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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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?

Исследование эффективности методов синхронизации времени для распределенного имитационного моделирования

С. 208–211.
Voznesenskaya T.
Language: Russian
Keywords: parallel and distributed simulationраспределенное имитационное моделирование

In book

Труды Всероссийской научной конференции "Высокопроизводительные вычисления и их приложения"
М.: Издательство МГУ, 2000.
Similar publications
Математическая модель для анализа производительности распределенных систем имитационного моделирования
Voznesenskaya T., Искусственный интеллект 2002 № 2 С. 74–78
Added: September 16, 2017
Математическая модель алгоритмов синхронизации времени для распределенного имитационного моделирования
Voznesenskaya T., В кн.: Программные системы и инструменты.Т. 1.: М.: Издательский отдел ф-та ВМиК МГУ им. М.В. Ломоносова, 2000. С. 56–66.
Added: September 16, 2017
Седьмая всероссийская научено-практическая конференция «Имитационное моделирование. Теория и практика" (ИММОД-2015)
[б.и.], 2015.
Цель конференции – распространение методов и средств имитационного моделирования для решения научных и практических задач, активизация творческой деятельности, поддержание и укрепление научно-производственного потенциала РФ. Организаторы и учредители конференции: − ФГБУН Институт проблем управления им. В.А. Трапезникова РАН, Москва; − НП «Национальное общество имитационного моделирования, Санкт-Петербург; − Институт информатики и автоматизации Российской Академии Наук, СанктПетербург; − ...
Added: February 29, 2016
Программные системы и инструменты.
М.: Издательский отдел ф-та ВМиК МГУ им. М.В. Ломоносова, 2000.
Added: June 2, 2015
Some Problems of the Simulation Model Efficiency and Flexibility
Mikov A., Kozlov A., Zamyatina E. et al., , in: Proceedings of 8th EUROSIM Congress on Modelling and Simulation "EUROSIM 2013», Сarrdiff, Wales, United Kingdom, 10-13 of September, ISBN 978-0-7695-5073-2.: Cardiff: IEEE Computer Society, 2013. P. 532–538.
Simulation remains one of the most popular and sometimes the only method of investigation of the complex dynamical systems nowadays. The problems become more and more complicate and so simulation models become complicate and large scale too. So the modelers have to reduce the time of simulation run and overall time of the simulation model ...
Added: March 13, 2015
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