Кафедра прикладної математики (ПМ)
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- ДокументКласифікація країн європейського союзу за основними соціально-економічними показниками методом головних компонент(ХНУРЕ, 2017) Гибкіна, Н. В.; Сидоров, М. В.; Стороженко, О. В.The classification problem of the European Union countries on the essential economic and social indicators in 1994-2016 is considered. To sorting the EU member into groups, it was suggested to use the method of main components based on 22 socio-economic indicators for each country. The obtained results graphically display the position of the countries on the plane of the first two main components.
- ДокументЗастосування методу згорткових нейронних мереж на графах для розв’язання задачі комівояжера(ХНУРЕ, 2023) Ільницький, В. Б.Integer programming problem is NP-complete which leads to difficulties in obtaining exact solution at large scales. The Travelling Salesman Problem (TSP) is a famous example, asking for the shortest possible route that visits each city and returns to the origin city. This problem can be represented as sequential decision making tasks on graphs, making them a good fit for machine learning approaches, such as graph neural networks, and potentially give a possibility to avoids expensive or specialized handcrafted solutions.
- ДокументМатематичні моделі та методи розпізнавання природного мовлення на основі нейронних мереж(ХНУРЕ, 2023) Петришин, А. Ю.This thesis centers around the comparison of neural network-based models for speech recognition in the presence of noise. The study reviews denoising autoencoders, CNNs, RNNs, and transformer-based architectures, and evaluates their suitability for different noise types and levels. Comparison is conducted by collecting and preprocessing dataset of noisy speech recordings and experimenting to compare the performance of these models in terms of recognition accuracy, robustness to different noise types and levels, and computational efficiency.
- ДокументРозпізнавання крадіжок у магазинах на записах камер відеоспостереження за допомогою комбінованої моделі CNN-RNN(ХНУРЕ, 2023) Сидоренко, Б. Ю.The recognition of human activities through surveillance has numerous applications across various fields. This article presents a proposed approach to identify shoplifting in camera-recorded video data using a neural classifier that combines two neural networks, specifically, convolutional and recurrent networks. The hybrid architecture consists of two parallel streams: initial and processed video fragments (histogram of oriented gradients and optical flow). The convolutional network extracts features from each frame of the video fragment, while the recurrent network processes the temporal information from sequences of frames as features to classify the activity.
- ДокументМоделювання нестаціонарних режимів течії газу по багатонитковій лінійній ділянці газопроводу(ХНУРЕ, 2023) Полятикін, А. О.This work is devoted to a problem statement of a modelling of nonstationary gas flow regimes along multi-line linear sections of the pipeline. The pipeline section consists of few parallel pipes of a given diameter. Modeling of such regimes of gas flow along a section of a gas pipeline of a given structure is used in emergency situations, when the parameters of the gas flow at the entrance and exit of a multi-threaded linear section of the gas pipeline change rapidly.