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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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Комплексный метод оценки показателей безотказности радиотехнических устройств космической аппаратуры

Известия высших учебных заведений. Приборостроение. 2021. Т. 64. № 4. С. 316–328.
Korolev P.

The paper presents the method for evaluating the electronic devices reliability indicators of spacecraft equipment. The method taking into account the following factors: the effectiveness of the quality management system functioning; compliance of the design results with the technical specification requirements; additional tests quality of the electronic devices; the quality of the implementation of the necessary tasks contained in the normative and technical documentation. The existing methodology for specifying the numerical value of the “production quality factor” included in the mathematical model of the failure rate has been improved. The advantage of the methodology lies in the constant monitoring of the necessary measures contained in the normative and technical documentation of the electronic devices at the design stage. The application effectiveness of the new method in assessing the target level of the electronic devices dependability indicators of spacecraft equipment is shown.

Research target: Electronics and Electrical Engineering Computer Science Engineering and Technology
Priority areas: management IT and mathematics engineering science
Language: Russian
Full text
DOI
Text on another site
Keywords: надежностьreliabilityкосмическая аппаратурабезотказностьDependabilityрадиотехническое устройствокоэффициент качества производстваproduction quality factorelectronic devicespacecraft equipmentunmanned automatic spacecraftнепилотируемый автоматический космический аппарат
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