Applied Aspects of Information Technology
The publication focuses on the practical implementation of the latest information technologies in industry, energy, financial technologies, healthcare, sociology, education, project management, ecology, and human safety.
Національний університет «Одеська політехніка». Інститут комп'ютерних систем
Parametric approach to improving significant indicators of thermoelectric systems for maintaining thermal conditions
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: reliability indicators, dynamic characteristics, thermoelectric material, efficiency, current mode The problem of improving the reliability of complex systems is fundamental, since it is impossible to achieve complete failure-free operation in real systems that interact with the external environment. In modern information systems, increasing the probability of failure-free operation by stabilizing the temperature of critical heat-loaded elements using a thermal control system remains a pressing issue. To solve this problem, a mathematical model of a thermoelectric cooler of a given design has been developed. The model is intended for comparative analysis and assessment of the influence of various combinations of parameters of the initial thermoelectric material of equal efficiency on the main performance indicators of the product under variable current load. The results of calculations of reliability indicators, dynamic and energy characteristics of single-stage thermoelectric coolers, and the geometry of thermoelectric element chains are presented for various combinations of parameters of the initial thermoelectric materials. It is shown that with a rational design of a single-stage thermoelectric cooler, it is possible to select a combination of parameters of the initial material with the same efficiency to reduce the operating current and increase the supply voltage at different thermal loads. Additional means of controlling the parameters of a thermoelectric cooler are the geometry of the thermocouple branches, the physical parameters of the initial thermocouple materials, the operating current, the supply voltage, and the failure rate. Analysis of the research results showed that it is possible to select a combination of raw material parameters that reduces the operating current by an average of 20% and reduces the relative failure rate by up to 40% under otherwise equal conditions.
Applied information technologies in energy engineering and automation
Applied information technology in energyengineering and automation
Method for automated control of rotor engine blade synchronization
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: automated control, computer modeling rotary-blade internal combustion engine, computer simulation, electrical synchronisation, torque control This study addresses the critical issue of synchronization accuracy in rotary-blade internal combustion engines, which directly influences their efficiency and operational reliability. Traditional mechanical synchronization methods suffer from significant drawbacks, including rapid wear, high maintenance costs, and limited modernization potential. To overcome these limitations, we propose an innovative electrical synchronization approach based on adaptive torque control of electric machines. The developed algorithm dynamically adjusts torque in real time according to the displacement of the bisector of the angle between the blades forming the combustion chamber, ensuring precise synchronization during each engine cycle. A mathematical model was formulated to describe the relationship between torque, bisector displacement, and engine dynamics. This model was implemented in a custom simulation environment developed in JavaScript with interactive visualization tools. Computer simulations were conducted under various operating conditions to evaluate the algorithm’s performance. Results demonstrate a substantial reduction in bisector displacement - from several degrees to thousandths of a degree and stabilization of cycle duration after only a few iterations. These findings confirm the algorithm’s ability to maintain synchronization accuracy and improve engine stability. The proposed approach offers practical benefits for the design of advanced rotary-blade internal combustion engines, reducing mechanical wear and enhancing energy efficiency. Future research will focus on optimizing the correction factor, adapting the algorithm for engines operating under variable loads and temperatures, and integrating PID-based control strategies for improved responsiveness.
computer modeling rotary-blade internal combustion engine
Technological platforms, tools, and standardization in the unified information space for automated unmanned aerial vehicle design
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: model-based engineering, product lifecycle, digital thread, tool interoperability, unmanned aircraft, systems integration Unmanned aerial vehicle development entails highly interdisciplinary processes, often hampered by fragmented digital toolchains and siloed data. This paper addresses the critical need for an integrated unified information space across the unmanned aerial vehicle lifecycle. We present a review of modern platforms and standards that enable end-to-end data continuity – the “digital thread” – from initial requirements through design, manufacturing, and operations. The aim is to synthesize current approaches to achieving interoperability and an authoritative source of truth for unmanned aerial vehicle projects. Key objectives include mapping prevalent Model-Based Systems Engineering and Product Lifecycle Management platforms, comparing lifecycle standards (e.g., systems modeling language, Standard for the Exchange of Product Data, Open Services for Lifecycle Collaboration, Open platform communications unified architecture) across development phases, proposing an integration framework to connect heterogeneous tools, and identifying gaps in adoption. In terms of methods, a structured literature review was conducted using IEEE Xplore, Scopus, and engineering databases. Search strings targeted unmanned aerial vehicle tool integration, Model-Based Systems engineering and Product Lifecycle Management interoperability, and digital thread case studies. Inclusion criteria focused on publications and standards addressing cross-domain data integration; industry white papers and standards documentation were also analyzed. The results reveal a rich landscape of standards for requirements management, system architecture, product data exchange, simulation interoperability, and IoT-based operational feedback. We provide a comparative analysis (including tables) of these standards versus lifecycle stages, and of integration approaches (Application Programming Interfaces, service buses, Open Services for Lifecycle Collaboration links, industrial Internet of Things protocols) against factors like latency, scalability, and security. A conceptual integration blueprint is outlined, leveraging open standards to connect systems modeling language models, Computer-Aided Design/Product Lifecycle Management data, and real-time sensor information into a cohesive environment. Discussion highlights trade-offs such as proprietary Product Lifecycle Management suites versus openness, the maturity and portability of Model-Based Systems Engineering models (systems modeling language version 1.0 limitations and systems modeling language version 2.0 prospects), and cloud vs. on-premises deployment given aerospace security International Traffic in Arms Regulations constraints. Organizational readiness (business process reengineering, stakeholder buy-in) emerges as a key success factor. In conclusion, unifying the information space can dramatically improve unmanned aerial vehicle development efficiency, traceability, and innovation. However, realizing this vision requires not only technical solutions but also adoption of standards and cultural change in engineering practices. The paper’s synthesis provides practical insights and a roadmap for both researchers and practitioners aiming to implement a consistent digital thread for complex aerospace systems.
Systems analysis, applied information systems and technologies
Systems analysis, applied information systems and technology
Research on improving a graph neural network model forcomputer network simulation
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Сomputer network modeling, machine learning, graph neural network, network parameters, node characteristics, optimization, network topology Modern computer networks face increasing challenges due to the growing complexity of their structure, dynamic traffic fluctuations, and the need to maintain high performance. Traditional approaches to network modeling often fail to accurately predict parameters such as latency or packet loss, as they have limited capacity to capture the specific characteristics of individual network elements. This highlights the relevance of developing novel methods that can adapt to real operating conditions and ensure efficient resource management. The aim of this study is to enhance network modeling methods by developing a model that incorporates the individual properties of network elements to improve the accuracy of parameter prediction and to optimize routing processes. The research objectives include the analysis of current modeling approaches, the design of an improved model based on machine learning techniques, the refinement of training algorithms, and the execution of experiments to evaluate the model’s effectiveness. Machine learning methods were applied in the implementation, with particular emphasis on a graph neural network, which enables the modeling of complex interdependencies among network elements. The proposed model integrates node-specific characteristics into the data processing pipeline, thereby ensuring adaptability to heterogeneous conditions. Experiments were conducted on multiple datasets representing real-world network topologies; with prediction accuracy assessed using several evaluation metrics. The results demonstrate that the proposed model provides higher accuracy in predicting network parameters compared to baseline approaches, exhibiting the ability to generalize to unseen topologies. The scientific novelty of the work lies in the incorporation of element-level characteristics into the modeling process, allowing for a more precise reflection of real-world conditions. The practical significance is manifested in the potential application of the model in network management systems for routing optimization and infrastructure cost reduction. The findings open new prospects for further development of modeling and management methods in modern networked systems.
Performance evaluation of photon mapping methods
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Computer graphics, photon mapping, rendering, machine learning This research presents a comprehensive performance evaluation of photon mapping techniques for global illumination, with a focus on real-time rendering and large-scale visual simulations. The study introduces a custom implementation of a KD-tree-based spatial indexing structure for photon retrieval, providing an empirically validated alternative to linear search in high-density photon environments. The integration of this method with controlled benchmarking constitutes the core novelty of the work. Five modern photon mapping strategies are investigated collectively without emphasizing individual advantages, including traditional formulations, progressive and stochastic progressive variants, Monte Carlo-based hybridization, and machine-learning-augmented methods. Experiments are conducted on diverse test scenes with varying geometric and lighting complexity, using both Central processing unit (CPU) and Graphics processing unit (GPU) platforms to measure scalability and efficiency under distinct resource constraints. Evaluation is based on rendering time, memory usage, and image quality indicators such as mean squared error and peak signal-to-noise ratio. A uniform testing pipeline ensures methodological consistency across hardware setups and photon emission scales. The KD-tree approach demonstrates improved query performance as photon count increases, reducing lookup overhead and enabling more efficient scene processing in dense illumination contexts. The findings are applicable to next-generation physically based rendering engines, interactive graphics applications, and high-fidelity simulation systems where computational cost and responsiveness are critical. The implementation insights, experimental results, and derived recommendations serve as a foundation for the selection and deployment of photon mapping algorithms in scenarios requiring accurate global illumination and scalable real-time visualization. The work supports design decisions in rendering architecture by quantifying trade-offs in algorithmic structure, hardware acceleration, and memory behavior. The novelty of this work lies in the implementation and evaluation of a custom KD-tree algorithm for photon retrieval, which has not been previously benchmarked in the context of large-scale photon mapping. Unlike purely theoretical analyses, this study provides empirical validation of KD-tree efficiency compared to linear search, offering practical insights into scalability trade-offs for real-time and high-fidelity rendering applications.
Computer science and software engineering
Hybrid graphs for code smells: a multi-level model for anti-pattern detection in software components
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Machine learning, software engineering, program analysis, graph representation learning, static analysis, uncertainty estimation, transfer learning, empirical evaluation The paper proposes a hybrid, multi-level method for detecting code smells and anti-patterns in software components, where structure, semantics, metrics, and evolution are treated as first-class signals. A heterogeneous Code Property Graph (Abstract Syntax Tree + Control-flow Graph + Program Dependence Graph) is constructed and enriched with textual embeddings from a pretrained code language model, classical quality metrics (Chidamber–Kemerer, Halstead), and version-control history (churn, co-change, recency). Local idioms are summarized via a sequence–graph encoder at the method/block level, component structure is aggregated by a relation-aware Graph Neural Network at the class/module level, and project context is propagated over a component-interaction graph. To support deployment in evolving codebases, an open-set head is introduced: energy, entropy, and stochastic variance are combined to enable calibrated abstention on unfamiliar patterns. The approach is evaluated on polyglot Java Virtual Machine corpora using time-aware, cross-project splits with multi-label targets (Long Method, God Class, Feature Envy, Data Class, Shotgun-Surgery–like, No-smell). Improvements in macro Area Under the Precision–Recall Curve and F1 overrule/metric baselines, Abstract Syntax Tree-only, and text-only models are observed, while FPR@95TPR is maintained or reduced. Withheld-class experiments show that open-set gating increases Area Under ROC for Open-Set Recognition and TNR@TPR and lowers calibration error, yielding probabilities suitable for thresholded automation and human triage. Cross-language transfer (train Java → test Kotlin/Scala) is shown to be stronger than with single-view models, aided by language-agnostic typing and per-project normalization. Incremental graph maintenance confines computation to changed regions, aligning inference time with CI/CD budgets. By exposing hierarchical attention and channel gates, explanations are produced that align with practitioner reasoning. It is concluded that hybrid graphs with hierarchical reasoning and selective prediction deliver detectors that are more accurate, transferable, and operationally safer for evolving software systems.
Methods for addressing labeled data scarcity in applied computer vision systems
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Machine learning, augmentation, convolution neural networks, labeled images, data generation, pseudolabeling This article systematizes approaches to addressing the shortage of labeled images in datasets for deep learning models, which constitute the core of modern computer vision applications. It covers methods such as transfer learning using pre-trained networks, weakly supervised and semi-automatic labeling techniques, and various sample expansion strategies through data augmentation. Particular attention is given to data augmentation as a widely applicable and technologically accessible approach. The article presents an experimental analysis of the impact of different augmentation methods, including classical transformations such as rotation, reflection, noise addition, and modern techniques like MixUp, CutMix, and AugMix, on the performance of convolutional neural networks in image classification tasks. Results demonstrate that even basic augmentation methods can substantially improve model generalization when training data are limited, and combining multiple strategies can approach the accuracy achievable with fully labeled datasets. In addition, the study explores the application of pseudo-labeling, a semi-automatic labeling method, with a focus on the RAF-DB dataset for facial expression recognition. By generating confident pseudo-labels for unlabeled samples, the model’s performance improved by up to two percent in overall accuracy and up to ten percent in individual class recognition. This highlights the potential of combining augmentation with pseudo-labeling to enhance model robustness in scenarios with scarce labeled data. An additional experiment using a basic CNN on the CIFAR-10 dataset confirmed the positive effect of augmentation on classification accuracy. The employed transformations included horizontal flipping, random cropping, and color jittering, resulting in a аfive percent increase in overall accuracy compared to models trained without augmentation. The findings indicate that integrating classical and advanced augmentation techniques with semi-automatic pseudo-labeling provides a practical and effective strategy for improving deep learning performance on limited datasets. This combined approach is particularly valuable for real-world applications where labeled data are scarce or costly to obtain, demonstrating a promising direction for further research and deployment in domains such as facial emotion recognition and general object classification.
Post-clustering interpretation of gene expression data using functional enrichment and network analysis
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Computational biology data analysis, bioinformatics, integrative analysis, gene expression data, post-clustering interpretation, functional enrichment, gene ontology, kyoto encyclopedia of genes and genomes, reactome, cytoscape, network-based analysis Clustering of gene expression profiles is a core technique used to reveal hidden biological structures and differentiate disease subtypes in high-dimensional biomedical datasets. Nevertheless, translating cluster structures into biologically meaningful insights requires integrative analytical strategies that go beyond unsupervised learning. In this work, we introduce a novel integrative computational approach that emphasizes post-clustering interpretation by combining statistical functional enrichment with network-based modeling. Clusters of gene expression profiles, previously identified in patients with distinct cancer types, were subjected to enrichment analysis using Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, and Reactome databases. The enrichment was performed with the g:Profiler tool, allowing the detection of significantly overrepresented biological processes, molecular functions, cellular components, and signaling pathways within each cluster. To visualize and further interpret the enriched functional categories, Cytoscape software was employed. Functional interaction networks were constructed using two key modules: ClueGO, which integrates Gene Ontology and pathway annotation into a functionally grouped network, and CluePedia, which expands these networks by showing relationships between genes and enriched terms. This network-based visualization enabled deeper biological interpretation and facilitated the identification of core functional themes. The analysis revealed that each gene cluster is associated with distinct biological processes, such as immune signaling, metabolic pathways, DNA repair, or cell cycle regulation. The novelty of the proposed approach lies in its systematic integration of enrichment statistics with graph-based visualization, ensuring both computational rigor and biological interpretability. These findings confirm that the method can extract biologically consistent knowledge from complex gene expression data. In summary, the study presents an innovative post-clustering interpretation strategy that bridges unsupervised machine learning and functional genomics. This approach advances the explainability of computational analysis and supports its application in disease subtyping, biomarker discovery, and personalized medicine research.
Generalization of the method for constructing GL-models of complex fault-tolerant multiprocessor systems with additional failure conditions
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: GL-models, non-basic fault-tolerant multiprocessor systems, consecutive two-dimensional system, mixed-type systems, reliability assessment The article is devoted to methods for constructing GL-models of fault-tolerant multiprocessor systems. GL-models can be used as behavioral models of such systems under failure flows to evaluate their reliability metrics through statistical experiments. The study considers two types of systems: consecutive two-dimensional systems and mixed-type systems. A consecutive two-dimensional system is defined as one in which components are arranged in the form of a rectangular matrix, and the system fails when a rectangular block of a certain size appears, consisting entirely of failed components. A mixed-type system fails if at least one of the following conditions is met: a specified number of arbitrary components have failed; a specified number of consecutive components have failed; or a rectangular block of a certain size, consisting entirely of failed components, appears within the component matrix. Currently, there are no formalized methods for constructing GL-models for the aforementioned types of systems. The objective of this work is to develop a universal method for constructing GL-models for both consecutive two-dimensional systems and mixed-type systems. It is shown that, to construct a GL-model for such a system, it is sufficient to determine the maximum number of failed components under which the system remains operational. Based on this threshold, a basic system model is constructed without considering additional failure conditions. Then, all combinations of component failures that lead to system failure are identified. The basic model is subsequently weakened at the vectors corresponding to these critical failure combinations. This paper presents, for the first time, an algorithm for constructing GL-models for consecutive two-dimensional systems and mixed-type systems. In addition, it introduces methods for calculating the maximum allowable number of component failures under which the system remains functional, as well as estimating the total number of failure combinations that result in system failure. Experimental results confirm that the proposed models adequately represent the real behavior of such systems under failure flows. Examples are provided to illustrate the GL-model construction process for both of the aforementioned system types.
non-basic fault-tolerant multiprocessor systems
Risk assessment and network description modules in a multi-level wireless sensor network risk assessment ontology
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Wireless sensor networks, multi-level ontology, risk analysis, sensor node, threat intensity, threat probability, Supervisory Control and Data Acquisition Wireless sensor networks have become widely used in industrial automation systems due to their ease of deployment, flexibility, and cost-effectiveness. They provide real-time monitoring, diagnostics, and control of technological processes without the need for complex cabling infrastructure. However, the operation of wireless sensor networks in production environments is accompanied by risks associated with limited energy resources of nodes, the impact of electromagnetic interference, data transmission delays, the inability to guarantee communication stability, and vulnerability to cyberattacks. These threats are exacerbated in complex environments where equipment is located in hard-to-reach places, and the network has limited bandwidth and variable topology. Such conditions require the construction of multi-level structures for network health analysis, risk assessment, and adaptive management. The aim of the research is to develop two functional modules of a multi-level ontology: a risk assessment module and a network description module, that allow for an integrated analysis of threats, vulnerabilities, and wireless sensor network parameters, taking into account their changing state. The research methodology is based on the construction of formalized mathematical models: the network description is implemented through a graph representation with the parameters of nodes, connections, and their dynamics, while the risk assessment is performed using a multifactor model that takes into account the probability of threats, the level of their impact, the criticality of network components, and external risks. The article presents examples of the application of modules based on typical production scenarios that demonstrate the relationship between the structural description of the network and the dynamic assessment of threats. Information from the network description module is used to calculate risks in real time, while the results of the risk analysis affect the dynamic adjustment of network operation parameters. The scientific novelty of the research lies in the integration of two formalized models within a single ontological system of risk-oriented wireless sensor network management. The practical significance lies in the possibility of implementing the proposed approaches in real industrial conditions to increase the reliability, flexibility, adaptability and information security of sensor networks. The proposed system is the basis for building self-adjusting smart networks capable of independently responding to changes in technical condition, threats and environmental parameters.
Supervisory Control and Data Acquisition
A hybrid method for detecting anomalous traffic in computer networks
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Anomaly detection, hybrid method, network traffic, packet signature, fuzzy logic, self-similarity, behavioral model, outbound traffic, intrusion detection, cybersecurity This study addresses the increasing difficulty of detecting anomalies in network traffic caused by growing threats to information and communication systems. Traditional intrusion detection systems often fail to adapt to new threats, particularly when analyzing outbound traffic, which may signal internal compromise. To overcome these limitations, the study proposes a hybrid detection method aimed at improving anomaly identification accuracy. The method integrates three components. First, traffic is classified using a signature-based approach with predefined sets of allowed and prohibited signatures. Second, self-similarity analysis with the Hurst coefficient detects long-term traffic patterns. Third, fuzzy logic is applied to interpret uncertain traffic characteristics, such as port numbers, protocols, intensity, and packet sizes, using linguistic variables and fuzzy rules. The research presents formalized models of both legitimate and malicious user behavior and a composite packet signature model for comprehensive traffic analysis. This approach enhances adaptability and reduces the proportion of unclassified traffic. Experimental validation using real and synthetic data confirms improved detection accuracy and a lower false positive rate compared to conventional methods. The scientific novelty lies in combining deterministic classification with fuzzy logic within a single detection pipeline, with a special emphasis on outbound traffic monitoring. The practical value of the proposed system is its suitability for integration into existing cybersecurity frameworks, contributing to more effective threat detection and reduced operational risks in evolving network environments.
A systematic approach to selecting architectural patterns for IoT development
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Internet of Things, architectural design patterns, pattern selection methodology, quality evaluation model, system architecture The increasing complexity and scale of Internet of Things (IoT) systems, especially within industrial environments, pose significant challenges in system design, including issues of security, interoperability, scalability, and efficient resource utilization. With a wide array of architectural patterns available to address these challenges, developers often struggle to select the most suitable solutions. This paper presents a systematic methodology for evaluating and choosing the best combinations of architectural design patterns tailored for various IoT deployment scenarios. The approach begins by analyzing existing IoT design patterns and modeling their key operational characteristics. A structured template is used to describe each pattern, facilitating consistency and comparability. These descriptions are evaluated using a quality model comprising criteria such as reliability, safety, usability, responsiveness, adaptability, durability, interoperability, and security. A weighted-sum model, with adjustable criterion weights, transforms qualitative assessments into quantitative aggregated scores. This enables objective ranking of patterns and supports defensible architectural decision-making. The methodology is validated through multiple case studies, including general-purpose IoT systems (e.g., smart homes) and Industry 4.0 environments. In each case, patterns are selected based on system-specific priorities. Notably, high-performing patterns such as Cloud-on-the-Loop, Closed-Loop Control, and Role-Based Access Control align well with known best practices and demonstrate the method’s practical applicability. Sensitivity analysis further confirms the approach's adaptability, illustrating how changes in evaluation weights significantly influence the resulting pattern rankings. This systematic methodology improves the reproducibility, transparency, and flexibility of IoT architecture design processes. It empowers developers to tailor architectural solutions to specific domain needs while maintaining alignment with industry standards. Future research will explore extending the methodology to emerging IoT sectors, constructing specialized pattern catalogs, and integrating the selection framework into automated design tools to further streamline the development of IoT systems.
Method for incremental control of consistency between structural and behavioral views of software architecture
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Model consistency, incremental verification, model synchronization, metamodel, ontological constraints, reliability The development of software engineering poses a challenge for researchers to maintain the integrity of models stored simultaneously in a lightweight text format and in a formally rich metadata view. The existence of two views ensures broad compatibility with developer tools and accurate reproduction of semantics, but creates the risk of discrepancies between structural and behavioral descriptions. The relevance of the research is determined by the need for methods that prevent the accumulation of contradictions without significantly affecting the speed of design iterations. The aim of this work is to provide a theoretical justification for an incremental approach that can guarantee the consistency of a metamodel with two views during any successive changes. To achieve this goal, a generalized metamodel has been formed that distinguishes between a structural view for static entities and a behavioral view for dynamic aspects. A correspondence relationship has been introduced between views, which describes pairs of equivalent elements and sets rules for their mutual consistency. The set of rules is formalized in the language of object invariants. Incrementalism is ensured by localizing changes: after editing, only those fragments that are directly involved in the modification are checked, so that the time spent remains proportional to the volume of the updated part. The result of applying the method is to prove the correctness of the proposed restrictions, which excludes the possibility of inconsistent model states. An analytical assessment of the complexity of the procedure confirms a linear dependence on the number of changed elements, which indicates the suitability of the approach for industrial-scale models. A demonstration control example, built on a representative domain, showed that the method detects inconsistency immediately after a single edit and proposes a sequence of actions sufficient to eliminate it without involving outside expertise. As a result, the work proposes a new formal methodology for maintaining consistency between views of a single model, which comprehensively combines localized verification with a declarative description of dependencies. The practical significance is manifested in the reduction of error correction costs, increased reliability of documentation, and the ability to integrate the method into modern modeling and continuous development environments, making it a promising tool for the development and maintenance of large corporate systems.
Heterogeneous swarm complex multilevel reconfiguration based on Swarm Chemistry
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Information technology, swarm intelligence, genetic algorithm, multi-agent systems, scan optimisation, swarm chemistry The need for the rapid and safe analysis of areas affected by emergencies is driving the search for innovative approaches in the field of autonomous systems. One such solution is the use of swarms of unmanned aerial vehicles to scan territories. This study presents a hybrid approach to the dynamic control of swarms of drones, combining self-organisation mechanisms based on ‘swarm chemistry’ with the optimisation of global parameters using an evolutionary algorithm. The proposed multi-level swarm reconfiguration module allows the behaviour of individual agents and the entire formation to adapt in response to environmental changes. A clustering module has been implemented to divide the scanning area into subregions, as well as a route planning system that considers the scanning width of each individual drone. To effectively distribute zones between agents, a proportional algorithm is employed that considers the functional capabilities of each device. Simulation results showed a reduction in mission execution time of more than twofold and an increase in the stability of swarm behaviour, even under conditions of heterogeneous composition and partial agent loss. This approach has significant application potential in emergency monitoring, victim search and damage assessment. This study formulates the architecture of a dynamic swarm reconfiguration system that can adapt to environmental changes in real time. This approach ensures the system’s flexibility and stability when performing tasks in difficult conditions. Further development of the model involves integrating machine learning methods to enhance adaptability and expanding to three-dimensional space to improve the accuracy and realism of simulations.
GL-models for analyzing multiprocessor systems tolerant to one and two failures
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: fault-tolerance, multiprocessor systems, reliability estimation, GL-models, non-basic systems, cyclic graph, edge function The work is devoted to the problem of constructing GL-models of behavior in the fault flow of non-basic fault-tolerant multiprocessor systems. We consider systems that are fault-tolerant to any one failure, and under a certain condition – to any two failures of their processors. It is assumed that this condition is satisfied by a Boolean expression that depends on the states of the system's processors. To build models of such systems, we use the previously proposed method based on combining the expressions of the edge functions of auxiliary basic GL-models corresponding to 1- and 2-fault-tolerant systems. Two alternative ways of constructing auxiliary K(2, n) models are considered. It is shown that the obtained GL-models are based on the same cyclic graphs and differ only in the expressions of their edge functions. The complexity of the expressions of the edge functions of the obtained models is analyzed for the cases of using auxiliary GL-models of each type. The formulas for estimating the complexity of these expressions (the number of elementary logical operations) depending on the number of processors in the system, in particular, for cases when this number is a power of two, are obtained. Also, for the case of using one of the auxiliary models, the possibility of further simplification of the expressions of some of the edge functions is revealed. It is determined in which cases, namely, for what number of system processors, it is advisable to use auxiliary models of one or another type. Experiments confirm that the GL-models constructed in the above way adequately reflect the behavior of the system. Examples are given that demonstrate the application of the considered method of constructing non-basic GL-models and confirm the correctness of estimation the complexity of expressions of their edge functions when using auxiliary models of various types. It has also been shown that further simplification of the expressions for the edge functions in GL-models under construction is possible, particularly by reordering the edges in auxiliary models. However, this possibility has not been investigated in the general case within this article.
Investigation of the efficiency of neural network models for developing a classifier of ophthalmic pathologies
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: machine learning, ophthalmic disease classification, fundus images, deep learning, VGG16, VGG19, EfficientNet, medical image analysis This study presents the development and evaluation of a machine learning-based system for the classification of ophthalmic diseases using fundus images. The dataset consists of images categorized into four main classes: cataract, diabetic retinopathy, glaucoma, and healthy eye. To ensure the accuracy and reliability of the models, the data underwent preprocessing steps, including outlier detection, normalization, balancing, and splitting into training and testing sets. Three deep learning models - VGG16, VGG19, and EfficientNet were utilized for disease classification. The experimental results demonstrated high prediction accuracy across different disease categories, with EfficientNet achieving the highest performance (up to 96.94% for diabetic retinopathy). The system allows users to upload eye images, select a model, and obtain diagnostic predictions with specified accuracy levels. The models were rigorously tested using the Python unittest framework, confirming their stability and reliability. The findings highlight the potential of machine learning in improving ophthalmic disease diagnosis, reducing diagnostic time, and enhancing medical decision-making. The integration of these models into medical practice can significantly improve the quality of healthcare services and assist doctors in providing more efficient and accurate diagnoses.
Using large language models for video processing in the agricultural industry
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: artificial intelligence, large language models, multimodal models, computer vision, video analysis, poultry monitoring, behavior recognition, precision agriculture, OpenCV, streamlit application Modern artificial intelligence technologies, particularly large language models, are increasingly being applied in agriculture to enhance automation, decision-making, and sustainability. This study presents a comprehensive analysis of large language models and their integration with computer vision and video processing for real-time livestock monitoring. A software system was developed that utilizes multimodal large language models to analyze poultry behavior from video streams, enabling the detection of anomalies, prediction of potential health issues, and automatic generation of recommendations for farmers. The system is based on a modular architecture and combines technologies such as OpenCV, FastAPI, and Streamlit. Comparative evaluation of models including GPT-4o, Claude 3.7, and LLaVA demonstrates their suitability for different agricultural tasks. The results confirm the effectiveness of large language model-based solutions in improving operational efficiency, reducing human intervention, and supporting precision agriculture. Despite high computational demands, the proposed approach significantly simplifies the deployment of intelligent monitoring systems and opens new opportunities for smart farming innovations.
Integration of physiological factors into a mathematical model of the human eye condition
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: human eye, mathematical modeling, integral index, nonlinear dependencies, optimization methods, adaptability, monitoring, prediction Mathematical models of the human eye serve as adaptive tools for analyzing and predicting ophthalmological parameters, considering their interrelations and individual patient characteristics. Their application in ophthalmology enhances the quality of diagnostics, monitoring, and treatment, ultimately improving patients' quality of life. The developed eye condition model is based on a mathematical function that integrates physiological eye parameters, each assigned a weight coefficient to determine its impact on the overall condition index. The model accounts for complex nonlinear interactions between parameters, allowing for a more accurate representation of physiological processes. To optimize the weight coefficients, the L-BFGS-B method is employed—an iterative algorithm that efficiently minimizes the loss function and ensures high accuracy in adapting the model to individual patient data.This model offers several advantages: it enables early diagnosis of diseases such as glaucoma, cataracts, and macular degeneration, tailors treatment plans based on individual patient parameters, and facilitates disease monitoring and progression prediction for timely therapy adjustments. Furthermore, the model can be integrated with modern technologies, including virtual and augmented reality systems, as well as artificial intelligence for automated diagnostics. Thus, the proposed mathematical model serves as a universal tool for analyzing eye conditions and developing innovative diagnostic and therapeutic technologies. By incorporating parameter interdependencies and their effects on the physiological state of the eye, it provides ophthalmologists with a powerful instrument for enhancing diagnostics, prediction, and disease monitoring in vision healthcare.
Thermal control of a complex of thermoelectric cooling devices
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: reliability indicators, dynamic characteristics, mass and dimensions, temperature difference, current mode The paper presents the results of research into the possibility of controlling a complex of thermoelectric cooling devices with parallel electrical connection in a non-uniform temperature field. Such formulation of the problem of creating systems for ensuring thermal conditions of radio-electronic equipment is relevant for on-board information systems, which are subject to stringent requirements for reliability and mass-size characteristics. Research are carried out for the most used temperature range from 295K to 250K in the range of power dissipation from 0.5 W to 15 W for different values of supply voltage. The model of the executive body of the system of thermal modes provision based on the complex of thermoelectric coolers with parallel electrical connection is developed. The model takes into account different dissipation power, non-uniform temperature field, different supply voltages and geometry of thermoelectric cell branches. The comparative analysis of basic parameters, reliability indices and dynamic functioning of the complex of thermoelectric coolers for different supply voltages, different temperature levels of cooling with the corresponding thermal load is carried out. The possibility of selecting the optimal supply voltage taking into account the limitations on the operating current, mass, energy, dynamic and reliability characteristics of the thermoelectric cooler complex is shown. The analysis of the research results has shown the possibility of choosing the nominal of supply voltages taking into account the limitations on mass-size, energy, dynamic and reliability characteristics for different geometry of thermoelements branches.
Hybrid detection of fuzzy duplicate texts: сosine similarity and transformers
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: hybrid methods, fuzzy duplicates, cosine similarity, transformer models, ukrainian language texts, content moderation systems This paper addresses the challenge of detecting texts that share the same meaning but differ in wording and structure. Such “fuzzy duplicates” are increasingly prevalent in user-generated content, media articles, and academic materials. Traditional TF-IDF-based methods with cosine similarity process data swiftly but often overlook deeper semantic nuances, especially in languages with free word order and complex morphology (for example, Slavic languages such as Ukrainian or Bulgarian, and agglutinative languages like Hungarian). Fully neural solutions (e.g., transformers) typically offer higher accuracy yet can be slow and computationally demanding. To tackle these issues, we propose a hybrid approach that integrates a simplified neural component with classical cosine similarity. The workflow normalizes text variants (correcting spelling and inflectional forms), converts them into semantic vectors using a lightweight transformer model, and then applies a dynamic threshold mechanism tuned to text genre (e.g., news vs. social media). Experiments on Ukrainian-language datasets suggest that this method balances accuracy and speed more effectively than a fully neural pipeline. The approach is novel in combining domain-specific preprocessing and lightweight neural embeddings for fuzzy duplicate detection in text, achieving approximately ten to twelve percent higher detection accuracy than known solutions while maintaining faster runtime than a full BERT model. Preliminary tests in editorial and plagiarism-checking scenarios indicate that the system more reliably identifies paraphrased content than purely statistical methods, thereby reducing the burden of manual verification. Overall, the hybrid design offers a practical compromise between detection performance and computational requirements, which is especially beneficial for resource-constrained applications in morphologically rich languages like Ukrainian or other Slavic languages. Future efforts will focus on extending morphological coverage to further improve reliability.
Using complex-valued neural networks for aircraft identification
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: complex-valued networks, radar signals, aircraft recognition, phase analysis, aviation safety, object identification This paper presents an approach to aircraft recognition using complex-valued neural networks. The objective of the article is to study the effectiveness of complex-valued neural networks for aircraft identification tasks based on radar data, the efficiency evaluated based on criteria such as classification accuracy, robustness to noise interference, the ability to maintain high accuracy with limited training data, and an optimal trade-off between accuracy and computational complexity. The study focuses on aircraft identification using phase and amplitude characteristics of radar signals, which are essential for aviation security and airspace monitoring. The research method includes theoretical analysis, modeling, and experimental testing. The paper discusses the architectural features of artificial neural networks that utilize complex numbers for signal processing. This approach enables the incorporation of phase information, which significantly improves the accuracy of radar data analysis. The results confirm that complex-valued neural networks surpass traditional models in recognition accuracy. Specifically, the inclusion of the phase component provides an increase in accuracy by up to eight and a half percent. Additionally, complex-valued neural networks demonstrate high resistance to noise interference, maintaining classification accuracy of up to ninety-two and three-tenths percent even at a noise level of thirty percent. Despite these advantages, the primary limitation of complex-valued neural networks is their higher computational complexity compared to real-valued models. This requires significant resources for training and implementation, which can be a critical factor for applications where real-time signal processing speed is essential. The study also explores optimization possibilities for artificial neural networks by developing hybrid approaches that combine the strengths of different network types and by simplifying architectures without compromising accuracy. The findings indicate that artificial neural networks are an effective tool for aircraft classification, particularly in complex signal environments and conditions with noise interference. These networks have significant potential for widespread use in both military and civilian airspace monitoring systems, providing enhanced accuracy and reliability in recognition tasks. The results obtained in this study open new opportunities for advancing aviation security technologies and automating aircraft recognition systems.
Development of a combined model for analyzing gas mixtures using machine learning methods
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: spectral analysis, gas mixtures, complex models, neural networks, optimization methods, machine learning Analysis of gas mixtures is an important task in spectroscopy, environmental monitoring, industrial control and scientific research. Accurate determination of component concentrations in complex gas environments requires advanced approaches that combine physical modeling and artificial intelligence methods. The use of neural networks in spectral analysis allows increasing the accuracy and stability of calculations under variable experimental conditions, which indicates the relevance of the work. The aim of the research is to develop a combined model for spectral light flux analysis that combines physical modeling of spectral absorption of gases with machine learning methods. This provides increased accuracy in determining the concentration of components in multicomponent gas mixtures and allows adaptive adjustment of analysis parameters depending on the measurement conditions. An integrated methodology is proposed, which includes modeling of spectral light flux based on Gaussian and Lorentzian absorption profiles, the use of the Bouguer-Lambert-Beer equations to determine gas concentrations, and training a neural network to predict the light flux. To assess the performance of the developed model, a series of numerical experiments were conducted with varying network parameters and optimizing the configuration. The results obtained confirmed the high efficiency of the model, which is reflected in the high value of the coefficient of determination and low values of the mean square error. The model was tested when changing gas concentrations and the length of the optical path, which confirmed its stability and adaptability. The study showed that the optimal configuration of the neural network includes three hidden layers with an optimal number of neurons, which provides a balance between accuracy and efficiency. A rectified linear activation function was used for stable convergence, and for weight optimization - an adaptive stochastic gradient descent method, which improves performance. The proposed method of combining physical modeling and machine learning provides high accuracy of gas mixture analysis and resistance to variations in external conditions. The scientific novelty of the study lies in the use of a combined approach, which allows adapting the model to a wide range of spectral characteristics. The practical significance of the work lies in the possibility of applying the developed methodology for industrial control, environmental monitoring, and laboratory research, providing a reliable tool for the analysis of complex gas mixtures.
Machine learning models for predicting payment status on an online car rental platform
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: ensemble models, financial risk assessment, logistic regression, Machine learning, naive bayes classifier, payment prediction, support vector machine It has been demonstrated that the detailed data collected on online platforms are heterogeneous, semantically inconsistent, and weakly structured. Therefore, the use of machine learning for their aggregation, structuring, and analysis is well-justified. As a case study for developing machine learning models, the task of predicting the payment behavior of clients on an online car rental platform was considered. Input data were automatically generated based on users’ actions on the platform. Subsequently, the data were aggregated and structured through feature engineering, time field transformation, and the removal of redundant attributes to enhance model quality. Five classification models were developed: Support Vector Machine, Naive Bayes classifier, Logistic Regression, and two ensemble models (Soft Voting and Stacking). The results showed that Logistic Regression and ensemble models (particularly Stacking) achieved the best precision and recall, making them the most reliable for predicting on-time payments. Ensemble models, especially stacking, demonstrated high efficiency by combining the strengths of different base models. Although SVM can account for complex relationships between features, it showed the weakest performance in distinguishing payment statuses. The findings contribute to a better understanding of customer payment behavior and highlight the importance of choosing appropriate classification models for financial risk assessment. Future research will focus on improving model performance through enhanced feature selection, class imbalance correction, and the integration of additional data sources such as customer credit history. The use of such models can significantly improve automated risk management and enhance decision-making efficiency for companies dealing with payment obligations.
ResJobFit - end-to-end artificial neural networks based technology for job-resume matching
Section: Software engineering аnd systems analysis Categories: Software engineering аnd systems analysis Keywords: artificial neural networks, IT systems, machine learning, NLP, transformers, text embedding, information retrieval With the ever-growing expansion of online recruitment, a reliable person-job matching has become increasingly crucial. Due to different experience, education and specialization requirements, as well as location considerations, specified in the job advertisement, various aspects should be taken into account for reliable matching and ranking of candidates. It has been shown that matching of resumes and vacancies can be approached as either pair classification or semantic similarity search based on embeddings. While classification approaches process each vacancy-resume pair sequentially, thus resulting in quadratic time complexity, independent text embeddings and ranking is a much more efficient and scalable solution, since it has linear time complexity. In this article semantic similarity search to rank suitability of candidates with regards to vacancies has been used. ResJobFit - an end-to-end Artificial Neural Networks based technology for job-resume matching is proposed. ResJobFit technology consists of Segmentation, Parsing, Summarization and HR Embedding Module models, and their outputs (vector and attributes defining each resume or job advertisement), as well as a Vector Database in which the records are stored. Unsupervised text embeddings training for HR domain encapsulating two novel training objectives - intra- and cross-section contrastive alignment is introduced. Pretrained BERT-base model is adapted by teaching it to match summary-last employment sections of the resume with parts of the same vacancy or employment section. As baselines TFIDF, BERT, E5 and GTE have been used. The proposed unsupervised training strategy was compared against SimCSE, DeCLUTR and ConFit approaches. NDCG, MAP and MRR are used as metrics for measuring accuracy of the designed algorithm. It has been shown that the novel training objective lets it achieve significant improvement in comparison to other unsupervised training approaches. Improvement of 11% in NDCG was achieved by adapting the DeCLUTR training strategy for the HR domain based on exploiting the structure of resumes over the classical DeCLUTR training strategy on the task of ranking summaries of vacancies and resumes generated by large language models. 2% and 6% have been achieved using ResJobFit and ResJobFit with requirements matching over state-of-the-art ConFit model on the task of ranking full-text vacancies and resumes.
Software engineering аnd systems analysis
Reducing the amount of computations at the second stage of an ensemble classifier with stacking on mine-detection drones
Section: Software engineering аnd systems analysis Categories: Software engineering аnd systems analysis Keywords: multilayer perceptron, neural network, thinning connections, pruning, weighting coefficients, classification quality Using drones to search for mines is a promising area that allows accelerating the process of demining an area and reducing the danger to people. To increase the probability of detecting mines, drones use sensors with different operating principles. Each type of sensor requires specialized processing, which is carried out at the first stage of the ensemble classifier with stacking. The sensor signals are combined at the second stage of the ensemble classifier, where a multilayer perceptron is usually used as a neural network. Acceleration of terrain survey requires that processing be carried out in real time on the computing equipment of the drone itself. This, in turn, requires a reduction in the amount of computation for all algorithms used on the drone. The article is devoted to reducing the amount of computation when implementing a multilayer perceptron. The original homogeneous structure of the perceptron, when each of the neurons of the previous layer has connections with all neurons of the next layer, is redundant, since it does not take into account the features of the processed data set. The article proposes a method for finding a balance between the dimensionality and number of layers of the perceptron, the time interval between the procedures of thinning connections, the training step and the number of connections removed at a time. Using thinning connections taking into account other parameters allows you to reduce the amount of calculations by 80% or more, while saving and even increasing the quality of classification. There are removed connections that do not make a noticeable contribution to the quality of classification, but introduce additional noise into the perceptron training process and the formation of the output result.
Development of the architecture of a computer aided diagnosis system in medicine
Section: Software engineering аnd systems analysis Categories: Software engineering аnd systems analysis Keywords: Computer-aided diagnosis, decision support systems, image analysis, image detection, neural networks, medicine With the development of information technology, automation of various production processes is an urgent task, and medical diagnostics is no exception. In recent decades, artificial intelligence and information technology have been widely used in computer diagnostic systems. However, as technology advances, so do the challenges. Not every system is optimized and fast, and traditional methods are fading into the background. Often, systems do not use cloud technologies and have unoptimized architectures. This all affects their performance and, accordingly, is an urgent problem. The study analyzes the methods used in computer diagnostic systems and compares them in terms of advantages and disadvantages. The scientific works related to computer diagnostic systems in medicine for specific tasks are analyzed. The existing architectures of computer diagnostic systems are analyzed, which made it possible to identify the use of consistent approaches to diagnosis. Based on the analyzed data, the purpose, objectives, object and subject of the study are determined. A new architecture has been developed that uses the capabilities of U-Net for image segmentation and convolutional neural networks for medical image classification. The developed architecture is designed to increase the speed and automation of diagnostic processes through the use of neural networks and, accordingly, reduce human intervention. The scientific novelty of the developed architecture lies in the parallel execution of medical image segmentation and classification tasks, which gives a potential increase in data processing speed, and in the availability of an image generator, which solves the problem of lack of test data for model training.
Recommendation system for financial decision-making using Artificial intelligence
Section: Software engineering аnd systems analysis Categories: Software engineering аnd systems analysis Keywords: Artificial Intelligence, machine learning, cognitive biases, financial decisions, ethics The rapid expansion of artificial intelligence (AI) in consumer markets presents challenges, particularly in how cognitive biases influence financial decision-making. These biases can lead to irrational spending, raising ethical concerns about AI’s role in such applications. This research explores how AI can enhance decision-making effectiveness and support consumers in making more rational financial choices. The focus is on developing an intelligent financial management system that applies modern AI algorithms to analyze financial behavior, detect anomalies, and offer personalized recommendations. The article considers a system for generating personalized financial recommendations based on large language models, which uses transaction history, predicted costs, and anomaly information to generate individual advice. Techniques include using Isolation Forest for identifying atypical financial actions and a combination of ARIMA and LSTM models for budget forecasting. The research also considers integrating these models with large language models (LLMs) to generate personalized recommendations. The methodological part of the work includes an analysis of existing models and their areas of application, defining data types and structures for processing, developing a system that integrates the available models, and testing it. The process of generating recommendations is described, which includes the stages of processing input data, forming context, generating recommendations and evaluating them taking into account user characteristics, such as risk level, financial goals and preferences. The generated recommendations are aimed at optimizing the user's financial behavior and can be adapted to different income levels. Special attention is paid to the ethical aspects of the system, which include ensuring confidentiality, fairness and transparency, as well as the importance of supporting user autonomy in making financial decisions. The system promotes responsible financial behavior by helping to avoid impulsive spending and increasing financial awareness without manipulation or imposing specific decisions.
Methods for refining the depth map obtained from depth sensors
Section: Software engineering аnd systems analysis Categories: Software engineering аnd systems analysis Keywords: Depth maps, 3D reconstruction, image processing, spatial data analysis, data refinement, sensor-based imaging, edge detection, noise reduction, depth sensing, computational imaging, augmented reality, autonomous systems Depth maps are essential in applications such as robotics, augmented reality, autonomous vehicles, and medical imaging, providing critical spatial information. However, depth maps from sensors like time-of-flight (ToF) and structured light systems often suffer from low resolution, noise, and missing data. Addressing these challenges, this study presents an innovative method to refine depth maps by integrating high-resolution color images. The proposed approach employs both hard- and soft-decision pixel assignment strategies to adaptively enhance depth map quality. The hard-decision model simplifies edge classification, while the soft-decision model, integrated within a Markov Random Field framework, improves edge consistency and reduces noise. By analyzing discrepancies between edges in depth maps and color images, the method effectively mitigates artifacts such as texturecopying and blurred edges, ensuring better alignment between the datasets. Key innovations include the use of the Canny edge detection operator to identify and categorize edge inconsistencies and anisotropic affinity calculations for precise structural representation. The soft-decision model introduces advanced noise reduction techniques, improving depth map resolution and preserving edge details better than traditional methods. Experimental validation on Middlebury benchmark datasets demonstrates that the proposed method outperforms existing techniques in reducing Mean Absolute Difference values, especially in high-upscaling scenarios. Visual comparisons highlight its ability to suppress artifacts and enhance edge sharpness, confirming its effectiveness across various conditions. This approach holds significant potential for applications requiring high-quality depth maps, including robotics, augmented reality, autonomous systems, and medical imaging. By addressing critical limitations of current methods, the study offers a robust, versatile solution for depth map refinement, with opportunities for real-time optimization in dynamic environments.
Computer investigation of voltage fluctuations on the power busbars of an arc steel-melting furnace
Section: Digital control of technical and social systems Categories: Digital control of technical and social systems Keywords: Аrc furnace, computer model, slag fluctuations, dispersion, stabilization, electromagnetic compatibility, artificial external characteristics, energy efficiency The intensification of production and the development of electrical technologies result in increased energy intensity and concentration of electrical loads. There is a growing number of nonlinear, phase-asymmetric, and rapidly changing dynamic electricity consumers. Typical representatives of such consumers are arc steel-melting furnaces (ASF). Their operation negatively impacts the quality indicators of electrical energy in distribution networks, necessitating the development of solutions to bring their values within regulatory standards. Traditionally, developed solutions are generally aimed at mitigating the consequences of their operation by increasing the power capacity of the energy system and implementing dynamic reactive power compensation installations. This article proposes a two-loop structure for an automatic control system (ACS) of the electrical regime of ASF. Unlike the aforementioned traditional approach, the solutions proposed in this study are primarily aimed at suppressing disturbances within the power electrical circuits of the ASF itself, significantly reducing the negative impact of their operation on the quality indicators of electrical energy, particularly on voltage fluctuations on the power busbars of the ASF. This is achieved through a substantial increase in responsiveness, phase-wise autonomy in disturbance control, and the expansion of the functional capabilities of the ACS for the electrical regime of the ASF to implement adaptive multi-criteria optimal control strategies. These properties are additionally provided to the control system by the inclusion of a high-speed electrical current control loop in its structure, which functionally enables the formation and rapid implementation of desired artificial external characteristics of the arc furnace. The effectiveness of the proposed solutions was examined using a created computer model of the ACS for the ASF DSP-200, which incorporated the proposed solutions. In the article, a comparative analysis was conducted through computer modeling of the dynamics, electromagnetic compatibility, and energy efficiency indicators of the proposed two-loop ACS and the serial power controller ARDM-T-12 for the ASF DSP-200 arc furnace. The obtained research results confirmed an increase in the dynamic accuracy of stabilizing the coordinates of the electrical regime and a reduction in voltage fluctuations and deviations in the electrical network. Specifically, the current dispersion of the arcs at different technological stages of melting decreased by 4.5-7 times, the electrical network voltage by 3-4 times, and the reactive power by 5-7.5 times.7.5.
Digital control of technical and social systems
Information technology for forecasting epidemiological threats based on the telegraphic equation
Section: Information systems and technology Categories: Information systems and technology Keywords: information technology, model, telegraphic equation, epidemiological threats, forecasting, modeling Based on the analysis, it is established that traditional approaches, such as the SIR (Susceptible-Infectious-Covered) and SEIR (Susceptible-Exposed-Infectious-Covered) models, do not provide sufficient forecasting accuracy and do not take into account the complex dynamics of the spread of infectious diseases. The need to develop a method that will improve forecasting accuracy and provide support for managerial decision-making to predict the spread of epidemiological threats based on the telegraphic equation is substantiated. The developed system allows for making effective management decisions aimed at reducing the negative impact of the epidemic on the population and medical infrastructure. The use of the telegraphic level allows us to predict the wave spread of infection, spatial and temporal delays, as well as sources of new infections, which ensures accurate forecasting of peak periods, the duration of the epidemic, and the workload of medical facilities. The developed method integrates the classical SIR model with the telegraphic level, which allows the modelling the dynamics of infection spread in a spatio-temporal environment. This method provides forecasting of the spatial and temporal dynamics of infection spread, taking into account wave effects, delays, and the influence of external factors. It provides an opportunity to accurately analyze key epidemic indicators, such as the peak of the disease, its duration, and the distribution of the burden on hospitals. The developed method and mathematical model based on the telegraphic level provided an appropriate level of accuracy in predicting the spatial and temporal dynamics of the spread of epidemiological threats. Testing the model on historical COVID-19 data showed that the average forecast error was 5...10%. This indicates the model's adequacy. In the case of high population mobility, the model accurately described the wave dynamics of the infection. The proposed decision support system includes a user-friendly interface with four tabs for entering model parameters, analyzing results, visualizing them, and generating recommendations. It allows to improve the accuracy of estimating the duration of the epidemic, peak loads, and some resources. The developed system is a tool for managers to support the adoption of governmental decisions aimed at predicting the infection of the population of regions and optimizing the use of medical resources. The results of the study can be used to plan epidemic response measures at the local, regional, and global levels. The proposed system ensures efficiency, flexibility, and accuracy, which are key to managing epidemiological situations in the face of modern challenges.