Zelinka / Brandstetter / Trong Dao AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application

1. Auflage 2019
ISBN: 978-3-030-14907-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 554, 998 Seiten

Reihe: Lecture Notes in Electrical Engineering

These proceedings address a broad range of topic areas, including telecommunication, power systems, digital signal processing, robotics, control systems, renewable energy, power electronics, soft computing and more. Today's world is based on vitally important technologies that combine e.g. electronics, cybernetics, computer science, telecommunication, and physics. However, since the advent of these technologies, we have been confronted with numerous technological challenges such as finding optimal solutions to various problems regarding controlling technologies, signal processing, power source design, robotics, etc. Readers will find papers on these and other topics, which share fresh ideas and provide state-of-the-art overviews. They will also benefit practitioners, who can easily apply the issues discussed here to solve real-life problems in their own work. Accordingly, the proceedings offer a valuable resource for all scientists and engineers pursuing research and applications in the above-mentioned fields.

Autoren/Hrsg.

Zelinka, Ivan

Brandstetter, Pavel

Trong Dao, Tran

Hoang Duy, Vo

Kim, Sang Bong

Inhaltsverzeichnis

1;Foreword;6
2;Contents;9
3;Computer Science;18
4;New Neuromorphic AI NM500 and Its ADAS Application;19
4.1;Abstract;19
4.2;1 Introduction;19
4.3;2 Neuromorphic Artificial Intelligence;21
4.4;3 NM500 Architecture;22
4.5;4 ADAS Application Using Image Learning and Recognition;23
4.5.1;4.1 Learning and Recognition of Traffic Information Images;23
4.5.2;4.2 Real-Time Performance and Hardware Implementation;25
4.6;5 Conclusions;27
4.7;Acknowledgement;28
4.8;References;28
5;Analyzing l1-loss and l2-loss Support Vector Machines Implemented in PERMON Toolbox;29
5.1;1 Support Vector Machines for Classifications;29
5.2;2 Hessian Regularization;32
5.3;3 No-Bias Data Classifications;33
5.4;4 PermonSVM: SVM Implementation on Top of PETSc;34
5.5;5 Numerical Experiments;35
5.6;6 Conclusions;38
5.7;References;39
6;Hybrid Fuzzy Neural Model Based Dempster-Shafer System for Processing of Diagnostic Information;40
6.1;1 Introduction;40
6.2;2 Elements of Dempster-Shaffer Theory;41
6.3;3 Practical Task Specifications;42
6.4;4 Adaptive Fuzzy Dempster-Shaffer Model;43
6.5;5 Hybrid Fuzzy Neural Model Based on Evidence Theory;44
6.6;6 HFNN Training;46
6.7;7 Conclusions;48
6.8;References;49
7;ANFIS and Fuzzy Tuning of PID Controller for STATCOM to Enhance Power Quality in Multi-machine System Under Large Disturbance;50
7.1;Abstract;50
7.2;1 Introduction;50
7.3;2 Configuration of the Studied System;52
7.3.1;2.1 SG Model;52
7.3.2;2.2 STATCOM Model;53
7.4;3 Design of Fuzzy-PID Self-tuning Controller;54
7.5;4 Design of ANFIS Controller for Tuning Gain PID;56
7.6;5 Simulation Results;58
7.7;6 Conclusion;59
7.8;References;59
8;Proposal of Electrode System for Measuring Level of Glucose in the Blood;61
8.1;Abstract;61
8.2;1 Introduction;61
8.3;2 Methods;62
8.4;3 Results;65
8.5;4 Conclusion;69
8.6;Acknowledgment;69
8.7;References;70
9;Substitution Rules with Respect to a Context;71
9.1;1 Introduction;71
9.2;2 TIL in Brief;73
9.3;3 Valid Substitutions in TIL;74
9.3.1;3.1 Three Kinds of Contexts;74
9.3.2;3.2 v-congruent, Equivalent, and Procedurally Isomorphic Constructions;75
9.3.3;3.3 Substitution of v-congruent Constructions;76
9.3.4;3.4 Substitution of Equivalent Constructions;77
9.3.5;3.5 Substitution of Procedurally Isomorphic Constructions;77
9.4;4 Implementation;77
9.4.1;4.1 Introduction to the Tool for TIL-Script Processing;78
9.4.2;4.2 Equivalence Classes of v-congruent, Equivalent, and Procedurally Isomorphic Constructions;79
9.4.3;4.3 Substitution of Constructions;79
9.4.4;4.4 Generating Process;80
9.5;5 Conclusion;81
9.6;References;81
10;Fuzzy Model Predictive Control for Discrete-Time System with Input Delays;83
10.1;Abstract;83
10.2;1 Introduction;83
10.3;2 Problem Statement;84
10.4;3 Model Predictive Controller Design;85
10.5;4 Simulated Example;90
10.6;5 Conclusion;92
10.7;References;92
11;An Improvement of Fuzzy-Based Control Strategy for a Series Hydraulic Hybrid Truck;94
11.1;Abstract;94
11.2;1 Introduction;94
11.3;2 System Description and Modeling;96
11.4;3 Control System Development;97
11.5;4 Simulation Results and Discussion;99
11.6;5 Conclusion;102
11.7;References;103
12;A New Approach Newton-Raphson Load Flow Analysis in Power System Networks with STATCOM;104
12.1;1 Introduction;104
12.2;2 Newton-Raphson Load Flow in Power System Without STATCOM;105
12.3;3 Newton-Raphson Load Flow in Power System with STATCOM;106
12.4;4 Design and Development of Software;109
12.4.1;4.1 Technologies and Platform;109
12.4.2;4.2 Programming;109
12.4.3;4.3 Data Input and Output;110
12.4.4;4.4 Structure of the Program;111
12.5;5 Test Systems and Results;111
12.5.1;5.1 Modified IEEE 30-Bus;111
12.5.2;5.2 IEEE 57-Bus;113
12.5.3;5.3 Computation Time and Number of Iterations;114
12.6;6 Conclusions;114
12.7;References;115
13;Neural Network for Smart Adjustment of Industrial Camera - Study of Deployed Application;117
13.1;1 Introduction;117
13.2;2 Problem Formulation;118
13.3;3 Proposed Solution;119
13.3.1;3.1 Image Acquisition by Industrial Camera;120
13.3.2;3.2 Feature Extraction;120
13.3.3;3.3 Neural Network for Camera Parameters Adjustment;122
13.4;4 Design of Feedforward Neural Network;123
13.4.1;4.1 Training and Validation Set;123
13.4.2;4.2 Training and Pruning;123
13.5;5 Evaluation of Proposed Approach;125
13.6;6 Conclusion and Future Work;127
13.7;References;128
14;Risk Assessment Approach to Estimate Security of Cryptographic Keys in Quantum Cryptography;130
14.1;1 Introduction;130
14.2;2 Risk Management;131
14.3;3 Quantum Cryptography;132
14.3.1;3.1 Quantum Key Distribution;133
14.3.2;3.2 Key Distillation;133
14.4;4 Risk-Based Approach in Quantum Cryptography;134
14.4.1;4.1 Probability of Eavesdropping;135
14.4.2;4.2 Impact to the Final Key;137
14.5;5 Conclusions;138
14.6;References;139
15;Wavelet Transform Decomposition for Fetal Phonocardiogram Extraction from Composite Abdominal Signal;141
15.1;Abstract;141
15.2;1 Introduction;141
15.3;2 State of the Art;141
15.4;3 Mathematical Apparatus;142
15.4.1;3.1 Discrete Wavelet Transform;142
15.5;4 Materials and Methods;143
15.5.1;4.1 Design of the DWT Based Extraction System;143
15.5.2;4.2 Dataset;144
15.6;5 Results and Discussion;145
15.7;6 Conclusion;147
15.8;Acknowledgment;147
15.9;References;148
16;A CUDA Approach for Scenario Reduction in Hedging Models;150
16.1;1 Introduction;150
16.2;2 Scenario Sampling Procedure;151
16.2.1;2.1 Modified Weighted Euclidean Distance Method;151
16.3;3 CUDA Implementation;152
16.3.1;3.1 Weighted Euclidean Distance Parallelization;153
16.3.2;3.2 Distance Calculation;153
16.3.3;3.3 Pivot Scenario Calculation;154
16.4;4 Experimentation and Results;155
16.5;5 Conclusion;158
16.6;References;159
17;Using a Strain Gauge Load Cell for Analysis of Round Punch;160
17.1;Abstract;160
17.2;1 Introduction;160
17.3;2 Round Punch;161
17.4;3 Measurement of Force;162
17.5;4 Results;164
17.6;5 Conclusion;168
17.7;Acknowledgment;169
17.8;References;169
18;Geometrical Computational Method to Locate Hypocenter by Signal Readings from a Three Receivers;170
18.1;1 Introduction;170
18.2;2 Solution via Tetrahedron;171
18.2.1;2.1 Unfolding of the Faces of a Tetrahedron;172
18.2.2;2.2 The Theorem of Three Perpendiculars: The Location Error;174
18.3;3 Example of a Real Seismic Event;174
18.4;4 Conclusion;176
18.5;References;176
19;An Intelligent Question-Answer System over Natural-Language Texts;178
19.1;Abstract;178
19.2;1 Introduction;178
19.3;2 TIL in Brief;180
19.4;3 Questions and Answers;181
19.4.1;3.1 Basic Classification of Empirical Questions and Answers;181
19.4.2;3.2 Presupposition of Propositions;182
19.4.3;3.3 Presupposition of Questions;183
19.5;4 Logical Analysis of Questions with Presupposition and Their Answers;186
19.5.1;4.1 The ‘If-then-else’ Function and the Analytic Schema;186
19.5.2;4.2 Wh-Questions;187
19.5.3;4.3 Exclusive-or Questions;188
19.6;5 Concluding Remarks;189
19.7;Acknowledgements;189
19.8;References;189
20;An Efficient Reduced Basis Construction for Stochastic Galerkin Matrix Equations Using Deflated Conjugate Gradients;191
20.1;1 Introduction;191
20.1.1;1.1 Problem Setting;192
20.2;2 Stochastic Galerkin Method;193
20.3;3 Reduced Basis Method;193
20.3.1;3.1 Rational Krylov Subspace Methods;194
20.3.2;3.2 Adaptive Selection of Space Expansion;195
20.4;4 Deflated Conjugate Gradients;196
20.5;5 Numerical Testing;196
20.5.1;5.1 Reduced Basis Convergence;197
20.5.2;5.2 Deflated CG Convergence;198
20.6;6 Conclusions;199
20.7;References;200
21;An Investigation on Signal Comparison by Measuring of Numerical Strings Similarity;201
21.1;1 Introduction;201
21.2;2 Data Type;202
21.3;3 Detection of Pairs of Similar Numerical Strings;203
21.4;4 Application of the Counter Algorithm to Identify Similar Signals;204
21.5;5 Comparison of the Proposed Technique with the Correlation Method on the Base the Same Data Set;206
21.6;6 Conclusion;208
21.7;References;208
22;Optimization;211
23;A Lightweight SHADE-Based Algorithm for Global Optimization - liteSHADE;212
23.1;Abstract;212
23.2;1 Introduction;212
23.3;2 From DE to liteSHADE;213
23.3.1;2.1 Shade;214
23.3.2;2.2 liteSHADE;216
23.4;3 Experimental Settings;217
23.4.1;3.1 SHADE and liteSHADE Settings;217
23.5;4 Results and Discussion;218
23.6;5 Conclusion;220
23.7;Acknowledgments;220
23.8;References;221
24;Pupil Localization Using Self-organizing Migrating Algorithm;222
24.1;1 Introduction;222
24.2;2 Related Work;223
24.3;3 Proposed Method;224
24.4;4 Experiments;227
24.5;5 Conclusion;230
24.6;References;230
25;Differential Evolution Algorithms Used to Optimize Weights of Neural Network Solving Pole-Balancing Problem;232
25.1;1 Introduction;232
25.2;2 Differential Evolution;233
25.3;3 Pole-Balancing Problem;234
25.4;4 Experiment;235
25.4.1;4.1 Results;236
25.5;5 Conclusion;240
25.6;References;241
26;The Use of Radial Basis Function Surrogate Models for Sampling Process Acceleration in Bayesian Inversion;243
26.1;1 Introduction;243
26.2;2 Bayesian Inversion and Posterior Sampling;244
26.3;3 Radial Basis Function Surrogate Models;247
26.4;4 Numerical Experiments;247
26.4.1;4.1 Surrogate Model Updates;248
26.4.2;4.2 Integration into the Sampling Algorithm;250
26.5;5 Conclusions;251
26.6;References;252
27;An Optimised Hybrid Group Method in Data Handling (GMDH) Network;254
27.1;1 Introduction;254
27.2;2 Group Method in Data Handling;255
27.2.1;2.1 Singular Value Decomposition and GMDH Coefficients;256
27.2.2;2.2 Discrete Differential Evolution Algorithm;256
27.3;3 Hybrid DE-GMDH;257
27.4;4 DE-GMDH Network Structure Optimization;260
27.4.1;4.1 Network Model;260
27.4.2;4.2 Non-redundant Ordered Network;260
27.5;5 Experimentation;262
27.6;6 Conclusion;264
27.7;References;264
28;A Better Indexing Method for Closest Open Location Policy in Forklift Warehouse Operation;266
28.1;Abstract;266
28.2;1 Introduction;266
28.3;2 Methodology;267
28.3.1;2.1 Assumption Made;267
28.3.2;2.2 Layout Design;267
28.3.3;2.3 Basic of Closest Open Location – COL Policy;268
28.3.4;2.4 Time-Based COL Policy;268
28.3.5;2.5 Distance-Based COL Policy;270
28.4;3 Simulation Software;271
28.5;4 Simulation Result;271
28.5.1;4.1 Rack Stability;271
28.5.2;4.2 Total Time;273
28.6;5 Conclusion;275
28.7;References;275
29;On-Line Efficiency-Optimization Control of Induction Motor Drives Using Particle Swarm Optimization Algorithm;276
29.1;Abstract;276
29.2;1 Introduction;276
29.3;2 Induction Motor Loss Model;277
29.4;3 Online Optimizing the Efficiency of Induction Motor Drives Based on the Particle Swarm Optimization Algorithm;281
29.5;4 Simulation Results;282
29.6;5 Conclusion;285
29.7;Acknowledgement;285
29.8;References;285
30;Introducing the Run Support Strategy for the Bison Algorithm;287
30.1;Abstract;287
30.2;1 Introduction;287
30.3;2 Bison Algorithm;288
30.4;3 Run Support Strategy;290
30.4.1;3.1 Run Support Strategy Movement Example;291
30.5;4 Methods and Results;292
30.6;5 Discussion;295
30.7;6 Conclusion;296
30.8;Acknowledgment;296
30.9;References;296
31;Optimizing Automated Storage and Retrieval Algorithm in Cold Warehouse by Combining Dynamic Routing and Continuous Cluster Method;298
31.1;Abstract;298
31.2;1 Introduction;298
31.3;2 Assumption Made and Layout Design;299
31.3.1;2.1 Assumption Made [1, 2];299
31.3.2;2.2 Layout Design;300
31.3.3;2.3 Vehicle Task Assignment;300
31.4;3 Storage Algorithm Base on Continuous Cluster Method [3];300
31.5;4 Auto – Localization;302
31.5.1;4.1 Storage and Retrieval Strategy;302
31.5.2;4.2 Determining Storage Location by A* Algorithm;302
31.6;5 Dynamic Routing Method by Time Window;303
31.7;6 Simulation and Result;305
31.7.1;6.1 Comparison About Travel Distance of 3 Storage Algorithms;306
31.7.2;6.2 The Efficiency of the Dynamic and Static Routing Algorithm Through the Time Consumption Comparison;307
31.8;7 Conclusion;307
31.9;References;307
32;Dependency of GPA-ES Algorithm Efficiency on ES Parameters Optimization Strength;309
32.1;Abstract;309
32.2;1 Introduction;309
32.3;2 Hybrid GPA-ES Algorithm;310
32.4;3 Experiments and Obtained Data;313
32.5;4 Conclusions;316
32.6;Acknowledgements;317
32.7;References;317
33;A Modified Bat Algorithm to Improve the Search Performance Applying for the Optimal Combined Heat and Power Generations;318
33.1;Abstract;318
33.2;1 Introduction;318
33.3;2 Formulation of Optimal Operation of Combined Heat and Power Generation Problem;319
33.4;3 Modified Bat Algorithms;321
33.5;4 Numerical Results;323
33.6;5 Conclusion;325
33.7;References;326
34;Prediction of Hourly Vehicle Flows by Optimized Evolutionary Fuzzy Rules;328
34.1;1 Introduction;328
34.2;2 Related Work;329
34.3;3 Evolutionary Fuzzy Rules;330
34.3.1;3.1 EFR Internals;331
34.3.2;3.2 Optimized FR;332
34.4;4 Experiments;334
34.5;5 Conclusions;337
34.6;References;338
35;A New Simple, Fast and Robust Total Least Square Error Computation in E2: Experimental Comparison;340
35.1;1 Introduction;340
35.2;2 Least Square Error Approximation;340
35.3;3 Total Least Square Error Approximation;341
35.3.1;3.1 Total Least Square Error - Goniometric Functions;343
35.3.2;3.2 Total Least Square Error - Parametric Form;343
35.4;4 Proposed Approach;344
35.5;5 Experimental Results;346
35.6;6 Conclusion;348
35.7;References;348
36;On the Self-organizing Migrating Algorithm Comparison by Means of Centrality Measures;350
36.1;1 Introduction;350
36.2;2 Motivation;351
36.3;3 Experiment Design;351
36.3.1;3.1 Self-organizing Migrating Algorithm;351
36.3.2;3.2 Networks;352
36.3.3;3.3 Network Model for the SOMA;353
36.3.4;3.4 Centrality Measures;353
36.3.5;3.5 Experiment Design;354
36.4;4 Results;355
36.5;5 Conclusion;356
36.6;References;357
37;A Brief Overview of the Synergy Between Metaheuristics and Unconventional Dynamics;359
37.1;Abstract;359
37.2;1 Introduction;359
37.3;2 Chaos Driven Metaheuristics;360
37.3.1;2.1 Chaos as the CPRNG;360
37.3.2;2.2 Other Unconventional Approaches with Chaos;364
37.4;3 Non-random Processes and Evolutionary Algorithms;364
37.5;4 Metaheuristics and Complex Networks;364
37.6;5 Conclusions;367
37.7;Acknowledgments;367
37.8;References;367
38;Telecommunications;372
39;An Examination of Outage Performance for Selected Relay and Fixed Relay in Cognitive Radio-Aided NOMA;373
39.1;1 Introduction;373
39.2;2 System Model;374
39.3;3 Performance of CR-NOMA with DF Relaying;375
39.3.1;3.1 Model 1: Selected Relay to Improve Performance in Primary Network;377
39.3.2;3.2 Model 2: Fixed Relay in Primary Network;381
39.4;4 Numerical Results;381
39.5;5 Conclusion;383
39.6;References;383
40;Throughput Analysis of Power Beacon-Aided Multi-hop Relaying Networks Employing Non-orthogonal Multiple Access with Hardware Impairments;385
40.1;Abstract;385
40.2;1 Introduction;385
40.3;2 Network Model;386
40.4;3 Throughput Evaluation;389
40.4.1;3.1 Channel Model;389
40.4.2;3.2 Throughput Analysis;390
40.5;4 Simulation Results;391
40.6;5 Conclusion;393
40.7;Acknowledgment;394
40.8;References;394
41;Optimum Selection of the Reference Signal for Correlation Receiver Applied to Marker Localization;396
41.1;Abstract;396
41.2;1 Introduction;396
41.3;2 Related Works;397
41.4;3 Model of the Locator – Marker System;397
41.5;4 Selection of the Optimum Reference Signal for the Correlation Receiver;400
41.6;5 Conclusion;402
41.7;Acknowledgment;402
41.8;References;402
42;Comparing of Transfer Process Data in PLC and MCU Based on IoT;404
42.1;Abstract;404
42.2;1 Introduction;404
42.2.1;1.1 Division and Development of IoT;405
42.3;2 Possibilities of Data Processing;406
42.4;3 Design of Solution;407
42.4.1;3.1 Industrial IoT Solution;407
42.4.2;3.2 Commercial IoT Solution;409
42.4.3;3.3 Comparing of Industrial and Commercial IoT Solutions;410
42.5;4 Conclusion;412
42.6;Acknowledgement;412
42.7;References;412
43;Protecting Gateway from ABP Replay Attack on LoRaWAN;414
43.1;1 Introduction;414
43.2;2 Testbed Infrastructure;415
43.3;3 Authentication Methods;416
43.4;4 Experimental Attack;417
43.5;5 ABP Detector for Protect Gateway;418
43.6;6 Description of the Algorithm;419
43.7;7 Test Process;420
43.8;8 Conslusion;421
43.9;References;422
44;Development of a Distributed VoIP Honeypot System with Advanced Malicious Traffic Detection;423
44.1;1 Introduction;423
44.2;2 State of the Art;424
44.3;3 Honeypot Components and Functions;424
44.4;4 Design and Implementation of the VoIP Honeypot;426
44.5;5 Interim Results;428
44.5.1;5.1 Attacks Analysis;428
44.5.2;5.2 Whois Providers Comparison;430
44.6;6 Conclusion;431
44.7;References;432
45;Proposal and Implementation of Probe for Sigfox Technology;434
45.1;1 Introduction;434
45.2;2 State of the Art;435
45.3;3 Overall Concept and Used Technology;435
45.3.1;3.1 Sigfox Radio Network Technology;436
45.3.2;3.2 Sigfox Module Telecom Design 1208;438
45.3.3;3.3 Bluetooth Module HC-05;438
45.3.4;3.4 Power Supply YwRobot MB102;438
45.4;4 Implementation and Results;438
45.5;5 Conclusion;441
45.6;References;441
46;IoT Approach to Street Lighting Control Using MQTT Protocol;443
46.1;Abstract;443
46.2;1 Introduction;443
46.3;2 Street Light Control Solutions;444
46.3.1;2.1 Luminaires;444
46.3.2;2.2 Control Systems;445
46.3.3;2.3 Advantages and Disadvantages of Common Control Solutions;445
46.4;3 Street Light as IoT;445
46.5;4 Protocol MQTT;446
46.6;5 L2Led L2LCM Luminaire;447
46.7;6 Testing Polygon BroadbandLIGHT;447
46.8;7 Implementation;449
46.9;8 Testing and Results;450
46.10;9 Conclusion;450
46.11;Acknowledgment;451
46.12;References;451
47;Materials;453
48;Temperature Dependence of Microstructure in Liquid Aluminosilicate;454
48.1;Abstract;454
48.2;1 Introduction;454
48.3;2 Calculation Method;455
48.4;3 Results and Discussion;456
48.5;4 Conclusion;461
48.6;Acknowledgment;461
48.7;References;461
49;Study on Effect of Parameters on Friction Stir Welding Process of 6061 Aluminum Alloy Tubes;463
49.1;Abstract;463
49.2;1 Introduction;463
49.3;2 Mathematical Model;464
49.3.1;2.1 Mathematical Thermal Equilibrium Model;464
49.3.2;2.2 Experiment;469
49.3.3;2.3 Design Option for Experiment Parameters Value Field;469
49.3.4;2.4 Experiment;470
49.3.5;2.5 Tensile Strength Testing for Welding Link;470
49.3.6;2.6 Normal Force {\varvec F}_{{\varvec z}} and Welding Force {\varvec F}_{{\varvec x}} Testing;470
49.3.7;2.7 Analyze the Result;470
49.4;3 Conclusion;473
49.5;References;473
50;Convergence Study of Different Approaches of Solving the Hartree-Fock Equation on the Potential Curve of the Hydrogen Fluoride;474
50.1;1 Introduction;474
50.2;2 Problem Setting;475
50.2.1;2.1 Standard Eigenproblem Approach;476
50.2.2;2.2 Optimization Approach;476
50.3;3 Methods of Solution;477
50.3.1;3.1 Solution of the Eigenproblem;478
50.3.2;3.2 Solution of the Optimization Problem;479
50.4;4 Numerical Experiments;479
50.5;5 Conclusion;482
50.6;References;483
51;Control Systems;485
52;Network Traffic Anomaly Detection in Railway Intelligent Control Systems Using Nonlinear Dynamics Approach;486
52.1;1 Introduction;486
52.2;2 Related Works;487
52.3;3 Proposed Approach;489
52.4;4 Computational Experiment;491
52.5;5 Conclusions and Future Work;493
52.6;References;494
53;Advanced Methods of Detection of the Steganography Content;495
53.1;Abstract;495
53.2;1 Introduction;495
53.3;2 State of the Art;496
53.4;3 Proposed Method;497
53.5;4 Results and Discussion;499
53.5.1;4.1 Classification Success Rate;500
53.5.2;4.2 Results of the Macroblock Filtering;501
53.5.3;4.3 Overall Contribution of the Proposed Method;502
53.6;5 Conclusion and Future Work;503
53.7;Acknowledgements;503
53.8;References;504
54;Robust Servo Controller Design Based on Linear Shift Invariant Differential Operator;505
54.1;Abstract;505
54.2;1 Introduction;505
54.3;2 Preliminaries;506
54.4;3 Robust Tracking Controller Design Method for MIMO System;508
54.5;4 Simulation Results;510
54.5.1;4.1 PI-MIMO Controller;511
54.5.2;4.2 The Proposed Servo Control System;511
54.5.3;4.3 Step Reference;511
54.5.4;4.4 Ramp Input;513
54.5.5;4.5 Parabolic Input;514
54.6;5 Conclusions;514
54.7;Acknowledgement;515
54.8;References;515
55;Servo Controller Design and Fault Detection Algorithm for Speed Control of a Conveyor System;516
55.1;Abstract;516
55.2;1 Introduction;516
55.3;2 Servo Controller Design;517
55.3.1;2.1 Modeling of a Belt Driven Transmission Section;517
55.3.2;2.2 Controller Design;518
55.4;3 Fault Detection Algorithm;519
55.4.1;3.1 Extended Kalman Filter;519
55.4.2;3.2 Fault Detection and Isolation Using EKF;520
55.5;4 Experimental Results;521
55.5.1;4.1 Experimental Results of Servo Controller Design;522
55.5.2;4.2 Experimental Result of Fault Detection Algorithm;522
55.6;5 Conclusion;524
55.7;Acknowledgement;524
55.8;References;524
56;A Control System for Power Electronics with an NXP Kinetis Series Microcontroller;525
56.1;Abstract;525
56.2;1 Introduction;525
56.3;2 The Control System Based on the NXP Kinetis MKV58F Microcontroller;526
56.3.1;2.1 eFlex PWM Modulators;526
56.3.2;2.2 ADC and HSADC Converter;526
56.3.3;2.3 DAC Converter;527
56.3.4;2.4 Rotary and Position Sensor Interface (ENC);527
56.3.5;2.5 Serial Communication Interfaces (SPI, I2C, CAN, UART);527
56.4;3 Control System Topology Design;527
56.5;4 PCB Design and 3D Modelling of the Control Unit;528
56.6;5 Software Development Platform and Support;528
56.6.1;5.1 MCUXpresso IDE;528
56.6.2;5.2 FreeMASTER;529
56.7;6 Experimental Results;529
56.8;7 Conclusion;530
56.9;Acknowledgement;530
56.10;References;531
57;A MIMO Robust Servo Controller Design Method for Omnidirectional Automated Guided Vehicles Using Polynomial Differential Operator;532
57.1;Abstract;532
57.2;1 Introduction;532
57.3;2 System Modeling;534
57.4;3 MIMO Robust Servo Controller Design;534
57.5;4 Simulation Results;538
57.5.1;4.1 Proposed Controller;538
57.5.2;4.2 Adaptive Controller;539
57.5.3;4.3 Ramp Reference Input;539
57.5.4;4.4 Parabolic Reference Input;540
57.6;5 Conclusions;542
57.7;Acknowledgement;542
57.8;References;542
58;Model Reference Adaptive Control Strategy for Application to Robot Manipulators;544
58.1;Abstract;544
58.2;1 Introduction;545
58.3;2 System Modeling;547
58.3.1;2.1 Problem Statement;547
58.3.2;2.2 Mathematical Modeling;547
58.4;3 Model Reference Adaptive Controller Design;548
58.4.1;3.1 Overview of Adaptive Control;548
58.4.2;3.2 Model Reference Adaptive Controller Design;548
58.4.3;3.3 Reference Motion Reconstruction Scheme;551
58.5;4 Experimental Studies;552
58.5.1;4.1 Implementation of the Proposed Controller;552
58.5.2;4.2 Controllers for Comparison;552
58.5.3;4.3 Experimental Setup;553
58.5.4;4.4 Experimental Results;553
58.6;5 Conclusion;557
58.7;Acknowledgment;557
58.8;References;557
59;Stabilization of Time-Varying Systems Subject to Actuator Saturation: A Takagi-Sugeno Approach;559
59.1;Abstract;559
59.2;1 Introduction;559
59.3;2 Problem Statement;560
59.3.1;2.1 Preliminaries: Time-Varying Parameters Polytopic Modeling;560
59.3.2;2.2 Saturated Control Problem Statement;562
59.4;3 Saturated State Feedback Control Law;562
59.4.1;3.1 Control Law;562
59.4.2;3.2 Control Problem Definition;563
59.5;4 Main Results;564
59.6;5 Numerical Example;567
59.7;6 Conclusion;570
59.8;References;570
60;Observer Based Control for Systems with Mismatched Uncertainties in Output Matrix;572
60.1;Abstract;572
60.2;1 Introduction;572
60.3;2 Problem Formulation;573
60.4;3 Main Theoretical Results;575
60.5;4 Conclusion;578
60.6;References;579
61;Nonlinear Disturbance Observer with Recurrent Neural Network Compensator;580
61.1;Abstract;580
61.2;1 Introduction;580
61.3;2 Disturbance Observer (DOB) for Nonlinear Systems Using Recurrent Neural Network;581
61.3.1;2.1 Recurrent Neural Network (RNN);581
61.3.2;2.2 Nonlinear Disturbance Observer with Recurrent Neural Network Compensator (Nonlinear DOB with RNN Compensator);582
61.4;3 Vertically-Articulated Two-Link Manipulator System for Simulation Study;584
61.4.1;3.1 Modeling;584
61.4.2;3.2 Angular Velocity Control System;585
61.4.3;3.3 Conventional Disturbance Observer to Be Compared;585
61.5;4 Simulation Study;586
61.5.1;4.1 RNN Training;586
61.5.2;4.2 Comparison with Conventional DOB;588
61.6;5 Conclusion;589
61.7;References;590
62;Parameters Estimation for Sensorless Control of Induction Motor Drive Using Modify GA and CSA Algorithm;591
62.1;Abstract;591
62.2;1 Introduction;591
62.3;2 The Current Based Model Reference Adaptive System (CB-MRAS) Model to Estimate the Speed of the IM;592
62.3.1;2.1 The Vector Controlled Model of IM with Sensorless Speed Based on CB-MRAS Method;592
62.3.2;2.2 The CB-MRAS Model to Estimate the Speed of the Induction Motor;593
62.4;3 Estimate Factors of CB-MRAS Equations Using GA and CucKoo Search Algorithm;594
62.4.1;3.1 How to Determine Optimal Parameters for CB-MRAS Model;594
62.4.2;3.2 The Difference Methods to Estimate Parameters of CB-MRAS Model;596
62.5;4 Simulation Results;599
62.5.1;4.1 The Used Parameters for Simulation, Graphs and Table of Data;599
62.5.2;4.2 The Speed Response of CB-MRAS Model with the Algorithms;600
62.5.3;4.2 The Speed Response of CB-MRAS Model with the Algorithms;600
62.6;5 Conclusion;601
62.7;Acknowledgement;602
62.8;References;602
63;Study on Algorithms and Path-Optimization for USV’s Obstacle Avoidance;603
63.1;Abstract;603
63.2;1 Introduction;603
63.3;2 Mathematical Model of a Ship;604
63.4;3 Guidance and Control Design;606
63.4.1;3.1 Guidance Design;606
63.4.2;3.2 Controller Design;610
63.5;4 Simulation Results;612
63.6;5 Conclusion;614
63.7;Acknowledgement;614
63.8;References;614
64;Visual Servoing Controller Design Based on Barrier Lyapunov Function for a Picking System;616
64.1;Abstract;616
64.2;1 Introduction;616
64.3;2 Basic Elements of Image Motion;617
64.3.1;2.1 Image-Based Visual Servoing (IBVS);619
64.3.2;2.2 Position-Based Visual Servoing (PBVS);619
64.4;3 Lyapunov Function;621
64.5;4 Controller Design Using Barrier Lyapunov Function;622
64.6;5 Simulation Result;622
64.7;6 Conclusion;625
64.8;Acknowledgment;626
64.9;Appendix;626
64.10;References;627
65;Designing a PID Controller for Ship Autopilot System;629
65.1;Abstract;629
65.2;1 Introduction;629
65.3;2 The Kinematic and Kinetic Equations;630
65.3.1;2.1 Reference Frames [1];630
65.3.2;2.2 Ship Kinematic Equations;632
65.4;3 PID Controller;633
65.4.1;3.1 Design of PID Controller with Acceleration Feedback;633
65.4.2;3.2 PID Controller with Acceleration Feedback for the Ship Autopilot;633
65.5;4 Simulation of the Ship Autopilot;634
65.5.1;4.1 Simulation Conditions;634
65.5.2;4.2 Simulation of the Ship Autopilot Using the PID Controller;635
65.6;5 Conclusion;638
65.7;References;638
66;The Rotor Initial Position Determination of the Hi-Speed Switch-Reluctance Electrical Generator for the Steam-Microturbine;639
66.1;1 Introduction;639
66.2;2 Problem Formulation;640
66.3;3 Method for the Rotor Initial Position Determination;641
66.4;4 An Example of Using the Proposed Method of the Hi-Speed Switch-Reluctance Electrical Generator Initial Position Determination;642
66.5;5 Simulation Results and Analysis;644
66.6;6 Conclusion and Perspectives;647
66.7;References;648
67;Stability and Chaotic Attractors of Memristor-Based Circuit with a Line of Equilibria;650
67.1;1 Introduction;650
67.2;2 Local Stability Analysis;651
67.3;3 Global Stability Analysis;652
67.4;4 Chaotic Attractors;653
67.5;5 Conclusion;654
67.6;References;654
68;Mechanical Engineering;656
69;Behavior of Five-Pad Tilting–Pad Journal Bearings with Different Pivot Stiffness;657
69.1;Abstract;657
69.2;1 Introduction;657
69.3;2 TEHD Bearing Model;658
69.4;3 Test-Rig and Bearing Under Test;659
69.5;4 Pivot Stiffness Calculation;661
69.6;5 Results and Discussions;663
69.7;6 Conclusions;666
69.8;References;667
70;Dynamic Characteristics of a Non-symmetric Tilting Pad Journal Bearing;668
70.1;Abstract;668
70.2;1 Introduction;668
70.3;2 Bearing Description;669
70.4;3 TEHD Bearing Model;671
70.5;4 Results and Discussion;672
70.5.1;4.1 Dynamic Coefficients Versus Rotor Rotational Speed;672
70.5.2;4.2 Dynamic Coefficients Versus Static Load;674
70.5.3;4.3 Dynamic Coefficients Versus Load Direction;675
70.6;5 Conclusions;678
70.7;References;678
71;Energy;680
72;DCM Boost Converter in CPM Operation for Tuning Piezoelectric Energy Harvesters;681
72.1;1 Introduction;681
72.2;2 Architecture Description;682
72.2.1;2.1 Model Transfer Function;683
72.2.2;2.2 Circuit Description and Operation;685
72.3;3 Digital Implementation;686
72.4;4 Validation;687
72.5;5 Conclusion;689
72.6;References;689
73;Effect of Weighting Coefficients on Behavior of the DTC Method with Direct Calculation of Voltage Vector;691
73.1;Abstract;691
73.2;1 Introduction;691
73.3;2 Description of the Method;692
73.4;3 Specification of the Measurement Conditions;694
73.5;4 Experimental Results;695
73.6;5 Conclusion;699
73.7;Acknowledgement;699
73.8;Appendix – Meaning of Symbols;699
73.9;References;700
74;A New Protocol for Energy Harvesting Decode-and-Forward Relaying Networks;701
74.1;1 Introduction;701
74.2;2 Network and Channel Models;703
74.2.1;2.1 Random Relay Selection (RRS) Scheme;704
74.2.2;2.2 Best Relay Selection (BRS) Scheme;705
74.3;3 Performance Analysis;706
74.3.1;3.1 Random Relay Selection Scheme;706
74.3.2;3.2 Best Relay Selection Scheme;707
74.4;4 Numerical Results and Discussion;707
74.4.1;4.1 Verification of the Effectiveness of Proposed Policy;707
74.4.2;4.2 Effect of Number of Relays (N);708
74.4.3;4.3 Effect of Minimum Active Energy (E0);709
74.4.4;4.4 Effect of Fading Severity Parameters (m1 and m2);709
74.5;5 Conclusions;710
74.6;References;711
75;Average Bit Error Probability Analysis for Cooperative DF Relaying in Wireless Energy Harvesting Networks;713
75.1;1 Introduction;713
75.2;2 System Model;714
75.3;3 Performance Analysis;716
75.3.1;3.1 Outage Performance with the End-to-end SNR;716
75.3.2;3.2 Ergodic Capacity with the End-to-end SNR;717
75.3.3;3.3 Average Bit Error Probability (ABEP);718
75.4;4 Numerical Results;719
75.5;5 Conclusion;721
75.6;References;721
76;LCCT vs. LLC Converter - Analysis of Operational Characteristics During Critical Modes of Operation;723
76.1;Abstract;723
76.2;1 Introduction;723
76.3;2 Transfer Characteristics of LLC and LCCT Converter;724
76.4;3 Comparison of LLC and LCCT Converter – Steady State, Critical Operational Modes;725
76.4.1;3.1 Steady-State Operation;726
76.4.2;3.2 Critical Dynamic Operation;728
76.5;4 Conclusion;730
76.6;Acknowledgements;731
76.7;References;731
77;Control Renewable Energy System and Optimize Performance by Using Weather Data;733
77.1;Abstract;733
77.2;1 Introduction;733
77.3;2 Basic Theory;734
77.3.1;2.1 Wind Turbine System;734
77.3.2;2.2 Photovoltaic System;734
77.3.3;2.3 Data Analyze;735
77.4;3 Methodology;736
77.4.1;3.1 Details About Renewable Energy System;736
77.4.2;3.2 Weather Data Algorithm;737
77.5;4 Simulation Result;739
77.6;5 Conclusion;742
77.7;References;743
78;Analysis of Efficiency and THD in 7-Level Voltage Inverters with Reduced Number of Switches;744
78.1;Abstract;744
78.2;1 Introduction;744
78.3;2 7-Level Inverter Topologies with Reduced Number of Switches;745
78.3.1;2.1 Symmetrical Topology of 7-Level Inverter with 4 Sources and 6 Switches;745
78.3.2;2.2 Asymmetrical Topology of 7-Level Inverter with 2 Sources and 7 Switches;746
78.3.3;2.3 Symmetrical Topology of 7-Level Inverter with 3 Sources and 6 Switches;747
78.4;3 Modulation Techniques for Multilevel Inverters;748
78.4.1;3.1 Subharmonic PWM;748
78.5;4 Simulation of 7-Level Inverter Topologies;749
78.5.1;4.1 Simulation Setup and Creation of Power Circuit;749
78.5.2;4.2 Evaluation of Total Harmonic Distortion;750
78.5.3;4.3 Evaluation of Power Losses and Efficiency;751
78.6;5 Results;752
78.7;6 Conclusion;754
78.8;Acknowledgment;754
78.9;References;754
79;Waste Management - Weighing-Machine Automation;755
79.1;1 Recycling of PET Material;755
79.2;2 Printing of Weights Label;757
79.2.1;2.1 Raspberry Pi 3;758
79.2.2;2.2 RS485 GPIO Shield for Raspberry Pi V3.0;758
79.2.3;2.3 USB Office Printer;759
79.2.4;2.4 Technology into Production;759
79.3;3 Software and Sequence of Printing Processes;760
79.3.1;3.1 Serial Communication;761
79.3.2;3.2 Working with Text Templates in LaTex;762
79.3.3;3.3 Printing;763
79.3.4;3.4 Cron and Deamons;763
79.4;4 Conclusion;764
79.5;References;765
80;Optimization of Voltage Model for MRAS Based Sensorless Control of Induction Motor;766
80.1;Abstract;766
80.2;1 Introduction;766
80.3;2 Flux Estimator with DC Offset Compensation;768
80.4;3 Compensation of VSI Nonlinearities;769
80.4.1;3.1 Deadtime and IGBT Delay Compensation;770
80.5;4 Optimization Results;773
80.6;5 Conclusion;775
80.7;References;776
81;Capability of Predictive Torque Control Method to Control DC-Link Voltage Level in Small Autonomous Power System with Induction Generator;777
81.1;Abstract;777
81.2;1 Introduction;777
81.3;2 Method;779
81.4;3 Results;781
81.5;4 Conclusion;784
81.6;Acknowledgement;784
81.7;References;785
82;Feasibility Structural Analysis of Engineering Plastic Reel Module for Carrying Wound High-Voltage Electric Transmission Line;786
82.1;Abstract;786
82.2;1 Introduction;787
82.3;2 Static Analysis Condition;788
82.4;3 Static Analysis Result;792
82.5;4 Conclusion;795
82.6;Acknowledgement;796
82.7;References;796
83;Improving Fault Tolerant Control to the One Current Sensor Failures for Induction Motor Drives;797
83.1;Abstract;797
83.2;1 Introduction;797
83.3;2 Fault Tolerant Control for Current Sensors;798
83.3.1;2.1 The Field Oriented Control Technique;799
83.3.2;2.2 FTC Unit in the FOC Induction Motor Drive Structure;801
83.4;3 Simulation Results;803
83.5;4 Conclusion;806
83.6;Acknowledgement;806
83.7;References;806
84;Impact of Parameter Variation on Sensorless Indirect Field Oriented Control of Induction Machine;807
84.1;Abstract;807
84.2;1 Introduction;807
84.3;2 Mathematical Modelling of IM;809
84.4;3 State Estimation and Filtering;809
84.4.1;3.1 The Rotor Magnetic Flux Estimator;810
84.4.2;3.2 The Pseudo Sliding Mode Observer and Angular Velocity Extractor;810
84.4.3;3.3 Observer for Load Torque Estimation and Rotor Speed Estimate Filtering;811
84.5;4 Simulation;812
84.6;5 Experimental Results;814
84.7;6 The Effect of Machine Parameters Error on Rotor Speed Estimation;815
84.7.1;6.1 The Effect of Rotor Resistance Error on Speed Estimation;816
84.7.2;6.2 The Effect of Stator Resistance Error on Speed Estimation;816
84.8;7 Conclusion;816
84.9;Acknowledgment;817
84.10;References;817
85;Validation the FEM Model of Asynchronous Motor by Analysis of External Radial Stray Field;818
85.1;Abstract;818
85.2;1 Introduction;818
85.3;2 Finding of Motor Failures;819
85.3.1;2.1 Identification of Motor – Determination the Number of Rotor Bars;820
85.3.2;2.2 Measurement of Rotor Bars Number by Slot Harmonic;821
85.3.3;2.3 Determination of Rotor Slots Number by Method of rpm Difference;822
85.3.4;2.4 Measurement of Broken Rotor Bar;823
85.3.5;2.5 2D FEM Model of Asynchronous Motor;824
85.4;3 Conclusion;826
85.5;Acknowledgement;826
85.6;References;827
86;Outage and Intercept Probability Analysis for Energy-Harvesting-Based Half-Duplex Relay Networks Assisted by Power Beacon Under the Existence of Eavesdropper;829
86.1;1 Introduction;830
86.2;2 System Model;831
86.3;3 Performance Analysis;833
86.3.1;3.1 Outage Probability;833
86.3.2;3.2 Intercept Probability;835
86.3.3;3.3 Reliable Communication Without Interception;837
86.4;4 Numerical Results;837
86.5;5 Conclusion;841
86.6;References;841
87;Design of Electrical Regulated Drainage with Energy Harvesting;843
87.1;Abstract;843
87.2;1 Introduction to Electrical Drainage;843
87.3;2 Energy Harvesting System;845
87.4;3 Block 6 – Power Supply from Voltage Drop in the Forward Direction;846
87.5;4 Block 7 – Power Supply in the Reverse Direction;847
87.6;5 Block 6 Parameters Based on Experiment Results;848
87.7;6 Block 7 Parameters Based on Experiment Results;849
87.8;7 Discussion;850
87.9;8 Conclusion;850
87.10;References;851
88;Analysis of Appliance Impact on Total Harmonic Distortion in Off-Grid System;852
88.1;Abstract;852
88.2;1 Introduction;852
88.3;2 Experiment Description;853
88.3.1;2.1 Platform Description;854
88.4;3 Testing and Results;855
88.5;4 Conclusion;856
88.6;Acknowledgement;856
88.7;References;857
89;Influencing of Current Sensors by an External Magnetic Field of a Nearby Busbar;858
89.1;Abstract;858
89.2;1 Introduction;858
89.3;2 Analysis of Erroneous Measurement;859
89.4;3 Method Used;861
89.5;4 Results and Analysis;862
89.5.1;4.1 Testing of Clamp Ammeters;862
89.5.2;4.2 Testing the Current Sensors of the Network Analyzer;865
89.6;5 Summary and Conclusion;866
89.7;Acknowledgement;867
89.8;References;867
90;A Model for Predicting Energy Savings Attainable by Using Lighting Systems Dimmable to a Constant Illuminance Level;868
90.1;Abstract;868
90.2;1 Introduction;868
90.3;2 Dynamic Model of Uniformly Overcast Sky;869
90.3.1;2.1 Diffuse Illuminance Calculation;870
90.4;3 Prediction Model;871
90.5;4 Verification of the Prediction Model;871
90.5.1;4.1 Artificial Lighting System Calculations;873
90.5.2;4.2 Prediction Model Application;874
90.6;5 Conclusion;876
90.7;Acknowledgments;876
90.8;References;876
91;Strategy of Metropolis Electrical Energy Supply;878
91.1;Abstract;878
91.2;1 Introduction and Problem Definition;878
91.3;2 Authors Proposals and Discussion;879
91.4;3 Conclusions and Future Proposals;886
91.5;References;886
92;Robotics;888
93;Attitude Control of Jumping Robot with Bending-Stretching Mechanism;889
93.1;Abstract;889
93.2;1 Introduction;889
93.3;2 Modelling of Jumping Robot;890
93.4;3 Output Zeroing Control;892
93.5;4 Computer Simulations;893
93.5.1;4.1 Evaluation of Robustness Against Torque Disturbance;894
93.5.2;4.2 Evaluation of Tracking Performance of Posture Angle {\varvec \theta}_{1} to Target Value {\varvec \theta}_{{\varvec d}};896
93.5.3;4.3 Evaluation of Attitude Maintaining Ability During Bending Stretching Motion;897
93.6;5 Conclusion;899
93.7;References;899
94;Geometric Foot Location Determination Algorithm for Façade Cleaning Robot;900
94.1;1 Introduction;900
94.2;2 Challenges for Façade Cleaning;902
94.3;3 Foot Location Algorithm;904
94.3.1;3.1 Initial Location;905
94.3.2;3.2 Middle Area;906
94.3.3;3.3 Final Location;907
94.3.4;3.4 Left Foot Location;907
94.4;4 Numerical Simulation;908
94.5;5 Conclusion;908
94.6;References;909
95;Smart Manipulation Approach for Assistant Robot;910
95.1;1 Introduction;910
95.1.1;1.1 Contributions;911
95.2;2 Theoretical Framework;911
95.2.1;2.1 Robot Modeling;912
95.2.2;2.2 Local Vision Algorithms;913
95.3;3 Results;915
95.3.1;3.1 Vision Experimental Results;915
95.3.2;3.2 Visual Assisted Manipulation Simulation and Experimental Results;916
95.3.3;3.3 Discussion;917
95.4;4 Conclusion;918
95.5;References;919
96;Computational Study on Upward Force Generation of Gymnotiform Undulating Fin;920
96.1;Abstract;920
96.2;1 Introduction;920
96.3;2 Problem Setting;921
96.4;3 Model of Simulation;922
96.4.1;3.1 Model of Gymnotiform Undulating Fin;922
96.4.2;3.2 Method;922
96.4.3;3.3 Simulation Model;924
96.5;4 Results;925
96.6;5 Conclusion;928
96.7;References;928
97;Modular Design of Gymnotiform Undulating Fin;930
97.1;Abstract;930
97.2;1 Introduction;930
97.3;2 Principle of Design;931
97.3.1;2.1 Principle of Gymnotiform Undulating Fin;931
97.3.2;2.2 Concept of Modular Design;932
97.3.3;2.3 Design of Power Transmission;933
97.4;3 Model and Results;934
97.5;4 Conclusion;936
97.6;References;936
98;Path Following Control of Automated Guide Vehicle Using Camera Sensor;938
98.1;Abstract;938
98.2;1 Introduction;938
98.3;2 System Description;939
98.4;3 System Modeling;939
98.5;4 Tracking Error and Error Measuring Using Camera Sensor;940
98.6;5 Controller Design;941
98.7;6 Simulation and Experimental Results;943
98.8;7 Conclusions;943
98.9;Acknowledgement;943
98.10;References;944
99;Binary Classification of Terrains Using Energy Consumption of Hexapod Robots;945
99.1;1 Introduction;945
99.2;2 Long Short-Term Memory;947
99.3;3 Methodology;948
99.3.1;3.1 Results;950
99.4;4 Conclusions and Future Work;953
99.5;References;953
100;The Movement of Swarm Robots in an Unknown Complex Environment;955
100.1;1 Introduction;955
100.2;2 The Method;956
100.2.1;2.1 The Movement of Swarm Robots;956
100.2.2;2.2 The Imaginary Environment;956
100.3;3 Particle Swarm Optimization Algorithm;958
100.4;4 Simulation Results;959
100.4.1;4.1 Setup Environment;959
100.4.2;4.2 The Results and Discussions;959
100.5;5 Conclusion;964
100.6;References;964
101;Image Processing;966
102;Contour Detection Method of 3D Fish Using a Local Kernel Regression Method;967
102.1;Abstract;967
102.2;1 Introduction;967
102.3;2 Contour Detection Method of 3D Fish Using a Local Kernel Regression Method;968
102.3.1;2.1 3D Point Cloud;968
102.3.2;2.2 Canny Edge Point Detection;969
102.3.3;2.3 Local Kernel Regression;971
102.4;3 Experiment Result;974
102.4.1;3.1 Kinect Camera;974
102.4.2;3.2 3D Point Cloud;974
102.4.3;3.3 Canny Edge Points Detection;975
102.4.4;3.4 Local Kernel Regression;975
102.5;4 Conclusions;976
102.6;Acknowledgments;976
102.7;References;976
103;Camera Based Tests of Dimensions, Shapes and Presence Based on Virtual Instrumentation;977
103.1;Abstract;977
103.2;1 Introduction;977
103.3;2 Automated Image Acquisition and Processing - HW for Machine Vision;978
103.4;3 Testing the Possibilities of an Automated System for Optical Inspection of Products;979
103.4.1;3.1 Implementation;979
103.4.2;3.2 Calibration;979
103.4.3;3.3 The Actual Image Processing;980
103.5;4 Conclusion;984
103.6;Acknowledgment;984
103.7;References;984
104;A 3D Scanner Based on Virtual Instrumentation Implemented by a 1D Laser Triangulation Method;986
104.1;Abstract;986
104.2;1 Introduction;986
104.2.1;1.1 1D Laser Triangulation;987
104.2.2;1.2 Virtual Instrumentation;988
104.3;2 Design and Principles of Implemented 3D Scanner;988
104.3.1;2.1 Mechanical Structure;989
104.4;3 3D Scanner Implementation;990
104.4.1;3.1 SW Application;991
104.5;4 Results;991
104.6;5 Conclusion;993
104.7;References;993
105;Author Index;995