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Sensors Data Fusion for Smart Decisions Making Using Interpretative Machine Learning Models

  • Sensor fusion is an important and crucial topic in many industrial applications. One of the challenging problems is to find an appropriate sensor combination for the dedicated application or to weight their information adequately. In our contribution, we focus on the application of the sensor fusion concept together with the reference to the distance-based learning for object classification purposes. The developed machine learning model has a bi-functional architecture, which learns on the one side the discrimination of the data regarding their classes and, on the other side, the importance of the single signals, i.e., the contribution of each sensor to the decision. We show that the resulting bi-functional model is interpretative, sparse, and simple to integrate in many standard artificial neural networks.

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Metadaten
Author:Feryel Zoghlami, Marika Kaden, Thomas Villmann, Germar Schneider, Harald Heinrich
URN:urn:nbn:de:bsz:mit1-opus4-122983
DOI:https://doi.org/10.48446/opus-12298
ISSN:1437-7624
Parent Title (German):26. Interdisziplinäre Wissenschaftliche Konferenz Mittweida
Publisher:Hochschule Mittweida
Place of publication:Mittweida
Document Type:Conference Proceeding
Language:English
Year of Completion:2021
Publishing Institution:Hochschule Mittweida
Contributing Corporation:Infineon Technologies Dresden GmbH & Co. KG
Release Date:2021/05/18
Tag:interpretable models; sensor fusion; sensors evaluation
Issue:002
Page Number:2
First Page:145
Last Page:146
Open Access:Frei zugänglich
Licence (German):License LogoUrheberrechtlich geschützt