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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.
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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Управление качеством проектирования изделий легкой промышленности через конфекционирование материалов в цифровой среде

Костюмология. 2022. Т. 7. № 1. С. 1–11.
Туханова В. Ю.

This article provides the study of the quality management of the light industry in the digital environment. The article deals with the process of the confectioning of materials for garments. It is considered with the modern scheme of supplying materials to enterprises of the Russian light industry. The author presents a diagram based on the analysis of the material confectioning process before the introduction of digital technologies. The goal is to study the quality indicators at all stages of the creation of light industry products, as well as to develop a methodology for designing the quality of garments in the digital environment. This research is a part of the doctoral dissertation. The article presents the results of an experiment by measuring the shrinkage of materials after ironing and washing. The purpose of the tests is to develop measures for the efficiency of an enterprise producing women's clothing under its own brand. The article presents a diagram of the analytics of the stages of the process of confectioning materials in a digital environment.

The algorithm for managing the quality of light industry products through materials packaging tools was implemented at an enterprise in Moscow as part of the Russian Federation's strategy for the transition to digital technologies in the production sector. The article substantiates that modern research in the field of introducing digital technologies into industrial production suggests that the next stage in the digitalization of processes will be to work on the implementation of artificial intelligence algorithms for the efficient operation of enterprises and competitiveness in the world market. The author proposes ways of introducing artificial intelligence technologies into the process of quality management in the garments design.

Research target: Chemical Technologies
Language: Russian
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Keywords: искусственный интеллектпроектированиетехнологияtechnologyquality managementуправление качествомdesignцифровая экономикаartificial intelligencedigital economygarmentsшвейные изделия
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