LEADER 02039nam 2200613Ia 450 001 9910778999403321 005 20230913214133.0 010 $a0-7914-9738-0 010 $a0-585-07768-1 035 $a(CKB)111004366788200 035 $a(OCoLC)614574088 035 $a(CaPaEBR)ebrary10019112 035 $a(SSID)ssj0000178043 035 $a(PQKBManifestationID)11167380 035 $a(PQKBTitleCode)TC0000178043 035 $a(PQKBWorkID)10221334 035 $a(PQKB)11743893 035 $a(MiAaPQ)EBC3406901 035 $a(Au-PeEL)EBL3406901 035 $a(CaPaEBR)ebr10019112 035 $a(OCoLC)923396749 035 $a(EXLCZ)99111004366788200 100 $a19891031h19901990 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIncentive pay and career ladders for today's teachers $ea study of current programs and practices /$fRichard M. Brandt 210 1$aAlbany, N.Y. :$cState University of New York Press,$d1990. 210 4$a©1990 215 $a1 online resource (xii, 286 pages) 225 1 $aSUNY series in educational leadership 300 $aBibliographic Level Mode of Issuance: Monograph 311 0 $a0-7914-0399-8 320 $aIncludes bibliographical references (p. [259]-275). 410 0$aSUNY series in educational leadership. 606 $aTeachers$xSalaries, etc$zUnited States 606 $aMerit pay$zUnited States 606 $aCompensation management$zUnited States 606 $aTeachers$xRating of$zUnited States 615 0$aTeachers$xSalaries, etc. 615 0$aMerit pay 615 0$aCompensation management 615 0$aTeachers$xRating of 676 $a331.2/813711/00973 700 $aBrandt$b Richard Martin$f1922-$01584618 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910778999403321 996 $aIncentive pay and career ladders for today's teachers$93868542 997 $aUNINA LEADER 04358nam 22005535 450 001 9910155297303321 005 20251230064025.0 024 7 $a10.1007/978-3-662-53806-7 035 $a(CKB)3710000000966179 035 $a(DE-He213)978-3-662-53806-7 035 $a(MiAaPQ)EBC4751417 035 $a(PPN)197133886 035 $a(EXLCZ)993710000000966179 100 $a20161130d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Cyber Physical Systems $eSelected papers from the International Conference ML4CPS 2016 /$fedited by Jürgen Beyerer, Oliver Niggemann, Christian Kühnert 205 $a1st ed. 2017. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer Vieweg,$d2017. 215 $a1 online resource (VII, 72 p. 24 illus., 19 illus. in color.) 225 1 $aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 311 08$a3-662-53805-9 311 08$a3-662-53806-7 327 $aA Concept for the Application of Reinforcement Learning in the Optimization of CAM-Generated Tool Paths -- Semantic Stream Processing in Dynamic Environments Using Dynamic Stream Selection -- Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment -- A Modular Architecture for Smart Data Analysis using AutomationML, OPC-UA and Data-driven Algorithms -- Cloud-based event detection platform for water distribution networks using machine-learning algorithms -- A Generic Data Fusion and Analysis Platform for Cyber-Physical Systems -- Agent Swarm Optimization: Exploding the search space -- Anomaly Detection in Industrial Networks using Machine Learning. . 330 $aThe work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS ? Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Prof. Dr. Oliver Niggemann is Professor for Embedded Software Engineering. His research interests are in the field of Distributed Real-time Software and in the fields of analysis and diagnosis of distributed systems. He is a board member of the inIT and a senior researcher at the Fraunhofer Application Center Industrial Automation INA located in Lemgo. Dr. Christian Kühnert is a senior researcher at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data-driven condition monitoring. . 410 0$aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 606 $aComputational intelligence 606 $aData mining 606 $aKnowledge management 606 $aComputational Intelligence 606 $aData Mining and Knowledge Discovery 606 $aKnowledge Management 615 0$aComputational intelligence. 615 0$aData mining. 615 0$aKnowledge management. 615 14$aComputational Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aKnowledge Management. 676 $a006.3 702 $aBeyerer$b Jürgen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNiggemann$b Oliver$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKühnert$b Christian$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910155297303321 996 $aMachine Learning for Cyber Physical Systems$91543087 997 $aUNINA