04945nam 2200697 450 991013748690332120230808212231.01-119-21214-61-119-21208-1(CKB)3710000000576660(EBL)4353592(OCoLC)925426792(SSID)ssj0001604114(PQKBManifestationID)16313913(PQKBTitleCode)TC0001604114(PQKBWorkID)14893752(PQKB)10634674(PQKBManifestationID)16314151(PQKB)24180540(MiAaPQ)EBC4353592(DLC) 2015040994(Au-PeEL)EBL4353592(CaPaEBR)ebr11150425(CaONFJC)MIL890222(EXLCZ)99371000000057666020160211h20162016 uy 0engur|n|---|||||txtccrRobust optimization world's best practices for developing winning vehicles /Subir Chowdhury, Shin TaguchiChichester, England :Wiley,2016.©20161 online resource (527 p.)Includes index.1-119-21209-X 1-119-21212-X Includes bibliographical references and index.Title Page; Copyright; Dedication; Preface; Acknowledgments; About the Authors; Chapter 1: Introduction to Robust Optimization; 1.1 What Is Quality as Loss?; 1.2 What Is Robustness?; 1.3 What Is Robust Assessment?; 1.4 What Is Robust Optimization?; Chapter 2: Eight Steps for Robust Optimization and Robust Assessment; 2.1 Before Eight Steps: Select Project Area; 2.2 Eight Steps for Robust Optimization; 2.3 Eight Steps for Robust Assessment; 2.4 As You Go through Case Studies in This Book; Chapter 3: Implementation of Robust Optimization; 3.1 Introduction; 3.2 Robust Optimization ImplementationPart One: Vehicle Level OptimizationChapter 4: Optimization of Vehicle Offset Crashworthy Design Using a Simplified Analysis Model; 4.1 Executive Summary; 4.2 Introduction; 4.3 Stepwise Implementation of DFSS Optimization for Vehicle Offset Impact; 4.4 Conclusion; References; Chapter 5: Optimization of the Component Characteristics for Improving Collision Safety by Simulation; 5.1 Executive Summary; 5.2 Introduction; 5.3 Simulation Models; 5.4 Concept of Standardized S/N Ratios with Respect to Survival Space; 5.5 Results and Consideration; 5.6 Conclusion; ReferencePart Two: Subsystems Level Optimization by Original Equipment Manufacturers (OEMs)Chapter 6: Optimization of Small DC Motors Using Functionality for Evaluation; 6.1 Executive Summary; 6.2 Introduction; 6.3 Functionality for Evaluation in Case of DC Motors; 6.4 Experiment Method and Measurement Data; 6.5 Factors and Levels; 6.6 Data Analysis; 6.7 Analysis Results; 6.8 Selection of Optimal Design and Confirmation; 6.9 Benefits Gained; 6.10 Consideration of Analysis for Audible Noise; 6.11 Conclusion; Chapter 7: Optimal Design for a Double-Lift Window Regulator System Used in Automobiles7.1 Executive Summary7.2 Introduction; 7.3 Schematic Figure of Double-Lift Window Regulator System; 7.4 Ideal Function; 7.5 Noise Factors; 7.6 Control Factors; 7.7 Conventional Data Analysis and Results; 7.8 Selection of Optimal Condition and Confirmation Test Results; 7.9 Evaluation of Quality Characteristics; 7.10 Concept of Analysis Based on Standardized S/N Ratio; 7.11 Analysis Results Based on Standardized S/N Ratio; 7.12 Comparison between Analysis Based on Standardized S/N Ratio and Analysis Based on Conventional S/N Ratio; 7.13 Conclusion; Further ReadingChapter 8: Optimization of Next-Generation Steering System Using Computer Simulation8.1 Executive Summary; 8.2 Introduction; 8.3 System Description; 8.4 Measurement Data; 8.5 Ideal Function; 8.6 Factors and Levels; 8.7 Pre-analysis for Compounding the Noise Factors; 8.8 Calculation of Standardized S/N Ratio; 8.9 Analysis Results; 8.10 Determination of Optimal Design and Confirmation; 8.11 Tuning to the Targeted Value; 8.12 Conclusion; Chapter 9: Future Truck Steering Effort Robustness; 9.1 Executive Summary; 9.2 Background; 9.3 Parameter Design; 9.4 Acknowledgments; ReferencesChapter 10: Optimal Design of Engine Mounting System Based on Quality EngineeringMotor vehiclesDesign and constructionRobust optimizationManufacturing processesMotor vehiclesDesign and construction.Robust optimization.Manufacturing processes.629.231Chowdhury Subir864493Taguchi ShinMiAaPQMiAaPQMiAaPQBOOK9910137486903321Robust optimization1929475UNINA03644nam 22005295 450 991029945960332120200705065051.03-319-73531-410.1007/978-3-319-73531-3(CKB)4100000002892262(DE-He213)978-3-319-73531-3(MiAaPQ)EBC6314099(MiAaPQ)EBC5589130(Au-PeEL)EBL5589130(OCoLC)1029870455(PPN)225553392(EXLCZ)99410000000289226220180319d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierMachine Learning for Text /by Charu C. Aggarwal1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (XXIII, 493 p. 80 illus., 4 illus. in color.) 3-319-73530-6 1 An Introduction to Text Analytics -- 2 Text Preparation and Similarity Computation -- 3 Matrix Factorization and Topic Modeling -- 4 Text Clustering -- 5 Text Classification: Basic Models -- 6 Linear Models for Classification and Regression -- 7 Classifier Performance and Evaluation -- 8 Joint Text Mining with Heterogeneous Data -- 9 Information Retrieval and Search Engines -- 10 Text Sequence Modeling and Deep Learning -- 11 Text Summarization -- 12 Information Extraction -- 13 Opinion Mining and Sentiment Analysis -- 14 Text Segmentation and Event Detection.Text analytics is a field that lies on the interface of information retrieval, machine learning, and natural language processing. This book carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This book covers text analytics and machine learning topics from the simple to the advanced. Since the coverage is extensive, multiple courses can be offered from the same book, depending on course level.Data miningArtificial intelligenceData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Data mining.Artificial intelligence.Data Mining and Knowledge Discovery.Artificial Intelligence.006.31Aggarwal Charu Cauthttp://id.loc.gov/vocabulary/relators/aut518673MiAaPQMiAaPQMiAaPQBOOK9910299459603321Machine Learning for Text2175811UNINA