دانلود کتاب Metaheuristic Algorithms in Industry 4.0 (Advances in Metaheuristics)

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Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface Editors Contributors 1. A Review on Cyber Physical Systems and Smart Computing: Bibliometric Analysis 1.1 Introduction 1.2 Data Selection and Extraction 1.3 Distribution of Publications along Different Verticals 1.3.1 Publications Analysed: Year-on-Year Basis 1.3.2 Research Directions of Publications 1.3.3 Popular Places 1.3.4 Productive Organizations and Researchers 1.4 Analysis along the Collaboration Vertical 1.4.1 Collaboration Strength amongst the Researchers 1.4.2 Collaboration Strength of Organizations 1.4.3 Collaborative Strength of Places 1.5 Analysis along the Citation Landscape 1.5.1 The Citation Landscape for Research Papers 1.5.2 The Citation Landscape for Researchers 1.5.3 The Citation Landscape for Organizations 1.5.4 The Citation Landscape for Places 1.6 Timeline Analysis and Burst Detection 1.6.1 Timeline Review Analysis 1.6.2 Keyword Burst Detection 1.6.3 References Burst Detection 1.7 Conclusion References 2. Design Optimization of Close-Fitting Free-Standing Acoustic Enclosure Using Jaya Algorithm 2.1 Introduction 2.2 Insertion Loss 2.2.1 Mathematical Model for Prediction of Insertion Loss 2.2.2 Need for Optimization 2.2.2.1 Effect of Variation in Panel Thickness (h) on IL 2.2.2.2 Effect of Variation in Source to Panel Distance (d) on IL 2.2.2.3 Effect of Variation in Internal Damping Coefficient (η) on IL 2.2.3 Optimization 2.2.3.1 Formulation of Optimization Problem 2.2.3.2 Optimization by Jaya Algorithm 2.2.3.3 Final Dimensions of the Enclosure 2.3 Experimentation 2.3.1 Experimental Set-up 2.3.2 Experimental Procedure 2.4 Results and Discussion 2.4.1 Theoretically Predicted Vs Experimentally Obtained Results 2.5 Conclusions and Future Scope References 3. A Metaheuristic Scheme for Secure Control of Cyber-Physical Systems 3.1 Introduction 3.2 Setup and Preliminaries 3.2.1 System Description 3.2.2 A Moving Target Defense Scheme Using Switching Controllers 3.2.3 Problem Formulation 3.3 System Analysis in the Absence of Cyber Attack 3.4 System Analysis in the Presence of Cyber Attack 3.4.1 A Detection Scheme for the Presence of Actuator Intrusion 3.4.2 An MTD Control Scheme to Mitigate Cyber Attack 3.5 Optimization of the Proposed MTD Control Scheme 3.5.1 Basic Algorithm of PSO 3.5.2 PSO-Based LQR Tuning 3.6 Simulation Example 3.7 Conclusions and Future Scope Acknowledgments References 4. Application of Salp Swarm Algorithm to Solve Constrained Optimization Problems with Dynamic Penalty Approach in Real-Life Problems 4.1 Introduction 4.2 Framework of Salp Swarm Algorithm 4.3 Dynamic Penalty Approach 4.4 Proposed Methodology 4.5 Design of Experiments 4.5.1 Selection of Orthogonal Array 4.5.2 Experimental Data 4.5.3 Problem Formulation 4.6 Results and Discussion 4.7 Conclusion References 5. Optimization of Robot Path Planning Using Advanced Optimization Techniques Notation 5.1 Introduction 5.1.1 Navigational Methodology Used for Mobile Robot Path Planning 5.1.1.1 Classical Approaches for Mobile Robot Navigation 5.1.1.2 Reactive Approaches for Mobile Robot Navigation 5.1.2 Classification of Navigation Strategy 5.2 Literature Review 5.3 Optimization Algorithms 5.3.1 Jaya Algorithm 5.3.2 Rao Algorithms 5.4 Applications of the Jaya and Rao Algorithms to Robot Path Planning Optimization 5.4.1 Case Study 1 5.4.1.1 Objective Function Formulation for Case Study 1 5.4.1.2 Obstacle Avoidance Behavior for Case Study 1 5.4.1.3 Goal Searching Behavior for Case Study 1 5.4.1.4 Case Study 1: Example 1 5.4.1.5 Case Study 1: Example 2 5.4.2 Case Study 2 5.4.2.1 Objective Function Formulation for Case Study 2 5.4.2.2 Obstacle Avoidance Behavior for Case Study 2 5.4.2.3 Goal Searching Behavior for Case Study 2 5.4.2.4 Case Study 2: Example 1 5.4.2.5 Case Study 2: Example 2 5.4.2.6 Case Study 2: Example 3 5.4.2.7 Case Study 2: Example 4 5.4.2.8 Case Study 2: Example 5 5.4.3 Case Study 3 5.4.3.1 Objective Function Formulation for Case Study 3 5.4.3.2 Obstacle Avoidance Behavior for Case Study 3 5.4.3.3 Target-Seeking Behavior for Case Study 3 5.4.3.4 Case Study 3: Example 1 5.4.3.5 Case Study 3: Example 2 5.4.3.6 Case Study 3: Example 3 5.4.4 Case Study 4 5.4.4.1 Objective Function Formulation for Case Study 4 5.4.4.2 Case Study 4: Example 1 5.4.4.3 Case Study 4: Example 2 5.5 Conclusions References 6. Semi-Empirical Modeling and Jaya Optimization of White Layer Thickness during Electrical Discharge Machining of NiTi Alloy Abbreviations 6.1 Introduction 6.1.1 Research Novelty 6.2 Method and Material 6.2.1 Experimental Details 6.2.2 Empirical Modeling 6.2.3 Jaya Optimization 6.2.4 Convergence Analysis for WLT 6.3 Results and Discussions 6.3.1 Comparative Analysis of WLT 6.3.2 WLT Evaluation Using ImageJ Software 6.3.3 Optimum Parameter Setting Using Jaya Technique 6.4 Conclusions References 7. Analysis of Convolution Neural Network Architectures and Their Applications in Industry 4.0 7.1 Introduction 7.2 Evolution of Convolution Neural Network Architectures 7.2.1 LeNet 7.2.1.1 Architecture Description 7.2.1.2 Limitations 7.2.2 AlexNet 7.2.2.1 Architecture Description 7.2.2.2 Limitations 7.2.3 GoogLeNet 7.2.3.1 Architecture Description 7.2.3.2 Limitations 7.2.4 VGG 7.2.4.1 Architecture Description 7.2.4.2 Limitations 7.2.5 ResNet 7.2.5.1 Architecture Description 7.2.5.2 Limitations 7.2.6 R-CNN 7.2.6.1 Architecture Description 7.2.6.2 Limitations 7.2.7 You Only Look Once (YOLO) 7.2.7.1 Architecture Description 7.2.7.2 Limitations 7.2.8 Generative Adversarial Networks (GANs) 7.2.8.1 Limitations 7.3 Applications of Convolution Neural Networks in Industry 4.0 7.3.1 Healthcare Sector 7.3.2 Automotive Sector 7.3.3 Fault Detection 7.4 Conclusion References 8. EMD-Based Triaging of Pulmonary Diseases Using Chest Radiographs (X-Rays) Abbreviations Symbols Chapter Organization 8.1 Introduction 8.1.1 Motivation for Building This Tool 8.1.2 Earth Mover’s Distance 8.1.3 Dataset 8.1.4 Parameter Settings 8.2 Results and Discussion 8.2.1 Conclusion and Anticipated Outcomes 8.2.2 Anticipated Outcomes References 9. Adaptive Neuro Fuzzy Inference System to Predict Material Removal Rate during Cryo-Treated Electric Discharge Machining Abbreviations 9.1 Introduction 9.2 Materials and Experimental Set-up 9.3 Results and Discussion 9.4 Conclusions References 10. A Metaheuristic Optimization Algorithm-Based Speed Controller for Brushless DC Motor: Industrial Case Study 10.1 Introduction 10.2 Speed Control of Sensorless BLDC Motor Drives 10.2.1 Mathematical Model of BLDC Motors 10.2.2 Sensorless Speed Control Scheme of BLDC Motors 10.2.2.1 Principle of Sensorless Position Detection 10.2.2.2 Sensorless Speed Control of BLDC Motors 10.2.2.3 Sensorless Control Strategy 10.3 Analysis of Metaheuristics Optimization Algorithm-Based Controller 10.3.1 Methods of Optimal Tuning of Controller 10.3.1.1 Analysis of Optimization Techniques Based on PID Controller 10.3.1.2 Analysis of Optimization Techniques Based on FOPID Controller 10.4 Metaheuristic Optimization Algorithm-Based Controller Tuning 10.4.1 Controller Design 10.4.2 Basic Structure of Optimal Tuning of Controller 10.4.3 Optimization Techniques Based on Controller Tuning for Brushless DC Motor 10.4.4 Effect of Controller Parameters 10.4.5 Problem Formulation 10.5 Results and Discussions 10.5.1 Simulink Model 10.5.2 Speed Response under Constant Load Condition 10.5.3 Speed Response under Varying Load Conditions 10.5.4 Speed Response under Varying Set Speed Conditions 10.5.5 Speed Response under Combined Operating Conditions 10.5.6 Mean, Standard Deviation and Convergence 10.6 Conclusion and Future Research Direction References 11. Predictive Analysis of Cellular Networks: A Survey List of Abbreviations 11.1 Introduction 11.2 Traffic Characteristics and Aspects of Analysis 11.2.1 Traffic Characteristics 11.2.1.1 Self-Similarity 11.2.1.2 Seasonality 11.2.1.3 Non-Stationarity 11.2.1.4 Multifractal 11.2.1.5 Long-Range Dependency (LRD) 11.2.1.6 Short-Range Dependency (SRD) 11.2.2 Aspects of Analysis 11.3 Overview of Predictive Analysis 11.3.1 Time-Series Analysis 11.3.2 CDR Analysis 11.3.3 Mobility and Location Analysis 11.4 Time-Series Analysis of Network Traffic 11.4.1 Stochastic Models 11.4.2 Research Contribution 11.5 CDR Analysis 11.5.1 Predicted Outputs 11.5.1.1 Mobility Analysis 11.5.1.2 Anomaly Detection 11.5.1.3 Social Influence Analysis 11.5.1.4 Voice Traffic Analysis 11.5.2 Big Data Analysis of CDR 11.6 Mobility and Location Analysis 11.6.1 Predicted Outputs 11.6.1.1 Moving Direction 11.6.1.2 Future Locations 11.6.1.3 User Trajectory 11.6.1.4 The Next Cell Id 11.6.2 Mobility Analysis 11.6.3 Location Analysis 11.7 Network Analysis for Special Parameters 11.7.1 Hotspot Detection 11.7.2 Holiday Traffic Prediction 11.7.3 Customer Churn Prediction 11.7.4 Fault Prediction 11.7.5 Anomaly Detection 11.8 Predictive Analysis-Enabled Applications 11.8.1 Resource Allocation 11.8.2 Handover Management 11.8.3 Location-Based Services 11.8.4 Interference Management 11.8.5 Energy Efficiency 11.9 Deep Learning in Predictive Analysis of Cellular Networks 11.9.1 Deep Learning State-of-the-Art 11.9.1.1 Convolutional Neural Networks 11.9.1.2 Recurrent Neural Networks 11.9.1.3 Deep Belief Networks 11.9.1.4 Autoencoders 11.9.1.5 Long Short-Term Memory 11.9.2 Deep Learning-Based Time-Series Analysis 11.9.3 Deep Learning-Based CDR Analysis 11.9.4 Deep Learning-Based Mobility & Location Analysis 11.10 Emerging Intelligent Networks 11.10.1 Characteristics of SON 11.10.1.1 Scalability 11.10.1.2 Stability 11.10.1.3 Agility 11.10.2 Classes of SON 11.10.2.1 Self-Configuration 11.10.2.2 Self-Optimization 11.10.2.3 Self-Healing 11.10.3 Applications of SON 11.10.3.1 Coverage and Capacity Optimization 11.10.3.2 Mobility Robustness Optimization 11.10.3.3 Mobility Load Balancing 11.10.3.4 RACH Optimization 11.11 Conclusion References 12. Optimization Techniques and Algorithms for Dental Implants – A Comprehensive Review 12.1 Introduction 12.1.1 Structural Optimization 12.1.2 Surface Morphology Optimization 12.1.3 Material Properties Optimization 12.2 FEA Aspect of Optimization Techniques 12.3 Optimization Techniques and Algorithms 12.3.1 Genetic Algorithm 12.3.2 Topology Optimization Algorithm 12.3.2.1 SKO (Soft Kill Option) 12.3.2.2 Solid Isotropic Material with Penalization (SIMP) 12.3.3 Particle-Swarm Optimization 12.3.4 Multiobjective Optimization Algorithm 12.3.5 Approximate Optimization 12.3.6 Uncertainty Optimization Algorithm 12.3.7 Memetic Search Optimization 12.4 Parameters for Optimization 12.4.1 Structural Parameters 12.4.2 Material Properties and Surface Morphology 12.4.3 Osseointegration, Implant Design, Surgical Technique and Excessive Loading 12.5 Complementary Techniques Used with Optimization Algorithms 12.5.1 Surrogate Models for Optimization Algorithms 12.5.1.1 Artificial Neural Networks (ANN) 12.5.1.2 Kriging Interpolation 12.5.1.3 Use of Support Vector Regression (SVR) 12.6 Conclusion Acknowledgement References Index