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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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Нейросемантические характеристики употребительных слов тувинского языка через призму комплексного психолингвистического эксперимента

Новые исследования Тувы. 2023. № 1. С. 170–185.
Kolmogorova A., Натпит А.

The purpose of the study is to describe the neurosemantic characteristics of 73 nouns used in the oral speech of the Tuvans: 55 are words of the Tuvan language, 18 are from Russian. We obtained the word list in a psycholinguistic experiment with 150 Tuvan-Russian bilinguals whose average age was 22.6 years. Five groups of noun stimuli were identified: culturally significant words (names of rituals and their elements, natural objects with a high symbolic load in the traditional worldview, kinship terms); zoonyms; names of household objects in Tuvan, names of household objects in Russian, and vocabulary of the everyday. The research problem lies in the field of joint inquiries of cognitive linguistics, neurosemantics and aphasiology. It is a case of searching for a neural correlate that is called the meaning of a word in traditional linguistics. According to F. Pulvermüller’s neurosemantic conception, such a correlate is a distributed structure of neurons. In being activated, they allow a person to get a “feeling” of a word and provide understanding. In order to conduct the experiment, we used a methodology already well-tested in perceptual semantics to identify such psycholinguistic properties of words as subjective frequency, age of acquisition, imageability, familiarity with a concept. The novelty of the work is that for the first time using the Tuvan language material we have found out which groups of words different psycholinguistic properties are essential for and which neurosemantic characteristics of words they correlate with (sensorimotor experience, visual perception, experience of engagement with a word). We hypothesize about factors affecting the identified characteristics of words and specify words that have neural correlates with the strongest connections. It has been found out that in case of culturally significant words the property “familiarity with a concept” turns out to be relatively high in value and the strongest in influence on other properties; in case of zoonyms it is “imageability”; in case of everyday vocabulary it is “subjective frequency” which strongly correlates with the “age of acquisition”. It is “familiarity with a concept” in case of names of household items in Tuvan. The subjective frequency has the greatest influence on other properties in Russian. The reflection of the informants has shown that the household sphere is most affected by the competition between words of the native and non-native languages. Those Tuvan words which are most deeply influenced by this competition have the lowest values of psycholinguistic properties.

Research target: Philology and Linguistics
Language: Russian
DOI
Keywords: bilingualismбилингвизмpsycholinguisticsэкспериментальная психолингвистикаTuvan languageтувинский язык
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