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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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Разработка измерителя ускорений в широких амплитудном и частотном диапазонах для лабораторной модели разлома

Сейсмические приборы. 2023. Т. 59. № 3. С. 58–74.
Кох В. В., Казначеев П. А., Майбук З. Я., Пономарев А. В.

The work is devoted to the development accelerometer for measurement of fast movements in laboratory experiment. In our previous experiments on the slider model of a seismogenic fault, it was revealed that the use of single sensitive acceleration sensor does not allow for qualitative and quantitative characterization of all observed movements. The high-amplitude movements could introduce the sensor into saturation, and the low-amplitude ones were on the response limit. To solve this problem, an accelerometer has been developed for measurement of acceleration in wide amplitude and frequency ranges. The accelerometer consists of two analog single-component piezoelectric sensors with a measuring range up to 500g and two digital three-component micro-electromechanical systems (MEMS) sensors with measuring ranges up to 8g and 400g. In shock-based tests, amplitudes and characteristic acceleration times were achieved, comparable to those of movements on the slider model of fault. The efficiency of the acceleration meter has been confirmed. It has been shown that the concurrent use of digital and analog sensors makes it possible to track non-uniformly scaled movements in more detail.

Language: Russian
DOI
Text on another site
Keywords: разломfaultshockизмерение ускоренийacceleration measurementlaboratory modelingлабораторное моделированиеMEMS sensorpiezoelectric sensorМЭМС-датчикипьезоэлектрические датчикиудар
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