LEADER 01814nam 2200361 450 001 9910513683303321 005 20231213215823.0 010 $a1-5044-7996-3 024 70$a10.1109/IEEESTD.2021.9656838 035 $a(CKB)4100000012203915 035 $a(NjHacI)994100000012203915 035 $a(EXLCZ)994100000012203915 100 $a20231213d2021 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$a2657-2021 - IEEE Guide for Energy Feedback System for DC Traction Power Supply System /$fInstitute of Electrical and Electronics Engineer 210 1$a[Place of publication not identified] :$cIEEE,$d2021. 215 $a1 online resource (51 pages) 330 $aIn the dc electric railways, when a train regenerates power, usually the power has to be consumed within the dc network because the dc traction power systems are often not reversible. Several technologies improve receptivity: energy consumption, energy feedback, and energy storage. Solution selection depends on the application. The energy feedback systems (EFSs) convert energy to ac power system as system dc voltage rises. Engineers are helped by this guide to decide where these EFSs can provide the greatest benefits, determine which design solutions will have the maximum effectiveness, and evaluate the costs and benefits of developing new EFS projects. 606 $aEnergy consumption 606 $aEnergy policy 615 0$aEnergy consumption. 615 0$aEnergy policy. 676 $a333.79 801 0$bNjHacI 801 1$bNjHacl 906 $aDOCUMENT 912 $a9910513683303321 996 $a2657-2021 - IEEE Guide for Energy Feedback System for DC Traction Power Supply System$92567222 997 $aUNINA LEADER 03299nam 22006015 450 001 9910255012203321 005 20200705022927.0 010 $a3-319-29206-4 024 7 $a10.1007/978-3-319-29206-9 035 $a(CKB)3710000000909051 035 $a(DE-He213)978-3-319-29206-9 035 $a(MiAaPQ)EBC6310741 035 $a(MiAaPQ)EBC5577828 035 $a(Au-PeEL)EBL5577828 035 $a(OCoLC)961104124 035 $a(PPN)196324947 035 $a(EXLCZ)993710000000909051 100 $a20161011d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntelligent Techniques for Data Science /$fby Rajendra Akerkar, Priti Srinivas Sajja 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XVI, 272 p. 121 illus., 57 illus. in color.) 311 $a3-319-29205-6 320 $aIncludes bibliographical references. 327 $aPreface -- Introduction -- Data Analytics -- Basic Learning Algorithms -- Fuzzy Logic -- Artificial Neural Networks -- Genetic Algorithms and Evolutionary Computing -- Other Metaheuristics and Classification Approaches -- Analytics and Big Data -- Data Analytics Using R -- Appendix I: Tools for Data Science -- Appendix II: Tools for Computational Intelligence. 330 $aThis textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions. The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism. 606 $aData mining 606 $aArtificial intelligence 606 $aKnowledge management 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aKnowledge Management$3https://scigraph.springernature.com/ontologies/product-market-codes/515030 615 0$aData mining. 615 0$aArtificial intelligence. 615 0$aKnowledge management. 615 14$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 615 24$aKnowledge Management. 676 $a006.312 700 $aAkerkar$b Rajendra$4aut$4http://id.loc.gov/vocabulary/relators/aut$0621740 702 $aSajja$b Priti Srinivas$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910255012203321 996 $aIntelligent Techniques for Data Science$92256428 997 $aUNINA