000921835 000__ 04310cam\a2200481Ii\4500
000921835 001__ 921835
000921835 005__ 20230306150642.0
000921835 006__ m\\\\\o\\d\\\\\\\\
000921835 007__ cr\cn\nnnunnun
000921835 008__ 190419s2020\\\\gw\a\\\\o\\\\\100\0\eng\d
000921835 020__ $$a9783662590843$$q(electronic book)
000921835 020__ $$a3662590840$$q(electronic book)
000921835 020__ $$z9783662590836
000921835 0247_ $$a10.1007/978-3-662-59084-3$$2doi
000921835 035__ $$aSP(OCoLC)on1097679394
000921835 035__ $$aSP(OCoLC)1097679394
000921835 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dOCLCF$$dSFB
000921835 049__ $$aISEA
000921835 050_4 $$aQ325.5
000921835 08204 $$a006.3/1$$223
000921835 1112_ $$aML4CPS (Conference)$$d(2017 :$$cLemgo, Germany)
000921835 24510 $$aMachine learning for cyber physical systems :$$bselected papers from the International Conference ML4CPS 2017 /$$cJürgen Beyerer, Alexander Maier, Oliver Niggemann, editors.
000921835 2463_ $$aML4CPS 2017
000921835 264_1 $$aBerlin, Germany :$$bSpringer Vieweg,$$c2020.
000921835 300__ $$a1 online resource (vii, 87 pages) :$$billustrations.
000921835 336__ $$atext$$btxt$$2rdacontent
000921835 337__ $$acomputer$$bc$$2rdamedia
000921835 338__ $$aonline resource$$bcr$$2rdacarrier
000921835 4901_ $$aTechnologien für die intelligente Automation, Technologies for Intelligent Automation,$$x2522-8579 ;$$vBand 11
000921835 5050_ $$aPrescriptive Maintenance of CPPS by Integrating Multi-modal Data with Dynamic Bayesian Networks -- Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensors -- Differential Evolution in Production Process Optimization of Cyber Physical Systems -- Machine Learning for Process-X: A Taxonomy -- Intelligent edge processing -- Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems -- Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis -- Verstehen von Maschinenverhalten mit Hilfe von Machine Learning -- Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Recongurable Architectures -- The Acoustic Test System for Transmissions in the VW Group.
000921835 506__ $$aAccess limited to authorized users.
000921835 520__ $$aThe work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 25th-26th, 2017. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Dr. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.
000921835 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 19, 2019).
000921835 650_0 $$aMachine learning$$vCongresses.
000921835 650_0 $$aCooperating objects (Computer systems)$$vCongresses.
000921835 7001_ $$aBeyerer, Jürgen,$$eeditor.
000921835 7001_ $$aMaier, Alexander,$$eeditor.
000921835 7001_ $$aNiggemann, Oliver,$$eeditor.
000921835 830_0 $$aTechnologien für die intelligente Automation, Technologies for Intelligent Automation ;$$vBd. 11
000921835 852__ $$bebk
000921835 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-662-59084-3$$zOnline Access$$91397441.1
000921835 909CO $$ooai:library.usi.edu:921835$$pGLOBAL_SET
000921835 980__ $$aEBOOK
000921835 980__ $$aBIB
000921835 982__ $$aEbook
000921835 983__ $$aOnline
000921835 994__ $$a92$$bISE