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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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?

Designing Data-Intensive Application System for Production Plans Data Processing and Near Real-Time Analytics

P. 1495–1500.
Suleykin A., Peter B. Panfilov

In this work, benchmarking of production plan processing applications based on data storage and analytics solutions using open source technologies was performed. The functional and component architecture of a digital framework for processing production plan files is presented, with special attention to the performance analysis of data processing based on the measurement of processing time for each data volume and memory used. The proposed architecture of the application system consists of digital services, including the downloading data from hundreds of production plan files, processing these files, converting them into JSON objects and inserting SQL into an in-memory database for real-time queries, where they are analyzed using KPI -based methods. The proposed system allows processing and analyzing production plans almost in real time, which enables decision makers to check and monitor the status of production plans in real time and take the necessary actions on demand and on time. The proposed digital framework was tested on data collected from real production plans of a truck manufacturing company.

Language: English
DOI
Text on another site
Keywords: productionreal-time systemscompaniescomputer architecturethroughputcomponent architecturesvolume measurement

In book

2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)
IEEE, 2022.
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