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20241018113403.0 |
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241018b |||||o|||| 00| 0 eng d |
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9781119906278 |
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hardcover |
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9781119906391 |
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electronic book |
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electronic book |
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electronic book |
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(OCoLC)1376309953 |
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YDX |
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eng |
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rda |
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YDX |
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YDX |
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eng |
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TA342 |
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.O68 2023 |
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| Classification number |
620.001/5196 |
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23/eng/20230505 |
| 245 00 - TITLE STATEMENT |
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| Title |
Optimization techniques in engineering : |
| Remainder of title |
advances and applications / |
| Statement of responsibility, etc |
edited by Anita Khosla... [and 3 others] |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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Hoboken, NJ : |
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John Wiley & Sons, Incorporated, |
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2023. |
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1 online resource. |
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text |
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txt |
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rdacontent. |
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computer |
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rdamedia. |
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online resource |
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cr |
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rdacarrier. |
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rdacc |
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http://rdaregistry.info/termList/RDAColourContent/1003. |
| 490 1# - SERIES STATEMENT |
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| Series statement |
Sustainable Computing and Optimization Series. |
| 505 0# - CONTENTS |
|---|
| Formatted contents note |
Table of Contents<br/>Preface xxi<br/><br/>Acknowledgment xxix<br/><br/>Part 1: Soft Computing and Evolutionary-Based Optimization 1<br/><br/>1 Improved Grey Wolf Optimizer with Levy Flight to Solve Dynamic Economic Dispatch Problem with Electric Vehicle Profiles 3<br/>Anjali Jain, Ashish Mani and Anwar S. Siddiqui<br/><br/>1.1 Introduction 4<br/><br/>1.2 Problem Formulation 5<br/><br/>1.2.1 Power Output Limits 6<br/><br/>1.2.2 Power Balance Limits 6<br/><br/>1.2.3 Ramp Rate Limits 7<br/><br/>1.2.4 Electric Vehicles 7<br/><br/>1.3 Proposed Algorithm 8<br/><br/>1.3.1 Overview of Grey Wolf Optimizer 8<br/><br/>1.3.2 Improved Grey Wolf Optimizer with Levy Flight 9<br/><br/>1.3.3 Modeling of Prey Position with Levy Flight Distribution 10<br/><br/>1.4 Simulation and Results 13<br/><br/>1.4.1 Performance of Improved GWOLF on Benchmark Functions 14<br/><br/>1.4.2 Performance of Improved GWOLF for Solving DED for the Different Charging Probability Distribution 14<br/><br/>1.5 Conclusion 29<br/><br/>References 34<br/><br/>xxi<br/><br/>vii<br/><br/>2 Comparison of YOLO and Faster R-CNN on Garbage Detection 37<br/>Arulmozhi M., Nandini G. Iyer, Jeny Sophia S., Sivakumar P., Amutha C. and Sivamani D.<br/><br/>2.1 Introduction 37<br/><br/>2.2 Garbage Detection 39<br/><br/>2.2.1 Transfer Learning-Technique 39<br/><br/>2.2.2 Inception-Custom Model 39<br/><br/>2.2.2.1 Convolutional Neural Network 40<br/><br/>2.2.2.2 Max Pooling 41<br/><br/>2.2.2.3 Stride 41<br/><br/>2.2.2.4 Average Pooling 41<br/><br/>2.2.2.5 Inception Layer 42<br/><br/>2.2.2.6 3*3 and 1*1 Convolution 43<br/><br/>2.2.2.7 You Only Look Once (YOLO) Architecture 43<br/><br/>2.2.2.8 Faster R-CNN Algorithm 44<br/><br/>2.2.2.9 Mean Average Precision (mAP) 46<br/><br/>2.3 Experimental Results 46<br/><br/>2.3.1 Results Obtained Using YOLO Algorithm 46<br/><br/>2.3.2 Results Obtained Using Faster R-CNN 46<br/><br/>2.4 Future Scope 48<br/><br/>2.5 Conclusion 48<br/><br/>References 48<br/><br/>3 Smart Power Factor Correction and Energy Monitoring System 51<br/>Amutha C., Sivagami V., Arulmozhi M., Sivamani D. and Shyam D.<br/><br/>3.1 Introduction 51<br/><br/>3.2 Block Diagram 53<br/><br/>3.2.1 Power Factor Concept 54<br/><br/>3.2.2 Power Factor Calculation 54<br/><br/>3.3 Simulation 54<br/><br/>3.4 Conclusion 56<br/><br/>References 57<br/><br/>4 ANN-Based Maximum Power Point Tracking Control Configured Boost Converter for Electric Vehicle Applications 59<br/>Sivamani D., Sangari A., Shyam D., Anto Sheeba J., Jayashree K. and Nazar Ali A.<br/><br/>4.1 Introduction 59<br/><br/>4.2 Block Diagram 60<br/><br/>4.3 ANN-Based MPPT for Boost Converter 64<br/><br/>4.4 Closed Loop Control 66<br/><br/>4.5 Simulation Results 67<br/><br/>4.6 Conclusion 70<br/><br/>References 70<br/><br/>5 Single/Multijunction Solar Cell Model Incorporating Maximum Power Point Tracking Scheme Based on Fuzzy Logic Algorithm 73<br/>Omveer Singh, Shalini Gupta and Shabana Urooj<br/><br/>5.1 Introduction 74<br/><br/>5.2 Modeling Structure 75<br/><br/>5.2.1 Single-Junction Solar Cell Model 75<br/><br/>5.2.2 Modeling of Multijunction Solar PV Cell 77<br/><br/>5.3 MPPT Design Techniques 80<br/><br/>5.3.1 Design of MPPT Scheme Based on P&O Technique 80<br/><br/>5.3.2 Design of MPPT Scheme Based on FLA 82<br/><br/>5.4 Results and Discussions 84<br/><br/>5.4.1 Single-Junction Solar Cell 84<br/><br/>5.4.2 Multijunction Solar PV Cell 86<br/><br/>5.4.3 Implementation of MPPT Scheme Based on P&O Technique 90<br/><br/>5.4.4 Implementation of MPPT Scheme Based on FLA 91<br/><br/>5.5 Conclusion 93<br/><br/>References 93<br/><br/>6 Particle Swarm Optimization: An Overview, Advancements and Hybridization 95<br/>Shafquat Rana, Md Sarwar, Anwar Shahzad Siddiqui and Prashant<br/><br/>6.1 Introduction 96<br/><br/>6.2 The Particle Swarm Optimization: An Overview 97<br/><br/>6.3 PSO Algorithms and Pseudo-Code 98<br/><br/>6.3.1 PSO Algorithm 98<br/><br/>6.3.2 Pseudo-Code for PSO 101<br/><br/>6.3.3 PSO Limitations 101<br/><br/>6.4 Advancements in PSO and Its Perspectives 102<br/><br/>6.4.1 Inertia Weight 102<br/><br/>6.4.1.1 Random Selection (RS) 102<br/><br/>6.4.1.2 Linear Time Varying (LTV) 103<br/><br/>6.4.1.3 Nonlinear Time Varying (NLTV) 103<br/><br/>6.4.1.4 Fuzzy Adaptive (FA) 103<br/><br/>6.4.2 Constriction Factors 104<br/><br/>6.4.3 Topologies 104<br/><br/>6.4.4 Analysis of Convergence 104<br/><br/>6.5 Hybridization of PSO 105<br/><br/>6.5.1 PSO Hybridization with Artificial Bee Colony (ABC) 105<br/><br/>6.5.2 PSO Hybridization with Ant Colony Optimization (aco) 106<br/><br/>6.5.3 PSO Hybridization with Genetic Algorithms (GA) 106<br/><br/>6.6 Area of Applications of PSO 107<br/><br/>6.7 Conclusions 109<br/><br/>References 109<br/><br/>7 Application of Genetic Algorithm in Sensor Networks and Smart Grid 115<br/>Geeta Yadav, Dheeraj Joshi, Leena G. and M. K. Soni<br/><br/>7.1 Introduction 115<br/><br/>7.2 Communication Sector 116<br/><br/>7.2.1 Sensor Networks 116<br/><br/>7.3 Electrical Sector 117<br/><br/>7.3.1 Smart Microgrid 117<br/><br/>7.4 A Brief Outline of GAs 118<br/><br/>7.5 Sensor Network’s Energy Optimization 120<br/><br/>7.6 Sensor Network’s Coverage and Uniformity Optimization Using GA 126<br/><br/>7.7 Use GA for Optimization of Reliability and Availability for Smart Microgrid 131<br/><br/>7.8 GA Versus Traditional Methods 135<br/><br/>7.9 Summaries and Conclusions 136<br/><br/>References 137<br/><br/>8 AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber–Reinforced Polymer (CFRP) Drilling Process 139<br/>Rohit Volety and Geetha Mani<br/><br/>8.1 Introduction 140<br/><br/>8.2 Methodology 142<br/><br/>8.3 AI-Based Predictive Modeling 143<br/><br/>8.3.1 Linear Regression 143<br/><br/>8.3.2 Random Forests 144<br/><br/>8.3.3 XGBoost 145<br/><br/>8.3.4 Svm 146<br/><br/>8.4 Performance Indices 146<br/><br/>8.4.1 Root Mean Squared Error (RMSE) 146<br/><br/>8.4.2 Mean Squared Error (MSE) 147<br/><br/>8.4.3 R 2 (R-Squared) 147<br/><br/>8.5 Results and Discussion 147<br/><br/>8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase 148<br/><br/>8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase 148<br/><br/>8.5.3 K Cross Fold Validation 149<br/><br/>8.6 Conclusions 151<br/><br/>References 152<br/><br/>9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery 155<br/>Jacob Vishal, Somdeb Datta, Sudipta Mukhopadhyay, Pravar Kulbhushan, Rik Das, Saurabh Srivastava and Indrajit Kar<br/><br/>9.1 Introduction 156<br/><br/>9.2 Literature Survey 157<br/><br/>9.3 Research Methodology 159<br/><br/>9.3.1 Dataset and Metrics 161<br/><br/>9.4 Result and Discussion 162<br/><br/>9.5 Conclusion 165<br/><br/>References 165<br/><br/>10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA 169<br/>Jyotirmayee Subudhi and P. Indumathi<br/><br/>10.1 Introduction 170<br/><br/>10.2 System Model 172<br/><br/>10.3 User Clustering 175<br/><br/>10.4 Optimal Power Allocation for EE-SE Tradeoff 176<br/><br/>10.4.1 Multiobjective Optimization Problem 177<br/><br/>10.4.2 Multiobjective PSO 178<br/><br/>10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA 180<br/><br/>10.5 Numerical Results 180<br/><br/>10.6 Conclusion 183<br/><br/>References 184<br/><br/>11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews 187<br/>Rishabh Singh, Akarshan Kumar and Mousim Ray<br/><br/>11.1 Introduction 188<br/><br/>11.1.1 Related Work 189<br/><br/>11.2 Materials and Methods 190<br/><br/>11.2.1 Data Cleaning and Pre-Processing 191<br/><br/>11.2.2 Feature Extraction 191<br/><br/>11.2.3 Classifiers 193<br/><br/>11.3 Results and Experiments 194<br/><br/>11.4 Conclusion 197<br/><br/>References 198<br/><br/>12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm 201<br/>Mintu Pal and Sibsankar Dasmahapatra<br/><br/>12.1 Introduction 202<br/><br/>12.2 Genetic Algorithm GA: An Evolutionary Computational Technique 203<br/><br/>12.3 Design of Multiobjective Optimization Problem 204<br/><br/>12.3.1 Decision Variables 204<br/><br/>12.3.2 Objective Functions 204<br/><br/>12.3.2.1 Minimization of Main Cutting Force 205<br/><br/>12.3.2.2 Minimization of Feed Force 205<br/><br/>12.3.3 Bounds of Decision Variables 205<br/><br/>12.3.4 Response Variables 206<br/><br/>12.4 Results and Discussions 206<br/><br/>12.4.1 Single Objective Optimization 206<br/><br/>12.4.2 Results of Multiobjective Optimization 208<br/><br/>12.5 Conclusion 212<br/><br/>References 212<br/><br/>13 Genetic Algorithm-Based Optimization for Speech Processing Applications 215<br/>Ramya.R, M. Preethi and R. Rajalakshmi<br/><br/>13.1 Introduction to GA 215<br/><br/>13.1.1 Enhanced GA 216<br/><br/>13.1.1.1 Hybrid GA 216<br/><br/>13.1.1.2 Interval GA 217<br/><br/>13.1.1.3 Adaptive GA 217<br/><br/>13.2 GA in Automatic Speech Recognition 218<br/><br/>13.2.1 GA for Optimizing Off-Line Parameters in Voice Activity Detection (VAD) 218<br/><br/>13.2.2 Classification of Features in ASR Using GA 219<br/><br/>13.2.3 GA-Based Distinctive Phonetic Features Recognition 219<br/><br/>13.2.4 GA in Phonetic Decoding 220<br/><br/>13.3 Genetic Algorithm in Speech Emotion Recognition 221<br/><br/>13.3.1 Speech Emotion Recognition 221<br/><br/>13.3.2 Genetic Algorithms in Speech Emotion Recognition 222<br/><br/>13.3.2.1 Feature Extraction Using GA for SER 222<br/><br/>13.3.2.2 Steps for Adaptive Genetic Algorithm for Feature Optimization 224<br/><br/>13.4 Genetic Programming in Hate Speech Using Deep Learning 225<br/><br/>13.4.1 Introduction to Hate Speech Detection 225<br/><br/>13.4.2 GA Integrated With Deep Learning Models for Hate Speech Detection 226<br/><br/>13.5 Conclusion 228<br/><br/>References 228<br/><br/>14 Performance of P, PI, PID, and NARMA Controllers in the Load Frequency Control of a Single-Area Thermal Power Plant 231<br/>Ranjit Singh and L. Ramesh<br/><br/>14.1 Introduction 231<br/><br/>14.2 Single-Area Power System 232<br/><br/>14.3 Automatic Load Frequency Control (ALFC) 233<br/><br/>14.4 Controllers Used in the Simulink Model 233<br/><br/>14.4.1 PID Controller 233<br/><br/>14.4.2 PI Controller 234<br/><br/>14.4.3 P Controller 234<br/><br/>14.5 Circuit Description 235<br/><br/>14.6 ANN and NARMA L2 Controller 236<br/><br/>14.7 Simulation Results and Comparative Analysis 237<br/><br/>14.8 Conclusion 239<br/><br/>References 240<br/><br/>Part 2: Decision Science and Simulation-Based Optimization 243<br/><br/>15 Selection of Nonpowered Industrial Truck for Small Scale Manufacturing Industry Using Fuzzy VIKOR Method Under FMCDM Environment 245<br/>Bipradas Bairagi<br/><br/>15.1 Introduction 246<br/><br/>15.2 Fuzzy Set Theory 248<br/><br/>15.2.1 Some Important Fuzzy Definitions 248<br/><br/>15.2.2 Fuzzy Operations 249<br/><br/>15.2.3 Linguistic Variable (LV) 250<br/><br/>15.3 Fvikor 251<br/><br/>15.4 Problem Definition 253<br/><br/>15.5 Results and Discussions 253<br/><br/>15.6 Conclusions 258<br/><br/>References 259<br/><br/>16 Slightly and Almost Neutrosophic gsα*—Continuous Function in Neutrosophic Topological Spaces 261<br/>P. Anbarasi Rodrigo and S. Maheswari<br/><br/>16.1 Introduction 261<br/><br/>16.2 Preliminaries 262<br/><br/>16.3 Slightly Neutrosophic gsα* – Continuous Function 263<br/><br/>16.4 Almost Neutrosophic gsα* – Continuous Function 266<br/><br/>16.5 Conclusion 274<br/><br/>References 274<br/><br/>17 Identification and Prioritization of Risk Factors Affecting the Mental Health of Farmers 275<br/>Hullash Chauhan, Suchismita Satapathy, A. K. Sahoo and Debesh Mishra<br/><br/>17.1 Introduction 275<br/><br/>17.2 Materials and Methods 277<br/><br/>17.2.1 ELECTRE Technique 278<br/><br/>17.3 Result and Discussion 281<br/><br/>17.4 Conclusion 293<br/><br/>References 294<br/><br/>18 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): An Application to Material Handling System Selection 297<br/>Bipradas Bairagi<br/><br/>18.1 Introduction 298<br/><br/>18.2 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): The Proposed Algorithm 300<br/><br/>18.3 Illustrative Example 303<br/><br/>18.3.1 Problem Definition 303<br/><br/>18.3.2 Calculation and Discussions 305<br/><br/>18.4 Conclusions 309<br/><br/>References 310<br/><br/>19 Evaluation of Optimal Parameters to Enhance Worker’s Performance in an Automotive Industry 313<br/>Rajat Yadav, Kuwar Mausam, Manish Saraswat and Vijay Kumar Sharma<br/><br/>19.1 Introduction 314<br/><br/>19.2 Methodology 315<br/><br/>19.3 Results and Discussion 316<br/><br/>19.4 Conclusions 320<br/><br/>References 321<br/><br/>20 Determining Key Influential Factors of Rural Tourism— An AHP Model 323<br/>Puspalata Mahaptra, RamaKrishna Bandaru, Deepanjan Nanda and Sushanta Tripathy<br/><br/>20.1 Introduction 324<br/><br/>20.2 Rural Tourism 325<br/><br/>20.3 Literature Review 326<br/><br/>20.4 Objectives 328<br/><br/>20.5 Methodology 328<br/><br/>20.6 Analysis 332<br/><br/>20.7 Results and Discussion 332<br/><br/>20.8 Conclusions 340<br/><br/>20.9 Managerial Implications 340<br/><br/>References 341<br/><br/>21 Solution of a Pollution-Based Economic Order Quantity Model Under Triangular Dense Fuzzy Environment 345<br/>Partha Pratim Bhattacharya, Kousik Bhattacharya, Sujit Kumar De, Prasun Kumar Nayak, Subhankar Joardar and Kushankur Das<br/><br/>21.1 Introduction 346<br/><br/>21.1.1 Overview 346<br/><br/>21.1.2 Motivation and Specific Study 346<br/><br/>21.2 Preliminaries 348<br/><br/>21.2.1 Pollution Function 348<br/><br/>21.2.2 Triangular Dense Fuzzy Set (TDFS) 349<br/><br/>21.3 Notations and Assumptions 350<br/><br/>21.3.1 Case Study 351<br/><br/>21.4 Formulation of the Mathematical Model 352<br/><br/>21.4.1 Crisp Mathematical Model 352<br/><br/>21.4.2 Formulation of Triangular Dense Fuzzy Mathematical Model 352<br/><br/>21.4.3 Defuzzification of Triangular Dense Fuzzy Model 353<br/><br/>21.5 Numerical Illustration 354<br/><br/>21.6 Sensitivity Analysis 355<br/><br/>21.7 Graphical Illustration 355<br/><br/>21.8 Merits and Demerits 358<br/><br/>21.9 Conclusion 358<br/><br/>Acknowledgement 359<br/><br/>Appendix 359<br/><br/>References 360<br/><br/>22 Common Yet Overlooked Aspects Accountable for Antiaging: An MCDM Approach 363<br/>Rajnandini Saha, Satyabrata Aich, Hee-Cheol Kim and Sushanta Tripathy<br/><br/>22.1 Introduction 364<br/><br/>22.2 Literature Review 365<br/><br/>22.3 Analytic Hierarchy Process (AHP) 367<br/><br/>22.4 Result and Discussion 372<br/><br/>22.5 Conclusion 373<br/><br/>References 373<br/><br/>23 E-Waste Management Challenges in India: An AHP Approach 377<br/>Amit Sutar, Apurv Singh, Deepak Singhal, Sushanta Tripathy and Bharat Chandra Routara<br/><br/>23.1 Introduction 378<br/><br/>23.2 Literature Review 379<br/><br/>23.3 Methodology 379<br/><br/>23.4 Results and Discussion 379<br/><br/>23.5 Conclusion 390<br/><br/>References 391<br/><br/>24 Application of k-Means Method for Finding Varying Groups of Primary Energy Household Emissions in the Indian States 393<br/>Tanmay Belsare, Abhay Deshpande, Neha Sharma and Prithwis De<br/><br/>24.1 Introduction 394<br/><br/>24.2 Literature Review 395<br/><br/>24.3 Materials and Methods 397<br/><br/>24.3.1 Data Preparation 397<br/><br/>24.3.2 Methods and Approach 397<br/><br/>24.3.2.1 Cluster Analysis 397<br/><br/>24.3.2.2 Agglomerative Hierarchical Clustering 397<br/><br/>24.3.2.3 K-Means Clustering 398<br/><br/>24.4 Exploratory Data Analysis 398<br/><br/>24.5 Results and Discussion 401<br/><br/>24.6 Conclusion 405<br/><br/>References 406<br/><br/>25 Airwaves Detection and Elimination Using Fast Fourier Transform to Enhance Detection of Hydrocarbon 409<br/>Garba Aliyu, Mathias M. Fonkam, Augustine S. Nsang, Muhammad Abdulkarim, Sandip Rashit and Yakub K. Saheed<br/><br/>25.1 Introduction 410<br/><br/>25.1.1 Airwaves 411<br/><br/>25.1.2 Fast Fourier Transform 412<br/><br/>25.2 Related Works 413<br/><br/>25.3 Theoretical Framework 415<br/><br/>25.4 Methodology 416<br/><br/>25.5 Results and Discussions 417<br/><br/>25.6 Conclusion 420<br/><br/>References 420<br/><br/>26 Design and Implementation of Control for Nonlinear Active Suspension System 423<br/>Ravindra S. Rana and Dipak M. Adhyaru<br/><br/>26.1 Introduction 423<br/><br/>26.2 Mathematical Model of Quarter Car Suspension System 426<br/><br/>26.2.1 Mathematical Model 426<br/><br/>26.2.2 Linearization Method for Nonlinear System Model 429<br/><br/>26.2.3 Discussion of Result 430<br/><br/>26.3 Conclusion 433<br/><br/>References 434<br/><br/>27 A Study of Various Peak to Average Power Ratio (PAPR) Reduction Techniques for 5G Communication System (5G-CS) 437<br/>Himanshu Kumar Sinha, Anand Kumar and Devasis Pradhan<br/><br/>27.1 Introduction 437<br/><br/>27.2 Literature Review 439<br/><br/>27.3 Overview of 5G Cellular System 440<br/><br/>27.4 Papr 441<br/><br/>27.4.1 Continuous Time PAPR 441<br/><br/>27.4.2 Continuous Time PAPR 442<br/><br/>27.5 Factors on which PAPR Reduction Depends 442<br/><br/>27.6 PAPR Reduction Technique 443<br/><br/>27.6.1 Scrambling of Signals 443<br/><br/>27.6.2 Signal Distortion Technique 446<br/><br/>27.6.3 High Power Amplifier (HPA) 447<br/><br/>27.7 Limitation of OFDM 447<br/><br/>27.8 Universal Filter Multicarrier (UMFC) Emerging Technique to Reduce PAPR in 5G 448<br/><br/>27.8.1 Transmitter of UMFC 448<br/><br/>27.8.2 Receiver of UMFC 450<br/><br/>27.9 Comparison Between Various Techniques 450<br/><br/>27.10 Conclusion 450<br/><br/>References 452<br/><br/>28 Investigation of Rebound Suppression Phenomenon in an Electromagnetic V-Bending Test 455<br/>Aman Sharma, Pradeep Kumar Singh, Manish Saraswat and Irfan Khan<br/><br/>28.1 Introduction 455<br/><br/>28.2 Investigation 458<br/><br/>28.2.1 Specimen for Tests 458<br/><br/>28.2.2 Design of Die and Tool 458<br/><br/>28.2.3 Configuration and Procedure 459<br/><br/>28.3 Mathematical Evaluation 460<br/><br/>28.3.1 Simulation Methodology 460<br/><br/>28.4 Modeling for Material 461<br/><br/>28.4.1 Suppressing Rebound Phenomenon 461<br/><br/>28.5 Conclusion 466<br/><br/>References 466<br/><br/>29 Quadratic Spline Function Companding Technique to Minimize Peak-to-Average Power Ratio in Orthogonal Frequency Division Multiplexing System 469<br/>Lazar Z. Velimirovic<br/><br/>29.1 Introduction 469<br/><br/>29.2 OFDM System 471<br/><br/>29.2.1 PAPR of OFDM Signal 472<br/><br/>29.3 Companding Technique 474<br/><br/>29.3.1 Quadratic Spline Function Companding 474<br/><br/>29.4 Numerical Results and Discussion 475<br/><br/>29.5 Conclusion 480<br/><br/>Acknowledgment 480<br/><br/>References 480<br/><br/>30 A Novel MCGDM Approach for Supplier Selection in a Supply Chain Management 483<br/>Bipradas Bairagi<br/><br/>30.1 Introduction 484<br/><br/>30.2 Proposed Algorithm 486<br/><br/>30.3 Illustrative Example 491<br/><br/>30.3.1 Problem Definition 491<br/><br/>30.3.2 Calculation and Discussions 492<br/><br/>30.4 Conclusions 498<br/><br/>References 499<br/><br/>Index 501 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
This 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. |
| 545 0# - BIOGRAPHICAL OR HISTORICAL DATA |
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| Biographical or historical note |
About the Author<br/>Anita Khosla, PhD, is a professor in the Department of Electrical and Electronics Engineering at Manav Rachna International Institute of Research and Studies, University, Faridabad. She is the editor of two books and more than 50 research papers in national, international journals and conferences.<br/><br/>Prasenjit Chatterjee, PhD, is a full professor of Mechanical Engineering and Dean (Research and Consultancy) at MCKV Institute of Engineering, West Bengal, India. He has more than 120 research papers in various international journals and peer-reviewed conferences. He has authored and edited more than 22 books on intelligent decision-making, fuzzy computing, supply chain management, optimization techniques, risk management, and sustainability modeling. Dr. Chatterjee is one of the developers of a new multiple-criteria decision-making method called Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS).<br/><br/>Ikbal Ali, PhD, is a professor in the Department of Electrical Engineering, Faculty of Engineering & Technology of Jamia Millia Islamia, New Delhi, India. His research work has been widely published and cited in refereed international journals/conferences of repute like IEEE. His research interests are in the fields of power systems, operation, and control; and smart grid technologies.<br/><br/>Dheeraj Joshi, PhD, is a professor in the Electrical Engineering Department, Delhi Technological University since 2015. He has published more than 200 publications in international/national journals and conferences. His areas of interest are power electronics converters, induction generators in wind energy conversion systems, and electric drives. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Engineering |
| General subdivision |
Mathematical models. |
| Authority record control number |
https://id.loc.gov/authorities/subjects/sh2008103076. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Khosla, Anita. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
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| Qualifying information |
Original |
| International Standard Book Number |
111990627X |
| -- |
9781119906278 |
| Record control number |
(OCoLC)1295806070. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
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| Display text |
Print version: |
| Main entry heading |
Khosla, Anita |
| Title |
Optimization Techniques in Engineering |
| Place, publisher, and date of publication |
Newark : John Wiley & Sons, Incorporated,c2023 |
| International Standard Book Number |
9781119906278. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
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| Uniform title |
Sustainable Computing and Optimization Series. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119906391 |
| Link text |
Full text available at Wiley Online Library Click here to view |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
|
| Item type |
EBOOK |