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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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Семейство логарифмических спиралей в гамильтоновых системах размерности 8 с управлением из круга

Дифференциальные уравнения. 2024. Т. 60. № 11. С. 1531–1540.
Ронжина М. И., Manita L.

We study a neighbourhood of a singular second-order extremal in optimal control problems that are affine in control in a disk. We consider the case in which the Hamiltonian system has dimension 8 and is a small (in the sense of the action of the Fuller group) perturbation of the Hamiltonian system of the generalized Fuller problem with control in the disk. For this class of problems, we prove the existence of extremals in the form of logarithmic spirals that reach the singular second-order extremal in a finite time, while the control performs infinitely many rotations around the circle.

Research target: Mathematics
Language: Russian
DOI
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Keywords: Особая экстремальраздутие особенностиsingular extremallogarithmic spirals blow-up of a singularityдвумерное управление из кругалогарифмическая спираль
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