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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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Enhancing the Effectiveness of Management Decisions in Public Transport through Multi-Agent Technologies

Ymer. 2025. Vol. 24. No. 11. P. 1–18.
Trofimov S.

This paper presents the development of a multi-agent system for managing the technical condition of bus fleets in public transport enterprises. The proposed solution aims to enhance management decision-making processes in vehicle maintenance through the implementation of intelligent agents and data analytics. The system is based on the interaction of two types of agents: "Bus," which collects and analyzes data on the vehicle's technical condition, and "Mechanic," responsible for maintenance planning. The system architecture encompasses a hardware complex for CAN bus data collection, GLONASS terminal for telemetry transmission, server-side software agents, and client applications. The results of a pilot system implementation on a bus are presented, confirming the effectiveness of the proposed solution. An economic analysis demonstrates the potential for optimizing operational costs of transport enterprises through the implementation of the developed system.

Research target: Economics and Management
Language: English
DOI
Text on another site
Keywords: predictive analyticspublic transportmulti-agent systemstelemetrytechnical maintenancemanagement automationsoftware agents
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