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Трансформация коммуникативного языка в русском православии в контексте постсекулярности: 2011–2013 гг.
С. 476–484.
In book
М.: Издательский дом НИУ ВШЭ, 2014.
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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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