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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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Usage of Clustering of Paley Graphs in Polar Coordinates for the Development of New Network on Chip Topologies

P. 419–423.
Alijon F. Fatullaev, Edward R. Rzaev, Aleksandr Yu. Romanov

The article presents a study of clustering of Paley graphs with the arrangement of prime numbers in polar coordinates and a comparison of the resulting groups in terms of their static parameters; the application of fault-tolerant self-organizing routing method for new topologies is also considered. This article is a continuation of a series of articles related to the study of circulant graphs and their modifications in the field of network-on-chip design.

Language: English
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Keywords: кластеризациямаршрутизацияclusteringсеть на кристаллеnetwork-on-chipcirculant graphГрафы ПэлиPaley graphs циркулянтный графSelf-Organizing Routing

In book

2022 International Russian Automation Conference (RusAutoCon)
IEEE, 2022.
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