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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.
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Atomic Patterns for Efficient Computation with Pattern Structures

P. 178–194.
Dudyrev E., Couceiro M., Kaytoue M., Sergei O. Kuznetsov, Napoli A.

Pattern Structures is a framework in FCA allowing objects to have complex descriptions, only requiring that the set of descriptions forms a complete meet-semi-lattice. However, some particular descrip tions or patterns, such as subgraphs and subsequences, do not necessarily ensure that every pair of descriptions has a unique infimum and ask for additional operations, e.g., anti-chain completion. Moreover, meet-based approaches struggle to generate non-trivial implications for complex data since, in general, they only output closed descriptions. For overcoming such limitations, we introduce in this paper an alternative view of pat tern structures based on the join operation and the so-called “atomic patterns”. Such atomic patterns correspond to join-irreducible descrip tions in the join-semi-lattice of all possible descriptions. They enable an efficient traversal of the description space and the computation of closures, minimal generators, pseudo-intents, implications among others, while showing very good computational performance.

Language: English
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Keywords: Formal Concept Analysis (FCA)Pattern StructuresAtomic PatternsDescription SpaceJoin-Irreducible Element
Publication based on the results of:
Complex language and semantic models in artificial intelligence (2025)

In book

Second International Joint Conference, CONCEPTS 2025, Cluj-Napoca, Romania, September 8–12, 2025, Proceedings. Conceptual Knowledge Structures. LNCS, volume 15941
Cham: Springer, 2025.
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