LEADER 01690nam0-2200493-i-450- 001 990009555660403321 005 20120412124122.0 010 $a978-0-8218-5275-0$bpaperback 035 $a000955566 035 $aFED01000955566 035 $a(Aleph)000955566FED01 035 $a000955566 100 $a20120412d2012----km-y0itay50------ba 101 0 $aeng 102 $aUS 105 $aa---a---101yy 200 1 $aTopics in complex analysis and operator theory$eThird Winter School in Complex Analysis and Operator Theory, February 2-5, 2010, Universidad Politécnica de Valencia, Valencia, Spain$fÓscar Blasco, José A. Bonet, José M. Calabuig, David Jornet, editors 210 $aProvidence$cAmerican Mathematical Society$aMadrid$cReal Sociedad Mathemática Espa?ola$d2012 215 $aXII, 252 p.$d26 cm 225 1 $aContemporary mathematics$v561 610 0 $aFunzioni di una variabile complessa$aAtti di conferenze 610 0 $aTeoria degli operatori$aAtti di conferenze 610 0 $aTeoria geometrica delle funzioni 610 0 $aClassi speciali di operatori lineari 610 0 $aAtti di conferenze di interesse specifico vario 676 $a515'.9 702 1$aBlasco,$bÓscar 702 1$aBonet,$bJosé A. 702 1$aCalabuig,$bJosé M. 702 1$aJornet,$bDavid 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a990009555660403321 952 $aC-1-(561$b25197$fMA1 959 $aMA1 962 $a30-06 962 $a47-06 962 $a30CXX 962 $a30HXX 962 $a30JXX 962 $a47BXX 962 $a00B25 996 $aTopics in complex analysis and operator theory$9853666 997 $aUNINA LEADER 01547aam 2200397I 450 001 9910710043503321 005 20151118015325.0 024 8 $aGOVPUB-C13-65872ef6370ac3fd1faef52229c18f62 035 $a(CKB)5470000002475843 035 $a(OCoLC)929881971 035 $a(EXLCZ)995470000002475843 100 $a20151118d1975 ua 0 101 0 $aeng 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aInvestigation of two techniques for application in test methods for detecting defects in spacecraft thermal-protection material /$fSeymour Edelman; Steven C. Roth; J. Franklin Mayo-Wells 210 1$aGaithersburg, MD :$cU.S. Dept. of Commerce, National Institute of Standards and Technology,$d1975. 215 $a1 online resource 225 1 $aNBSIR ;$v75-776 300 $a1975. 300 $aContributed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aTitle from PDF title page. 320 $aIncludes bibliographical references. 700 $aEdelman$b Seymour$01397677 701 $aEdelman$b Seymour$01397677 701 $aMayo-Wells$b J. Franklin$01390793 701 $aRoth$b Steven C$01397678 712 02$aUnited States.$bNational Bureau of Standards. 801 0$bNBS 801 1$bNBS 801 2$bGPO 906 $aBOOK 912 $a9910710043503321 996 $aInvestigation of two techniques for application in test methods for detecting defects in spacecraft thermal-protection material$93524177 997 $aUNINA LEADER 05001nam 22006735 450 001 9910506407703321 005 20250522004754.0 010 $a3-030-77696-4 024 7 $a10.1007/978-3-030-77696-1 035 $a(CKB)4950000000280544 035 $a(MiAaPQ)EBC6788047 035 $a(Au-PeEL)EBL6788047 035 $a(OCoLC)1281956159 035 $a(PPN)258297018 035 $a(DE-He213)978-3-030-77696-1 035 $a(EXLCZ)994950000000280544 100 $a20211020d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplication of Machine Learning and Deep Learning Methods to Power System Problems /$fedited by Morteza Nazari-Heris, Somayeh Asadi, Behnam Mohammadi-Ivatloo, Moloud Abdar, Houtan Jebelli, Milad Sadat-Mohammadi 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (390 pages) 225 1 $aPower Systems,$x1860-4676 311 08$a3-030-77695-6 320 $aIncludes bibliographical references and index. 327 $aChapter 1. Power System Challenges and Issues -- Chapter 2. Introduction and literature review of power system challenges and issues -- Chapter 3. Machine learning and power system planning: opportunities, and challenges -- Chapter 4. Introduction to Machine Learning Methods in Energy Engineering -- Chapter 5. Introduction and Literature Review of the Application of Machine Learning/Deep Learning to Control Problems of Power Systems -- Chapter 6. Introduction and literature review of the application of machine learning/deep learning to load forecasting in power system -- Chapter 7. A Survey of Recent particle swarm optimization (PSO)-Based Clustering Approaches to Energy Efficiency in Wireless Sensor Networks -- Chapter 8. Clustering in Power Systems Using Innovative Machine Learning/Deep Learning Methods -- Chapter 9. Voltage stability assessment in power grids using novel machine learning-based methods -- Chapter 10. Evaluation and Classification of cascading failure occurrence potential dueto line outage -- Chapter 11. LSTM-Assisted Heating Energy Demand Management in Residential Buildings -- Chapter 12. Wind Speed Forecasting Using Innovative Regression Applications of Machine Learning Techniques -- Chapter 13. Effective Load Pattern Classification by Processing the Smart Meter Data Based on Event-Driven Processing and Machine Learning -- Chapter 14. Prediction of Out-of-step Condition for Synchronous Generators Using Decision Tree Based on the Dynamic data by WAMS/PMU -- Chapter 15. The adaptive neuro-fuzzy inference system model for short-term load, price and topology forecasting of distribution system -- Chapter 16. Application of Machine Learning for Predicting User Preferences in Optimal Scheduling of Smart Appliances -- Chapter 17. Machine Learning Approaches in a Real Power System and Power Markets. 330 $aThis book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses. Offers innovative machine learning and deep learning methods for dealing with power system issues; Provides promising solution methodologies; Covers theoretical background and experimental analysis. 410 0$aPower Systems,$x1860-4676 606 $aElectric power distribution 606 $aElectric power production 606 $aMachine learning 606 $aEnergy policy 606 $aEnergy policy 606 $aEnergy Grids and Networks 606 $aElectrical Power Engineering 606 $aMachine Learning 606 $aEnergy Policy, Economics and Management 615 0$aElectric power distribution. 615 0$aElectric power production. 615 0$aMachine learning. 615 0$aEnergy policy. 615 0$aEnergy policy. 615 14$aEnergy Grids and Networks. 615 24$aElectrical Power Engineering. 615 24$aMachine Learning. 615 24$aEnergy Policy, Economics and Management. 676 $a621.31028563 702 $aNazari-Heris$b Morteza 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910506407703321 996 $aApplication of machine learning and deep learning methods to power system problems$92899860 997 $aUNINA