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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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University Teaching Load Distribution Algorithm

P. 152–155.
Vladlena D. Markvirer, Ekaterina A. Karnaukhova

This article presents an overview of the main algorithms used in resource distribution problems. The description, input and output data, as well as peculiarities and complexity of these algorithms are considered. The paper proposes a modified algorithm for finding vapor combinations in a graph, which allows solving the problem of optimal load distribution between the teachers of a structural subdivision of the university and the disciplines that this subdivision is obliged to implement in an academic year. The need for modification of the algorithm is argued by the unevenness and variability of the subject area, multi-criteria load assignment and the need to take into account subjective characteristics of the decision on allocation.

Language: English
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Keywords: graph algorithmsteaching load distributiongraph algorithms application

In book

023 International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS)
023 International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS)
Petrozavodsk: IEEE, 2023.
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