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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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Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
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The Integration Of Edge Computing Into Iot Application Using Advantedge Platform, Case Study: Connection Technology (Wi‐Fi, 4G, 5G)

P. 106–111.
Dayoub A., Koucheryavy E.

As the number of Internet of Things (IoT) devices that access the internet increases, generating data has correspondingly increased. As a result, the need to improve Internet speeds to facilitate data trans‐ mission is increasing, along with growing requirements for computing resources and storage capacity in centralized servers. In response to these challenges and to facilitate real‐time data processing while re‐ ducing network bandwidth consumption, the integration of edge computing has emerged as a promising avenue. This research paper focuses on merging edge computing with IoT applications, using a simula‐ tion platform called AdvantEdge. In this context, we delve deeper into the use of connectivity technolo‐ gies such as Wi‐Fi, 4G and 5G by user equipment (UE) to establish connections with edge infrastructure. The main goal is to solve problems related to latency and network bandwidth usage inherent in IoT ap‐ plications.

Language: English
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Keywords: Internet of ThingsMulti‐access Edge ComputingIoT applicationWi‐Fi, 4G, 5G

In book

Информационно-телекоммуникационные технологии и математическое моделирование высокотехнологичных систем: материалы Всероссийской конференции с международным участием, Москва, РУДН, 08–12 апреля 2024 года.
Dayoub A. M.: RUDN, 2024.
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