000 03943nam a22005175i 4500
001 978-3-662-48838-6
003 DE-He213
005 20200421111837.0
007 cr nn 008mamaa
008 160219s2016 gw | s |||| 0|eng d
020 _a9783662488386
_9978-3-662-48838-6
024 7 _a10.1007/978-3-662-48838-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
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 2015 /
_cedited by Oliver Niggemann, J�urgen Beyerer.
250 _a1st ed. 2016.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer Vieweg,
_c2016.
300 _aVI, 121 p. 12 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTechnologien f�ur die intelligente Automation, Technologies for Intelligent Automation
505 0 _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.
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.
650 0 _aEngineering.
650 0 _aKnowledge management.
650 0 _aData mining.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aKnowledge Management.
700 1 _aNiggemann, Oliver.
_eeditor.
700 1 _aBeyerer, J�urgen.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783662488362
830 0 _aTechnologien f�ur die intelligente Automation, Technologies for Intelligent Automation
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-662-48838-6
912 _aZDB-2-ENG
942 _cEBK
999 _c55358
_d55358