E-Book, Englisch, Band 465, 1086 Seiten, eBook
Duy / Dao / Zelinka AETA 2017 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application
1. Auflage 2018
ISBN: 978-3-319-69814-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 465, 1086 Seiten, eBook
Reihe: Lecture Notes in Electrical Engineering
ISBN: 978-3-319-69814-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1;Foreword;6
2;Contents;8
3;Keynote Speech;19
4;Under-Actuated Systems: Nonlinear Control Showcase;20
4.1;Abstract;20
4.2;1 Nonlinear Control Theory;20
4.3;2 Under-Actuated Systems;21
4.4;3 Velocity Constrained Systems (Driftless System);21
4.5;4 Velocity Constrained Systems (Constant Drift);23
4.6;5 Under-Actuated Systems Without Gravity;24
4.7;6 Under-Actuated Systems with Gravity;25
4.8;7 Bilinear Systems;25
4.9;8 Motion Control (Unstable Zero-Dynamics);26
4.10;9 Motion Control (Periodic Motion);27
4.11;10 Motion Control (Snake);29
4.12;11 Concluding Comment;29
4.13;References;29
5;An Overview of Cyber Insecurity and Malicious Uses of Cyberspace;32
5.1;Abstract;32
5.2;1 Introduction;32
5.3;2 Cyber Insecurity;35
5.4;3 Malicious Uses of Cyberspace;37
5.5;4 Conclusions;40
5.6;References;40
6;Computer Science;41
7;A New Android Botnet Classification for GPS Exploitation Based on Permission and API Calls;42
7.1;Abstract;42
7.2;1 Introduction;42
7.3;2 Related Work;43
7.4;3 Methodology;44
7.4.1;3.1 Dataset;46
7.4.2;3.2 Feature Selection;46
7.4.3;3.3 Feature Extraction;47
7.4.4;3.4 Classification;48
7.5;4 Results and Discussion;48
7.6;5 Conclusion and Future Work;50
7.7;Acknowledgment;50
7.8;References;50
8;A Protocol for Securing E-Voting System;53
8.1;Abstract;53
8.2;1 Introduction;53
8.3;2 Analyze Some Existed Protocols;54
8.4;3 A New Proposed Protocol;56
8.5;4 The Experimental Results of the Proposed Protocol;60
8.6;5 Assessing the Security of the Proposed Protocol;60
8.6.1;5.1 Analyzing the Cryptographic Method Applied to Developing the E-Voting Protocol;60
8.6.2;5.2 Comparing the Developed Protocol with the Esoteric Protocol and Analyzing the Ability to Overcome the Risks of the E-Voting System;61
8.7;6 Conclusion;62
8.8;References;62
9;Processing Big Data in Field of Marketing Models Using Apache Spark;64
9.1;1 Introduction;64
9.2;2 Overview of Apache Spark;64
9.2.1;2.1 Cluster Manager;66
9.3;3 Experiment Design;66
9.3.1;3.1 Preprocessing Data;66
9.3.2;3.2 Input Data Set;67
9.3.3;3.3 Data Clustering;68
9.3.4;3.4 Cloud Computing;69
9.3.5;3.5 Report Outputs;70
9.3.6;3.6 Performance;71
9.4;4 Results;72
9.5;5 Conclusion;72
9.6;References;73
10;Firefly Algorithm: Enhanced Version with Partial Population Restart Using Complex Network Analysis;74
10.1;Abstract;74
10.2;1 Introduction;74
10.3;2 Firefly Algorithm;75
10.4;3 Proposed Complex Network Approach;76
10.4.1;3.1 Centralities;76
10.4.2;3.2 Partial Population Restart Control;78
10.5;4 Test Functions;80
10.6;5 Results;80
10.7;6 Conclusion;82
10.8;Acknowledgements;82
10.9;References;82
11;L-SHADE Algorithm with Distance Based Parameter Adaptation;84
11.1;Abstract;84
11.2;1 Introduction;84
11.3;2 Differential Evolution;85
11.3.1;2.1 Initialization;85
11.3.2;2.2 Mutation;86
11.3.3;2.3 Crossover;86
11.3.4;2.4 Selection;86
11.4;3 L-SHADE;87
11.4.1;3.1 Initialization;87
11.4.2;3.2 Mutation;88
11.4.3;3.3 Crossover;88
11.4.4;3.4 Historical Memory Updates;88
11.4.5;3.5 Linear Decrease in Population Size;89
11.5;4 Distance Based Parameter Adaptation;91
11.6;5 Experiments;91
11.7;6 Results and Discussion;91
11.8;7 Conclusion;94
11.9;Acknowledgements;94
11.10;References;94
12;Optimization;96
13;Application of the Box-Behnken Model Design to the Optimization of Process Parameters in the Convection-Drying of Channa Striata Fish;97
13.1;Abstract;97
13.2;1 Introduction;97
13.3;2 Materials and Methods;98
13.3.1;2.1 Materials;98
13.3.2;2.2 Methods;98
13.3.3;2.3 Determination of Optimized Parameters of Convection-Drying Channa Striatafish;99
13.3.4;2.4 Experimental Designs;100
13.4;3 Results and Discussion;101
13.4.1;3.1 Optimized Parameters of Convection-Drying Channa Striata Fish;101
13.4.2;3.2 Experiment Verified the Optimal Conditions and Evaluated the Authenticity of the Model of Drying Fish;104
13.5;4 Conclusions;104
13.6;Acknowledgements;105
13.7;References;105
14;A Study on Real-World Disaster Evacuation System with Mathematical Network Problem Solving Algorithm;107
14.1;Abstract;107
14.2;1 Introduction;107
14.3;2 Algorithm-Applied Disaster Evacuation Systems;108
14.3.1;2.1 Analysis on the Application Algorithm;108
14.3.2;2.2 Comparison with Existing Algorithms;110
14.4;3 The Application of Disaster Evacuation System to Actual Field;112
14.4.1;3.1 The Configuration of Disaster Evacuation System Applied with Algorithm;112
14.4.2;3.2 The Application Test in the Actual Field;114
14.5;4 Conclusion;116
14.6;References;117
15;Optimal Threshold Policies for Robust Data Center Control;118
15.1;1 Introduction;118
15.2;2 Background and Problem Definition;120
15.2.1;2.1 Markov Decision Process;120
15.2.2;2.2 Data Center Control;120
15.2.3;2.3 Threshold Policy;124
15.2.4;2.4 Robust MDP;124
15.3;3 Optimality of Threshold Policies;124
15.3.1;3.1 Threshold Policy in Multidimensional Case;125
15.3.2;3.2 Finite Horizon;125
15.3.3;3.3 Solving Method;126
15.4;4 Experimental Results;126
15.5;5 Conclusion;127
15.6;References;127
16;A Review of Real-World Applications of Particle Swarm Optimization Algorithm;129
16.1;Abstract;129
16.2;1 Introduction;129
16.3;2 Particle Swarm Optimization (PSO);130
16.4;3 Review of Recent Applications of PSO Based Methods;131
16.5;4 Conclusion;134
16.6;Acknowledgements;134
16.7;References;134
17;Differential Evolution for Constrained Industrial Optimization;137
17.1;Abstract;137
17.2;1 Introduction;137
17.3;2 Related Works and Motivation;138
17.4;3 Problem Design;138
17.4.1;3.1 Waste Processing Batch Reactor;138
17.4.2;3.2 Constrained Optimization;139
17.4.3;3.3 Optimized Parameters;140
17.5;4 Differential Evolution;140
17.5.1;4.1 Canonical DE;141
17.5.2;4.2 Success-History Based Adaptive Differential Evolution;141
17.6;5 Experiment Results;142
17.7;6 Conclusion and Results Analysis;144
17.8;Acknowledgements;145
17.9;References;145
18;Telecommunications;147
19;Multi-sensor Data Fusion Technique to Detect Radiation Emission in Wireless Sensor Networks;148
19.1;Abstract;148
19.2;1 Introduction;148
19.3;2 Main Characteristics of the System;149
19.4;3 Physical Characteristics and Mathematical Model;150
19.5;4 Sensors Data Fusion;153
19.6;5 Conclusions;156
19.7;Acknowledgements;156
19.8;References;156
20;Analytical Study of the IEEE 1609.4 MAC in Vehicular Ad Hoc Networks;158
20.1;1 Introduction;158
20.2;2 Analytical Model;160
20.3;3 Model Validation;165
20.4;4 Conclusion;166
20.5;References;166
21;Enhanced Self-sorting Based MAC Protocol for Vehicular Ad-Hoc Networks;168
21.1;1 Introduction;168
21.2;2 Related Works;168
21.3;3 Protocol Description;169
21.3.1;3.1 Queue Formation;170
21.3.2;3.2 Channel Reservation;171
21.3.3;3.3 Safety Message Forwarding and WSA Handshaking;171
21.3.4;3.4 Service Data Transmission;171
21.4;4 Performance Analysis;172
21.4.1;4.1 Packet Delivery Ratio of Safety Packets;172
21.4.2;4.2 Throughput of Service Application;172
21.4.3;4.3 Performance Evaluation;173
21.5;5 Conclusion;174
21.6;References;175
22;Adaptive TDMA and CSMA-Based MAC Protocols for Vehicular Ad Hoc Networks: A Survey;176
22.1;1 Introduction;176
22.2;2 Adaptive TDMA and CSMA-Based MAC Protocols;178
22.2.1;2.1 A Dedicated Multi-channel MAC Protocol;179
22.2.2;2.2 A Hybrid Efficient and Reliable MAC Protocol;179
22.2.3;2.3 An Efficient Time Slot Acquisition on the Hybrid MAC Protocol;180
22.2.4;2.4 An Efficient and Fast Broadcast Frame Adjustment Algorithm in the Hybrid MAC Protocol;180
22.2.5;2.5 Coordinated Multi-channel MAC Protocol;181
22.3;3 Challenges in Adaptive TDMA and CSMA-Based MAC Protocols;182
22.4;4 Conclusion;182
22.5;References;183
23;Electronics;185
24;A Low-Power Embedded IoT Microprocessor Design and Validation;186
24.1;Abstract;186
24.2;1 Introduction;186
24.3;2 The Low-Power Design Techniques for an Embedded IoTMicroprocessor (ARM-Lite RISC);187
24.4;3 The Proposed Low-Power Design Techniques;188
24.4.1;3.1 Low-Power Scheme-1: Gated-Clock;188
24.4.2;3.2 Low-Power Scheme-2: Memory-Interleaving;189
24.4.3;3.3 Low-Power Scheme-3: Structure-Reorder;190
24.4.4;3.4 Low-Power Scheme-4: Instruction-Coding;191
24.5;4 Experimental Results Analysis;192
24.6;5 Conclusions;194
24.7;References;195
25;A Deep Through-Microhole Fabricated Inside a Glass Optical Fiber by Use of a Near Ultraviolet Femtosecond Laser;196
25.1;Abstract;196
25.2;1 Introduction;196
25.3;2 Experiment;197
25.4;3 Results and Discussion;198
25.5;4 Conclusion;200
25.6;Acknowledgement;200
25.7;References;200
26;MicroEYE: A Wireless Multiple-Lenses Panoramic Endoscopic System;201
26.1;Abstract;201
26.2;1 Introduction;201
26.2.1;1.1 Global Minimally Invasive Surgery Analysis;201
26.2.2;1.2 Background of the Proposed MicroEYE Project;202
26.2.3;1.3 MicroEYE: A Two-Lenses Wireless Panoramic Endoscope;203
26.3;2 The Conventional Image Stitching Techniques;204
26.4;3 The Proposed Image Stitching Technique;204
26.5;4 The Hardware System with Chip Design Validation;205
26.6;5 The Low-Power ME Chip Design Technique (Clustered Voltage Scaling, CVS);208
26.7;6 System Integration, in-Vivo Experiment and Demonstration;209
26.8;7 Conclusions;210
26.9;References;211
27;Enhancement of Distributed Fiber Optic Vibration Sensors;212
27.1;Abstract;212
27.2;1 Introduction;212
27.3;2 State of the Art;213
27.4;3 Design of Novel Pulse Frequency Multiplier;215
27.5;4 Conclusion;219
27.6;Acknowledgement;219
27.7;References;219
28;Materials;221
29;Improving the Optical Properties of the 8500 K In-cup Packaging WLEDs by Using the Green-emitting CaF2:Ce3+, Tb3+ Phosphor;222
29.1;Abstract;222
29.2;1 Introduction;222
29.3;2 Physical Model and Mathematical Description;224
29.4;3 Results and Discussions;225
29.5;4 Conclusions;227
29.6;Acknowledgements;227
29.7;References;228
30;Simulation Study of Microstructure of the Amorphous ZnO;230
30.1;Abstract;230
30.2;1 Introduction;230
30.3;2 Calculation Method;231
30.4;3 Results and Discussion;232
30.5;4 Conclusion;235
30.6;Acknowledgment;235
30.7;References;236
31;The Thixoforming Process with Different Pressing Speed for Aluminum Material;237
31.1;Abstract;237
31.2;1 Introduction;237
31.3;2 The Die Design for Thixoforming Process;238
31.3.1;2.1 Experimental Parameters;238
31.3.2;2.2 The Die Design for the Thixoforming Process;239
31.3.3;2.3 Simulation and Experiment of the Pressing Step in the Thixoforming Process;242
31.4;3 Simulation Results;242
31.5;4 Experiment;246
31.6;5 Microstructure of Thixoforming Billet;248
31.7;6 Conclusion;249
31.8;References;250
32;The Impact of Distance Between Two Phosphor Layers on Luminous Flux and Color Quality of Remote Phosphor Package;251
32.1;Abstract;251
32.2;1 Introduction;251
32.3;2 Simulation and Computation;252
32.4;3 Simulation Results and Discussion;254
32.5;4 Conclusions;257
32.6;References;257
33;A Novel Approach to Fabricate Silicon Nanowire Field Effect Transistor for Biomolecule Sensing;259
33.1;Abstract;259
33.2;1 Introduction;259
33.3;2 Material and Method;260
33.4;3 Results and Discussion;261
33.5;4 Conclusion;265
33.6;References;266
34;Simulation of Ridge-Waveguide AlGaInP/GaInP Multiple-Quantum Well Diode Lasers;267
34.1;Abstract;267
34.2;1 Introduction;267
34.3;2 Laser Structure;268
34.4;3 Results and Discussions;269
34.5;4 Conclusions;271
34.6;References;272
35;Signal Processing;273
36;Experimental Evaluation of Steady State Visual Evoked Potentials for Brain Machine Interface;274
36.1;Abstract;274
36.2;1 Introduction;274
36.3;2 Steady State Visual Evoked Potentials (SSVEP);275
36.4;3 Experimental Methods;276
36.4.1;3.1 Experimental Design;276
36.4.2;3.2 Brainwave Preprocessing;277
36.5;4 Classification of SSVEP Response;278
36.6;5 Experiment;279
36.6.1;5.1 Experimental Conditions;279
36.6.2;5.2 Evaluation of Classification Performance;280
36.6.3;5.3 Evaluation of Recognition Speed and Information Transfer Capability;282
36.7;6 Conclusion;283
36.8;References;283
37;EMG-Based Interface Multi-degree of Freedom and Optionality;285
37.1;1 Introduction;285
37.2;2 MIMO-NARX Model;286
37.2.1;2.1 NARX Model;287
37.2.2;2.2 Became NARX Model into Multi Input and Multi Output;288
37.3;3 Bayesian Network;288
37.4;4 Measurement of Forearm Movements;289
37.4.1;4.1 Measurement of Myoelectric and Joint Angle;289
37.4.2;4.2 Processing Myoelectric;290
37.5;5 Estimating Joint Angle of Forearm Movements by MIMO-NARX Model;290
37.6;6 To Decide the Number of Terms of NARX Model by Bayesian Network;292
37.7;7 Conclusion;294
37.8;References;294
38;Motion and Force Estimation Based on the NARX with an EMG Signal;295
38.1;1 Introduction;295
38.2;2 Measurement and Preparation of the EMG Signal;296
38.3;3 System Identification Based on the EMG Signal;297
38.3.1;3.1 RLS Method;298
38.3.2;3.2 The NARX Model;299
38.4;4 Model Construction Based on the EMG Signal and the Wrist Angle;300
38.4.1;4.1 System Configuration of Metering Experiment for the Angle;300
38.4.2;4.2 Estimation Result of Angle;301
38.5;5 Model Construction Based on EMG Signal and Force;302
38.5.1;5.1 System Configuration of Metering Experiment for Force;302
38.5.2;5.2 Estimation Result of Force;303
38.6;6 Conclusion;304
38.7;References;304
39;The Estimation Method of Force by Using Kinect Camera;306
39.1;1 Introduction;306
39.2;2 Measurement Condition;307
39.3;3 The Driving Torque Estimation Method by Using Newton Euler Method;308
39.4;4 Physical Parameter Estimation;310
39.4.1;4.1 Joint Angle Estimation;310
39.4.2;4.2 Mass Estimation Method;312
39.4.3;4.3 Inertia Tensor Estimation;312
39.5;5 Verification of Estimating Result;313
39.6;6 Conclusion;315
39.7;References;316
40;Image Processing;317
41;Real-Time Root Monitoring of Hydroponic Crop Plants: Proof of Concept for a New Image Analysis System;318
41.1;1 Introduction;318
41.2;2 System Design;320
41.2.1;2.1 Setup and Operation;321
41.2.2;2.2 Root Measurement;322
41.2.3;2.3 Problem Identification;322
41.3;3 Results;324
41.3.1;3.1 Root Measurements;324
41.3.2;3.2 Problem Identification;325
41.4;4 Further Work;327
41.5;5 Conclusion;327
41.6;References;327
42;A New Approach of 2D Measurement of Injury Rate on Fish by a Modified K-means Clustering Algorithm Based on L*A*B* Color Space;329
42.1;Abstract;329
42.2;1 Introduction;329
42.3;2 Calculation for Injury Rate;330
42.4;3 Experimental Results;334
42.5;4 Conclusion;337
42.6;Acknowledgements;338
42.7;References;338
43;Determination of the Fish Surface Area and Volume Using Ellipsoid Approximation Method Applied for Image Processing;339
43.1;Abstract;339
43.2;1 Introduction;339
43.3;2 Mathematical Modeling;340
43.3.1;2.1 Preparation of Samples;340
43.3.2;2.2 Surface Area and Volume of Ellipsoid;341
43.3.3;2.3 The Partition Disc to Determine Surface Area and Volume;342
43.3.4;2.4 Image Processing;345
43.3.5;2.5 Image Acquisition and Analysis;345
43.4;3 Results and Discussion;346
43.4.1;3.1 Fish Shape Extraction;346
43.4.2;3.2 Surface Area and Volume Computation;346
43.5;4 Conclusion;348
43.6;Acknowledgement;348
43.7;Appendix A;348
43.8;Appendix B;350
43.9;References;352
44;Recognition and Grasping Objects from 3D Environment by Combining Depth and Color Stereo Image in the Mobile Picking Robot System;353
44.1;Abstract;353
44.2;1 Introduction;353
44.3;2 System Description;354
44.4;3 Proposed Algorithm;355
44.4.1;3.1 Mapping RGB Image into Depth Map;355
44.4.2;3.2 Object Recognition;355
44.4.3;3.3 Grasping Object;356
44.5;4 Experimental Results;358
44.6;5 Conclusion;361
44.7;Acknowledgments;361
44.8;References;361
45;Control Systems;363
46;Aerial Attitude Control of Hopping Robots Using Reaction Wheels: Evaluation of Prototype II in the Air;364
46.1;Abstract;364
46.2;1 Introduction;364
46.3;2 Estimation Method of Attitude Angle Using 9 Axis Motion Sensors;366
46.4;3 Aerial Attitude Control System Design;367
46.5;4 Experiment of Aerial Attitude Control;370
46.5.1;4.1 Experimental Setup;370
46.5.2;4.2 Experimental Results;371
46.6;5 Conclusion;374
46.7;References;374
47;Reference Trajectory Generation of Laser Beam in Consideration of Response Speed of Laser Processing Machine;376
47.1;Abstract;376
47.2;1 Introduction;376
47.3;2 System Identification of Galvanometer Scanner;377
47.3.1;2.1 Overview of System Configuration;377
47.3.2;2.2 System Identification;378
47.4;3 Generation of Modified Reference Trajectory Using Inverse Characteristics;379
47.4.1;3.1 Modified Reference Trajectory Generation Algorithm;379
47.4.2;3.2 Verification by Simulation;379
47.5;4 Experiment;380
47.5.1;4.1 Structure of Experimental Apparatus;380
47.5.2;4.2 Procedure and Conditions;381
47.5.3;4.3 Experimental Results of System Identification;382
47.5.4;4.4 Evaluation Results of Laser Beam Trajectory;382
47.6;5 Conclusion;385
47.7;References;385
48;State Estimation of Internal Combustion Engine Based on Mathematical Model;386
48.1;1 Introduction;386
48.2;2 Experimental System;388
48.3;3 Engine Model;389
48.3.1;3.1 Piston-Crank Mechanism;389
48.3.2;3.2 Combustion Process;390
48.3.3;3.3 Air Intake System;391
48.4;4 State Estimation Applying the UKF;392
48.4.1;4.1 Construction of Spreading System;392
48.4.2;4.2 Discretization of Engine Model;393
48.4.3;4.3 Verification of Estimated Results;393
48.5;5 Conclusions;395
48.6;References;395
49;Analysis of the Relationship Between Operational Mastery Process and Balance Capability in Daily Life for Unstable Personal Vehicles;397
49.1;1 Introduction;397
49.2;2 Model Derivation;398
49.2.1;2.1 Balance Board Model;399
49.2.2;2.2 Integrates the Model of the Balance Board and the Human;400
49.3;3 Control System Design;402
49.3.1;3.1 Linearization;402
49.3.2;3.2 Model-Based Stabilizing Control Based on State Dependent Riccati Equation;403
49.3.3;3.3 Verification of Simulation Result;404
49.4;4 Conclusion;406
49.5;References;406
50;Vertical Motion Control of Crane Without Load Position Information Using Nonlinear Control Theory;408
50.1;Abstract;408
50.2;1 Introduction;408
50.3;2 Mathematical Model;409
50.4;3 Controller Design;411
50.5;4 Experimental Results;414
50.5.1;4.1 Parameters of Motor-Winch System;414
50.5.2;4.2 Experimental Data Acquisition;415
50.5.3;4.3 The Relationship Between the Load Position and Rope Tension Force;416
50.5.4;4.4 Experimental Results;416
50.6;5 Conclusions;418
50.7;Acknowledgment;418
50.8;References;418
51;Feedback Control of Antagonistic-Type Twisted and Coiled Polymer Actuator;419
51.1;Abstract;419
51.2;1 Introduction;419
51.3;2 Method;420
51.4;3 Numerical Simulations;421
51.4.1;3.1 System Identification;421
51.4.2;3.2 Simulation Results;423
51.5;4 Experiments;424
51.5.1;4.1 Experiment Results;424
51.5.2;4.2 PID Control Experiment;424
51.6;5 Conclusion;426
51.7;Acknowledgements;427
51.8;References;427
52;Performance Evaluation of Grasping Force Control Based on Fall Velocity Control of Grasping Object for Telemanipulation Systems;428
52.1;Abstract;428
52.2;1 Introduction;428
52.3;2 Modeling of the Control Plant;429
52.3.1;2.1 Modeling of a Grasping Object;429
52.3.2;2.2 Modeling of the Slave Device;431
52.4;3 Grasping Force Control System;432
52.4.1;3.1 Design of the Falling Velocity Control System;432
52.4.2;3.2 Algorithm to Modify Grasping Force;433
52.4.3;3.3 Design of the Grasping Force Control System;434
52.5;4 Experiment;434
52.5.1;4.1 Experimental Setup;434
52.5.2;4.2 Performance Evaluation of the Falling Velocity Control System;435
52.5.3;4.3 Performance Evaluation of the Grasping Force Control System;436
52.6;5 Conclusion;437
52.7;References;437
53;Damping Control of Suspended Load for Truck Cranes in Consideration of Control Input Dimension;439
53.1;Abstract;439
53.2;1 Introduction;439
53.3;2 Modeling;440
53.3.1;2.1 Modeling of Crane Boom;440
53.3.2;2.2 Augmented System of Crane Boom with Valve Controller;441
53.3.3;2.3 Augmented Crane System Including Suspended Load Dynamics;443
53.4;3 Control System Design;443
53.5;4 Experiment of Load Damping Control of Truck Crane;445
53.5.1;4.1 Experimental Procedure;445
53.5.2;4.2 Experimental Result;448
53.6;5 Conclusion;449
53.7;References;449
54;Model-Based Clustering of Time Series Based on State Space Generative Models;450
54.1;Abstract;450
54.2;1 Introduction;450
54.3;2 Model Specification;451
54.3.1;2.1 Finite Mixture Model;451
54.3.2;2.2 Time Series Generative Model;452
54.3.3;2.3 Hierarchical Model Overview;454
54.4;3 Model Estimation;455
54.4.1;3.1 MCMC Initialization;455
54.4.2;3.2 MCMC Iteration Until Convergence;456
54.4.3;3.3 Model Identification and Cluster Assignment;458
54.5;4 Conclusions;458
54.6;References;459
55;Dynamic Programming Based Adaptive Optimal Control for Inverted Pendulum;460
55.1;Abstract;460
55.2;1 Introduction;460
55.3;2 Adaptive Optimal Control Design;461
55.4;3 Simulation Results;468
55.5;4 Conclusion;469
55.6;References;470
56;State Estimation of a Yoyo Based on a Model with Elasticity of a String;471
56.1;1 Introduction;471
56.2;2 Problem Establishment;472
56.3;3 Yoyo Model with the Elasticity of the String;472
56.4;4 State Estimation Based on the Yoyo Model;475
56.5;5 Conclusion;479
56.6;References;479
57;PI Sliding Mode Control for Active Magnetic Bearings in Flywheel;481
57.1;Abstract;481
57.2;1 Introduction;481
57.3;2 Active Magnetic Bearing System in Flywheel Modeling;482
57.4;3 PI Sliding Mode Control Design;484
57.4.1;3.1 PI Sliding Surface;484
57.4.2;3.2 Control Law Design;486
57.5;4 Simulation;486
57.6;5 Conclusion;490
57.7;References;490
58;A Study for Learning Method of Modified PID Controller with On-line Hybrid Genetic Algorithm;491
58.1;Abstract;491
58.2;1 Introduction;491
58.3;2 Learning of PID Controller;492
58.3.1;2.1 Controlled System;492
58.3.2;2.2 Limitation of PID Parameter’s Solution Domain;492
58.3.3;2.3 Limitation of Time Range for Object Function;496
58.3.4;2.4 Modified PID Controller;496
58.3.5;2.5 Simulation with Hybrid GA;497
58.4;3 Conclusion;500
58.5;References;500
59;Backstepping-Based Adaptive Velocity Tracking Controller Design for a Winding Spindle System;501
59.1;1 Introduction;501
59.2;2 System Modeling;502
59.3;3 Controller Design;504
59.4;4 Experimental Results;508
59.5;5 Conclusion;511
59.6;References;512
60;Study on Optimized Guidance and Robust Control for the Ship Maneuvering;513
60.1;Abstract;513
60.2;1 Introduction;513
60.3;2 Mathematical Model of a Ship;514
60.4;3 Guidance and Control Design;515
60.4.1;3.1 Guidance Design;515
60.4.2;3.2 Maneuvering Control Design;517
60.5;4 Simulation Results;520
60.6;5 Conclusion;522
60.7;Acknowledgement;522
60.8;References;522
61;Advanced Control Strategy of Dynamic Voltage Restorers for Distribution System Using Sliding Mode Control Input-Output Feedback Linearization;524
61.1;Abstract;524
61.2;1 Introduction;524
61.3;2 Overview of DVR System;525
61.3.1;2.1 System Modeling;525
61.3.2;2.2 Generation of Voltage References;526
61.4;3 Proposed Control Strategy Using Sliding Mode Input-Output Feedback Linearization;527
61.4.1;3.1 Input-Output Feedback Linearization Control;527
61.4.2;3.2 Sliding Mode Input-Output Feedback Linearization Controller;528
61.5;4 Simulation Results;530
61.6;5 Conclusions;533
61.7;Acknowledgment;533
61.8;References;533
62;Optimal Pump Scheduling to Pressure Management for Large-Scale Water Distribution Systems;535
62.1;1 Introduction;535
62.2;2 Formulation of MINLP for Efficient Optimization of Pressure Management;537
62.2.1;2.1 Modeling of Pumping Stations;537
62.2.2;2.2 Formulation of Pressure Management Regulation;537
62.2.3;2.3 Decomposition of the MINLP;539
62.3;3 Case Study;539
62.4;4 Conclusions;543
62.5;References;543
63;Model Predictive Control with Both States and Input Delays;545
63.1;Abstract;545
63.2;1 Introduction;545
63.3;2 Model Predictive Control Design;546
63.4;3 Main Results;547
63.5;4 Simulated Example;551
63.6;5 Conclusion;555
63.7;References;555
64;On the Hamiltonian Approach to the Collocated Virtual Holonomic Constraints in the Underactuated Mechanical Systems;557
64.1;1 Introduction;557
64.2;2 Preliminary Definitions and Results;559
64.3;3 Hamiltonian Description;562
64.4;4 Main Results;563
64.5;5 Case Study: The Mechanical Four Link Chain;566
64.5.1;5.1 Generalized Acrobot - Engaging the Unactuated Cyclic Variable;566
64.5.2;5.2 The Mechanical Four Link Chain, the Collocated VHCs and GA;568
64.6;6 Conclusions and Outlooks;570
64.7;References;570
65;Robotics;572
66;Continuous Genetic Algorithm Aiding to Quadcopter Controller Design;573
66.1;Abstract;573
66.2;1 Introduction;573
66.3;2 Quadcopter Configuration;574
66.4;3 The Continuous GA Optimal PID Controller;575
66.5;4 Simulation Results;576
66.6;5 Conclusion;580
66.7;References;581
67;Control System Design of Four Wheeled Independent Steering Automatic Guided Vehicles (AGV);582
67.1;Abstract;582
67.2;1 Introduction;582
67.3;2 System Modeling;583
67.4;3 Control System Design;584
67.5;4 Simulation Result;586
67.6;5 Conclusion;588
67.7;Acknowledgment;588
67.8;References;588
68;A Guide to Selecting Path Planning Algorithm for Automated Guided Vehicle (AGV);589
68.1;Abstract;589
68.2;1 Introduction;589
68.3;2 Path Planning Algorithm;590
68.4;3 System Configuration;593
68.5;4 Simulation and Experimental Results;594
68.6;5 Conclusion;597
68.7;Acknowledgment;597
68.8;References;597
69;Path Following Control of Bike Robot;599
69.1;1 Introduction;599
69.2;2 Bicycle Robot;600
69.3;3 Path Generation;601
69.3.1;3.1 Proposed Method;601
69.3.2;3.2 Algorithm;602
69.4;4 Numerical Simulation;604
69.4.1;4.1 Path Generation;604
69.4.2;4.2 Path Following;605
69.5;5 Concluding Remarks;607
69.6;References;608
70;Online Training the Radial Basis Function Neural Network Based on Quasi-Newton Algorithm for Omni-directional Mobile Robot Control;609
70.1;Abstract;609
70.2;1 Introduction;609
70.3;2 Radial Basis Function Neural Networks;610
70.4;3 The Quasi-Newton Algorithm;611
70.5;4 Adaptive Sliding Mode Control with Radial Basis Function Neural Network for Omni-directional Mobile Robot;612
70.5.1;4.1 Omni-directional Mobile Robot Model;612
70.5.2;4.2 Adaptive Sliding Mode Controller Design for Trajectory Tracking;613
70.5.3;4.3 Simulation Results;615
70.6;5 Conclusion;618
70.7;References;618
71;A Study on Looking for Shortest Trajectory of Mobile Robot Using A* Algorithm;619
71.1;Abstract;619
71.2;1 Introduction;619
71.3;2 Mobile Robot Design;620
71.3.1;2.1 Mechanical Design;620
71.3.2;2.2 Electrical Design;621
71.3.3;2.3 Kinematic Problem;621
71.4;3 Looking for Shortest Trajectory Algorithm;623
71.4.1;3.1 Review of A* Algorithm;623
71.4.2;3.2 Example About Using A* Algorithm;623
71.5;4 Simulation;625
71.6;5 Experimental Results;627
71.7;6 Conclusion;628
71.8;References;628
72;Independent Joint Control System Design Method for Robot Motion Reconstruction;629
72.1;Abstract;629
72.2;1 Introduction;629
72.3;2 System Modeling;630
72.3.1;2.1 Problem Statement;630
72.3.2;2.2 Method Demonstration;631
72.3.3;2.3 Mathematical Model;631
72.4;3 Control Design;633
72.4.1;3.1 Control Design for the First Joint and the Second Joint;634
72.4.2;3.2 Control Design for the Other Joints;635
72.5;4 Experiment;636
72.5.1;4.1 Experimental Setup;636
72.5.2;4.2 Experimental Results;639
72.6;5 Conclusion and Future Work;639
72.7;Acknowledgement;639
72.8;References;640
73;Walking Figure Generating in Consideration of Ground Reaction Force;641
73.1;1 Introduction;641
73.2;2 Construction of Disturbance Observer;642
73.2.1;2.1 Design Overview;642
73.2.2;2.2 Disturbance observer;642
73.2.3;2.3 Modeling of Legs of a Hexapod Robot;644
73.2.4;2.4 Design of Disturbance Observer;645
73.3;3 Estimation of Ground Reaction Force by Disturbance Observer;646
73.4;4 Conclusion;648
73.5;References;648
74;Study on the Combined Underwater Tracked Vehicle System with a Rock Crushing Tool;649
74.1;Abstract;649
74.2;1 Introduction;649
74.3;2 Assumption and Terminology for Simulation;650
74.3.1;2.1 Basic Assumptions;650
74.3.2;2.2 Terminology;651
74.4;3 Analysis of Force and Moment of the RC Tool;652
74.4.1;3.1 Analysis of Cutting Forces;652
74.4.2;3.2 Resistance Forces;654
74.4.3;3.3 Analysis of the Traction of the UTV;655
74.4.4;3.4 Analysis of the Moment of the RC Tool;655
74.5;4 Application Example;656
74.5.1;4.1 System Description;656
74.5.2;4.2 Steps of Calculation for Design Process;657
74.5.3;4.3 Results and Discussion;657
74.6;5 Conclusion;659
74.7;Acknowledgements;659
74.8;References;659
75;Designing Optimal Trajectories and Tracking Controller for Unmanned Underwater Vehicles;660
75.1;Abstract;660
75.2;1 Introduction;660
75.3;2 Depth Motion Equation of UUV;661
75.4;3 Designing Optimal Trajectories;661
75.4.1;3.1 Time-Optimal Trajectories (TOTs);661
75.5;4 Tracking Controller;665
75.5.1;4.1 Control Algorithm;665
75.5.2;4.2 Sliding Mode Control Law;665
75.6;5 Simulation and Discussion;666
75.6.1;5.1 Simulation 1;668
75.6.2;5.2 Simulation 2;668
75.7;6 Conclusion;669
75.8;Acknowledgements;670
75.9;References;670
76;Simulation and Experiment of Underwater Vehicle Manipulator System Using Zero-Moment Point Method;671
76.1;1 Introduction;671
76.2;2 Dynamic Modeling;672
76.3;3 Redundancy Analysis;673
76.4;4 Simulation;676
76.5;5 Experiment;677
76.6;6 Conclusion;677
76.7;References;678
77;Development of Ray-Type Underwater Glider;679
77.1;1 Introduction;679
77.2;2 Dynamic Modeling;680
77.2.1;2.1 Coordinate System and Mathematical Model;680
77.2.2;2.2 Dual-Buoyancy Engine Design;682
77.3;3 Motion Performance Analysis;683
77.3.1;3.1 Simulation 1 - Underwater Glider Model;683
77.3.2;3.2 Simulation 2 - Dual-Buoyancy Analysis;685
77.4;4 Conclusion;687
77.5;References;687
78;Modeling and Evaluating Motion Performance of Robotic Fish with a Pair of Non-uniform Pectoral Fins;688
78.1;Abstract;688
78.2;1 Introduction;688
78.3;2 Dynamic Modeling for 2D Motion;689
78.3.1;2.1 Kinematic Model;690
78.3.2;2.2 Dynamic Model;690
78.4;3 Swimming Modes and Mechanical Efficiency;694
78.5;4 Simulation Results and Discussion;695
78.6;5 Conclusions and Future Works;696
78.7;References;697
79;Modeling and Control System Design of Four Wheel Independent Steering Automatic Guided Vehicle (AGV);698
79.1;Abstract;698
79.2;1 Introduction;698
79.3;2 System Modeling;699
79.4;3 Control System Design;700
79.5;4 Simulation Result;702
79.6;5 Conclusion;705
79.7;Acknowledgment;705
79.8;References;705
80;Development of a Module Robot for Glass Façade Cleaning Robot;706
80.1;1 Introduction;706
80.2;2 System Design for Glass Façade Cleaning Robot;708
80.3;3 Trajectory Generation;710
80.3.1;3.1 Inverse Kinematics;710
80.3.2;3.2 5th Degree Polynomial Interpolation;711
80.3.3;3.3 Gait Generation Based on Sequential Control;713
80.4;4 Experiment;713
80.5;5 Conclusion;715
80.6;References;715
81;Tracking Controller Design for Omni-Directional Automated Guided Vehicles Using Backstepping and Model Reference Adaptive Control;717
81.1;1 Introduction;717
81.2;2 System Modeling;718
81.3;3 Controller Design;720
81.4;4 Simullation Results;723
81.5;5 Conclusion;726
81.6;References;726
82;Path Planning for Automatic Guided Vehicle with Multiple Target Points in Known Environment;728
82.1;Abstract;728
82.2;1 Introduction;728
82.3;2 Path Planning Algorithm;729
82.3.1;2.1 D* Lite Path Planning Algorithm;732
82.3.2;2.2 Modified D* Lite Path Planning Algorithm;733
82.4;3 Simulation and Experimental Results;735
82.5;4 Conclusion;737
82.6;Acknowledgement;737
82.7;References;737
83;Modeling of Rolling Locomotion on a Reconfigurable Quadruped Robot with Servos for State Estimation;738
83.1;1 Introduction;738
83.2;2 Problem Establishment;739
83.3;3 Model of the Rolling Reconfigurable Robot with Servos;740
83.4;4 Simulations of Rolling Locomotion on the Reconfigurable Quadruped Robot;743
83.5;5 Conclusion;745
83.6;References;745
84;Study on Determining the Number of Fin-Rays of a Gymnotiform Undulating Fin Robot;747
84.1;Abstract;747
84.2;1 Introduction;747
84.3;2 System Formulation;748
84.4;3 Experimental Result;751
84.5;4 Conclusion;753
84.6;References;754
85;Study on Design, Analysis and Control an Underwater Thruster for Unmanned Underwater Vehicle (UUV);755
85.1;Abstract;755
85.2;1 Introduction;755
85.3;2 Mechanism Design;756
85.3.1;2.1 Propeller;757
85.3.2;2.2 Thruster Housing;758
85.3.3;2.3 Magnetic Coupling;758
85.4;3 Simulation;760
85.4.1;3.1 Dynamic Model of BLDC;760
85.4.2;3.2 Control Algorithm for BLDC;761
85.4.3;3.3 Simulation Results;763
85.5;4 Experiments;764
85.6;5 Conclusion;765
85.7;Acknowledgements;766
85.8;References;766
86;Design and Implement a Fuzzy Autopilot for an Unmanned Surface Vessel;767
86.1;Abstract;767
86.2;1 Introduction;767
86.3;2 Ship Dynamics;768
86.3.1;2.1 The First-Order Nomoto Model;768
86.3.2;2.2 Disturbance;768
86.4;3 Identification of Nomoto Model Using Kempf’s Zig-zag Maneuver;769
86.5;4 Fuzzy Logic Controller;770
86.6;5 Simulation and Experiment;772
86.6.1;5.1 Experimental Identification of Nomoto Model;773
86.6.2;5.2 Step Response Simulation;774
86.6.3;5.3 Step Response Experimental;775
86.7;6 Conclusion;776
86.8;References;777
87;Heading and Depth Control of Autonomous Underwater Vehicles via Adaptive Neural Network Controller;778
87.1;Abstract;778
87.2;1 Introduction;778
87.3;2 Dynamic Model;779
87.4;3 Description of the ANNAI (Adaptive Neural Network by Adaptive Interaction) Controller;780
87.5;4 Heading and Depth Control System;781
87.5.1;4.1 ANNAI-Based Heading Control System;781
87.5.2;4.2 ANNAI-Based Depth Control System;782
87.6;5 Simulation Results;783
87.7;6 Conclusion;786
87.8;References;786
88;Nonlinearities Compensation Method for Application to Robot Manipulators Using Time-Delay Estimation;788
88.1;Abstract;788
88.2;1 Introduction;788
88.3;2 System Modeling;789
88.3.1;2.1 Problem Formulation;789
88.3.2;2.2 Method Demonstration;790
88.3.3;2.3 Mathematical Model;791
88.4;3 Control Design;793
88.4.1;3.1 Control Structure with Time-Delay Estimation (TDE);793
88.4.2;3.2 Stability Analysis;794
88.5;4 Experiment Studies;794
88.5.1;4.1 Experimental Setup;794
88.5.2;4.2 Experimental Results;795
88.6;5 Conclusion;798
88.7;Acknowledgement;798
88.8;References;798
89;Motor Control;800
90;Development of the Removable Electric Drive System for Wheelchair Running on Public Road;801
90.1;1 Introduction;801
90.2;2 Driving System Design;802
90.2.1;2.1 Needs for Wheelchair Users;802
90.2.2;2.2 Derivation of Load During Running;803
90.2.3;2.3 Selection Result of a Motor and a Gear Ratio;805
90.3;3 Experimental Model Production;805
90.3.1;3.1 Motor Identification;805
90.3.2;3.2 Identification Result of Motor;806
90.3.3;3.3 Producted Experimental Model;807
90.4;4 Verification Experiments;808
90.4.1;4.1 The Requirements of the Driving Inspection;808
90.4.2;4.2 Verification Results;808
90.5;5 Conclusion;809
90.6;References;810
91;PI-Based Fuzzy Speed Controller with PWM Direct Torque Control for Induction Motor Drive;811
91.1;Abstract;811
91.2;1 Introduction;811
91.3;2 Fuzzy Logic Design for Speed Controller;813
91.4;3 Experimental Results;814
91.5;4 Conclusions;819
91.6;Acknowledgement;819
91.7;References;819
92;Fast Maximum Power Point Tracking Control for Variable Speed Wind Turbines;821
92.1;Abstract;821
92.2;1 Introduction;821
92.3;2 Modeling of Wind Turbine Systems;822
92.4;3 Control of MPPT;824
92.4.1;3.1 Conventional Optimal Torque Control;824
92.4.2;3.2 Proposed Torque Control;825
92.4.3;3.3 Optimization of Control System Dynamics;826
92.5;4 Simulation Results;827
92.6;5 Conclusions;829
92.7;References;829
93;Feedback-Linearization-Based Direct Power Control of DFIG Wind Turbine Systems Under Unbalanced Grid Voltage;830
93.1;Abstract;830
93.2;1 Introduction;830
93.3;2 Modeling of DFIG;831
93.3.1;2.1 Modeling of DFIG Under Normal Grid Condition;831
93.3.2;2.2 Behaviors of DFIG Under Unbalanced Voltage Condition;833
93.4;3 Feedback Linearization;835
93.5;4 Simulation Results;836
93.6;5 Conclusions;838
93.7;References;838
94;High-Gain Observer Based Output Feedback Controller for a Two-Motor Drive System: A Separation Principle Approach;840
94.1;Abstract;840
94.2;1 Introduction;840
94.3;2 Problem Statement;841
94.4;3 High Gain Observer of Multi-motor Systems;843
94.5;4 Sliding Mode Control of Multi-motor Systems;845
94.6;5 Output Feedback Control Design of Multi-motor Systems;845
94.7;6 Simulation Results;847
94.8;7 Conclusions;849
94.9;References;849
95;A Fuzzy-Based Supervisory Controller Development for a Series Hydraulic Hybrid Vehicle;850
95.1;Abstract;850
95.2;1 Introduction;850
95.3;2 System Modeling;851
95.4;3 Control System Development;853
95.5;4 Simulation Results and Discussion;856
95.6;5 Conclusion;859
95.7;References;859
96;Rotor Time Constant Estimation of Induction Motor Using Online PI-Adaptive and GA-Adaptive Model;860
96.1;Abstract;860
96.2;1 Introduction;860
96.3;2 Mathematical Model of the Vector Controlled Induction Motor;862
96.4;3 Model Reference Adaptive System Method (MRAS);863
96.5;4 GA Adaptive Model Method;864
96.6;5 Simulation Results;866
96.6.1;5.1 The Simulation Results with Rotor Time Constant Is Constant;867
96.6.2;5.2 Simulated Results with Rotor Time Constants Change;869
96.7;6 Conclusion;870
96.8;Acknowledgement;870
96.9;References;870
97;Estimate Parameters of Induction Motor Using ANN and GA Algorithm;872
97.1;Abstract;872
97.2;1 Introduction;872
97.3;2 Mathematical Model of Induction Motor for Parameter Estimation;873
97.4;3 Parameters Estimation of Induction Motor Using ANN Algorithm;877
97.5;4 Parameter Estimation of Induction Motor Using GA Algorithm;878
97.6;5 Simulation Results;879
97.6.1;5.1 Simulation Results with ANN Algorithm;879
97.6.2;5.2 Simulation Results with GA Algorithm;880
97.7;6 Conclusion;881
97.8;Acknowledgement;881
97.9;References;881
98;Study on Hybrid Method for Efficiency Optimization of Induction Motor Drives;883
98.1;Abstract;883
98.2;1 Introduction;883
98.3;2 Mathematical Model of an Induction Motor;884
98.4;3 Efficiency Optimal Control;886
98.4.1;3.1 Power Lost Control Algorithm;886
98.4.2;3.2 Search Control Algorithm;887
98.5;4 The Simulation Results and Discussion;889
98.5.1;4.1 Parameters of Motor Model with Iron Loss;889
98.5.2;4.2 Simulation Results and Discussion;890
98.6;5 Conclusion;892
98.7;Acknowledgement;892
98.8;References;892
99;Power Systems;894
100;A New Optimal Algorithm for Multi-objective Short-Term Fixed Head Hydrothermal Scheduling with Emission Control Consideration;895
100.1;Abstract;895
100.2;1 Introduction;895
100.3;2 Formulation of Multi-objective Dispatch;897
100.4;3 Novel Cuckoo Search Algorithm;898
100.4.1;3.1 The First Modification;899
100.4.2;3.2 The Second Modification;899
100.5;4 Numerical Results;901
100.6;5 Conclusion;904
100.7;References;904
101;A Study of the Power Factor Improvement by Using Harmonic Filter in Busan Urban Railway Substation with Thyristor Rectification Method;906
101.1;Abstract;906
101.2;1 Introduction;906
101.3;2 Rectification Method of Using Thyristor;907
101.4;3 Design and Simulation of Harmonic Filter;907
101.4.1;3.1 Specifications of the 12 Pulse Thyristor Dual Converter;907
101.4.2;3.2 Design and Simulation of Harmonic Filter;908
101.4.2.1;3.2.1 Simulation of Harmonic Current and Power Before Installation of Harmonic Filter;909
101.4.2.2;3.2.2 Calculation of Harmonic Filter Capacity;909
101.4.2.3;3.2.2 Calculation of Harmonic Filter Capacity;909
101.4.2.4;3.2.3 Simulation of Harmonic Current and Power After Installing Harmonic Filter;911
101.5;4 Conclusions;912
101.6;Acknowledgement;912
101.7;References;912
102;Variable Structure Load Frequency Control of Power System;913
102.1;Abstract;913
102.2;1 Introduction;913
102.3;2 Two-Area LFC System;914
102.4;3 Variable Structure Load Frequency Control Design;915
102.5;4 Simulation Tests;917
102.6;5 Conclusions;919
102.7;References;919
103;Quantum-Behaved Bat Algorithm for Combined Economic Emission Dispatch Problem with Valve-Point Effect;921
103.1;Abstract;921
103.2;1 Introduction;921
103.3;2 Problem Formulation;922
103.3.1;2.1 Economic Dispatch (ED);923
103.3.2;2.2 Emission Dispatch;923
103.3.3;2.3 Constraints;923
103.3.4;2.4 Combined Economic Emission Dispatch (CEED);924
103.4;3 Methodology;924
103.5;4 Result and Analysis;926
103.6;5 Conclusion;929
103.7;Acknowledgments;930
103.8;References;930
104;Modified Flower Pollination Algorithm for Solving Economic Dispatch Problem;932
104.1;Abstract;932
104.2;1 Introduction;932
104.3;2 Problem Formulation;933
104.3.1;2.1 Objective Function;933
104.3.2;2.2 Constraints;933
104.3.3;2.3 Processing Power Balance Constraint;934
104.4;3 Modified Flower Pollination Algorithm for ELD Problem;935
104.4.1;3.1 Conventional Flower Pollination Algorithm (CFPA);935
104.4.2;3.2 Modified Flower Pollination Algorithm (MFPA);936
104.5;4 Implementation of the MFPA for the Considered Problem;936
104.5.1;4.1 Initialization;936
104.5.2;4.2 The First Solution Generation via Local Search;937
104.5.3;4.3 The Second Solution Generation via Global Search;937
104.5.4;4.4 The Termination Criterion of the Search Process;937
104.6;5 Numerical Results;938
104.7;6 Conclusion;939
104.8;References;940
105;Optimal Load Frequency Control in an Isolated Power System;941
105.1;Abstract;941
105.2;1 Introduction;942
105.3;2 Mathematical Model of an Isolated Power System;943
105.3.1;2.1 Mathematical Modelling of Generator;943
105.3.2;2.2 Mathematical Modelling of Load;943
105.3.3;2.3 Mathematical Modelling for Prime Mover;944
105.3.4;2.4 Mathematical Modelling of Governor;944
105.4;3 Optimal Load-Frequency Controller Design;946
105.5;4 Simulation Results;948
105.6;5 Conclusion;950
105.7;References;950
106;Energy;952
107;A Research on the Thermal Daily Performance of Hybrid Solar Collector with Fin-and-Tube Heat Exchanger in Winter;953
107.1;Abstract;953
107.2;1 Introduction;954
107.3;2 Experimental Apparatus and Method;955
107.3.1;2.1 Experimental Apparatus;955
107.3.2;2.2 Experimental Method;956
107.4;3 Results and Discussion;957
107.5;4 Conclusion;961
107.6;Acknowledgments;961
107.7;References;962
108;A Study on the Heat Exchange Performance of Hybrid Solar Collector with Air to Water Heat Exchanger Type;963
108.1;Abstract;963
108.2;1 Introduction;964
108.3;2 Experimental Apparatus and Method;964
108.3.1;2.1 Experimental Apparatus;964
108.3.2;2.2 Experimental Method;966
108.4;3 Results and Discussion;967
108.5;4 Conclusion;970
108.6;Acknowledgments;970
108.7;References;970
109;Numerical Design of Solar Collector Trough System for Integrated Solar Combine Cycle;972
109.1;Abstract;972
109.2;1 Introduction;972
109.3;2 Proposed Model of an Integrated Solar Combine Cycle;973
109.3.1;2.1 Integrated Solar Combine Cycle;973
109.3.2;2.2 Design an Integrated Solar Combine Cycle;973
109.3.3;2.3 Design Solar Trough;975
109.4;3 Description and Computation of the Solar Thermal Collector;976
109.4.1;3.1 The Total Intensity of Solar Radiation on the Surface of the Earth;976
109.4.2;3.2 Calculating the Heating Process;977
109.4.3;3.3 Calculating the Heat Capacity;980
109.4.4;3.4 Calculating Number of the Needed Solar Trough;981
109.5;4 Calculate Characteristic Parameters of the Sample Device;981
109.6;5 Conclusion;983
109.7;References;984
110;Mechanical Engineering;985
111;Effect of Minimum Quantity Lubrication on Surface Roughness in Milling Machining;986
111.1;Abstract;986
111.2;1 Introduction;986
111.3;2 Experiment Setup;988
111.3.1;2.1 Design Experiment;988
111.3.2;2.2 Mathematical Model;989
111.4;3 Experimental Details;992
111.4.1;3.1 Single-Factor Experiments;993
111.4.2;3.2 Multi-factor Experiments;994
111.5;4 Conclusion;996
111.6;References;996
112;Slip Analysis on a Non-holonomic Continuously Variable Transmission Using Magic Formula;998
112.1;Abstract;998
112.2;1 Introduction;999
112.3;2 Non-holonomic Continuously Variable Transmissions;1000
112.4;3 Friction Model and Experimental Results;1001
112.4.1;3.1 Planar Friction Model Using Magic Formula;1001
112.4.2;3.2 Test Bench Setup and Experimental Results;1002
112.5;4 Numerical Results Using First-Order Time Delayed Slip;1003
112.6;5 Conclusions;1007
112.7;Acknowledgement;1007
112.8;References;1008
113;Sterilization of Edible Bird Nest Product Utilize Microwave Technology;1009
113.1;Abstract;1009
113.2;1 Introduction;1009
113.3;2 Methodology;1010
113.3.1;2.1 Microwave Applicator Design;1010
113.3.2;2.2 Sterilization Experimental Design;1012
113.4;3 Results;1014
113.4.1;3.1 Microwave Design Results;1014
113.4.2;3.2 Sterilization Results;1016
113.5;4 Conclusion and Discussion;1018
113.6;References;1019
114;Forming Limit Diagram Prediction of AA6061-T6 Sheet Using a Microscopic Void Growth Model;1021
114.1;Abstract;1021
114.2;1 Introduction;1021
114.3;2 Ductile Fracture Model;1022
114.4;3 Numerical Implementation;1023
114.5;4 Experimental Work;1023
114.6;5 Numerical Simulation;1025
114.6.1;5.1 Calibration of the Material Parameters;1025
114.6.2;5.2 Forming Limit Diagram;1026
114.7;6 Conclusion;1030
114.8;Acknowledgements;1030
114.9;References;1030
115;Numerical Analysis of LBV150 ROV Thruster Performance Under Open Water Test Condition;1032
115.1;Abstract;1032
115.2;1 Introduction;1032
115.3;2 LBV150 ROV Thruster’s Thrust Test-Rig;1034
115.4;3 3D Modeling of the LBV150 ROV Thruster;1035
115.5;4 Numerical Analysis of the SeaBotix LVB150 ROV Thruster;1036
115.5.1;4.1 Mesh Generation for Simulation;1036
115.5.2;4.2 Simulation of Dynamics of the SeaBotix LBV150 ROV Thruster;1037
115.6;5 Conclusions;1041
115.7;Acknowledgement;1041
115.8;References;1041
116;A Method for Tuning the Frequency Series Wave Speed in Hydraulic Flexible Hose;1042
116.1;Abstract;1042
116.2;1 Introduction;1042
116.3;2 Three-Transducer Method with Frequency Series Wave Speed;1043
116.3.1;2.1 Wall Model of Flexible Hose;1044
116.3.2;2.2 Function of Wave Speed on a 3 Parameters Flexible Tube Model;1044
116.3.3;2.3 Function of Wave Speed on a 5 Parameters Flexible Tube Model;1046
116.4;3 Calculation of Wave Speed;1047
116.4.1;3.1 An Experiment with Three-Transducer Method with Frequency Series Wave Speed;1047
116.4.2;3.2 Trial and Error Method;1048
116.4.3;3.3 Parameter Tuning with RCGA;1048
116.5;4 Conclusion;1051
116.6;Acknowledgement;1051
116.7;References;1051
117;Optimal Design of RFECT System for Inspection of 16-inch Ferromagnetic Pipe;1052
117.1;Abstract;1052
117.2;1 Introduction;1052
117.3;2 Optimal Design of RFECT System;1053
117.3.1;2.1 Exciter Coil Design;1053
117.3.2;2.2 Receiver Coil Design;1057
117.4;3 Verification by Experiment;1059
117.5;4 Conclusion;1061
117.6;References;1062
118;Building Management Algorithms in Automated Warehouse Using Continuous Cluster Analysis Method;1063
118.1;Abstract;1063
118.2;1 Introduction;1063
118.3;2 Methodology;1064
118.3.1;2.1 Basic of ABC Storage, Closest Open Location - COL Policy and Continuous Cluster Method;1064
118.3.2;2.2 Assumption Made;1065
118.3.3;2.3 Layout Design;1066
118.3.4;2.4 Automated Storage and Retrieval Algorithm;1067
118.4;3 Simulation Software;1068
118.5;4 Simulated Result;1068
118.5.1;4.1 Comparison of Continuous Cluster Algorithm and Random Algorithms;1069
118.5.2;4.2 Comparison of Continuous Cluster Algorithm and ABC Policy;1071
118.6;5 Conclusion;1072
118.7;References;1072
119;Development of VR Based Authoring Tool for Smart Factory;1073
119.1;Abstract;1073
119.2;1 Introduction;1073
119.3;2 Related Works;1074
119.4;3 VR Factory System;1076
119.4.1;3.1 Architecture;1076
119.4.2;3.2 VR Visualization;1076
119.4.3;3.3 VR Based Authoring Tool;1077
119.4.4;3.4 Simulation;1078
119.5;4 Experiment;1080
119.6;5 Conclusion;1080
119.7;References;1081
120;Author Index;1083
