{"product_id":"optimization-techniques-in-engineering-advances-and-applications-hardback-9781119906278","title":"Optimization Techniques in Engineering; Advances and Applications (Hardback) 9781119906278","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOptimization Techniques in Engineering\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eAdvances and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAnita Khosla (Edited by), Khosla (Author), Prasenjit Chatterjee (Edited by), Ikbal Ali (Edited by), Dheeraj Joshi (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119906278, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 23 May 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e544 pages\u003cbr\u003e22.9 x 15.2 x 3.2 cm, 0.953 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eOPTIMIZATION TECHNIQUES IN ENGINEERING\u003c\/b\u003e \u003cp\u003e\u003cb\u003eThe book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThis book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I — Soft Computing and Evolutionary-Based Optimization; and Part II — Decision Science and Simulation-Based Optimization, which contains application-based chapters. \u003c\/p\u003e\n\u003cp\u003eReaders and users will find in the book: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAn overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics;\u003c\/li\u003e \u003cli\u003eAn in-depth treatment of contributions to optimal learning and optimizing engineering systems;\u003c\/li\u003e \u003cli\u003eMaps out the relations between optimization and other mathematical topics and disciplines;\u003c\/li\u003e \u003cli\u003eA problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eResearchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eAcknowledgment xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Soft Computing and Evolutionary-Based Optimization 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Improved Grey Wolf Optimizer with Levy Flight to Solve Dynamic Economic Dispatch Problem with Electric Vehicle Profiles 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnjali Jain, Ashish Mani and Anwar S. Siddiqui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.2 Problem Formulation 5\u003c\/p\u003e \u003cp\u003e1.2.1 Power Output Limits 6\u003c\/p\u003e \u003cp\u003e1.2.2 Power Balance Limits 6\u003c\/p\u003e \u003cp\u003e1.2.3 Ramp Rate Limits 7\u003c\/p\u003e \u003cp\u003e1.2.4 Electric Vehicles 7\u003c\/p\u003e \u003cp\u003e1.3 Proposed Algorithm 8\u003c\/p\u003e \u003cp\u003e1.3.1 Overview of Grey Wolf Optimizer 8\u003c\/p\u003e \u003cp\u003e1.3.2 Improved Grey Wolf Optimizer with Levy Flight 9\u003c\/p\u003e \u003cp\u003e1.3.3 Modeling of Prey Position with Levy Flight Distribution 10\u003c\/p\u003e \u003cp\u003e1.4 Simulation and Results 13\u003c\/p\u003e \u003cp\u003e1.4.1 Performance of Improved GWOLF on Benchmark Functions 14\u003c\/p\u003e \u003cp\u003e1.4.2 Performance of Improved GWOLF for Solving DED for the Different Charging Probability Distribution 14\u003c\/p\u003e \u003cp\u003e1.5 Conclusion 29\u003c\/p\u003e \u003cp\u003eReferences 34\u003c\/p\u003e \u003cp\u003exxi\u003c\/p\u003e \u003cp\u003evii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Comparison of YOLO and Faster R-CNN on Garbage Detection 37\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArulmozhi M., Nandini G. Iyer, Jeny Sophia S., Sivakumar P., Amutha C. and Sivamani D.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 37\u003c\/p\u003e \u003cp\u003e2.2 Garbage Detection 39\u003c\/p\u003e \u003cp\u003e2.2.1 Transfer Learning-Technique 39\u003c\/p\u003e \u003cp\u003e2.2.2 Inception-Custom Model 39\u003c\/p\u003e \u003cp\u003e2.2.2.1 Convolutional Neural Network 40\u003c\/p\u003e \u003cp\u003e2.2.2.2 Max Pooling 41\u003c\/p\u003e \u003cp\u003e2.2.2.3 Stride 41\u003c\/p\u003e \u003cp\u003e2.2.2.4 Average Pooling 41\u003c\/p\u003e \u003cp\u003e2.2.2.5 Inception Layer 42\u003c\/p\u003e \u003cp\u003e2.2.2.6 3*3 and 1*1 Convolution 43\u003c\/p\u003e \u003cp\u003e2.2.2.7 You Only Look Once (YOLO) Architecture 43\u003c\/p\u003e \u003cp\u003e2.2.2.8 Faster R-CNN Algorithm 44\u003c\/p\u003e \u003cp\u003e2.2.2.9 Mean Average Precision (mAP) 46\u003c\/p\u003e \u003cp\u003e2.3 Experimental Results 46\u003c\/p\u003e \u003cp\u003e2.3.1 Results Obtained Using YOLO Algorithm 46\u003c\/p\u003e \u003cp\u003e2.3.2 Results Obtained Using Faster R-CNN 46\u003c\/p\u003e \u003cp\u003e2.4 Future Scope 48\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 48\u003c\/p\u003e \u003cp\u003eReferences 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Smart Power Factor Correction and Energy Monitoring System 51\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmutha C., Sivagami V., Arulmozhi M., Sivamani D. and Shyam D.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 51\u003c\/p\u003e \u003cp\u003e3.2 Block Diagram 53\u003c\/p\u003e \u003cp\u003e3.2.1 Power Factor Concept 54\u003c\/p\u003e \u003cp\u003e3.2.2 Power Factor Calculation 54\u003c\/p\u003e \u003cp\u003e3.3 Simulation 54\u003c\/p\u003e \u003cp\u003e3.4 Conclusion 56\u003c\/p\u003e \u003cp\u003eReferences 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 ANN-Based Maximum Power Point Tracking Control Configured Boost Converter for Electric Vehicle Applications 59\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSivamani D., Sangari A., Shyam D., Anto Sheeba J., Jayashree K. and Nazar Ali A.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 59\u003c\/p\u003e \u003cp\u003e4.2 Block Diagram 60\u003c\/p\u003e \u003cp\u003e4.3 ANN-Based MPPT for Boost Converter 64\u003c\/p\u003e \u003cp\u003e4.4 Closed Loop Control 66\u003c\/p\u003e \u003cp\u003e4.5 Simulation Results 67\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 70\u003c\/p\u003e \u003cp\u003eReferences 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Single\/Multijunction Solar Cell Model Incorporating Maximum Power Point Tracking Scheme Based on Fuzzy Logic Algorithm 73\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eOmveer Singh, Shalini Gupta and Shabana Urooj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 74\u003c\/p\u003e \u003cp\u003e5.2 Modeling Structure 75\u003c\/p\u003e \u003cp\u003e5.2.1 Single-Junction Solar Cell Model 75\u003c\/p\u003e \u003cp\u003e5.2.2 Modeling of Multijunction Solar PV Cell 77\u003c\/p\u003e \u003cp\u003e5.3 MPPT Design Techniques 80\u003c\/p\u003e \u003cp\u003e5.3.1 Design of MPPT Scheme Based on P\u0026amp;O Technique 80\u003c\/p\u003e \u003cp\u003e5.3.2 Design of MPPT Scheme Based on FLA 82\u003c\/p\u003e \u003cp\u003e5.4 Results and Discussions 84\u003c\/p\u003e \u003cp\u003e5.4.1 Single-Junction Solar Cell 84\u003c\/p\u003e \u003cp\u003e5.4.2 Multijunction Solar PV Cell 86\u003c\/p\u003e \u003cp\u003e5.4.3 Implementation of MPPT Scheme Based on P\u0026amp;O Technique 90\u003c\/p\u003e \u003cp\u003e5.4.4 Implementation of MPPT Scheme Based on FLA 91\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 93\u003c\/p\u003e \u003cp\u003eReferences 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Particle Swarm Optimization: An Overview, Advancements and Hybridization 95\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShafquat Rana, Md Sarwar, Anwar Shahzad Siddiqui and Prashant\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 96\u003c\/p\u003e \u003cp\u003e6.2 The Particle Swarm Optimization: An Overview 97\u003c\/p\u003e \u003cp\u003e6.3 PSO Algorithms and Pseudo-Code 98\u003c\/p\u003e \u003cp\u003e6.3.1 PSO Algorithm 98\u003c\/p\u003e \u003cp\u003e6.3.2 Pseudo-Code for PSO 101\u003c\/p\u003e \u003cp\u003e6.3.3 PSO Limitations 101\u003c\/p\u003e \u003cp\u003e6.4 Advancements in PSO and Its Perspectives 102\u003c\/p\u003e \u003cp\u003e6.4.1 Inertia Weight 102\u003c\/p\u003e \u003cp\u003e6.4.1.1 Random Selection (RS) 102\u003c\/p\u003e \u003cp\u003e6.4.1.2 Linear Time Varying (LTV) 103\u003c\/p\u003e \u003cp\u003e6.4.1.3 Nonlinear Time Varying (NLTV) 103\u003c\/p\u003e \u003cp\u003e6.4.1.4 Fuzzy Adaptive (FA) 103\u003c\/p\u003e \u003cp\u003e6.4.2 Constriction Factors 104\u003c\/p\u003e \u003cp\u003e6.4.3 Topologies 104\u003c\/p\u003e \u003cp\u003e6.4.4 Analysis of Convergence 104\u003c\/p\u003e \u003cp\u003e6.5 Hybridization of PSO 105\u003c\/p\u003e \u003cp\u003e6.5.1 PSO Hybridization with Artificial Bee Colony (ABC) 105\u003c\/p\u003e \u003cp\u003e6.5.2 PSO Hybridization with Ant Colony Optimization (aco) 106\u003c\/p\u003e \u003cp\u003e6.5.3 PSO Hybridization with Genetic Algorithms (GA) 106\u003c\/p\u003e \u003cp\u003e6.6 Area of Applications of PSO 107\u003c\/p\u003e \u003cp\u003e6.7 Conclusions 109\u003c\/p\u003e \u003cp\u003eReferences 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Application of Genetic Algorithm in Sensor Networks and Smart Grid 115\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGeeta Yadav, Dheeraj Joshi, Leena G. and M. K. Soni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 115\u003c\/p\u003e \u003cp\u003e7.2 Communication Sector 116\u003c\/p\u003e \u003cp\u003e7.2.1 Sensor Networks 116\u003c\/p\u003e \u003cp\u003e7.3 Electrical Sector 117\u003c\/p\u003e \u003cp\u003e7.3.1 Smart Microgrid 117\u003c\/p\u003e \u003cp\u003e7.4 A Brief Outline of GAs 118\u003c\/p\u003e \u003cp\u003e7.5 Sensor Network’s Energy Optimization 120\u003c\/p\u003e \u003cp\u003e7.6 Sensor Network’s Coverage and Uniformity Optimization Using GA 126\u003c\/p\u003e \u003cp\u003e7.7 Use GA for Optimization of Reliability and Availability for Smart Microgrid 131\u003c\/p\u003e \u003cp\u003e7.8 GA Versus Traditional Methods 135\u003c\/p\u003e \u003cp\u003e7.9 Summaries and Conclusions 136\u003c\/p\u003e \u003cp\u003eReferences 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber–Reinforced Polymer (CFRP) Drilling Process 139\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRohit Volety and Geetha Mani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 140\u003c\/p\u003e \u003cp\u003e8.2 Methodology 142\u003c\/p\u003e \u003cp\u003e8.3 AI-Based Predictive Modeling 143\u003c\/p\u003e \u003cp\u003e8.3.1 Linear Regression 143\u003c\/p\u003e \u003cp\u003e8.3.2 Random Forests 144\u003c\/p\u003e \u003cp\u003e8.3.3 XGBoost 145\u003c\/p\u003e \u003cp\u003e8.3.4 Svm 146\u003c\/p\u003e \u003cp\u003e8.4 Performance Indices 146\u003c\/p\u003e \u003cp\u003e8.4.1 Root Mean Squared Error (RMSE) 146\u003c\/p\u003e \u003cp\u003e8.4.2 Mean Squared Error (MSE) 147\u003c\/p\u003e \u003cp\u003e8.4.3 R 2 (R-Squared) 147\u003c\/p\u003e \u003cp\u003e8.5 Results and Discussion 147\u003c\/p\u003e \u003cp\u003e8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase 148\u003c\/p\u003e \u003cp\u003e8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase 148\u003c\/p\u003e \u003cp\u003e8.5.3 K Cross Fold Validation 149\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 151\u003c\/p\u003e \u003cp\u003eReferences 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery 155\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJacob Vishal, Somdeb Datta, Sudipta Mukhopadhyay, Pravar Kulbhushan, Rik Das, Saurabh Srivastava and Indrajit Kar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 156\u003c\/p\u003e \u003cp\u003e9.2 Literature Survey 157\u003c\/p\u003e \u003cp\u003e9.3 Research Methodology 159\u003c\/p\u003e \u003cp\u003e9.3.1 Dataset and Metrics 161\u003c\/p\u003e \u003cp\u003e9.4 Result and Discussion 162\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 165\u003c\/p\u003e \u003cp\u003eReferences 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA 169\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJyotirmayee Subudhi and P. Indumathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 170\u003c\/p\u003e \u003cp\u003e10.2 System Model 172\u003c\/p\u003e \u003cp\u003e10.3 User Clustering 175\u003c\/p\u003e \u003cp\u003e10.4 Optimal Power Allocation for EE-SE Tradeoff 176\u003c\/p\u003e \u003cp\u003e10.4.1 Multiobjective Optimization Problem 177\u003c\/p\u003e \u003cp\u003e10.4.2 Multiobjective PSO 178\u003c\/p\u003e \u003cp\u003e10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA 180\u003c\/p\u003e \u003cp\u003e10.5 Numerical Results 180\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 183\u003c\/p\u003e \u003cp\u003eReferences 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews 187\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRishabh Singh, Akarshan Kumar and Mousim Ray\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 188\u003c\/p\u003e \u003cp\u003e11.1.1 Related Work 189\u003c\/p\u003e \u003cp\u003e11.2 Materials and Methods 190\u003c\/p\u003e \u003cp\u003e11.2.1 Data Cleaning and Pre-Processing 191\u003c\/p\u003e \u003cp\u003e11.2.2 Feature Extraction 191\u003c\/p\u003e \u003cp\u003e11.2.3 Classifiers 193\u003c\/p\u003e \u003cp\u003e11.3 Results and Experiments 194\u003c\/p\u003e \u003cp\u003e11.4 Conclusion 197\u003c\/p\u003e \u003cp\u003eReferences 198\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm 201\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMintu Pal and Sibsankar Dasmahapatra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 202\u003c\/p\u003e \u003cp\u003e12.2 Genetic Algorithm GA: An Evolutionary Computational Technique 203\u003c\/p\u003e \u003cp\u003e12.3 Design of Multiobjective Optimization Problem 204\u003c\/p\u003e \u003cp\u003e12.3.1 Decision Variables 204\u003c\/p\u003e \u003cp\u003e12.3.2 Objective Functions 204\u003c\/p\u003e \u003cp\u003e12.3.2.1 Minimization of Main Cutting Force 205\u003c\/p\u003e \u003cp\u003e12.3.2.2 Minimization of Feed Force 205\u003c\/p\u003e \u003cp\u003e12.3.3 Bounds of Decision Variables 205\u003c\/p\u003e \u003cp\u003e12.3.4 Response Variables 206\u003c\/p\u003e \u003cp\u003e12.4 Results and Discussions 206\u003c\/p\u003e \u003cp\u003e12.4.1 Single Objective Optimization 206\u003c\/p\u003e \u003cp\u003e12.4.2 Results of Multiobjective Optimization 208\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 212\u003c\/p\u003e \u003cp\u003eReferences 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Genetic Algorithm-Based Optimization for Speech Processing Applications 215\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRamya.R, M. Preethi and R. Rajalakshmi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction to GA 215\u003c\/p\u003e \u003cp\u003e13.1.1 Enhanced GA 216\u003c\/p\u003e \u003cp\u003e13.1.1.1 Hybrid GA 216\u003c\/p\u003e \u003cp\u003e13.1.1.2 Interval GA 217\u003c\/p\u003e \u003cp\u003e13.1.1.3 Adaptive GA 217\u003c\/p\u003e \u003cp\u003e13.2 GA in Automatic Speech Recognition 218\u003c\/p\u003e \u003cp\u003e13.2.1 GA for Optimizing Off-Line Parameters in Voice Activity Detection (VAD) 218\u003c\/p\u003e \u003cp\u003e13.2.2 Classification of Features in ASR Using GA 219\u003c\/p\u003e \u003cp\u003e13.2.3 GA-Based Distinctive Phonetic Features Recognition 219\u003c\/p\u003e \u003cp\u003e13.2.4 GA in Phonetic Decoding 220\u003c\/p\u003e \u003cp\u003e13.3 Genetic Algorithm in Speech Emotion Recognition 221\u003c\/p\u003e \u003cp\u003e13.3.1 Speech Emotion Recognition 221\u003c\/p\u003e \u003cp\u003e13.3.2 Genetic Algorithms in Speech Emotion Recognition 222\u003c\/p\u003e \u003cp\u003e13.3.2.1 Feature Extraction Using GA for SER 222\u003c\/p\u003e \u003cp\u003e13.3.2.2 Steps for Adaptive Genetic Algorithm for Feature Optimization 224\u003c\/p\u003e \u003cp\u003e13.4 Genetic Programming in Hate Speech Using Deep Learning 225\u003c\/p\u003e \u003cp\u003e13.4.1 Introduction to Hate Speech Detection 225\u003c\/p\u003e \u003cp\u003e13.4.2 GA Integrated With Deep Learning Models for Hate Speech Detection 226\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 228\u003c\/p\u003e \u003cp\u003eReferences 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Performance of P, PI, PID, and NARMA Controllers in the Load Frequency Control of a Single-Area Thermal Power Plant 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRanjit Singh and L. Ramesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 231\u003c\/p\u003e \u003cp\u003e14.2 Single-Area Power System 232\u003c\/p\u003e \u003cp\u003e14.3 Automatic Load Frequency Control (ALFC) 233\u003c\/p\u003e \u003cp\u003e14.4 Controllers Used in the Simulink Model 233\u003c\/p\u003e \u003cp\u003e14.4.1 PID Controller 233\u003c\/p\u003e \u003cp\u003e14.4.2 PI Controller 234\u003c\/p\u003e \u003cp\u003e14.4.3 P Controller 234\u003c\/p\u003e \u003cp\u003e14.5 Circuit Description 235\u003c\/p\u003e \u003cp\u003e14.6 ANN and NARMA L2 Controller 236\u003c\/p\u003e \u003cp\u003e14.7 Simulation Results and Comparative Analysis 237\u003c\/p\u003e \u003cp\u003e14.8 Conclusion 239\u003c\/p\u003e \u003cp\u003eReferences 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Decision Science and Simulation-Based Optimization 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Selection of Nonpowered Industrial Truck for Small Scale Manufacturing Industry Using Fuzzy VIKOR Method Under FMCDM Environment 245\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBipradas Bairagi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 246\u003c\/p\u003e \u003cp\u003e15.2 Fuzzy Set Theory 248\u003c\/p\u003e \u003cp\u003e15.2.1 Some Important Fuzzy Definitions 248\u003c\/p\u003e \u003cp\u003e15.2.2 Fuzzy Operations 249\u003c\/p\u003e \u003cp\u003e15.2.3 Linguistic Variable (LV) 250\u003c\/p\u003e \u003cp\u003e15.3 Fvikor 251\u003c\/p\u003e \u003cp\u003e15.4 Problem Definition 253\u003c\/p\u003e \u003cp\u003e15.5 Results and Discussions 253\u003c\/p\u003e \u003cp\u003e15.6 Conclusions 258\u003c\/p\u003e \u003cp\u003eReferences 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Slightly and Almost Neutrosophic gsα*—Continuous Function in Neutrosophic Topological Spaces 261\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eP. Anbarasi Rodrigo and S. Maheswari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 261\u003c\/p\u003e \u003cp\u003e16.2 Preliminaries 262\u003c\/p\u003e \u003cp\u003e16.3 Slightly Neutrosophic gsα* – Continuous Function 263\u003c\/p\u003e \u003cp\u003e16.4 Almost Neutrosophic gsα* – Continuous Function 266\u003c\/p\u003e \u003cp\u003e16.5 Conclusion 274\u003c\/p\u003e \u003cp\u003eReferences 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Identification and Prioritization of Risk Factors Affecting the Mental Health of Farmers 275\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHullash Chauhan, Suchismita Satapathy, A. K. Sahoo and Debesh Mishra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 275\u003c\/p\u003e \u003cp\u003e17.2 Materials and Methods 277\u003c\/p\u003e \u003cp\u003e17.2.1 ELECTRE Technique 278\u003c\/p\u003e \u003cp\u003e17.3 Result and Discussion 281\u003c\/p\u003e \u003cp\u003e17.4 Conclusion 293\u003c\/p\u003e \u003cp\u003eReferences 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): An Application to Material Handling System Selection 297\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBipradas Bairagi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 298\u003c\/p\u003e \u003cp\u003e18.2 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): The Proposed Algorithm 300\u003c\/p\u003e \u003cp\u003e18.3 Illustrative Example 303\u003c\/p\u003e \u003cp\u003e18.3.1 Problem Definition 303\u003c\/p\u003e \u003cp\u003e18.3.2 Calculation and Discussions 305\u003c\/p\u003e \u003cp\u003e18.4 Conclusions 309\u003c\/p\u003e \u003cp\u003eReferences 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Evaluation of Optimal Parameters to Enhance Worker’s Performance in an Automotive Industry 313\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajat Yadav, Kuwar Mausam, Manish Saraswat and Vijay Kumar Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 314\u003c\/p\u003e \u003cp\u003e19.2 Methodology 315\u003c\/p\u003e \u003cp\u003e19.3 Results and Discussion 316\u003c\/p\u003e \u003cp\u003e19.4 Conclusions 320\u003c\/p\u003e \u003cp\u003eReferences 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Determining Key Influential Factors of Rural Tourism— An AHP Model 323\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePuspalata Mahaptra, RamaKrishna Bandaru, Deepanjan Nanda and Sushanta Tripathy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 324\u003c\/p\u003e \u003cp\u003e20.2 Rural Tourism 325\u003c\/p\u003e \u003cp\u003e20.3 Literature Review 326\u003c\/p\u003e \u003cp\u003e20.4 Objectives 328\u003c\/p\u003e \u003cp\u003e20.5 Methodology 328\u003c\/p\u003e \u003cp\u003e20.6 Analysis 332\u003c\/p\u003e \u003cp\u003e20.7 Results and Discussion 332\u003c\/p\u003e \u003cp\u003e20.8 Conclusions 340\u003c\/p\u003e \u003cp\u003e20.9 Managerial Implications 340\u003c\/p\u003e \u003cp\u003eReferences 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Solution of a Pollution-Based Economic Order Quantity Model Under Triangular Dense Fuzzy Environment 345\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePartha Pratim Bhattacharya, Kousik Bhattacharya, Sujit Kumar De, Prasun Kumar Nayak, Subhankar Joardar and Kushankur Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 346\u003c\/p\u003e \u003cp\u003e21.1.1 Overview 346\u003c\/p\u003e \u003cp\u003e21.1.2 Motivation and Specific Study 346\u003c\/p\u003e \u003cp\u003e21.2 Preliminaries 348\u003c\/p\u003e \u003cp\u003e21.2.1 Pollution Function 348\u003c\/p\u003e \u003cp\u003e21.2.2 Triangular Dense Fuzzy Set (TDFS) 349\u003c\/p\u003e \u003cp\u003e21.3 Notations and Assumptions 350\u003c\/p\u003e \u003cp\u003e21.3.1 Case Study 351\u003c\/p\u003e \u003cp\u003e21.4 Formulation of the Mathematical Model 352\u003c\/p\u003e \u003cp\u003e21.4.1 Crisp Mathematical Model 352\u003c\/p\u003e \u003cp\u003e21.4.2 Formulation of Triangular Dense Fuzzy Mathematical Model 352\u003c\/p\u003e \u003cp\u003e21.4.3 Defuzzification of Triangular Dense Fuzzy Model 353\u003c\/p\u003e \u003cp\u003e21.5 Numerical Illustration 354\u003c\/p\u003e \u003cp\u003e21.6 Sensitivity Analysis 355\u003c\/p\u003e \u003cp\u003e21.7 Graphical Illustration 355\u003c\/p\u003e \u003cp\u003e21.8 Merits and Demerits 358\u003c\/p\u003e \u003cp\u003e21.9 Conclusion 358\u003c\/p\u003e \u003cp\u003eAcknowledgement 359\u003c\/p\u003e \u003cp\u003eAppendix 359\u003c\/p\u003e \u003cp\u003eReferences 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Common Yet Overlooked Aspects Accountable for Antiaging: An MCDM Approach 363\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajnandini Saha, Satyabrata Aich, Hee-Cheol Kim and Sushanta Tripathy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 364\u003c\/p\u003e \u003cp\u003e22.2 Literature Review 365\u003c\/p\u003e \u003cp\u003e22.3 Analytic Hierarchy Process (AHP) 367\u003c\/p\u003e \u003cp\u003e22.4 Result and Discussion 372\u003c\/p\u003e \u003cp\u003e22.5 Conclusion 373\u003c\/p\u003e \u003cp\u003eReferences 373\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 E-Waste Management Challenges in India: An AHP Approach 377\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmit Sutar, Apurv Singh, Deepak Singhal, Sushanta Tripathy and Bharat Chandra Routara\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 378\u003c\/p\u003e \u003cp\u003e23.2 Literature Review 379\u003c\/p\u003e \u003cp\u003e23.3 Methodology 379\u003c\/p\u003e \u003cp\u003e23.4 Results and Discussion 379\u003c\/p\u003e \u003cp\u003e23.5 Conclusion 390\u003c\/p\u003e \u003cp\u003eReferences 391\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Application of k-Means Method for Finding Varying Groups of Primary Energy Household Emissions in the Indian States 393\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTanmay Belsare, Abhay Deshpande, Neha Sharma and Prithwis De\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 394\u003c\/p\u003e \u003cp\u003e24.2 Literature Review 395\u003c\/p\u003e \u003cp\u003e24.3 Materials and Methods 397\u003c\/p\u003e \u003cp\u003e24.3.1 Data Preparation 397\u003c\/p\u003e \u003cp\u003e24.3.2 Methods and Approach 397\u003c\/p\u003e \u003cp\u003e24.3.2.1 Cluster Analysis 397\u003c\/p\u003e \u003cp\u003e24.3.2.2 Agglomerative Hierarchical Clustering 397\u003c\/p\u003e \u003cp\u003e24.3.2.3 K-Means Clustering 398\u003c\/p\u003e \u003cp\u003e24.4 Exploratory Data Analysis 398\u003c\/p\u003e \u003cp\u003e24.5 Results and Discussion 401\u003c\/p\u003e \u003cp\u003e24.6 Conclusion 405\u003c\/p\u003e \u003cp\u003eReferences 406\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Airwaves Detection and Elimination Using Fast Fourier Transform to Enhance Detection of Hydrocarbon 409\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGarba Aliyu, Mathias M. Fonkam, Augustine S. Nsang, Muhammad Abdulkarim, Sandip Rashit and Yakub K. Saheed\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 410\u003c\/p\u003e \u003cp\u003e25.1.1 Airwaves 411\u003c\/p\u003e \u003cp\u003e25.1.2 Fast Fourier Transform 412\u003c\/p\u003e \u003cp\u003e25.2 Related Works 413\u003c\/p\u003e \u003cp\u003e25.3 Theoretical Framework 415\u003c\/p\u003e \u003cp\u003e25.4 Methodology 416\u003c\/p\u003e \u003cp\u003e25.5 Results and Discussions 417\u003c\/p\u003e \u003cp\u003e25.6 Conclusion 420\u003c\/p\u003e \u003cp\u003eReferences 420\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Design and Implementation of Control for Nonlinear Active Suspension System 423\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRavindra S. Rana and Dipak M. Adhyaru\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 423\u003c\/p\u003e \u003cp\u003e26.2 Mathematical Model of Quarter Car Suspension System 426\u003c\/p\u003e \u003cp\u003e26.2.1 Mathematical Model 426\u003c\/p\u003e \u003cp\u003e26.2.2 Linearization Method for Nonlinear System Model 429\u003c\/p\u003e \u003cp\u003e26.2.3 Discussion of Result 430\u003c\/p\u003e \u003cp\u003e26.3 Conclusion 433\u003c\/p\u003e \u003cp\u003eReferences 434\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 A Study of Various Peak to Average Power Ratio (PAPR) Reduction Techniques for 5G Communication System (5G-CS) 437\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHimanshu Kumar Sinha, Anand Kumar and Devasis Pradhan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 437\u003c\/p\u003e \u003cp\u003e27.2 Literature Review 439\u003c\/p\u003e \u003cp\u003e27.3 Overview of 5G Cellular System 440\u003c\/p\u003e \u003cp\u003e27.4 Papr 441\u003c\/p\u003e \u003cp\u003e27.4.1 Continuous Time PAPR 441\u003c\/p\u003e \u003cp\u003e27.4.2 Continuous Time PAPR 442\u003c\/p\u003e \u003cp\u003e27.5 Factors on which PAPR Reduction Depends 442\u003c\/p\u003e \u003cp\u003e27.6 PAPR Reduction Technique 443\u003c\/p\u003e \u003cp\u003e27.6.1 Scrambling of Signals 443\u003c\/p\u003e \u003cp\u003e27.6.2 Signal Distortion Technique 446\u003c\/p\u003e \u003cp\u003e27.6.3 High Power Amplifier (HPA) 447\u003c\/p\u003e \u003cp\u003e27.7 Limitation of OFDM 447\u003c\/p\u003e \u003cp\u003e27.8 Universal Filter Multicarrier (UMFC) Emerging Technique to Reduce PAPR in 5G 448\u003c\/p\u003e \u003cp\u003e27.8.1 Transmitter of UMFC 448\u003c\/p\u003e \u003cp\u003e27.8.2 Receiver of UMFC 450\u003c\/p\u003e \u003cp\u003e27.9 Comparison Between Various Techniques 450\u003c\/p\u003e \u003cp\u003e27.10 Conclusion 450\u003c\/p\u003e \u003cp\u003eReferences 452\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Investigation of Rebound Suppression Phenomenon in an Electromagnetic V-Bending Test 455\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAman Sharma, Pradeep Kumar Singh, Manish Saraswat and Irfan Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 455\u003c\/p\u003e \u003cp\u003e28.2 Investigation 458\u003c\/p\u003e \u003cp\u003e28.2.1 Specimen for Tests 458\u003c\/p\u003e \u003cp\u003e28.2.2 Design of Die and Tool 458\u003c\/p\u003e \u003cp\u003e28.2.3 Configuration and Procedure 459\u003c\/p\u003e \u003cp\u003e28.3 Mathematical Evaluation 460\u003c\/p\u003e \u003cp\u003e28.3.1 Simulation Methodology 460\u003c\/p\u003e \u003cp\u003e28.4 Modeling for Material 461\u003c\/p\u003e \u003cp\u003e28.4.1 Suppressing Rebound Phenomenon 461\u003c\/p\u003e \u003cp\u003e28.5 Conclusion 466\u003c\/p\u003e \u003cp\u003eReferences 466\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Quadratic Spline Function Companding Technique to Minimize Peak-to-Average Power Ratio in Orthogonal Frequency Division Multiplexing System 469\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLazar Z. Velimirovic\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 469\u003c\/p\u003e \u003cp\u003e29.2 OFDM System 471\u003c\/p\u003e \u003cp\u003e29.2.1 PAPR of OFDM Signal 472\u003c\/p\u003e \u003cp\u003e29.3 Companding Technique 474\u003c\/p\u003e \u003cp\u003e29.3.1 Quadratic Spline Function Companding 474\u003c\/p\u003e \u003cp\u003e29.4 Numerical Results and Discussion 475\u003c\/p\u003e \u003cp\u003e29.5 Conclusion 480\u003c\/p\u003e \u003cp\u003eAcknowledgment 480\u003c\/p\u003e \u003cp\u003eReferences 480\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 A Novel MCGDM Approach for Supplier Selection in a Supply Chain Management 483\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBipradas Bairagi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction 484\u003c\/p\u003e \u003cp\u003e30.2 Proposed Algorithm 486\u003c\/p\u003e \u003cp\u003e30.3 Illustrative Example 491\u003c\/p\u003e \u003cp\u003e30.3.1 Problem Definition 491\u003c\/p\u003e \u003cp\u003e30.3.2 Calculation and Discussions 492\u003c\/p\u003e \u003cp\u003e30.4 Conclusions 498\u003c\/p\u003e \u003cp\u003eReferences 499\u003c\/p\u003e \u003cp\u003eIndex 501\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430994931992,"sku":"9781119906278","price":127.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119906278.jpg?v=1784768215","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/optimization-techniques-in-engineering-advances-and-applications-hardback-9781119906278","provider":"Freshly Printed Books","version":"1.0","type":"link"}