Факультет прикладних комп'ютерних технологій (ДМетІ) <br> Дніпровський металургійний інститут (ДМетІ)

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UK: Факультет прикладних комп'ютерних технологій (ДМетІ)
Дніпровський металургійний інститут (ДМетІ) EN: Faculty of Applied Computer Technology
Dnipro Metallurgical Institute

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Aircraft Detection in Aerial Imagery Based on YOLO Architectures
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Radionov, Yevhen; Hnatushenko, Volodymyr V.
ENG: The study is devoted to determining the most efficient YOLO-based architecture for the task of aircraft detection in high-resolution aerial imagery. A comparative analysis was conducted across YOLO models v8 through v11 under three experimental conditions: using pre-trained (raw) models, fine-tuning the models on a domain-specific dataset, and fine-tuning models to a dataset enhanced through a proposed image preprocessing method. The evaluation considered both accuracy and inference performance metrics. The proposed methodology reduced the false negative rate from 19.5% to 3.2% at a confidence threshold of 0.75, underscoring its effectiveness in enhancing target visibility under challenging imaging conditions such as low contrast or background clutter.
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.
Analysis and Research of the Causes and Course of Degradation of Lithium Batteries
(EDP Sciences-Web of conferences, 2024) Buriak, Serhii Yu.; Gololobova, Oksana O.; Havryliuk, Volodymyr I.; Serdiuk, Tetiana M.; Voznyak, Oleh M.; Manachyn, Ivan O.
ENG: Energy storage devices based on lithium technology are confidently leading the respective market due to their significant advantages over other technologies in the industry. Despite their relatively recent history of appearance, they managed to undergo many modifications of both physical and chemical components. One of the constant goals of all research in this field is the formation of knowledge about the degradation processes occurring inside a given chemical current source, and ways to influence them. Systematization and identification of the fundamental reasons for the decrease in the performance of lithium batteries still remains a topical issue of today, and therefore is considered in this article. And no matter how studied this issue looks, taking into account the existing many long-term experimental data of a huge number of scientists and a number of different types of companies, but still, optimization of work is impossible without identifying and eliminating as many destructive factors as possible in battery operation. The difficulty of this process lies also in the fact that, taking into account all the high-tech production processes in the world, there are no two identical lithium current sources. On the example of a single battery, the ability to maintain high performance, close to nominal, was demonstrated from a source that, due to its lifetime, should not have had them. The data obtained during the experiment, which confirmed the high performance, show once again that the issue of degradation of lithium current sources can and should be studied further.
Analysis of Air Dust Pollution in the Transport Compartment of the Launch Vehicle at the Stage of the Pre-launch Preparation
(Printing House “Technologija”, Kaunas, Lithuania, 2024) Biliaiev, Mykola M.; Biliaieva, Viktoriia V.; Kozachyna, Vitalii A.; Kozachyna, Valeriia V.; Mashykhina, Polina B.; Semenenko, Pavlo
ENG: At the stage of the pre-launch preparation, it is necessary to fulfill very strict environment conditions inside the main fairing where the satellite is located. Namely, it is very important to predict dust concentration inside the main fairing and especially near satellite surface during forced ventilation. To predict air dust pollution inside of main fairing 2D fluid dynamics numerical model has been developed. The governing equations include equation of potential flow to simulate air flow inside the main fairing and equation of pollutant dispersion. Also, empirical model has been used to calculate the number of dust particles fall to the satellite surface. Implicit finite difference schemes of splitting have been used for numerical integration of governing equations. The computer code has been developed on the basis of proposed numerical model. The results of computational experiments to estimate dust concentration field inside the main fairing of the launch vehicle are presented.
Analysis of Changes in Global Warming Potential during Enrichment and Production of Battery-Grade Graphite Using Electrothermal Fluidized Bed Technology
(IOP Publishing Ltd, 2024) Hubynskyi, Semen M.; Sybir, Artem; Fedorov, Serhii S.; Usenko, Andrii Yu.; Hubynskyi, Mykhailo V.; Vvedenska, Tetyana
ENG: The greenhouse gas emissions during the production of anode class graphite for the conditions of Ukraine have been calculated. Conventional technologies and technologies using electrothermal fluidized bed (EFB) for natural and synthetic graphite have been studied. Calculations are carried out with respect to the whole technological chain, starting from extraction and processing of raw materials and ending with finishing processing (coating). As a result, it is shown that the technology of using EFB for purification of natural graphite and graphitization of synthetic graphite is competitive in terms of global warming potential (GWP). In the production of natural graphite using thermal purification with EFB instead of chemical purification, emissions of greenhouse gases practically remain at the same level. At the same time, the use of acids is eliminated, and the environmental impact associated with them is reduced. Production of synthetic graphite of anodic quality in EFB furnaces allows to reduce greenhouse gases (GHG) emissions by 40-50% in comparison with traditional graphitization technologies in Acheson and Kastner furnaces. The effect is achieved by reducing energy and raw material consumption.
Analysis of Methodologies for Carbon Stock Estimation in Forests
(Український державний університет науки і технологій, ННІ «Інститут промислових та бізнес технологій», ІВК «Системні технології», Дніпро, 2022) Kavats, Olena O.; Khramov, Dmitriy A.; Sergieeiva, Kateryna L.; Vasyliev, Volodymyr V.
ENG: Current approaches to carbon stock estimation in forest ecosystems are discussed. Datasets containing biomass and carbon stock estimates that can be used for training/validation in machine learning are described. Examples of applying the remote approach to assessing forest biomass over large areas are analyzed. To estimate the forest carbon stocks in Ukraine, the most promising is the remote approach, which combines ground-based and satellite measurements for forest classification and statistical modeling of carbon stocks. For training and validation of machine learning algorithms, it is proposed to use the GEDI Biomass Map covering most of the territory of Ukraine — from the southern borders to the latitude of Chernihiv in the north. A prototype of forest biomass estimating product in Ukraine can be based on publicly available MODIS NBAR data, SRTM DEM, ECMWF climate data and use the Random Forest machine learning method.
Analysis of Monolithic and Microservice Architectures Features and Metrics
(Хмельницький національний університет, Україна, 2021) Selivorstova, Tatjana V.; Klishch, Sergey M.; Kyrychenko, Serhii; Guda, Anton I.; Ostrovskaya, Kateryna Yu.
ENG: In this paper the information technologies stack is presented. These technologies are used during network architecture deployment. The analysis of technological advantages and drawbacks under investigation for monolithic and network architectures will be useful during of cyber security analysis in telecom networks. The analysis of the main numeric characteristics was carried out with the aid of Kubectl. The results of a series of numerical experiments on the evaluation of the response speed to requests and the fault tolerance are presented. The characteristics of the of monolithic and microservice-based architectures scalability are under investigation. For the time series sets, which characterize the network server load, the value of the Hurst exponent was calculated. The research main goal is the monolithic and microservice architecture main characteristics analysis, time series data from the network server accruing, and their statistical analysis. The methodology of Kubernetes clusters deploying using Minikube, Kubectl, Docker has been used. Application deploy on AWS ECS virtual machine with monolithic architecture and on the Kubernetes cluster (AWS EKS) were conducted. The investigation results gives us the confirmation, that the microservices architecture would be more fault tolerance and flexible in comparison with the monolithic architecture. Time series fractal analysis on the server equipment load showed the presence of long-term dependency, so that we can treat the traffic implementation as a self-similar process. The scientific novelty of the article lies in the application of fractal analysis to real time series: use of the kernel in user space, kernel latency, RAM usage, caching of RAM collected over 6 months with a step of 10 seconds, establishing a long-term dependence of time series data. The practical significance of the research is methodology creation of the monolithic and microservice architectures deployment and exploitation, as well as the use of time series fractal analysis for the network equipment load exploration.
Analysis of Relationships between Parameters of the National Forest Inventory of Finland: Case Study of Mesic Forest
(Geological Society Publishing House, London, UK, 2025) Kavats, Olena O.; Khramov, Dmitriy; Sergieieva, Kateryna L.
ENG: The use of satellite images and machine learning in addition to in situ data in national forest inventories enables covering large areas and significantly reduces costs. However, such combined inventories provide modelled stand properties, the relationships between which are not well understood. An approach to investigating linear and non-linear relationships between forest inventory parameters is proposed. It is applied to a study of the Multi-Source National Forest Inventory (MS-NFI) stand properties for the case of mesic forests. The relationships between MS-NFI parameters and stand reflectance in the visible, red edge, near infrared and short-wave infrared spectral regions were investigated for the Sentinel-2 satellite sensor. Linear models of canopy reflectance as a function of forest stand and elevation properties were developed. These models allowed to assess the comparative influence of MS-NFI parameters on stand reflectance as well as the monthly dynamics of this influence during the season (May–August 2019). Linear relationships between forest inventory parameters were investigated using a correlation matrix. Generalized additive models were used to investigate non-linear pairwise relationships between forest inventory parameters. The proposed approach can be applied to assess the impact of stand features obtained from conventional ground-based forest inventory on forest canopy reflectance.
Application of Biomass Pellets for Iron Ore Sintering
(Trans Tech Publications Ltd, Switzerland, 2021) Kieush, Lina; Koveria, Andrii; Qiao Zhu, Zuo; Boyko, Maksym M.; Sova, Artem; Yefimenko, Vadym
ENG: Purpose. The use of biomass as fuel might solve several technological and environmental issues and overcome certain challenges of sinter production. In particular, as revealed by comprehensive analyses, biomass can be used as fuel for iron ore sintering. In this study, we investigate the use of some raw and pyrolysis-processed biomass pellet types, namely wood, sunflower husks (SFH), and straw, for iron ore sintering. In the experiments, the pyrolysis temperature was set to 673, 873, 1073, and 1273 K, and the proportion of biomass in the fuel composition was set to 25%. It was established that the addition of biofuels to the sintering blend leads to an increase in the gas permeability of the sintered layer. The analysis of the complex characteristics of the sintering process and the sinter strength showed the high potential of wood and sunflower husk pellets pyrolyzed at 1073 and 873 K, respectively, for iron ore sintering. The analysis of the macrostructure of the sinter samples obtained using biomaterials revealed that with higher pyrolysis temperatures; the materials tend to have greater sizes and higher amounts of pores and cracks. The composition analyses of the resultant sinters revealed that with higher temperature, the FeO content of the sinters tends to increase.
Application of Neural Networks for Prediction Financial Time Series
(Scientific Publishing Center “Sci-conf.com.ua”, Perfect Publishing, 2024) Prokofiev, Taras; Ostrovska, Kateryna Yu.
ENG: The article discusses some aspects and features of the use of neural networks for forecasting financial time series for the purpose of making a profit. The use of neural networks to analyze financial information is a promising alternative (or complement) to traditional research methods. Due to their adaptability, the same neural networks can be used to analyze several instruments and markets, while the patterns found by a player for a specific instrument using technical analysis methods may work worse or not work at all for other instruments.
Application of the GoldenRAM AI Platform for Monitoring Mining Activities Using Earth Observation Data
(Український державний університет науки і технологій, ІВК «Системні технології», Дніпро, 2026) Kavats, Olena O.; Sergieieva, Kateryna L.; Matselyukh, T. B.
ENG: This paper analyzes the potential of Earth remote sensing data and artificial intelligence for monitoring mining sites. The study investigates the remote assessment of the value of the deposit using multispectral imagery, time-series data, and digital elevation models. Particular attention is paid to the GoldenRAM platform, which ensures synergy between satellite data, GIS, and machine learning for automated change detection. The core innovation of this study lies in the implementation of the Artificial Intelligence Knowledge Processors (AIKP) approach, which serves as a modular foundation for a fundamental system development architecture. This methodology enables seamless integration of AI-assisted development and advanced geospatial analytics, producing a reliable framework for cross-domain monitoring. Integrating these technologies facilitates continuous remote monitoring, dynamic quarry assessment, and verification of extraction volumes. The proposed approach is highly relevant for monitoring areas with limited physical access and can be adapted for environmental analysis systems in industrial regions of Ukraine.
Application of Two-Dimensional Padé-Type Approximations for Image Processing
(National University «Zaporizhzhia Polytechnic», Zaporizhzhia, 2023) Olevskyi, V. I.; Hnatushenko, Volodymyr V.; Korotenko, G. M.; Olevska, Yu. B.; Obydennyi, Ye. O.
ENG: Context. The Gibbs phenomenon introduces significant distortions for most popular 2D graphics standards because they use a finite sum of harmonics when image processing by expansion of the signal into a two-dimensional Fourier series is used in order to reduce the size of the graphical file. Thus, the reduction of this phenomenon is a very important problem. Objective. The aim of the current work is the application of two-dimensional Padé-type approximations with the aim of elimination of the Gibbs phenomenon in image processing and reduction of the size of the resulting image file. Method. We use the two-dimensional Padé-type approximants method which we have developed earlier to reduce the Gibbs phenomenon for the harmonic two-dimensional Fourier series. A definition of a Padé-type functional is proposed. For this purpose, we use the generalized two-dimensional Padé approximation proposed by Chisholm when the range of the frequency values on the integer grid is selected according to the Vavilov method. The proposed scheme makes it possible to determine a set of series coefficients necessary and sufficient for construction of a Padé-type approximation with a given structure of the numerator and denominator. We consider some examples of Padé approximants application to simple discontinuous template functions for both formulaic and discrete representation. Results. The study gives us an opportunity to make some conclusions about practical usage of the Padé-type approximation and about its advantages. They demonstrate effective elimination of distortions inherent to Gibbs phenomena for the Padé-type approximant. It is well seen that Padé-type approximant is significantly more visually appropriate than Fourier one. Application of the Padé-type approximation also leads to sufficient decrease of approximants’ parameter number without the loss of precision. Conclusions. The applicability of the technique and the possibility of its application to improve the accuracy of calculations are demonstrated. The study gives us an opportunity to make conclusions about the advantages of the Padé-type approximation practical usage.
Applying Machine Learning Techniques to Analyze Forest Fire Impacts on Sentinel-2 Imagery across Ukraine
(Український державний університет науки і технологій, ННІ ≪Інститут промислових та бізнес технологій≫, ІВК ≪Системні технології≫, Дніпро, 2026) Hnatushenko Viktoriia V.; Udovyk, Iryna M.; Heipke, Christian; Hnatushenko, Maksym V.
ENG: Forest fires pose severe ecological and socio-economic threats, necessitating efficient tools for rapid damage assessment. This study presents a machine learning approach for detecting burnt forest areas in Ukraine using multispectral Sentinel-2 imagery. A new manually annotated dataset was developed for training semantic segmentation models, addressing the scarcity of open data for the region. The proposed convolutional neural network, based on an encoder–decoder architecture with Xception blocks, effectively captures spectral patterns associated with fire damage. Experiments conducted on Sentinel-2 Level-2A imagery of the Kinburn Peninsula (October 2022) demonstrate high detection performance, achieving an Intersection over Union (IoU) of 95%. The results confirm the model’s capability for accurate burnt-area mapping and highlight its potential for broader applications in regional fire monitoring and environmental management.
Attributes and Metrics of Trust Based Models in Cloud Security
(Publishing House "Helvetica", 2026) Bobrenok, Viacheslav V.; Guda, Anton I.
ENG: Cloud environments became a popular solution for hosting and managing infrastructure and data for businesses in different domains: cloud computing service providers’ revenue in 2018 amounted to approximately 217 billion US dollars, in 2022 - 481 billion, and the forecast for 2028 includes a profit figure of more than 1 trillion US dollars. But as organizations migrate from traditional on-premises infrastructures to cloud platforms, conventional perimeter-based security approaches have become insufficient due to the absence of clear network boundaries and the rise of remote access. It introduced a new set of security challenges. For example, in 2021, losses to companies from the leakage of confidential information amounted to an average of 3.5 million US dollars, and losses from attacks aimed at destroying or damaging IT infrastructure amounted to 4.6 million US dollars. Thus, these issues must be resolved to facilitate further adoption of cloud technologies. Trust based models might be a solution for some of these challenges as the evolution of trust models in cloud security reflects a shift from static to dynamic and adaptive mechanisms. By shifting from implicit trust to continuous verification and contextual awareness, these models provide a more robust framework for protecting sensitive information and maintaining secure access in cloud ecosystems. This work is an attempt to discover key attributes of trust based models and how they can be used to create a mechanism for securing data and workloads in cloud environments. It is achieved by conducting an extensive review of existing security threats in cloud environments as well as a systematic analysis of key characteristics of trust based models and their applicability for mitigation of these threats. As a result, this work discovers key attributes of trust based models which can be used to implement new security mechanisms for cloud environments. Such mechanisms should be better suited for handling the dynamic nature of such environments. Even though securing cloud environments remains a complex task, the attributes described in this research can be used to create new tools and methodologies which can greatly simplify it and facilitate further adoption of cloud technologies.
Automated Building Damage Detection on Digital Imagery Using Machine Learning
(Dnipro University of Technology, Ukraine, 2023) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: Purpose. To develop an automated method based on machine learning for accurate detection of features of a damaged building on digital imagery. Methodology. This article presents an approach that employs a combination of unsupervised machine learning techniques, specifically Principal Component Analysis (PCA), K-means clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to identify building damage resulting from military conflicts. The PCA method is utilized to identify principal vectors representing the directions of maximum variance in the data. Subsequently, the K-means method is applied to cluster the feature vector space, with the predefined number of clusters reflecting the number of principal vectors. Each cluster represents a group of similar blocks of image differences, which helps to identify significant features associated with fractures. Finally, the DBSCAN method is employed to identify areas where points with similar characteristics are located. Subsequently, a binary fracture mask is generated, with pixels exceeding the threshold being identified as fractures. Findings. The introduced methodology attains an accuracy rate of 98.13 %, surpassing the performance of conventional methods such as DBSCAN, PCA, and K-means. Furthermore, the method exhibits a recall of 82.38 %, signifying its ability to effectively detect a substantial proportion of positive examples. Precision of 58.54 % underscores the methodology’s capability to minimize false positives. The F1 Score of 70.90 % demonstrates a well-balanced performance between precision and recall. Originality. DBSCAN, PCA and K-means methods have been further developed in the context of automated detection of building destruction in aerospace images. This allows us to significantly increase the accuracy and efficiency of monitoring territories, including those affected by the consequences of military aggression. Practical value. The results obtained can be used to improve automated monitoring systems for urban development and can also serve as the basis for the development of effective strategies for the restoration and reconstruction of damaged infrastructure.
Automatic Compensation of the Mill RolL Eccentricity in Terms of Limited Speed of Hydraulic Compression Devices
(Dnipro University of Technology, Dnipro, 2025) Boyko, O.; Kuvaiev, Victor; Potap, Oleg; Potap, M.; Rybalchenko, Maria O.
ENG: Purpose. To reduce deviation of vertical dimension (thickness) of rolled products from the specified value by enhancing the accuracy and shortening the setup time of an eccentricity compensation subsystem of mill rolls based on substantiation of an eccentricity compensation method. This method is based on an active search algorithm to determine the actual eccentricity parameters in real time, taking into account the actual response time of hydraulic compression devices (HCD) and investigating its effectiveness through simulation computer modelling. Methodology. The research was based on the analytical determination of the frequency characteristics of the AGC system in sheet metal rolling, considering the actual response time of HCD of a rolling mill as well as a comprehensive model of a rolling process in a quarto mill with rolling movement and an automatic thickness control system (ATCS) that compensates for eccentricity. The study was conducted by comparing the results of computer simulation modelling of the improved ATCS, whose algorithm took into account the HCD response time, with the performance indicators of the previous system, which did not consider this factor. Findings. It has been established that under the AGC thickness control conditions, the measured amplitude of a variable component of thickness does not match the amplitude of eccentricity due to the finite response time of HCD. The frequency characteristics of the AGC system have been determined analytically, taking into account the actual response time of HPD in a rolling mill. An improved procedure for determining the actual eccentricity amplitude in real time has been substantiated, which involves a temporary reduction in the HCD speed within the initial rolling section. A structure for an automated control system has been proposed for practical implementation of this procedure. It has been demonstrated that the proposed solutions allow for a threefold reduction in thickness variations caused by eccentricity compared to the corresponding performance indicators of the known eccentricity compensation systems with the AGC thickness control. Originality. The influence of the HCD response time on the accuracy of AGC thickness control systems for rolled products has been established. An approximate linear relationship has been identified between the ratio of the amplitude of thickness fluctuations caused by eccentricity and the amplitude of roll gap fluctuations relative to the roll speed and HCD response time under the AGC algorithm thickness control conditions. The improved procedure for determining the actual eccentricity amplitude in real time has been substantiated. Practical value. The effectiveness is substantiated of implementing an improved active search algorithm for determining the eccentricity parameters of mill rolls under the limited HCD response conditions in real time. This approach allows for a threefold reduction in the sheet thickness variability caused by roll eccentricity compared to the performance indicators of the known AGC thickness control systems, thereby ensuring the production of high-precision rolled products in Ukrainian sheet rolling mills.
Blasphemers of the Tsar and God: The "Offensive Cases" of Early 20th-Century Yekaterinoslav
(ENIGMA CORPORATION, Praha, 2025) Savchenko, Serhiy V.; Vysotskyi, Oleksandr
ENG: This study aims to reconstruct the “voice of the people” in early 20th-century Yekaterinoslav Province by analyzing cases of verbal offenses against the imperial family, religious institutions, as well as state authorities. Using archival police reports and legal documents, the research examines of how these offenses functioned as both spontaneous outbursts and structured forms of social protest, revealing broader societal tensions. The objective of the present study is to explore the role of blasphemy and political insults in expressing dissatisfaction with tsar Nicholas II’s perceived weakness, misfortune, and failure to meet traditional expectations of rulership. By placing these offenses in the context of popular culture, anti-monarchism, and delegitimization of power, this study challenges traditional revolutionary interpretations that frame them solely as symptoms of class struggle. Instead, it argues that many participants did not reject monarchy as an institution but rather criticized the reigning sovereign’s perceived incompetence. The increase in documented offenses was not only a reflection of growing unrest but also a consequence of expanding police surveillance and bureaucratic mechanisms that politicized expressions of frustration. The findings provide new perspectives on popular geopolitics, showing that admiration for foreign powers, particularly Japan, sometimes accompanied anti-monarchical rhetoric. Additionally, this research enhances the understanding of how informal communication networks helped spread oppositional sentiment, further undermining the legitimacy of autocratic rule. By examining these overlooked sources, the study reinterprets the intersections of popular culture, local history, and political resistance in the late Russian Empire.
CFD Modeling of Traffic-related Air Pollution in Street Canyon
(Printing House “Technologija”, Kaunas, Lithuania, 2024) Biliaiev, Mykola M.; Biliaieva, Viktoriia V.; Berlov, Oleksandr V.; Kozachyna, Vitalii A.; Kozachyna, Valeriia V.; Yakubovska, Zinaida M.
ENG: High pollution levels are often observed in urban street canyons. Different mathematical models are intensively used to predict pollution levels in urban street canyons. In this paper quick computing 3D CFD model is proposed to compute wind flow over buildings and pollutant dispersion in street canyon. To simulate wind flow over buildings 3D equation of potential flow has been used. Pollutant concentration field has been modelled using three-dimensional equation of pollutant dispersion. Governing equations are also included simplified equations to describe pollutants chemical transformations in atmosphere. To solve numerically governing equations implicit difference schemes have been used. The computer code to realize the proposed numerical models has been developed. Results of numerical experiments are presented.
Change in Slag Composition and Sulfur Content of Hot Metal in the Process Chain of Blast Furnace — Hot Metal Desul furization Complex — Converter (BOF)
(Publishing House “Akademperiodyka”, Kyiv, 2024) Shevchenko, A. P.; Kysliakov, Volodymyr G.; Dvoskin, B. V.; Manachyn, Ivan O.
ENG: Introduction. Modern conditions of iron and steel making industry require production of high-quality competitive metal products. Thus, the removal of sulfur at the lowest cost has been becoming increasingly important. Problem Statement. The major amount of sulfur in iron and steel making comes with charge materials in sintering blast furnace production. When using out-of-furnace processing of hot metal in hot metal desulfurization and slag removal facilities, the degree of hot metal desulfurization can be 75—99%. This ensures the production of hot metal with a sulfur content in the range of 0.002—0.015%. Purpose. The analysis of changes in the sulfur content of hot metal and in the slag composition in the process chain of steel production, followed by the development of technical solutions and process methods to eliminate the resulfurization of hot metal. Materials and Methods. Our calculations, based on the actual data of Ukrainian and Chinese iron and steel making facilities. The selected samples of slag and hot metal have been analyzed with the use of raster spectral microscopy methods. In the studies of sulfur content at various stages of smelting, the method of material balance calculation has been employed. Results. In the slag phase, along with systems of CaO ∙ SiO2 ∙ Al2O3 type with different ratios of components containing 0.2—3.5% sulfur, CaxSiyAlz type systems containing up to 1% sulfur have been detected. In the beads, the sulfur content varies within 0.1—0.85%. Sulfur is present in the form of sulfides of (Fe, Mn)S type, mainly MnS, while in non-metallic inclusions of the beads, the sulfur content ranges within 15—30%. The residing ladle slag after desulfurization should not exceed 0.5—0.7 kg/t of hot metal. Conclusions. To prevent the resulfurization of hot metal during its discharge from a blast furnace, it is advisable to rationalize ladle slag modes, by adjusting ladle slag composition, increasing the degree of ladle cleaning from the slag residing from previous loads and inducing a slag cover in the absence of ladle slag. The conducted studies have shown that sulfur from the slag does not return to the hot metal and resulfurization does not occur, which is explained by the protective effect of residual magnesium.
Coefficient of Local Loss of Mechanical Energy of the Flow for a Mixture of Charge Materials
(Dnipro University of Technology, 2021) Selegej, Andriy Mikolayovich; Ivaschenko, Valeriy; Golovko, Vjacheslav Iljich; Kiriya, R.; Kvasova, Luydmila Sergijvna
ENG: Purpose. To determine the dependence of the coefficient of local losses of mechanical energy of flow of a twocomponent mixture of charge material on its depth, content of components, and average equivalent diameter of particles in the case of their freedispersed motion. Methodology. The value of the coefficient of local losses of mechanical energy was determined by the value of the hydraulic resistance of the fluid during its movement in open channels and pipes. In this paper, methods were used of comparative analysis, mathematical modeling and forecasting of dynamic processes in the flow of granular material. findings. Based on the results of theoretical studies, a mathematical model was obtained, the use of which allows calculating the coefficient of local losses of mechanical energy for the flow of a twocomponent mixture of charge materials with agglomerate particle sizes from 15 to 50 mm, pellets from 6 to 12 mm, coke from 10 to 60 mm. The developed model with satisfactory accuracy makes it possible to evaluate the movement of the charge from the indicated materials along the paths of the charging devices of blast furnaces at a speed in the range from 1.5 to 20 m/s and to determine the trajectories of the mixture of charge materials on the top with an accuracy of 0.2 m. It is noted that the calculation of the above coefficient by the known techniques is not accurate enough, which is associated with the uncertainty in the choice of a single average equivalent diameter of the particles of the two component charge. Comparative analysis of the developed model with the known models and experimental data indicates that the accuracy of calculating the dynamic parameters of a twocomponent flow of charge materials using the developed model increases by 5–10 % in comparison with calculations using the previously known models. Originality. For the first time, regularities of changes in the coefficient of internal mechanical losses of a twocomponent flow of charge materials from its depth, content of components, average equivalent particle diameters when moving along the paths of charging devices of blast furnaces have been established. practical value. Mathematical dependencies have been developed and can be used to determine the technological parameters of the charge of a modern blast furnace with different characteristics of the granulometry of the charge and the ratios of its components. This will increase the accuracy of predicting the course of the process under consideration, the degree of automation of the control systems for the technological process of the charge supply of blast furnaces, will make it possible to use expensive charge materials more efficiently, reduce energy consumption and reduce the harmful impact on the environment.
Comparative Analysis of Activation Functions in U-Net for Binary Water Segmentation using Sentinel-2 Imagery
(CEUR-WS Team, Aachen, Germany, 2025) Kundenko, Pavlo; Hnatushenko, Viktoriia V.; Tsaryk, Vladyslav Yu.; Dmytriieva, Iryna S.
ENG: The study examines how different activation functions influence the performance of a U-Net model applied to binary water-body segmentation in Sentinel-2 imagery. Using an identical training setup for each experiment, six nonlinearities—ReLU, Leaky ReLU, ELU, PReLU, Swish and RReLU—are individually substituted into the network while all other parameters remain fixed. Comparative evaluation on a held-out validation set reveals that Leaky ReLU provides the most balanced trade-off between precision and recall, making it the preferred choice for accurate water-mask generation. PReLU offers a similar but slightly lower performance, whereas ELU excels at capturing additional water pixels at the cost of more false positives. The findings highlight the importance of activation-function selection in remote-sensing segmentation tasks and suggest further exploration of advanced nonlinearities and larger, more diverse datasets to enhance generalization.
Comparative Analysis of Classification Methods for High-Resolution Optical Satellite Images
(Khmelnytskyi National University, Khmelnytskyi, 2024) Hnatushenko, Volodymyr V.; Kashtan, Vita; Chumychov, Denys; Nikulin, Serhii
ENG: High-resolution satellite image classification is used in various applications, such as urban planning, environmental monitoring, disaster management, and agricultural assessment. Traditional classification methods are ineffective due to the complex characteristics of high-resolution multichannel images: the presence of shadows, complex textures, and overlapping objects. This necessitates selecting an efficient classification method for further thematic data analysis. In this study, a comprehensive assessment of the accuracy of the most well-known classification methods (parallelepiped, minimum distance, Mahalanobis distance, maximum similarity, spectral angle map, spectral information difference, binary coding, neural network, decision tree, random forest, support vector machine, K-nearest neighbour, and spectral correlation map) is performed. This study comprehensively evaluates various classification algorithms applied to high-resolution satellite imagery, focusing on their accuracy and suitability for different use cases. To ensure the robustness of the evaluation, high-quality WorldView-3 satellite imagery, known for its exceptional spatial and spectral resolution, was utilized as the dataset. To assess the performance of these methods, error matrices were generated for each algorithm, providing detailed insights into their classification accuracy. The average values along the main diagonal of these matrices, representing the proportion of correctly classified pixels, served as a key metric for evaluating overall effectiveness. Results indicate that advanced machine learning approaches, such as neural networks and support vector machines, consistently outperform traditional techniques, achieving superior accuracy across various classes. Despite their high average accuracy, a deeper analysis revealed that only some algorithms are universally optimal. For instance, some methods, such as random forests or spectral angle mappers, exhibited strength in classifying specific features like vegetation or urban structures but performed less effectively for others. This underscores the importance of tailoring algorithm selection to the specific objectives of individual classification tasks and the unique characteristics of the target datasets. This study can be used to select the most effective method of classifying the earth's surface, depending on the tasks of further thematic analysis of high-resolution satellite imagery. Furthermore, it highlights the potential of integrating machine learning-based approaches to enhance the accuracy and reliability of classification outcomes, ultimately contributing to more practical applications.
Comparative Mathematical Analysis of Transmission and Axial Disc Brakes
(Український державний університет науки і технологій, Дніпро, 2024) Monia, Andrii G.; Bychkova, D. M.
ENG: Purpose. A comparative study of axial and transmission disc-pad brakes. The task of the work is the theoretical determination of the braking torque and the force of pressing the pads against the disk in different braking modes, as well as the determination of the area of optimal operating modes of the indicated brakes. The methods. Comparative mathematical analysis. Findings. With equal dimensions of the two types of disc brakes and an even distribution of the braking torque between the wheel pairs, the transmission creates a greater braking torque on each of the four wheels of the traction section due to the gear ratio of the axial gearbox. Installing a disc brake on the axle of a wheel pair with a central location of the drive gear wheel allows you to change the masses of the half-axles, which means to eliminate self-oscillations that destroy the drive axle under the action of resonant torsional vibrations. The heavier the rolling stock of the train being transported, or the greater the slope of the track on the descent, the smaller the braking torque can be applied to the wheel pair, to exclude its blocking and clutch failure. The obtained results show that the smaller the moments of inertia and stiffness of the wheels, half-axles and transmission elements (gear wheels, shafts, etc.), the smaller the braking torque required to stop the locomotive on the same braking path. But the higher the speed of the train before braking, the greater the braking torque should be. Originality. The obtained results show that the smaller the moments of inertia and stiffness of the wheels, half-axles and transmission elements (gear wheels, shafts, etc.), the smaller the braking torque required to stop the locomotive on the same braking path. But the higher the speed of the train before braking, the greater the braking torque should be. Practical value. Taking into account the above-mentioned features of disc brakes, multilevel backup of brake systems of heavy mining locomotives operating on track slopes of up to 50 ‰ should be considered justified and necessary. Such locomotives should have both disc transmission brakes, as more efficient, and disc axle brakes as safer.
Complex of Mathematical Models and Methods to Calculate Pressure Effect on Sulfide Distribution in Steel
(Хмельницький національний університет, Україна, 2021) Selivyorstova, Tetjana V.; Selivyorstov, Vadim Yu.; Kuznecov, Vitaliy V.
ENG: Primary objective is to develop computational method to analyze digital pictures of sulfide prints, helping obtain qualitative image characteristics, and to formulate mathematical model of the distribution of sulphide inclusions to determine specific features of the pressure effect on the macrostructure formation of carbon steel castings flooded into the uncooled mold. The research was carried out using images of sulfide prints of templates cut of steel cylindrical castings; L500 steel was applied. The castings result from industrial tests of a method of gas-dynamic effect on the fusion in the foundry forms under the conditions of a casthouse of Dnipropetrovsk aggregate plant PJSC. Digital pictures of sulfide prints, obtained in terms of the increased rate of gas pressure and maximum pressure, were binarized; defective fra gments were removed; and zo ning took place. The developed computational method has been applied for fragments of images, representing different zones; data arrays have been received containing sizes and amounts of inclusions in the fragment. The developed computational method to analyze digital images of sulfide prints has been implemented. ASImprints software support has helped obtain qualitative characteristics of images; namely, distribution of amount of the certain-size sulfide inclusions. The computational method to analyze digital images of sulfide prints has made it possible to study the set of patterns of sulfide prints. The dependences have been obtained, describing specific features of sulfide inclusion distribution while varying gas-dynamic pressure method in terms of fusion in the casting form. It has been demonstrated that the distribution describes effectively the power-series distribution to compare with the exponential one. Mathematical model of the power -series distribution parameter dependence upon pressure has been developed. Deviation of the distribution parameters in terms of the experimental values and the model values has been evaluated. The research demonstrates the ways to apply an algorithm of simple recursive casting for quantitative analysis of digital images of sulfide prints. Use of ASImprints, being software implementation of the computational method to analyze digital images of sulfide prints making it possible to obtain qualitative characteristics of images, has helped identify that the increased pressure within a casting-device for gas injection system results in the increased specific amount of inclusions and the decreased specific zone of sulfide inclusions respectively. It has been defined that exponential function describes reliably the nature of sulfide inclusion distribution in the digital image of sulfide print. The research has demonstrated that fragments of a sulfide print, belonging to one zone, are statistically homogeneous. Thus, it is possible to analyze quantitively digital image zone of a sulfide print on its fragment. Mathematical model of dependence of sulfide inclusion distribution in carbon-steel castings in terms of gas-dynamic effect on fusion solidifying in a mold has been developed. The model may be applied to predict sulfide inclusion distribution within the selected zones of cross section of the cylindrical castings solidifying in the uncooled mold in terms of the preset mode of gas-dynamic effect.
Computer Modeling of Harmful Impurities Transfer
(Scientific Publishing Center “Sci-conf.com.ua”, 2021) Moroz, Borys Ivanovych; Shvachych, Gennady Grygorovych; Chorna, Valentyna Ivanivna; Voroshylova, Nataliiya Volodymyrivna
ENG: The paper considers solutions to the ecology problems, which set is formulated from cause-effect relationships. According to the adopted model, the equation’s coefficients for the harmful impurities transfer are attributed to the causal features of the process. Herein, the setting of cause-and-effect links is the goal of the ecology’s direct problems. Along with direct methods of mathematical modeling of harmful impurities transfer in the atmosphere from pollution sources, the paper considers the formulation and methods of solving inverse problems, which essence is to estimate the input parameters based on actual information about the modeled system, known from the experiment. Based on the research results, a software package was developed to implement the solution of the coefficient inverse problems of ecology using the mathematical modeling method.
Computer Modeling of Territory Flooding in the Event of an Emergency at Seredniodniprovska Hydroelectric Power Plant
(Dnipro University of Technology, Ukraine, 2022) Ivanov, D. V.; Hnatushenko, Volodymyr V.; Kashtan, Vita Yu.; Garkusha, I. M.
ENG: Purpose. Computer modeling of territory flooding in the event of an emergency at Seredniodniprovska Hydroelectric Power Plant (HPP). Methodology. The computer model of possible territory flooding at Seredniodniprovska HPP is developed using simulation modeling methods and geometric and hydrological approaches and considers initial boundary conditions of the water-engineering system. Calculations of the wave break height and the half-divided cross-sectional area of the river bed were made and a three-dimensional model of the territory flooding was built using the Python language and ArcGIS Desktop software. Findings. The data for each creation of the hydraulic node, namely the depth and width of the flooded territory, were calculated. This allowed analyzing the macro level considering the triangulation model of the surface. The wave break parameters and flaps (intersections) were taken into account in case of a dam break at a hydroelectric power plant or a rise in the water level. A mathematical model, and a 3D model were developed, and a forecast of the flood zone due to an emergency was made using satellite survey data. Originality. The mathematical method received further development for calculating flood territories in the event of an emergency at Seredniodniprovska Hydroelectric Power Plant, taking into account the parameters of the breakthrough wave and the calculation of cross-sections for the cases when a hydroelectric dam breaks or the water level rises; the method uses one-dimensional and two-dimensional systems of Saint-Venant equations, and geometric and hydrological approaches. A three-dimensional model of the territory flooding is developed to predict possible consequences. Practical value. The obtained results can be used to model the flooding of the territory located near dangerous hydro-technical objects, such as dams, dikes as well as to forecast flooded territories during the construction of drainage and protective structures.
Computer System for Mechanisms Diagnosis
(Ukrainian State University of Science and Technologies, Dnipro, 2022) Ivashchenko, Valeriy; Shvachych, Gennady; Sushko, Larysa
ENG: The computer system proposed in this work is aimed at solving the problem of automating a comprehensive assessment of the technical functioning of mechanisms. The system’s computational equipment have the minimum necessary computing requirements. No additional paid software is required for installation. Unlike existing systems, the proposed one has a moderate cost. For the majority of industrial enterprises, this factor is crucial when choosing the most beneficial computer system. In addition, the developed system is simple and comfortable to use. Thus, the system has an intuitive and intelligible interface for the operator, which allows the operator to quickly familiarize themselves with it and put it to use immediately; the system monitors the correctness entries in the electronic history - it corrects basic fields that are not properly indicated (repair data, repair requests, part price, etc.). The system has the ability to add individual templates for a specific unit. Unlike existing systems, the proposed system is multifunctional.
Computer Technology for Satellite Imagery Processing in Nature Management Problem Solving Using Lineament Analysis
(Український державний університет науки і технологій, ІВК «Системні технології», Дніпро, 2023) Kashtan, Vita Yu.; Nikulin, Serhii; Hnatushenko, Volodymyr V.; Sergieieva, Kateryna; Korobko, Olha; Ivanov, Denys
ENG: This study focuses on analyzing the techniques used to highlight lineaments in images. Various mask algorithms, including the widely used optimal Kenny detector, were employed to identify brightness boundaries. Additionally, several quality criteria were developed to assess the accuracy of boundary selection. Based on the results of the analysis, conclusions were drawn regarding the effectiveness of different pre-processing methods for space images, along with recommendations to streamline data processing and analysis and enhance the reliability of results. Our analysis of image processing methods for selecting brightness boundaries revealed that the most effective approach involves applying filters to the source images to increase the number of selected boundaries while maintaining their integrity and length.
Construction of a Kinetic Equation of Carbon Removal for Controlling Steel Melting in the Metallurgical System "Cupola Furnace – Small Converter"
(TECHNOLOGY CENTER PC, Kharkiv, 2025) Makarenko, Dmytro M.; Selivorstova, Tetiana V.; Dotsenko, Yuriy V.; Osypenko, Iryna O.; Dzevochko, Oleksandr M.; Pereverzieva, Alevtyna M.; Dzevochko, Alona I.
ENG: The object of research in the paper is the process of steelmaking in a small converter, which works in tandem with a cupola furnace. The existing problem is that the control of the process of obtaining steel in an oxygen converter is complicated by the need to determine in real time the current chemical composition of the melt, in particular carbon. This is due to the fact that the rate of carbon removal is too high, as a result of which the process of carbon removal is transient. Therefore, it is too difficult to implement regulation based on feedback on continuous measurement. The presence of the specified problem requires solutions related to the possibilities of developing or improving software control of the process. It is shown that in certain sections of the process within each time section of oxygen purging of the melt in the converter, the kinetic curve has a linear form with a constant coefficient value in front of the inlet mine. But the value of the initial coefficient for each equation that describes the process within its limits changes. This allows to state that in case of a change in the initial condition, the kinetic curves shift relative to each other in parallel. On this basis, a system of equations has been constructed that describes the process of carbon removal in a small oxygen converter that receives liquid iron from a cupola furnace. It has been shown that to use the obtained system of equations, it is necessary to know the initial carbon content in the melt discharged from the cupola furnace, and it depends on the method of oxygen supply to the cupola furnace. Based on the modeling of this process in two variants – using a “sharp blow” and supplying oxygen to the air blown into the tuyeres, a nomogram has been constructed. It allows to determine the initial carbon content for the practical use of the obtained system of equations. Using the obtained system makes it possible to determine the time after which oxygen cutoff should be made. This will allow to decide to implement software control of the melt blowing process in the converter. The presented study will be useful for machine-building enterprises that have foundry shops in their structure, where cast iron is smelted for the manufacture of castings.
Construction of a Mathematical Model of the Heat and Mass Transfer Process in the Main Fairing of a Launch Vehicle at the Pre-Launch Preparation Stage
(TECHNOLOGY CENTER PC, Kharkiv, 2025) Biliaiev, Mykola M.; Biliaieva, Viktoriia V.; Rusakova, Tetiana I.; Kozachyna, Vitalii A.; Semenenko, Pavlo V.; Berlov, Oleksandr V.; Kirichenko, Pavlo S.; Hrudkina, Nataliia S.; Voitenko, Yuliia V.; Dolzhenkova, Olena V.
ENG: This study investigates the sequential and continuous formation of thermal fields in the main fairing of a launch vehicle when using protective screens. While thermostating, it is necessary to predict the risk in overheating the payload body and, if necessary, take measures to reduce the temperature near the payload. An engineering solution to this problem can be found through the use of protective screens of various configurations inside the main fairing. These screens reduce the heat flow from the heated outer wall of the fairing to the payload surface. However, there are no standard methods for solving this problem. To evaluate the effectiveness of this protection, a numerical model based on the fundamental equations of continuum mechanics has been constructed. The modeling equations include the energy equation and the equation of motion of a non-viscous gas. Using the numerical model built, a computational experiment was conducted, which confirmed the effectiveness of using protective screens to shield the payload body from excessive heating. The computer time required to perform the computational experiment is 3 seconds. This makes it possible to perform a significant number of calculations in a working day. The proposed simple technical means for protecting the payload from excessive heating could be used in the design of new models for rocket technology. Applying these screens slightly reduces the need for large volumes of clean air. The numerical model built could be used at specialized organizations at the “for-sketch” design stage. Numerical experiments have shown that the use of protective screens inside the main fairing makes it possible to achieve a temperature 2–4°C lower than the maximum permissible temperature near the payload.
Data Flow Management in Information Systems Using Blockchain Technology
(Dnipro University of Technology, Dnipro, 2024) Sytnyk, Roman; Hnatushenko, Viktoriia V.
ENG: Purpose. Improving the process of information transfer for critical infrastructure sectors and enterprises through new approaches to real-time tracking of goods, services, and equipment, ensuring secure and transparent data integration and auditing of data flows in information systems using blockchain technologies. Methodology. This research moves away from traditional centralized data management systems based on SQL and no-SQL databases by implementing a decentralized, immutable system built on blockchain technology. This uses the principles of the Merle tree in a digital ledger within blockchain technology to verify data integrity and smart contracts to automate key data flow processes. By tracking goods and equipment through supply chains on the blockchain, this approach ensures product authenticity, provenance, and transparency in real time. In addition, it creates a secure and transparent audit trail for all data in the system compared to conventional centralized data management systems based on SQL and no-SQL databases. Findings. The developed blockchain-based approach improves data security, transparency, automation, and trust in managing data flows. Compared to traditional systems, it offers unique advantages such as immutability, decentralized management, and improved traceability. But while offering numerous advantages, blockchain also faces some limitations in terms of scalability and system complexity. Originality. Digital ledger and blockchain methods have been further developed in the context of designing information systems and data flow management systems based on blockchain algorithms in the context of Industry 4.0. This allows increasing data security, transparency, automation, and trust in data flow management. Practical value. The proposed approach is used to design information and data flow management systems based on blockchain algorithms. This improves the quality of data flow management in industrial enterprises and critical infrastructure, as well as supply chains.
Decentralized Information System for Supply Chain Management Using Blockchain
(RWTH Aachen, Germany, 2022) Sytnyk, Roman; Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.
ENG: Development of international and domestic trade, globalization, creation of longer and more complex supply chains, increase in sales of goods and similar trends lead to an increase in requirements and load on information systems that manage and monitor the shipments of goods, resources and products. The aim of this paper is to make improvements to the existing approaches of building and designing logistics information systems. The paper proposes usage of blockchain technology in order to simplify and make more transparent the processes of monitoring and managing the movement of products between different equal participants in logistics supply chain information systems. A prototype of the supply chain information system based on the use of blockchain technology and smart-contracts using a decentralized Ethereum virtual machine was developed and studied in comparison with traditional approaches.
Deep Learning-Based Segmentation of Multi-Temporal Satellite Imagery for Flood Detection
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: A deep learning-based pixel-based flood zone segmentation approach is proposed using multi-temporal satellite images and topographic and hydrological information. It is proposed to combine heterogeneous data (satellite images before and after the flood, digital elevation model, and hydrographic characteristics) into a single input tensor, allowing the neural network to consider the area's spatial and temporal dynamics and morphometric features. The architecture of the model ensures the preservation of the spatial detail of the flooded area through skip-connection mechanisms, which contributes to the correct identification of flood boundaries. Comparative analysis with FCNN, DeepLabv3, and BASNet confirmed the superiority of the proposed approach (F1-score 82%, Dice 82% for the category 'flooded areas'), which indicates its effectiveness for accurately detecting flooded areas.
Detecting Extraordinary Application Memory Use by Analyzing Memory Screenshots
(Public Organization "Ukrainian Assembly of Doctors of Sciences in Public Administration", Kyiv, 2024) Guk, Natalia A.; Mitikov, Nikolay Y.; Selivyorstova Tatjana V.
ENG: This study investigates excessive memory consumption in .NET applications, with a focus on identifying inefficiencies in memory allocations that lead to unnecessary resource usage. Real-world processing system memory snapshots were gathered using ProcDump, and managed heaps were thoroughly inspected with WinDBG to uncover memory usage patterns and the distribution of space among different types. ClrMD was employed to further analyze runtime data and offer optimization recommendations targeted at reducing the overall memory footprint. To validate these optimizations, Benchmark.NET performance tests were conducted using 'application' data to measure memory usage before and after the suggested changes. The analysis uncovered that user-defined types were responsible for consuming significantly more memory than required. This overconsumption was due to overallocation, largely driven by an overestimation of the necessary data range for the objects, despite domain-specific data consistently fitting within smaller numeric ranges. The mismatch between object design and actual data requirements led to memory inefficiencies. After conducting targeted optimizations based on the real characteristics of the stored data and adhering to memory alignment principles, the study succeeded in significantly reducing the application's memory consumption. These optimizations resulted in memory savings potentially measured in gigabytes, demonstrating the effectiveness of aligning object design with data representation. The research underscores the value of memory snapshot analysis as a tool for identifying and mitigating excessive memory usage in .NET applications. It advocates for a more deliberate object design strategy, one that takes into account the actual range and size of the data being handled. Such an approach can result in significant performance improvements and more efficient memory management. This study offers practical insights for developers aiming to enhance the memory efficiency of .NET applications, contributing to more sustainable and scalable software systems.
Detection of Forest Fire Consequences on Satellite Images Using a Neural Network
(German Society for Photogrammetry, Remote Sensing and Geoinformation, 2023) Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.; Kashtan, Vita; Heipke, Christian
ENG: The objective of this research is the detection of burnt forest areas from Sentinel-2 imagery. The proposed algorithm uses an approach based on convolutional neural networks (CNN). The functionality of the created system allows solving the task, starting from the moment of receiving the input data, image preprocessing and ending with the export of a hot-spot fire polygonal file describing the area that was burnt. These results are compared to methods based on the dNBR and a variant of BAIS2 called dBAIS2, which are generated from measurements in the near and middle IR channels of the Sentinel images. The proposed algorithm was tested on Sentinel satellite images acquired from June to September 2021for the Tizi Ouzou region, Algeria. We found it to have an overall accuracy of 97%, outperforming the results obtained from dNBR and dBAIS2 by large margins.
Determining the Optimal Composition of Low-Basicity Slags Using Pegmatite for Electromelting Processes
(TECHNOLOGY CENTER PC, Kharkiv, 2026) Proidak, Yurii S.; Gorobets, Anton P.; Zhadanos, Oleksandr V.; Rybalchenko, Mariia O.
ENG: This study focuses on the process of slag formation and its performance during steelmaking using carbon charge, when remelting alloyed scrap in electric steelmaking units, and in secondary metallurgy units. One of the relevant issues is the use of alternative materials, such as alkali aluminosilicates, capable of replacing conventional slag components without compromising the quality of the slag. This study reports scientifically proven conditions for replacing fluorspar in the composition of slags used in remelting technologies and secondary metallurgy with domestic mineral raw materials – pegmatites, which contain up to 10–15% of the total alkali metal oxides Na2O and K2O. The effect of Na2O and K2O on the rheological characteristics of the slag melt in the CaO-SiO2+(Na2O, K2O) system has been confirmed. A series of experimental meltings was carried out to establish the slag-forming regime in a ladle-furnace when fluorspar in the solid slag-forming mixture is completely replaced with pegmatites. A comparative analysis of the compositions of refining slags for the current industrial technology and the experimental technology has been performed. A significant increase in slag fluidity and a desulfurization level of the metal comparable to that of the current technology were established, despite a decrease in slag basicity to 1,8–2,0, which is consistent with the requirements of remelting technologies. Chemical analysis of the metal and slag compositions was conducted for the experimental meltings at the stages of semi-product melting and steel treatment in the ladle furnace. The metal from the experimental meltings fully met the requirements of the normative and technical documentation. Thus, this work provides a theoretical justification for an innovative secondary-metallurgy technology using alkali aluminosilicate pegmatite as a part of slag forming mixtures. The metal quality indicators in terms of sulfur content confirm the effectiveness of the devised technology with partial or complete replacement of fluorspar with pegmatite during remelting or secondary steelmaking.
Developing Software to Solve Certain Problems of Inventory Management
(Видавництво «Молодий вчений», 2022) Lozovska, Lyudmila I.; Bandorina, Lily M.
ENG: In modern conditions of intensifying competition in consumer markets, stable operation of enterprises is ensured by the implementation of strategies for maximum satisfaction of consumer demand for goods and related services. Planning such strategies requires improvements in various enterprise systems, in particular, the inventory management system based on the rationalization and optimization of product flows, use of appropriate models and methods for these tasks. Every company strives to satisfy the customer at the highest possible level. This task requires a significant investment in inventory. At the same time, an equally important task is to find new opportunities to reduce all types of costs and increase profits, therefore, from the point of view of finance, the ideal state is when there is no stock while the production needs of the company are met in full. But such a situation is absolutely impossible in real conditions. In order to work as efficiently as possible and reach acceptable financial indicators, it is necessary to effectively manage inventory stock at the enterprise. An essential feature of inventory modeling process is uncertainty in real conditions, which is associated with the inaccuracy or incompleteness of information about demand, supply, time delays of ordered goods, product spoilage and other parameters of the logistics system. This necessitates an effective inventory management mechanism in conditions of uncertainty. Solving this problem requires calculation of exact date and quantity of each subsequent order. Most modern software packages for inventory management are quite difficult to understand or require large financial expenses. In addition, most practical applications are designed for deterministic parameters of demand and production schedule. In fact, demand has a high level of uncertainty, which requires improved algorithms using stochastic theory of inventory management. Thus, a number of issues related to this field still remain unresolved or incompletely resolved. Most publications consider the classic toolkit with some well-known modifications. However, classic models are quite difficult to apply in in real life because the ideal conditions are difficult to achieve in practice. The models do not take into account the limitations imposed by internal and external factors. Based on this, we have created software that would perform the required task. The final product of the research is a software product that enables warehouse operators and managers to optimize drafting a schedule of production inventory deliveries in conditions of demand uncertainty, as well as controlling deliveries based on an economic-mathematical model of inventory management in conditions of demand uncertainty. The software is able to determine the optimal period of delivery of several types of resources, total costs for storage and ordering, the effect of the obtained savings, speeding up and simplifying the dispatcher's work.
Development and Research of a Chatbot Using the Linguistic Core of Amazon Lex V2
(CEUR-WS Team, Aachen, Germany, 2024) Hnatushenko, Viktoriia V.; Ostrovska, Kateryna Yu.; Nosov, Valerii
ENG: The main of this research is to develop and explore the configuration of a text and voice recognition system, integrate it into a specialized application, and deploy the application in a cloud environment. Amazon Lex service is built on chatbots that support Natural Language Understanding (NLU) and voice recognition. The developed chatbot elevates the user experience while engaging with voice consultants by offering flexible customization options. A chatbot has been designed with interactive text input fields and voice recording functions. The server architecture of the application is configured for seamless data transmission through the AWS SDK to Amazon Lex. The input information undergoes processing to ensure the generation of responses that are dynamically displayed on the web page. The structure of all intents – simulating banking services such as checking card balance, transaction history, and more. Testing the intents was done by creating a dataset with possible user statements and automated runs. The developed chatbot was tested through 6 runs, each consisting of up to 5 statements for recognition. The accuracy of text input recognition ranged from 60% to 99%, with voice input recognition accuracy being 10% lower.
Development of a JFET Model with Increased Accuracy: Measurements of Wrangling Data, Acquisition and Model Analysis
(CEUR-WS Team, Aachen, Germany, 2025) Hnatushenko, Viktoriia V.; Guda, Anton I.; Zimoglyad, Andrew Yu.; Zhurba, Anna O.
ENG: Junction gate field – effect transistors have a significant role in the modern electronics. Simulation of the electronic schematics is a crucial part of the modern devices development. At the present time an existent model are used. A drawbacks of the exiting JFET models, with are commonly used during electronic schematics simulation a described. For the tasks of precision simulation the simple approximation functions and switching conditions lead to accuracy loss. A general purpose and specialized hardware and software complex was created to acquire measurement data. This measurement complex gives as possibility to acquire measurement data in automatic and semi-automatic modes. A bulk amount of data about selected JFET species was collected. According to this data a new model was proposed. This model allows us identify parameters in sequence, which significantly decreases the possibility of the identification errors. Proposed model requires more complex calculations to achieve results, and more data to conduct parametric identification. But as the result, new model provides better agreement with experimental data, especially in low-voltage regimes. New model allows us to decrease simulation error level from the 20% to 1—5%. The proposed model provides better qualitative conforming to the experimental data.
Development of a Linear-Scaling Consensus Mechanism of the Distributed Data Ledger Technology
(Springer, Singapore, 2022) Shvachych, Gennady G.; Pobochii, Ivan A.; Sashchuk, Hanna; Dzhus, Oleksandr; Khylko, Olena; Busygin, Volodymyr
ENG: The paper proposes and explores a new blockchain system that operates on a linearly scalable consensus mechanism. This selection method confirms the shard through shares voting and scalable random generation by VDF (Verifiable Delay Function) and VRF (Verifiable Random Function). The system analyzes available consensus mechanisms, sharding, and the age of distributed randomness. It is energy efficient, fully scalable, secure, with fast consensus. Compared to available methods, the improved shard method performs network connection and transaction verification and reveals the state of the blockchain. The threshold has a sufficiently low coefficient for small validators to participate in the network and receive rewards. The proposed sharding process runs securely due to a distributed randomness (DRG) process that is unpredictable, impartial, and verified. The network is constantly overloaded to prevent slow adaptive Byzantine malicious validators. Contrary to other sharding blockchains that require Proof-of-Work to select validators, the proposed consensus is attributed to Proof-of-Stake, therefore, energy-efficient. Herein the consensus is achieved by a BFT algorithm which is linearly scalable and faster than PBFT.