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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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?

Библиометрические индикаторы в ресурсах Clarivate

Гл. 3. С. 177–220.
Pislyakov V.

This chapter examines bibliometric indicators related to citedness of journals, authors, research groups, organizations and whole countries. The author discusses usage of various bibliometric indicators: the impact factor, average citedness, share of uncited papers, Eigenfactor and Article Influence Score, Hirsch and Hirsch-type indices, and others. A special section investigates indicators of chronological distribution of references. Particular attention is paid to normalized indicators, including indicators normalized by research disciplines and by publication sources, as well as to creation of normalized citation profiles. The introductory section deals with the basics of bibliometric analysis and features of citation databases. The final section emphasizes importance of informed and responsible use of bibliometric indicators in research policy-making, funding allocation, and faculty and research personnel recruitment.

Language: Russian
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Keywords: библиометрические индикаторыWeb of Sciencehighly cited papersвысокоцитируемые статьиWeb of Sciencebibliometric indicatorsHirsch indexиндекс Хиршаnormalized citednessнормализованная цитируемость

In book

Руководство по наукометрии: индикаторы развития науки и технологии, второе издание
Руководство по наукометрии: индикаторы развития науки и технологии, второе издание
Акоев М. А., Маркусова В. А., Москалева О. В., Pislyakov V. Екатеринбург: Издательство Уральского университета, 2021.
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