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News
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.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Алгоритмы и программное обеспечение автоматизации процессов измерений и обработки данных оперативной диагностики ионосферы и ионосферных радиолиний

Журнал радиоэлектроники. 2022. № 10.
Shiriy A.

The article contains the main architectural principles and technical solutions in the development of the software part of the hardware and software complex for the operational diagnostics of the ionosphere and ionospheric radio channels, mainly through remote ground-based radiosonding. A brief description of measurement techniques and measuring equipment is given. The tasks of automating the measurement process itself and data recording have been solved, which allow organizing the work of the complex according to a given schedule without direct human participation. The tasks of extracting information from the obtained experimental data of radiosonding and ionosphere diagnostics have also been solved, including the possibilities for working with the hierarchy of experimental data, including the possibility of degeneration of derived data, the possibility of viewing available data and the possibility of batch processing of large data arrays to obtain and study their statistical characteristics. The organization of processing when collecting statistics of variations of intermode delays, amplitude-frequency characteristics of partial modes of propagation of short waves, as well as classification of these data according to the so-called empirical multipath models are considered; examples of results are given. The prospects and preliminary results of applying the achievements of the machine learning to solve the problems of automating the extraction of information from radiosonding data are shown; the software of marking data for the formation of training samples is described. The plans for further research are the development of quality metrics and the choice of optimal architectures of deep neural networks, as well as the development of markup tools, including instruments of cross-monitoring the results.

Research target: Computer Science Earth Sciences Electronics and Electrical Engineering
Language: Russian
DOI
Text on another site
Keywords: автоматизация измеренийобработка ионограмм
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