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001			95064
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007			cr cnu---unuuu
008			161224s2017 gw ob 000 0 eng d
020			_a3662537516
020			_a3662537524 _q(electronic bk.)
020			_a9783662537510
020			_a9783662537527 _q(electronic bk.)
020			_z9783662537510 _q(print)
035			_a(OCoLC)966484958 _z(OCoLC)965128607 _z(OCoLC)974650779 _z(OCoLC)1005772307
040			_aEBLCP _cEBLCP _dN$T _dGW5XE _dOCLCF _dAZU _dUAB _dIDEBK _dCOO _dYDX _dOCLCQ _dUPM _dIOG _dESU _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dVT2 _dU3W _dES-MaUEC _bspa
041	1		_aeng _bger _heng
050		4	_aTK7872.D48 _bM665 2017 EB
066			_c(S
100	1		_aMönks, Uwe.
245	1	0	_aInformation fusion under consideration of conflicting input signals _cUwe Mönks.
264		1	_aBerlin, Germany _bSpringer _c2017
300			_a1 recurso en línea (249 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aTechnologien für die intelligente Automation _vBd. 4
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aIntroduction -- Scientific State of the Art -- Preliminaries -- Multilayer Attribute-based Conflict-reducing Observation -- Evaluation -- Summary.
520	3		_aThis work proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) and the µBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. In addition, a sensor defect detection method, which is based on the continuous monitoring of sensor reliabilities, is presented. The performances of the contributions are shown by their evaluation in the scope of both a publicly available data set and a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The author Dr.-Ing. Uwe Mönks studied Electrical Engineering and Information Technology at the OWL University of Applied Sciences (Lemgo), Halmstad University (Sweden), and Aalborg University (Denmark). Since 2009 he is employed at the Institute Industrial IT (inIT) as research associate with project leading responsibilities. During this time he completed his doctorate (Dr.-Ing.) in a cooperative graduation with Ruhr-University Bochum. His research interests are in the area of multisensor and information fusion, pattern recognition, and machine learning.
650		7	_aDetectores _2embne _0(OCoLC)fst00891594 _0 _9140157
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-662-53752-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
880			_6520-00/(S _aThis work proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) and the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. In addition, a sensor defect detection method, which is based on the continuous monitoring of sensor reliabilities, is presented. The performances of the contributions are shown by their evaluation in the scope of both a publicly available data set and a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The author Dr.-Ing. Uwe Mönks studied Electrical Engineering and Information Technology at the OWL University of Applied Sciences (Lemgo), Halmstad University (Sweden), and Aalborg University (Denmark). Since 2009 he is employed at the Institute Industrial IT (inIT) as research associate with project leading responsibilities. During this time he completed his doctorate (Dr.-Ing.) in a cooperative graduation with Ruhr-University Bochum. His research interests are in the area of multisensor and information fusion, pattern recognition, and machine learning.
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95064 _d95064 _x1