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000753921 019__ $$a941696378$$a943826763
000753921 020__ $$a9783662488386$$q(electronic book)
000753921 020__ $$a3662488388$$q(electronic book)
000753921 020__ $$z9783662488362
000753921 0247_ $$a10.1007/978-3-662-48838-6$$2doi
000753921 035__ $$aSP(OCoLC)ocn940545238
000753921 035__ $$aSP(OCoLC)940545238$$z(OCoLC)941696378$$z(OCoLC)943826763
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000753921 08204 $$a006.3/1$$223
000753921 1112_ $$aML4CPS (Conference)$$d(2015 :$$cLemgo, Germany)
000753921 24510 $$aMachine learning for cyber physical systems$$h[electronic resource] :$$bselected papers from the International Conference ML4CPS 2015 /$$cOliver Niggemann, Jürgen Beyerer (eds.).
000753921 264_1 $$aBerlin :$$bSpringer Vieweg,$$c2016.
000753921 300__ $$a1 online resource (vi, 121 pages) :$$billustrations.
000753921 336__ $$atext$$btxt$$2rdacontent
000753921 337__ $$acomputer$$bc$$2rdamedia
000753921 338__ $$aonline resource$$bcr$$2rdacarrier
000753921 4901_ $$aTechnologien für die intelligente Automation, Technologies for Intelligent Automation
000753921 5050_ $$aDevelopment of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control -- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks -- Forecasting Cellular Connectivity for Cyber- Physical Systems: A Machine Learning Approach -- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation -- Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission -- Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for an Industrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems.
000753921 506__ $$aAccess limited to authorized users.
000753921 520__ $$aThe work 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 Lemgo, October 1-2, 2015. 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.
000753921 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 23, 2016).
000753921 650_0 $$aMachine learning$$vCongresses.
000753921 650_0 $$aCooperating objects (Computer systems)$$vCongresses.
000753921 7001_ $$aNiggemann, Oliver,$$eeditor.
000753921 7001_ $$aBeyerer, Jürgen,$$eeditor.
000753921 77608 $$iPrint version:$$aNiggemann, Oliver$$tMachine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2015$$dBerlin, Heidelberg : Springer Berlin Heidelberg,c2015$$z9783662488362
000753921 830_0 $$aTechnologien für die intelligente Automation.
000753921 852__ $$bebk
000753921 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-662-48838-6$$zOnline Access$$91397441.1
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000753921 983__ $$aOnline
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