LEADER 05890nam 22007455 450 001 9910433248603321 005 20230126214926.0 010 $a3-662-62746-9 024 7 $a10.1007/978-3-662-62746-4 035 $a(CKB)4100000011679175 035 $a(DE-He213)978-3-662-62746-4 035 $a(MiAaPQ)EBC6436125 035 $a(Au-PeEL)EBL6436125 035 $a(OCoLC)1231609193 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/33766 035 $a(PPN)252511794 035 $a(EXLCZ)994100000011679175 100 $a20201223d2021 uy 0 101 0 $aeng 135 $aurnn#---mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning for cyber physical systems $eselected papers from the international conference ML4CPS 2020 ; Berlin, Germany, March 12-13, 2020 /$feditors, Jürgen Beyerer, Alexander Maier, Oliver Niggemann 205 $a1st edition 2021. 210 $cSpringer Nature$d2021 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer Vieweg,$d2021. 215 $a1 online resource (VII, 130 p. 42 illus., 25 illus. in color.) 225 1 $aTechnologies for Intelligent Automation,$x2522-8579 ;$v13 311 1 $a3-662-62745-0 327 $aPreface -- Energy Profile Prediction of Milling Processes Using Machine Learning Techniques -- Improvement of the prediction quality of electrical load profiles with artficial neural networks -- Detection and localization of an underwater docking station -- Deployment architecture for the local delivery of ML-Models to the industrial shop floor -- Deep Learning in Resource and Data Constrained Edge Computing Systems -- Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis -- Proposal for requirements on industrial AI solutions -- Information modeling and knowledge extraction for machine learning applications in industrial production systems -- Explanation Framework for Intrusion Detection -- Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning -- Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks -- First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems -- Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data. 330 $aThis open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS ? Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Dr. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. Prof. Oliver Niggemann got his doctorate in 2001 at the University of Paderborn with the topic "Visual Data Mining of Graph-Based Data". He then worked for almost 8 years in leading positions in the industry. From 2008-2019 he held a professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo/Germany. Until 2019 Prof. Niggemann was also deputy head of the Fraunhofer IOSB-INA, which works in industrial automation. On April 1, 2019 Prof. Niggemann took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut-Schmidt-University in Hamburg / Germany. There he does research at the Institute for Automation Technology IfA in the field of artificial intelligence and machine learning for cyber-physical systems. 410 0$aTechnologies for Intelligent Automation,$x2522-8579 ;$v13 606 $aMachine learning$vCongresses 610 $aCyber-physical systems, IoT 610 $aCommunications Engineering, Networks 610 $aComputer Systems Organization and Communication Networks 610 $aCyber-Physical Systems 610 $aComputer Engineering and Networks 610 $aMachine Learning 610 $aArtificial Intelligence 610 $aCognitive Robotics 610 $aInternet of Things 610 $aComputational intelligence 610 $aComputer-based algorithms 610 $aSmart grid 610 $aOpen Access 610 $aIndustry 4.0 610 $aElectrical engineering 610 $aCybernetics & systems theory 610 $aCommunications engineering / telecommunications 610 $aComputer networking & communications 615 0$aMachine learning 676 $a621.38 700 $aBeyerer$b Jürgen$4edt$01029954 702 $aBeyerer$b Jürgen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMaier$b Alexander$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 $a9910433248603321 996 $aMachine Learning for Cyber Physical Systems$93018774 997 $aUNINA