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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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Редуцирование модели электрической схемы с учетом характеристик внешних цепей

Информационные технологии. 2018. Т. 24. № 9. С. 563–572.
Гурарий М. М., Жаров М. М., Русаков С. Г., Ulyanov S.

Main stages of projective model order reduction (MOR) methods for electrical circuits include model construction by Pade approximation, implementation of Krylov subspace methods for essential decrease of computational noise, application of congruent transform to save circuit passivity, using block-Krylov methods for multiport circuits and multipoint rational-Krylov algorithms to mitigate the redundancy of reduced models. The considerable drop in the redundancy was achieved by applying singular value decomposition and by adaptive choosing the expansion points to be used for the moment matching. Thus, further progress in redundancy elimination cannot be provided by the modifications improvement of basis construction algorithms, and other directions to decrease redundancy should be considered. Known methods suppose direct connection of signal sources to network ports, thus resulting in the network solutions that often cannot exist in the framework of real surrounding. Then the reduction algorithm to provide required accuracy for such solutions excessively increases the model order

Language: Russian
DOI
Keywords: схемотехническое моделированиеавтоматизация проектированияdesign automationcircuit simulationKrylov subspaceподпространство Крыловаmodel order reductionlinear subcircuitprojection methodsпонижение порядка моделилинейная подсхемапроекционные методы
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