E-Book, Englisch, Band 632, 880 Seiten, eBook
Kasruddin Nasir / Ahmad / Saari InECCE2019
1. Auflage 2020
ISBN: 978-981-15-2317-5
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
Proceedings of the 5th International Conference on Electrical, Control & Computer Engineering, Kuantan, Pahang, Malaysia, 29th July 2019
E-Book, Englisch, Band 632, 880 Seiten, eBook
Reihe: Lecture Notes in Electrical Engineering
ISBN: 978-981-15-2317-5
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book presents the proceedings of the 5th International Conference on Electrical, Control & Computer Engineering 2019, held in Kuantan, Pahang, Malaysia, on 29th July 2019. Consisting of two parts, it covers the conferences’ main foci: Part 1 discusses instrumentation, robotics and control, while Part 2 addresses electrical power systems. The book appeals to professionals, scientists and researchers with experience in industry.The conference provided a platform for professionals, scientists and researchers with experience in industry.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1;Preface;7
2;Contents;10
3; Instrumentation, Control and Artificial Systems;19
3.1; Position Control of Pneumatic Actuator Using Cascade Fuzzy Self-adaptive PID;20
3.1.1;1 Introduction;20
3.1.2;2 Mathematical Model of Pneumatic System;21
3.1.3;3 Cascade Fuzzy Self-adaptive PID Controller Design;23
3.1.4;4 Results and Discussion;25
3.1.5;5 Conclusion;29
3.1.6;References;30
3.2; Effect of Excitation Frequency on Magnetic Response Induced by Front- and Back-Side Slits Measured by a Differential AMR Sensor Probe;32
3.2.1;1 Introduction;33
3.2.2;2 Experimental Setup;34
3.2.2.1;2.1 MFL Probe;34
3.2.2.2;2.2 Measurement System;35
3.2.3;3 Results and Discussions;37
3.2.3.1;3.1 Front Side and Back Side Measurement;37
3.2.3.2;3.2 Graph of Slope of Trendline of the Delta Values of Real and Imaginary Parts of the Front Side and Back Side Measurement;39
3.2.4;4 Conclusions;40
3.2.5;References;41
3.3; Model-Free PID Controller Based on Grey Wolf Optimizer for Hovering Autonomous Underwater Vehicle Depth Control;42
3.3.1;1 Introduction;43
3.3.2;2 HAUV Model;44
3.3.3;3 Controller Design;45
3.3.3.1;3.1 PID Controller;45
3.3.3.2;3.2 Grey Wolf Optimization (GWO);46
3.3.4;4 Results and Discussion;48
3.3.5;5 Conclusion;51
3.3.6;References;51
3.4; Experimental Study of Optimization of Electrode Dimension for Non-invasive Electrical Resistance Tomography Application;53
3.4.1;1 Introduction;53
3.4.2;2 The Basic Principle of ERT;54
3.4.3;3 Methodology;55
3.4.4;4 Results and Discussion;58
3.4.5;5 Conclusion;61
3.4.6;References;61
3.5; A Fictitious Reference Iterative Tuning Method for Buck Converter-Powered DC Motor Control System;62
3.5.1;1 Introduction;62
3.5.2;2 Problem Statements;64
3.5.2.1;2.1 Buck-Converter Powered Dc Motor Model;65
3.5.3;3 Algorithm for Controller Tuning;66
3.5.3.1;3.1 A Brief Review of SKF Algorithm;66
3.5.3.2;3.2 Fictitious Reference Iterative Tuning;67
3.5.4;4 Numerical Example;69
3.5.5;5 Conclusion;71
3.5.6;References;72
3.6; Depth Evaluation of Slits on Galvanized Steel Plate Using a Low Frequency Eddy Current Probe;74
3.6.1;1 Introduction;74
3.6.2;2 Measurements;75
3.6.2.1;2.1 Magnetic Sensor Probe;75
3.6.2.2;2.2 Measurement System and Test Sample;76
3.6.3;3 Results and Discussion;77
3.6.3.1;3.1 Signal Analysis;77
3.6.3.2;3.2 Frequency Response Characteristic;78
3.6.4;4 Conclusion;80
3.6.5;References;81
3.7; Sensitivity Maps Preparation for Electrical Capacitance Tomography Using Finite Element Approach;82
3.7.1;1 Introduction;83
3.7.2;2 Sensitivity Map Generation;84
3.7.2.1;2.1 Experimental Set-Up;85
3.7.2.2;2.2 Post-processing Data;87
3.7.3;3 Results and Discussion;87
3.7.4;4 Conclusion;89
3.7.5;References;89
3.8; Infrared Thermal Sensor for a Low Cost and Non-invasive Detection of Skin Cancer;91
3.8.1;1 Introduction;92
3.8.2;2 Device Design and Instrumentation;92
3.8.3;3 Device Characterization and Feasibility Study;95
3.8.3.1;3.1 Comparative Measurement of Object Temperature;95
3.8.3.2;3.2 Non-invasive Measurement of Skin Temperature;96
3.8.4;4 Results;96
3.8.4.1;4.1 Comparative Measurement of Object Temperature;96
3.8.4.2;4.2 Non-invasive Measurement of Skin Temperature;96
3.8.5;5 Discussion;97
3.8.6;6 Conclusion;98
3.8.7;References;99
3.9; T-Way Strategy for Sequence Input Interaction Test Case Generation Adopting Fish Swarm Algorithm;100
3.9.1;1 Introduction;100
3.9.2;2 Proposed T-Way Strategy Adopting Fish Swarm Algorithm;103
3.9.3;3 Results and Discussion;106
3.9.3.1;3.1 Comparison Results of TSFSSA Based on Experiment 1;110
3.9.3.2;3.2 Comparison Results of TSFSSA Based on Experiment 2;110
3.9.3.3;3.3 Comparison Results of TSFSSA Based on Experiment 3;110
3.9.4;4 Conclusion and Future Work;111
3.9.5;References;111
3.10; Development of AC and DC Drive Coils for a Small Volume Magnetic Particle Imaging System;113
3.10.1;1 Introduction;113
3.10.2;2 Methodology;115
3.10.2.1;2.1 Development of AC Drive Coil;115
3.10.2.2;2.2 Resonant Circuit for AC Drive Coil;116
3.10.2.3;2.3 DC Excitation Unit and Passive Butterworth Filter;117
3.10.2.4;2.4 Experimental Setup;118
3.10.3;3 Result and Discussions;119
3.10.3.1;3.1 Resonance Frequency of AC Excitation Coil;119
3.10.3.2;3.2 Field Free Line and DC Excitation Field;120
3.10.4;4 Conclusion;122
3.10.5;References;122
3.11; A Diversity-Based Adaptive Synchronous-Asynchronous Switching Simulated Kalman Filter Optimizer;124
3.11.1;1 Introduction;124
3.11.2;2 The Simulated Kalman Filter;126
3.11.3;3 The Proposed Diversity-Based Adaptive Synchronous-Asynchronous Switching SKF (DASAS-SKF);127
3.11.4;4 Experiment Result and Discussion;129
3.11.5;5 Conclusion;134
3.11.6;References;135
3.12; Combinatorial Test Suite Generation Strategy Using Enhanced Sine Cosine Algorithm;138
3.12.1;1 Introduction;138
3.12.2;2 Overview of T-Way Testing;139
3.12.3;3 Related Works;141
3.12.4;4 Original Sine Cosine Algorithm and Its Enhancement;142
3.12.5;5 Experimental Results and Conclusion;144
3.12.6;References;146
3.13; Classification of Lubricant Oil Geometrical Odor-Profile Using Cased-Based Reasoning;149
3.13.1;1 Introduction;150
3.13.2;2 Methodology;150
3.13.2.1;2.1 Data Measurement;151
3.13.2.2;2.2 Data Pre-Processing;152
3.13.2.3;2.3 Feature Extraction;152
3.13.2.4;2.4 Intelligent Classification;153
3.13.2.5;2.5 Performance Measure;154
3.13.3;3 Result and Discussion;155
3.13.3.1;3.1 Data Normalization and Odor-Pattern Establishment Process;155
3.13.3.2;3.2 CBR Performance Measure;157
3.13.4;4 Conclusion;160
3.13.5;References;160
3.14; Optimization of Quaternion Based on Hybrid PID and P? Control;162
3.14.1;1 Introduction;162
3.14.2;2 Quaternion Math;163
3.14.3;3 Quaternion Based Quadrotor Modelling;166
3.14.4;4 Controller Synthesis;167
3.14.5;5 Genetic Algorithm;168
3.14.6;6 Result and Discussion;169
3.14.7;7 Conclusion;172
3.14.8;References;173
3.15; Elimination-Dispersal Sine Cosine Algorithm for a Dynamic Modelling of a Twin Rotor System;175
3.15.1;1 Introduction;175
3.15.2;2 Elimination-Dispersal Sine Cosine Algorithm;177
3.15.3;3 Benchmark Functions Test;178
3.15.4;4 Twin Rotor System;179
3.15.5;5 Parametric Modelling of a Twin Rotor System;179
3.15.6;6 Result and Discussion;181
3.15.6.1;6.1 Result of the Benchmark Functions Test;181
3.15.6.2;6.2 Result of the Parametric Modelling;183
3.15.7;7 Conclusion;185
3.15.8;References;185
3.16; The Investigation of Meat Classification Based on Significant Authentication Features Using Odor-Profile Intelligent Signal Processing Approach;187
3.16.1;1 Introduction;188
3.16.2;2 Methodology;189
3.16.2.1;2.1 Sample Preparation;189
3.16.2.2;2.2 Experimental Setup;190
3.16.2.3;2.3 Data Measurement;191
3.16.2.4;2.4 Data Pre-processing;191
3.16.2.5;2.5 Feature Extraction;191
3.16.2.6;2.6 CBR Classification;192
3.16.2.7;2.7 Performance Measure;192
3.16.3;3 Results and Discussion;193
3.16.3.1;3.1 Feature Extraction;193
3.16.3.2;3.2 CBR Classification;194
3.16.3.3;3.3 Performance Measure;197
3.16.4;4 Conclusion;198
3.16.5;References;198
3.17; The Study of Raw Water Based on Quality Parameter Using Smell-Print Sensing Device;200
3.17.1;1 Introduction;200
3.17.2;2 Methodology;202
3.17.2.1;2.1 Raw Water Sampling and Preparation;202
3.17.2.2;2.2 Data Pre-processing;203
3.17.2.3;2.3 Features Extraction;203
3.17.2.4;2.4 CBR Classification;203
3.17.2.5;2.5 Performance Measure;204
3.17.3;3 Result and Discussion;204
3.17.3.1;3.1 Data Measurement;204
3.17.3.2;3.2 Data Pre-processing;204
3.17.3.3;3.3 Features Extraction;205
3.17.3.4;3.4 Performance Measure;206
3.17.4;4 Conclusion;209
3.17.5;References;209
3.18; Camera Orientation Determination Based on Copper Wire Spool Shape;211
3.18.1;1 Introduction;212
3.18.2;2 Theoretical Background;213
3.18.3;3 Proposed Algorithm;214
3.18.3.1;3.1 Copper Wire Spool Detection;215
3.18.3.2;3.2 Camera and Spool Alignment;215
3.18.4;4 Experimental Results and Discussion;217
3.18.5;5 Conclusion;221
3.18.6;References;223
3.19; A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem;225
3.19.1;1 Introduction;225
3.19.2;2 The Problem of Software Module Clustering;227
3.19.3;3 Symbiotic Organism Search and Its Modification;227
3.19.4;4 Benchmarking Case Studies;230
3.19.5;5 Discussion;232
3.19.6;6 Conclusion;233
3.19.7;References;233
3.20; Classification of Agarwood Types (Malaccensis and Crassna) Between Oil and Smoke Using E-Nose with CBR Classifier;236
3.20.1;1 Introduction;236
3.20.2;2 Methodology;238
3.20.2.1;2.1 Data Measurement;239
3.20.2.2;2.2 Data Pre-Processing;240
3.20.2.3;2.3 Features Extraction;240
3.20.2.4;2.4 CBR Classification;240
3.20.2.5;2.5 Performance Measurement;241
3.20.3;3 Results and Discussion;241
3.20.4;4 Conclusion;246
3.20.5;References;247
4; Applied Electronics and Computer Engineering;249
4.1; SCAR-CNN: Secondary-Classification-After-Refinement Convolutional Neural Network for Fine-Grained Categorization;250
4.1.1;1 Introduction;250
4.1.2;2 Related Work;251
4.1.3;3 Methodology;253
4.1.3.1;3.1 Datasets;253
4.1.3.2;3.2 Part Localization;254
4.1.3.3;3.3 Primary Classification;255
4.1.3.4;3.4 Secondary Classification;256
4.1.4;4 Experiments;257
4.1.4.1;4.1 Image Up-Scaling Method;257
4.1.4.2;4.2 First Classification Scenario;258
4.1.4.3;4.3 Second Classification Scenario;259
4.1.4.4;4.4 Overall Result;260
4.1.5;5 Conclusion;260
4.1.6;References;261
4.2; Forecasting Road Deaths in Malaysia Using Support Vector Machine;264
4.2.1;1 Introduction;265
4.2.2;2 Methodology;266
4.2.2.1;2.1 Datasets;267
4.2.2.2;2.2 SVM Model Development;267
4.2.3;3 Results and Discussion;268
4.2.4;4 Conclusion;269
4.2.5;References;270
4.3; Investigation of Dimensionality Reduction on Numerical Attribute Features in a Finger Vein Identification System;271
4.3.1;1 Introduction;271
4.3.2;2 Related Work;272
4.3.3;3 Propose Method;273
4.3.3.1;3.1 Finger Vein Database;273
4.3.3.2;3.2 Image Processing;274
4.3.3.3;3.3 Feature Extraction;274
4.3.3.4;3.4 Principal Components Analysis (PCA) Dimensionality Reduction;278
4.3.3.5;3.5 Support Vector Machine (SVM);278
4.3.4;4 Experimental Results and Discussion;280
4.3.5;5 Conclusion;283
4.3.6;References;283
4.4; Intelligent Gender Recognition System for Classification of Gender in Malaysian Demographic;285
4.4.1;1 Introduction;285
4.4.2;2 Related Works;286
4.4.3;3 Method;287
4.4.3.1;3.1 System Architecture;288
4.4.3.2;3.2 Convolutional Neural Network Technique;289
4.4.4;4 Results and Discussion;292
4.4.4.1;4.1 Prototype Operation;292
4.4.4.2;4.2 Performance in Malaysian Demographic;293
4.4.5;5 Conclusion;296
4.4.6;References;297
4.5; A Novel Approach Towards Tamper Detection of Digital Holy Quran Generation;298
4.5.1;1 Introduction;298
4.5.2;2 Related Works;300
4.5.3;3 Methodology;301
4.5.3.1;3.1 System Design;301
4.5.3.2;3.2 Jaro-Winkler Similarity;303
4.5.3.3;3.3 Performance Measure Parameters;303
4.5.4;4 Implementation Details;304
4.5.5;5 Experimental Results and Analysis;304
4.5.6;6 Conclusion;308
4.5.7;References;308
4.6; A Comparative Study of AFM-Assisted Direct and Least-Square Attitude Determination Algorithm;310
4.6.1;1 Introduction;311
4.6.2;2 Ambiguity Resolution;311
4.6.2.1;2.1 Ambiguity Function Method (AFM);311
4.6.2.2;2.2 New Method Based on AFM;313
4.6.3;3 Attitude Determination;314
4.6.3.1;3.1 DAD Method (Direct Attitude Determination);314
4.6.3.2;3.2 LSAD Method (Least Square Attitude Determination;317
4.6.4;4 Verification and Analysis;318
4.6.4.1;4.1 Two Antenna Experiment;318
4.6.4.2;4.2 Three Antenna Experiments;318
4.6.5;5 Conclusions;321
4.6.6;References;321
4.7; Design and Development of Wearable Human Activity Recognition for Healthcare Monitoring;323
4.7.1;1 Introduction;323
4.7.2;2 System Modelling and Design;325
4.7.3;3 Results and Discussions;327
4.7.4;4 Conclusion;331
4.7.5;References;331
4.8; Region of Interest Extraction of Finger-Vein Image Using Watershed Segmentation with Distance Transform;333
4.8.1;1 Introduction;333
4.8.2;2 Proposed ROI Extraction Method;335
4.8.2.1;2.1 Finger Vein Image Segmentation and Edge Detection;336
4.8.2.2;2.2 Finger Vein Image Orientation Correction;336
4.8.2.3;2.3 ROI Extraction;338
4.8.3;3 Results and Discussions;340
4.8.3.1;3.1 Comparison of the Proposed and Previously Proposed Finger Edge Detection Methods;340
4.8.3.2;3.2 Comparison of Various ROI Extraction Methods;342
4.8.4;4 Conclusion;344
4.8.5;References;344
4.9; The Classification of Skateboarding Trick Manoeuvres Through the Integration of Image Processing Techniques and Machine Learning;346
4.9.1;1 Introduction;347
4.9.2;2 Methodology;348
4.9.2.1;2.1 Experimental Setup;348
4.9.2.2;2.2 Data Collection;348
4.9.2.3;2.3 Image Processing;348
4.9.2.4;2.4 Machine Learning;350
4.9.3;3 Results and Discussion;353
4.9.4;4 Conclusion;354
4.9.5;References;355
4.10; Review and Analysis of Risk Factor of Maternal Health in Remote Area Using the Internet of Things (IoT);356
4.10.1;1 Introduction;356
4.10.2;2 Analysis of Risk Factor during Pregnancy;357
4.10.2.1;2.1 Literature Search and Selecting Risk Factors Intensity;357
4.10.2.2;2.2 Analyzing the Common Risk Parameters from an Existing Diabetes Dataset for Women;359
4.10.2.3;2.3 Analyzing Sample Real Data to Explore the Status of Accuracy;360
4.10.3;3 The Experimental Result Over Pima-Indians-Diabetes Data Set;360
4.10.3.1;3.1 Classification Accuracy;360
4.10.3.2;3.2 Making Prediction;361
4.10.4;4 Proposed System Model;362
4.10.4.1;4.1 Integrated Model;362
4.10.4.2;4.2 Model of IoT Device;362
4.10.5;5 Conclusion;363
4.10.6;References;363
4.11; Recent Trends and Open Challenges in EEG Based Brain-Computer Interface Systems;365
4.11.1;1 Introduction;366
4.11.2;2 Recent Advancements in BCI System;366
4.11.2.1;2.1 Data Acquisition for BCI;366
4.11.2.2;2.2 EEG Modalities Used in BCI;367
4.11.2.3;2.3 Data Pre-Processing;368
4.11.2.4;2.4 Feature Extraction and Classification;368
4.11.2.5;2.5 EEG Based BCI Technologies;369
4.11.3;3 Challenges in BCI System with Possible Solutions;369
4.11.3.1;3.1 Absence of Standard Data Acquisition Protocol;372
4.11.3.2;3.2 Low ITR of BCI System;372
4.11.3.3;3.3 Lack of Autonomous Operation of BCI System/Expert Dependency of BCI System;373
4.11.3.4;3.4 Dissimilar Performance Evaluation Metric of BCI System;373
4.11.4;4 Conclusions;373
4.11.5;References;374
4.12; Early Rubeosis Iridis Detection Using Feature Extraction Process;377
4.12.1;1 Introduction;377
4.12.2;2 Research Background;378
4.12.3;3 Methodology;380
4.12.4;4 Result and Discussion;382
4.12.5;5 Conclusion;383
4.12.6;References;385
4.13; Multi-hop File Transfer in WiFi Direct Based Cognitive Radio Network for Cloud Back-Up;386
4.13.1;1 Introduction;387
4.13.2;2 The System Model;388
4.13.2.1;2.1 Routing Protocol Selection;388
4.13.2.2;2.2 Cognitive Radio Network (CRN) Activity Implementation;388
4.13.2.3;2.3 WiFi Direct Based Multi-hop Data Transfer and Cloud Synchronization;390
4.13.2.4;2.4 System Implementation;390
4.13.3;3 Performance Analysis;390
4.13.4;4 Conclusion and Future Works;395
4.13.5;References;396
4.14; The Multifocus Images Fusion Based on a Generative Gradient Map;398
4.14.1;1 Introduction;399
4.14.2;2 Methodology;400
4.14.2.1;2.1 Multifocus Images Fusion;401
4.14.2.2;2.2 Multifocus Images Fusion with Generative Gradient Map;402
4.14.2.3;2.3 The Generative Gradient Map;404
4.14.2.4;2.4 Morphology Filtering;405
4.14.2.5;2.5 Guided Image Filter;406
4.14.3;3 Test and Results;406
4.14.4;4 Conclusions and Future Work;409
4.14.5;References;409
4.15; A Comparative Analysis of Four Classification Algorithms for University Students Performance Detection;411
4.15.1;1 Introduction;412
4.15.2;2 Literature Review;412
4.15.3;3 Methodology;414
4.15.3.1;3.1 Data Collection;414
4.15.3.2;3.2 Null Data Reduction;416
4.15.3.3;3.3 Label Encoding;416
4.15.3.4;3.4 Feature Selection;417
4.15.4;4 Results and Discussions;418
4.15.5;5 Conclusions and Future Directions;419
4.15.6;References;420
4.16; Open-Set Face Recognition in Video Surveillance: A Survey;421
4.16.1;1 Introduction;421
4.16.2;2 Closed-Set Versus Open-Set Face Recognition;422
4.16.3;3 Literature Review;424
4.16.3.1;3.1 One-Class Classification Based Approaches;424
4.16.3.2;3.2 Two-Class Classification Based Approaches;425
4.16.3.3;3.3 Multi-class Classification Based Approaches;426
4.16.4;4 Current Problems and Future Research;427
4.16.5;5 Performance Evaluation;428
4.16.6;6 Face Video Surveillance Databases;429
4.16.7;7 Conclusion;430
4.16.8;References;430
4.17; Hardware Development of Auto Focus Microscope;433
4.17.1;1 Introduction;434
4.17.2;2 Methods;435
4.17.2.1;2.1 Hardware Design;436
4.17.2.2;2.2 Circuit Interfacing;437
4.17.2.3;2.3 System Workflow;438
4.17.3;3 Results and Discussions;440
4.17.3.1;3.1 Fine and Corse Knob;440
4.17.3.2;3.2 Sputum Image Analysis;443
4.17.4;4 Conclusion and Future Works;444
4.17.5;References;445
4.18; Overview on Fingerprinting Authentication Technology;446
4.18.1;1 Introduction;446
4.18.2;2 Fingerprint Characteristics;447
4.18.2.1;2.1 Identification;447
4.18.2.2;2.2 Permanence;447
4.18.3;3 Authentication System;448
4.18.3.1;3.1 Data Acquisition;449
4.18.3.2;3.2 Feature Extraction;450
4.18.3.3;3.3 Matching;452
4.18.4;4 System Security;453
4.18.5;5 Future Technology;454
4.18.6;6 Conclusion;456
4.18.7;References;456
4.19; Bandwidth and Gain Enhancement of a Modified Ultra-wideband (UWB) Micro-strip Patch Antenna Using a Reflecting Layer;458
4.19.1;1 Introduction;459
4.19.2;2 Antenna Design;460
4.19.2.1;2.1 Calculation of the Associated Parameters;460
4.19.2.2;2.2 Reflecting Layer (A Second Layer of Antenna) Design;463
4.19.3;3 Results and Discussions;463
4.19.4;4 Conclusion;466
4.19.5;References;467
4.20; Oil Palm Tree Detection and Counting in Aerial Images Based on Faster R-CNN;469
4.20.1;1 Introduction;469
4.20.2;2 Methodology;471
4.20.3;3 Experiments;473
4.20.3.1;3.1 Experimental Set-Up;473
4.20.3.2;3.2 Results and Discussion;473
4.20.4;4 Conclusions;475
4.20.5;References;476
4.21; EEG Pattern of Cognitive Activities for Non Dyslexia (Engineering Student) due to Different Gender;477
4.21.1;1 Introduction;478
4.21.2;2 Methodology;479
4.21.2.1;2.1 Subjects;479
4.21.2.2;2.2 Data Collection;480
4.21.2.3;2.3 Analysis of Data;481
4.21.3;3 Results and Discussions;481
4.21.3.1;3.1 Distribution Results;481
4.21.3.2;3.2 Results of Average;483
4.21.3.3;3.3 Correlation;485
4.21.4;4 Conclusion;485
4.21.5;References;486
4.22; Intelligent Autism Screening Using Fuzzy Agent;488
4.22.1;1 Introduction;488
4.22.2;2 Related Works;489
4.22.3;3 Methodology;490
4.22.3.1;3.1 Overview of Fuzzy Agents;490
4.22.3.2;3.2 The Working Mechanism of Fuzzy Agent for Autism Screening;491
4.22.4;4 Results and Discussion;493
4.22.5;5 Conclusions;495
4.22.6;References;495
4.23; Ultra Wide Band (UWB) Based Early Breast Cancer Detection Using Artificial Intelligence;497
4.23.1;1 Introduction;498
4.23.2;2 Methodology;499
4.23.2.1;2.1 The Experimental Set Up and Data Collection;500
4.23.2.2;2.2 Feature Extraction;502
4.23.2.3;2.3 Artificial Neural Network (ANN);502
4.23.3;3 Result and Discussion;503
4.23.4;4 Conclusion;505
4.23.5;References;506
4.24; Design and Analysis of Circular Shaped Patch Antenna with Slot for UHF RFID Reader;508
4.24.1;1 Introduction;508
4.24.2;2 Antenna Geometry and Design;510
4.24.3;3 Results and Discussions;512
4.24.3.1;3.1 Result of Simulation of the Proposed Antenna;512
4.24.3.2;3.2 Parametric Studies;514
4.24.4;4 Conclusion;516
4.24.5;References;517
4.25; Analysis of EEG Features for Brain Computer Interface Application;519
4.25.1;1 Introduction;519
4.25.2;2 Methodology;520
4.25.2.1;2.1 EEG Measurement and Protocol;521
4.25.2.2;2.2 Preprocessing and Feature Extraction;521
4.25.3;3 Result and Discussion;524
4.25.3.1;3.1 EEG Raw Data;524
4.25.3.2;3.2 Filtered EEG Data;524
4.25.3.3;3.3 Average Power Spectral Density;526
4.25.3.4;3.4 Average Spectral Centroid;526
4.25.3.5;3.5 Average Log Energy Entropy;527
4.25.4;4 Conclusion;529
4.25.5;References;529
4.26; Hybrid Sampling and Random Forest Based Machine Learning Approach for Software Defect Prediction;531
4.26.1;1 Introduction;532
4.26.2;2 Related Works;532
4.26.3;3 Data Pre-processing;533
4.26.3.1;3.1 Removing Highly Correlated Features;533
4.26.3.2;3.2 Feature Scaling;533
4.26.3.3;3.3 Feature Selection;534
4.26.3.4;3.4 Algorithms for Random Forest Importance and Chi-Square;535
4.26.4;4 Proposed Methodology;536
4.26.4.1;4.1 Imbalanced Dataset;537
4.26.5;5 Experiment Results;537
4.26.5.1;5.1 Evaluation Matrices;537
4.26.5.2;5.2 Results and Analysis;538
4.26.5.3;5.3 Results Discussion;542
4.26.6;6 Conclusion;542
4.26.7;References;543
4.27; kNN and SVM Classification for EEG: A Review;544
4.27.1;1 Introduction;544
4.27.2;2 EEG Signal Processing Architecture;546
4.27.3;3 Previous Studies;547
4.27.3.1;3.1 k-Nearest Neighbour;547
4.27.3.2;3.2 Support Vector Machine;548
4.27.3.3;3.3 Comparison Studies;550
4.27.4;4 Discussion;551
4.27.5;5 Conclusion;552
4.27.6;References;552
4.28; Flexible Graphene-Silver Nanowires Polydimethylsiloxane (PDMS) Directional Coupler;555
4.28.1;1 Introduction;555
4.28.2;2 Experimental Method;556
4.28.2.1;2.1 Graphene–Silver Nanowires Dispersion;556
4.28.2.2;2.2 PDMS Substrate;557
4.28.2.3;2.3 Directional Coupler Design;557
4.28.2.4;2.4 Four-Point Probe Technique;558
4.28.3;3 Results and Discussion;559
4.28.4;4 Conclusion;562
4.28.5;References;564
4.29; Investigating the Possibility of Brain Actuated Mobile Robot Through Single-Channel EEG Headset;566
4.29.1;1 Introduction;566
4.29.2;2 Methodology;568
4.29.2.1;2.1 Data Acquisition Protocol;568
4.29.2.2;2.2 Feature Extraction and Classification;571
4.29.2.3;2.3 Translational Algorithm and Device Command;572
4.29.2.4;2.4 Hardware Setup;573
4.29.3;3 Result and Discussion;573
4.29.4;4 Conclusion;576
4.29.5;References;576
4.30; Campus Hybrid Intrusion Detection System Using SNORT and C4.5 Algorithm;578
4.30.1;1 Introduction;579
4.30.2;2 Related Works;579
4.30.3;3 Hybrid Intrusion Detection System;580
4.30.3.1;3.1 Component of Hybrid Intrusion Detection System;580
4.30.3.2;3.2 Intrusion Detection System (IDS);581
4.30.4;4 Research Methods;581
4.30.4.1;4.1 Stage of Analysis;581
4.30.4.2;4.2 Stage of Design;582
4.30.4.3;4.3 Stage of Implementation;583
4.30.4.4;4.4 Stage of Enforcement;586
4.30.4.5;4.5 Stage of Enhancement;589
4.30.5;5 Conclusions and Recommendations;589
4.30.6;References;590
4.31; Image Segmentation of Women’s Salivary Ferning Patterns Using Harmony Frangi Filter;591
4.31.1;1 Introduction;591
4.31.2;2 Research Method;593
4.31.2.1;2.1 Hessian Matrix as Edge Detection Method;594
4.31.2.2;2.2 Harmony Frangi Filter;597
4.31.2.3;2.3 Block Diagram;598
4.31.3;3 Results and Analysis;599
4.31.3.1;3.1 Stages of the Image Segmentation Process;599
4.31.3.2;3.2 Measurement of Segmentation;605
4.31.4;4 Conclusion;606
4.31.5;References;608
4.32; Autonomous Self-exam Monitoring for Early Diabetes Detection;609
4.32.1;1 Introduction;609
4.32.2;2 Research Background;610
4.32.3;3 Methodology;612
4.32.3.1;3.1 Data Collection;612
4.32.3.2;3.2 Segmentation;613
4.32.3.3;3.3 Threshold;615
4.32.3.4;3.4 Feature Extraction;615
4.32.3.5;3.5 Classification;615
4.32.4;4 Result and Discussion;616
4.32.5;5 Conclusion;617
4.32.6;References;618
4.33; Quantitative Assessment of Remote Code Execution Vulnerability in Web Apps;619
4.33.1;1 Introduction;620
4.33.2;2 Literature Review;620
4.33.3;3 RCE Exploitation Process;621
4.33.4;4 Methodology;622
4.33.4.1;4.1 Data Collection;622
4.33.4.2;4.2 Pre-Processing Phase;623
4.33.5;5 Results;623
4.33.6;6 Discussions;626
4.33.7;7 Conclusion;627
4.33.8;References;627
5; Sustainable Energy and Power Engineering;629
5.1; A Salp Swarm Algorithm to Improve Power Production of Wind Plant;630
5.1.1;1 Introduction;630
5.1.2;2 Problem Framework;631
5.1.2.1;2.1 Salp Swarm Algorithm (SSA) Mathematical Model;632
5.1.2.2;2.2 SSA Approach to Increase Wind Plant Power Production;633
5.1.3;3 Results and Discussion;633
5.1.3.1;3.1 Wind Plant Dynamic Model;634
5.1.3.2;3.2 Example of a Singular Row Wind Plant Consisting of 10 Turbines;635
5.1.4;4 Conclusions;637
5.1.5;References;638
5.2; Improvement of Performance and Response Time of Cascaded Five-Level VSC STATCOM Using ANN Controller and SVPWM During Period of Voltage Sag;640
5.2.1;1 Introduction;641
5.2.2;2 Proposed STATCOM;642
5.2.2.1;2.1 Cascaded 5-Level VSC;643
5.2.2.2;2.2 SVPWM Circuit Scheme;644
5.2.2.3;2.3 Control Circuit of STATCOM;645
5.2.2.4;2.4 ANN Controller;646
5.2.3;3 Simulation Results;647
5.2.3.1;3.1 Case A: Voltage Sag Engendered by SLG Fault;647
5.2.3.2;3.2 Case B: Voltage Sag Engendered by LL Fault;650
5.2.4;4 Conclusion;650
5.2.5;References;652
5.3; Development of Maximum Power Point Tracking for Doubly-Fed Induction Generators in Wind Energy Conversion Systems;654
5.3.1;1 Introduction;655
5.3.2;2 MPPT in DFIG-WECS;656
5.3.3;3 CS Algorithm Based MPPT in DFIG-WECS;657
5.3.4;4 Numerical Result;658
5.3.5;5 Conclusion;663
5.3.6;References;663
5.4; Development of PV Module Power Degradation Analyzer;665
5.4.1;1 Introduction;665
5.4.2;2 Literature Review;666
5.4.2.1;2.1 PV Module’s Parameters;666
5.4.2.2;2.2 Recent Studies;667
5.4.3;3 Methodology;668
5.4.3.1;3.1 Overview;668
5.4.3.2;3.2 System Design;668
5.4.4;4 Result and Discussion;670
5.4.4.1;4.1 Comparison with I–V Tracer;670
5.4.4.2;4.2 Degradation Calculation;671
5.4.5;5 Conclusion;672
5.4.6;References;673
5.5; Direct Power Control Method of Maximum Power Point Tracking (MPPT) Algorithm for Pico-Hydrokinetic River Energy Conversion System;675
5.5.1;1 Introduction;675
5.5.2;2 Pico-Hydrokinetic System Configuration;677
5.5.2.1;2.1 Turbine Model;677
5.5.2.2;2.2 Analysis of PMSG and Rectifier;679
5.5.2.3;2.3 Design of DC Boost Converter;680
5.5.3;3 MPPT Control Algorithm;681
5.5.3.1;3.1 Hill-Climbing Search Algorithm;681
5.5.3.2;3.2 Proposes Modify Hill-Climbing Search with PI Current Controller;682
5.5.4;4 Results and Discussion;684
5.5.5;5 Conclusion;685
5.5.6;References;686
5.6; Load Estimation of Single-Phase Diode Bridge Rectifier Using Kalman Filter;688
5.6.1;1 Introduction;688
5.6.2;2 Kalman Filter Method-Based on RC Estimation;689
5.6.2.1;2.1 Kalman Filter;691
5.6.3;3 Theoretical Analysis;692
5.6.4;4 Simulation Results and Analysis;693
5.6.5;5 Conclusion;698
5.6.6;References;698
5.7; A Study on Residual Current Device Nuisance Tripping Due to Grounding Resistance Value;700
5.7.1;1 Introduction;700
5.7.2;2 Experiment Arrangement;701
5.7.3;3 RCD Performance Due to Grounding Resistance Value;702
5.7.3.1;3.1 Effect of Grounding Resistance;702
5.7.3.2;3.2 Effect of Load and Grounding Resistance Value;704
5.7.4;4 Conclusion;705
5.7.5;References;706
5.8; DC-Link Protection for Grid-Connected Photovoltaic System: A Review;707
5.8.1;1 Introduction;707
5.8.2;2 Photovoltaic (PV) System;708
5.8.3;3 Impacts of Grid Faults on Grid-Connected PV System;708
5.8.4;4 Conventional Protection Schemes of Grid-Connected PV System;710
5.8.5;5 Proposed New Protection Scheme (Zero State Protection Scheme);715
5.8.6;6 Conclusion;717
5.8.7;References;717
5.9; An Improved Efficiency of Solar Photo Voltaic System Applications by Using DC-DC Zeta Converter;719
5.9.1;1 Introduction;719
5.9.2;2 Detailed Arrangement of Projected System;721
5.9.2.1;2.1 SPV Based Zeta Converter Control;721
5.9.2.2;2.2 Scheme of Projected System;722
5.9.3;3 Control of Projected System;725
5.9.3.1;3.1 INC-MPPT Algorithm;725
5.9.3.2;3.2 BLDC Motor Under Electronic Commutation;726
5.9.4;4 BLDC Speed Controller Method;726
5.9.4.1;4.1 Control Speed of BLDC Motor;728
5.9.5;5 MATLAB/Simulation Results;728
5.9.6;6 Conclusion;730
5.9.7;References;733
5.10; Hydrophobic Sol-Gel Based Self-cleaning Coating for Photovoltaic Panels;734
5.10.1;1 Introduction;735
5.10.2;2 Experimental Details;735
5.10.2.1;2.1 Materials;735
5.10.2.2;2.2 Fabrication of VTT Sol-Gel Based Coating;736
5.10.2.3;2.3 Characterization;737
5.10.3;3 Results and Discussions;739
5.10.3.1;3.1 Experimental Preparation of VTT Sol;739
5.10.3.2;3.2 Heat Treatment of the Coating;740
5.10.3.3;3.3 Transmittance and Transparency;740
5.10.4;4 Conclusions;744
5.10.5;References;745
5.11; Effect of Graphene Oxide Nanoparticles on Thermal Properties of Paraffin Wax;747
5.11.1;1 Introduction;748
5.11.2;2 Methods;749
5.11.2.1;2.1 Fabrication of Nano-enhanced PCM;749
5.11.2.2;2.2 Analysis Methods;749
5.11.2.3;2.3 Theoretical;751
5.11.3;3 Results and Discussion;752
5.11.3.1;3.1 Melting and Solidifying Time;752
5.11.3.2;3.2 Temperature of Melting Process;753
5.11.3.3;3.3 Differential Scanning Calorimetry Curve;756
5.11.3.4;3.4 Total Heat Stored;759
5.11.4;4 Conclusion;760
5.11.5;References;760
5.12; Reliability Performance of Low Voltage (LV) Network Configuration;762
5.12.1;1 Introduction;762
5.12.2;2 Reliability Input;764
5.12.2.1;2.1 LV Distribution Network;764
5.12.2.2;2.2 Mean Fault Rates and Repair Times;765
5.12.3;3 Reliability Assessment;767
5.12.3.1;3.1 Reliability Method;767
5.12.3.2;3.2 Reliability Indices;769
5.12.4;4 Result;769
5.12.5;5 Discussion;770
5.12.6;6 Conclusion;771
5.12.7;References;771
5.13; Detailed Non-Linear Constrained Multi-Objective Optimal Operation of Power Systems Including Renewable Energy Sources;773
5.13.1;1 Introduction;773
5.13.2;2 Renewable Power Sources;774
5.13.2.1;2.1 Solar Power;774
5.13.2.2;2.2 Wind Power;775
5.13.3;3 Optimal Operation of Integrated Power Systems;775
5.13.3.1;3.1 Objective Function;776
5.13.3.2;3.2 Constraint Condition;777
5.13.4;4 Optimal Operation of an Integrated Power System Using a MCS Algorithm;778
5.13.4.1;4.1 CS Algorithm;779
5.13.4.2;4.2 MCS Algorithm;779
5.13.5;5 Numerical Results;780
5.13.6;6 Conclusion;785
5.13.7;References;786
5.14; Voltage Sag Immunity Testing for AC Contactors in Industrial Environment;787
5.14.1;1 Introduction;787
5.14.1.1;1.1 Voltage Sag;787
5.14.1.2;1.2 AC Contactors;789
5.14.2;2 Previous Research;790
5.14.2.1;2.1 Laboratory Testing;790
5.14.3;3 Methodology;792
5.14.3.1;3.1 Selection of AC Contactors;792
5.14.3.2;3.2 Practical Test Setup;793
5.14.3.3;3.3 Test Procedure;793
5.14.3.4;3.4 Selection of Variables;794
5.14.4;4 Result and Analysis;794
5.14.4.1;4.1 New and Ageing Contactor;794
5.14.5;5 Conclusion;797
5.14.6;References;798
5.15; Vertical Axis Wind Turbines: An Overview;799
5.15.1;1 Introduction;799
5.15.2;2 Wind Energy;800
5.15.2.1;2.1 Wind Power;800
5.15.2.2;2.2 Global Expansion of Wind Energy Generation;801
5.15.2.3;2.3 Implementation of Wind Energy Generation in Malaysia;801
5.15.3;3 Wind Turbine Classification;802
5.15.3.1;3.1 Horizontal Axis Wind Turbine (HAWT);802
5.15.3.2;3.2 Vertical Axis Wind Turbine (VAWT);802
5.15.3.3;3.3 Savonius Type;804
5.15.3.4;3.4 Darrieus Type;805
5.15.3.5;3.5 Hybrid Savonius–Darrieus VAWT;808
5.15.4;4 Proposed Design Methodology for Hybrid VAWT;810
5.15.5;5 Future Development of VAWTs for Energy Generation;811
5.15.6;6 Conclusion;811
5.15.7;References;812
5.16; Hyperheuristics Trajectory Based Optimization for Energy Management Strategy (EMS) of Split Plug-In Hybrid Electric Vehicle;814
5.16.1;1 Introduction;814
5.16.2;2 HEV Free Model;815
5.16.3;3 Methodology;816
5.16.4;4 Result;819
5.16.5;5 Discussion;822
5.16.6;6 Conclusion;822
5.16.7;References;825
5.17; Utilization of Filter Harmonic Current Based on Shunt HPF Within the Acceptable IEEE-519 Standard;826
5.17.1;1 Introduction;826
5.17.2;2 System Description;827
5.17.2.1;2.1 Simulation Results and Discussion;829
5.17.3;3 Conclusion;834
5.17.4;References;834
5.18; Vehicle-to-Grid as Frequency Regulator in a Micro Grid System;836
5.18.1;1 Introduction;836
5.18.2;2 Plug-in Hybrid Electric Vehicle Charging Load Profile (PHEVCLP);837
5.18.2.1;2.1 Vehicle Daily Mileage Analysis;838
5.18.2.2;2.2 Vehicle Arrival Time Analysis;839
5.18.2.3;2.3 PHEV Load Charging Profile;840
5.18.2.4;2.4 Model of Vehicle-to-Grid (V2G) System;842
5.18.3;3 Results and Discussion;843
5.18.3.1;3.1 Increases of Residential Load Due to the PHEV Charging;843
5.18.3.2;3.2 Different Charging Level and Its Impact on System Frequency;844
5.18.4;4 Conclusion;849
5.18.5;References;849
5.19; Development of PV Module Hotspot Detector;851
5.19.1;1 Introduction;851
5.19.2;2 Literature Review;852
5.19.3;3 Methodology;854
5.19.4;4 Results and Discussion;856
5.19.5;5 Conclusion;860
5.19.6;References;860
5.20; Comparative Analysis for LED Driver with Analog and Digital Controllers;861
5.20.1;1 Introduction;861
5.20.2;2 Modelling of the Controller;863
5.20.3;3 Parameter Calculations;863
5.20.4;4 Simulation;867
5.20.5;5 Conclusions;871
5.20.6;References;872
5.21; Characterization of Positive Porous Electrode Felt for Organic Redox Flow Battery Application;874
5.21.1;1 Introduction;874
5.21.2;2 Methodology;875
5.21.2.1;2.1 Material Preparation;875
5.21.2.2;2.2 Felt Preparation;876
5.21.2.3;2.3 Electrochemical Investigation;876
5.21.3;3 Result and Discussion;877
5.21.4;4 Conclusion;879
5.21.5;References;879
