title: ÐÐ¾Ð´ÐµÐ»Ñ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ñ Ð»ÐµÐ´Ð¾Ð²ÑÑ Ð½Ð°Ð³ÑÑÐ·Ð¾Ðº Ð¿ÑÐ¸ ÐµÐ³Ð¾ Ñ ÑÑÐ¿ÐºÐ¾Ð¼ Ð´ÑÐ¾Ð±Ð»ÐµÐ½Ð¸Ð¸, Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ðµ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ´Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Model for describing ice loads during its brittle failure, neural network segmentation of the time series
Creators: name: Ð¡ÐµÑÐ¾Ð², ÐÐ»ÐµÐºÑÐ°Ð½Ð´Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð ÑÐ´Ñ (Ð¼Ð°Ñ.) Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ
ÐÐµÐ´
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Ð¼ÐµÑ Ð°Ð½Ð¸Ð·Ð¼ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
Ð»ÐµÐ´Ð¾Ð²ÑÐµ Ð½Ð°Ð³ÑÑÐ·ÐºÐ¸
ÐºÐ²Ð°Ð·Ð¸Ð´ÐµÑÐµÑÐ¼Ð¸Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
segmentation
attention
signal generation
ice load
quasi-deterministic model

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description: The subject of the graduate qualification work is Â«Model for describing ice loads during its brittle failure, neural network segmentation of the time seriesÂ». This work is devoted to the development of a model for describing the processes that occur in ice loads during its crushing and the development of a neural network model for segmentation of time series. As a result of the research, a convolutional neural network model was obtained based on the encoder-decoder architecture. The model was optimized to the minimum size without loss of accuracy, and a new type of attention layer was proposed. Sawtooth patterns of ice loads were extracted using the developed neural network, their statistics confirmed the proposed linear quasi-deterministic model of the ascending part of the patterns. The applicability of the neural network model was studied for different types of experiments, the model showed high segmentation accuracy not only on the data of model ice experiments, but also on field data. The segmentation results were compared using a neural network model and using machine learning methods, which substantiated the need to work with time series using deep learning methods. A data generation tool has been developed that allows automatic labeling of large amounts of data. The neural network was trained on synthetic time series of cardiograms and showed high segmentation accuracy of real heart rhythms, thus proving the applicability of the developed neural network on any type of time series.