Персона: Овчинникова, Анастасия Олеговна
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Институт лазерных и плазменных технологий
Стратегическая цель Института ЛаПлаз – стать ведущей научной школой и ядром развития инноваций по лазерным, плазменным, радиационным и ускорительным технологиям, с уникальными образовательными программами, востребованными на российском и мировом рынке образовательных услуг.
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Анастасия Олеговна
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- ПубликацияОткрытый доступA novel approach of the grain structure modelling in the framework of polycrystalline specimen and EBSD experiment simulation(2019) Ovchinnikova, A. O.; Savyolova, T. I.; Овчинникова, Анастасия Олеговна© 2019 Published under licence by IOP Publishing Ltd.In this work the influence of the grain structure model in polycrystalline specimen on the results of the simulated EBSD experiment is studied. In the framework of EBSD experiment simulation the improved model of the polycrystalline with the grains in form of ellipses is used. Such a model is proposed to describe structural features of the real specimen more elaborately than one with grains as circles. Matching the improved polycrystalline model with the initial one is performed by comparison of the texture entities, restored from the EBSD measurements of specimens, with reference ones, calculated for modelled specimens. The EBSD experiment simulation is conducted for both specimens models under varying of the experiment parameters: A scanning step and a tolerance angle.
- ПубликацияТолько метаданныеMEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer(2021) Vasilyev, A. N.; Zubarev, I. P.; Kozyrskiy, B. L.; Shishkin, S. L.; Ovchinnikova, A. O.; Овчинникова, Анастасия Олеговна© Copyright © 2021 Ovchinnikova, Vasilyev, Zubarev, Kozyrskiy and Shishkin.Gaze-based input is an efficient way of hand-free human-computer interaction. However, it suffers from the inability of gaze-based interfaces to discriminate voluntary and spontaneous gaze behaviors, which are overtly similar. Here, we demonstrate that voluntary eye fixations can be discriminated from spontaneous ones using short segments of magnetoencephalography (MEG) data measured immediately after the fixation onset. Recently proposed convolutional neural networks (CNNs), linear finite impulse response filters CNN (LF-CNN) and vector autoregressive CNN (VAR-CNN), were applied for binary classification of the MEG signals related to spontaneous and voluntary eye fixations collected in healthy participants (n = 25) who performed a game-like task by fixating on targets voluntarily for 500 ms or longer. Voluntary fixations were identified as those followed by a fixation in a special confirmatory area. Spontaneous vs. voluntary fixation-related single-trial 700 ms MEG segments were non-randomly classified in the majority of participants, with the group average cross-validated ROC AUC of 0.66 ± 0.07 for LF-CNN and 0.67 ± 0.07 for VAR-CNN (M ± SD). When the time interval, from which the MEG data were taken, was extended beyond the onset of the visual feedback, the group average classification performance increased up to 0.91. Analysis of spatial patterns contributing to classification did not reveal signs of significant eye movement impact on the classification results. We conclude that the classification of MEG signals has a certain potential to support gaze-based interfaces by avoiding false responses to spontaneous eye fixations on a single-trial basis. Current results for intention detection prior to gaze-based interface’s feedback, however, are not sufficient for online single-trial eye fixation classification using MEG data alone, and further work is needed to find out if it could be used in practical applications.
- ПубликацияОткрытый доступВлияние параметров проведения EBSD эксперимента на достоверность вычисления текстурных характеристик поликристаллического материала(НИЯУ МИФИ, 2017) Антонова, А. О.; Овчинникова, Анастасия Олеговна; Савелова, Т. И.