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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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Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
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Модификация МДП-структур сильнополевой инжекцией электронов и электронным облучением

С. 242–252.
Андреев Д. В., Bondarenko G., Столяров А. А.
Language: Russian
Full text
Keywords: МДП-структураоблучениемодификацияirradiationmodificationMIS structure
Publication based on the results of:
Theoretical and experimental investigation of influence of the concentrated energy flows and temperature on electrophysical properties of functional materials (2015)

In book

Труды XXV Международной конференции "Радиационная физика твердого тела" (Севастополь, 6-11 июля 2015г.)
Труды XXV Международной конференции "Радиационная физика твердого тела" (Севастополь, 6-11 июля 2015г.)
М.: ФГБНУ "НИИ ПМТ", 2015.
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