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News
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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Алгоритм поиска оптимального расположения сенсоров для решения задачи мониторинга пространства

Программные продукты и системы. 2016. № 3. С. 60–66.
Кочкаров А. А., Яцкин Д. В.

This paper is devoted to the task of space monitoring, algorithm and software package for solving it. From this problem the authors go on to the problem of detection, and then – to the problem of geometrical sensors location. It is proposed to use a decentralized robotic network to solve this problem. Limitations and assumptions for sensors location problem leading to the problem of covering space. The problem parameters are reasonable discretized. The problem is discussed in detail from a mathematical point of view, the algorithm to solve it is developed, its complexity is estimated. Mathematical and software models were made. Software package that implements the specified algorithm were developed. The map of the area and a number of physical parameters of the sensor serve as input data to the software package. Package allows to calculate points in space, with placed where devices (detectors) the monitoring problem will be considered to be solved. In this software package produced a series of tests. Tests showed efficiency, correctness and optimality of the developed approaches and algorithm. Due to the low polynomial computational complexity of the algorithm, the program can find a solution set of monitoring tasks for the particular case of monitoring of large areas. Also a number of sensors can be used reaches 1000.

Language: Russian
Full text
Keywords: мониторингmonitoringпрограммный комплексsoftware packageтеория множествmobile robotsensorset theoryUAVБПЛАсенсорdetectioncoverage algorithmалгоритмы покрытияпокрытие пространстваspace coveragerobotic networkgroup managementобнаружениемобильный сенсорсеть сенсоровгрупповое управление
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