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999			_c108137 _d108137
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007			cr nn 008mamaa
008			180820s2018 gw | s |||| 0|eng d
020			_a9783662578056
024	7		_a10.1007/978-3-662-57805-6 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aT58.4 _b2018 EB
245	0	0	_aIMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency : _bIntelligent Methods for the Factory of the Future _cedited by Oliver Niggemann, Peter Schüller.
264		1	_aBerlin, Heidelberg _bSpringer Berlin Heidelberg : _bImprint: Springer Vieweg _c2018.
300			_a (1 recurso en línea VII, 129 páginas, 52 ilustraciones, 29 ilustraciones a color)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	1		_aTechnologien für die intelligente Automation, Technologies for Intelligent Automation _x2522-8579 _v8
490			_aEngineering (Springer-11647)
500			_aEBSPRINGER_ENGINEERING_FEB2019
505	0		_aConcept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems -- Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory -- Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps -- Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps -- A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes -- Validation of similarity measures for industrial alarm flood analysis -- Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause.
506	0		_aOpen Access
520			_aThis open access work presents selected results from the European research and innovation project IMPROVE which yielded novel data-based solutions to enhance machine reliability and efficiency in the fields of simulation and optimization, condition monitoring, alarm management, and quality prediction. The Editors 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. Dr. Peter Schüller is postdoctoral researcher at Technische Universität Wien. His research interests are hybrid reasoning systems that combine Knowledge Representation and Machine Learning and applications in the fields of Cyber-Physical systems and Natural Language Processing.
988			_aEBSPRINGER_ENGINEERING_FEB2019
650		7	_2embne _aGestión de proyectos _9167300
650		7	_2embne _9669785 _aEficiencia industrial
700	1		_aNiggemann, Oliver _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9100247
700	1		_aSchüller, Peter _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service) _9106996
776	0	8	_iPrinted edition: _z9783662578049
776	0	8	_iPrinted edition: _z9783662578063
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-662-57805-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b06/2019 _dz _feng _ggw _h0 _ejf _zSI