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  • Модификация высокоуровневой модели NoCModel 2.0 для моделирования сетей на кристалле с циркулянтными топологиями
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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
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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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Модификация высокоуровневой модели NoCModel 2.0 для моделирования сетей на кристалле с циркулянтными топологиями

С. 23–30.
Прилепко П. М., Romanov A., Lezhnev E.
Language: Russian
Full text
DOI
Text on another site
Keywords: маршрутизациясеть на кристаллетопология сети на кристаллевысокоуровневое моделирование
Publication based on the results of:
Development of a hybrid model for the design and simulation of networks-on-chip (2020)

In book

Проблемы разработки перспективных микро- и наноэлектронных систем (МЭС-2020).
Вып. 4. , ИППМ РАН, 2020.
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На основе идеологии нечёткой логики предложены алгоритмы и набор правил для выявления перегрузок маршрутизаторов сети на кристалле (Network-on-Chip, NoC). Разработанные алгоритмы позволяют рассчитывать вероятность перегрузки маршрутизаторов и для различных сценариев нагрузки строить тепловые карты состояния сети. Многочисленные примеры показали эффективность предложенных алгоритмов своевременного выявления перегрузок, что даёт широкую возможность адаптации протоколов маршрутизации в сетях NoC ...
Added: March 16, 2026
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