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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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Proceedings of the international scientific and practical conference “Methods and methodology of archaeological sciences in archaeological field research”, 18 September 2019, Al-Farabi National Kazakh University

Almaty : Kazakh Universiteti Almaty, 2019.
Under the general editorship: G. M. Mutanov

Proceedings of a conference on the application of scientific methods in archaeological fieldwork, mostly in Kazakhstan

Chapters
Geoarchaeology at Dzhankent: Urbanization and state formation on the Northern Silk Road
Arzhantseva I., Haerke H. G., Tazhekeev A., , in: Proceedings of the international scientific and practical conference “Methods and methodology of archaeological sciences in archaeological field research”, 18 September 2019, Al-Farabi National Kazakh University.: Almaty: Kazakh Universiteti Almaty, 2019. P. 4–11.
Report on the methods (geophysics, geomorphology, palaeobotany, micromycology) used in the fieldwork in the early medieval town of Dzhankent (Kazakhstan) ...
Added: October 26, 2019
Priority areas: humanitarian
Language: Kazakh
Keywords: Kazakhstangeoarchaeologyarchaeology
Proceedings of the international scientific and practical conference “Methods and methodology of archaeological sciences in archaeological field research”, 18 September 2019, Al-Farabi National Kazakh University
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