CRUST

Common Repository of the University of Science and Technologies

Welcome to the Common Repository of the University of Science and Technologies (CRUST)!

CRUST is an electronic archive created to publish, accumulate, store and provide free full-text access to publications.

These publications have a scientific, educational, and methodological purpose. They are created by teachers, researchers, employees, graduate students, or students of the university (student publications are published with the review of a supervisor).

CRUST also provides university employees with the opportunity to publish their own scientific papers.

For contacts: [email protected]

The institutional repository of the Ukrainian State University of Science and Technologies

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Recent Submissions

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Aircraft Detection with Deep Neural Networks and Contour-Based Methods
(National University "Zaporizhzhia Polytechnic", Zaporizhzhia, 2024) Radionov, Y. D.; Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Kazymyrenko, O. V.
ENG: Context. Aircraft detection is an essential task in the military, as fast and accurate aircraft identification allows for timely response to potential threats, effective airspace control, and national security. The use of deep neural networks improves the accuracy of aircraft recognition, which is essential for modern defense and airspace monitoring needs. Objective. The work aims to improve the accuracy of aircraft recognition in high-resolution optical satellite imagery by using deep neural networks and a method of sequential boundary traversal to detect object contours. Method. A method for improving the accuracy of aircraft detection on high-resolution satellite images is proposed. The first stage involves collecting data from the HRPlanesv2 dataset containing high-precision satellite images with aircraft annotations. The second stage consists of preprocessing the images using a sequential boundary detection method to detect object contours. In the third stage, training data is created by integrating the obtained contours with the original HRPlanesv2 images. In the fourth stage, the YOLOv8m object detection model is trained separately on the original HRPlanesv2 dataset and the dataset with the applied preprocessing, which allows the evaluation of the impact of additional processed features on the model performance. Results. Software that implements the proposed method was developed. Testing was conducted on the primary data before preprocessing and the data after its application. The results confirmed the superiority of the proposed method over classical approaches, providing higher aircraft recognition accuracy. The mAP50 index reached 0.994, and the mAP50-95 index reached 0.864, 1% and 4.8% higher than the standard approach. Conclusions. The experiments confirm the effectiveness of the proposed method of aircraft detection using deep neural networks and the process of sequential boundary traversal to detect object contours. The results indicate this approach’s high accuracy and efficiency, which allows us to recommend it for use in research related to aircraft recognition in high-resolution images. Further research could focus on improving image preprocessing methods and developing object recognition technologies in machine learning.
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Оподаткування підприємств
(Український державний університет науки і технологій, Дніпро, 2025) Распопова, Юлія Олександрівна; Акімова, Тетяна Валеріївна
UKR: Посібник охоплює зміст навчальної дисципліни «Оподаткування підприємств». Розкриті методологічні засади функціонування податкової системи, особливості її адміністрування; обов’язкові елементи, що визначаються під час встановлення загальнодержавних та місцевих податків (зборів) в Україні. Наочність викладеного матеріалу дисципліни забезпечена використанням табличного та графічного методів надання інформації. Наведені завдання для самоконтролю у вигляді тестових завдань відкритого типу та практичних ситуаційних завдань. Навчальний посібник призначений для студентів спеціальності 071 «Облік і оподаткування» всіх форм навчання. Може бути корисним студентам економічних спеціальностей, викладачам, фахівцям-практикам, керівникам підприємств.
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Land Cover Mapping with Sentinel-2 Imagery Using Deep Learning Semantic Segmentation Models
(CEUR-WS Team, Aachen, Germany, 2024) Hnatushenko, Viktoriia V.; Honcharov, Oleksandr
ENG: Land cover mapping is essential for environmental monitoring and evaluating the effects of human activities. Recent studies have demonstrated the effective application of particular deep learning models for tasks such as wetland mapping. Nonetheless, it is still ambiguous which advanced models developed for natural images are most appropriate for remote sensing data. This study focuses on the segmentation of agricultural fields using satellite imagery to distinguish between cultivated and non-cultivated areas. We employed Sentinel-2 imagery obtained during the summer of 2023 in Ukraine, illustrating the nation's varied land cover. The models were trained to differentiate among three principal categories: water, fields, and background. We chose and optimised five advanced semantic segmentation models, each embodying distinct methodological methods derived from U-Net. Upon examination, all models exhibited robust performance, with total accuracy spanning from 80% to 89.2%. The highest-performing models were U-Net with Residual Blocks and U-Net with Residual Blocks and Batch Normalisation, whereas U-Net with LeakyReLU Activation exhibited much quicker inference times. The findings suggest that semantic segmentation algorithms are highly effective for efficient land cover mapping utilising multispectral satellite images and establish a dependable benchmark for assessing future advancements in this domain.
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Geometric Fractals' Constructive-Synthesizing Models using Ontological Means
(CEUR-WS Team, Aachen, Germany, 2024) Kuropiatnyk, Olena S.; Shynkarenko, Viktor I.; Zhuchyi, Larysa I.; Lyakhova, Maria
ENG: The paradigm of constructive-synthesizing modelling is based on the idea of the world as a collection of different structures. The development of constructive-synthesizing modelling provides an opportunity to automate the formation of structures. Automation possibilities depend on the degree of formalization and the quality of the corresponding models. In this work, the formalization of constructive-synthesizing models is enriched by the ontological representation of knowledge. This approach is demonstrated in the formation and display of geometric fractals. The developed models are implemented by software tools using Java and Apache Jena framework. It is possible to change the basic elements of fractals based on their ontological representation.
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Government Strategy for Enhancing the Infrastructure of International Freight Railway Corridors: Economic and Strategic Considerations
(Learning Gate, Brooklyn, NY, USA, 2025) Tymoshchuk, Yaroslav; Hrebeniuk, Halyna M.; Selishchev, Sergii; Suray, Inna; Zadoia, Viacheslav O.
ENG: The article examines the state strategy for developing the infrastructure of international freight railway corridors in the European and Asia-Pacific regions from 2022 to 2023. It considers the impact of public investment on the formation of infrastructure projects through macroeconomic analysis. It describes programs that help optimize freight transport and stimulate trade development between continents. Public investment in the infrastructure of railway corridors in the EU has increased by 12% over the past five years, indicating a strategic interest in their further development. The article highlights the essence of international partnership and trade cooperation based on foreign investment to modernize railway networks. Special attention is paid to the strategic infrastructure goals for logistics corridors and transport hubs. The role of railway corridors passing through China and the European Union territory is identified. The competition between regions for leadership in transit hubs, economic influence, and control of transport corridors is investigated. At the micro level, trends in the formation of modal transport and the construction of heavy railway infrastructure are analyzed. The efficiency of the G7 countries in increasing the capacity of freight networks is studied. The problems of the states with regulatory protectionism measures have been identified. The article provides a comparative analysis of the leading companies in global freight corridors that are of strategic importance for the functioning of the economy.