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001430936 001__ 1430936
001430936 003__ OCoLC
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001430936 020__ $$a9783662627464$$q(electronic bk.)
001430936 020__ $$a3662627469$$q(electronic bk.)
001430936 020__ $$z9783662627457
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001430936 0247_ $$a10.1007/978-3-662-62746-4$$2doi
001430936 035__ $$aSP(OCoLC)1237484309
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001430936 049__ $$aISEA
001430936 050_4 $$aQ325.5
001430936 08204 $$a006.3/1$$223
001430936 1112_ $$aML4CPS (Conference)$$n(5th :$$d2020 :$$cBerlin, Germany)
001430936 24510 $$aMachine learning for cyber physical systems :$$bselected papers from the international conference ML4CPS 2020 /$$cJürgen Beyerer, Alexander Maier, Oliver Niggemann, editors.
001430936 24630 $$aML4CPS 2020
001430936 264_1 $$aBerlin :$$bSpringer Vieweg,$$c[2021]
001430936 300__ $$a1 online resource (vii, 130 pages) :$$billustrations (some color)
001430936 336__ $$atext$$btxt$$2rdacontent
001430936 337__ $$acomputer$$bc$$2rdamedia
001430936 338__ $$aonline resource$$bcr$$2rdacarrier
001430936 347__ $$atext file
001430936 347__ $$bPDF
001430936 4901_ $$aTechnologien für die intelligente Automation = Technologies for intelligent automation,$$x2522-8579 ;$$vBand 13
001430936 500__ $$aInternational conference proceedings.
001430936 504__ $$aIncludes bibliographical references.
001430936 5050_ $$aPreface -- Energy Profile Prediction of Milling Processes Using Machine Learning Techniques -- Improvement of the prediction quality of electrical load profiles with artificial 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.
001430936 5060_ $$aOpen access$$5GW5XE
001430936 520__ $$aThis open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Dr. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. Prof. Oliver Niggemann got his doctorate in 2001 at the University of Paderborn with the topic "Visual Data Mining of Graph-Based Data". He then worked for almost 8 years in leading positions in the industry. From 2008-2019 he held a professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo/Germany. Until 2019 Prof. Niggemann was also deputy head of the Fraunhofer IOSB-INA, which works in industrial automation. On April 1, 2019 Prof. Niggemann took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut-Schmidt-University in Hamburg / Germany. There he does research at the Institute for Automation Technology IfA in the field of artificial intelligence and machine learning for cyber-physical systems.
001430936 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 25, 2021).
001430936 650_0 $$aMachine learning$$vCongresses.
001430936 650_0 $$aCooperating objects (Computer systems)$$vCongresses.
001430936 650_0 $$aComputer engineering.
001430936 650_0 $$aInternet of things.
001430936 650_0 $$aEmbedded computer systems.
001430936 650_0 $$aElectrical engineering.
001430936 650_0 $$aComputer organization.
001430936 650_6 $$aApprentissage automatique$$vCongrès.
001430936 650_6 $$aObjets coopérants (Systèmes informatiques)$$vCongrès.
001430936 650_6 $$aOrdinateurs$$xConception et construction.
001430936 650_6 $$aInternet des objets.
001430936 650_6 $$aSystèmes enfouis (Informatique)
001430936 650_6 $$aGénie électrique.
001430936 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001430936 655_7 $$aConference papers and proceedings.$$2lcgft
001430936 655_7 $$aActes de congrès.$$2rvmgf
001430936 655_0 $$aElectronic books.
001430936 7001_ $$aBeyerer, Jürgen,$$eeditor.
001430936 7001_ $$aMaier, Alexander,$$eeditor.
001430936 7001_ $$aNiggemann, Oliver,$$eeditor.
001430936 7760_ $$z3662627450
001430936 830_0 $$aTechnologien für die intelligente Automation ;$$vBand 13.$$x2522-8579
001430936 852__ $$bebk
001430936 85640 $$3Springer Nature$$uhttps://link.springer.com/10.1007/978-3-662-62746-4$$zOnline Access$$91397441.2
001430936 909CO $$ooai:library.usi.edu:1430936$$pGLOBAL_SET
001430936 980__ $$aBIB
001430936 980__ $$aEBOOK
001430936 982__ $$aEbook
001430936 983__ $$aOnline
001430936 994__ $$a92$$bISE