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Saadallah/etal/2022d: Simulation and Sensor Data Fusion for Machine Learning Application

Bibtype Article
Bibkey Saadallah/etal/2022d
Author Amal, Saadallah and Felix, Finkeldey and Jens, Buß and Katharina, Morik and Petra, Wiederkehr and Wolfgang, Rhode
Ls8autor Morik, Katharina
Saadallah, Amal
Title Simulation and Sensor Data Fusion for Machine Learning Application
Journal Advanced Engineering Informatics
Abstract The performance of machine learning algorithms depends to a large extent on the amount and the quality of data
available for training. Simulations are most often used as test-beds for assessing the performance of trained models
on simulated environment before deployment in real-world. They can also be used for data annotation, i.e, assigning
labels to observed data, providing thus background knowledge for domain experts. We want to integrate this knowledge
into the machine learning process and, at the same time, use the simulation as an additional data source. Therefore,
we present a framework that allows for the combination of real-world observations and simulation data at two levels,
namely the data or the model level. At the data level, observations and simulation data are integrated to form an enriched
data set for learning. At the model level, the models learned from observed and simulated data separately are combined
using an ensemble technique. Based on the trade-o between model bias and variance, an automatic selection of the
appropriate fusion level is proposed. Our framework is validated using two case studies of very di erent types. The
first is an industry 4.0 use case consisting of monitoring a milling process in real-time. The second is an application in
astroparticle physics for background suppression.
Year 2022
Projekt SFB876-B3



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