Information Fusion Under Consideration of Conflicting Input Signals [electronic resource] /
By: Mönks, Uwe [author.].
Contributor(s): SpringerLink (Online service).
Series: Technologien für die intelligente Automation, Technologies for Intelligent Automation: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer Vieweg, 2017Edition: 1st ed. 2017.Description: XIX, 240 p. 58 illus., 35 illus. in color. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9783662537527.Subject(s): EXTC Engineering
| Signal, Image and Speech Processing | Robotics and Automation | Artificial IntelligenceDDC classification: 006.3 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
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Springer Nature eBookSummary: This 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.
This 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.
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