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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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О задаче верификации для одного класса автоматов реального времени

С. 257–260.
Zakharov V., Винарский Е. М.
Language: Russian
Text on another site
Keywords: разрешимостьmodel checkingверификация моделей программтемпоральная логикаdecidabilityreal time automataконечный автомат реального времениtemporal logic
Publication based on the results of:
Synthesis, recovery, conformance checking, and other methods for the analysis of process models and distributed information systems (2019)

In book

Материалы XIII Международного семинара "Дискретная математика и ее приложения" имени академика О.Б. Лупанова (Москва, МГУ, 17-22 июня 2019)
М.: Изд-во механико-математического факультета МГУ, 2019.
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