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Optimization techniques in engineering : (Record no. 12954)

MARC details
000 -LEADER
fixed length control field 11424cam a22005537i 4500
001 - CONTROL NUMBER
control field on1376309953
003 - CONTROL NUMBER IDENTIFIER
control field OCoLC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240523125544.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu---unuuu
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230419s2023 nju o 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency YDX
Language of cataloging eng
Description conventions rda
Transcribing agency YDX
Modifying agency YDX
-- DG1
-- ORMDA
-- SFB
-- OCLCF
-- OCLCO
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119906391
Qualifying information electronic book
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119906393
Qualifying information electronic book
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119906384
Qualifying information electronic book
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119906385
Qualifying information electronic book
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781119906278
Qualifying information hardcover
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 111990627X
Qualifying information hardcover
029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC)
OCLC library identifier AU@
System control number 000074218546
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1376309953
037 ## - SOURCE OF ACQUISITION
Stock number 9781119906278
Source of stock number/acquisition O'Reilly Media
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA342
Item number .O68 2023
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 620.001/5196
Edition number 23/eng/20230505
049 ## - LOCAL HOLDINGS (OCLC)
Holding library MAIN
245 00 - TITLE STATEMENT
Title Optimization techniques in engineering :
Remainder of title advances and applications /
Statement of responsibility, etc. edited by Anita Khosla... [and 3 others].
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Hoboken, NJ :
Name of producer, publisher, distributor, manufacturer John Wiley & Sons, Incorporated,
Date of production, publication, distribution, manufacture, or copyright notice 2023.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
490 1# - SERIES STATEMENT
Series statement Sustainable Computing and Optimization Series
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Description based on online resource; title from digital title page (viewed on May 05, 2023).
520 ## - SUMMARY, ETC.
Summary, etc. OPTIMIZATION TECHNIQUES IN ENGINEERING The 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. 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. Readers and users will find in the book: An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics; An in-depth treatment of contributions to optimal learning and optimizing engineering systems; Maps out the relations between optimization and other mathematical topics and disciplines; A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems. Audience Researchers, 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.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- Part 1: Soft Computing and Evolutionary-Based Optimization -- Chapter 1 Improved Grey Wolf Optimizer with Levy Flight to Solve Dynamic Economic Dispatch Problem with Electric Vehicle Profiles -- 1.1 Introduction -- 1.2 Problem Formulation -- 1.2.1 Power Output Limits -- 1.2.2 Power Balance Limits -- 1.2.3 Ramp Rate Limits -- 1.2.4 Electric Vehicles -- 1.3 Proposed Algorithm -- 1.3.1 Overview of Grey Wolf Optimizer -- 1.3.2 Improved Grey Wolf Optimizer with Levy Flight -- 1.3.3 Modeling of Prey Position with Levy Flight Distribution -- 1.4 Simulation and Results -- 1.4.1 Performance of Improved GWOLF on Benchmark Functions -- 1.4.2 Performance of Improved GWOLF for Solving DED for the Different Charging Probability Distribution -- 1.5 Conclusion -- References -- Chapter 2 Comparison of YOLO and Faster R-CNN on Garbage Detection -- 2.1 Introduction -- 2.2 Garbage Detection -- 2.2.1 Transfer Learning-Technique -- 2.2.2 Inception-Custom Model -- 2.3 Experimental Results -- 2.3.1 Results Obtained Using YOLO Algorithm -- 2.3.2 Results Obtained Using Faster R-CNN -- 2.4 Future Scope -- 2.5 Conclusion -- References -- Chapter 3 Smart Power Factor Correction and Energy Monitoring System -- 3.1 Introduction -- 3.2 Block Diagram -- 3.2.1 Power Factor Concept -- 3.2.2 Power Factor Calculation -- 3.3 Simulation -- 3.4 Conclusion -- References -- Chapter 4 ANN-Based Maximum Power Point Tracking Control Configured Boost Converter for Electric Vehicle Applications -- 4.1 Introduction -- 4.2 Block Diagram -- 4.3 ANN-Based MPPT for Boost Converter -- 4.4 Closed Loop Control -- 4.5 Simulation Results -- 4.6 Conclusion -- References -- Chapter 5 Single/Multijunction Solar Cell Model Incorporating Maximum Power Point Tracking Scheme Based on Fuzzy Logic Algorithm -- 5.1 Introduction.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 5.2 Modeling Structure -- 5.2.1 Single-Junction Solar Cell Model -- 5.2.2 Modeling of Multijunction Solar PV Cell -- 5.3 MPPT Design Techniques -- 5.3.1 Design of MPPT Scheme Based on P&amp -- O Technique -- 5.3.2 Design of MPPT Scheme Based on FLA -- 5.4 Results and Discussions -- 5.4.1 Single-Junction Solar Cell -- 5.4.2 Multijunction Solar PV Cell -- 5.4.3 Implementation of MPPT Scheme Based on P&amp -- O Technique -- 5.4.4 Implementation of MPPT Scheme Based on FLA -- 5.5 Conclusion -- References -- Chapter 6 Particle Swarm Optimization: An Overview, Advancements and Hybridization -- 6.1 Introduction -- 6.2 The Particle Swarm Optimization: An Overview -- 6.3 PSO Algorithms and Pseudo-Code -- 6.3.1 PSO Algorithm -- 6.3.2 Pseudo-Code for PSO -- 6.3.3 PSO Limitations -- 6.4 Advancements in PSO and Its Perspectives -- 6.4.1 Inertia Weight -- 6.4.2 Constriction Factors -- 6.4.3 Topologies -- 6.4.4 Analysis of Convergence -- 6.5 Hybridization of PSO -- 6.5.1 PSO Hybridization with Artificial Bee Colony (ABC) -- 6.5.2 PSO Hybridization with Ant Colony Optimization (ACO) -- 6.5.3 PSO Hybridization with Genetic Algorithms (GA) -- 6.6 Area of Applications of PSO -- 6.7 Conclusions -- References -- Chapter 7 Application of Genetic Algorithm in Sensor Networks and Smart Grid -- 7.1 Introduction -- 7.2 Communication Sector -- 7.2.1 Sensor Networks -- 7.3 Electrical Sector -- 7.3.1 Smart Microgrid -- 7.4 A Brief Outline of GAs -- 7.5 Sensor Network's Energy Optimization -- 7.6 Sensor Network's Coverage and Uniformity Optimization Using GA -- 7.7 Use GA for Optimization of Reliability and Availability for Smart Microgrid -- 7.8 GA Versus Traditional Methods -- 7.9 Summaries and Conclusions -- References -- Chapter 8 AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber-Reinforced Polymer (CFRP) Drilling Process -- 8.1 Introduction.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 8.2 Methodology -- 8.3 AI-Based Predictive Modeling -- 8.3.1 Linear Regression -- 8.3.2 Random Forests -- 8.3.3 XGBoost -- 8.3.4 SVM -- 8.4 Performance Indices -- 8.4.1 Root Mean Squared Error (RMSE) -- 8.4.2 Mean Squared Error (MSE) -- 8.4.3 R2 (R-Squared) -- 8.5 Results and Discussion -- 8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase -- 8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase -- 8.5.3 K Cross Fold Validation -- 8.6 Conclusions -- References -- Chapter 9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Research Methodology -- 9.3.1 Dataset and Metrics -- 9.4 Result and Discussion -- 9.5 Conclusion -- References -- Chapter 10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA -- 10.1 Introduction -- 10.2 System Model -- 10.3 User Clustering -- 10.4 Optimal Power Allocation for EE-SE Tradeoff -- 10.4.1 Multiobjective Optimization Problem -- 10.4.2 Multiobjective PSO -- 10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA -- 10.5 Numerical Results -- 10.6 Conclusion -- References -- Chapter 11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews -- 11.1 Introduction -- 11.1.1 Related Work -- 11.2 Materials and Methods -- 11.2.1 Data Cleaning and Pre-Processing -- 11.2.2 Feature Extraction -- 11.2.3 Classifiers -- 11.3 Results and Experiments -- 11.4 Conclusion -- References -- Chapter 12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm -- 12.1 Introduction -- 12.2 Genetic Algorithm GA: An Evolutionary Computational Technique -- 12.3 Design of Multiobjective Optimization Problem.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 12.3.1 Decision Variables -- 12.3.2 Objective Functions -- 12.3.3 Bounds of Decision Variables -- 12.3.4 Response Variables -- 12.4 Results and Discussions -- 12.4.1 Single Objective Optimization -- 12.4.2 Results of Multiobjective Optimization -- 12.5 Conclusion -- References -- Chapter 13 Genetic Algorithm-Based Optimization for Speech Processing Applications -- 13.1 Introduction to GA -- 13.1.1 Enhanced GA -- 13.2 GA in Automatic Speech Recognition -- 13.2.1 GA for Optimizing Off-Line Parameters in Voice Activity Detection (VAD) -- 13.2.2 Classification of Features in ASR Using GA -- 13.2.3 GA-Based Distinctive Phonetic Features Recognition -- 13.2.4 GA in Phonetic Decoding -- 13.3 Genetic Algorithm in Speech Emotion Recognition -- 13.3.1 Speech Emotion Recognition -- 13.3.2 Genetic Algorithms in Speech Emotion Recognition -- 13.4 Genetic Programming in Hate Speech Using Deep Learning -- 13.4.1 Introduction to Hate Speech Detection -- 13.4.2 GA Integrated With Deep Learning Models for Hate Speech Detection -- 13.5 Conclusion -- References -- Chapter 14 Performance of P, PI, PID, and NARMA Controllers in the Load Frequency Control of a Single-Area Thermal Power Plant -- 14.1 Introduction -- 14.2 Single-Area Power System -- 14.3 Automatic Load Frequency Control (ALFC) -- 14.4 Controllers Used in the Simulink Model -- 14.4.1 PID Controller -- 14.4.2 PI Controller -- 14.4.3 P Controller -- 14.5 Circuit Description -- 14.6 ANN and NARMA L2 Controller -- 14.7 Simulation Results and Comparative Analysis -- 14.8 Conclusion -- References -- Part 2: Decision Science and Simulation-Based Optimization -- Chapter 15 Selection of Nonpowered Industrial Truck for Small Scale Manufacturing Industry Using Fuzzy VIKOR Method Under FMCDM Environment -- 15.1 Introduction -- 15.2 Fuzzy Set Theory -- 15.2.1 Some Important Fuzzy Definitions -- 15.2.2 Fuzzy Operations.
590 ## - LOCAL NOTE (RLIN)
Local note John Wiley and Sons
Provenance (VM) [OBSOLETE] Wiley Online Library: Complete oBooks
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Engineering
General subdivision Mathematical models.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Ing�enierie
General subdivision Mod�eles math�ematiques.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Engineering
General subdivision Mathematical models
Source of heading or term fast
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Khosla, Anita.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
International Standard Book Number 111990627X
-- 9781119906278
Record control number (OCoLC)1295806070
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information 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
Uniform title Sustainable Computing and Optimization Series.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://onlinelibrary.wiley.com/doi/book/10.1002/9781119906391">https://onlinelibrary.wiley.com/doi/book/10.1002/9781119906391</a>
938 ## -
-- YBP Library Services
-- YANK
-- 19764167
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-- 92
-- INLUM

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