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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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Introducing RusDraCor – A TEI-Encoded Russian Drama Corpus for the Digital Literary Studies

P. 28–31.
Fischer F., Orlova T., Skorinkin D., Palchikov G., Tyshkevich N. B.

We describe the creation of a corpus of Russian-language drama, comprising hundreds of texts from the middle of the 18th century to the first third of the 20th century. Texts are encoded in the XML-based markup standard TEI, the focus is on extra-linguistic, structural annotations, although additional annotation layers can be added easily.

Language: English
Full text
Keywords: network analysisdigital humanitiestheory of drama
Publication based on the results of:
A Digital Take on the History of Literature: Large-Scale Network Analysis of Russian Drama /Сетевой анализ русской драмы (2017)

In book

Corpus Linguistics 2017 Abstracts
[б.и.], 2017.
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