LEADER 04503nam 22006255 450 001 9910253965803321 005 20200706024513.0 010 $a3-662-48838-8 024 7 $a10.1007/978-3-662-48838-6 035 $a(CKB)3710000000602463 035 $a(EBL)4415577 035 $a(SSID)ssj0001653617 035 $a(PQKBManifestationID)16433141 035 $a(PQKBTitleCode)TC0001653617 035 $a(PQKBWorkID)14982445 035 $a(PQKB)10225133 035 $a(DE-He213)978-3-662-48838-6 035 $a(MiAaPQ)EBC4415577 035 $a(PPN)192221566 035 $a(EXLCZ)993710000000602463 100 $a20160219d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning for Cyber Physical Systems $eSelected papers from the International Conference ML4CPS 2015 /$fedited by Oliver Niggemann, Jürgen Beyerer 205 $a1st ed. 2016. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer Vieweg,$d2016. 215 $a1 online resource (124 p.) 225 1 $aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8579 300 $aDescription based upon print version of record. 311 $a3-662-48836-1 327 $aDevelopment of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control -- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks -- Forecasting Cellular Connectivity for Cyber- Physical Systems: A Machine Learning Approach -- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation -- Prognostics Health  Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission -- Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for an Industrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems. 330 $aThe work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS ? Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. 410 0$aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8579 606 $aComputational intelligence 606 $aData mining 606 $aKnowledge management 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aKnowledge Management$3https://scigraph.springernature.com/ontologies/product-market-codes/515030 615 0$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 $a006.31 702 $aNiggemann$b Oliver$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBeyerer$b Jürgen$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910253965803321 996 $aMachine Learning for Cyber Physical Systems$91543087 997 $aUNINA