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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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Data and Reference Semantic-Based Simulator of DB-nets with the Use of Renew Tool

P. 453–465.
Rigin A., Shershakov Sergey

Complexity of software systems is constantly growing, which is even more aggravated by concurrency of processes in systems, so modeling and validating such systems is necessary for detecting and eliminating failures. One of the most well-known formalisms for solving this problem is Petri nets and their extensions such as colored Petri nets, reference nets, and similar models. Many software systems use databases for stor- ing persistent data. However, Petri nets and their mentioned extensions are not designed for modeling persistent data manipulation since these formalisms aim at modeling a control  ow of the considered systems.  DB-nets, as a novel formalism, aim to solve this problem by providing three following layers: (1) the control layer represented by a colored Petri net with extensions, (2) the data logic layer, which allows to retrieve and update the persistent data, and (3) the persistence layer representing a relational database for storing the persistent data. To date, there are no publicly available software tools that implement simulation of db-net models with reference semantics support. The paper presents a novel software tool for the db-nets simulation. The simulator is developed as a pure plugin for the Renew (Reference Net Workshop) software tool without modifying existing Renew source code. Such an approach for development of the simulator allows to reinforce existing Renew refer- ence semantics. The SQLite embeddable relational DBMS is used as a base tool to implement the db-net persistence layer. In the paper, the- oretical foundations and architecture of the developed simulator are de- scribed. The results of the work can be used in research projects that involve modeling complex software systems with persistent data for both academic and industry-oriented applications.

Language: English
DOI
Text on another site
Keywords: Petri netsDB-netsReference NetsRenew ToolRelational DBMSPersistent DataSoftware ModelingSoftware Validation
Publication based on the results of:
Process models: checking conformance with the observed behavior, automated discovery and behavioral analysis (2021)

In book

Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers
Vol. 12602. , Springer, 2021.
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