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Italia$v22 702 1$aLa China$b, Sergio$3SUNV002772 712 $aUTET$3SUNV000072$4650 801 $aIT$bSOL$c20181109$gRICA 912 $aSUN0071731 950 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA$d00 CONS XV.D.36 (19.1) $e00 FT 35228 995 $aUFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA$gFT$h35228$kCONS XV.D.36 (19.1)$op$qa 996 $aTutela dei diritti. 1$91401998 997 $aUNICAMPANIA LEADER 05733nam 22007695 450 001 9910372753003321 005 20251230064628.0 010 $a9783662584859 010 $a3662584859 024 7 $a10.1007/978-3-662-58485-9 035 $a(CKB)4100000007223594 035 $a(DE-He213)978-3-662-58485-9 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/32392 035 $a(PPN)243764421 035 $a(MiAaPQ)EBC31281916 035 $a(Au-PeEL)EBL31281916 035 $a(MiAaPQ)EBC5929350 035 $a(ODN)ODN0010071702 035 $a(oapen)doab32392 035 $a(EXLCZ)994100000007223594 100 $a20181217d2019 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 $eSelected papers from the International Conference ML4CPS 2018 /$fedited by Jürgen Beyerer, Christian Kühnert, Oliver Niggemann 205 $a1st ed. 2019. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer Vieweg,$d2019. 215 $a1 online resource (VII, 136 pages) 225 1 $aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 ;$v9 311 08$a9783662584842 311 08$a3662584840 327 $aMachine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project -- Deduction of time-dependent machine tool characteristics by fuzzy-clustering -- Unsupervised Anomaly Detection in Production Lines -- A Random Forest Based Classifer for Error Prediction of Highly Individualized Products -- Web-based Machine Learning Platform for Condition-Monitoring -- Selection and Application of Machine Learning-Algorithms in Production Quality -- Which deep artifificial neural network architecture to use for anomaly detection in Mobile Robots kinematic data -- GPU GEMM-Kernel Autotuning for scalable machine learners -- Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria -- A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance -- Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality -- Enabling Self-Diagnosis of AutomationDevices through Industrial Analytics -- Making Industrial Analytics work for Factory Automation Applications -- Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems -- LoRaWan for Smarter Management of Water Network: From metering to data analysis. 330 $aThis Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS ? Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Dr. ChristianKühnert is a senior researcher at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data-driven condition monitoring. Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo. 410 0$aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 ;$v9 606 $aComputational intelligence 606 $aComputer engineering 606 $aComputer networks 606 $aTelecommunication 606 $aData mining 606 $aComputational Intelligence 606 $aComputer Engineering and Networks 606 $aCommunications Engineering, Networks 606 $aData Mining and Knowledge Discovery 615 0$aComputational intelligence. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aTelecommunication. 615 0$aData mining. 615 14$aComputational Intelligence. 615 24$aComputer Engineering and Networks. 615 24$aCommunications Engineering, Networks. 615 24$aData Mining and Knowledge Discovery. 676 $a006.3 686 $aCOM021030$aCOM043000$aTEC009000$aTEC041000$2bisacsh 700 $aBeyerer$b Jürgen$01029954 702 $aBeyerer$b Ju?rgen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKuhnert$b Christian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNiggemann$b Oliver$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910372753003321 996 $aMachine Learning for Cyber Physical Systems$93018774 997 $aUNINA