LEADER 05817nam 22006495 450 001 996465882303316 005 20200704085236.0 024 7 $a10.1007/11536406 035 $a(CKB)1000000000213170 035 $a(SSID)ssj0000316576 035 $a(PQKBManifestationID)11225823 035 $a(PQKBTitleCode)TC0000316576 035 $a(PQKBWorkID)10275073 035 $a(PQKB)11293935 035 $a(DE-He213)978-3-540-31855-2 035 $a(MiAaPQ)EBC3067793 035 $a(PPN)12309657X 035 $a(EXLCZ)991000000000213170 100 $a20100721d2005 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aCase-Based Reasoning Research and Development$b[electronic resource] $e6th International Conference on Case-Based Reasoning, ICCBR 2005, Chicago, IL, USA, August 23-26, 2005, Proceedings /$fedited by Hector Munoz-Avila, Francesco Ricci 205 $a1st ed. 2005. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2005. 215 $a1 online resource (XVI, 656 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v3620 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-31855-0 311 $a3-540-28174-6 320 $aIncludes bibliographical references and index. 327 $aInvited Talks -- The Virtue of Reward: Performance, Reinforcement and Discovery in Case-Based Reasoning -- Learning to Optimize Plan Execution in Information Agents -- Cased-Based Reasoning by Human Experts -- Scientific Papers -- Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game -- An Ensemble of Case-Based Classifiers for High-Dimensional Biological Domains -- Language Games: Solving the Vocabulary Problem in Multi-Case-Base Reasoning -- Evaluation and Monitoring of the Air-Sea Interaction Using a CBR-Agents Approach -- A Comparative Analysis of Query Similarity Metrics for Community-Based Web Search -- A Case-Based Approach for Indoor Location -- P2P Case Retrieval with an Unspecified Ontology -- Autonomous Internal Control System for Small to Medium Firms -- The Application of a Case-Based Reasoning System to Attention-Deficit Hyperactivity Disorder -- Reasoning with Textual Cases -- Using Ensembles of Binary Case-Based Reasoners -- Transfer in Visual Case-Based Problem Solving -- Generating Estimates of Classification Confidence for a Case-Based Spam Filter -- Improving Gene Selection in Microarray Data Analysis Using Fuzzy Patterns Inside a CBR System -- CBR for State Value Function Approximation in Reinforcement Learning -- Using CBR to Select Solution Strategies in Constraint Programming -- Case-Based Art -- Supporting Conversation Variability in COBBER Using Causal Loops -- Opportunities for CBR in Learning by Doing -- Navigating Through Case Base Competence -- A Knowledge-Intensive Method for Conversational CBR -- Re-using Implicit Knowledge in Short-Term Information Profiles for Context-Sensitive Tasks -- Acquiring Similarity Cases for Classification Problems -- A Live-User Evaluation of Incremental Dynamic Critiquing -- Case Based Representation and Retrieval with Time Dependent Features -- The Best Way to Instil Confidence Is by Being Right -- Cooperative Reuse for Compositional Cases in Multi-agent Systems -- Evaluating the Effectiveness of Exploration and Accumulated Experience in Automatic Case Elicitation -- HYREC: A Hybrid Recommendation System for E-Commerce -- Extending jCOLIBRI for Textual CBR -- Critiquing with Confidence -- Mapping Goals and Kinds of Explanations to the Knowledge Containers of Case-Based Reasoning Systems -- An Approach for Temporal Case-Based Reasoning: Episode-Based Reasoning -- How to Combine CBR and RBR for Diagnosing Multiple Medical Disorder Cases -- Case-Based Student Modeling Using Concept Maps -- Learning Similarity Measures: A Formal View Based on a Generalized CBR Model -- Knowledge-Rich Similarity-Based Classification -- Autonomous Creation of New Situation Cases in Structured Continuous Domains -- Retrieval and Configuration of Life Insurance Policies -- Analogical and Case-Based Reasoning for Predicting Satellite Task Schedulability -- Case Adaptation by Segment Replanning for Case-Based Planning Systems -- Selecting the Best Units in a Fleet: Performance Prediction from Equipment Peers -- CCBR?Driven Business Process Evolution -- CBR for Modeling Complex Systems -- CBE-Conveyor: A Case-Based Reasoning System to Assist Engineers in Designing Conveyor Systems. 410 0$aLecture Notes in Artificial Intelligence ;$v3620 606 $aArtificial intelligence 606 $aMathematical logic 606 $aInformation technology 606 $aBusiness?Data processing 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aIT in Business$3https://scigraph.springernature.com/ontologies/product-market-codes/522000 615 0$aArtificial intelligence. 615 0$aMathematical logic. 615 0$aInformation technology. 615 0$aBusiness?Data processing. 615 14$aArtificial Intelligence. 615 24$aMathematical Logic and Formal Languages. 615 24$aIT in Business. 676 $a006.3/3 702 $aMunoz-Avila$b Hector$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRicci$b Francesco$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Conference on Case-Based Reasoning 906 $aBOOK 912 $a996465882303316 996 $aCase-Based Reasoning Research and Development$9771966 997 $aUNISA LEADER 05004nam 2200805Ia 450 001 9911019334103321 005 20200520144314.0 010 $a9786613371652 010 $a9781283371650 010 $a1283371650 010 $a9781118097724 010 $a1118097726 010 $a9780470648223 010 $a0470648228 010 $a9780470647998 010 $a047064799X 024 7 $a10.1002/9780470647998 035 $a(CKB)3400000000000262 035 $a(EBL)700531 035 $a(SSID)ssj0000477037 035 $a(PQKBManifestationID)11305774 035 $a(PQKBTitleCode)TC0000477037 035 $a(PQKBWorkID)10502260 035 $a(PQKB)11418569 035 $a(MiAaPQ)EBC700531 035 $a(CaBNVSL)mat05732781 035 $a(IDAMS)0b000064814ebff1 035 $a(IEEE)5732781 035 $a(MiAaPQ)EBC4031874 035 $a(Au-PeEL)EBL4031874 035 $a(CaPaEBR)ebr11108075 035 $a(CaONFJC)MIL337165 035 $a(OCoLC)705354450 035 $a(PPN)256056854 035 $a(Perlego)1011116 035 $a(EXLCZ)993400000000000262 100 $a20100223d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 13$aAn introduction to wavelet modulated inverters /$fS.A. Saleh, M. Azizur Rahman 205 $a1st ed. 210 $aPiscataway, NJ $cIEEE Press ;$a[Hoboken, N.J.] $cJohn Wiley$dc2011 215 $a1 online resource (168 p.) 225 1 $aIEEE Press series on power engineering 300 $aDescription based upon print version of record. 311 08$a9780470610480 311 08$a0470610484 320 $aIncludes bibliographical references (p. 135-141) and index. 327 $aFrontmatter -- Introduction to Power Inverters -- Wavelets and the Sampling Theorem -- Modeling of Power Inverters -- Scale-Based Linearly Combined Wavelets -- Single-Phase Wavelet Modulated Inverters -- Three-Phase Wavelet Modulated Inverters -- Appendix A: Nondyadic MRA for 3f WM Inverters. 330 $aAn authoritative guide to designing and constructing wavelet functions that accurately model complex circuits for better performanceThis is the first book to provide details, analysis, development, implementation, and performances of wavelet modulated (WM) inverters, a novel technique that keeps power systems stable and minimizes energy waste while enhancing power quality and efficiency. Written by experts in the power electronics field, it provides step-by-step procedures to implement the WM technique for single- and three-phase inverters. Also presented are key sample performance results for the new WM power inverters for different load types, which demonstrate the inverters' simplicity, efficacy, and robustness.Beginning with the fundamentals of inverter technology, the book then describes wavelet basis functions and sampling theory with particular reference to the switching model of inverters. From there, comprehensive chapters explain:. The connection between the non-uniform sampling theorem and wavelet functions to develop an ideal sampling-reconstruction process to operate an inverter. The development of scale-based linearly combined basis functions in order to successfully operate single-phase WM inverters. Performances of single-phase WM inverters for static, dynamic, and non-linear loads. The simulation and experimental performances of three-phase wavelet modulated voltage source inverters for different loads at various operating conditionsThe book establishes, for the first time, a direct utilization of different concepts of the sampling theorem and signal processing in accurate modeling of the operation of single- and three-phase inverters. Figures are provided to help develop the basis of utilizing concepts of the sampling, signal processing, and wavelet theories in developing a new tool and technology for inverters. Also included are easy-to-follow mathematical derivations, as well as procedures and flowcharts to facilitate the implementation of the WM inverters. These items make this unique reference of great interest to academic researchers, industry-based researchers, and practicing engineers. It is ideally suited for senior undergraduate and graduate-level students in electrical engineering, computer engineering, applied signal processing, and power electronics courses. 410 0$aIEEE Press series on power engineering. 606 $aElectric inverters 606 $aModulation (Electronics) 606 $aWavelets (Mathematics) 615 0$aElectric inverters. 615 0$aModulation (Electronics) 615 0$aWavelets (Mathematics) 676 $a621.3815/322 700 $aSaleh$b S. A$0845879 701 $aRahman$b M. Azizur$g(Mohammad Azizur)$0845880 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019334103321 996 $aAn introduction to wavelet modulated inverters$91888756 997 $aUNINA