LEADER 01690nam 2200385 450 001 9910172603703321 005 20171006080617.0 010 $a1-5044-0393-2 035 $a(CKB)3710000001362050 035 $a(WaSeSS)IndRDA00078421 035 $a(NjHacI)993710000001362050 035 $a(EXLCZ)993710000001362050 100 $a20171006d1962 || | 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAIEE standard for semiconductor rectifier components 210 1$aNew York :$cIEEE,$d1962. 215 $a1 online resource (31 pages) 330 $aDefinitions given herein apply specifically to semiconductor components used for rectification or control of electric power, or both. For the purpose of this standard, a semiconductor rectifier component is a semiconductor rectifier cell, rectifier diode, or rectifier stack. Note that the name of the actual semiconductor material (selenium, silicon, etc.) may be substituted in place of the word "semiconductor" in the name of the components.Only those definitions likely to be needed by the user are included. 606 $aSemiconductor rectifiers$xStandards 606 $aElectric current rectifiers$xStandards 606 $aElectric current rectifiers 615 0$aSemiconductor rectifiers$xStandards. 615 0$aElectric current rectifiers$xStandards. 615 0$aElectric current rectifiers. 676 $a621.3815322 801 0$bWaSeSS 801 1$bWaSeSS 906 $aDOCUMENT 912 $a9910172603703321 996 $aAIEE standard for semiconductor rectifier components$92576726 997 $aUNINA LEADER 04229nam 22007095 450 001 9910734092203321 005 20241120135444.0 010 $a3-030-96917-7 024 7 $a10.1007/978-3-030-96917-2 035 $a(MiAaPQ)EBC7015874 035 $a(Au-PeEL)EBL7015874 035 $a(CKB)23736967100041 035 $aEBL7015874 035 $a(AU-PeEL)EBL7015874 035 $a(DE-He213)978-3-030-96917-2 035 $a(PPN)265864461 035 $a(EXLCZ)9923736967100041 100 $a20220611d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms /$fby Tome Eftimov, Peter Koro?ec 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (141 pages) 225 1 $aNatural Computing Series,$x2627-6461 300 $aDescription based upon print version of record. 311 08$aPrint version: Eftimov, Tome Deep Statistical Comparison for Meta-Heuristic Stochastic Optimization Algorithms Cham : Springer International Publishing AG,c2022 9783030969165 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Metaheuristic Stochastic Optimization -- Benchmarking Theory -- Introduction to Statistical Analysis -- Approaches to Statistical Comparisons -- Deep Statistical Comparison in Single-Objective Optimization -- Deep Statistical Comparison in Multiobjective Optimization -- DSCTool: A Web-Service-Based E-Learning Tool -- Summary. 330 $aFocusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts: Part I: Introduction to optimization, benchmarking, and statistical analysis ? Chapters 2-4. Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms ? Chapters 5-7. Part III: Implementation and application of Deep Statistical Comparison ? Chapter 8. 410 0$aNatural Computing Series,$x2627-6461 606 $aArtificial intelligence 606 $aStochastic analysis 606 $aStatistics 606 $aArtificial Intelligence 606 $aStochastic Analysis 606 $aStatistics 606 $aOptimització matemàtica$2thub 606 $aIntel·ligència artificial$2thub 608 $aLlibres electrònics$2thub 615 0$aArtificial intelligence. 615 0$aStochastic analysis. 615 0$aStatistics. 615 14$aArtificial Intelligence. 615 24$aStochastic Analysis. 615 24$aStatistics. 615 7$aOptimització matemàtica 615 7$aIntel·ligència artificial. 676 $a519.3 676 $a519.6 700 $aEftimov$b Tome$01241171 702 $aKoros?ec$b Peter$f1977- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734092203321 996 $aDeep statistical comparison for meta-heuristic stochastic optimization algorithms$92997594 997 $aUNINA