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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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The New Promising Network-on-Chip Topologies Development Using Hierarchical Method

P. 819–824.
Rzaev E., Ryzhov A., Romanov A.

This work is a logical continuation of previous
works related to the study of promising topologies for
networks-on-chip based on the direct and root product of
circulant graphs. In this article, we describe and compare the
static characteristics of the hierarchical method of constructing
more complex structures compared to the root and direct
products of graphs. Also in this paper, the result of the analysis
of the integration of atomic structures (the smallest structural
units of scalable topologies) into various levels of hierarchical
structure is presented. A comparison of the obtained structures
with the previously studied structures, which are the results of
the application of the root product of Paley graphs, is presented.

Language: English
Full text
DOI
Text on another site
Keywords: SoCPaley graphsNoCaccreditation associationshierarchical method

In book

2022 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)
Sochi: IEEE, 2022.
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