Detection of Driving Dynamics Anomalies Using Deep Learning
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Publisher
Springer Fachmedien Wiesbaden
Link
https://link.springer.com/content/pdf/10.1007/978-3-658-45010-6_24
Reference15 articles.
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2. Cimniak V, Rether D, Bodza S, Grill M, Bargende M (2021) A new class of ai-based engine models. In: Liebl J, Beidl C, Maus W (eds) Internationaler Motorenkongress 2021, Proceedings, Springer Fachmedien Wiesbaden, Wiesbaden, pp 267–282, https://doi.org/10.1007/978-3-658-35588-3_16
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4. Ding K, Ding S, Morozov A, Fabarisov T, Janschek K (2019) On-line error detection and mitigation for time-series data of cyber-physical systems using deep learning based methods. In: 2019 15th European Dependable Computing Conference, IEEE, Piscataway, NJ, pp 7–14, https://doi.org/10.1109/EDCC.2019.00015
5. Feng L (2023) Predicting output responses of nonlinear dynamical systems with parametrized inputs using lstm. IEEE Journal on Multiscale and Multiphysics Computational Techniques 8:97–107, https://doi.org/10.1109/JMMCT.2023.3242044
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