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  4. A region-based machine learning approach for self-diagnosis of a 4D digital thermal twin
 
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2022
Conference Paper
Titel

A region-based machine learning approach for self-diagnosis of a 4D digital thermal twin

Abstract
In this paper, we explore the applicability of machine learning as a tool for self-diagnosis of 4D digital twins with a focus on simulated surface temperatures. Generation of digital twins involves abstractions, simplification, and the closed-world assumption. Hence, performing thermal simulation in order to obtain surface temperatures involves not only mathematical modeling of the physical phenomena, but also temporal uncertainties on external conditions. To identify the types of simulation inaccuracies, our proposed method is based on thermal image comparison, i.e. the corresponding measured thermal image and the simulated thermal image resulting from the 4D digital twin. First, a statistical necessary condition is defined to obtain regions of interest in the simulated image. Second, after manual labeling of these regions into the two inaccuracy classes, we conduct a detailed feature analysis and subsequently train our Random Forest classifier. The results show a good separability of the two classes despite the limited training data, allowing to achieve values of overall accuracy around 93.5%.
Author(s)
Strauß, Eva
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Bulatov, Dimitri
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Hauptwerk
ISPRS TC IV 17th 3D GeoInfo Conference 2022
Konferenz
3D GeoInfo Conference 2022
Thumbnail Image
DOI
10.5194/isprs-annals-x-4-w2-2022-265-2022
Language
English
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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
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