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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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RNGAVXLIB: Program library for random number generation, AVX realization

Computer Physics Communications. 2016. Vol. 200. P. 402–405.
Guskova M.S., Barash L.Yu., Shchur L.N.

We present the random number generator (RNG) library RNGAVXLIB, which contains fast AVX realizations of a number of modern random number generators, and also the abilities to jump ahead inside a RNG sequence and to initialize up to 1019 independent random number streams with block splitting method. Fast AVX implementations produce exactly the same output sequences as the original algorithms. Usage of AVX vectorization allows to substantially improve performance of the generators. The new realizations are up to 2 times faster than the SSE realizations implemented in the previous version of the library (Barash and Shchur, 2013), and up to 40 times faster compared to the original algorithms written in ANSI C.

Research target: Physics Computer Science
Priority areas: IT and mathematics engineering science
Language: English
Full text
DOI
Keywords: Monte Carlo simulationsStatistical methodsRandom number generationAdvanced Vector Extensions
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