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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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?

Competition Level Estimation at Day-ahead Wholesale Electricity Market for its Evolution

P. 324–327.
Sergey Kechin, Ilya Dolmatov, Tatiana Gass

In our paper we estimate the competition level of the day-ahead market (DAM) as a whole
and of its parts (ZSP) using the Panzar-Rosse model. We identified that competition on the
whole DAM has limited power. At the First Pricing zone 10 of 15 ZSP (67%) have limited
competition intensity while at the Second Pricing zone in 2 of 5 ZSP (40%) competition is
limited. Other ZSP have weak competition. We also mention different effects on price of
the same variables at zones with limited and weak competition.

Language: English
Text on another site
Keywords: competitionday-ahead electricity market

In book

International Research Conference “GSOM Emerging Markets Conference-2020”
St. Petersburg State University Graduate School of Management, 2020.
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