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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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High-resolution capability of adaptive antenna arrays for communication systems

Журнал радиоэлектроники. 2002. No. 7.
S.A. Tiraspolsky, Gerebryakov G. V.

In this paper we investigate comparison methods of different geometric configurations of adaptive antenna arrays for communications on purpose to estimate directions-of-arrival (DOA) of several external signals. The investigated antenna configurations have four elements and eleven wavelengths array size. The best high-resolution algorithm and the best array configuration are defined by numerical simulations.

Research target: Mathematics Computer Science Physics Electronics and Electrical Engineering
Language: English
Text on another site
Keywords: оценка параметровканал связиantenna arraysDirection of Arrival Estimationmultipath propagationантенная решеткамноголучевое распространение сигналовWireless communication
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