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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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Analysis of Requirements for Next-Generation Complex Urban and Transportation System Simulation

P. 1–5.
Stepanyants V., Romanov A.

Transportation planning progressively includes more parameters with new technologies arising on the market, new problems and factors being considered, and an importance of interactions with other urban domains being realized. To evaluate the operations of a transportation system simulation is widely needed. However, state-of-art simulation tools do not cover all the desired parameters. This paper analyzes the functional requirements for next-generation complex urban and transportation system simulation. For this purpose, we have assessed the existing practices and future needs for urban and transportation simulation. As an effort to address identified challenges, an overall conceptual model for a next-generation urban transportation simulation tool was synthesized. The obtained conclusions can be further used for the development or modification of simulation environments. Directions for further research are discussed.

Language: English
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Keywords: transportation systemscomplex systemsconceptual modeltraffic simulationnanoscopic simulationintegrated simulation environmentartificial transportation systems

In book

2022 Dynamics of Systems, Mechanisms and Machines (Dynamics)
IEEE, 2022.
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