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  • Дисковые микрорезонаторы с нарушением симметрии: численный расчет
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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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Дисковые микрорезонаторы с нарушением симметрии: численный расчет

С. 514–515.
Verbus V. A., Степихова М. В., Сергеев C. М., Новиков А. В., Красильник З. Ф.
Language: Russian
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Keywords: дисковые микрорезонаторы

In book

Нанофизика и наноэлектроника. Материалы XX Международного симпозиума (Нижний Новгород, 14–18 марта 2016 г.). В 2 т.
Т. 2. , Н. Новгород: Издательство Нижегородского госуниверситета, 2016.
Similar publications
Фотолюминесценция дисковых резонаторов, сформированных на базе светоизлучающих структур n(Ge QDs-Si)/SOI
Verbus V. A., Сергеев C. М., Степихова М. В. et al., В кн.: Нанофизика и наноэлектроника. Материалы XX Международного симпозиума (Нижний Новгород, 14–18 марта 2016 г.). В 2 т.Т. 2.: Н. Новгород: Издательство Нижегородского госуниверситета, 2016. С. 722–723.
The paper presents the results of studies of disk microresonators (with diameters of 5-40 microns) formed on the basis of light-emitting structures n(GeQDs-Si)/SOI. The research was conducted using microphotoluminescence (microphone) in the wavelength range of 1.3–1.7 µm, corresponding to a range of the emissivity of the structures with nanoislands of Ge(Si) (Povey). The contribution of ...
Added: March 9, 2017
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