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020 _a9783662584859
_9978-3-662-58485-9
024 7 _a10.1007/978-3-662-58485-9
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
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082 0 4 _a006.3
_223
245 1 0 _aMachine Learning for Cyber Physical Systems
_h[electronic resource] :
_bSelected papers from the International Conference ML4CPS 2018 /
_cedited by Jürgen Beyerer, Christian Kühnert, Oliver Niggemann.
250 _a1st ed. 2019.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer Vieweg,
_c2019.
300 _aVII, 136 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,
_x2522-8579 ;
_v9
505 0 _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 Automation Devices 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.
506 0 _aOpen Access
520 _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. 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. 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.
650 0 _aComputational intelligence.
650 0 _aComputer organization.
650 0 _aElectrical engineering.
650 0 _aData mining.
650 1 4 _aComputational Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/T11014
650 2 4 _aComputer Systems Organization and Communication Networks.
_0http://scigraph.springernature.com/things/product-market-codes/I13006
650 2 4 _aCommunications Engineering, Networks.
_0http://scigraph.springernature.com/things/product-market-codes/T24035
650 2 4 _aData Mining and Knowledge Discovery.
_0http://scigraph.springernature.com/things/product-market-codes/I18030
700 1 _aBeyerer, Jürgen.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKühnert, Christian.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aNiggemann, Oliver.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783662584842
776 0 8 _iPrinted edition:
_z9783662584866
830 0 _aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,
_x2522-8579 ;
_v9
856 4 0 _uhttps://doi.org/10.1007/978-3-662-58485-9
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912 _aZDB-2-SOB
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