LEADER 05752nam 22006735 450 001 9910866569003321 005 20240620125226.0 010 $a3-031-47062-1 024 7 $a10.1007/978-3-031-47062-2 035 $a(CKB)32323149400041 035 $a(MiAaPQ)EBC31502925 035 $a(Au-PeEL)EBL31502925 035 $a(DE-He213)978-3-031-47062-2 035 $a(EXLCZ)9932323149400041 100 $a20240620d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Cyber-Physical Systems $eSelected papers from the International Conference ML4CPS 2023 /$fedited by Oliver Niggemann, Jürgen Beyerer, Maria Krantz, Christian Kühnert 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (130 pages) 225 1 $aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 ;$v18 311 $a3-031-47061-3 327 $aCausal Structure Learning using PCMCI+ and Path Constraints from Wavelet-based Soft Interventions -- Reinforcement Learning from Human Feedback for Cyber-Physical Systems: On the Potential of Self-Supervised Pretraining -- Using ML-based Models in Simulation of CPPSs: A Case Study of Smart Meter Production -- Deploying machine learning in high pressure resin transfer molding and part post processing: a case study -- Development of a Robotic Bin Picking Approach based on Reinforcement Learning -- Control Reconfiguration of CPS via Online Identification using Sparse Regression (SINDYc) -- Using Forest Structures for Passive Automata Learning -- Domain Knowledge Injection Guidance for Predictive Maintenance -- Towards a systematic approach for Prescriptive Analytics use cases in smart factories -- Development of a standardized data acquisition prototype for heterogeneous sensor environments as a basis for ML applications in pultrusion -- A Digital Twin Design for conveyor belts predictive maintenance -- Augmenting explainable data-driven models in energy systems: A Python framework for feature engineering. 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 Hamburg (Germany), March 29th to 31st, 2023. 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. Oliver Niggemann held the professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo (Germany) from 2008 to 2019 and was also deputy head of the Fraunhofer IOSB-INA until 2019. In 2019, he took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut Schmidt University in Hamburg. His research at the Institute for Automation Technology is in the field of artificial intelligence and machine learning for cyber-physical systems. Prof. Dr.-Ing. Jürgen Beyerer is a full professor for informatics at the Institute for Anthropomatics and Robotics at the Karlsruhe Institute of Technology KIT and director of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Research interests include automated visual inspection, signal and image processing, active vision, metrology, information theory, fusion of data and information from heterogeneous sources, system theory, autonomous systems and automation. Dr. Maria Krantz is a Postdoc at the Helmut Schmidt University in Hamburg. Her main research interests are causality in Cyber-Physical Systems and applications of diagnosis algorithms in production systems. Dr. Christian Kühnert is senior scientist 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 analytics for cyber-physical systems. 410 0$aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$x2522-8587 ;$v18 606 $aCooperating objects (Computer systems) 606 $aComputer engineering 606 $aComputer networks 606 $aArtificial intelligence 606 $aNeural networks (Computer science) 606 $aCyber-Physical Systems 606 $aComputer Engineering and Networks 606 $aArtificial Intelligence 606 $aMathematical Models of Cognitive Processes and Neural Networks 615 0$aCooperating objects (Computer systems). 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aArtificial intelligence. 615 0$aNeural networks (Computer science). 615 14$aCyber-Physical Systems. 615 24$aComputer Engineering and Networks. 615 24$aArtificial Intelligence. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 676 $a621.38 700 $aNiggemann$b Oliver$01356466 701 $aBeyerer$b Jürgen$01029954 701 $aKrantz$b Maria$01746862 701 $aKühnert$b Christian$0754943 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910866569003321 996 $aMachine Learning for Cyber-Physical Systems$94178610 997 $aUNINA