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710	2		_aSpringerLink (Online service) _9106996
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008			181217s2019 gw a o |||| 0|eng d
020			_a9783662584859
024	7		_a10.1007/978-3-662-58485-9 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aTK7895.E42 _b2019 EB
245	0	0	_aMachine Learning for Cyber Physical Systems : _bSelected papers from the International Conference ML4CPS 2018 _cedited by Jürgen Beyerer, Christian Kühnert, Oliver Niggemann.
264		1	_aBerlin, Heidelberg _bImprint: Springer Vieweg _c2019
300			_a1 recurso en línea (VII, 136 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aTechnologien für die intelligente Automation Technologies for Intelligent Automation _x2522-8579 _v9
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
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	3		_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.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _aSistemas embebidos _vCongresos y asambleas _9667201
650		7	_2embne _9144553 _aArquitectura de redes informáticas _vCongresos y asambleas
700	1		_aBeyerer, Jürgen. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9100176
700	1		_aKühnert, Christian. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aNiggemann, Oliver. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9100247
776	0	8	_iPrinted edition: _z9783662584842
776	0	8	_iPrinted edition: _z9783662584866
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-662-58485-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b11/2019 _ek _zSI