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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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О максимальном разрезе в случайном гиперграфе

Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика). 2021. Т. 501. С. 26–30.
Zakharov P., Shabanov D. A.

This paper deals with the problem of finding the max-cut for random hypergraphs. We consider the classical binomial model H(n,k,p) of a random k-uniform hypergraph on n vertices with probability p=p(n). The main results generalize previously known facts for the graph case and show that in the sparse case (when the expected number of edges is  cn for some fixed c>0 independent of n) there exists $\gamma(c, k, q)>0$, such that the ratio of the maximal cut of $H(n, k, p)$ to the number of vertices converges in probability to $\gamma(c, k, q)$. Moreover, we obtain some bounds for the value of $\gamma(c, k, q)$.

Research target: Mathematics
Language: Russian
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Keywords: задачи оптимизацииoptimization problemsслучайные гиперграфыhypergraphsгиперграфыrandom hypergraphscut of a hypergraphinterpolation methodразрез гиперграфаметод интерполяции
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