TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Zoghlami, Feryel
A1  - Kaden, Marika
A1  - Villmann, Thomas
A1  - Schneider, Germar
A1  - Heinrich, Harald
T1  - Sensors Data Fusion for Smart Decisions Making Using Interpretative Machine Learning Models
T2  - 26. Interdisziplinäre Wissenschaftliche Konferenz Mittweida
N2  - 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.
KW  - sensor fusion
KW  - sensors evaluation
KW  - interpretable models
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mit1-opus4-122983
SN  - 1437-7624
SS  - 1437-7624
U6  - https://doi.org/10.48446/opus-12298
DO  - https://doi.org/10.48446/opus-12298
IS  - 002
SP  - 145
EP  - 146
S1  - 2
PB  - Hochschule Mittweida
CY  - Mittweida
ER  -