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  • Исследование возможности применения генетических алгоритмов к решению задач планирования работы электроподвижного состава метрополитена
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Subject
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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Исследование возможности применения генетических алгоритмов к решению задач планирования работы электроподвижного состава метрополитена

Электроника и электрооборудование транспорта. 2017. № 6. С. 37–40.
Чжо М. А., Sidorenko V.

This paper is a part of continuation publications on the topic of genetic algorithms application to the scheduling of underground rolling stock maintenance. The authors implemented the adaptive crossover selection algorithms within the genetic algorithm framework according to the features of the scheduling problem. Then the authors investigated the possibilities of genetic algorithm by using the different crossover types in solving rolling stock scheduling problem, and the influence of parameters in genetic algorithm on the results obtained. The results of genetic algorithm application are described.

Research target: Computer Science Economics and Management Construction Mechanics and Mechanical Engineering Effective Natural Resource Management
Priority areas: management IT and mathematics business informatics engineering science
Language: Russian
Full text
Keywords: оптимизацияoptimizationтехническое обслуживаниепланированиеschedulingгенетический алгоритмgenetic algorithmelectrical rolling stockэлектроподвижной составtechnical maintenance
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