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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 integration of edge computing into IoT application using AdvantEDGE platform, case study: mobility

Ch. 259. P. 1–5.
Dayoub A.

As the number of the Internet of things (IoT) devices connected to the internet increases, the amount of data explodes. This leads to a higher demand for internet speed during data transfer and more requirements for computing power and storage in central servers. To address these issues and enable real-time data processing while reducing network bandwidth utilization, edge computing has become a promising solution. This article focuses on the development of the IoT application on the edge computing platform in case of the mobility of the User Equipment (UE) to solve problems like latency and network bandwidth occupation for IoT applications while moving between two zones. The methodology involves simulating a network model to evaluate performance metrics such as latency and bandwidth under mobility conditions. Results demonstrate that latency is reduced to approximately 30-50 ms during UE mobility, highlighting the efficacy of Multi-access Edge Computing (MEC) in IoT systems. These findings suggest that edge computing can significantly enhance the performance of time-sensitive IoT applications.

Language: English
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Keywords: mobilityInternet of Things (IoT)mobile edge computing

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Proceedings of the 2025 7th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)
Dayoub A. United States of America: IEEE, 2025.
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