2025

Постійне посилання на фонд

https://ela.kpi.ua/handle/123456789/78636

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A multifactor model for detecting propaganda in textual data
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Gavrilenko, Olena; Feshchenko, Kyryl
Detecting elements of propaganda in large volumes of textual data is currently one of the key tools in combating the information warfare taking place worldwide. This paper presents a multifactor model for determining the level of propaganda in a publication. The analyzed publications included text-based news articles and social media posts, which were processed using both quantitative and semantic text analysis methods. The model was constructed using the method of linear convolution, which enables the integration of multiple heterogeneous indicators into a unified value reflecting the degree of propaganda. The proposed model considers thirteen indicators, each of which, when exhibiting a high value, signals the potential presence of propaganda within a text. The indicators encompass lexical, syntactic, and semantic characteristics such as emotional tone, subjective evaluation, presence of manipulative triggers, and calls to action. The value of each indicator was calculated using methods of statistical analysis, intelligent data analysis, and machine learning. An algorithm for determining the influence level of each factor was proposed, as well as a scale for assessing the overall level of propaganda. For every analyzed publication, a utility function value was computed to quantify its propaganda intensity. The threshold value of this utility function – beyond which a publication is considered propagandistic – was defined as the sample mean across the dataset. This approach allows for an objective classification of textual materials without the need for expert labeling. The advantage of the developed method lies in the fact that each indicator is derived exclusively from empirical statistical data and validated computational procedures, ensuring the elimination of human subjectivity. The study demonstrates that the modified multifactor model can serve as a universal analytical tool for detecting propaganda in various types of textual data, thereby enhancing the transparency and reliability of media content analysis.
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A Multimodal Retrieval-Augmented Generation System with ReAct Agent Logic for Multi-Hop Reasoning
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Yuvzhenko, Denys; Chymshyr, Viacheslaw; Shymkovych, Volodymyr; Znova, Kyrylo; Nowakowski, Grzegorz; Telenyk, Sergii
The rapid advancement of generative artificial intelligence models significantly influences modern methods of information processing and user interactions with information systems. One of the promising areas in this domain is Retrieval-Augmented Generation (RAG), which combines generative models with information retrieval methods to enhance the accuracy and relevance of responses. However, most existing RAG systems primarily focus on textual data, which does not meet contemporary needs for multimodal information processing (text, images, tables). The research object of this work is a multimodal RAG system based on ReAct agent logic, capable of multi-hop reasoning. The main emphasis is placed on integrating textual, graphical, and tabular information to generate accurate, complete, and relevant responses. The system's implementation utilized the ChromaDB vector storage, the OpenAI embedding generation model (text-embedding-ada-002), and the GPT-4 language model. The purpose of the study is the development, deployment, and empirical evaluation of the proposed multimodal RAG system based on the ReAct agent approach, capable of effectively integrating diverse knowledge sources into a unified informational context. The experimental evaluation utilized the Global Tuberculosis Report 2024 by the World Health Organization, containing various textual, graphical, and tabular data. A specialized test set of 50 queries (30 textual, 10 tabular, 10 graphical) was created for empirical analysis, allowing comprehensive testing of all aspects of multimodal integration. The research employed methods such as semantic vector search, multi-hop agent-based planning with ReAct logic, and evaluations of answer accuracy, answer recall, and response latency. Additionally, an analysis of response speed dependence on query volume was conducted. The obtained results confirmed the high efficiency of the proposed approach. The system demonstrated an answer accuracy of 92%, answer recall of 89%, and ensured complete (100%) coverage of all data types. The average response time was approximately 5 seconds, meeting interactive system requirements. Optimal parameters were experimentally determined (for example, parameter k = 6, classification threshold 0.35, and up to three reasoning iterations), ensuring the best balance among completeness, speed, and operational efficiency. The study's findings highlighted significant advantages of the multimodal agent-based approach compared to traditional textual RAG solutions, confirming the promising direction for further research.
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Approach to hybrid load management in Fat-Tree web clusters
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Radchenko, Kostiantyn; Chernenkyi, Artem
The paper presents an approach to hybrid load management in a web cluster that is capable of providing adaptive request balancing based on load prediction and resilience to random web server failures. The proposed architecture is built upon the Fat-Tree topology, which ensures high scalability, structural redundancy, and efficient routing within the cluster network. The developed system performs load forecasting using moving average methods and Erlang-based queueing models, enabling the estimation of overload probabilities and proactive redistribution of computational resources. Four representative simulation scenarios were analyzed: baseline load, peak load, dynamic traffic variations, and random server failures. The obtained results demonstrate enhanced system reliability, reduced average response time, and more balanced utilization of cluster resources. In the context of rapidly growing web services and user traffic volumes, the issue of maintaining high reliability and efficiency of clustered infrastructures becomes increasingly significant. Even with robust topologies such as Fat-Tree, irregular traffic patterns and sudden surges in client requests can cause local overloads and performance degradation. Random node failures further complicate cluster management, necessitating the use of adaptive and predictive control mechanisms. The proposed model integrates Fat-Tree network simulation with statistical forecasting algorithms, forming the basis for proactive load management. This integration allows for minimizing service degradation risks, dynamically responding to workload changes, and maintaining stable operation of web infrastructures under partial node failures. The architecture shows strong potential for real-time implementation in large-scale distributed web systems. It can be further enhanced by incorporating machine learning or wavelet-based forecasting methods to improve the accuracy of load estimation and system adaptability.
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Automatic Network Reconfiguration Method with Dynamic IP Address Management
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Haidai, Anatolii; Klymenko, Iryna
In the context of growing cyber threats, systems capable not only of detecting anomalies in the operation of network infrastructure but also of promptly responding to them without administrator intervention are becoming increasingly relevant. This paper proposes a method for automatic network reconfiguration based on the integration of the Zabbixmonitoring system with the pfSensenetwork gateway functionality. Such a system enables centralized control of the operating system status, resource usage, and network activity, while also allowing for automatic changes to host IP addresses, routing adaptation, and connection restrictions according to defined security policies.The aim of the study is to develop a method for automatic network monitoring and reconfiguration with dynamic IP address changes to improve the effectiveness of cyber threat mitigation. The object of the study is the processes of information security management in computer networks. The subject of the study includes methods of anomaly detection and automatic response through modification of network parameters using Zabbixand pfSense.In the context of automatic response to detected threats, the method of comprehensive monitoring of client host operating systems has been formalized, including subsequent analysis of logs, user actions, resource load, network port usage, and interaction with external services. A methodology for network reconfiguration after anomaly detection has been developed and implemented: in particular, changing the IP address while maintaining functionality in a minimal network access configuration and isolating the node using pfSense. Scripts for Windows client OS were employed, interacting with the Zabbixand pfSenseAPIs, thus ensuring dynamic and fully automated operation.Testing results of the proposed system in a simulated environment confirm its effectiveness. Compared to manual or partially automated solutions, incident response time was reduced, and the risk of attack propagation within the network was minimized.
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Comparative analysis of LCNet050 and MobileNetV3 architectures in hybrid quantum–classical neural networks for image classification
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Khmelnytskyi, Arsenii; Gordienko, Yuri
This study explores the impact of classical backbone architecture on the performance of hybrid quantum-classical neural networks in image classification tasks. Hybrid models combine the representational power of classical deep learning with the potential advantages of quantum computation. Specifically, this research employs a quanvolutional neural network architecture in which a quantum convolutional layer, based on a four-qubit Ry circuit, preprocesses input images before classical processing. Despite the growing interest in hybrid models, few studies have systematically investigated how variations in classical architecture design affect the overall performance of hybrid quantum-classical neural networks. To address this gap, we compare two lightweight convolutional backbones – MobileNetV3Small050 and LCNet050 – integrated with an identical quantum preprocessing layer. Both models are evaluated on the CIFAR-10 dataset using 5-fold stratified cross-validation. Performance is assessed using multiple metrics, including accuracy, macro- and micro-averaged area under the curve, and class-wise confusion matrices. The results indicate that the LCNet-based hybrid model consistently outperforms its MobileNet counterpart, achieving higher overall accuracy and area under the curve scores, along with improved class balance and robustness in distinguishing less-represented classes. These findings underscore the critical role of classical backbone selection in hybrid quantum-classical architectures. While the quantum layer remains fixed, the synergy between quantum preprocessing and classical feature extraction significantly affects model performance. This study contributes to a growing body of work on quantum-enhanced learning systems by demonstrating the importance of classical design choices. Future research may extend these insights to alternative datasets, deeper or transformer-based backbones, and more expressive quantum circuits.
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DDOS attack detection with data imperfections using machine learning algorithms
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Dremov, Artem; Volokyta, Artem
The issue of DDoS attacks remains a prevalent one even in recent years. Modern environment is highlydynamic and is characterized by a large amount of traffic flow. Existing research covers several models,techniques and approaches to detecting DDoS traffic, which aim to optimize the detection in controlleddatasets. However, unintentional noise or data corruption may lower the efficacy of such methods. As such,determining most effective ways to detect DDoS traffic in conditions of data imperfections is necessary forreliable network performance.Therefore, the object of this research Is the usage of machine learning algorithms for detection ofincoming DDoS attacks. The purpose of this research is to determine the performance of ways to detectincoming DDoS attacks with machine learning algorithms based on detection accuracy, while simulatingimperfect data conditions. The study also examines the impact of class rebalancing on modified data.To achieve the aim of this research a variety of machine learning algorithms were implemented andtested on aCIC-DDoS2019dataset. The data is modified by removing values and introducing noise, tested,the classes are resampled and the dataset is tested again. The goal is to achieve over 90% accuracy in aclassification task of the type of DDoS attack and to determine how much the changes affect the performanceof the algorithms.The results of the testing indicated that several solutions reach the target mark and changes to thedataset in realistic conditions do not significantly affect the final result. However, all models tested showa decrease in accuracy compared to unmodified data with more complex models showing higher resilience(smaller decrease in accuracy). In addition, resampling of the data shows comparable decrease in accuracyof the models with more complex models being affected less.The results of this study may be used in development of an algorithm of repairing the corrupted dataor development of models more resistant to such data changes. Additionally, the results of this study maybe used when considering models for practical implementations of a DDoS traffic classification system.
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Decentralized Task Allocation Method in Hierarchical IoT Systems Using Fuzzy Logic
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Rolik, Oleksandr; Nahaiko, Dmytro
The use of fog and edge computing extends the computational capabilities of IoT systems to the network edge, contributing to the minimization of delays during task execution. Osmotic computing complements distributed computing by providing seamless integration between computational environments through dynamic migration of micro-elements across different hierarchy tiers according to current load conditions and resource availability. However, within the concept of osmotic computing, a key challenge remains the effective management of task allocation under conditions of uncertainty, dynamism, and heterogeneity of the IoT environment. The aim of this study is to improve the efficiency of resource utilization and task allocation in hierarchical IoT systems based on osmotic computing under uncertain and dynamically changing environmental conditions. The object of the study is the process of task allocation in multi-tier IoT systems that include cloud, fog, and edge computing. The subject of the study is methods and models for task allocation and computing resource management in IoT systems using the osmotic computing paradigm. The paper presents a three-tier hierarchical management model built on cloud, fog, and edge environments, which implements a centralized-decentralized management approach. Each tier is represented by a set of computing nodes and a management system that performs local task allocation, resource state monitoring, and micro-element management. The management system of the lower tier is subordinate to the higher-tier management system in the hierarchy. A method for decentralized task allocation in hierarchical IoT systems using fuzzy logic has been developed. The allocation method includes two decision-making stages using a fuzzy inference system: determining the direction of task allocation and selecting the optimal computing node for its execution. The determination of task allocation direction is carried out based on task characteristics, and the suitability rating of computing nodes is determined considering task execution latency, resource utilization efficiency, and load balancing. The task is assigned to the node with the maximum rating. The use of fuzzy logic ensures rational decision-making under conditions of uncertainty in real-time, which is characteristic of highly heterogeneous and dynamic IoT environments. Experimental modeling and investigation of the method were carried out using the iFogSim simulation environment. The research results show that the percentage of locally executed tasks remains virtually unchanged with different numbers of tasks, indicating stability in decision-making. Increasing the intensity of task generation leads to an increase in task computation latency due to increased load on computing nodes, while task assignment latency and response latency remain unchanged. The method demonstrated adaptability in task allocation for different types of tasks.
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Deep Q-learning policy optimization method for enhancing generalization in autonomous vehicle control
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Drahan, Mykhailo; Pysarenko, Andrii
The development of autonomous vehicle control policies based on deep reinforcement learning is a principal technical problem for cyber-physical systems, fundamentally constrained by the high dimensionality of state spaces, inherent algorithmic instability, and a pervasive risk of policy over-specialization that severely limits generalization to real-world scenarios. The object of this investigation is the iterative process of forming a robust control policy within a simulated environment, while the subject focuses on the influence of specialized reward structures and initial training conditions on policy convergence and generalization capability. The study's aim is to develop and empirically evaluate a deep Q-learning policy optimization method that utilizes dynamic initial conditions to mitigate over-specialization and achieve stable, globally optimal adaptive control. The developed method formalizes two optimization criteria. First, the adaptive reward function serves as the safety and convergence criterion, defined hierarchically with major penalties for collision, intermediate incentives for passing checkpoints and a continuous minor penalty for elapsed time to drive efficiency. Second, the mechanism of dynamic initial conditions acts as the policy generalization criterion, designed to inject necessary stochasticity into the state distribution. The agent is modeled as a vehicle equipped with an eight-sensor system providing 360 degrees coverage, making decisions from a discrete action space of seven options. Its ten-dimensional state vector integrates normalized sensor distance readings with normalized dynamic characteristics, including speed and angular error. Empirical testing confirmed the policy's vulnerability under baseline fixed-start conditions, where the agent demonstrated over-specialization and stagnated at a traveled distance of approximately 960 conventional units after 40,000 episodes. The subsequent application of the dynamic initial conditions criterion successfully addressed this failure. By forcing the agent to rely on its generalized state mapping instead of trajectory memory, this approach successfully overcame the learning plateau, enabling the agent to achieve full, collision-free track traversal between 53,000 and 54,000 episodes. Final optimization, driven by penalty, reduced the total track completion time by nearly half. This verification confirms the method's value in producing robust, stable, and efficient control policies suitable for integration into autonomous transport cyber-physical systems.
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Detection Method of Fraudulent Payment Transaction Based on C-Score Metric
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Korynetskyi, Dmytro; Stetsenko, Inna V.
Fraud detection for payment transactions is a cost-sensitive task, as the costs associated with misclassification – such as missing a fraudulent transaction or incorrectly blocking a legitimate one – can vary significantly depending on business priorities. Traditional evaluation metrics, particularly the F1-score, ignore this asymmetry, creating a need for more flexible approaches. This research focuses on developing a method for building adaptive, cost-sensitive fraud detection systems. The aim is to develop a method that enables the practical application of the cost-sensitive C-score metric to configure a multi-level decision logic. The paper also presents a possible software architecture for its implementation. The proposed two-phase method (offline calibration and online scoring) uses the C-score metric to determine multiple decision thresholds corresponding to different business scenarios. Its validation was conducted on the public “Credit Card Fraud Detection” dataset using the XGBoost algorithm. The Synthetic Minority Over-sampling Technique (SMOTE) was applied to overcome the severe class imbalance in the data, and a comparison was made against the traditional F1-score-based approach. The experimental results showed that the proposed approach allows for the identification of two distinct thresholds from a single classifier. The first threshold ensures high precision, making it suitable for automated blocking of payment transactions with minimal false positives. The second threshold, focused on high recall, enables the selection of suspicious payment transactions for subsequent manual review. It was also confirmed that the SMOTE significantly contributed the model's class separation ability, thereby increasing the reliability of calibrating these thresholds. Based on the method, a practical blueprint for a service-oriented architecture is proposed for creating flexible and configurable anti-fraud systems.
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Drone Swarm Control Model Based on High-Level Petri Nets
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Ivankov, Valentyn; Novotarskyi, Mykhailo
The rapid growth of unmanned aerial vehicle (UAV) applications in the modern world imposes significant demands on the reliability of control logic. An error in the sequence of stages can lead at best to inefficient battery usage or violations of airspace regulations, and at worst to an accident with loss of the vehicle and potential harm. Control is usually implemented using scripts or behavior trees, which complicates maintenance. The reason is that the size of the source files quickly increases, and when it becomes necessary to add new functionality or modify existing logic, there is a risk of introducing vulnerabilities by failing to account for all possible situations. This is why High-Level Petri Nets (HLPN) were chosen, as this method addresses the problem of formally describing the control system and allows the system to be easily scaled or modified in any way. The aim of the study is to develop and validate a model based on HLPN that will serve as the single source of truth for UAV swarm control. In the proposed model, the places correspond to flight stages, and the tokens carry numerical parameters such as battery charge, coordinates, and telemetry. Thus, a single scheme simultaneously describes discrete events and constraints. For each transition, conditions are formalized to verify the possibility of its execution, such as checking the minimum required battery level or verifying location. The methodology includes several stages. First, the network structure is formally defined. Then, based on this structure, a Python model is built that implements the developed network, controls movement between states, and ensures the correct sequence of transition firings. After developing the model, testing and analysis of the obtained results are performed. The results show that using HLPNs to build a model for verifying commands in a discrete mode indeed ensures a correct description of transitions between states and increases the reliability and survivability of the developed control system model, while also significantly reducing maintenance efforts. The developed model is easily adaptable to route changes, addition of sensors, or functional expansion.
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Environment for Tuning Parameters of a Multithreaded Program Developed Using a Dependency Graph
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Nesterenko, Kostiantyn; Stetsenko, Inna V.
With the advent of multi-core central processors, multithreading has become the most widespread practice for improving program execution performance. However, the development of a multithreaded program remains a rather complex process. To simplify this process and enhance the performance of the resulting program, various methods for managing thread-based execution are often employed.One such method is the method ofmanaging the execution of tasks of a multithreaded program according to a given dependency graph. This method significantly reduces the resource intensity of program development and increases program performance by employing a lockless approach to multithreaded programming.Nevertheless, the challenge of efficient utilization of computational resources remains relevant and can only be addressed through the careful design of parallel computations. In particular, identifying the configuration parameters for a multithreaded program that ensure optimal resource utilization is a resource-intensive and complex task, even for highly qualified specialists.This study examines existing approaches to tuning the parameters of multithreaded programs to achieve the mostefficient execution. It proposes the use of an environment for tuning multithreaded program parameters based on the method of managing the execution of tasks of a multithreaded program according to a given dependency graph. The accuracy of the resource efficiency metrics obtained through this environment was experimentally validated. A practical example demonstrates the application of the environment in the development of a multithreaded program. The use of the environment also facilitates the configuration process of multithreaded program parameters
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Evaluation of the effectiveness of two approaches to building damage detection with satellite imagery
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Oliinyk, Yurii; Rumiantsev, Oleksii
This study addresses the approaches for satellite image analysis to assess infrastructure damage. Themain aim is to conduct a comprehensive comparative analysis of the effectiveness of two key machinelearning approaches: specialized semantic segmentation based on theU-Netarchitecture and generalizedvisual analysis using large vision-language models. The object of the research is the process of quantitativelybenchmarking these two distinct approaches to determine their practical applicability for multi-class damageclassification.The research material is the publicly availablexView2dataset. The methods involved two parallelexperiments. For the semantic segmentation approach, aU-Netmodel with anEfficientNet-B4encoderwas implemented and trained on 6-channel input data (”before” and ”after” images) using a combinedDiceandFocalloss function. For the vision-language models approach, the open-sourceLLaVA-1.5-7Bmodelwas evaluated in a zero-shot mode using advanced prompt engineering for an aggregative counting task.To enable a direct comparison, the standardJaccard indexwas calculated based on the aggregated objectcounts for each damage class.The results of the experiments revealed a significant performance disparity. The specializedU-Netmodeldemonstrated high effectiveness, achieving an intersection over union score of 0.6141 on the test set. Incontrast, theLLaVAmodel proved unsuitable for accurate quantitative analysis, yielding an extremely lowJaccard indexof approximately 0.063, primarily due to its systemic failure to correctly identify and countobjects (𝑅𝑒𝑐𝑎𝑙𝑙≈0.07). The scientific novelty lies in being the first study to quantitatively document thisorder-of-magnitude capability gap, confirming that for tasks requiring high-precision mapping, specializedsegmentation models remain the indispensable tool.
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Hexacopter-Based Cyber-Physical System for Water Sampling with Adaptive Path Planning and Multi-Drone Coordination
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Pysarenko, Andrii; Rolik, Oleksandr
The object of this study is a hexacopter-based cyber-physical system designed for autonomous water sampling to support environmental monitoring, addressing the problem of inefficient control under dynamic conditions. The subject focuses on integrating physical flight control and water sampling operations with cyber supervisory functions, including real-time waypoint navigation, task scheduling, and multi-drone coordination, validated as a current system component. The research investigates the system’s performance under payload variations and wind disturbances, ensuring robustness and precision in adverse environments. The purpose is to improve efficiency of water sampling through this CPS, achieving enhanced flight stability and positioning accuracy via a cascade PID control system, optimizing mission planning with adaptive cyber strategies, and increasing scalability through multi-drone operations. This approach aims to surpass traditional UAV systems by using physical-cyber integration for precise, robust, and scalable water quality assessment. The methodology combines simulation-based and analytical techniques to develop and assess the hexacopter CPS. A 6-degree-of-freedom mathematical model, based on Newton-Euler equations, was constructed in MATLAB/Simulink to simulate hexacopter dynamics, incorporating payload and wind effects. The cascade PID control system was tuned using the Ziegler-Nichols method, with iterative optimization to reduce overshoot and settling time across three scenarios: 1 kg static payload, 1.5 kg dynamic payload, and 5 m/s wind. The cyber supervisory system, implemented in ROS 2, employs graph-based algorithms (Dijkstra’s for waypoint navigation, list-scheduling for task allocation) and a consensus protocol for multi-drone coordination, tested in a 500x500 m² environment. Performance metrics, such as position root mean square error (RMSE) and attitude errors, were analyzed to evaluate system effectiveness. Results demonstrate significant improvements in water sampling capabilities. The cascade control system achieved a 40–50% reduction in position RMSE and maintained attitude errors within ±0.8° to ±1.2°, ensuring stable flight. The cyber-physical framework reduced mission time by 15% through adaptive path optimization, while multi-drone coordination increased sampling coverage by 20%, enhancing scalability. These outcomes reflect the system’s precision and robustness that highlight novel control and coordination strategies with practical value for environmental monitoring. The study provides a foundation for future ecological applications.
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Hybrid Path Planning Method for Unmanned Ground Vehicles Swarm in Dynamic Environments
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Rudnytskyi, Myroslav; Klymenko, Iryna
Unmanned ground vehicles (UGVs) have significant potential across various applications. These include automation of the agricultural tasks, inspection and maintenance within construction and industrial sectors, automation of complex assembly processes and infrastructure repairs, explosives disposal, automation of logistical operations, search-and-rescue missions, and expeditions to hard-to-reach or hazardous areas. However, a key challenge limiting their widespread deployment is autonomous navigation, which remains a significant problem due to dynamic environments characterized by constantly changing obstacle configurations, unpredictable scenarios, and the need for rapid real-time decision-making to ensure safe and stable movement. The object of this paper is a hybrid path planning for the autonomous navigation of unmanned ground vehicles swarm within a simulated environment. The research aims to develop autonomous navigation method for the unmanned ground vehicles swarm by employing a hybrid approach designed to enhance the efficiency of obstacle avoidance and improve the adaptability to dynamic environments. To achieve this goal, a novel autonomous swarm navigation method based on a hybrid approach is proposed. This approach differs from existing solutions by employing the A* path planning algorithm with incorporated traversal costs on the map for global-level navigation and the artificial potential field (APF) algorithm, that supports linear and V-shaped formations for local-level navigation. The research findings indicate that the proposed method allows the swarm to perform optimal path planning, considering traversal costs, and effectively avoid local minimum problems that are inherent to the artificial potential field method. The successful performance of the method within the simulated environment demonstrates its potential for future validation in real-world scenarios and practical applications involving swarms of unmanned ground vehicles operating in challenging environments. At the same time, the study identified challenges related to swarm size scalability in narrow spaces, defining directions for further improvements.
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Hybrid Voting Model for Decentralized Autonomous Organizations with Dynamic Quorum
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Serebriakov, Roman; Klymenko, Iryna
The article examines the problem of balancing security and flexibility in decision-making mechanisms within decentralized autonomous organizations (DAOs), which operate without centralized control through the use of smart contracts. To this end, two main voting models employed in DAOs are analyzed: the conjunctive model, which requires unanimous approval of a proposal by all participant groups, and the disjunctive model, where approval from a single group is sufficient. Both models have significant advantages and drawbacks: the former ensures a high level of security and protection of all parties’ interests but considerably slows down the decision-making process, while the latter provides speed and scalability but introduces risks of centralized influence. In response to these challenges, a hybrid voting model is proposed, in which the type of logic is determined by the nature of the proposal. Specifically, critical changes, such as updates to governance rules or quorum parameters, must involve all groups, whereas routine operational matters can be decided through a simplified disjunctive procedure. The implemented smart contract architecture supports both mechanisms and enables DAOs to dynamically adjust quorum thresholds through separate governance proposals. To evaluate the effectiveness of the model, a simulation of 1,000 voting processes was conducted under four different scenarios of participant activity: balanced, one-sided, and low overall participation. The results showed a reduction in the probability of deadlock situations and an increase in the share of successful votes when hybrid logic was applied, particularly under conditions of low or asymmetric participation. In addition, special attention was given to gas cost optimization: the disjunctive approach allows vote counting to be stopped once a quorum is reached by one group, thus reducing overall computational expenses. Therefore, the proposed solution appears promising for both financial DAOs and decentralized infrastructures, particularly the Internet of Things, where speed, scalability, and secure coordination are especially important.
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Intelligent traffic management method in software-defined networks based on behavioral classification and adaptive priority service
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Oboznyi, Dmytro; Kulakov, Yurii
The growing complexity of modern enterprise network environments demands sophisticated traffic management solutions that can provide quality of service (QoS) guarantees for encrypted and heterogeneous flows. Existing traffic management approaches face significant challenges when dealing with encrypted protocols and diverse application requirements, resulting in performance degradation for critical services and inefficient resource utilization. This paper addresses the problem of intelligent traffic management in software-defined networks through behavioral classification and adaptive priority service mechanisms. The study examines the development and implementation of an integrated traffic management method that combines behavioral deep packet inspection, class-based queuing, and weighted random early detection algorithms. The research investigates how behavioral flow characteristics remain observable in encrypted traffic environments and how these patterns can be leveraged for effective QoS provisioning. The proposed method utilizes packet timing patterns, connection behaviors, and flow statistics to classify traffic without relying on payload inspection or predefined port assignments. Experimental validation through discrete-event simulation demonstrates significant performance improvements compared to traditional first-in-first-out mechanisms. The behavioral classification component achieves over 95% classification accuracy. The experimental results demonstrate up to 97.5% improvement in latency performance and 0% packet loss for high-priority traffic. Integrating behavioral traffic recognition with adaptive queue management within a programmable network framework provides an effective and innovative approach to maintaining stable service quality in encrypted, multi-service environments. The proposed method is compatible with existing software-defined network controllers and can be deployed without modification of application protocols or infrastructure components.
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Mathematical Model of Clustering of Informational Messages with Indicators of Activity for the Information Content by Tone and Areas of Society Activity
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Pysarchuk, Oleksii; Baran, Danylo
The mathematicalmodel of clustering of information messages has been further developed, which is based on the frequency analysis of their tonality using Natural Language Processing methodologies with the support of large language models; OLAP visualization of clustering results and is distinguished by an established system of indicators of information content activity by areas of society activity with hierarchical compression of incoming Big Data arrays, which determines the database model for their storage. This provides an improvement to the analysis of information messages in global information networks by taking into account many factors in the areas of society activity.The main idea and goal of the mathematical model for clustering information messages is to implement a sequence of preparation stages for detecting critical activity of the information content in global media. In practice, this is the establishment of a list and the determination of indicator values that measure content activity in primary messages, followed by their transformation into a time series –a systematized dataset. In the conditions of high density of the flow of occurrence, dynamics of development, and transformation of information content, a Big Data structure of information messages is taken into account. Therefore, the clustering model, apart from division by informational features, should provide the hierarchical compression of incoming Big Data arrays.Research objective: development of a mathematical model of clustering information messages with indicators of information content activity by tone and spheres of activity of society.Research subject: methods of clustering information messages.Research object: process of clustering information messages.
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Method for combining CNN-based features with geometric facial descriptors in emotion recognition
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Zinchenko, Liudmyla
This study presents a method for combining CNN-based visual features with geometric facial descriptors to improve the accuracy of emotion recognition in static images. The method integrates deep convolutional embeddings extracted from a pre-trained ResNetV2_101 model within the ML.NET framework with handcrafted geometric features computed from facial landmarks. Open-source datasets containing labeled emotional categories were used for experiments. At the first stage, deep image embeddings were obtained through transfer learning. At the second stage, 68 facial landmarks were detected to calculate distances and proportional relationships such as interocular distance, mouth width, eyebrow height, and other geometry-based indicators. These visual and geometric representations were concatenated into a unified feature space and classified using a multiclass linear model. The hybrid method achieved approximately 4% higher accuracy than the baseline CNN model relying solely on pixel-level features (from about 63% to 67%), confirming that combining heterogeneous features enhances generalization and robustness. The results also highlight that geometric descriptors act as stabilizing factors, compensating for noise, occlusions, and lighting variations that degrade CNN-only models. The developed pipeline demonstrates the feasibility of integrating interpretable geometric cues with deep embeddings directly in C# using ML.NET. The research novelty lies in proposing an interpretable hybrid model for emotion recognition that improves reliability while maintaining compatibility with .NET-based applications. The approach offers an accessible solution for developers working within enterprise .NET ecosystems, enabling direct deployment without cross-language integration. Future research will focus on extending the model toward multimodal emotion analysis that incorporates speech, gesture, and physiological signals to enhance contextual understanding of affective states. Additionally, the hybrid model can serve as a diagnostic tool for studying emotion dynamics in psychological or behavioral research.
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Method for Detecting Anomalous Bearing Measurements Based on the Analysis of Distribution Density Histogram
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Starodubtsev, Illia; Pysarchuk, Oleksii
Subject of the research. Statistical models and algorithmic techniques for processing bearing-measurement datasets with the goal of detecting and filtering anomalous values. Object of the research. Methods for handling outliers in bearing-measurement datasets. Purpose of the work. To develop a method that improves the reliability of determining the direction to a radio-emission source in a single-station radio direction-finding system. The article proposes a method for detecting anomalous measurements by analysing the histogram of their probability-density distribution. Propagation characteristics of very-high-frequency radio waves complicate radio direction finder operation, producing both random and systematic anomalies. The approach examines bearing-density histogram intervals rather than individual measurements, enabling an integrated assessment of sample structure and anomaly identification. The method’s performance was assessed on bearing datasets of 100, 500 and 1000 measurements at confidence levels of 80–95 %. The evaluation of the method’s robustness was made for small and large arrays. The study analysed the influence of systematic and random anomalies on histogram construction and confidence-interval determination. Key metrics were bearing-estimation accuracy, root-mean-square error and method stability across varying confidence levels. Results show the method minimises the impact of anomalous measurements at the tails and within the main cluster. Experiments demonstrate a reduced root-mean-square error and a consistent rise in direction-finding accuracy after anomaly removal. These findings confirm the technique’s potential for further development and integration into synthesised radio direction-finding systems, where bearing reliability and precision are critical.
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Method for On-line Acceleration of Dependent Operation Chains Using Redundant Code on FPGA with System of Linear Equations Example
(National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Verbovskyi, Illya; Zhabin, Valerii
This study examines methods for accelerating the execution of dependent operation chains in on-line mode through parallel processing of operands at the bit level in redundant code on field-programmable gate arrays (FPGA). The object of research is the hardware implementation of the Thomas algorithm for solving systems of linear equations with tridiagonal matrices on FPGA platforms. The aim is to develop a method for accelerating dependent operation chains in on-line mode using redundant code with minimization of pin count requirements. The methodology employs algorithmic analysis, hardware modeling using Active HDL, performance evaluation based on timing characteristics and resource utilization on Altera Cyclone III EP3C5E144 platform, with verification performed using Quartus. The results reveal bottlenecks in traditional FPGA implementations of the Thomas algorithm and demonstrate that the proposed optimized method provides over threefold performance improvement while maintaining constant pin count regardless of operand bit depth. The developed computing module architecture enables bit-wise parallel data processing and supports a modified version of the Thomas algorithm adapted for on-line operation. The scientific novelty lies in combining redundant code with on-line computation techniques to simultaneously achieve computational acceleration and hardware implementation simplification. The practical value is determined by the applicability of the proposed approach to resource-constrained FPGA platforms, ensuring efficient implementation of computationally intensive algorithms with dependent operation chains.

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