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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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Автоматическое размещение графа на основе метода физических аналогий

С. 93–97.
Коломейченко М. И., Polyakov I. V., Chepovskiy A.

This paper describes an automatic graph layout ”peacock’s tail”, which is based on force-directed graph drawing algorithm. Also presented its modified faster version called ”fast peacock’s tail.” This approach proved its efficiency on big social network graphs.

Language: Russian
Full text
Keywords: визуализация графа graph analysisgraph visualizationанализ графаgraph layoutавтоматическое размещение графа

In book

Труды Международной научной конференции Московского физико-технического института (государственного университета) и Института физико-технической информатики (SCVRT1516).
М., Протвино: Институт физико-технической информатики, 2016.
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