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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.
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Automated Formal Verification of Model Transformations Using the Invariants Mechanism

P. 59–73.
Boris Ulitin, Eduard Babkin, Tatiana Babkina, Arsenii Vizgunov

The article is devoted to the problem of automated formal verification of modeling artifacts during engineering of digital transformations. Automation significantly increases the quality of model transformations since many manual errors are eliminated. However, the formal checking the correctness of such automation remains an open question. One more problem is the dependence of the procedure for checking the correctness of transformations on the modeling languages of the source and target models. In the article we represent the solution, based on the formalism of invariant checking, that allows modelers to formally test the correctness of model transformation regardless of a modeling language.

Language: English
DOI
Keywords: model transformationmodel checkinggraph transformationformal verificationInvariants

In book

Lecture Notes in Business Information Processing
Issue 365: Perspectives in Business Informatics Research. , Switzerland: Springer, 2019.
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