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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.
May 25, 2026
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May 25, 2026
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Лазерная модификация титановой пленки на поверхности оптических волноводов в ниобате лития

Письма в Журнал технической физики. 2020. Т. 46. № 17. С. 51–54.
Тронев А. В., Парфенов М. В., Соломонов Н. А., Ionov A. M., Божко С. И., Ильичев И. В., Агрузов П. М., Шамрай А. В.
Research target: Materials Technologies Physics
Priority areas: engineering science
Language: Russian
Full text
DOI
Text on another site
Keywords: интегральная оптикаНиобат литияlithium niobateintegrated opticsoptical waveguideоптический волновод,
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