Anomaly data synthesis and detection via domain randomization

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Published Sep 4, 2023
Joonha Jun Jongsoo Lee

Abstract

The demand for a large amount of data necessary for learning is increasing with the great development of artificial intelligence. The synthesis of engineering data is challenging in that it is not only to combine data, but also to proceed with data synthesis while keeping the engineering characteristics intact. To address this problem, this work proposes a synthesis and detection model of anomalous data utilizing domain randomization. This model learns data from existing systems to identify patterns and synthesizes new data by itself with domain randomization. The learned model can accurately detect anomaly data in the system in various environments.
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Keywords

domain randomization, pattern recognition, anomaly detection, data synthesis, engineering data

References
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, P. Abbeel. (2017). Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. IEEE/RSJ International Conference on Intelligent Robots and Systems.

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Y. Chen, D. J. Kempton, A. Ahmadzadeh, R. A. Angryk. (2021). Towards Synthetic Multivariate Time Series Generation for Flare Forecasting. ICAISC 2021, Artificial Intelligence and Soft Computing.
Section
Regular Session Papers