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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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Лексические инновации и классификация уральских языков

С. 361–375.
Zhivlov M.
Language: Russian
DOI
Text on another site
Keywords: Historical Linguisticsсравнительно-историческое языкознаниеUralic languagesуральские языкигенеалогическая классификация языков мираlanguage classification
Publication based on the results of:
The inner and outer dynamics of cultural processes in the Oriental societies and its traditional foundations: languages, history and religion (2022)

In book

Hämeenmaalta Jamalille: kirja Tapani Salmiselle
Helda Open Books, 2022.
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