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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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Eyetracking and vegetatics data in a cognitive load task

Mendeley. 2024.
Alshanskaia E., Martynova O., Portnova G., Liaukovich K.

Eyetracking and vegetatics data in a cognitive load task

Published: 10 July 2024| Version 1 

Description

Raw eyetracking and vegetatics data in a cognitive load task with additional false feedback in the second block.

Research target: Biology Computer Science Psychology
Language: English
DOI
Text on another site
Keywords: база данныхdatabaseeye-trackingвегетативная нервная система человекаайтрекингautonomic nervous system
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