attacks such as tampering and counterfeiting. The identification method based on the fingerprint of the device only concentrates on considering the conceptual system design and simulation, but scarely refers to the actual Miettinen system implementation. This research proposes the thesis on improving k-means clustering algorithm identification and mobile terminal device fingerprint identification, to prevent static features from being attacked, and use dynamic feature analysis to enhance network terminal device identification security.

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2 Characteristic Behavior Recognition of Wireless Terminal Equipment in Power System 2.1 Basic Principles of Characteristic Behavior Recognition IoT terminal device characteristic behavior recognition is used to automatically identify the type of specific device or terminal in the IoT. Authorized users send a specific networking terminal request to the terminal detection module through the browser or program. The terminal detection module use Nmap technology to scan the corresponding device and obtain the fingerprint of the terminal device. The feature behavior recognition module classifies and identifies the device fingerprint by calling the classifier trained of k-means algorithm, so as to detect and return the type of device. The main control program of the server side calls the terminal detection module to automatically scan and collect the fingerprint data of the corresponding terminal, and calls the classifier trained of k-means algorithm to classify and identify the device fingerprint. It will determine the specific device type of the networking terminal. And then, it returns the identification result of the networking terminal to the users. Figure 1 is an application scenario of the method for identifying characteristic behaviors of an IoT terminal device.

Fig. 1. Application scenarios of IoT terminal equipment

2.2 The Overall Process of Characteristic Behavior Recognition When an authorized user enters the terminal Internet Protocol (IP) and MAC to be queried, the system first determines whether the IP and MAC addresses exist. If it exists,

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Nmap scans the operating system of the IP and the open port number, and then imports the obtained data into the database. The training model is generated by performing classification training on the data that has been imported into the database. Based on the training model, the fingerprint behavior characteristic recognition of the terminal device is performed. The overall process is as follows: Step 1: Data import and check if the IP and MAC addresses in the data exist. Step 2: If it exists and import the database, if there is no import blacklist. Step 3: Data export and training through k-means algorithm to obtain the model. Step 4: Detect whether IP and MAC addresses exist in the device fingerprint database. Step 5: If it exists and scan the model, if it does not exist and add it to the blacklist. Step 6: Get the final recognition result. 2.3 Basic Principles of Improved K-Means Algorithm The core idea of k-means algorithm is: first randomly select k initial clustering centers from the data set Ci (1 ≤ i ≤ k), calculate the Euclidean distance between the remaining data objects and the cluster center Ci , find the cluster center Ci closest to the target data object, and assign the data object to the cluster corresponding to the cluster center Ci . Then calculate the average value of the data objects in each cluster as the new cluster center, and perform the next iteration until the cluster center no longer changes or the maximum number of iterations stops. The formula for calculating the Euclidean distance between the data object and the cluster center in space is:    m  2 xj − Cij d (x, Ci ) =  (1) j=1

where x is the data object, Ci is the ith cluster center, m is the dimension of the data object, and xj , Cij is the jth attribute value of x and Ci . The sum of the squared error and SSE of the entire data set is: SSE =

k  

2

|d (x, Ci )|

(2)

i=1 x∈Ci

Among them, the size of SSE indicates the quality of the clustering result, and k is the number of clusters. The improved k-means clustering algorithm in this paper is to move the local clustering center to a position that is more conducive to classification, in order to solve the local optimal problem that the traditional k-means clustering algorithm is easy to fall into. The following is the improvement method. The optimal k is determined according to the contour coefficient. The k objects are used in the sample book as the initial cluster center. Each object in the data space is assigned to its nearest cluster, and the different

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clustering centers are recalculated. Then re-cluster according to the new clustering center and reevaluate whether it should replace the clustering center. It is easy to cause deformation error when changing the cluster center. The method of measuring deformation error is the n samples form a European-style space. The distance d (xi , x0 ) between an object xi in a cluster and the European-style space center x0 can be calculated. The distance d (φ, x0 ) between the cluster center φ and the European-style space center x0 can also be calculated. In the same cluster, if change the cluster center to other objects in the cluster, and the deformation error formula caused by replacing the cluster center point φ is:    n  n     2  D =  d (xi , x0 )2 − nk d (φn , x0 )2 −  d (xi , x0 )2 − nk d φp , x0 (3) i=1

i=1

In the formula, D is the deformation error, nk is the number of clustering objects, n is the number of objects in Euclidean space, φn is the new cluster center, and φp is the old cluster center. In order to change the cluster center accurately, it can be described as follow. If at least one of the objects in a cluster is replaced as the new cluster center, the deformation error would be caused D < 0. Then let D which has the largest absolute value as the new cluster center. Otherwise, the cluster center is unchanged. The implementation steps of the improved k-means clustering algorithm are as follows. Step 1: Use the maximum contour coefficient to determine the optimal k value, and select k objects as the initial cluster center. Step 2: Assign each object in the sample space to the cluster nearest to it, and recalculate the cluster center. Step 3: If the objects in the cluster are no longer reassigned, keep the existing cluster, and then go to step 4. Step 4: According to the D clustering center movement rule based on the deformation error described above, if a clustering center is moved to a better position to reduce the overall deformation error sum, it is moved to a better position, and then goes to step 2.

3 Experimental Simulation and Analysis In order to compare the classification effect, the three different classification algorithms, decision tree, support vector machine, and k-means, are used to build feature behavior recognition. The same training data set train models. Using the training set and validation set to verify the classifier effect. All algorithm’s effect analysis and verification are simulated by an Intel Core i7 processor computer. The specific experimental environment configuration parameters are CPU: Intel Core i7-9700K, its core number is 8 cores, memory: 32 GB, hard disk: 1 TB mechanical hard disk (HDD), 256 GB Solid State Drive (SDD), system: Windows 10 professional. In this method, TP is the real example, FN is the false negative example, FP is the false positive example, and TN is the true negative example. The precision indicates

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that among the examples identified as positive examples, the proportion of true positive examples is also called accuracy. Its calculation formula is: F=

TP TP + FP

(4)

The recall rate indicates how many of the examples that should be positive are correctly identified. The calculation formula is: M =

TP TP + FN

(5)

The N1 score takes into account the accuracy and recall, and is defined as the harmonic average of the two. The calculation formula is:

 1 1 1 1 + (6) = × N1 2 F M The Nα score is the general form of the N1 score, which is defined as the weighted harmonic average of precision and recall. It can express different preferences for precision or recall. The calculation formula is: 

α2 1 1 1 + (7) = · Nα 1 + α2 F M where α > 0 is a parameter. When α = 1, the Nα score is degraded to N1 score. When α < 1, the accuracy has a greater impact, and when α > 1, the recall rate has a greater impact. The comparison of the recognition accuracy of the three classifiers is shown in Fig. 2. It can be seen from Fig. 2 that the classifier trained by k-means has a relatively high accuracy in the classification processing of the training data set and the validation data set. Its average accuracy is 96.4%. The classifier obtained by support vector machine has a lower accuracy. The average accuracy of it is just 91%. Although the decision tree classifier has good simulation results on the training data set, the accuracy on the validation data set is relatively on the low side. Its average accuracy is 94.5%. Figure 3 shows the recognition rate of different IoT devices. Among them, the recognition rate of the decision tree classifier is 80%, the recognition rate of the support vector machine classifier is 75%, and the recognition rate of the k-means classifier is 98%. Because different IoT devices have different fingerprint characteristic information, the same classifier has different recognition rates for different IoT devices. In terms of the recognition rate of IoT and non-IoT devices, the k-means method is better than the support vector machine method. The support vector machine method is not much different from the decision tree method. In terms of the specific IoT device recognition rate, the decision tree method is slightly different. The k-means method is superior to the support vector machine method, and the k-means method is superior to the decision tree method. In summary, the k-means classifier has high accuracy in the recognition of characteristic behaviors of IoT terminal devices, and is the preferred method for classifier construction.

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Fig. 2. Comparison of recognition accuracy of three classifiers

Fig. 3. Identification rate of IoT and non-IoT devices

4 Conclusion Aiming at improving the problem of characteristic behavior recognition of wireless terminal equipment in power system, this paper proposes an access control model based on the combination of device fingerprint and improving k-means clustering algorithm for multimodal wireless terminal equipment recognition. In addition, the research simulates corresponding wireless terminal equipment characteristic behavior recognition.

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This method uses Nmap scanning technology to collect device fingerprints, and applies machine learning methods to analyze fingerprints in order to identify devices. K-means algorithm, decision tree, and support vector machine are used to construct a classifier and evaluate the accuracy of the recognition system on the device fingerprint dataset. The results show that the improved k-means classifier has significant advantages.

References 1. Zhang, L., Wang, Z, Wang, C., Ma, Y., Xiang, C.: Design and implementation of intelligent identification system for IoT terminals.J. Chongqing Univ. Posts Telecommun. 31(04), 443– 450 (2019) 2. Lin, C., Tong, H., Yu, W., Xin, Y., Zhang, R.: Distribution grid intelligent state monitoring and fault handling platform based on ubiquitous power Internet of Things.J. Northeast Electr. Power Univ. 39(04), 1–4 (2019) 3. Shi, Z.: Research of Identification Methods of Network Terminal Devices Based on Decision Trees.Hubei University (2018) 4. Zhu, J., Chan, D.S., Prabhu, M.S., Natarajan, P., Hu, H., Bonomi, F.: Improving web sites performance using edge servers in fog computing architecture. In: IEEE Seventh International Symposium on Service-oriented System Engineering, Redwood City, pp. 25–28 (2013) 5. Sun, L., Lv, L., Zhang, X., Liu, G.: The application of intelligent optimization algorithm in the reactive power optimization of the distributed power distribution network. J. Northeast Electr. Power Univ. 37(04), 27–31 (2017) 6. Wu, Y., Lv, W., Li, C., Teng, X.: Smart meter data privacy protection scheme based on authentication and aggregation encryption. J. Northeast Electr. Power Univ. 38(05), 91–96 (2018) 7. Tang, L., Yang, Z., Wang, M.: Improve K-means algorithms of cluster method by GA. Math. Stat. Appl. Probab. (04), 58–64 (1997) 8. Chen, E., Wang, S., Ning, Y., Wang, X.: The design and implementation of clustering algorithm using representative data. Pattern Recogn. Artif. Intell. 14(04), 417–422 (2001) 9. Lin, K., Chen, M., Deng, J., Hassan, M.M., Fortino, G.: Enhanced fingerprinting and trajectory prediction for IoT localization in smart buildings. IEEE Trans. Autom. Sci. Eng. 13(3), 1294– 1307 (2016) 10. Hagen, S., Kemper, A.: Model-based planning for state-related changes to infrastructure and software as a service instances in large data centers. In: Proceedings of IEEE 3rd International Conference on Cloud Computing, Miami, FL, pp.11–18 (2010) 11. Mori, T., Utsunomiya, Y., Tian, X., Okuda, T.: Queueing theoretic approach to job assignment strategy considering various inter-arrival of job in fog computing. In: 19th Asia-Pacific Network Operations and Management Symposium, Seoul, South Korea, pp. 151–156 (2017) 12. Xiao, Q., Wang, J., Zhu, Y.: Intelligent terminal device identification method of internet of things. Telecommun. Sci. 33(02), 3–8 (2017) 13. Meidan, Y., Bohadana, M., Shabtai, A., Guarnizo, D.J., Ochoa, M., Tippenhauer, O.N., Elovici, Y.: ProfillIoT: a machine learning approach for IoT device identification based on network traffic analysis. In: Proceedings of the ACM Symposium on Applied Computing, pp. 506–509 (2017) 14. Miettinen, M., Frassetto, T., Sadeghi, A., Marchal, S., Asokan, N., Hafeez, I., Tarkoma, S.: IoT sentinel demo: automated device-type identification for security enforcement in IoT. In: IEEE 37th International Conference on Distributed Computing Systems, Atlanta, GA, pp. 2511–2514 (2017)

Security Transmission Technology of WSN Based on Trust Management Mechanism in Power System Yong Wang1 , Lei Li1 , Qiang Ma1 , Ti Guan1 , Yujie Geng1 , Shici Li2(B) , and Jianpo Li2 1 State Grid Shandong Electric Power Company, Jinan 250000, China 2 School of Computer Science, Northeast Electric Power University, Jilin 132012, China

[email protected], [email protected]

Abstract. With the rapid economy development and increasing power demand, in order to achieve data sharing and complementarity, accessing wireless sensor networks (WSN) to power data communication networks for transmission is facing huge security challenges. The trust management mechanism based on residual energy proposed in this paper first evaluates the trust of nodes by the number of successful interactions and the number of failed interactions between nodes, establishes a trust management model, and effectively judges malicious nodes to ensure security. And secondly ensures the secure transmission of data. At the same time, the remaining energy of the node is considered and the evaluation index is set by weighting the remaining energy and trust value to select the optimal node to transmit data. The experimental results show that this method can effectively ensure the security and efficiency of network information transmission, and effectively solve the problem of wireless sensor network transmission in power systems. Keywords: Power system transmission · Wireless sensor networks · Trust management · Residual energy

1 Introduction Wireless sensor networks (WSN) are multi-hop self-organizing network system formed by a large number of miniature sensor nodes deployed in the monitoring area through radio communication. Its purpose is to cooperatively sense, collect and process information of monitored objects in the network coverage area and send it to observers [1–3]. WSN have been widely used in electric power systems with the increase in intelligence and automation of power systems. During this process, the electrical, switching, and analog data obtained from the sensor nodes may need to be uploaded to the plant, station control center or dispatch center for analysis and monitoring, so the WSN need to be connected to the existing data sharing and complementarity in electric power data communication networks [4]. During the communication of electric power information data, the confidentiality of task execution, the reliability of data generation, and the security of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_53

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data transmission must be guaranteed to prevent data from being answered or accessed by illegal users.

2 WSN Security Technology Aiming at the security problems of WSN, the current research on WSN security technologies is mainly focused on the following aspects. 2.1 Cryptography There are some insecure factors in WSN. It is necessary to use scientific cryptographic techniques to ensure the security of network communications. At the same time, in the current environment, where multiple communication devices are commonly used, it is necessary to use corresponding different cryptographic technologies for encryption. The current standards for evaluating whether cryptography is suitable for WSN are the code length, data length, processing time, and energy consumption of cryptographic algorithms. Compared with the asymmetric key algorithm, the symmetric key algorithm has the characteristics of low calculation complexity and low energy consumption. So it has been regarded as the mainstream cryptographic technology in WSN. 2.2 Key Management The key is a parameter that is the data entered in the algorithm to convert plaintext to ciphertext or convert ciphertext to plaintext. Key management is a comprehensive technology including all aspects from key generation to key destruction, mainly in the management system, management protocol and key generation, distribution, replacement, and injection. At present, there are some classic key management technologies. The random key pre-distribution scheme proposed by Teng et al. [5], they proved that the number of key pools, the capacity of the key pool, and the network connectivity rate are between the known network size and the expected number of neighbor nodes. There is a computable relationship, which greatly reduces the key storage capacity of each node. Compared with the random key pre-distribution scheme, the LEAP deterministic key management scheme proposed by Eschennuer and Gligor [6] has increased the computation and storage space requirements of the nodes. But it can guarantee that nodes that need to exchange data must have a shared key. 2.3 Secure Routing Some common routing protocols, mainly considering routing efficiency and energy saving, have not considered routing security, so they are vulnerable to various routing attacks [7]. Therefore, secure routing protocols are the core of WSN routing security and have become the most active research topics. Various secure routing protocols have been proposed one after another. A typical cryptographic algorithm-based secure routing protocol SPINS [8] includes SNEP and μ TESLA security modules to achieve data

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confidentiality and point-to-multipoint broadcast authentication to ensure data security. The intrusion-tolerant routing protocol INSENS [9] not only draws on some ideas of the SPINS protocol but also limits flooding attacks by methods such as each node sharing only one key with the base station, discarding duplicate messages and building multipath routing. Solve the problem of node resource constraints by transferring complex tasks such as routing table calculation from sensor nodes to relatively rich base stations.

3 Trust Management Mechanism Since traditional security technologies are mainly used to resist external attacks on wireless sensor networks, they cannot effectively resolve internal attacks that occur when nodes are captured. Trust management mechanisms are considered to complement traditional security technologies based on cryptography. Marsh first introduced the theory of trust in sociology in economics transactions [10] which laid the foundation for the application of the trust model in the computer field. In 1996, the trust management mechanism was first introduced into e-commerce and gradually introduced into the Internet, P2P networks, grids, and other networks [11]. Trust management mechanism is now widely regarded as an effective mechanism to solve open network security problems. Many research scholars have done a lot of research work on the establishment and management of trust models and achieved good results. Some scholars reviewed the existing trust management systems [12] and summarized the following advantages. 1. Preventing sybil attacks: In the centralized management method, the digital certificate provided by the user is issued and verified by the certificate authority to ensure its uniqueness thereby increasing the cost of obtaining multiple identities. In the distributed management method, it does not rely on an authorized entity, but uses a binding “unique” identifier to detect the multiple identities of nodes by using the cooperation between nodes in the network or form a trust network by evaluating trusted resources. 2. Reduce rumors: Audits are conducted through undeniable digital signatures and other methods to prevent rumors. 3. Reduce the impact of rumor spreads: Provide credibility of other nodes through trusted nodes or use statistical methods to establish a modified feedback system to reduce the impact of rumor spread. 4. Prevent short-term denigration attacks: It is necessary to increase the trust cost of newly added nodes that is the lower trust value of newly added nodes requires a certain amount of time to gradually increase. 5. Reduce the risk of denial of service attacks: Stochastic technology is used to select participants for the calculation of trust value to reduce the probability of malicious nodes obtaining the trust value. However, researchers are more concerned about the establishment of trust models and do not consider the performance of the network. Therefore, the research on trust management mechanism in WSN still faces great challenges.

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4 Trust Management Mechanism Based on Residual Energy Trust evaluation will be performed on nodes in the trust management mechanism. Set thresholds to divide trust zones. Finally, the nodes in the trusted area are used for transmission and the nodes in untrusted area should be avoided to transmit data as much as possible. However, the node residual energy is not taken into account in this process. If the node’s trust value is high but the residual energy is low, the node will die during data transmission, which will cause the failed information transmission. Therefore, in view of this phenomenon, this paper proposes a trust management mechanism based on residual energy which uses a weighted method to comprehensively evaluate the node trust value and residual energy. Then it will select the nodes with high comprehensive evaluation to transmit information to ensure the security and reliability of WSN transmission. The trust management mechanism based on residual energy is mainly divided into two steps: node trust value calculation and comprehensive evaluation based on trust value and residual energy. 1. Node trust value calculation In the planar WSN, all nodes have the same function and are deployed in the same position. The network topology is simple. All the nodes can collect data. At the same time, it acts as a relay node for data forwarding operations and performs data transmission to the sink node in a multi-hop manner. Therefore, on the basis of ensuring a sufficient number of interactions, the nodes are evaluated for trust through the number of successful interactions and the number of failed interactions between the nodes. Each node manages and maintains the trust information of neighboring nodes. Comprehensively evaluates the trust value of this node through other nodes to ensure the security of the node. Raising node j trust value Tj calculation formula: 1  Sij , j = 1, 2, . . . , n, i = j n−1 Sij + Fij n

Tj =

(1)

i=0

where Sij is the successful interactions number between sensor node i and sensor node j, Fij is the failed interactions number between sensor node i and sensor node j, n is the number of sensor nodes in the cluster. 2. Comprehensive evaluation based on trust value and residual energy Put forward the node reliability parameter evaluation index. After calculating the node trust value, the trust value and the remaining energy are used to obtain the node trust energy parameter. The trust energy parameter Pj of node j is: Pj = a · Ej + b · Tj

(2)

where a is the weighting factor of nodes residual energy, Ej is the residual energy of node j, b is the weighting factor of nodes trust value, and Tj is the trust value of node j.

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The weighting factor of nodes residual energy a and the weighting factor of nodes trust value b satisfy the following conditions: a+b=1

(3)

where a and b can be adjusted according to specific conditions, usually a = b = 0.5. Set energy constraints for the nodes residual energy Ej : Ej > Emin

(4)

where Emin is the minimum energy to send and receive operation. Before the comprehensive evaluation of trust value and residual energy, it will be judged whether the remaining energy of the node satisfies the energy constraint conditions. The nodes that meet the conditions are selected for comprehensive evaluation. After the network completes the comprehensive evaluation of the trust value and the remaining energy, it will obtain the energy parameters of all nodes that currently meet the energy constraints. When the network needs to perform the task of transmitting information, the reliability parameters of these nodes are sorted and the node with the good reliability parameters of the nodes is preferentially selected as the next hop node. Aiming at the security problem of WSN, a trust management mechanism is used to evaluate the trust of nodes. At the same time, in the process of trust evaluation, other nodes are used to comprehensively evaluate the node to ensure the fair trust evaluation. Comprehensive evaluation using the method of weighting the remaining energy of the node and the trust value of the node while ensuring the safety and reliability of the node. Realize the selection of the optimal transmission node to perform the transmission task.

5 System Simulation Analysis The experimental platform used in this experiment is TRMSim-WSN, a simulation platform in JAVA environment specially used for testing the trust or reputation management mechanism of wireless sensor networks. The test parameters used are shown in Table 1. The proposed method is compared with the classic trust management model RFSN and the improved model flat-RTMS trust model (Rasch-based trust management model under a flat structure) mainly in the average remaining energy ratio, the average report rate of event data and the malicious node detection rate under different malicious node ratios. In the experiment, the network monitoring area is 100 m × 100 m flat rectangular area. Each sensor node is a static node and is randomly distributed in the network monitoring area. Figures 1, 2, and 3 show that this method is compared with the classic trust management model RFSN and the improved model flat-RTMS trust model in the average remaining energy at 30% malicious node ratio, the rate of event data reporting in the environment varies with the ratio of malicious nodes, and the detection rate of malicious nodes in the environment varies with the ratio of malicious nodes. Both of them are better than the other two methods. The simulation results show that the trust management mechanism based on residual energy can effectively identify malicious nodes in the network, improve the event reporting rate and data forwarding rate, and effectively reduce node energy consumption and prolong the life cycle of the entire network.

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Table 1. Basic test parameter Parameter

Value

Event sensing range

20 m

Distance threshold d0 70 m Node initial energy

2J

Esensing

1 nJ/bit

Ereceiving

1 nJ/bit

Eelec

50 nJ/bit

εfs

10 pJ/bit/m2

εemp

0.0013 pJ/bit/m4

Fig. 1. Average remaining energy ratio at 30% malicious node ratio

6 Conclusion This paper proposes a trust management mechanism based on residual energy which is divided into two steps, node trust value calculation and comprehensive evaluation of trust value and residual energy. The proposed nodes trust energy parameter evaluation index effectively solve the problem of secure transmission of wireless sensor networks in

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Fig. 2. Event data reporting rate at different malicious node ratios

Fig. 3. Average malicious node detection rate at different malicious node ratios

the electric power system while ensuring the remaining energy of the nodes, effectively extend the life cycle of the network.

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References 1. Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, Y.: A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002) 2. Qu, Z., Zhang, Y., Xin, P., Li, J., Hu, K.: An energy internet routing algorithm on hypergraph based minimum-energy loss. J. Northeast Electr. Power Univ. 37(6), 93–99 (2017) 3. Callaway, E.H.: Wireless Sensor Networks: Architectures and Protocols, pp. 41–62. CRC Press LLC (2004) 4. Teng, Z., Qu, Z., Zhang, L., Guo, S.: Research on vehicle navigation BD/DR/MM integrated navigation positioning. J. Northeast Electr. Power Univ. 37(4), 98–101 (2017) 5. Eschennuer, L., Gligor, V.D.: A key-management scheme for distributed sensor networks. In: The 9th ACM Conference on Computer and Communications Security, Washington, pp. 41– 47 (2002) 6. Zhu, S., Setia, S., Jajodia, S.: LEAP: efficient security mechanisms for large-scale distributed sensor networks. ACM Trans. Sens. Netw. 2(4), 62–72 (2004) 7. Shi, B., Wang, Y., Li, W., Sun, G.: Study on communication network effect on system performance of wide area control. J. Northeast Electr. Power Univ. 38(6), 29–34 (2018) 8. Adrian, P., Robert, S., Tygar, J.D., Wen, V., Culler, D.E.: SPINS: security protocols for sensor networks. Wireless Netw. 8(5), 521–534 (2002) 9. Deng, J., Han, R., Mishra, S.: INSENS: intrusion-tolerant routing for wireless sensor networks. Comput. Commun. 29(2), 216–230 (2006) 10. Marsh, S.P.: Formalising “trust as a computational concept”. Ph.D. dissertation. University of Stirling, Scotland (1994) 11. Blaze, M., Feigenbaum, J., Lacy, J.: Decentralized Trust Management. In: IEEE Computer Society, Oakland, CA, pp. 164–173 (1996) 12. Kevin, H., David, Z., Cristrna, N.-R.: A survey of attack and defense techniques for reputation systems. ACM Comput. Surv. 42(1), 1–31 (2009)

Survey of Attack Detection and Defense Technologies in Wireless Sensor Networks Jianpo Li1 , Shici Li1(B) , Tao Yang2 , Yan Xie3 , and Guoge Zhang3 1 School of Computer Science, Northeast Electric Power University, Jilin 132012, China

[email protected]

2 State Grid Jiangxi Information & Telecommunication Company, Nanchang 330096, China 3 State Grid Jixi Electric Power Supply Company, Jixi 158100, China

Abstract. With the development of wireless sensor networks (WSN), security issues have gradually become one of the research hotspots of WSN, and they are receiving widespread attention from scholars in various countries. Based on the introduction of the security issues of WSN, this article focuses on the various WSN attack principles, detection, and defense technologies. First, analyze the security requirements of WSN for different application backgrounds. Second, the common WSN attack methods include false routing information attack, Sybil attack, node replication attack, HELLO flood attack, sinkhole attack, selective forwarding attack and wormhole attack divide into two types: camouflaged attacks and non-camouflaged attacks introduce their attack principles, introduce its attack principle and schematic separately, and then analyze the current detection and defense technologies for these attack methods and summarize the results in tables. Finally the existing problems and prospects of WSN security are discussed which makes the research direction of security issues clearer. Keywords: Wireless sensor networks · Attack detection · Security defense

1 Introduction Wireless sensor network (WSN) is a multihop self-organizing network system formed by a large number of sensor nodes deployed in the monitoring area through wireless communication [1]. Because WSN is often deployed in unmanned or dangerous areas, both external perception and correct data transmission are prerequisites for the normal operation of WSN. However, current sensor networks are usually deployed in a workfriendly environment by default and positioning technology for security incidents is considered less. At the same time due to the limitations of the WSN’s own energy, bandwidth, processing, and storage capacity [2], the security issue of WSN has become one of the main issues hindering its development and application. The research on this issue is also one of the current research hotspots in the WSN field [3, 4]. The open distribution of WSN and the characteristics of wireless broadcast communication have security risks. WSN with different application backgrounds have different © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_54

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protection requirements for information. The security requirements of WSN are mainly manifested in the following aspects: confidentiality, integrity, authenticity, usability, freshness, robustness, and access control.

2 Wireless Sensor Networks Attack Methods and Their Detection and Defense Technologies In view of the current security issues of WSN, this paper divides the attack methods of wireless sensor networks into two types: camouflaged attacks and non-camouflaged attacks. Camouflaged attacks are when an attacking node masquerades its identity or behavior to attack. Non-camouflaged attacks are a direct attack by the attacking node. 2.1 Camouflaged WSN Attacks False routing information attack. False routing information attack by deceiving, tampering, and retransmitting routing information, attackers can create routing loops to attract or reject network traffic. The schematic is shown in Fig. 1, node A transmits information to node B under normal conditions, but because node A was attacked by false routing information, it transmitted information to node B via node C instead. False error messages are formed, routing routes are extended, and end-to-end delay is increased.

C A

B

Fig. 1. Schematic diagram of false routing information attack

False routing information attack detection and defense technologies. In traditional networks, packet-related attack is a very important threat. The usual countermeasures for this attack are encryption and authentication. Sybil attack. Sybil attack node can either steal the identity of other nodes or forge an identity that does not exist on the network, thus having multiple different identities in front of other nodes in the network. The schematic is shown in Fig. 2, node F is a Sybil attack node disguised as both node B and node C. When node B wants to communicate with node D and node C wants to communicate with node E, it will send information to attack node F disrupting the normal operation of the network. Sybil attack detection and defense technologies. Ref. [5] mainly discussed wireless device resource testing methods and random key predistribution methods to identify witch attacks. A witch detection method based on received signal strength indication (RSSI) uses RSSI ratios from multiple receivers to detect Sybil attacks. The method of detecting the Sybil attack by using the neighbor node relationship is to verify the node identity. Using a one-way key chain and Merkle hash tree at the same time enables each node to authenticate the identity of other nodes in the network, thereby preventing Sybil attack.

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A B

C F D

E

Base station Fig. 2. Schematic diagram of Sybil attack

Node replication attack. Node replication attack refers to the fact that after the sensor node captures the sensor node, it obtains important information such as the key and node ID. Through this information, a large number of duplicate nodes are forged and distributed to the network for destructive activities. The schematic is shown in Fig. 3, and node B is captured to replicate nodes B1 and B2. The copied node has all the legal information of the captured node. It can also forge or tamper with related routing information, causing routing confusion.

Fig. 3. Schematic diagram of node replication attack

Node replication attack detection and defense technologies. The detection methods of node replication attack can be divided into three categories: central detection, local detection, and broadcast detection, the current research is mainly focused on broadcast detection, the initial broadcast detection was flooded with node verification information (location information and ID), and the nodes receiving the information recorded the verification information. When a node receives a message with the same ID and a different location, it finds the replication node. This method is energy intensive and requires a lot of storage space. Some improvements are proposed based on this method. The main idea is to select a certain number of verification nodes by some method and send the verification information to the verification nodes or verify at the intersection of verification lines. A partition-based node replication attack detection method detects node replication attack by partitioning the deployment area and establishing the coordinates based on the number of hops. Reference [6] proposed a detection mode based on group deployment knowledge to identify and revoke replica nodes and send the position declaration information to the corresponding group authentication to reduce communication and energy consumption overhead. Reference [7] proposed a location-based cryptographic mechanism for defense, using identity-based cryptography (IBC) to generate location-based key (LBK) based on node location information. Then the bilinear and symmetric properties of bilinear mapping are

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used to implement neighbor node authentication and key pair establishment. Reference [8] addresses the shortcomings of Ref. [6] in that it cannot resist key compromise impersonation (KCI) attack and cannot achieve forward secrecy proposes a location-based capture-tolerant key management model. It does not require any pairing and mapping to point hash operations and is more suitable for WSN. HELLO flood attack. Some routing protocols require nodes to continuously send HELLO packets to claim to be neighbors, but when a strong malicious node broadcast HELLO packets with high power, the node receiving this packet will consider the malicious node to be their neighbor. The schematic is shown in Fig. 4, the attack node broadcast system overhead packets with sufficient power to allow all nodes in the network to receive messages, so that nodes A, B, C, and D will consider them as neighbor nodes. In future routing, these nodes may use the path of the malicious node makes the network unable to operate normally.

A

B

C

D

Fig. 4. Schematic diagram of HELLO flood attack

HELLO flood attack detection and defense technologies. Virendra Pal Singh et al. proposed a technique for detecting and preventing HELLO flood attack based on received signal strength (RSS) in WSN using the AODV protocol in Ref. [9] which is the fixed signal strength of the sensor nodes and compared the RSS of each received HELLO packets with this threshold. Nodes that are far away from the opponent will incorrectly classify the opponent as “friend”. Utilizing a RAEED protocol to prevent HELLO flood attacks, this protocol uses improved two-way authentication and the key exchange characteristics of INSENS and LEAP to reduce the number of messages exchanged and the percentage of message loss. Sinkhole attack. The sinkhole attacker claims to be able to provide a single-hop high-quality path to the base station node, thereby attracting each neighboring node of the attacking node to change the network transmission direction, and forward the packet sent to the base station node to the sinkhole attacker, which seriously damages the network load Balance also provides a platform for other attack methods, as shown in Fig. 5. Sinkhole attack detection and defense technologies. The first method to detect sinkhole attack involves base station in the detection process, resulting in high communication costs for the protocol. The base station floods the network with a request message containing the ID of the affected node, and the affected node responds to the base station with a message containing its ID, next-hop ID and related costs. The received information is then used by the base station to construct a network flow graph for identifying sinkholes. Multipath routing and probabilistic routing are more effective methods

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Base staƟon

Fig. 5. Schematic diagram of sinkhole attack

to resist sinkhole attack. Geographic routing is also used to suppress the occurrence of sinkhole attack. 2.2 Non-camouflaged Attacks Selective forwarding attack. The attacker comes from the captured malicious node. After the source node establishes a path to transmit information to the target node, the malicious node selectively forwards or refuses to forward any data packet after receiving the data packet. The schematic is shown in Fig. 6, attacking node C only forwards the attacker’s own information to node D or forwards it to another attacking node E along the wrong path, which causes the data packet to fail to reach its destination, causing data loss and network communication confusion. In severe cases, the trust mechanism of nodes in the network may also be broken.

E A

B

C

D

Fig. 6. Schematic diagram of selective forwarding attack

Selective forwarding attack detection and defense technologies. Ref. [10] first deploys a detection node, obtains the node’s packet forwarding rate and entropy function through the detection node, and then uses these values to calculate the reputation value of each node. If the reputation value is lower than the preset threshold, the node is judged as a selective forwarding attack node. The rank value of the computing node is used to detect the attacking node by comparing the difference between the rank value of the node and the parent node. Use deployed drones to observe network nodes, collect data and then calculate the number of data packets received by each node. Then use sequential probability ratio test (SPRT) tests to detect malicious nodes. Reference [11] proposed a cooperative defense method for selective delivery attack based on SoRCA-based optimal route algorithm (SBORA). Use fuzzy logic to resist selective forwarding attack and use fuzzy logic to determine the number of routes to be established based on network energy and the number of malicious nodes. Reference [12] proposed a polynomial-based defense mechanism polynomial-based defense mechanism

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countering the selective forwarding attacks (PDMCS) for selective forwarding attack. In the scheme, time data packets and event data packets are divided into multiple data subpackets. Wormhole attack. Wormhole attack is a message receiving–replaying attack method. The schematic is shown in Fig. 7, and it establishes a tunnel (wormhole link) between two attacking nodes A and B. Attacking node A records the received data packet in area U, passes this packet to attacking node B in area V and then replays it. This attack will have a great impact on network topology, data traffic, and data security.

Fig. 7. Schematic diagram of wormhole attack

Wormhole attack detection and defense technologies. SeLRoc proposed a wormhole attack detection method based on the unique characteristics of the sector and the communication distance violation characteristics, and realized the safe positioning against wormhole attack by identifying and removing the attacked beacon nodes. HiLRoc further considers the rotatable characteristics of the antenna and multiple wireless transmission power levels based on SeLRoc, which effectively improves the positioning accuracy. Multidimensional scaling visualization of wormhole (MDS-VOW) method, in drawing theory, multidimensional scaling technology is used to reconstruct the topology of the network. Visual drawing can be used to detect the network topology distorted by wormhole attack. Measure the distribution of the number of neighbor nodes or the distance distribution between all node pairs, and use statistical calculation methods to detect wormhole attacks. Packet binding is used to prevent wormhole attacks. Packet bondage is to add binding information to each data packet to limit the transmission distance of the packet. The Senleash method [13] determines the transmission direction and angle of the signal source by adding a directional antenna module and determines whether wormhole attack node exists in the network by measuring the physical characteristics of the signal. The round-trip time (RTT) method [14] against wormhole attack adds a clock synchronization module. According to the above WSN attack methods and their detection and defense technologies, the characteristics of various attack methods are shown in Table 1.

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Attack method

Camouflaged attack

Attack characteristics

Detection and defense technology

False routing information attack

Yes

Deceiving, tampering, and retransmitting routing information

Encryption, authentication

Sybil attack

Yes

Steal or forge the node identity

RSSI detection and neighbor relationship detection

Node replication attack

Yes

Forge a large number of Central, local, replicated nodes broadcast detection, key management, etc.

HELLO flood attack Yes

High-power broadcast HELLO packets

RSS detection, two-way authentication

Sinkhole attack

Yes

Attract nodes to change transmission direction

Multipath, probabilistic, and geographic routing

Selective forwarding No attack

Selectively or refuse to forward packets

SPRT test, fuzzy logic

Wormhole attack

Form a wormhole link for receiving and replaying operations

Beacon node identification, label authentication, and packet bondage

No

3 Problems and Prospects At present, domestic and foreign scientific research institutions have researched the security of WSN and their routing, proposed some effective security mechanisms and achieved certain results, but there are still many unresolved problems. 1. Improve the accuracy of sensor node positioning under special circumstances. Finding the accurate location information of a node through positioning technology is the basis for the normal operation of all routing protocols. 2. Enhance fault tolerance and intrusion tolerance of sensor networks. Information interaction between sensor communication nodes is often disrupted and disturbed by various environmental factors or attackers. 3. Protect key node location information. When monitoring, detecting, and tracking objects are sensitive targets or valuables, and it is particularly important to protect the location information of the monitored objects [15].

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4 Conclusion This article first analyzes the current development status of WSN security and then analyzes the security requirements of WSN. The attack methods of WSN are divided into two types, camouflaged and non-camouflaged, and their principles are introduced. On this basis, the detection and defense technologies for these attack methods are analyzed, and finally the problems and prospects of WSN security are discussed.

References 1. Wang, S.: Prediction and analysis of systemic network security model based on data mining. J. Northeast Electr. Power Univ. 39(6), 91–93 (2019) 2. Qu, Z., Zhang, Y., Xin, P., Li, J., Hu, K.: An energy internet routing algorithm on hypergraph based minimum-energy loss. J. Northeast Electr. Power Univ. 37(6), 93–99 (2017) 3. Pang, L., Jiao, L., Wang, Y.: Design and analysis of secure routing protocols in wireless sensor networks. Chin. J. Sens. Actuators 21(9), 1629–1634 (2008) 4. Shi, B., Wang, Y., Li, W., Sun, G.: Study on communication network effect on system performance of wide area control. J. Northeast Electr. Power Univ. 38(6), 29–34 (2018) 5. James, N., Elniae, S., Dawn, S., Adrian, P.: The sybil attack in sensor networks: analysis & defenses. In: Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks, New York, pp. 259–268 (2004) 6. Ho, J., Liu, D., Wright, M., Das, S.: Distributed detection of replica node attacks with group deployment knowledge in wireless sensor networks. Ad Hoc Netw. 7(8), 1476–1488 (2009) 7. Zhang, Y., Liu, W., Lou, W., Fang, Y.: Location-based compromise-tolerant security mechanisms for wireless sensor networks. IEEE J. Sel. Areas Commun. 24(2), 247–260 (2015) 8. Duan, M., Xu, J.: An efficient location-based compromise-tolerant key management scheme for sensor networks. Inf. Process. Lett. 111(11), 503–507 (2011) 9. Virendra, P., Aishwarya, S., Anand, U., Sweta, J.: Signal strength based hello flood attack detection and prevention in wireless sensor networks. Int. J. Comput. Appl. 62(15), 1–6 (2013) 10. Hu, Y., Wu, Y., Wang, H.: Detection of insider selective forwarding attack based on monitor node and trust mechanism in WSN. Wirel. Sens. Netw. 6(1), 237–248 (2014) 11. Ren, F., Lin, C., Liu, W.: Congestion control in IP networks. Chin. J. Sens. Actuators 26(9), 1025–1034 (2003) 12. Jiang, W., Sun, J., Wang, Z.: Passive queue management algorithm with two random packet loss. J. Syst. Simul. 23(5), 987–997 (2011) 13. Hu, R., Dong, X., Wang, D.: SenLeash: a restricted defense mechanism against wormhole attacks in wireless sensor network. J. Commun. 34(10), 65–75 (2013) 14. Subha, S., Sankar, U.: Message authentication and wormhole detection mechanism in wireless sensor networks. In: IEEE 9th International Conference on Intelligent Systems and Control, Coimbatore, pp. 693–696 (2015) 15. Teng, Z., Qu, Z., Zhang, L., Guo, S.: Research on vehicle navigation BD/DR/MM integrated navigation positioning. J. Northeast Electr. Power Univ. 37(4), 98–101 (2017)

A Wireless Hijack Attack on Power Consumption System of Power Metering Automation Xiao Yong1 , Jin Xin1 , Feng Junhao1 , and Zhang Zitong2(B) 1 Electric Power Research Institute, CSG, Guangzhou 510633, China 2 Guangzhou Haiyi Information Security Technology Co., Ltd., Guangzhou 510600, China

[email protected], [email protected]

Abstract. By setting up the simulation environment of the automatic power metering system, analyzing the security of the system, and the information security risk obtained from the analysis process is verified through the attack experiment. Firstly, the security of the communication protocol and authentication method of the system is studied to find out the existing security problems. Secondly, the attack platform is built to carry out wireless hijacking attack, and the master station is forged to issue control instructions to the acquisition terminal, so as to achieve the purpose of remote power outage. The experimental results show that the security problem of communication protocol and authentication method will lead to the risk of wireless hijacking attack and finally threatens the information security of power consumption system of power metering automation. Keywords: Information security · Communication protocol · Authentication · Wireless hijacking attack

1 Introduction Power metering automation can be divided into information detection, video, and marketing businesses. Its business scope includes power generation and transformation side (power plants, substations) and power consumption side (large customers, lowvoltage customers). Its main businesses include load management and control, automatic power data collection, distribution and transformation monitoring, centralized meter reading, etc., to achieve the integration of power supply and power data collection and management [1]. In 2015, Ukraine’s power grid was attacked, resulting in a large-scale blackout [2]. In 2016, Israel’s power grid suffered a large-scale attack, resulting in the forced shutdown of relevant power equipment [3]. In 2019, Venezuela’s power grid suffered two attacks, resulting in a large-scale blackout [4], and with the frequent occurrence of these security events, research institutions at home and abroad pay more and more attention to the information security of smart grid. It can be found from the recent grid security events that most of the attacks are to penetrate the power production intranet through apt attacks © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_55

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[5], and finally get the relevant control system permissions, and carry out large-scale power outage and other damage operations. Accordingly, the power grid company will take a series of security measures to strengthen the information security construction of the power production intranet. Most of the meters and acquisition terminals of the power consumption side system of the power metering automation are in the position of unmanned monitoring, vulnerable to physical attacks, and logical attacks of the analysis application and protocol [6]. Whether we can bypass the high-cost attack on the grid internal network is the ultimate goal of this paper.

2 Introduction of Power Consumption Side of Power Metering Automation 2.1 Operation Mechanism of Power Side System of Metering Automation System Figure 1 shows the topological structure of the system at the power consumption side of the power metering automation. The electricity meters and acquisition terminals are installed in the physical area of residents or factories. They run DL/T through RS485 bus or PLC power carrier 645-2007 protocol for communication. The GPRS module built in the collection terminal will send the collection data to the operator through the GSM network with the uplink communication protocol of the measurement automation terminal, and then the operator will transmit the collection data to the main station of the measurement automation system of the power grid company through the traditional route exchange [7]. In the same way, the process of sending control instructions by the metering automation power side system is the reverse process of data acquisition.

Metering system

RJ45 Wired transmission

Operator GPRS wireless transmission

Acquisition terminal

Acquisition terminal RS485

Base station

PLC

Base station

Power meter

Power meter

Public buildings

Residential housing

Fig. 1. Topology of power metering automation’s power consumption system

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2.2 Power Side System Protocol of Power Metering Automation Basic Principles of DL/T 645-2007 Protocol. The protocol is a half duplex communication mode of master–slave structure. The handheld unit or other data terminal is the master station, and the multi-functional electric energy meter is the slave station. Each multifunction meter has its own address code. The establishment and removal of the communication link are controlled by the information frame sent by the master station. The byte transfer sequence is shown in Fig. 2.

Fig. 2. Byte transfer sequence

Information frame is the basic unit of information transmission, which is used to transmit effective information or data. The format of the information frame is shown in Fig. 3.

Fig. 3. Information frame format

Introduction to Communication Protocol of Uplink Terminal. The protocol adopts the half duplex or unbalanced transmission communication mode of master–slave structure, with the metering automation system as the master station, and the metering automation terminal actively addresses the master station through the network channel or half

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duplex channel. Each terminal has its own IP address and communication address. The establishment of the communication link is initiated by the slave station, and the master station performs the master–slave Q and A mode or the terminal active upload mode to the terminal. Each frame consists of seven fields: frame start character, length, control domain, address domain, application layer link user data, checksum, and frame end character. Each part consists of several bytes, and its structure is shown in Fig. 4.

Fig. 4. Data frame format

3 Safety Analysis of Power Consumption Side of Metering Automation System 3.1 No Encrypted Transmission and Simple Verification Mechanism Although the DL/T 645-2007 protocol stipulates that the password authentication of electric energy meter is required for high-risk instructions such as switching off and power-off, due to the reason of clear text transmission, it can be monitored and intercepted from the protocol, and the default password of electric energy meter of the same model and the same batch is usually the same. Once the attacker establishes communication with the electric energy meter or collection terminal, he can carry out malicious activities from illegal data frame according to the definition of protocol or protocol. Data encryption can effectively prevent monitoring behavior, such as using elliptic curve cryptography (ECC) technology [8] to ensure the security of data transmission process. The verification code can be generated by independent authentication key combined with protected data. For example, Galois message authentication code mode (GMAC) is used to calculate the MAC of the message by multiplication operation in Galois domain value, enter an independent authentication key and the content to be protected to get the message verification code. The tamper cannot copy the trusted message verification code because there is no authentication key [9].

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3.2 One-Way Identity Authentication Mechanism The identity authentication is required before the communication between the acquisition terminal and the power metering automation system master station. The IP address and port of the master station shall be set in the acquisition terminal in advance. Both sides encapsulate the uplink communication protocol in the application layer of TCP protocol for communication. After the acquisition terminal sends the request confirmation frame, the master station immediately judges whether the device is a legal device after receiving it, and replies to the confirmation frame after checking in the database. Then the master station will periodically send heartbeat message to maintain the conversation. The identity authentication mechanism of the collection terminal and the power metering terminal is one-way authentication, that is, the master station can check whether the collection terminal is legal, while the collection terminal cannot judge the authenticity of the master station. For two-way identity authentication, the identity authentication mechanism based on the combination of symmetric and asymmetric algorithms can be adopted between the master station and the terminal, in which the security medium of the master station uses the cipher machine and the security module is embedded in the terminal device [10]. 3.3 One-Way Authentication of GSM Network The one-way authentication mechanism causes the pseudo base station to implement man in the middle attack to obtain the transmission data in the GSM network. Although enabling A5 algorithm can properly alleviate this problem, the decision of whether to enable the encryption algorithm is in the base station rather than the mobile terminal. The pseudo base station built by the attacker can turn off the encryption algorithm and communicate with the mobile terminal [11]. The acquisition terminal integrated with GPRS module is a typical mobile terminal in GSM network. To solve the problem of one-way authentication in GSM network, we need to design and develop a new authentication process and strengthen the identity authentication of mobile terminal to base station. For example, the two-way authentication protocol mentioned in Ref. [12] can solve this problem.

4 Wireless Hijacking Attack Experiment of Power Side System of Measurement Automation 4.1 Experimental Environment The acquisition terminal configures its built-in GPRS module, installs SIM card, and configures the remote master station IP and port information to connect the remote master station for data upload and receiving the master station control command. The master station uses the server to install and test the application of the master station, configures and collects the terminal equipment information, and completes the equipment authentication access. The attacked environment is designed as shown in Fig. 5.

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GSM Motorola C118

Computer

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USPR B200

Collectio n terminal

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Lamp

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Fig. 5. Experimental environment

The main function of attack end 1 is to detect the strongest absolute radio frequency channel number (ARFCN) in the physical area of the attacked environment [13]. In this experiment, signal interference is used to forcibly destroy the connection between the attacked GPRS module and the real base station. At the same time, the attack end 2 sends the pseudo base station signal of the same ARFCN, forcing the GPRS module in the attacked environment to quickly access the environment of the attack end 2. The attack end 1 uses the virtual machine to install the open source mobile communication baseband (OSMOCOMBB) platform in cooperation with the external device Motorola C118 to detect the signal, identify the current strongest ARFCN value, and provide data support for attack end 2 to forge the ARFCN. The attack end 2 is the core attack environment of this experiment. open base transceiver station (OPENBTS) is installed and operated by personal computer, and the external USRP B200 hardware device is used for wireless signal reception and transmission. At the attack end, two laptops run Wireshark and the script used to forge the master station at the same time. 4.2 Experiment Process 1. Attack message Combination. Listen to the communication between the unidirectional energy meter of the attacked environment and the acquisition terminal, and get the DL/T 645-2007 protocol switch message. According to the definition of transparent transmission instruction of uplink terminal communication protocol, the transparent transmission instruction of switching and power-off function meeting the protocol is generated. 2. Scan Current Signal Strongest ARFCN. The attack end 1 runs the osmocombb platform, external Motorola c118 for base station detection, and runs the scanning

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script to find the base station with the strongest signal. The scanning results of ARFCN are arranged in order of signal strength from large to small. The current strongest base station is ARFCN = 121, and the signal strength is −62 dB. Wireless Hijacking. The attack end 2 runs and configures the OPENBTS command line. Viewing the status, it is found that the hijacked GPRS module has been hijacked successfully. Sniff Master Configuration Information. Run Wireshark in OPENBTS platform of attack end 2, filter the TCP message with source IP address. In the application layer protocol, you can find the confirmation frame of terminal uplink protocol message, check the destination IP address and port information of the data packet, and get the real IP address and port of the main station. Configure Attack End 2 Network Environment. Modify the address of eth0 local network card as the primary address, and configure Iptables so that the IP address of the hijacked device can communicate with the local eth0 address. Optimize the Configuration of the Second Base Station at the Attack End. Under the condition that the physical distance between the attack end 2 and the attacked environment and the signal environment are determined, the signal strength of the base station in the attack end 2 is affected by two parameters: path loss value C1 and cell reselection value C2 . The value of parameter C1 directly affects the signal quality of the attack end 2 transmitting base station. C1 = RLA_C − RXLEV_ACCESS_MIN − MAX((MS_TXPWR_MAX _CCH − P), 0)

(1)

where RLA_C is average receiving level of GPRS module, RXLEV_ACCESS_MIN is the minimum reception level allowed by GPRS module, MS_TXPWR_MAX _CCH is the maximum power level allowed by the control channel when the GPRS module is connected, P is GPRS module maximum transmit power level. The value of path loss criterion parameter C2 affects the base station reselection of GPRS module in the attacked environment. C2 = C1 + CELL_RESELECT_OFFSET − TEMPORARY_OFFSET ∗ H (PENALTY _TIME − T )

(2)

When PENALTY_ TIME = 31, C2 = C1 + CELL_RESELECT_OFFSET

(3)

where CELL_RESELECT_OFFSET is the cell reselection offset, which is used to manually correct parameter C2 , TEMPORARY_OFFSET is temporary offset TO, PENALTY_TIME is the penalty time PT, which determines the action time of TO. T is a timing parameter with an initial value of 0. When the GPRS module of the attacked environment chooses to access the base station transmitted by the attack end 2, the first step is to ensure that the base station C1 > 0 in the attack end 2, and C2 meets the following formula: C2 − CELL_RESELECT_HYSTERESIS > C2

(4)

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5 Experimental Results The pseudo master station sends the control command script to show that it has received the request connection protocol actively sent by the acquisition terminal, and at the same time, it sends the uplink protocol message of switching off power supply while replying to the confirmation packet. It successfully realizes the wireless hijacking attack. This kind of wireless hijacking attack has the characteristics of low cost and great damage effect. In the actual attack, it can realize the simultaneous power outage in some areas, which has a huge adverse impact on the lives of residents, industrial production. It may also become a means for illegal elements to tamper with the measurement data and realize illegal economic behaviors such as stealing electricity and electricity.

6 Conclusion This paper analyzes the structure of communication protocol, system business logic and equipment communication mode, and finds out the available security risks. Through the establishment of experimental environment, it successfully hijacks the acquisition terminal by using multiple security holes, and issues effective pull message to promote the power meter to pull the power-off, which successfully demonstrates the wireless hijack power-off attack. Through the experiment to achieve the whole malicious attack, we can find and locate the security problem, more effectively foresee and prevent the possible future smart grid security events.

References 1. Du, H.: Research of the Communication System on Power Distribution Automation System. South China University of Technology, Guangzhou (2013) 2. Liang, G., Weller, S.R., Zhao, J.: The 2015 Ukraine blackout: implications for false data injection attacks. IEEE Trans. Power Syst. 32(4), 3317 (2017) 3. Li, Z., Tong, W., Jin, X.: Construction of cyber security defense Hierarchy and cyber security testing system of smart grid: thinking and enlightenment for network attack events to national power grid of Ukraine and Israel. Autom. Electr. Power Syst. 40(8), 147 (2016) 4. Zhu, C.: Behind the blackout in Venezuela. State Grid 5, 72 (2019) 5. Dong, N., Zhang, J., Liu, W.: Problem and countermeasures on APT defense in power grid enterprises. Hebei Electr. Power 35(4), 25 (2016) 6. Zhou, L.: An identification and IDS based scheme for smart meter data acquisition. J. Shanghai Univ. Electr. Power 33(4), 348 (2017) 7. He, M.: Deeply applied approaching of centralized metering automation system. Rural Electr. 12, 40 (2018) 8. Liang, J.: Design on data transmission system of intelligent energy meter based on elliptic curve cryptosystem. Ind. Instrum. Autom. 5, 112 (2018) 9. Xu, J., Xiong, J., Cao, Z.: Design of smart meter communication security based on standard encryption algorithm. Electr. Meas. Instrum. 55(17), 127 (2018) 10. Wang, C.: Research on identity authentication based on the combination of symmetric and asymmetric algorithms. Sci. Technol. 27(2), 264 (2017) 11. Pannu, M., Bird, R., Gill, B.: Investigating vulnerabilities in GSM security. In: 2015 International Conference and Workshop on Computing and Communication

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12. Wu, Q.: Analysis and Design Precautions for GSM’S Main Security Vulnerabilities. Beijing University of Posts and Telecommunications, Beijing (2009) 13. Trevor, P.: Wireless system selection optimization using absolute radio frequency channel number table: US08768354B2 (2013)

Research on Real-Time Deformation Measurement of Structural Frame Based on Data Driven Zhou Yan(B)

, Jun Weng, and Qing Wang

Beijing Institute of Radio Measurement, Beijing 100854, China [email protected], [email protected], [email protected]

Abstract. In this paper, a structural frame deformation measurement system based on stress and strain measurement is introduced. Based on the data-driven method and the k-nearest neighbor algorithm (kNN) in machine learning, the real-time deformation estimation of the radar antenna structure frame is studied. The flowchart of structure frame deformation analysis is introduced, including the basic algorithm, sample expansion technology, deformation estimation, etc. The key factors and assumptions of reliability and stability of this method are discussed. The key point of this method is that the training samples should be collected in more typical working cases, and the acquisition data should be reliable. Then, an example with several typical working cases as training samples is given, by finite element method, which provides the feasibility of this method; the best selection of parameter k is also discussed by simulation experiment training. Finally, an experiment is given to verify the feasibility of this method. Keywords: Data driven · kNN · Structural frame · Deformation · Algorithm

1 Introduction With the rapid development of modern equipment, higher and higher requirements are imposed on the performance of radars, and the size and weight of phased array radars are constantly increasing. In structural design, traditional methods usually improve structural rigidity by optimizing the design, reduce the deformation of the array, and ensure the radar works normally. As the size of the radar antenna increases, it has become more and more difficult to improve the design of the rigidity. Stiffness will bring a significant increase in structural weight, which will have a large negative impact on radar maneuverability. Also, a large increase in cost is usually unacceptable. Therefore, by measuring the deformation of the radar structural frame in real time, compensating and correcting the radar electrical signal become a more economical and effective method. Large radar antenna arrays and structural frames are more sensitive to temperature loads. Under summer temperature differences and uneven solar radiation, it can cause a deformation of the 10 mm level, which greatly affects the accuracy of the radar. Also, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_56

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some structures use material combinations with different thermal expansion coefficients. The effect of temperature load is more prominent. Harbin Institute of Technology has conducted in-depth research on the key issues of the FAST structure safety and accuracy control of the 500 m radio telescope, provided the necessary support for its construction, operation, and maintenance, and established a numerical model of the sunlight temperature field of the FAST reflector structure in a real environment. According to the characteristics of FAST active displacement, a variety of methods such as statistical analysis, structural finite element analysis, and parameter optimization identification, etc., were synthesized. Structural fault diagnosis methods were proposed, and a specialized FAST structural health monitoring system was designed [1, 2]. Currently, more mature real-time measurement methods usually use optical measurement methods. Nanjing Institute of Electronic Technology measured the plane accuracy of the antenna array at different working angles through a digital photogrammetry system, and it performed precision and gravity pre-deformation correction based on the measured data [3]. Shang Yang et al. introduced the progress of large-scale structural deformation monitoring camera measurement, introduced the subgrade settlement measurement of large structures such as high-speed railways and tunnels, and the deformation measurement of the antenna base of the Yuan Wang surveying ship [4]. However, the optical measurement system must be set up outside the measured object, which has poor maneuverability and is greatly affected by the environment. Nanjing Institute of Electronic Technology has studied the real-time measurement of the radar antenna array’s dynamic deformation by acceleration sensors [5]. The measurement system based on acceleration possesses high reliability, but the integration will cause large errors, and slow variables cannot be measured, and the measurement of the deformation caused by the temperature to large structural frames cannot be achieved by this method. Few studies have estimated the real-time deformation of the structure by indirectly measuring the structural frame strain. Based on strain measurement, Shengdi Han calculated the deformation of the antenna structure by matrix mapping and studied the sensor arrangement [6]. Kefal et al. used inverse finite element analysis, to predict deformation and health monitoring of the structure based on strain measurement [7]. Most of the algorithms for inferring structural deformation through strain measurement make use of strain modal. This method is more suitable for dynamic prediction, and it is usually not particularly satisfactory for static and quasi-static deformation prediction. In recent years, data science has made remarkable achievements. Big data, machine learning, artificial intelligence, and deep learning are widely applied in many fields [8, 9]. This paper is based on a data-driven method and uses the k-nearest neighbor (kNN) algorithm in machine learning to conduct real-time deformation estimation of the radar antenna structural frame. This method is also suitable for real-time deformation of large structural frames of other mobile equipment estimates.

2 Deformation Measurement of Structural Frame There are many methods to measure the deformation of the structural frame, and the common ones are listed as below:

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1. Direct measurement based on optical measuring equipment. This is the most immediate and obvious way to measure structural deformation. But its accuracy of the result is easily affected by the environment, and the high cost also makes it impossible for real-time measurement in any environment. 2. Indirect measurement based on acceleration. By integrating the acceleration, the deformation is obtained. It is reliable, but the error caused by integral makes it inappropriate to measure the slow deformation, especially the ones caused by temperature. 3. Indirect measurement based on stress and strain. This method supposes to measure the strain of multi-points and convert the strain to deformation through some algorithms. Therefore, the reliability and the feasibility are determined by the algorithms. 4. Combine the above three methods and complement one another, thus applicable to any cases. The real-time deformation measurement system of the structure frame is displayed in Fig. 1, including stress and strain sensor, data acquisition system, data analysis system, and so on.

Fig. 1. Schematic diagram of real-time structural frame measurement system

The strain is usually measured by resistive transducer or fiber optic sensors. Since fiber optic sensors can be set up in series arrangement and taking fewer acquisition channels, with strong anti-interference ability, less effect by temperature, vibration, and shock, it is more appropriate to measure the strain in structure frame.

3 Algorithm for Deformation of Structure Frame 3.1 Data-Driven Methods In the proposed real-time deformation measurement system of the structural frame, the algorithm is the key point. Such algorithms can be divided into two classes: the ones based on models and the data-driven ones. Algorithms based on model demand a simulation model so that the response of the structure can be calculated. However, to build such a simulation model, a lot of experiments and trials with uncertain results need to be carried out. Besides, such algorithms cannot be applied in a different model, which makes it expensive.

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Data-driven algorithms have developed rapidly in the recent twenty years, combining big data, machine learning, and artificial intelligence; more and more valuable algorithms are proposed and applied in different fields. Such methods, without any physical model or prior knowledge, are capable of learning the inner relation among the data with only some samples. Furthermore, with the data increasing, such a model is optimized step by step. In this paper, we supposed to use kNN to calculate the deformation of structural frame. 3.2 kNN for Deformation of Structural Frame Assumption Before the analysis, we give some assumption as follows: 1. The structure system is stable, which means the relation between strain and displacement would not change according to time or the state of the structure. 2. The deformation of the structural frame is little that is the strain and the displacement are linear correlation.

kNN algorithm [10] Commonly, kNN is a robust and accurate machine learning algorithm that allows any kind of input and being less affected by abnormal value. It works by finding the k-nearest samples in training data according to some kind of distance and then predicts the result via such k-nearest samples. As shown in Fig. 2, the blue points are training data, while the red star is the test sample. By finding the k-nearest blue points, we can predict some similar information according to these k-nearest blue points. In the problem of calculating the deformation of the structural frame, the deformation of the test sample is obtained as the weighted mean of the k-nearest training samples. Flowchart of the analysis of structural frame deformation The process and the key point of collecting training calibration samples and test samples are shown as follows: 1. Collect the training calibration samples, in order to determine the relationship between strain and displacement in different working cases. 2. Although the cost of collection calibration samples is expensive, the relation between strain and displacement in different working cases is very important to train a realtime deformation calculation model. 3. Train the model with calibration samples, determine the parameters, and apply it in the project. 4. When practicing, collect the strain data from sensors and obtain the real-time deformation via the well-trained model.

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Fig. 2. Schematic diagram of kNN algorithm

In the process of collecting training data, it is assumed that the sensors are arranged in n points, and collect the strain both in x-axis and y-axis direction, while the deformation of m points is also collected so that we obtained the strain vector and the deformation vector.. If there are L training samples, then the training data S = {E, D} are combined with the strain matrix E2n×L and the deformation matrix Dm×L . 1. Enlarge the training data via adding the mirror samples, that is 1 1 E2n×2L = [E2n×L − E2n×L ], Dm×2L = [Dm×L − Dm×L ], S 1 = {E 1 , D1 }.

(1)

2. Enlarge the training data again via structural symmetry plane and obtain training data S 2 . 3. By adding random error, such as stochastic perturbation, drift error, and the nonconvergence, finally obtain the training data S  . 0 according to the test data is as The process of calculating the deformation dm×1 0 follows e2n×1 :

1. Use Euclidean distance that is: r(x, y) =



(xi − yi )2 .

(2)

i

2. Normalize each sample in training data according to the strain vector, while scale the vector with the same parameter, that is, for each sample, it makes  ∗ deformation  e  = 1 and d ∗ = 1 dm×1 , and obtain training data S ∗ . 2n×1 m×1 |e2n×1 |

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 0∗   = 1 with normalization parameter defined as 3. Normalize the test sample, e2n×1 follows: 1 a =  0 . e  2n×1

(3)

0∗ i∗ 4. Calculate the distance ri between test sample e2n×1 and each training sample e2n×1 , and the distance collection is r = {ri }. 5. Put the distance r in order, and select the k smallest distance r ∗ , whose responding training samples are S ∗ . Calculate the weight:

αi =

(ri

+ 0.01)2

1 k

1 j (rj +0.01)2

, i = 1, 2, . . . , k.

(3)

6. Then the deformation of test sample can be obtained as follows: 0 dm×1 =a

k 

∗ αi dm×1 .

(4)

i

After calculating the deformation of test sample, evaluation and optimization should be carried out: 1. Calculate the error between the predicted one and the real one, including the maximum error errormax and the error rate:   0 d − dreal  . (5) error% = |dreal | 2. Build the evaluation function according to maximum error errormax and error rate error% , and optimize the parameter k. 3. Since the normalized samples √ locate on the hypersphere with radio as 1, the maximum distance of two points is 2. When there is greater difference between training samples and test samples, which means when the minimum distance is larger than 0.5, system should pop up a warning that the prediction result is unreliable and even gives the confidence possibility.

Notices When the training samples are determined and the parameters are optimized, this system is able to make a real-time prediction. And with the training samples increased, the method can be trained more reliably and robustly. The key point of this method is that the training samples should be collected in more typical working cases, and the acquisition data should be reliable. Because the algorithm is robust, the prediction results are less affected by random errors. And in practice, no matter the deformation is caused by gravity or temperature, the deformation can be easily predicted.

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4 Example Take a simulation flat as an example, by finite element method; we can obtain several training samples. Specifically, this flat is supported by 4 hinges, and 24 sensors are arranged on it to collect the displacement, as shown in Fig. 3.

Fig. 3. Schematic diagram of flatbed

We set up seven working cases as training data: (1) normal gravity; (2) bias gravity; (3) uniform wind load; (4) uniform temperature; (5) local multiple concentrated force; (6) different temperature between right and left; and (7) different temperature between up and down. When the test samples are under such typical working cases, the prediction error is very small, as shown in Fig. 4.

Fig. 4. Comparison of estimated deformation and real deformation of uniform temperature load when the training set is better represented

If the working situation of the test sample is not included in the training samples, it may cause some prediction error. In this experiment, we select one working case as a test working case, while the other as training working cases, and in each working case we generate 20 samples randomly and set k as 8 in the test process. The result

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is shown in Fig. 5 when the test working case has uniform temperature, the prediction error is small, while the test working case is local multiple concentrated force, and the prediction error is large. And the maximum error and the error rate in each working case are shown in Fig. 6, only when the test working case is local multiple concentrated force, the prediction is unsatisfied, which is rare in practice.

(a) Uniform temperature load

(b) Local multi-point concentrated force load

Fig. 5. Comparison of estimated deformation and real deformation when the test sample is a uniform temperature load and local multi-point concentrated force

Fig. 6. Comprehensive error rate and minimum distance for different test samples

Eliminate the local multiple concentrated force working case; consider the best selection of parameter k. In this study, we take the error rate as the evaluation criterion, and for each k value, repeat the experiment 100 times and calculate the mean error rate. The result is shown in Fig. 7, as we can see, when k is larger than 70, the mean error rate becomes larger as k increases. And the best k should be set in the range from 40 to 60. Furthermore, consider the time of calculation; we are supposed to set k as 40.

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Fig. 7. Relationship between k and average comprehensive error rate

5 Experiment In this section, we carried out the experiment in a real flat as shown in Fig. 8. Strain sensors are laid out in the back of the flat, and displacement is measured via laser in the front. The deformation is caused by a screw rod.

Fig. 8. Testing strain sensors and deformation measurement on a flat plate

The prediction result and the real displacement are compared in Fig. 9, the error is small, and this method is confirmed.

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Fig. 9. Test results

6 Conclusion This paper proposes a kNN-based method to make a real-time deformation prediction of the structural frame, including the application scenario, processing flowchart, and evaluation indicator. Finally, we analyze its feasibility through a simulation example and a real experiment and confirmed that this method is effective to make an accurate real-time deformation prediction of the structural frame, even another similar structural frame.

References 1. De Rijk, R., Rushton, M., Khajepour, A., et al.: Out-of-plane vibration control of a planar cable-driven parallel robot. IEEE-ASME Trans. Mechatron. 23(4), 1684–1692 (2018) 2. Tang, X., Chai, X., Tang, L., et al.: Accuracy synthesis of a multi-level hybrid positioning mechanism for the feed support system in FAST. Robot. Comput.-Integr. Manuf. 30(5), 565– 575 (2014) 3. Yongzhong, Y., Chuanjing, L.: Application of digital photogrammetry in parabolic antenna type surface accuracy and gravity deformation testing. New Technol. Prod. 2(4), 9–10 (2014) 4. Yang, S., Qi-feng, Y., Banglei, G., et al.: Recent advances of videometrics for large-scale structure deformation monitoring. J. Exp. Mech. 32(5), 593–600 (2017) 5. Changwu, W., Lihao, P.: A real time measuring system of operational deformation of radar antenna. Mod. Radar 28(12), 90–92 (2006) 6. Shengdi, H.: Strain Sensor Placement for Deformation of Antenna Structures. Xidian University (2013) 7. Kefal, A., Oterkus, E., et al.: A quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoring. Eng. Sci. Technol. Int. J. 19, 1299–1313 (2016) 8. Wang, E.K., Chen, C.-M., Hassan, M.M., Almogren, A.: A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain. Futur. Gener. Comput. Syst. 108, 135–144 (2020) 9. Wu, J.M.-T., Tsai, M.-H., Xiao, S.-H., Liaw, Y.-P.: A deep neural network electrocardiogram analysis framework for left ventricular hypertrophy prediction. J. Ambient Intell. Hum. Comput. https://doi.org/10.1007/s12652-020-01826-1 10. Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)

A Sensor Fusion Method for In-Station Articulation of Train Zhao-Qing Liu(B) , Xing-Yuan Song, Yi-Hao Chen, and Zhen-Ni Yang Department of Automatic Test and Control, Harbin Institute of Technology, Science Park of Harbin Institute of Technology, No. 2, Yikuang Street, Nangang District, Harbin 150080, China [email protected], [email protected]

Abstract. Under the background of the traditional method of manual carriage hooking, this paper studies and validates a sensor fusion method to provide data fusion information for the automatic articulation study based on millimeter-wave radar and monocular camera sensor hardware platform. We build the train model for joint calibration, process the trace point clustering, and optimize the traditional radar Kalman filtering method which improves the stability of radar signal. After the experimental verification, the sensor fusion system in this paper is effective in such complex environment like the train station to provide effective information fusion of the foreground. Compared with the single sensor, the method greatly enhanced target carriage in front of the train for bearing awareness, for subsequent automatic articulated method ensures the real-time performance and reliability of the data source. Keywords: Carriage articulation · Sensor fusion · Millimeter-wave radar · Signal filter

1 Introduction In the current railway system, manual methods are still mainly used for train control in important safety areas such as carriage articulating, train warehousing, or obstacle avoidance on the way [1]. For example, the train operator and the guide in the railway station shall cooperate with each other in accordance with relevant procedures after naked-eye observation and discrimination. However, this method is complicated, inefficient, and less controllable, and the accident rate is high due to the severe influence of weather. Nowadays, with the continuous growth of intelligent railway ecology, new requirements have been put forward for many new railway construction research topics which includes the above problems. The use of automatic train active safety technology to replace manual work has become the key research direction. Similar to automobile active safety, the automatic environment perception capability of train is the basis of active safety technology, and the auxiliary perception system based on millimeter-wave radar is the major method at present. Millimeter-wave radar can provide high accuracy and powerfully real-time distance ranging information in a © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_57

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large scope, but its shortcomings of high false alarm rate and low target positioning accuracy are also obvious, which causes great limitations for the scene of intelligent environment perception and detection of trains. In this case, multi-sensor fusion technology has become the optimal choice to adapt to the perception and detection needs of complex environment [2]. The camera can provide more standardized spatial information than the radar [3], and furthermore, it is more feasible using camera to achieve target detection method. The incorporation of the camera as a visual sensor for fusion system [4] can make up for the drawback of the millimeter-wave radar, and on the other hand, radar will also similarly cover up the inadequacy of the camera ranging accuracy. In this paper, we use the complementary advantages of millimeter-wave radar and camera, and propose a sensor fusion method oriented to the automatic car articulation in the train station which implements real-time multi-sensor information fusion with good accuracy and up to 30 FPS. At the same time, an improved Kalman filter which contributes toward stability of distance information is adopted to adapt to complex in-station scenes.

2 Related Work Nowadays, many years of technology accumulation have been used in sensor fusion system for different perspectives, but some research fields are still under development. For example, in SLAM, when the scene of the actor (robot) is relatively chaotic, the single source information cannot meet the higher requirements of the actor on localization and mapping. Therefore, using multiple sensors to detect complex terrain and information fusion is a better method. Nowadays, a better scheme is Mingyang Li et al. use vision sensor and IMU fusion based on multi-state constrained Kalman filter (MSCKF) to achieve multi-source information association and detect the lost information in each group of data. Another research hotspot on sensor fusion is in the field of autonomous driving. Various on-board sensors, such as GPS, radar, and infrared sensors, have their own features. Through the fusion data collected and processed by them, a description of the car and its surrounding environment is created to provide a complete view of driving status. Scholars such as Mujica [5] and companies like WAYMO have taken advantage of this to support and develop radar-based autonomous driving systems which is forming a research path away from visual sensors. On the other hand, the autonomous driving scheme represented by Tesla takes the vision sensor as the basis, other sensors (ultrasonic wave, millimeter-wave radar) as the auxiliary technical route, and supports the selfdeveloped machine learning algorithm chain.

3 Sensors Calibration The data of radar and camera are subordinate to their respective coordinate systems. So, it is necessary to calibrate the data of the two, and unify them into one coordinate system to complete the sensor fusion, as shown in Fig. 1. According to the shape of the train, the vertical plane of the central axis is selected as the reference plane to meet the convenience of test calculation, and the mathematical modeling between the train and the sensor is carried out through the level and rangefinder. Therefore, we make the train

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modeling of a regular cube, and the millimeter-wave radar is installed at the bottom of the central axis of the train as the origin of the world coordinate system to build the coordinate transformation algorithm which can meet the hypothesis.

Fig. 1. Coordinate transformation relation

3.1 Camera Calibration Camera calibration is divided into two steps: camera matrix calculation and coordinate system transformation calculation. Firstly, the standard checkerboard calibration method was used to determine the camera’s internal parameters. For the convenience of calculation, a horizontal caliper is used to install and fix the camera position so that the normal line of the camera projection plane is parallel to the central axis of the train model. At the same time, a right-angle caliper is used to ensure that both the camera and the radar transmitter are on the vertical plane of the central axis. Finally, the relative distance between the camera and the radar is calculated to be L. 3.2 Joint Calibration After the installation and calibration of the camera and the millimeter-wave radar, the joint calibration will be started. As shown in Fig. 2, we set the coordinates of the target point object(x, y, z) in the world coordinate system as in the millimeter-wave radar coordinate system and P(Xc , Yc , Zc ) in the camera coordinate system. Because the X r Or Z r plane is parallel to the Y c Oc Z c plane, the external camera parameters is fixed as ⎛ ⎞ 1000 RT = ⎝ 0 1 0 L ⎠ (1) 0010 And, the depth d is given by the millimeter-wave radar information, so all the parameters for transformation are obtained. Assuming that the target point in the pixel coordinate system is P(u, v), the coordinate order conversion formula ⎛ ⎞ ⎞ ⎛ ⎞ ⎛ x u fx 0 cx ⎜y⎟ ⎟ d ⎝ v ⎠ = ⎝ 0 fy cy ⎠RT ⎜ ⎝z⎠ 0 0 1 1 1

(2)

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Fig. 2. Joint calibration model

can be obtained. To test the accuracy of the joint calibration, we transform the coordinates of the calibrated target pixels and the corresponding radar data to location information as the result observed by the system, and compare it with the true value obtained by laser rangefinder. We choose a stationary regular target as the test object and list the result of different distance in Table 1. Table 1. Joint calibration test Measurement (radar data Z r , pixel coordinates (u, v))

Observed value (m) (longitudinal, lateral)

True value (m) (longitudinal, lateral)

20.70, (902, 475)

20.70, −1.10

20.12, −1.02

22.20, (906, 475)

22.20, −1.08

22.01, −1.07

24.30, (911, 475)

24.30, −1.08

24.28, −1.07

26.70, (915, 475)

26.70, −1.07

26.19, −1.03

27.50, (917, 475)

27.50, −1.04

28.02, −0.99

30.50, (994, 475)

30.50, 1.10

30.10, 1.02

31.90, (992, 475)

31.90, 1.09

32.02, 1.03

34.50, (988, 475)

34.50, 1.03

34.29, 0.98

36.00, (986, 475)

36.00, 1.01

36.26, 1.00

38.40, (986, 475)

38.40, 1.07

38.19, 1.01

As shown in Table 1, the standard error of lateral distance (0.052 m) and the standard error of longitudinal distance (0.351 m) can be found, in which the lateral accuracy is related to the correctness of the joint calibration system, while the longitudinal accuracy is determined by the radar performance index.

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On the other hand, time synchronization of sensor data is required after model fusion. Firstly, record the data collection entrance time stamp of each sensor using software clock as a benchmark. Then, in the data buffer stage, the nearest many-to-one binding is carried out on the two data to ensure the fusion accuracy and to minimize the data loss rate.

4 Radar Signal Processing The environment of the train working field is complex and exists many irrelevant interferers. If only linear Kalman filter is used in the original data of millimeter-wave radar, there will still be some errors. So, it is necessary to improve the filter processing of radar for this situation. In this paper, an improved Kalman filter algorithm with stronger adaptability of low stationary signal is selected for processing complex scenes. 4.1 Signal Preprocessing Before filtering, it is necessary to preprocess the original trace point signal. The carriage target belongs to a large surface target. Clustering the radar trace point can increase the stability of the target point and reduce the noise of the interferers. This paper adopts a DBSCAN algorithm for millimeter-wave radar signals [7], which draws attention to the connectivity between data based on elliptical density and continuously expands clusters based on connectable data to find the final clustering results, which exactly conforms to the characteristics of millimeter-wave radar signals. In the algorithm, firstly, set the neighborhood density threshold parameter MinPts according to the site specification of train working field and the specifications of train carriage, check the Eps neighborhood of each trace point of the current frame. If the neighborhood contains number of trace point more than MinPts, then build a cluster centered on the point. Then, iterate over the aggregation density direct trace signal, ending the current frame process when all trace are accessed. Figure 3 shows the result of clustering the radar data frames at a certain time. It can be seen that in the case of complex environment, the small-area objects that obviously do not belong to the train target can be effectively excluded by fine tune of the parameters, so as to provide high-cleanliness data for the subsequent processing of specific requirements. 4.2 Signal Modification Based on Improved Kalman Filter The signal filtering of millimeter-wave radar generally uses linear Kalman filtering, but Kalman filtering has some limitations in such complex scenes as train working field. Since the target points are frequently blocked in this kind of complex environment, and Kalman filter will appear to be lost to the blocked targets, the train acceleration is low and the radar data truth value is basically stable, in order to ensure the low error of the radar signal filter under the strong noise that is different from the gaussian model such as the occlusions; the M-robust estimation method [6] is used for reference in this paper to improve the adaptability of the linear Kalman filter. Firstly, replace the correction and update process xˆ k = xˆ k− + Kk (yk − H xˆ k− )

(3)

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Fig. 3. Clustering results

in linear Kalman filter. Establish the error covariance matrix representing linear regression c and decompose it into terms of C × C T using Cholesky decomposition to calculate the state value X(k) and the optimal estimate Y (k). Then, use the Huber score function  d |y| − d 2 /2, |y| ≥ d (4) L(y) = |y| < d y2 /2, And BFGS [7] method to do the nonlinear optimization replacing the least squares. If an outlier trace point representing a occlusions is observed and the error is too large, the derivative of the loss function can be controlled in a small value, accordingly inhibiting the outliers in the error calculation. The clustering central trace point is used as the system input in the point trace tracking process. For the above reasons, these variables are reset to absolute values at each iteration, and some additional storage and movement operations are required compared with Kalman filtering, which cause iteration efficiency slightly slower than Kalman filtering. As shown in Fig. 4, based on the filtering result of a real radar signal for a tracking sequence, it can be seen that the modified result is significantly better than the linear Kalman filtering in the case of strong noise of occlusion.

5 Over-All Experiment The overall experimental hardware platform adopts a continental millimeter-wave radar [8] and a 30FPS monocular camera. The radar uses CAN for communication transmission, and the front-end of the fusion system software is a cross-platform Qt framework,

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Fig. 4. Improved Kalman filter estimation results

and the back-end is an OpenCV computer vision library. In order to accurately test the capability of the system, the test was carried out on the complex road surface in this paper. The situation of a large number of occlusion and interfering objects in the scene is similar to that in the train work field. At the same time, in order to make the system have the function of automatic verification, the target detector is added to the system to assist the verification of fusion performance. The fusion renderings are shown in Fig. 5. The experiment first tested the target objects of different surface areas by setting the target area parameters of pedestrians and large vehicles, respectively. The results showed that the system could clearly extract the target corresponding to the current object area and eliminate other interference points. Secondly, the experiment tested the presence of dynamic occlusions, and the results showed that the radar data still maintain a good stability when non-target vehicles came into the field of vision or became occlusions.

Fig. 5. The experiment visualization

Therefore, through the above experiments, it can be concluded that the sensor fusion system in this paper can effectively filter the interference and accurately locate the radar

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information on the target object in real time. At the same time, the overall experimental results further indicate that the system in this paper can implement the fusion of sensor information about the front target carriage in the train work field similar to the complex road surface, and the real-time performance of the system in this paper can reach 30 FPS after testing. In this way, the data throughput speed and accuracy of the subsequent real-time data processing are completely guaranteed.

6 Conclusion and Outlook In this paper, we propose and validate a sensor fusion method for in-station articulation of train. This method uses the complementary advantages of camera and millimeterwave radar for data fusion. At the same time, the improved Kalman filter makes the radar signal more stable in the situation. Compared with a single sensor, the method in paper greatly enhances the orientation perception ability of the train to the front target carriage and guarantees the real-time and reliability of the data source. On the other hand, in terms of experimental purposes, it is also the future experimental direction to continue the research on the automatic articulation method and enter the train work field for testing. Finally, for the cross-platform aspect, the hardware platform on which the system in this paper is based can be transplanted from the desktop computer to the embedded system as a follow-up work.

References 1. Xu, M., Feng, Y.-P., Lu, Z.-M.: Fast feature extraction based on multi-feature classification for color image. J. Inf. Hiding Multim. Signal Process. 10(2), 338–345 (2019) 2. Wang, X., Xu, L., Sun, H., et al.: On-road vehicle detection and tracking using MMW radar and monovision fusion[J]. IEEE Trans. Intell. Transp. Syst. 17(7), 2075–2084 (2016) 3. Wang, E.K., Zhang, X., Wang, F., Wu, T.-Y., Chen, C.-M.: Multilayer dense attention model for image caption. IEEE Access 7, 66358–66368 (2019) 4. Wang, X., Wang, X., Chu, S.-C., Roddick, J.F.: Spatiotemporal Fusion algorithm for singletime phase high resolution remote sensing image based on sparse representation. J. Network Intell. 4(3), 100–108 (2019) 5. Mujica, F.: Scalable electronics driving autonomous vehicle technologies. Texas Instruments (2014) 6. Durovic, Z.M., Kovacevic, B.D.: Robust estimation with unknown noise statistics. IEEE Trans. Autom. Control 44(6), 1292–1296 (1999). https://doi.org/10.1109/9.769393 7. Moritz, P., Nishihara, R., Jordan, M.: A linearly-convergent stochastic L-BFGS algorithm. In: Artificial Intelligence and Statistics, pp. 249–258 (2016) 8. Continental MMW radar introduction, https://autonomoustuff.com/product/continental-ars408-21/. Last accessed 2020/04/21

Intelligent Fault Diagnosis Using Limited Data Under Different Working Conditions Based on SEflow Model and Data Augmentation Sijue Li1

, Gaoliang Peng1(B) , Daoyong Mao2 , Zhiyu Zhu3 , Mengyu Ji1 , and Yuanhang Chen1

1 State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92 Xidazhi

Street, Harbin 150001, Heilongjiang Province, China {lisijue,pgl7782}@hit.edu.cn, [email protected], [email protected] 2 China Electronics Technology Group Corporation no. 38 Research Institute, Hefei, Anhui Province, China [email protected] 3 Department of Computer Science, City University of Hong Kong, Hong Kong, China [email protected]

Abstract. Accurate fault diagnosis of machine components is quite important for normal operation of equipment. Nowadays, artificial intelligent methods have been widely researched in fault diagnosis of rolling element bearings (REB). However, due to the variation of machine working conditions, the diagnosis accuracy always degrade seriously. Besides, as it is really hard to achieve large amounts of labeled health condition signals from real equipment, data deficiency is another trouble. Both issues impede the practical application of data-driven fault diagnosis. So as to solve the problems, a data augmentation method SEflow based on squeezeand-excitation networks (SEnet) and flow-generative model is proposed. Proposed SEflow can learn the data distributions from limited data, then generate augmented signals among different machine working conditions. The experiments applied on bearing datasets and ball screw signals verify the effectiveness of proposed method on solving domain adaption and data deficiency. Keywords: Intelligent fault diagnosis · Flow-based generative model · Data augmentation · Domain adaption · Deep learning

1 Introduction With the recent rapid development of machine learning and deep learning techniques in computer science [1–4], many kinds of artificial intelligent technologies have already been researched in fault diagnosis of REB [5, 6]. Therefore, the insist demands of effective fault diagnosis have drawn great attention [4]. Haidong et al. proposed a deep autoencoder feature learning method for fault diagnosis of rotating machinery with strong © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_58

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robust [7]. Jafar Zarei used a pattern recognition technique to detect fault in induction motors bearing by constructing artificial neural networks (ANN), the biggest difference of this method is using time-domain signals as training sets rather than frequency-domain ones [8]. Li et al used support vector machine (SVM) which is based on pseudo Wigner– Ville Distribution (PWVD) to process the acoustic emission signals instead of vibration signals for the diagnose rotor defect depth [9]. The above measures hammer at improving diagnosis accuracy in condition of complete data, including different working conditions, intact failure labels and enough data size, which pave the way for following well-designed classifiers. Nevertheless, the ideal is fullness, the reality is skinny. Due to the frequent changes of working conditions, the diagnosis accuracy always deteriorate severely under actual situation. In order to solve the problem, L. W. et al proposed a deep model based domain adaption for fault diagnosis (DAFD) to solve the cross-domain discrepancy [10]. Zhang et al. using domain adaptive convolutional neural networks (DACNN) to address intelligent fault diagnosis under varying working conditions [11]. However, in real industrial environment, it is a common phenomenon that people can only get limited small numbers of data from real equipment. So data deficiency is another serious issue besides domain adaption. Inspired by the concept of flow-based generative models and SEnet, we proposed SEflow to realize cross-domain fault diagnosis and data augmentation on machine signals. SEflow can learn the potential features among different working conditions. After the training phase, inverse transformation is applied to generate data from one condition to another. The experiments on bearing datasets and ball screw signals verify the effectiveness of proposed method. To our best knowledge, it is the first attempt to address the domain adaption with data deficiency problems at the same time. The main contributions can be summarized as follows: 1. An SEflow architecture based on SEnet and flow-generative model is proposed to learn potential distributions using limited data collected from machine equipment. The augmentation signals from one working conditions to another are generated to improve domain adaption and data augmentation. 2. Different data augmentation methods are utilized as comparison. Besides, the impact of signal amounts in training set and data augmentation level is discussed. 3. Experiment results on CWRU bearing dataset are presented to show the effectiveness of proposed method on solving cross-domain diagnosis and data deficiency.

2 Theoretical Background 2.1 Normalizing Flow As indicated in Fig. 1, a normalizing flow realizes the transformation of a probability density through a series of invertible transformation functions, which can complete better and more powerful distribution approximation [12]. Through a chain of transformations, normalizing flow can transform the initial simple distribution to a complex one by using stacked invertible transformation functions.

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Fig. 1. Diagram of multivariable invertible normalizing flow

z is the samples from distribution π(z), which is always a simple tractable density, such as Gaussian distribution. According to Fig. 1, zi−1 ∼ πi−1 (zi−1 ), zi = fi (zi−1 ) and zi−1 = fi−1 (zi ). Considering the relation between πi and πi−1 , the following functions can be achieved.    dfi (zi−1 ) −1  πi (zi ) = πi−1 (zi−1 )det . (1) dzi−1  The algorithm of the function is:    dfi (zi−1 )  . log(πi (zi )) = log(πi−1 (zi−1 )) − logdet dzi−1 

(2)

Therefore, the relational expression of x ∼ q(x) and z0 ∼ π0 (x0 ) is shown as below. log q(x) = log π0 (z0 ) −

N  i=1

   dfN (zN −1 )  . logdet dzN −1 

(3)

Hence, easily calculated distribution logarithmic expression log q(x) can be implemented to logarithmic likelihood equation.    N   dfN (zN −1 )   Ex∼p(x) [log q(x)] = Ex∼p(x) log π0 (z0 ) − . (4) logdet dzN −1  i=1

2.2 Squeeze-and-excitation Networks (SEnets) SEnets was designed to establish the interdependency among feature maps of input obviously [13]. By achieving the importance of each feature map by training, SEnets can promote the useful features and suppress features that are not useful for current task. As SEnets is a module network which can be embedded into many models. So here, we combined it with flow model to get better effects. The sketch map of SEnets is shown in Fig. 2. It is composed of three main operations: Squeeze, Excitation, and Scale.

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Fig. 2. Structure of SEnets

3 Design of Proposed Model and System Framework In order to solve the data deficiency and domain adaptive problem, the data augmentation framework SEflow based on SEnet and flow-generative model is proposed. In training part, SEflow can learn the latent characteristic according to input vibration signals. Then training data and modified high-dimensional latent variables are utilized to generate target domain working condition signals. The overall structure is shown in Fig. 3.

Fig. 3. Architecture of proposed data augmentation method −1 fSE is SEflow model, and it can extract data features fSE is the inverse form to generate real signals. The relationship of them just as encoder and decoder. Training fSE with loss backward can obtain high-dimensional features FH . Then, data from source domain DH and target domain DT is considered as inputs to get source domain features FS and target domain features FT . SEflow can realize feature interpolation to achieve features −1 is applied to generate signals DS→T . FS→T from source to target domain. Then, fSE The domain of generated data can cover each working conditions in training data. So, on one side, we can generate plentiful vibration signals to realize data augmentation. On the other side, as the domain of generative signals is controllable, it can address domain adaptive among different working conditions. Densenet is connected after above steps, functions as a classifier.

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3.1 Model Training of Proposed Frame The proposed SEflow is an important part in training phase, and it contains norm, permutation layer, and coupling SEnet layer. Norm layer When signals enter Norm layer, the first step is parameter initialization. The mean of pixel value in every dimension (except channel dimension) will be calculated, namely the average. Then, variation can be achieved as the absolute value of the difference between input and average. The structure of Norm is shown in Fig. 4.

Fig. 4. Norm layer in SEflow

In normalization step, center and scale operation can be applied based on input, average, and variation. Here, the input signals and output signals are defined as zin , zout , mean and variance of inputs can be expressed as μ, and σ . Then, the function of Norm layer can be shown as: zout =

zin − μ . σ

(5)

Permutation layer In permutation layer as shown in Fig. 5a, a random orthogonal matrix is generated initially. Through LU decomposition, permutation matrix P, lower triangular matrix L and upper triangular matrix U can be obtained. Keeping P remain unchanged, fix the sign of diagonal elements of U , make the diagonal elements of L to be one.

Fig. 5. Permutation layer and coupling SEnet layer in SEflow

It is observed in Fig. 5a that L and U are needed to be optimized. Parameter matrix W is composed of original matrix P and changed matrix L and U . The log-determinant of

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W equals the sum of logarithm of diagonal elements. In the 1 × 1 convolution operation, W serves as weight. As the group, height and width are all one, and the dimensions of output are the same as input ones. If the output of permutation is defined as zout-per . The equation sets can be described as:  W = PLU . (6) zout-per = W ∗ zout Coupling SEnet layer When vibration signals enter coupling layer, it is separated into two parts, z1 and z2 , along channel dimension. z1 is sent to coupling convolution layers, which are composed of three ReLu convolutions. The hidden layers are 256. In the first two layers, data will be put into Norm after convolution operation. At the end of coupling convolution layers, zero initialization will be applied. The structure is shown in Fig. 5b. If the outputs of coupling convolution and SEnet are defined as z1 and z1 . The total equation set can be described as: ⎧ z1 = z2 = Split(zout-per ) ⎪ ⎪ ⎪  ⎪ z ⎪ 1) 1 = CoupleConv2D(z ⎪  

⎪ ⎨ H W F 2 z1 (i, j) z1 = z1 • σ F1 δ H ×W ⎪ i=1 j=1 ⎪ ⎪ ⎪   ⎪ ⎪ ⎪ z3 = z 2 • z 1 + z1 ⎩ zf = Concat(z1 , ze )

(7)

4 Experiments Analysis 4.1 Introduction of Datasets and Compared Augmentation Method CWRU rolling bearing dataset is provided by the bearing data center of Case Western Reserve University. As one of the most commonly used bearing dataset, it contains four bearing health conditions: normal (NL); outer race fault (OF); inner race fault (IF), and ball fault (BF) under four different load conditions and rotate speeds. More detailed information can be obtained in [14]. The test rig is presented in Fig. 6. 4.2 Compared Augmentation Techniques Various data augmentation methods are applied on the diagnosis process to indicate the improvement and effectiveness of our proposed SEflow method, including adding Gaussian noise (GN) [16], signal translation (ST) [17], amplitude shifting (AS) [18], and WGAN-GP [19]. WGAN-GP is an improved version of WGAN. The penalty gradient functions can satisfy the Lipschitz constrain instead of weight clipping. Because of the regular terms, WGAN-GP can be trained more stable and generate more accurate data compared with

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Fig. 6. CWRU bearing signal acquisition device [15]

WGAN, which makes it to have great potential in data augmentation. The function of WGAN-GP is shown as follows:   (8) E [D(x)] − E [D(˜x)] − λ E [(∇xˆ D(ˆx)2 − 1)2 ]. x∼Ps

xˆ ∼Pt

xˆ ∼Pxˆ

D(x) and D(˜x) are distributions of source domain and target domain. The difference of two expectations represents distance of two distributions. The penalty term with coefficient λ is proposed to satisfy 1-Lipschitz condition and brings the better loss optimization and more stable convergence. 4.3 Results on CWRU Dataset Results of different methods In this section, the experiment under different working conditions with scarce data is shown. The comparison among various data augmentation is presented to solve domain adaption and verify the effectiveness of proposed methods. The test grouping is in accordance with different fault diameters. All tests are the mean value of five times to reduce the influence of random interference. Signals from condition 0 to 3 of three fault modes and one health states are sent to SEflow for training. The trained model transferred input signals to high-dimensional features, and reversed model was used to generate work other conditions’ data. Source condition and generated signals are used as training set, while target condition belongs to testing set, i.e., the domain adaption task C1 → C4 means that under the same faults, data of C1 working condition and generated signals are used as training set in neural network to test signals of C2 condition. The diagnosis results were changed by diverse augmentation coefficient. All possible situations were considered and the overall performance was represented by average accuracy. The best results of above three techniques are listed with other methods in Table 1. In the table, NA mean no augmentation. GN , AS and ST are abbreviations of Gaussian noise, amplitude shifting, and signal translation. The generative model WGAN-GP and proposed SEflow is introduced as comparison.

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Methods Train:Test

NA 1:1

NA 0.1:1

Working conditions

GN 1:1

AS 1:1

ST 1:1

γgaus (0.1)

γscal (1.5)

γtrans (40)

WGAN-GP 1:1

SEflow 1:1

C2→C1

76.4

53.5

73.0

89.9

84.9

62.8

91.3

C3→C1

68.7

54.6

64.3

62.1

69.7

68.6

94.8

C4→C1

67.2

45.8

69.1

65.0

53.5

57.3

92.4

C1→C2

63.5

58.4

69.8

71.9

71.5

69.2

85.2

C3→C2

93.3

55.5

93.5

89.1

92.2

77.7

88.4

C4→C2

82.3

62.9

84.2

77.1

80.4

69.1

88.7

C1→C3

62.2

46.2

69.4

65.2

67.4

65.5

81.6

C2→C3

91.5

62.1

90.3

85.4

91.6

72.5

88.9

C4→C3

87.3

55.3

86.1

77.8

88.3

69.2

86.3

C1→C4

59.7

60.9

62.7

67.8

72.8

64.1

79.6

C2→C4

85.6

55.9

79.8

81.9

87.9

66.9

88.2

C3→C4

91.2

55.9

87.3

81.1

85.7

70.8

85.6

Average

77.5

55.6

77.5

76.2

78.8

67.7

87.6

In NA, two circumstances: enough training data (1:1) and lacked training data (0.1:1), are considered. It can be learned that the scale factor (Train: Test) influenced a lot on cross-domain fault diagnosis. Compared with NA (0.1:1), all augmentation means could improve the results. Even WGAN −GP, the worst of them improved 12.3%. However, other methods could not get obvious compared with NA (1:1). The proposed SEflow obtained evident improvements in contrast with NA (1:1), and the average cross-domain diagnosis accuracy can be 87.6%. The results displayed the effectiveness of SEflow on domain adaption and data augmentation.

5 Conclusions In this paper, a data augmentation method named SEflow based on SEnet and flowgenerative model is presented for the rolling bearings and ball screw fault diagnosis under data deficiency and limited work conditions. Proposed model can transfer the original data to latent variables in high-dimensional space by optimizing the log-likelihood equation exactly. Implementing the feature interpolation in high-dimensional space can realize the domain transfer from original domain to target work condition, which called controllable domain adaption in the research. The SEflow can take advantage of the interpolated features to generate augmentation data which cover various work conditions. Experiments on CWRU datasets ball screw test rig are put into practice to show

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the improvement of the proposed model. The comparison with other data augmentation methods have showed great improvement of proposed method. It is true that other augmentation techniques can also bring positive enhancement; however, they are not as effective as SEflow framework. The research on data amounts shows that generally more augmented data means better enhancement effects. But more data equals more computation costs, and how to balance the fault diagnosis results and computational efficiency remains to be studied. There are still some problems to be explored. The comparative data augmentation methods are limited, what if combining them to a new way augmentation? How to simplify the structure and remain the generative effects as well? These questions will be paid attention in further research.

References 1. Socher, R., Huval, B., Bath, B., Manning, C.D., Ng, A.Y. (ed.): Convolutional-recursive deep learning for 3d object classification. In: Advances in Neural Information Processing Systems (2012) 2. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015) 3. Wang, E.K., Chen, C.-M., Hassan, M.M., Almogren, A.: A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain. Futur. Gener. Comput. Syst. 108, 135–144 (2020) 4. Wu, J.M.-T., Tsai, M.-H., Xiao, S.-H., Liaw, Y.-P.: A deep neural network electrocardiogram analysis framework for left ventricular hypertrophy prediction. J. Ambient Intell. Hum. Comput. https://doi.org/10.1007/s12652-020-01826-1 5. Zhang, W., Li, C.H., Peng, G.L., Chen, Y.H., Zhang, Z.J.: A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech. Syst. Signal Pr. 100, 439–453 (2018) 6. Jia, F., Lei, Y.G., Guo, L., Lin, J., Xing, S.B.: A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing 272, 619–628 (2018) 7. Shao, H.D., Jiang, H.K., Zhao, H.W., Wang, F.A.: A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech. Syst. Signal Pr. 95, 187–204 (2017) 8. Zarei, J.: Induction motors bearing fault detection using pattern recognition techniques. Expert Syst. Appl. 39(1), 68–73 (2012) 9. Li, X., Wang, K., Jiang, L.J.J.: The Application of AE Signal in Early Cracked Rotor Fault Diagnosis with PWVD and SVM. JSW 6(10), 1969–1976 (2011) 10. Lu, W., Liang, B., Cheng, Y., Meng, D., Yang, J., Zhang, J.: Deep model based domain adaptation for fault diagnosis. IEEE Trans. Ind. Electron. 64(3), 2296–2305 (2016) 11. Zhang, B., Li, W., Li, X.L., See-Kiong, N.G.: Intelligent fault diagnosis under varying working conditions based on domain adaptive convolutional neural networks. Ieee Access 6, 66367– 66384 (2018) 12. Rezende, D.J., Mohamed, S.J.: Variational inference with normalizing flows (2015) 13. Hu, J., Shen, L., Sun, G. (eds.): Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018) 14. Smith, W.A., Randall, R.B.: Rolling element bearing diagnostics using the case western reserve university data: a benchmark study. Mech. Syst. Signal Pr. 64–65, 100–131 (2015) 15. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C. (eds.): Improved training of wasserstein gans. In: Advances in Neural Information Processing Systems (2017) 16. Li, X., Zhang, W., Ding, Q., Sun, J.Q.: Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. J. Intell. Manuf. 31(2), 433–452 (2020)

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17. Cagli, E., Dumas, C., Prouff, E.: Convolutional neural networks with data augmentation against jitter-based countermeasures. In: International Conference on Cryptographic Hardware and Embedded Systems, 25 Sept 2017, pp. 45–68. Springer, Cham 18. Huang, L., Pan, W., Zhang, Y., Qian, L., Gao, N., Yuan, W.: Data augmentation for deep learning-based radio modulation classification. IEEE Access 8, 1498–1506 (2019) 19. Gao, X., Deng, F., Yue, X.: Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty. Neurocomputing 396, 487–494 (2020)

The Unified Framework of Deep Multiple Kernel Learning for Small Sample Sizes of Training Samples Jing Liu1 , Tingting Wang2 , and Yulong Qiao1(B) 1 College of Information and Communication Engineering, Harbin Engineering University,

Harbin 150001, China [email protected] 2 School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China

Abstract. Recently, deep learning technologies have rapidly developed. They have shown excellent performances in many fields. However, deep learning networks have weak adaptability to small sample sizes. In this paper, we proposed a novel depth-width-scaling multiple kernel learning unified framework. It has the ability to adjust the architecture according to the input data. We optimize the estimation of leave-one-out error by using the span bound instead of the dual objective function. Finally, we choose a large number of datasets from the UCI benchmark datasets for classification tasks. The experimental results show that different frameworks have different performances on the same datasets. Our DWS-MKL algorithm obtains better classification results than the state-of-the-art MKL algorithms, as determined through comparison. The encouraging results demonstrate that our method has the potential to improve the generalization performance of the model. Keywords: Deep learning · Multiple kernel learning · Depth-width-scaling · Span bound

1 Introduction With the developing of the deep learning theory, the researchers have proved the deep architecture is feasible and effective in several applications. The framework of deep learning-based multi-kernel machine is effective framework, and the learning method has been widely used in image analysis [1, 2], image annotation [3], image classification [4], image segmentation [5], anomaly detection [6], and other practical applications. Researcher proposed the deep kernel learning, namely LMKL [7], and in the other work, the estimated value of missing an error is adjusted instead of the double objective function [8]. Mathematically, it has been proved that multi-layer can improve the richness of representation, and the researchers combine support vector machine and multiple classifiers and use adaptive back propagation algorithm to update coefficients and weights [9–12]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_59

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2 Proposed Depth-Width-Scaling Deep Kernel Learning Network 2.1 Framework Take a binary classification problem, for example, a classifier based on a kernel function is built from a given training set of m samples D = {(x1 , y1 ), (x2 , y2 ), . . . , (xm , ym )} where xi ∈ Rn and yi ∈ {−1, 1}, i = 1, 2, . . . , m. Let φ(x) represents the function mapping x from Rn space to a higher feature space H where the samples are linearly separable. Define a function k(xi , xj ) that satisfies k(xi , xj ) = φ(xi )φ(xj ). k(xi , xj ) as the kernel function. Moreover, the kernel matrix K that is always semi-positive definite is defined as follows: ⎤ ⎡ k(x1 , x1 ) · · · k(x1 , xj ) · · · k(x1 , xm ) .. .. .. ⎥ ⎢ .. .. ⎥ ⎢ . . . . . ⎥ ⎢ ⎥ (1) K =⎢ , x ) · · · k(x , x ) · · · k(x , x ) k(x i 1 i j i m ⎥ ⎢ ⎥ ⎢ . . . . . .. .. .. .. .. ⎦ ⎣ k(xm , x1 ) · · · k(xm , xj ) · · · k(xm , xm ) The decision function of the classifier is:  m

f (x) = sign αi yi k(xi , x) + b

(2)

i=1

where αi are some dual coefficients, and b is the bias of the decision function f (x). Both of them can be learned from samples. The optimization problem is: 1 ω2 + C ξi (α,b,ξ ) 2 m

min

i=1

where ω =

m

s.t. yi (αi k(xi , x) + b) ≥ 1 − ξi , ξi ≥ 0, C > 0, i = 1, 2, . . . , m

i=1 αi yi xi , ξi

(3)

is the slack variable, and C is a regularization parameter. K(xi , x) =

M

θi ki (xi , x),

i=1

s.t. θi ≥ 0, M

θi = 1

(4)

i=1

where M is the total number of basic kernels, θi is the combination coefficients, while ki (xi , x) is a positive definite kernel. It is obvious that the K(xi , x) of MKL is a simple linear combination of M base kernels. From (2), the decision function of MKL is easily expressed in (5): M

m f (x) = sign θk αi yi k(xi , x) + b (5) k=1

i=1

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The “shallow” architecture of the general MKL is too flat to handle more complex tasks. The deep model has been proven to be useful in extracting richer features in mathematical representations. The deep architecture in MKL can yield considerably better performance. In this paper, we are interested in more flexible networks. Motivated by Aiolli and Donini [13], we introduce the depth-width-Scaling multiple kernel learning framework, named as DWS-MKL. The detailed architecture is depicted in Fig. 1.

Fig. 1. Framework of depth-width-scaling multiple kernel learning. There are M base kernels d ,w d ,w km at each combined kernel Kd ,w . The final combined kernel is Kf . θm represents the weights

In our framework, the depth and width of the network can be viewed as “layers” and “channels,” respectively. The inputs of primal layers are original datasets in Euclidean space. The intermediate hidden layers are connected with each other directly; that is, the output of the last middle combined kernels is the input of the next base kernels. Unlike the intermediate combined kernels, the final kernel is the average of all kernels from different channels. The original data are mapped to a higher-dimensional feature space (i.e., Hilbert space) through this extremely complex network. The scaling framework is powerful enough to fit diverse large-scale datasets. In classification tasks, we connect the final kernel with a classifier (i.e., SVM), as shown in Fig. 2. It aims to learn the decision function of the classifier. In this paper, we achieve a framework of DWS-MKL in which the depth and width vary between 1 and 3. 2.2 Algorithm A general L-layer DMKL network can be formulated as K (L) (x, y) = φ (L) (φ (L−1) (. . . φ (1) (x))) · φ (L) (φ (L−1) (. . . φ (1) (y)))

(6)

For the linear kernel k(x, y) = x·y, the l-order stays the same [11]; that is, k (l) (x, y) = = x · y. Then, the decision function is  n

f (x) = sign αi yi (xi · x) + b (7)

k (1) (x, y)

i=1

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Fig. 2. Classification network. The DWS-MKL can solve the nonlinear classification problem by connecting the classifier after the last combined kernel Kf . y1 , y2 , . . . , ym is m categories

A p-power polynomial kernel is given by k(x, y) = α(x · y + β)p , where α and β are free parameters. The corresponding decision function is

 n p αi yi (α(xi · x) + β) + b (8) f (x) = sign i=1



2 In the case of a Gaussian kernel, k(x, y) = exp − ||x−y|| , and the decision function 2 2σ is formulated as:

 n   ||xi − x||2 +b (9) αi yi exp − f (x) = sign 2σ 2 i=1

In this paper, we implement a 3-depth and 3-width network as an example. Therefore, we derive the third-order kernel function in Table 1. Table 1. Formulation of three-order kernel functions Kernel function

Formulation

Linear kernel

k (3) (x, y) = k (2) (x, y) = k(x, y) = x · y

Polynomial kernel k (2) (x, y) = (α(k(x, y) + β)p ) k (3) (x, y) = (α(k (2) (x, y) + β)p ) Gaussian kernel

k (2) (x, y) = e

−

1 (1−k(x,y)) σ2

k (3) (x, y) = e

−

1 (1−k (2) (x,y)) σ2

The Unified Framework of Deep Multiple Kernel Learning …

The total decision function of DWM-MKL is  m

f (x) = sign αi yi k(xi , x; θi ) + b

489

(10)

i=1

We choose one linear kernel, two polynomial kernels, and one Gaussian kernel as base kernels in our framework. The nature of the DWS-MKL optimization problem is to learn both combined weights θid ,w and the dual coefficients αi .

3 Experiments In this section, we conduct two experiments. The aim of the first experiment is to verify the performance of the framework with different depths and depth widths as well as to compare our results with some state-of-the-art MKL algorithms. We choose 21 public sub-datasets in the UCI benchmark, in which the datasets include binary classification tasks and multiple classification tasks. In comparison experiments, we only use 14 of 21 datasets for binary classification. We divide the datasets into a training set and a test set (50% each). The specific description is listed in Table 2. The features of the datasets include low (i.e., 1) and high (i.e., 64) dimensions. All datasets except spambase have small sample sizes. The minimum is 100. This setup poses a great challenge to the deep learning method. In all of our experiments, each combination of kernels is composed of four base kernels, as follows: 1. Linear kernel. 2. RBF kernel with σ . We compute σ using the Euclidean distance. 3. Polynomial kernel with degrees of 2 and 3. The free parameters are α = 1 and β = 1. For training, the maximum number of iterations is set to 100. The learning rate is usually set to lr = 1E − 5. The penalty coefficient C of the SVM is empirically fixed to 10. All algorithms are implemented in MATLAB. The SVM classifier is generated by the open-source LIBSVM. The performance of the algorithm is determined by the average classification accuracy. All parameters in these published algorithms are kept consistent with the paper in which they are described. The depth and width of our framework are decided by five-fold cross-validation. We organized the results into the following Table 3. In the last column of Table 3, the first bracket is the depth and width of our framework, and the second bracket is the average rank. We also highlight the best results in each row. As shown in the last row, the average rank of our method is 1.92, and SM1MKL and SimpleMKL have average ranks of 2.86 and 2.93, respectively, and subsequently, EasyMKL and L2MKL have average ranks of 3.07 and 3.36. In terms of the average rank, our method, DWS-MKL, outperforms all other MKL algorithms. From a statistical point of view, there are eight frameworks that are combined by 3-width in our method regardless of the depth. This work proves that a wider width can improve the performance

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Features Training set Test set

Australian

14

Air

345

345

64

178

181

Breast

1

138

139

Cleve

13

148

148

Cloud

1

511

513

Diabetes

8

384

384

Ecoli

7

166

170

Fertility

9

50

50

German

20

500

500

3

152

154

Indian liver patient

10

291

292

Ionosphere

32

157

194

Haberman

Iris

4

75

75

Mammographic Mass

4

374

374

Pima

8

384

384

Seeds

7

105

105

Sonar

60

103

105

Spambase

57

2300

2301

Tic-Tac-Toc

9

479

479

Vertebral column

6

155

155

13

88

90

Wine

of the framework when combined with the appropriate depth. In summary, DWS-MKL outperforms the MKL algorithm and improves the classification performance of the algorithm.

4 Conclusion In this paper, we propose a new depth-width-scaling multiple kernel learning framework for different datasets. We then use the gradient projection method to optimize the error with the span bound. We conduct a performance verification experiment and a comparison experiment. The first experiments indicates the necessity of different depth and width architectures. The other experiments demonstrates that the DWS-MKL algorithm outperforms state-of-the-art MKL algorithms in generalization performance, which indicates the effectiveness of the DWS-MKL framework. In future, the algorithm can be applied

72.20 (4)

74.02 (1)

92.78 (1)

72.19 (5)

78.38 (1)

86.66 (1)

92.09 (2)

75.00 (2)

72.90 (5)

3.07

German

Haberman

Ionosphere

Mass

Pima

Sonar

Spambase

Tic-Tac-Toc

Column

Average rank

2.93

81.29 (4)

68.05 (3)

79.70 (4)

76.69 (3)

78.38 (1)

76.73 (2)

91.47 (3)

72.36 (5)

70.00 (5)

77.34 (1)

99.80 (1)

84.45 (1)

69.78 (4)

84.30 (4)

SimpleMKLb [14]

a Avaliable at: https://github.com/okbalefthanded/EasyMKL b Avaliable at: https://github.com/maxis1718/SimpleMKL c Avaliable at: https://sites.google.com/site/xinxingxu666/ d The implementation is obtained in footnote 4

69.78 (4)

81.08 (4)

Cleve

74.21 (5)

69.06 (5)

Breast

Diabetes

84.63 (3)

Australian

Cloud

EasyMKLa [13]

Dataset

86.45 (2) 3.36

2.86

67.84 (4)

75.66 (5)

74.28 (4)

77.34 (4)

76.20 (3)

89.69 (4)

73.02 (3)

72.80 (3)

76.56 (2)

61.20 (5)

81.75 (2)

70.50 (1)

84.05 (5)

L2MKLd [16, 17]

86.45 (2)

67.84 (4)

91.91 (3)

72.38 (5)

77.34 (4)

76.20 (3)

89.69 (4)

72.72 (4)

73.20 (2)

76.56 (2)

73.29 (3)

81.75 (2)

70.50 (1)

85.79 (1)

SM1MKLc [15]

Table 3. Classification performance by comparing with existing MKL algorithm

1.92

88.38 (3, 1) (1)

92.69 (2, 1) (1)

93.09 (1, 3) (1)

84.76 (1, 3) (2)

78.12 (3, 3) (3)

78.87 (2, 1) (1)

91.75 (1, 1) (2)

74.02 (1, 3) (1)

73.60 (3, 2) (1)

74.73 (3, 3) (4)

75.43 (1, 3) (2)

80.40 (3, 3) (5)

70.50 (1, 1) (1)

85.50 (3, 3) (2)

Our method (D, W)

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to more complex tasks, such as image object detection, speech recognition, video multitasking classification, and so on. It can also be used on more large-scale datasets, such as hyperspectral image data, satellite remote sensing data, radar data, etc. Acknowledgements. This work is supported by the National Science Foundation of China under Grant No. 61871142, Science and Technology Foundation of National Defense Key Laboratory of Science and Technology on Parallel and Distributed Processing Laboratory(PDL) under Grant No. 6142110180406, Science and Technology Foundation of ATR National Defense Key Laboratory under Grant No. 6142503180402, China Academy of Space Technology (CAST) Innovation Fund under Grant No. 2018CAST33, Joint Fund of China Electronics Technology Group Corporation and Equipment Pre-Research under Grant No. 6141B08231109.

References 1. Ker, J., Wang, L., Rao, J., et al.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2017) 2. Wu, G., Kim, M., Wang, Q., et al.: Scalable high-performance image registration framework by unsupervised deep feature representations learning. IEEE Trans. Biomed. Eng. 63(7), 1505–1516 (2016) 3. Jiu, M., Sahbi, H.: Nonlinear deep kernel learning for image annotation. IEEE Trans. Image Process. 26(4), 1820–1832 (2017) 4. Wang, C., Shi, J., Zhang, Q., et al.: Histopathological image classification with bilinear convolutional neural networks. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4050–4053. IEEE (2017) 5. Li, W., Qian, X., Ji, J.: Noise-tolerant deep learning for histopathological image segmentation. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3075–3079. IEEE (2017) 6. Kumar, N., Rajwade, A.V., Chandran, S., et al.: Kernel generalized gaussian and robust statistical learning for abnormality detection in medical images. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 4157–4161. IEEE (2017) 7. Zhuang, J., Tsang, I.W., Hoi, S.C.H.: Two-layer multiple kernel learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 909–917 (2011) 8. Strobl, E.V., Visweswaran, S.: Deep multiple kernel learning. In: 2013 12th International Conference on Machine Learning and Applications, vol. 1, pp. 414–417. IEEE (2013) 9. Rebai, I., BenAyed, Y., Mahdi, W.: Deep multilayer multiple kernel learning. Neural Comput. Appl. 27(8), 2305–2314 (2016) 10. Rebai, I., BenAyed, Y., Mahdi, W.: Deep architecture using Multi-Kernel Learning and multiclassifier methods. In: 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), pp. 1–6. IEEE (2015) 11. Rebai, I., BenAyed, Y., Mahdi, W.: Deep kernel-SVM network. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 1955–1960. IEEE (2016) 12. Le, L., Hao, J., Xie, Y., et al.: Deep kernel: learning kernel function from data using deep neural network. In: 2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT), pp. 1–7. IEEE (2016) 13. Aiolli, F., Donini, M.: EasyMKL: a scalable multiple kernel learning algorithm. Neurocomputing 169, 215–224 (2015)

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14. Rakotomamonjy, A., Bach, F.R., Canu, S., et al.: SimpleMKL. J. Mach. Learn. Res. 9(Nov), 2491–2521 (2008) 15. Xu, X., Tsang, I.W., Xu, D.: Soft margin multiple kernel learning. IEEE Trans. Neural Netw. Learn. Syst. 24(5), 749–761 (2013) 16. Cortes, C., Mohri, M., Rostamizadeh, A.: L2 regularization for learning kernels. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 109–116. AUAI Press (2009) 17. Kloft, M., Brefeld, U., Sonnenburg, S., et al.: Lp-norm multiple kernel learning. J. Mach. Learn. Res. 12(Mar), 953–997 (2011)

Quasiconformal Mahalanobis Distance-Based Kernel Mapping Machine Learning for Hyperspectral Data Classification Jing Liu and Yulong Qiao(B) College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China [email protected]

Abstract. In this paper, we present quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification algorithm for the internet-based hyperspectral data retrieval. The contributions include three points: The quasiconformal mapping-based multiple kernels learning network framework is proposed for hyperspectral data classification, and the Mahalanobis distance kernel function is as the network nodes with the higher discriminative ability than Euclidean distance-based kernel function learning, and the objective function of measuring the class discriminative ability is proposed to seek the optimal parameters of the quasiconformal mapping projection. Experiments show that the proposed scheme is effective to the hyperspectral image classification and retrieval. Keywords: AIoT · Data retrieval · Hyperspectral data · Machine learning · Kernel machine

1 Introduction Hyperspectral data-based machine learning is feasible and effective method to extract the features for the image retrieval. The machine learning methods are divided into unsupervised and supervised learning. The unsupervised learning includes multi-dimensional scaling, NMF, ICA, neighborhood preserving embedding, locality preserving projection (LPP) [1], and other computing methods [2], as the supervised learning, generalized discriminant analysis [3], uncorrelated discriminant vectors analysis [4], and some accelerate algorithm [5, 6]. In recent years, the kernel-based machine learning algorithms were presented for the feature extraction; this paper proposes an improved kernel function supervised kernel-based LPP, local structure supervised feature extraction [7], kernel subspace LDA [8], kernel MSE [9], and quasiconformal mapping-based kernel machine [10]. In the algorithm, we proposed the quasiconformal mapping-based kernel learning for hyperspectral data classification for data retrieval in the Internet environment. And, the Mahalanobis distance kernel function is applied to extract the nonlinear feature, with higher discriminative ability than Euclidean distance-based kernel function learning. The objective function of quasiconformal kernel learning is created with Fisher © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_60

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criterion which is proposed to seek the optimal parameters of the quasiconformal mapping projection. The proposed scheme is effective to the hyperspectral image retrieval under the Internet environment.

2 Proposed Algorithm Given a sample set X ∈ RN , x, y, z are the samples in the sample set; if a function d : X × X → R+ defined in the vector space satisfies the following properties, then d is called a distance measure function: Symmetry: d (x, y) = d (y, x); Nonnegative: d (x, y) ≥ 0; Distinguishability: d (x, y) = 0 ⇔ x = y; Triangle inequality: d (x, y) + d (y, z) ≥ d (x, z). In the metric function that satisfies the above properties, the Euclidean distance is the most common distance metric function, which measures the absolute distance between spatial sample points, which is defined as:  (1) d (x, y) = (x − y)T (x − y) The Euclidean distance is characterized by simple calculation, but since the absolute distance measured is directly related to the coordinates of the position of each point, the adaptability to the data is poor in terms of feature scale and coupling degree between features. Cosine similarity mainly measures the consistency of direction between two vectors, which is defined as: d (x, y) = 1 −

x•y xy

(2)

Compared with the Euclidean distance, the cosine similarity measures the angle between the space vectors, which reflects the difference in the direction of the vector and is insensitive to the absolute value. The Minkowski distance is a general expression of a class of distance functions. Given two N-dimensional vectors: x = {x1, , x2 , . . . , xN } and y = {y1 , y2 , . . . , yN }, the Minkowski distance is defined as: 1/ P  N  P (xs − ys ) (3) d (x, y) = s=1

where P takes a different value, the distance is derived as a different type of distance.  When P = 1, the distance is called Manhattan distance, that is,d (x, y) = ( N s=1 |xs − ys |). This distance is used to indicate the absolute wheelbase sum of the two points on the standard coordinate system. When P = 2, the distance is called as the Euclidean distance. When P → ∞, the Minkowski distance is called the Chebyshev distance, that is, d (x, y) = maxs (|xs − ys |), which represents the maximum value of the numerical difference between the coordinates. Similar to the Euclidean distance, the Minkowski distance is still related to the dimension of the feature, and the correlation between the features is not considered. The Mahalanobis distance was proposed by P.C. Mahalanobis. It is defined as:   (4) d (x, y) = (x − y)T S−1 (x − y) = (x − y)T M(x − y)

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where S is the data correlation matrix, M = S − 1 is Mahalanobis matrix. When M is a unit array, the Mahalanobis distance degenerates into a Euclidean distance, indicating that the Euclidean distance is a special case of the Mahalanobis distance. The greatest advantage of the Mahalanobis distance is the ability to remove the coupling between various features and is scale-invariant. Obviously, the traditional approach does not meet the diverse task requirements, nor does it make full use of the information contained in the sample features. Therefore, it is necessary to construct a suitable distance metric function by learning the multidimensional information provided by the sample features for specific problems, so as to provide the best expression of feature similarity. Linear metric learning refers to obtaining a new metric function by linear transformation, that is, the form of the mapping function f is f (x) = AT x, where A is a projection matrix. The purpose of learning is to be able to find a suitable matrix A. In the case where the initial distance measure is Euclidean distance, the new measure function is:  (5) d˜ (x, y) = (AT x − AT y)T (AT x − AT y) At this time, for the real matrix A, if M = AAT , M is a semi-positive symmetric matrix. The formula can be written as follows:  d˜ (x, y) = (AT x − AT y)T (AT x − AT y)  = (x − y)T AAT (x − y)  (6) = (x − y)T M(x − y) The above formula is similar to the Mahalanobis distance metric in form, but the traditional Mahalanobis distance metric uses the inverse of the covariance matrix as the Mahalanobis matrix. The M in the above equation expands to the semi-positive array, representing a more general Mahalanobis matrix, so linear metric learning is also known as Mahalanobis metric learning. From the perspective of using sample information, the traditional Mahalanobis matrix only uses the internal structure of the data, focusing on describing the distribution properties of the data, while the Mahalanobis matrix obtained by the metric learning makes full use of the relationship between the feature and the category label and focus on features that adequately reflect sample class differences to achieve a better metric function. In general, a metric learning problem can be transformed into a constrained optimization problem: min LX (M) + λr(M) M

s.t. cX (M)

(7)

where LX (M) represents the loss function on the training set X, and r(M) is a regularization term, which is used to correct the over-fitting, and λ is the preset regularization factor, which is used to adjust the influence degree of the regularization term in the training process. cX (M) is the constraint on the training set. Different learning algorithms can be derived depending on the difference of loss function, the regularization term, and the constraints.

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Fisher criterion: The method is based on the pairwise constraint information provided by the sample as the priori information to minimize the similar sample pairs and control the distance between the non-similar sample pairs as the basic idea to construct a convex optimization problem and achieve the purpose of learning the Mahalanobis distance N matrix. First, given a sample set X : {xi }M i=1 ⊂ R , depending on whether the sample pairs belong to the same category, two constraint sets can be obtained: a homogeneous constraint set W and a non-like constraint set B. If the categories of the sample pairs are the same (similar), then the sample pair belongs to the set W, and if the categories of the sample pairs are not the same (similar), the sample pair belongs to the set B. If the Euclidean distance is used as the initial distance metric, considering that the postlearning metric can make the distance between the pairs of similar samples as small as possible, a convex optimization problem can be constructed:  2 dM (xi , xj ) min M≥0

(xi ,xj )∈W



s.t.

dM (xi , xj ) ≥ 1

(8)

(xi ,xj )∈B 2 (x , x )= (x − x )T M(x − x ), M are semi-positive definite matrices. The where dM i j i j i j constraint is added mainly to remove the trivial solution of M = 0. In the specific solution, iterative update method can be used to solve. In each iteration, the mature Newton downhill method is used to perform the gradient descent process to obtain an updated Mahalanobis matrix. The matrix is then iteratively mapped onto the constraint set. Although the algorithm is relatively simple in implementation, but the corresponding calculation amount is large in the case of large data size because the algorithm needs to construct all pairs of similar samples and non-similar samples in the whole dataset. And, the convergence speed of the algorithm is also slow. First, by introducing the projection matrix A, the distance between the pair of points becomes:  (9) dM (xi , xj ) = (AT xi − AT xj )T (AT xi − AT xj )

Considering the constraint set W, after the action of the projection matrix A, the sum of the squares of the distances between all pairs of points is:  dW = (AT xi − AT xj )T (AT xi − AT xj ) (10) (xi ,xj )∈W

The sum of the squares of the distances between all pairs of points in constraint set B can be calculated as:  (AT xi − AT xj )T (AT xi − AT xj ) dB = (xi ,xj )∈B

= tr(AT SB A)

(11)

Considering an excellent projection matrix A, it should maximize the distance between the samples in the constraint set B and reduce the distance between the samples

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in the constraint set W. Therefore, an objective function can be constructed by using the ratio of dW and dB to get an optimization problem, the optimal A* for the solution is: A∗ = arg max AT A=I

tr(AT SB A) tr(AT SW A)

(12)

Thus, calculating the Mahalanobis distance matrix is M∗ = A∗ (A∗ )T after learning.

3 Experiments and Analysis The performance of the proposed intelligent hyperspectral instrument is evaluated. The accuracy of spectrum classification is an important index to evaluate the performance of spectrum classification. The experiment was carried out on two sensing datasets of hyperspectral imager, i.e., Indian pine dataset and Pavia university dataset. The dataset of Indian pine is based on airborne platform, under the various spectral and the spatial resolutions. The data includes 224 0.4–2.5 μm bands. Nine kinds of 145 × 145 pixels images are realized in the experiment. The data collection at the University of Pavia is based on a reflective optical system imaging spectrometer (rosis). The data includes 115 bands. In the experiment, the performance of nine kinds of 610 × 340 images is verified. Except for the feature dimensions of the participating categories, the rest of the two experiments were identical. In the use of classification features, considering the computational efficiency and stability of the Mahalanobis matrix, the dimension of original spectral features is reduced by PCA. After the dimension reduction, the features are normalized to eliminate the deviation caused by the sampling method. For the first experiment, the top 30 principal components are selected to participate in the classification, that is, the feature dimension was 30. For experiment 2, the first 40 principal components are selected to participate in the classification, that is, the feature dimension is 40. On the classifier settings, the preset parameter values are selected by cross-validation by a standard multi-class SVM. In the kernel function setting, the Gaussian kernel function and the Mahalanobis Gaussian kernel function are used as the basis kernel functions, respectively. The scale parameter σ is set between [0.01, 2], and the number of basis kernels is 10. In terms of the evaluation index, the overall classification accuracy (OA) and Kappa coefficient (KC) are used as performance evaluation indicators, and information such as classifier training time, test time, and support vector number are collected. In the comparison method, the average multi-kernel and different multiple kernel learning methods are used as the multi-kernel combination coefficient algorithm, and the Euclidean distance Gaussian kernel and the Mahalanobis Gaussian kernel are, respectively, used for comparison. In this experiments, we implement four algorithms as follows. In the experiments, we evaluate the performance of the performance on quasiconformal kernel mapping on the two databases. The performance of quasicionformal kernel mapping is testified and evaluated with the polynomial kernel and Gaussian kernel. We have the kernel sparse representation classifier (KSRC) and support vector classifier (SVC) for classification. For comparisons, we also implement other algorithms, including SVM, RMKL-SVM, and POL-KSRC. The experimental results on two datasets are shown in Tables 1 and 2.

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Table 1. Performance on the Indian Pines data (%) Class

1

2

3

4

5

6

7

8

9

10

11

12

SVC 49.32 58.73 96.45 39.26 65.82 93.65 62.92 85.33 99.01 65.83 72.33 58.41 (polynomial) SVC (Gaussian)

78.02 73.65 99.16 76.92 80.52 97.12 79.78 89.80 99.79 83.64 86.04 80.74

KSRC 51.83 59.68 96.13 49.12 78.56 93.87 62.83 84.72 98.23 67.57 75.27 60.77 (polynomial) KSRC (Gaussian)

77.84 76.47 99.12 75.56 79.06 97.42 82.71 88.73 98.69 83.93 86.38 81.12

SVC (Q-kernel)

78.32 80.49 99.97 82.55 90.25 99.29 82.70 98.57 99.89 86.85 90.26 84.46

QMK (Q-kernel)

79.45 83.56 99.82 83.45 92.63 99.41 82.87 98.30 99.24 87.86 91.02 85.66

Table 2. Performance on the Pavia University data (%) Class SVM SVM MKL MKL KSRC KSRC Proposed (polynomial) (Gaussian) (polynomial) (Gaussian) (polynomial) (Gaussian) (Q-kernel) 1

83.93

84.45

84.24

88.22

84.93

85.42

92.24

2

85.23

91.45

90.29

93.15

86.26

92.45

94.36

3

70.24

74.14

74.61

78.23

71.27

75.13

83.27

4

87.27

90.47

90.26

89.22

88.28

91.45

91.93

5

96.28

97.62

97.23

97.24

97.23

98.65

98.73

6

70.41

78.77

77.47

84.26

71.47

79.72

84.15

7

69.33

71.13

70.16

76.27

69.92

72.17

82.62

8

76.24

81.53

80.22

82.93

77.26

82.56

86.27

9

98.56

99.42

99.24

99.26

98.85

99.93

99.93

In the comparison method, the average multi-kernel and different multiple kernel learning methods are used as the multi-kernel combination coefficient algorithm, and the Euclidean distance Gaussian kernel and the Mahalanobis Gaussian kernel are, respectively, used for comparison. In this experiments, we implement four algorithms as follows. Euclidean-MKL: The Euclidean distance kernel function. Each kernel function is combined according to the same weight, that is, the combination coefficient of each kernel function is the reciprocal of the number of kernel functions. See the description of literature for details. Mahalanobis-MKL1: The Mahalanobis distance kernel function is used for kernel learning. Euclidean-MKL2: The Euclidean distance kernel function is used. Mahalanobis-MKL2: The Mahalanobis distance kernel function is used, and the kernel learning is same to Euclidean-MKL2. The experimental results of different methods on Indian Pines dataset are shown in Tables 3 and 4. The experimental results of different methods on Pavia University dataset

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are shown in Tables 5 and 6. As these results, the proposed Mahalanobis distance kernel have the highest performance on the accuracy. Table 3. OA(%) of different methods on Indian Pines dataset Method

Feature dimension 10

20

30

40

50

57.23 ± 2.13 65.42 ± 1.95 67.98 ± 1.90 72.05 ± 1.24 74.23 ± 1.16

Euclidean-MKL1

Mahalanobis-MKL1 62.25 ± 2.45 67.84 ± 2.33 71.97 ± 1.51 74.69 ± 1.23 75.10 ± 0.98 59.10 ± 2.24 66.75 ± 1.82 71.08 ± 1.92 73.90 ± 1.24 76.07 ± 1.21

Euclidean-MKL2

Mahalanobis-MKL2 62.04 ± 2.34 68.63 ± 2.43 73.34 ± 1.59 74.97 ± 1.29 77.12 ± 1.10

Table 4. Kappa coefficient of different methods on Indian Pines dataset Method

Feature dimension 10

20

30

40

50

Euclidean-MKL1

0.532 0.598 0.595 0.678 0.696

Euclidean-MKL2

0.544 0.612 0.663 0.698 0.728

Mahalanobis-MKL1 0.565 0.641 0.682 0.712 0.732 Mahalanobis-MKL2 0.570 0.648 0.689 0.729 0.742

Table 5. OA(%) of different methods on Pavia University dataset Method

Feature dimension 10

Euclidean-MKL1

20

30

40

50

61.98 ± 3.16 67.78 ± 3.24 71.35 ± 2.39 75.35 ± 1.62 78.69 ± 1.12

Mahalanobis-MKL1 62.53 ± 3.23 69.34 ± 2.78 72.80 ± 2.10 76.24 ± 1.19 79.78 ± 1.05 Euclidean-MKL2

61.92 ± 3.21 66.97 ± 3.28 71.76 ± 2.39 75.56 ± 1.67 79.24 ± 1.33

Mahalanobis-MKL2 62.89 ± 3.22 69.42 ± 2.81 73.10 ± 2.23 76.34 ± 1.23 79.86 ± 1.16

4 Conclusion In this paper, we present the quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification algorithm. The contributions of the algorithm lies in the following points: The quasiconformal mapping-based multiple kernels learning network framework is proposed for hyperspectral data classification, and the Mahalanobis distance kernel function is as the network nodes with the higher discriminative ability than Euclidean distance-based kernel function learning, and the objective

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Table 6. Kappa coefficient of different methods on Pavia University dataset Method

Feature dimension 10

20

30

40

50

Euclidean-MKL1

0.548 0.608 0.658 0.704 0.721

Euclidean-MKL2

0.549 0.611 0.662 0.710 0.729

Mahalanobis-MKL1 0.554 0.619 0.672 0.716 0.734 Mahalanobis-MKL2 0.555 0.622 0.678 0.721 0.741

function of measuring the class discriminative ability is proposed to seek the optimal parameters of the quasiconformal mapping projection. Experiments show that the proposed scheme is effective to the hyperspectral image classification. The proposed algorithm has advantages on the large training samples constructing with the data from the Internet, including images, videos, and other information. Acknowledgements. This work is supported by the National Science Foundation of China under Grant No. 61871142.

References 1. Afzal, A., Asharaf, S.: Deep kernel learning in core vector machines. Pattern Anal. Appl. 21(3), 721–729 (2018) 2. Li, J., Pan, J., Chu, S.: Kernel class-wise locality preserving projection. Inf. Sci. 178(7), 1825–1835 (2008) 3. Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Comput. 12(10), 2385–2404 (2000) 4. Wang, L., Chan, L., Xue, P.: A criterion for optimizing kernel parameters in KBDA for image retrieval. IEEE Trans. Syst., Man Cybern.-Part B: Cybern. 35(3), 556–562 (2005) 5. Pan, J.S., Hu, P., Chu, S.C.: Novel parallel heterogeneous meta-heuristic and its communication strategies for the prediction of wind power. Processes 7(11), 845–856 (2019) 6. Pan, J., Lee, C.: Novel systolization of subquadratic space complexity multipliers based on toeplitz matrix-vector product approach. IEEE Trans. Very Large Scale Integr. Syst. 27(7), 1614–1622 (2019) 7. Zhao, H., Sun, S., Jing, Z., Yang, J.: Local structure based supervised feature extraction. Pattern Recogn. 39(8), 1546–1550 (2006) 8. Huang, J., Yuen, P., Chen, W., Lai, J.: Kernel subspace LDA with optimized kernel parameters on face recognition. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 187–192 (2004) 9. Tian, A., Chu, S., Pan, J.: A compact pigeon-inspired optimization for maximum short-term generation mode in cascade hydroelectric power station. Sustainability 12(3), 345–351 (2020) 10. Xie, X., Li, B., Chai, X.: A framework of quasiconformal mapping-based kernel machine with its application to hyperspectral remote sensing. Measurement 80, 270–280 (2016)

Research on Time-Delay Estimation of PMSM Driving System Based on RLS Method Zhong-zhen Chen1,2,3(B) , Dong-wei He1,2,3 , Li-sang Liu1,2,3 , Jian-xing Li1,2,3 , and Kuo-Chi Chang3,4,5 1 Technical Development Base of Industrial Integration Automation of Fujian Province, Fujian

University of Technology, Fuzhou 350118, China [email protected] 2 National Demonstration Center for Experimental Electronic Information and Electrical Technology Education, Fujian University of Technology, Fuzhou 350118, China 3 School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China 4 College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 5 Department of Business Administration, North Borneo University College, Sabah, Malaysia

Abstract. Under the influence of time-delay factors, the robustness of PMSM driving system reduces. Aiming at this phenomenon, a time-delay estimation method based on the least square (RLS) method for PMSM driving system was proposed. A difference equation-type mathematic model of PMSM driving system under the excitation voltage signal was built, considering time-delay effect. Based on which the equations of time-delay parameter were obtained. The parameters of difference equation are identified using the RLS method, and then the timedelay parameter was estimated by solving the proper equation after analysis. The simulation results showed that the proposed method could effectively estimate the time-delay parameter of the PMSM driving system, provide more accurate value for compensating the time-delay effect of the PMSM, and improve the robustness of the PMSM driving system. Keywords: PMSM · Recursive least square method · Pade approximate discretization · Time delay

1 Introduction Because of the characteristics of PMSM, such as high power density, high efficiency, simple structure and small size, it is widely used in industrial automation. But the driving control of PMSM was affected by the time delay factor. It reduces the performance and robustness of the PMSM driving system. Therefore, it was of great significance to measure the time delay of PMSM drive system for improving the stability and performance of the PMSM drive system [1, 2]. In PMSM driving system, time-delay effects were caused by processor operation, current conditioning circuit, AD sampling, digital control, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_61

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To estimate the time-delay parameter, Liao et al. built a motor drive model considering the influence of time delay [3], and a current loop controller with virtual impedance was designed to eliminate the effect of time delay but the delay-time parameter was given without experimental analysis. Wang et al. [4] proposed a time-delay process frequency domain identification algorithm by introducing the Pade approximation and the integral least square index. This method had a satisfactory effect on the simulation of large time-delay processes, but there is a large error in the time-delay process like PMSM driving the motor-driven system and the calculation was complex. It is not suitable for the estimation of the time delay of the motor-driven system, Lou et al. [5] used step DC voltage as input excitation signal and output current sampling value, and identified motor parameters based on recursive least square method. The advantage of this method is that input and output can fully reflect the characteristics of each frequency band of the system, which is simple and easy to realize. In this paper, a difference equation-type mathematic model of the PMSM driving system with time-delay factor was built using Pade approximation and bilinear transformation discretization, meanwhile, a group of the nonlinear equations with time-delay value were formed based on that the RLS method was adopted to realize the identification of the parameters of the difference equation, then the time-delay parameter was estimated by solving the nonlinear equations.

2 PMSM Driving System Model with Time-Delay Factor The voltage equation of PMSM in d–q-axis is  Vd = Rid + Ld didtd − ωe Lq iq di Vq = Riq + Lq dtq + ωe (ϕf + Ld id )

(1)

where R is the resistance of stator windings; ωe is the electrical rotor angular speed; ϕf is the magnetic flux linkage; Ld and Lq are the d- and q-axis inductance, respectively; Vd and Vq are the d- and q-axis voltage, respectively, id , iq are the d- and q-axis current, respectively. When the PMSM is injected with three-phase symmetrical voltage of high frequency and low voltage, the motor will maintain static state, which means the electric rotor angular speed is zero. The voltage equation of the PMSM in formula (1) should be changed to the form as follow.  Vd = Rid + Ld didtd (2) di Vq = Riq + Lq dtq Because there is no coupling between the voltage equations of the d- and q-axis in (2), obviously only one equation is needed for the estimation of the time-delay parameter when voltage and current data can be acquired; in this paper, the q-axis voltage equation is selected to derive the model of the permanent magnet synchronous motor driving system because of the weak saturation effect of the q-axis magnetic field.

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Using Laplace transform, from (2), we can derive the transfer function for the motor q-axis: Gq (s) =

Iq 1 = Vq R + Lq s

(3)

In the actual operation of PMSM driving system, the digital control produces the time-delay effect, due to the processor, current conditioning circuit, AD sampling, and other factors [6]. Therefore, considering all the time-delay factors of the PMSM driving system as a pure delay unit, based on formula (3), we can get the following model: G(s) =

Iq 1 e−Td s = Vq R + Lq s

(4)

where: Td is the overall time-delay parameter. For the digital control system, the time delay is not always an integral multiple of a sampling period obviously. In order to estimate the time-delay precisely, the transfer function model method needs to be linearized. Pade approximation is an efficient and convenient rational function approximation form, and it can not only achieve the linearization approximation of the time-delay unit but also make the model reach a very high accuracy approximation. The Pade approximation is used to approximate the time-delay unit e−τ s with rational polynomials, so the transfer function with time-delay unit, could be expressed as [7]: l j k j j=0 (l + k − j)l!(−τ s) j=0 (l + k − j)k!(−τ s) −τ s / (5) ≈ e j!(l − j)! j!(k − j)! where (5): l and k is order number,τ is delay time. In Pade approximation, the higher the order of l and k is chosen, the more perfect approaching performance the delay element e−τ s can be achieved. But the system is more complex and the calculation is more complex. The delay error of using the first-order or higher-order Pade approximation relative to the motor itself is not large. In order to simplify the measurement parameters of motor time delay and reduce the corresponding calculation amount, therefore, the first-order Pade approximation is more suitable for the calculation of motor time delay; the first-order Pade formula is: e−τ s = e−Td s =

−Td s/2 + 1 Td s/2 + 1

(6)

Using the first-order Pade transformation of formula (6), formula (4) could be rewritten as: G(s) =

Iq −Td s/2 + 1 1 = Vq R + Lq s Td s/2 + 1

(7)

Because the digital control system must be discrete, for the sake of implementation, the transfer function form model should be transform into discrete form. Adopting bilin−1 ear transformation in the form of s = T2 · 1−z , we can derived the difference equation 1+z −1 form model as follow from (7),

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G(z) =

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    T 2 − Td T + 2T 2 z −1 + T 2 + Td T z −2      =  2 RT + 2Td Lq + 2Lq T + Td RT + 2RT 2 − 4Td Lq z −1 + RT 2 + 2Td Lq − 2Lq T − Td RT z −2

(8) 10( −  4)s/; =    B0  where 2 T − Td T , B1 = 2T 2 , B2 = T 2 + Td T , A0 = RT 2 + 2Td L + 2LT + Td RT ,     A1 = 2RT 2 + 4Td L , A2 = RT 2 + 2Td L − 2LT − Td RT ; where B0 , B1 , B2 are the coefficients corresponding to the numerator of the difference equation, A0 , A1 , A2 are the coefficients corresponding to the denominator of the difference Eq. (8) can be rewritten as: G(z) =

B0 + B1 z −1 + B2 z −2 I = V A0 + A1 z −1 + A2 z −2

(9)

From equations above, we can easily deduce that we can estimate the time-delay parameter if we can identify the coefficients in (9).

3 Time Delay Parameter Estimate Solving time-delay parameters, this paper used voltage as the excitation signal to connect the motor model with time delay. Through sampling the current output and voltage data, the difference equation of the actual motor model was identified by the recursive least square method. The expression form of the discrete theoretical difference equation was consistent with that of the motor model through parameter identification. The coefficients of the equation and the parameters identified by the actual difference equation can form some equations with unknown delay parameters, so that the most accurate delay parameters could be solved by equation fitting. 3.1 Principle of Recursive Least Square (RLS) Method According to the black box structure of SISO system, the mathematical model of least square method can be obtained as follows [8]: z(k) = −a1 z(k − 1) − a2 z(k − 2) · · · an z(k − n) + b0 u(k) + b1 u(k − 1) · · · bn u(k − n)

(10)

The model defined according to formula (9) is changed to the least square format: z(k) = hT (k)θ + n(k)

(11)

 where, hT (k) = −z(k − 1) · · · −z(k − n) u(k − 1) · · · u(k − n) ; k is determined by  T the length of input and output data. θ = a1 · · · an b0 · · · bn is the parameter value to be estimated. n(k) is noise.

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In order to solve the above problems, the RLS method is proposed. In this method, the estimated values of the identified parameters are modified by many iterations, so the real-time online identification of parameters is realized, and the accuracy of the identification parameters is improved. The RLS formula is [8, 9]: ⎧ T ⎨ P(k) = P(k − 1) − K(k)h (k)P(k − 1) T K(k) = P(k − 1)h(k)[h (k)P(k − 1)h(k) + 1]−1 ⎩ θ (k) = θ (k − 1) + K(k)[z(k) − hT (k)θ (k − 1)]



(12)

3.2 Time Delay Parameter Estimation For the sake of RLS method, the second-order parameter identification model of PMSM is obtained from (9). G(z) =

B0 /A0 + (B1 /A0 )z −1 + (B2 /A0 )z −2 y(k) = u(k) 1 + (A1 /A0 )z −1 + (A2 /A0 )z −2

(13)

The coefficients of the difference equation identified by the RLS method equal to the coefficients in (13) theoretically. By this method, in Eq. (13), some nonlinear equations with unknown delay parameters were obtained: ⎧ ⎪ b0 = B0 /A0 ⎪ ⎪ ⎪ ⎪ ⎨ b1 = B1 /A0 (14) b2 = B2 /A0 ⎪ ⎪ ⎪ a1 = A1 /A0 ⎪ ⎪ ⎩ a = A /A 2 2 0 In formula (14): b0 , b1 , b2 , a1 , a2 are the identification coefficients. According to Eq. (14), a set of equations with time delay Td is obtained, and the optimal time delay Td value can be obtained by fitting the equations. However, since there are still errors between the theoretical coefficients and the identification parameters, it is necessary to make a comparative analysis of the identification errors; the errors are expressed as: ⎧ ⎪ ε1 = (b0 − B0 /A0 )/b0 ⎪ ⎪ ⎪ ⎪ ⎨ ε2 = (b1 − B1 /A0 )/b1 (15) ε3 = (b2 − B2 /A0 )/b2 ⎪ ⎪ ⎪ ε4 = (a1 − A1 /A0 )/a1 ⎪ ⎪ ⎩ ε = (a − A /A )/a 2 2 0 2 5 According to the error results of Eq. (15), the equation with reasonable error range should be selected, and the equation with serious error should be ignored to solve the fitting of time-delay value. That insure that the accurate time-delay parameter Td can be obtained.

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4 Simulation Verification and Analysis The validity and rationality of the proposed method are verified by the simulation using Simulink, and the simulation model is shown in Fig. 1. The simulation model is composed of voltage input, motor driving system, and data sampling system. And two types of PMSM are tested, and the parameters of PMSM are: R = 1.19 , Lq = 0.0159H and R = 1.05 , Lq = 0.00328H respectively. The voltage is Um = 30 sin(2π × 433) + 20sin(2π × 2.3)V. The system sampling rate is 10 kHz. Firstly, a simulation is carried out to verify the feasibility of the recursive least square method for parameter identification. Assuming that the motor parameters are R = 1.19 , Lq = 0.0159H and the time delay parameter is Td = 1.5 × 10−4 s. As shown in Fig. 2, after the 40 iterations of the recursive least square method, all the identified parameters converge, which verifies the effectiveness of the proposed method.

Fig. 1. Simulation model

1.2 1 b

0.8

b

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0.6

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a

0.2

a

0 1 2 1 2

0 -0.2 -0.4 -0.6 -0.8 -1 0

10

20

30

40

50

60

70

80

90

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Number of iterations

Fig. 2. Diagram of parameter identification

Secondly, a simulation is carried out to verify the accuracy of the proposed model. Under the same voltage excitation signal, the output currents of the identified motor model and the motor driving system are compared. As shown in Fig. 3, from the two

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current curves, we can find that the output currents of the identified motor model and the motor driving system are basically the same, and the error converges to zero after 0.06 s. So the proposed model is accuracy enough to be used to estimate the time-delay parameter. 20

Current output Identification model

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output current

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-5 0 0.005

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4 3 2 1 0 -1 0

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Fig. 3. Comparison of output currents

Thirdly, a serial of simulations is carried out to figure out the proper solution for the time-delay parameter Td . Set two sets of motor parameters (R = 1.19 , Lq = 0.0159H and R = 1.05 , Lq = 0.00328H), and choose different time-delay parameter (1.1 × 10−4 ∼ 3 × 10−4 ). The error of the identified coefficients and theoretical coefficients of Pade approximate discretized model are listed in Tables 1 and 2. From Fig. 4, we can find that the errors of ε1 , ε2 , ε3 , and ε5 reach more than 30%. In contrast, the error of

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ε4 is always kept below 10%. In order to get accurate Td value, the equation with large error is ignored; in this paper, the fourth equation with reasonable error is used to solve Td . Table 1. Identification and error analysis of theoretical parameters (R = 1.19 , Lq = 0.0159H) Error%

Td × 10−4 s 1.1

1.3

1.5

1.7

1.9

2.1

2.4

2.7 16.3

ε1 %

71.5

20.2

−15.6

−23.3

−13.5

−1.5

9.7

3 28.0

ε2 %

16.4

−7.3

−37.5

−55.7

−46.5

−24.0

1.4

18.5

42.4

ε3 %

1.1

19.4

25.5

25.1

20.6

13.6

3.1

−6.8

−32.4

ε4 %

−3.9

−7.0

−6.8

−5.1

−3.1

−1.4

0.1

1.1

2.1

ε5 %

−469.3

−130.6

−61.0

−30.3

−14.2

−5.6

0.5

3.3

6.0

Table 2. Identification and error analysis of theoretical parameters (R = 1.05 , Lq = 0.00328H) Error%

Td × 10−4 s 1.1

1.3

1.5

1.7

1.9

2.1

2.4

2.7

3

ε1 %

71.3

18.7

−23.6

−42.5

−42.9

−26.0

−18.8

−33.6

−11.3

−7.6

ε2 %

16.6

ε3 %

1.0

−42.0

−75.0

−89.6

−63.7

−51.7

−105.1

−26.7

19.58

26.2

26.8

24.1

18.0

12.3

12.9

1.1

ε4 %

−3.7

−7.0

−6.7

−4.9

−2.9

−1.1

0.4

1.3

2.3

ε5 %

−465.9

−131.3

−61.0

−30.0

13.9

−4.7

1.0

3.5

6.2

Fig. 4. Error comparison between identification coefficients and theoretical coefficients

Through the analysis above, the equation a1 = A1 /A0 is taken to solve Td . Set R = 1.19 , Lq = 0.0159H, the estimated error of serial of simulation are compared,

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  where the error is defined as: Td_ e − Td_ r /Td_ r × 100%, where Td_ e and Td_ r are the estimated and real time-delay parameter. As shows in Table 3 and Fig. 5, it can be concluded that the maximum error reach −14.42% when the time delay is 1.5 × 10−4 s, and the minimum error is when the time delay is 2.4 × 10−4 . The average error is −5.16% . The error analysis shows that the error of the estimated time-delay parameter is within a reasonable range; the proposed method is feasibility. Table 3. Estimated error comparison (R = 1.19 , Lq = 0.0159H) Td_ r × 10−4 s 1.1 Td_ e

× 10−4 s

Error%

1.017 −7.55

1.3

1.5 1.119

−13.92

1.7 1.284

−14.4

1.9 1.497

−11.94

1.746 −8.11

2.1

2.4

2.7

3

2.013 2.411 2.809 3.275 −4.14

0.46

4.04

9.16

Fig. 5. Estimated error of Td

5 Conclusion In this paper, a model of PMSM driving system with time delay was built, and a timedelay parameter estimation method was proposed based on the RLS method: the equations of time-delay parameter were obtained using the first-order Pade approximate and discretization, and then the parameters of the equation were identified by the RLS method, finally the time-delay parameter Td was solved. The simulations were carried out to verify the proposed method, and the results demonstrated that the proposed method could estimate the time-delay parameter of PMSM driving system more accurately. However, the estimation error is large in some conditions, which needs to be solved in the future research.

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Acknowledgements. The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported in part by Scientific Research Fund in FJUT under Grant GYZ18178, in part by the Natural Science Foundation of Fujian Province under Grant 2018J01640 and 2018J01507, and in part by the Education and research projects for young teachers in Fujian Provincial Education Department under Grant JAT170369 and in part by the Technology Development Base of Fujian Industrial Integration Automation Industries.

References 1. Sun, Y., Ling, L.: Current robust control for ironless linear permanent magnet synchronous motor. J. Electr. Eng. 5, 21–25 (2014) 2. Wang, W., Xi, X.: An improved pi regulator for current loop of pmsm taking one-step-delay into consideration. Proc. CSEE 34(12), 1882–1888 (2014) 3. Liao, Y., Li, F., Lin, H.: Virtual impedance-based current control for permanent magnet synchronous machine. zhongguodianjigongchengxuebao. Proceedings of the Chinese Society of Electrical Engineering, vol. 37, no. 19, pp. 5759–5766 (2017) 4. Wang, X., Shao, H.: A novel frequency domain identification method for systems with time delay based on integral least-square index. shanghaijiaotongdaxuexuebao/J. Shanghai Jiaotong Univ. 36(4), 539–542 (2002) 5. Lou, X., Chen, T., Zhu, S., et al.: Research on parameter identification technology of permanent magnet synchronous motor based on RLS Instrumentation and Automation,no 09, 2019,pp: 71–74 6. Li, F., Liao, Y., Lin, H.: A modified strategy for the current controller employing active resistance for permanent magnet synchronous machines. ZhongguoDianjiGongchengXuebao/Proc. Chin. Soc. Electr. Eng. 37(15), 4495–4502 (2017). Liu, C., Chen, P., Gao, J., et al.: An approximation method of digital control delay in inverter. J. Power Supply 13(3), 55–61 (2015) 7. Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Futur. Gener. Comput. Syst. 108, 445–453 (2020). ISSN 0167-739X. https://doi.org/10.1016/j.future.2020. 03.006 8. Xian-Liang, S., Wu, C.-F.: RLS parameter identification and emulate based on matlab/simulink. Microprocessors 06, 44–46 (2005) 9. Xin, Q., Wang, C., Yu, K.: Parameter identification of permanent magnet synchronous motor based on recursive least square method. Torpedo Technol. 22(6), 0452–0456 (2014)

Cache Learning Method for Terrific Detection of Atrial Fibrillation Mohamed Ezzeldin A. Bashir1 , Abdul Hakim H. M. Mohamed1 , Akbar Khanan1 , Fadi Abdel Muniem Abdel Fattah1 , Ling Wang2 , and Keun Ho Ryu3(B) 1 College of Business Administration, Al-Sharqiyah University, Ibra, Oman

{mohamed.bashir,abdulhakim.mohamed,akbar.khanan, fadi.fattah}@asu.edu.om 2 Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin City, China [email protected] 3 Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea [email protected]

Abstract. Atrial Fibrillation AF reported as the most occurring heart arrhythmia. Steadfast detection of AF in ECG monitoring systems is considerable for early treatment and health risks reduction. Various ECG mining and analysis efforts have addressed a wide variety of technical issues. However, the morphological descriptors are changing along the time within the different patients. As a result, the classification model constructed using old training data is not accurate enough to detect AF. This paper presents an outstanding dynamic learning method to achieve better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed 96.2%, 99.7%, and 99.4% for sensitivity, specificity, and overall accuracy, respectively. Accordingly, the proposed Cache learning method can be introduced to improve the performance of the AF intelligent detection systems. Keywords: Atrial Fibrillation (AF) · Electrocardiogram (ECG) · And dynamic learning

1 Introduction Atrial Fibrillation (AF) causes the heart to beat in irregular basis, which causes pushing blood ineffectively then the patient suffers from the lack of oxygen. Usually, it affects old peoples over the seventies [1]. AF can upgrade the stroke risk percentage up to 15–20% [2]. The patient with AF, reporting abnormal atrial activation in electrocardiogram (ECG), due to the random electrical signals generated by sinus node that reflects in continues circulating waves [3]. The ventricular response accordingly in the wrong manner because of the numerous fibrillatory waves circulate unevenly across the atrial © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_62

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myocardium. Hence, there is a miss synchronization between atrial rhythm and ventricular rhythm [4]. There are some methods developed to enumerate and detect AF regarding different features related to the ECG parameters [5]. But there are several limitations like the outsized morphological disparity of heart rhythm between different people and in the same person in different situations like sleeping or running or in a silent situation. In this paper, we propose the Cache learning method to enhance the development of a real-time AF detecting system, concerning the contest for training the classifiers model with a fresh dataset. This paper presents a summary of the related work, the proposed technique, experimental works, and the conclusion.

2 Related Work There are two main philosophies to train the classifier model, the first one based on splitting the dataset into two main groups with different percentages. One of them usually uses to train the classifier during the training time and another group uses to make sure that the classifier is working effectively by testing it and measuring sensitivity, specificity, and accuracy with an unobserved dataset offered in another group of data. Usually [6], the challenge of the morphologies ECG waveforms that differs from time to time with the patient and from different patients concerning sex, age, etc., is not considered. Accordingly, the performance of the classifier looks accepted when using the same patient’s dataset for learning it, while its performance often drops very much when the testing process is done through a group of patients. The literature shows that researches and merchants attempt to concord the mentioned problem in this approach by increasing the amount of dataset that uses for training as much as they can to overcome this problem, but using a very big training dataset reflects in the complexity of the system in terms of development and troubleshooting. Additionally, it is unbearable to familiarize the classifier with all ECG waveforms from all anticipated patients [7]. The second approach basses on developing a classifier model to monitor a specific patient [8]. Splitting down the database according to each patient is very costly especially in a huge database. Furthermore, it is very rare to find patients to involve them in the process of development especially they are sick, and it is very hard for them to spend time in laboratories. Hu et al. [9] introduced a technique that makes the training process self-adapting by utilizing the mixture-of-expert (MOE) to obtain patient readjustment. The self-adapting training method saves the efforts of dividing the database manually. Nevertheless, there are several downsides such as weakness of the sensitivity that generated due to the comparison between different modules, not only that but also it is very expansive because the system depends on developing a local expert for each patient need to be monitor. Furthermore, using more than one classifier at the same time maximizes the chance of mistakes. In previous works, Bashir et al. [8] proposed a nested ensemble method to relief the self-adapting technique’s problem by adapting the training dataset in a dynamic manner using fresh data, not only that but also acclimatizing the ECG morphological features related to each heart arrhythmia to improve the accuracy. Even though the performance is outstanding but harmonizing the dynamic training side with customized ECG feature side is very expensive.

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3 Cache Learning Method Cache technique attempts to provide a very smart dynamic learning process by offering a renewed training set of data to overcome changes of ECG morphological features, which keep changing through time. IT has four steps, as shown in (Fig. 1). The preliminary learning phase screening, development, and the elimination phase.

Fig. 1. Cache learning technique

3.1 Preliminary Learning The learning process begins with half of the overall data randomly. In this step, there is no kind of check or measurement, also there is not any kind of improvement. 3.2 Screening TrustM (x) is the overall trust index that indicates the usability of the training set (x) according to the label assigned to the different types of arrhythmia. The TrustM (x) is elaborated by L M (x) the local trust index, which accumulatively when classifying specific arrhythmia accurately using a specific set of ECG features. The L M (x) increases rapidly by one unit every time the list of features nominated to detect specific arrhythmia

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succeeds to detect arrhythmia. Formula (1) details the calculation of the L M (x).  LM (x) = βf (F, i).C S (x)

515

(1)

f ∈features

The weight of the exact feature to detect specific arrhythmia is represented by (F), (f ) represents an index of a specific feature, and the weight of the overall class of features C S (x) that succeed to detect arrhythmia is given by formula (2).  C S (x) = βf (F, i) (2) f ∈features

The class of features (F) used to detect specific arrhythmia (i) is passed to the Function β f (F, i). If the class of the features (F) succeed to detect the Arrhythmia (i), the function returns (+1) and if it fails to detect arrhythmia, it returns (−1).  +1 if label(x) = i βf (F, i) = (3) −1 otherwise The overall trust index TrustM (x) obtained by utilizing L M (x) the local trust index in formula (4) and the sigmoid function (0.5 < TrustM (X) < 1) that calculate in formula (5). Trust (X ) = sigmoid M

n 

LM (x)

(4)

x=1

sigmoid(x) =

1 1 + exp(x)

(5)

3.3 Development The dataset that performs oddly is going to be modified either partially or completely by a new fresh training dataset. The development phase has two sub-steps. The first one is labeling the impractical features and or class of features then, substitute it/them with a new selected feature or class. The labeling phase is conducted in the current training set utilized to detect arrhythmias. The current training set is grouped into a set of classes the class is going to be removed if its overall trust score is below than threshold δ remove . Formula 6 explain the calculation of class score: if C S (x) < δremove then remove

(6)

The removed class will be copied in a simple design file called Cache. It will not send back to the main database. The cache arranges according to the class score of the class, Cache allocates the classes in ascending way. The position inside the Cache is dynamic which means every time a removed class arrives, the Cache checks its score

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and compares it against the other class scores then the class takes the right position. The Cache file capacity is limited to 50% of the overall data size to keep the percentage of the training dataset fixed and not more to the half of the overall data. The process refers to the Cache to choose from the top to replace the removed class and if the performance not improved, then finally, a new class from the main database fetched accidentally. The chance PC to choose specific class is relative to the overall trust that explained in formula (4)     C S xselected/removed C   p xselected/removed = (7)  S j C xj Calculation of the probability of the two classes the removed (x removed ) and the selected (x selected ) are considered to evade selecting a recently removed class. The current training set is updated by the fresh selected class. Consequently, the screening and development continued and repeated always to keep the classifier performance effective. Starting from the second modification stage, the substitutions take place from the Cache, and not from the main database. The selection of the substituted category is depending on the C S (x), so the highest class on the top of the Cache will be nominated. The movement from the Cache is taking place when the class is nominated twice and fails then returns to the cache after it removed from the current learning dataset. Therefore, it replaced by fresh class from the main database by considering a random probability mentioned in formula (7). The screening and development steps cause continuous movement of the classes from the current training set to the Cache and from Cache to the main database. All classes have the same possibility to be selected in the ingoing update process without any consideration of their previous performances. 3.4 Elimination When partial moderation repeated many times without noticeable improvement in the performance of the classifier, then the development process becomes useless. Hitting Cache rapidly increases the computation cost very clearly. Accordingly, there is a strong need to replace the whole current learning group of data with a new one. The elimination process removes all the classes in the current group of the training set (x) if the defect score DS (x) is less than the threshold θ remove calculation of the defect score is in Formula (8). if DS (X ) > θremove then remove

(8)

Likewise, the elimination process takes place through the cache like the development process expect the first learning process when the group is selected randomly. The whole cache contains will be removed, if it is reactivated and removed from the active learning set two times. Nevertheless, a fresh set of data nominated but not in a random manner. Formula (7) can be utilized for this purpose and restarted from the beginning again. Bear in mind that, the ratio of the training dataset to the validation dataset is not changing during both development and elimination stages.

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4 Experimental Work A database developed in the University of California, Irvine [10], is used in the experimental work of this paper. The database is introduced by Waikato Environment for Knowledge Analysis (WEKA). It contains 279 features and 452 records [11]. The normal arrhythmia seen in classes from 01 to 15. There are more than fifteen kinds of different arrhythmias like Old Inferior Myocardial Infarction, Left bundle branch block, Right bundle branch block, Sinus bradycardia, Sinus tachycardia, degree AV block, degree AV block, left ventricular hypertrophy, Ventricular Premature Contraction (PVC), Atrial Fibrillation or Flutter is one of them. Besides the normal rhythm. WEKA 3.6.1 environment is used to conduct the experimental works in personal computer intel CORE i3, 1.70 GHz speed. 4.00 GB RAM. The threshold defined for δ remove to be 1.0 in Formula (6) while θ remove is set to be 0.5 in Formula (8). The cache learning method is experimented to detect AF. The cases of AF were increased in the total database by duplicating the cases, generating 21 cases of Atrial Fibrillation among the different arrhythmia and normal rhythm. The Cache learning method is equipped by the J48 algorithm as a classifier. The sensitivity, specificity, and accuracy are measured to detail the performance of the Cache method. A confusion matrix is used to present the performance of the classifier, where the FN, FP, TN, and TP, represent the false negative, false positive, true negative, and true positive, respectively. The performance of the classifier J45 when feeding by Cache learning to diagnose the Atrial Fibrillation (AF) is summarized in Table 1. It illustrates the performance of the Cache learning method with different ECG morphological features. Table 1. Performance of cache learning using P, QRS, and T ECG parameters Parameters

TP

FN

FP

TN

Sensitivity (%)

Specificity (%)

Accuracy (%)

QRS only

17

3

4

225

85.5

98.3

97.2

QRS + P

18

3

3

225

85.7

98.7

97.5

QRS + P + T

19

1

1

228

96.2

99.7

99.4

The results show that the performance of the cache learning technique improves to some extent with the QRS complex features when added to the P-wave features comparing with the performance with features related to the QRS complex alone,.while the performance of the classifier is improved very clearly when using all features related to the ECG morphological parameters P, QRS, and T. The literature shows that, so many researchers are utilizing the only QRS mainly the R parameter or P to detect AF. There is not any attempt to utilize the other waves or introducing all waves together. The reason is that such an attempt will increase the computational cost [12, 13]. Although the detection of the AF very relates to the R and or P waves, the other waves also affected the AF somehow. The result that summarized in Table 2 illustrates the improvement in the performance of the classifier when introducing all features related to all ECG morphological parameters. It is noticeable, the classifier learned by the Cache learning

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technique is unique especially when introducing all ECG features related to the P, QRS, and T waves. Table 2. Performance of Cache method against other techniques to detects AF Author

Database Method

Sensitivity (%) Specificity (%) Accuracy (%)

Christov et al. [12]

ECG

P wave

95.7

–

98.8

Fukunami et al. ECG [13]

P wave

91

76

–

Trigger methods [7]

ECG

QRS + P + T 95.0

99.6

99.2

Cache learning

ECG

QRS + P + T 96.2

99.7

99.4

5 Conclusion In this paper, the Cache method was introduced to detects AF arrhythmia very effectively and efficiently. Cache technique attempts to provide a very smart self-motivated learning process by offering rehabilitated training sets of data to overcome variations of ECG morphological features, which keep changing through time. Various approaches have been used to measure the performance of the Cache learning method. The results validate the outstanding performance of the Cache learning techniques, especially when using all ECG parameters. Acknowledgements. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2019K2A9A2A06020672 and No. 2020R1A2B5B02001717).

References 1. Bashir, M.E.A., Lee, D.G., Minghao, P., Shon, H.S., Ryu, K.H.: Crucial ECG parameters for precise atrial fibrillation revealing. In: The 4th International Conf FITAT 2011, Chanjue, South Korea 2. Rajendra, U., Sankaranarayanan, M., Nayak, J., Xiang, C., Tamura, T.: Automatic identification of cardiac health using modeling techniques: a comparative study. Inf. Sci. 178, 457–4582 (2008) 3. Rodrigues, J., Goni, A., Illarramendi, A.: Real-time classification of ECG on a PDA. Trans. IT B.med. IEEE 23–33 (2005) 4. Bashir, M.E.A., Akasha, M., Lee, D.G., Yi, M., Ryu, K.H., Bae, E.J., Cho, M., Yoo, C.: Highlighting the Current Issues with Pride Suggestions for Improving the Performance of Real-Time Cardiac Health Monitoring. DEXABilbao, Spain (2010)

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5. Bortolan, G., Jekova, I., Christov, I.: Comparison of four methods for premature ventricular contractions and normal beats clustering. Com. Card. 30, 921–924 (2005) 6. Bashir, M.E.A., Lee, D.G., Li, M., Bae, J.W., Shon, H.S., Cho, M.C., et al.: Trigger learning and ECG parameter customization for remote cardiac clinical care information system. IEEE Trans. Inf. Technol. Biomed. 16(4), 561–571 (2012) 7. Bashir, M.E.A., Ryu, K.S., Yun, U., Ryu, K.H.: Pro-detection of atrial fibrillation using a mixture of experts. IEICE Trans. Inf. Syst. 12, 2982–2990 (2012) 8. Bashir, M.E.A., Akasha, M., Lee, D.G., Yi, M., Ryu, K.H., Bae, E.J., Cho, M., Yoo, C.: Nested ensemble technique for excellence real-time cardiac health monitoring. BioComp LasVegas USA (2010) 9. Christov, I., Bortolan, G.: Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks. Phys. Meas. 1281–1290 (2004) 10. UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLRepository.html 11. WEKA web site, http://www.cs.waikato.ac.nz/~ml/weka/index.html 12. Christov, I., Bortolan, G., Daskalov, I.: Sequential analysis for automatic detection of atrial fibrillation and flutter. Comput. Cardiol. 293–296 (2001) 13. Fukunami, M., Yamada, T., Ohmori, M., Kumagai, K., Umemoto, K., Sakai, A., Kondoh, N., Minamino, T., Hoki, N.: Detection of patients at risk for paroxysmal atrial fibrillation during sinus rhythm by P wave-triggered signal-averaged electrocardiogram. Circul. 83, 162–169 (1991)

Enhanced the Depth of Integral Image Display by Using Barrier Array Yulian Cao

and Ganbat Baasantseren(B)

National University of Mongolia, Ulaanbaatar 14201, Mongolia [email protected]

Abstract. Integral imaging (InIm) is one of the most promising technologies for producing full color three-dimensional (3D) images with full parallax. However, the depth of the InIm display is shallow. We proposed a method to enhance the depth of InIm display using static barrier array. We created the barrier array the same size as the lens array and put it in front of the lens array. Observed the image on the diffuser, a clear 3D image is created. From the experimental results, the depth of the InIm display is enhanced. Keywords: Integral imaging display · Elemental image · Barrier · 3-D display

1 Introduction InIm was first proposed by Lippmann [1] in 1908; various methods have been studied to implement 3D image display systems [2–8]. Integral imaging is a promising 3D display technology because it does not require any special glasses and provides auto-stereoscopic images with both horizontal and vertical parallaxes, continuous viewpoints within the viewing angle, display full color and real-time 3D animated images, multiple observers can see 3D images freely within the viewing angle, it provides natural depth perception with relatively low eye-fatigue, but it has a limited viewing-resolution, low viewing angle, and short image-depth range. Therefore, we propose new methods to enhance the depth range of InIm display. Heejin et al. [9] used a composite lens array or a stepped lens array to enhance the depth of InIm display. They used the stepped lens array; this method can enhance the depth of InIm display, but the InIm display become thick. Kim et al. [10] used a floating display system based on integral imaging enhanced depth range, but the resolution and brightness of the system are degraded. Lee et al. [11] proposed to enhance depth of InIm display system with electrically variable image planes using polymer-dispersed liquid-crystal layers, and this method enables control of the location of image planes electrically, without any mechanical movement and enhances the depth. However, the price of this investment will be high. This paper proposes to enhance the depth range of InIm display, which uses barrier array. The experiment proved the feasibility of this method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2_63

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2 Depth Range of InIm Display The InIm display system has a lens array and a two-dimensional (2D) display panel. The rays displayed in the display panel pass through the lens array and are represented as a 3D image in space. Figure 1 shows the structure of an InIm display. Each lens creates a real image of the elemental image in the central depth plane (CDP) focal plane. The distance of the CDP is calculated using the Gaussian lens equation as follows:

Fig. 1. Propagation of light beam in an InIm display

LC = f · g/(g − f ),

(1)

where g is distance between the 2D display panel and the lens array and f is the focal length of the lens array. Element images through the lens array create a clear 3D InIm on the CDP. If the distance of the InIm is longer or shorter than the CDP, the image is repeated or clipped, and the size of one pixel of the InIm becomes larger and the image is scattered. The integrated point affects the depth of the InIm display. Therefore, we need to determine the quantitative criteria for the depth range of the InIm display. Figure 2 shows the pixel PI of InIm and 2D display pixel. Calculate the size of the PI geometrically optical: PI = (LC · PD )/g,

(2)

where PD is the pixel size of the 2D display panel. We take the distance between the front depth plane (FDP) and the rear depth plane (RDP) as the depth range of InIm display. The FDP and RDP are defined by the distance at which the integrated pixel size equals the size of PI . Suppose that three light beams from the elemental image pixels (EIP1 , EIP2 , and EIP3 ) are focused on the corresponding elemental lenses L2 , L3 , and L4 and are focused on the CDP, as shown in Fig. 1. These beams cross at the integrated image point PI at the CDP. From Fig. 2, L F and L R are given by: LF = LC · (PL + PI )/PL ,

(3)

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Fig. 2. Structure of InIm display

LR = LC · (PL − PI )/PL ,

(4)

where PL is the pitch of lens array. From Eqs. (3) and (4), calculate the depth range of InIm display: d = LF − LR = 2LC · PI /PL .

(5)

The depth of the lens array to be used for the experiment, PL = 50 mm, the focal length f = 90 mm, PD = 0.6 mm, g = 120 mm, d = 25.92 mm from Eq. (5). It is too short.

3 Proposed Method Depth of field (DOF) is defined as the area in a projected image, forward and after the focal plane, which also appears to be in focus in the image. Figure 3 shows the camera lens focus the light on the image plane sensor. Objects at different distances, although identical in size, will create spots of different sizes (and blur) at the image plane. When passing light through a lens and focus that light to form an image on a piece of film, the area of the image that is in true focus is razor-thin the focal plane. Figure 4 shows the camera aperture, the size of the opening of the lens, not only controls how much light enters a lens, it affects the depth of field. The large aperture is shallow the DOF. The small aperture is long the DOF. Here f is the focal length of the camera lens. Figure 4 compares the image taken at large and low apertures, respectively. In Fig. 4b, the background is clearly visible, and the background is invisible in Fig. 4a. This shows that if the size of the aperture is small, the depth will increase, so we have proposed a method of using a barrier.

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Fig. 3. Objects at different distances the camera lens focus the light will create spots of different sizes (and blur) at the image plane sensor

Fig. 4. Aperture depth of field comparison

4 Experimental Results 4.1 Effect of Barrier on the Depth The effect of barrier size on lens depth was compared in three cases. In the experiment, a lens array of size 5 cm × 5 cm was used, so the barrier was made of the same size, uand by sing MATLAB, creating two barriers of 3 cm × 3 cm and 1 cm × 1 cm in the center, as shown in Fig. 5. A black 5 cm × 5 cm square with a white border on a 2- display is placed behind a lens by creating 5 points with a spacing of 14 mm. Five points through the lens array created a 5 points InIm, according to experimental results, as shown in Table 1. Five points created a clear image at different distances from the lens array, respectively. According to Table 1, we will describe the following three aspects. Figure 6 shows when without barrier the 3rd ray on the 2D display passes through the lens to create

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Fig. 5. Aperture size when a 3 cm × 3 cm, b 1 cm × 1 cm Table 1. When has a barrier and without barrier, 5 points through the lens array created a clear position of InIm, distance from the lens array is show Barrier

5 points InIm distance from the lens array

Without barrier

280

310

360

310

280

3 cm × 3 cm barrier 290

330

360

330

290

1 cm × 1 cm barrier 355

356

360

356

355

1 (mm) 2 (mm) 3 (mm) 4 (mm) 5 (mm)

a clear image on the CDP. The 2nd and 4th rays created a clear image at L 2 = L 4 = 310 mm, while the 1st and 5th rays created a clear image at L 1 = L 5 = 280 mm.

Fig. 6. Propagation of light without barrier

Figure 7a shows a 3 cm × 3 cm barrier, where is on front of the lens and repeat the previous experiment, the 3rd ray passes through the lens to create a clear image at the

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CDP. The 2nd and 4th rays have a clear image at L 2 = L 4 = 330 mm, while the rays 1st and 5th have a clear image at L 1 = L 5 = 290 mm.

Fig. 7. Propagation of light when a the barrier is 3 cm × 3 cm and b 1 cm × 1 cm

Figure 7b shows a 1 cm × 1 cm barrier, the 3rd ray passes through the lens to create a clear image at the CDP. The 2nd and 4th rays have a clear image at L 2 = L 4 = 356 mm, while the 1st and 5th rays have a clear image at L 1 = L 5 = 355 mm. In the above experiment, the diffuser was placed on the CDP at a distance of 360 mm from the lens. Figure 8 shows the InIm image obtained when had a barrier or without a barrier. Figure 8a shows without a barrier, the image is large and blurred from the center to the edge. Figure 8b shows when the barrier is 3 cm × 3 cm, the image relatively clear from the center to the edge. Figure 8c shows when the barrier is 1 cm × 1 cm, the image so clear from the center to the edge. According to experimental results show that the small barrier reduces distortion.

Fig. 8. Experimental results when a without barrier, b 3 cm × 3 cm barrier, and c 1 cm × 1 cm barrier

4.2 Enhanced the Depth Range of the 3-D Display by Using Barrier Array A black square of size 5 cm × 5 cm is used to create in the center a white square of size 1 cm × 1 cm, as shown in Fig. 9a. Dig out the white part after printing it on the paper until the cover is full. Put this barrier array in front of the lens array to a size of 5 cm × 5 cm.

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Fig. 9. a Barrier array and b using MATLAB created the elemental image of E and S

In Fig. 9b, MATLAB is used to create an element image of ‘E’ and ‘S’, created an InIm of the two images ‘E’ and ‘S’, and used a diffuser to determine the location. Figure 10 shows the experimental setup of InIm display. First, ‘E’ and ‘S’ are placed on the CDP and created element images. Figure 11 shows with barrier array and without barrier array. Figure 11a shows without barrier array, E and S are blurred from the center to the edge. Figure 11b, the InIm of E and S is clear when had the barrier array.

Fig. 10. Experimental setup of InIm display

In two sets of experiments, ‘E’ and ‘S’ were changed the distance of 5 mm from CDP to FDP and RDP compared to the traditional method. The first experiment was performed by putting a diffuser on an E image, as shown in Fig. 12. The second repeat of the previous experiment by putting the diffuser on ‘S’, as shown in Fig. 13. Let us compare the last two images in Fig. 12a without barrier array and Fig. 12b have a barrier array. When without barrier, ‘E’ is blurred from the center to the edge, when it has a barrier array, ‘E’ is clear from the center to the edge. Compared the last two S images in Fig. 13a, b produces the same results as before.

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Fig. 11. InIm ‘E’ and ‘S’, when a without barrier and b with barrier array experimental results

Fig. 12. When the diffuser is put on the E a without barrier array and b with barrier array

Fig. 13. When the diffuser is put on the S a without barrier array and b with barrier array

5 Conclusion In this paper, we proposed a barrier array. The barrier array put in front of the lens array this method that provides a clear 3D image to the observer. Experimental results show that enhanced the depth of InIm display by using barrier array but reduce the intensity of the InIm. The new approach used just a static barrier array. Therefore, it is possible

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to apply for all InIm display. The intensity of InIm display is low, so we will decide this disadvantage in the future work.

References 1. Lippmann, G.: La Photographie Integrale. Comptes-Rendus Academie des Sciences 146, 446–451 (1908) 2. Ives, H.E.: Optical properties of a Lippmann lenticulated sheet. J. Opt. Soc. Am. 21(3), 71–176 (1931) 3. Dodgson, N.A.: Autostereoscopic 3D displays. Computer 38(8), 31–36 (2005) 4. Davies, N., McCormick, M., Brewin, M.: Design and analysis of an image transfer system using microlens arrays. Opt. Eng. 33(11), 3624–3633 (1994) 5. Okano, F., Hoshino, H., Arai, J., Yuyama, I.: Real-time pickup method for a three-dimensional image based on integral photography. Appl. Opt. 36(7), 1598–1603 (1997) 6. Erdmann, L., Gabriel, K.J.: High-resolution digital integral photography by use of a scanning microlens array. Appl. Opt. 40(31), 5592–5599 (2001) 7. Jang, J.-S., Javidi, B.: Improved viewing resolution of three-dimensional integral imaging with nonstationary microoptics. Opt. Lett. 27(5), 324–326 (2002) 8. Liao, H., Iwahara, M., Hata, N., Dohi, T.: High-quality integral videography using a multiprojector. Opt. Express 12(6), 1067–1076 (2004) 9. Choi, H., Park, J.H., Hong, J., Lee, B.: Depth-enhanced integral imaging with a stepped lens array or a composite lens array for three-dimensional display. Jpn. Soc. Appl. Phys. 43(8A), 730–731 (2004) 10. Kim, J., Min, S.W., Kim, Y., Cho, S.W., Heejin, C., Lee, B.: A depth-enhanced floating display system based on integral imaging. In: SPIE JBO webinar on Proceedings, pp. 60551F1–9. Society of Photo-Optical Instrumentation Engineers digital library, United States (2006) 11. Kim, Y., Choi, H., Kim, J., Cho, S.W., Kim, Y., Park, G., Lee, B.: Depth-enhanced integral imaging display system with electrically variable image planes using polymer-dispersed liquid-crystal layers. Appl. Opt. 46(18), 3766–3773 (2007)

Author Index

A Ahmad, Shoaib, 32, 41, 169 Amesimenu, Governor David Kwabena, 32, 41, 57, 123, 160, 169, 178 Ariunaa, Orgilbat, 74

B Baasantseren, Ganbat, 9, 82, 520 Badarch, Zorig, 9 Bashir, Mohamed Ezzeldin A., 512 Batbayar, S., 16 Bat-Ulzii, Sh., 16 Bi, Huijing, 323

C Cao, Pengcheng, 1 Cao, Yulian, 520 Chagnaadorj, Bymba-Ochir, 82 Cha, Hyo Soung, 246, 262, 268, 274 Chang, Fu-Hsiang, 32, 41, 57, 114, 123, 148, 160, 169, 178 Chang, Kuo-Chi, 1, 32, 41, 57, 99, 106, 114, 123, 148, 160, 169, 178, 219, 228, 502 Chen, Cuijuan, 314 Chen, Hang, 289 Chen, Jianfei, 357 Chen, Li-Wen, 219 Chen, Yi-Hao, 467 Chen, Yuanhang, 475 Chen, Zhong-zhen, 502 Choi, Kui Son, 246, 262, 268, 274

Chu, Kai-Chun, 32, 41, 57, 114, 123, 148, 160, 169, 178 Chu, Shu-Chuan, 228

D Dalkhaa, Nomin-Erdene, 9, 82 Deng, Hui-Qiong, 99, 106, 160 Do Thanh Tung, Hoang, 253

F Fattah, Fadi Abdel Muniem Abdel, 512 Feng, Li, 363, 372 Feng, Shiji, 281

G Gakiza, Joram, 41, 178 Gao, Jun, 407 Gao, Kai Tai, 92 Gao, Lifang, 340 Gao, Yuxi, 289 Geng, Yujie, 416, 432 Guan, Ti, 416, 432 Guo, Shaoyong, 340 Guo, Xiaoli, 363, 372

H Haque, Shamim MdObaydul, 41 He, Dong-wei, 502 He, Xian-Kang, 228 Hsu, Tsui-Lien, 123, 178 Huang, Guilin, 398

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 J.-S. Pan et al. (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 211, https://doi.org/10.1007/978-981-33-6420-2

529

530 Hwangbo, Yul, 238 Hwang, Yong Ha, 262, 268 Hwang, Young Ha, 246

J Jiang, Shi-Jie, 148 Jiao, Yang, 357 Jia, Zihang, 332 Jigjidsuren, Battogtokh, 9 Ji, Lu, 323 Ji, Mengyu, 475 Jin, Zhexing, 363 Jung, Dongoh, 50 Junhao, Feng, 448

K Kang, Ha Ye Jin, 246, 262, 268, 274 Khanan, Akbar, 512 Kim, Jae Ho, 246, 262, 268, 274 Kim, Tae-Sung, 133

L Lee, Jong Yun, 50 Lee, Sang-Hoon, 133 Lee, Sang Won, 246, 262, 268, 274 Liang, Wen Long, 211 Liao, Youguo, 281 Li, Dong Wei, 305 Li, Hong Na, 204 Li, Jianpo, 381, 389, 398, 407, 416, 424, 432, 440 Li, Jian-xing, 502 Li, Junhua, 281 Li, Lei, 416, 432 Li, Menghua, 289 Lin, Wenru, 314 Li, Pei-Qiang, 1, 99, 106 Li, Peiya, 140 Li, Qimeng, 340 Li, Qin-Bin, 99, 106 Li, Shici, 432, 440 Li, Sijue, 475 Li, Tong, 188 Liu, Han, 357 Liu, Hang Yu, 211 Liu, Jing, 485, 494 Liu, Li-sang, 502 Liu, Shutang, 281 Liu, Xin, 389, 398 Liu, Xing, 407 Liu, Yi Min, 188, 204

Author Index Liu, Zhao-Qing, 467 Lü, Ji-Xiang, 228 Luo, Jie, 99, 106 Lu, Yu, 92

M Ma, Chao, 340 Mao, Daoyong, 475 Ma, Qiang, 416, 432 Meng, Bo, 305 Meng, Jiale, 140 Mohamed, Abdul Hakim H. M., 512 Munkhbat, Khongorzul, 50 Munkhjargal, Zoljargal, 74 Munkhtsetseg, N., 16

N Namsraijaw, Choijamts, 82 Namsrai, Oyun-Erdene, 16

O Omer, Abdalaziz Altayeb Ibrahim, 32, 57, 160

P Pan, Jeng-Shyang, 228 Pan, Tien-Szu, 228 Park, Hyun Woo, 238 Peng, Gaoliang, 475 Phuong, Vuong Quang, 253 Pu, Wei Jian, 196

Q Qiao, Yulong, 485, 494 Qi, Wen, 24 Qu, Chaoyang, 372

R Ryu, Keun Ho, 50, 238, 512 Ryu, Kwang Sun, 246, 262, 268, 274

S Saeed, Saeed I. A., 381 Shyirambere, Gilbert, 57, 169 Song, Xing-Yuan, 467 Sung, Tien-Wen, 32, 66, 123 Sun, Peidong, 1

Author Index Sun, Shengze, 424 Sun, Tieli, 363, 372 Sun, Yuhan, 363, 372 Sun, Zefan, 140

T Tan, Jun, 298 Turatsinze, Elias, 114

V Van, Binh Ngo, 253

W Wang, Hong-Jiang, 148 Wang, Hsiao-Chuan, 41, 57, 123, 148, 169, 178 Wang, Li, 305 Wang, Ling, 92, 188, 196, 204, 211, 512 Wang, Ming-Tsung, 114, 123 Wang, Qing, 457 Wang, Rui, 389, 398 Wang, Ruo-Bin, 348 Wang, Shaoying, 340 Wang, Shiyuan, 363 Wang, Tingting, 485 Wang, Wenting, 389, 398 Wang, Yong, 357, 416, 432 Wang, Zhihui, 340 Weng, Jun, 457

X Xie, Hua, 196 Xie, Wen Ce, 188, 204 Xie, Yan, 381, 440 Xin, Jin, 448 Xu, Jie, 281 Xu, Lin, 348

531 Y Yan, Dang-Kang, 348 Yang, Huifeng, 340 Yang, Jihai, 407 Yang, Paite, 24 Yang, Tao, 381, 407, 440 Yang, Zhen-Ni, 467 Yan, Li-Jun, 228 Yan, Zhou, 457 Yao, Shuowang, 416 Yi, Kenan, 332 Yin, Yueqin, 424 Yong, Xiao, 448 You, Na Young, 246, 262, 268, 274

Z Zhang, Fuquan, 289 Zhang, Guoge, 381, 440 Zhang, Hao, 398 Zhang, Huajian, 424 Zhang, Li Yan, 196 Zhang, Wen-Ji, 219 Zhang, Xin, 66 Zhang, Zhantu, 424 Zhang, Zhongkai, 1 Zhao, Baohua, 66 Zhao, Xiaobo, 323, 332 Zhao, Xiaohong, 389 Zhao, Yang, 389 Zheng, Lingbin, 289 Zheng, Ri-Jing, 219 Zheng, Rong-Jin, 99, 106 Zhou, Tie Hua, 92, 188, 196, 204, 211 Zhou, Weiwei, 298 Zhou, Yao, 219 Zhou, Yu-Wen, 32, 114, 160 Zhu, Xiaohui, 323, 332 Zhu, Zhiyu, 475 Zitong, Zhang, 448