Control, Navigation and Communication Systems. Academic Journal
Journal represent the research results on the development and improvement of control, navigation and communication systems in various areas
Національний університет «Полтавська політехніка імені Юрія Кондратюка»
SYSTEMATIC ANALYSIS OF THE CAUSES OF NAVIGATIONAL INCIDENTS DURING SHIP MOORING AND WAYS TO IMPROVE THE INTERNATIONAL REGULATORY FRAMEWORK
Relevance. Due to the increasing size of merchant ships and the load on port infrastructure, the issue of ensuring safety during the mooring process is becoming strategically important. Frequent incidents in ports around the world indicate the imperfection of existing practices and the fragmentation of the implementation of modern risk management methods, in particular Bridge Resource Management (BRM). The object of study is navigation incidents during mooring of large-tonnage vessels. Purpose - to systematically analyze the factors that cause mooring incidents and to formulate recommendations for improving the international regulatory framework and risk management procedures in ports. Methodology. The study uses comparative analysis of incidents, expert risk assessment, classification of causes based on a factor model, and analysis of international IMO regulations (SOLAS, STCW, MSC.255(84), etc.). Results. The study confirmed that the key causes are excessive approach speed, inconsistency of towing support actions, human factor, and lack of standardized BRM procedures on the approach to the berth. Comparison of practices in different countries has shown uneven implementation of simulator training and intelligent decision support systems. Conclusions. The paper provides recommendations for the harmonization of regional practices that promote the development of decision support systems and the introduction of simulator training for ship crews. The expediency of integrating BRM in the port and developing adaptive risk management protocols, taking into account the real state of infrastructure provision, is proved.
Copyright (c) 2025 Sergiy Voliansky, Yuriy Buchkovsky, Oleksiy Melnyk, Andriy Voloshyn http://creativecommons.org/licenses/by-nc/4.0
RESEARCH OF FLIGHT PLANNING METHODS OF UNMANNED AERIAL VEHICLES IN COMPLEX FLIGHT CONDITIONS FOR MONITORING THE STATE OF CRITICAL INFRASTRUCTURE ELEMENTS
Relevance. The vast majority of modern UAV flight path planning algorithms designed for real-time onboard application do not take into account UAV dynamics, which negatively affects the accuracy and optimality of route planning, especially when tracking moving objects. Object of research: processes of monitoring critical infrastructure elements with UAVs. Purpose of the article. evaluation of methods for flight planners of unmanned aerial vehicles in difficult flight conditions for monitoring the condition of critical infrastructure elements. Research results. The article presents a comparative analysis of existing methods for constructing a UAV flight route during the observation of objects - elements of critical infrastructure objects, namely the exhaustive search method and the greedy algorithm with the improved algorithm proposed by the author. When modeling the emulation of the UAV flight using the MATLAB package, twenty scenarios were developed. For each scenario, a list of five objects to be monitored was selected. The objects were selected as dynamic and stationary. Based on the analysis of the considered scenarios, conclusions were formulated about the effectiveness of the improved method. Conclusions. The routes constructed using the improved method completely coincided with the routes constructed using the exhaustive search method. At the same time, the calculation time is significantly lower than the existing methods, which allows using the improved algorithm as part of the on-board software complex and quickly constructing and changing the route depending on the situation with respect to the monitored objects. Using an identifier in the control system circuit to compensate for the influence of the wind allows reducing the flight time along the route by 15%.
Copyright (c) 2025 Ruslan Kulish http://creativecommons.org/licenses/by-nc/4.0
ENHANCING TRUSTWORTHINESS OF IOT-ENABLED AUTOMATED VEHICLE LOCALIZATION SYSTEMS
Relevance. Autonomous vehicles rely on multi-sensor localization systems operating within IoT infrastructures, creating interconnected vulnerabilities from sensor anomalies, network failures, and cybersecurity threats that require comprehensive solutions addressing both vehicle-level and infrastructure-level reliability challenges. The object of research is IoT-enabled automated vehicle localization systems requiring trustworthy operation under adverse conditions, including sensor malfunctions, GPS spoofing attacks, and infrastructure failures. Purpose of the article is to develop and validate a unified resilience framework that integrates transformer-based anomaly detection for in-vehicle sensor streams with federated learning agents deployed across IoT edge gateways, ensuring sub-second recovery from infrastructure failures while maintaining localization accuracy. Research results. The proposed framework achieves 9498% anomaly detection accuracy while maintaining localization errors below 0.5 meters during fault conditions. The federated learning component demonstrates 40% reduced communication overhead compared to centralized approaches, with sub-second failover capabilities during infrastructure failures. Explainable ML integration provides interpretable alerts through transformer attention mechanisms, enabling real-time system diagnostics. Conclusions. The unified framework successfully addresses critical challenges in autonomous vehicle deployment by combining multi-layer anomaly detection, coherent reliability broadcasting, and explainable AI techniques, providing a comprehensive foundation for trustworthy autonomous vehicle operation in IoT-enabled smart city environments.
Copyright (c) 2025 Olena Sevostianova , Nataliia Kosenko , Vladlen Filippov , Maksym Diachenko , Ivan Kharakhaichuk http://creativecommons.org/licenses/by-nc/4.0
RESEARCH OF FACTORS OF INTERANNUAL AND INTRA-SEASONAL VARIABILITY OF THE MONSOON DEPRESSION IN THE INDIAN OCEAN
Monsoon depressions (MDs) – areas of low pressure with cyclonic circulation that form over the Bay of Bengal - have a significant impact on weather conditions in the northern Indian Ocean during the summer monsoon. They are characterised by gale-force winds, high waves, intense cloud cover and heavy rainfall, posing a serious threat to maritime navigation and coastal infrastructure in the region. The recurrence and intensity of MDs are subject to significant fluctuations in time over a wide range of scales, from intra-seasonal to inter-decadal. Understanding the factors that control the variability of the MD regime is essential to reduce risks and losses in the maritime transport sector, which plays a key role in the global economy. In this paper, based on a comprehensive analysis of reanalysis data, observations, and numerical experiments, we investigate the large-scale dynamic and thermodynamic factors that determine the MD regime on interannual and intra-seasonal time scales. It is shown that changes in atmospheric circulation and ocean surface temperature in recent decades have contributed to a decrease in the number of MDs over the Bay of Bengal. At the same time, the results of regional model experiments indicate a possible increase in the frequency of the MDs in the context of the ongoing warming of the World Ocean. The analysis of intra-seasonal variations revealed statistically significant links between the MD activity and fluctuations in the intensity and position of the Intratropical Convergence Zone (ICZ). The quantitative estimates obtained in this work provide a basis for improving the quality of weather forecasts on sea lanes in the northern Indian Ocean and optimising strategies to minimise weather risks for the maritime industry.
Copyright (c) 2025 Georgiy Tomchakovsky http://creativecommons.org/licenses/by-nc/4.0
SUBSTITUTION OF RESEARCH ON ANTENNA SYSTEMS IN SHORTWAVE AND ULTRASHORTWAVE RANGES
To improve the short-wave antenna based on low-positioned vibrators, an analysis of current trends in the development of new and modernization of existing antenna systems for short-wave radio communications was conducted. The possibility of studying the use of low-positioned horizontal vibrators is due to the fact that it is possible to create rather complex antenna arrays with large gain coefficients from available materials at ionospheric wave antenna sites and not only for them. Approaches to improving antenna systems depending on the type of radio wave propagation, physical parameters of antenna-feeder devices and their directional properties were analyzed. In this regard, an approach to antenna synthesis and the use of this approach in further work to improve the antenna system and obtain the necessary directional characteristics for organizing stable radio communications were substantiated. It is proposed to develop a method of constructive synthesis based on the methods of induced electromotive forces and mirror reflection. At the same time, a set of scientific tasks has been defined to achieve new results, classical mathematical methods have been selected, with the help of which it will be possible to calculate the electromagnetic field created by an improved antenna system. To achieve the set goal, the main directions have been defined, such as: improving the mathematical model of the antenna system based on low-lying radiators; the next step is to improve the mathematical method for calculating the electromagnetic field taking into account the radial components of the electric and magnetic fields; and, as a result, to obtain a convenient tool, to develop a methodology for the constructive synthesis of an improved short-wave antenna system from low-lying radiators with the ability to change the direction of the antenna radiation pattern in the vertical and horizontal planes. The article has formed an objective function that determines the main parameters that need to be achieved. Controlled and uncontrolled variables have been determined, on which the objective function of the specified process directly depends. In conclusion, an approach to calculating the efficiency indicator for evaluating the obtained parameter value from their previous values is presented.
Copyright (c) 2025 Oleh Berdnykov , Serhii Mazor , Tetiana Khranovska , Pavlo Dimitrov http://creativecommons.org/licenses/by-nc/4.0
DESIGN AND IMPLEMENTATION OF A SOFTWARE-DEFINED SPECTRUM ANALYZER BASED ON PLUTO-SDR
This article studies how a Software-Defined Radio (SDR) can work as a real-time spectrum analyzer and compares two spectrum analyzer program designs for PlutoSDR. The aim of the article: to demonstrate how an SDR can be used as a spectrum analyzer that makes fast sweeps and clearly displays data over wide frequency spans. We compare two approaches to the program design: (1) a step-by-step sweep and (2) a fast sweep with fewer tuner steps and RBW/VBW used during signal processing. The results obtained: the second program has shorter sweep times and more stable power level estimates across the band. It also makes burst signals easier to see and supports continuous monitoring like an entry-level Real-Time Spectrum Analyzer (RTSA). Conclusions: an SDR configured with proper RBW/VBW and efficient rendering can provide good-enough real-time spectrum analysis for educational and engineering tasks.
Copyright (c) 2025 Mykola Bikchentayev, Bohdan Boriak http://creativecommons.org/licenses/by-nc/4.0
DEVELOPMENT AND ANALYSIS OF A NUMERICAL MODEL FOR CALCULATING THE BRIGHTNESS TEMPERATURE OF THE ATMOSPHERE IN THE MAPLE ENVIRONMENT BASED ON ITU-R RECOMMENDATIONS
The article is devoted to the development and analysis of a simulation model for calculating the downward luminous temperature, implemented in the Maple environment based on ITU-R recommendations. The purpose of the article is to create a methodology and simulation model for numerical modelling of luminance temperature, as well as to evaluate the effectiveness of iterative and recursive methods, taking into account their performance and practical application. The resear ch tasks include the development of algorithms for calculating luminance temperature, the implementation of the model in Maple, the analysis of the dependence of luminance temperature on elevation angles and frequencies, the comparison of iterative and recursive approaches in terms of key parameters, and the evaluation of the model's accuracy relative to reference data. The results demonstrate the correctness of the model, confirmed by the proximity of the calculated luminous temperature values to the actual data. The iterative method proved to be faster, while the recursive approach better illustrates the physical model. Numerical modelling reflects the dependence of brightness temperature on elevation angles, which is consistent with physical principles. Field of application: covers meteorology (analysis of atmospheric radiation), telecommunications (assessment of 5G/6G signal attenuation), remote sensing of the Earth, and education (demonstration of RTE and algorithms). Prospects include the integration of scattering and real-time data.
Copyright (c) 2025 Olha Zhyla, Volodymyr Kosharskyi http://creativecommons.org/licenses/by-nc/4.0
MATHEMATICAL MODEL OF A DATA FLOW MANAGEMENT SYSTEM IN A CLUSTER-BASED MULTICONTROLLER SDN
The problem of load balancing among controllers and ensuring network state consistency is one of the key challenges in multicontroller Software-Defined Networks (SDN). To address this issue, it is essential to develop adequate mathematical models that accurately describe the processes of data flow management. The subject of this study is the data flow control system within a clustered multicontroller SDN environment. The objective of the research is to develop a mathematical model that accounts for the dynamic clustering of SDN controllers when receiving data from network switches, enabling balanced load distribution across controllers and facilitating rapid reallocation of links between controllers and switches. The following results have been obtained. A three-tier hierarchical clustering approach for control and data transmission/reception devices within the system has been proposed. The relationships between controllers and OpenFlow switches within the developed clustered architecture have been formalized. Computational analysis of the control element loads has been performed. Conclusion. The proposed mathematical model enables efficient load balancing among controllers and ensures dynamic redistribution of workloads in the event of controller failure.
Copyright (c) 2025 Stanislav Myhal http://creativecommons.org/licenses/by-nc/4.0
MATHEMATICAL MODEL OF THE COMMUNICATION RADIUS OF A MOBILE GROUND-BASED RETRANSMITTER TAKING INTO ACCOUNT THE DYNAMIC ORIENTATION OF THE ANTENNA
Increased requirements for the efficiency of mobile repeaters in hybrid networks such as FANET and LoRa necessitate the creation of simplified models for assessing connectivity. The object of the study is the process of radio signal propagation between a mobile repeater and a set of nodes in a dynamic environment. The purpose of the article is to develop a simplified mathematical model of the communication radius that takes into account the speed of movement, antenna orientation, and signal loss due to obstacles. The proposed model reduces computation time by 10–15% with an error of no more than 15% compared to the complex model. It is suitable for rapid modelling of large FANET/LoRa networks, provides a balance between accuracy and speed, and can be used to optimise the location of mobile repeaters in telecommunications systems.
Copyright (c) 2025 Oleksii Mykhailichenko http://creativecommons.org/licenses/by-nc/4.0
TECHNOLOGY OF SEMANTIC TRANSFORMATION OF INFORMATION MESSAGES IN THE INDUSTRIAL INTERNET OF THINGS ENVIRONMENT
The relevance of the research is the development and implementation of the technology of semantic transformation of information messages, which will increase the efficiency of data processing and promote the development of intelligent industrial process control systems. The purpose of the research: the development of the technology of semantic transformation of information messages in the industrial Internet of Things environment, which provides a consistent representation, interpretation and exchange of data between heterogeneous devices and systems. Results. The article proposes the architecture of a heterogeneous gateway for the industrial Internet of Things. The possibility of implementing the technology of a semantic gateway, which solves the problems of interaction of various applied technologies at the metadata level, is proven. In a heterogeneous environment, such a gateway is responsible for the transformation of industrial Internet of Things protocols. An analysis of data transmission protocols used in IIoT networks for the purpose of semantic transformation is carried out. Conclusion. The proposed approach allows to increase the level of interoperability, reduce information losses during message transmission, and create a basis for building intelligent data processing services in industrial cyber-physical systems.
Copyright (c) 2025 Serhii Pyrozhenko, Serhii Datsenko http://creativecommons.org/licenses/by-nc/4.0
POWER MICROWAVE SENSOR ON PARTIALLY FILLED RECTANGULAR WAVEGUIDES BY DIELECTRIC
In automatic power stabilization systems the functions of the measuring element are performed by a power sensor. It is proposed to implement the microwave module receiver paths for a number of mobile radio engineering systems on partially filled waveguides by dielectric. The output power in the microwave range is one of the most important parameters of microwave signal sources and amplifiers, microwave transmitting paths and radioengineering systems of various purposes in general. The ability to use a microwave signal source depends on the level of microwave output power. In radar stations, groundbased microwave telecommunication stations and satellite transmission systems the microwave transmitter power determines the range of the system. The article presents the schemes of the microwave transmission path of a digital troposcatter station and the design of a microwave power sensor on a waveguide partially filled by dielectric. Since there are microwave radiosystems, including digital troposcatter stations, operating in several frequency subranges, it is not necessary to approximate the entire range with orthogonal polynomials of equalwave nature. The article suggests that for such microwave radio systems it is more appropriate to use Legendre orthogonal polynomials and Gegenbauer orthogonal polynomials. The calculated data are shown in the form of graphs, where the mapping coefficients obtained from fourth- and fifthorder polynomials are compared. The conclusions of the article indicate that such microwave power sensors can be used in a nodal digital troposcatter station, a highspeed digital troposcatter station and in combined digital troposcatter stations.
Copyright (c) 2025 Vitaly Pochernyaev, Mariia Mahomedova, Natalia Syvkova, Oleh Yastreba http://creativecommons.org/licenses/by-nc/4.0
DISTANCE MEASUREMENT USING TDOA METHOD BASED ON LORA PROTOCOL
The article focuses on the implementation of distance measurement between objects using the Time Difference of Arrival (TDoA) method, specifically through the Round-Trip Time of Flight (RTToF) technique, based on the LoRa protocol and utilized by the SX1280 transceiver. The aim of the article is to detail the temporal characteristics of packet exchange and formalize the calculation of distance. The key parameters significantly affecting measurement accuracy are identified as Bandwidth (BW) and Spreading Factor (SF). The research includes a comprehensive analysis of error sources, such as Reference Oscillator Error (timing offset) and Multipath Propagation, and provides formulas for their quantification. A multi-stage correction methodology is proposed, incorporating Frequency Error Correction (based on FEI), LNA Compensation (based on RSSI), Statistical Filtering (using the median), Polynomial Curve Correction, and Environment-Specific Correction. The results obtained from field measurements indicate that with this comprehensive correction approach, a ranging precision of approximately 1 meter can be achieved in controlled line-of-sight conditions. Furthermore, practical outdoor applications demonstrate mean errors below 10 meters even at distances exceeding 1 km. Conclusions: The implementation and comprehensive correction of RTToF measurements using the LoRa-based SX1280 module allow for the achievement of high accuracy while maintaining an acceptable balance between precision, time-on-air (energy consumption), and communication range, which is crucial for object detection and positioning systems.
Copyright (c) 2025 Vitalii Rudenko http://creativecommons.org/licenses/by-nc/4.0
SYNTHESIS OF THE DIGITAL TWIN OF THE LOGICAL-DYNAMIC INFORMATION AND EVENTS MANAGEMENT SYSTEM FOR THE SECURITY OF COMPUTER SYSTEMS OF THE MOBILE CELLULAR INFORMATION AND COMMUNICATION NETWORK
The article focuses on the aspect of information security and notes that currently the modern mobile information and communication cellular network is one of the most vulnerable and at the same time important objects of the critical information infrastructure of the state. It serves a wide range of users who make decisions for the organization of public administration, and also provides digital communication to a number of other important systems from the population to departmental structures. That is why this network acts as a priority object in the context of organizing effective management of its information security events. To organize the reliable functioning of this important object, a new approach to the synthesis of a digital twin of the information and security event management system of computer systems of the cellular mobile information and communication network is proposed. The proposed synthesis is based on a logical-dynamic approach to modeling security events in modern computer systems, attack scenarios and mechanisms for responding to these information security incidents by forming appropriate effective control influences. The architecture of the digital twin, the algorithm for its synthesis are considered, and possible approaches for implementing the integration of this virtual object with such platforms as Wazuh, Streamlit, Neo4j, AWS IoT are proposed. Verification and testing are carried out using the example of a DDoS scenario, and the results of the synthesis algorithm implementation are presented. The effectiveness of the model in detecting threats and adapting to intensive changes in the security environment of the computer system of the mobile digital network is shown.
Copyright (c) 2025 Ievgen Samborskyi, Heorhii Krykhovetskyi http://creativecommons.org/licenses/by-nc/4.0
MEANS OF NORMALISING THE LEVELS OF MAGNETIC FIELDS OF LOCAL SOURCES
The protective properties of soft magnetic metal alloys for shielding ultra-low frequency magnetic fields have been investigated. The dependence of the shielding coefficients of industrial frequency magnetic fields by the most common electrical steels on the induction of the shielded magnetic field has been established. It has been determined that this dependence is non-monotonic. The presence of the maximum shielding coefficient is due to the achievement of the saturation induction of the material. The values of the shielding coefficients depend on the specific losses in steel, which are reference values. It has been shown that for the manufacture of shielding structures, it is advisable to use low-grade electrical steels, which are least suitable for their main application – the manufacture of cores and magnetic conductors. A two-layer structure is proposed for shielding highintensity magnetic fields, for example, in built-in transformers. A two-layer structure made of electrical steel with a gap between the layers has higher industrial frequency magnetic field shielding coefficients than several layers of the same steel. This reduces the weight of the shielding structure and increases its efficiency. Filling the gap between the layers reduces equipment noise, which is a regulatory parameter. The dependence of the magnetic field shielding coefficients of amorphous cobalt alloys on the induction of an external magnetic field has been studied. This dependence is non-monotonic, which must be taken into account in the design of shielding structures. Amorphous ferromagnetic alloys are also effective in shielding low-frequency magnetic fields. The advantages of such alloys are high magnetic permeability and low thickness. Thermomagnetic treatment of amorphous alloys significantly increases their protective properties. The feasibility of developing a composite material with a filler of finely dispersed amorphous soft magnetic alloy is shown.
Copyright (c) 2025 Nataliia Burdeina, Yaroslav Pidlisnyi http://creativecommons.org/licenses/by-nc/4.0
RESEARCH ON THE EFFECTIVENESS OF THIN NOISE BARRIERS IN CONFINED SPACES
The possibilities of using thin noise barriers to reduce acoustic impact on workers and the population in confined spaces have been investigated. Such conditions are typical for construction noise when working in densely built-up areas. The most convenient mathematical apparatus for predicting the effectiveness of an acoustic screen is presented. The measured amplitudefrequency characteristics of noise reduction indices depending on the area of the acoustic screen are given. An acceptable convergence of calculated and experimental data has been established. It has been established that thin screens are insufficiently effective in the low-frequency part of the sound spectrum. The influence of openings in the protective structure on noise reduction indices has been investigated. It has been established that at medium and high frequencies, openings become critical for noise protection. At the same time, at low frequencies, they have practically no effect on the effectiveness of the screen. Noise reduction indices in octave frequency bands and their changes with distance from the screen to the sound receiver were measured. It was found that at low frequencies, sound levels can increase (negative indices) due to interference phenomena. It is proposed to give screens resonant properties in confined spaces. It is advisable to choose the sound frequency with the maximum amplitude as the resonant frequency. To increase the effectiveness of the screen in the mid and high frequency ranges, it is advisable to manufacture two-layer panels and fill the gap between the layers with noise-absorbing porous material.
Copyright (c) 2025 Valentyn Glyva, Mykhailo Kashlev http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZATION OF THE CALCULATION APPARATUS FOR DESIGNING POROUS SOUND-ABSORBING MATERIALS
The main drawback of existing models of sound propagation through sound-absorbing porous materials is the complexity of their practical application. All calculations are presented in an imaginary form, which increases the volume of calculations and complicates the process of automating the design of sound-absorbing materials of the required efficiency. Therefore, it is advisable to convert all calculations into a real form, which will simplify the process of automating the design of soundabsorbing materials and structures. It has been shown that the use of empirical and semi-phenomenological models for preliminary assessment of the sound-absorbing properties of porous materials is difficult due to their dependence on numerous parameters that cannot be measured directly. It is proposed to optimise the calculation apparatus based on the Johnson-Champ-Allard-LaFarge (JCAL) model. It is shown that the JCAL model can be reduced to two non-acoustic parameters – porosity and airflow resistance, which can be determined quite accurately experimentally, taking into account their correlation with the four remaining parameters. The porosity of the material is easily determined by weighing test samples, and the air flow resistance can be measured in a standard impedance tube or on a laboratory setup for determining air flow resistance using air flow and a pressure sensor. Reducing the number of model parameters speeds up the prediction of sound absorption characteristics and makes the model more applicable to optimisation strategies and automated design of protective porous materials. The creation of application software for the automation of sound insulation design with high calculation speed will allow the optimisation of the selection of the required material parameters by trial and error. The proposed changes significantly reduce the computational complexity, increasing the suitability of the calculation apparatus for real-time applications and large-scale modelling.
Copyright (c) 2025 Grygorii Krasnianskyi, Yana Biruk http://creativecommons.org/licenses/by-nc/4.0
PRINCIPLES OF DEVELOPMENT OF ELECTROMAGNETIC SCREENS WITH SMALL DIMENSIONS AND WEIGHT
The possibilities of developing thin-film materials for shielding ultra-high and higher frequency electromagnetic radiation are analysed. It is shown that coatings with a thickness less than a quarter of the wavelength of the incident electromagnetic wave (400–500 μm) do not provide low reflection coefficients when applied to a metal surface. The reduction of reflection coefficients to 0.7–0.6 occurs randomly at different frequencies. This is due to resonance phenomena in the coating. The frequency band of minimum reflection is narrow and has no practical significance. It has been shown that it is difficult to achieve an acceptable balance of magnetic and electrophysical properties of a thin coating to minimise reflection coefficients. This is due to the dependence of the final product's performance not only on the magnetic and dielectric properties of the components and their concentrations, but also on the morphology of the particles, their distribution in the matrix body, and the polymerisation mode. In addition, magnetic and electrophysical properties have an amplitude-frequency dependence. It has been proven that in order to ensure low electromagnetic wave reflection coefficients and high electromagnetic energy absorption, it is advisable to use a multilayer structure. The layers must have different magnetic and electrophysical properties. At the same time, the lowest filler concentrations are in the upper layer. These concentrations should be at the limit of the percolation effect – a sharp increase in the electrical conductivity of the composition. These concentrations can be estimated using the theory of electrodynamics of continuous media. If possible, it is advisable to provide a dielectric layer between the layers. The presence of boundaries increases the overall effectiveness of the protective coating.
Copyright (c) 2025 Kyrylo Nikolaiev, Artem Bilyk, Kostiantyn Sapozhnykov http://creativecommons.org/licenses/by-nc/4.0
PARALLEL IMPLEMENTATION OF VOICE SIGNAL PROCESSING METHODS ON MULTICORE CPU AND GPU
Relevance. Systems such as voice assistants and speaker identification tools, which operate based on voice signal processing, have become increasingly widespread. The performance of such systems depends on the volume of data and operating conditions. Processing large collections of voice signals or ensuring real-time operation requires high-performance computing. Such performance can be achieved with massively parallel systems, including multiprocessor clusters or discrete GPUs. Object of research is the organisation of parallel computing processes in voice signal processing tasks using the capabilities of modern processor architectures. Purpose of the article is to develop a parallel voice signal processing system with adapted algorithms for a multi-core CPU and an integrated and discrete GPU. Research results. Comparative analysis revealed that for small loads (such as voice assistants and personal applications), CPU usage is sufficient to ensure efficient computation with low latency. However, when processing large datasets and performing streaming analytics, the proposed parallel approach, implemented on both the CPU and GPU, reduces execution time by 25-30% compared to a sequential implementation on the CPU. Conclusions. Research has shown that parallelism on the CPU is suitable for stages of voice signal processing that require a small amount of computation. At the same time, discrete GPUs can be utilised in stages with intensive computational tasks such as MFCC calculation, spectral subtraction, and wavelet filtering.
Copyright (c) 2025 Maksym Bondarenko, Heorhii Ivashchenko http://creativecommons.org/licenses/by-nc/4.0
BLOCK CIPHER MODE BASED ON HUFFMAN CODING FOR NIBBLES
Relevance. The relevance of this work is driven by the need for cryptographic solutions that combine high security, efficient compression, and low computational overhead, which is particularly important for IoT, mobile, and embedded systems. The purpose of the article is to research the development and analysis of a block cipher mode based on Huffman coding for nibbles, with integrated hash protection mechanisms and support for both global and per-block processing. Object of research: the use of Huffman transformation of half-bytes (nibbles) to create a block cipher mode. Research results. Experiments have shown that for data with low nibble entropy (H < 3.5 bits/nibble), the proposed mode reduces the size of encrypted data by 10– 20% without significant performance loss, ensuring error resistance and integrity preservation. Conclusion: the integration of statistical coding and symmetric encryption with adaptive parameter selection provides a balanced solution for secure and efficient information transmission under resource constraints.
Copyright (c) 2025 Maksym Glavchev, Yuliia Hlavcheva, Heorhii Molchanov http://creativecommons.org/licenses/by-nc/4.0
MODELS AND METHODS FOR BUILDING A DECENTRALIZED IOT SYSTEM – ESP32-BASED SENSORS USING THE MQTT PROTOCOL
Relevance. The relevance is due to the growing interest in ESP32-based IoT systems, the limitations of centralized systems, and the potential of the MQTT protocol. Article subject. Methods for implementing decentralized WSNs and their components. Purpose. The article's main purpose is to propose a decentralized architecture for Wireless Sensor Networks (WSNs). It aims to shift from the traditional centralized model, where all sensor nodes depend on a single hub, to a more resilient system where each node possesses direct internet access. The goal of this proposed design is to significantly boost the reliability and overall productivity of the network by eliminating the single point of failure inherent in a centralized setup. Article results. The article culminates in a specific and cost-effective recommendation of components to build a decentralized sensor node. These include: the ESP32 module, communication modules, a development environment, a data transfer protocol, and brokers to ensure its operation. Conclusions. Based on an analysis of available hardware and software, the article concludes that building a reliable and cost-effective decentralized Wireless Sensor Network (WSN) is highly feasible.
Copyright (c) 2025 Oleh Drozd, Oleksii Sytnyk, Maksym Nesterenko http://creativecommons.org/licenses/by-nc/4.0
METHODS OF DATA MINING USING MACHINE LEARNING
Relevance . In the context of the continuous growth of information volumes across various fields of the economy, science, and technology, the problem of effective processing and analysis of large-scale data has become increasingly urgent. Traditional analytical methods are no longer capable of providing fast and accurate extraction of useful knowledge and patterns from massive information flows. The response to this challenge lies in the methods of data mining, which are based on modern machine learning technologies. These methods enable the automatic discovery of hidden patterns, the generation of accurate predictions, and the support of data-driven decision-making in real time. Given the rapid development of digitalization, artificial intelligence, and the need for prompt decision-making in a competitive environment, the relevance of developing and improving data mining methods is growing steadily. The object of research is process of data mining using machine learning methods, namely the set of algorithms, models, tools, and approaches that ensure the detection of hidden patterns, anomalies, and structures in large volumes of heterogeneous information. Purpose of the article. This study explores contemporary approaches to intelligent data analysis based on machine learning techniques and assesses their effectiveness across a range of application domains. The article aims to provide a structured overview of state-of-the-art algorithms and to evaluate their respective advantages and limitations in processing large-scale and highdimensional datasets. Research results. A systematic analysis of key data mining methods based on machine learning algorithms was carried out. It was found that the most effective approaches for processing large and heterogeneous datasets include classification, clustering, regression analysis, and dimensionality reduction techniques. Deep neural networks demonstrated effectiveness when applied to unstructured data such as text, images, and time series. The study revealed that the appropriate choice of algorithm depends not only on the data type but also on the specific nature of the task. A comparative assessment of tools showed that the Python ecosystem offers the greatest flexibility, while AutoML platforms simplify model deployment for users with limited programming experience. The research also included a review of recent publications that confirm the practical value of machine learning in real-world use cases. Overall, the findings indicate that machine learning is a driving force behind the evolution of data mining methods, enabling accurate, scalable, and adaptive data processing in the context of modern digital transformation. Conclusions. Machine learning has significantly expanded the capabilities of intelligent data analysis by enabling the automatic detection of patterns, forecasting, and decisionmaking based on large volumes of information. The study demonstrates the effectiveness of various algorithms in tasks such as classification, clustering, regression, and deep learning. Python-based tools and cloud platforms have been identified as the most convenient environments for implementing analytical models. A promising direction lies in the development of explainable AI and hybrid approaches that combine algorithmic precision with domain-specific expertise.
Copyright (c) 2025 Dmytro Diachenko, Mykhailo Prokopchyk, Vladyslav Rovenchak, Andriy Frolov http://creativecommons.org/licenses/by-nc/4.0
COMPARATIVE ANALYSIS OF REAL-TIME GESTURE RECOGNITION METHODS BASED ON MEDIAPIPE, OPENCV, AND YOLOV8
The subject of the article is real-time gesture recognition methods based on computer vision, designed for integration into human-computer interaction (HCI) systems, particularly for device control via a webcam. The purpose of the work is a comparative analysis of the efficiency of three modern methods MediaPipe, OpenCV, and YOLOv8 by evaluating their performance using key metrics (FPS and Detection Rate) for detecting basic gestures. The article solves the following tasks: identifying the impact of external factors (lighting, background) on gesture recognition, implementing algorithms for three basic gestures (open palm, index finger up and down), conducting experimental testing and modeling in the Python environment. The following methods are used: computer vision and image processing (in particular, segmentation, keypoint tracking, and object detection); machine learning based on CNN (LeNet, YOLO); analysis of datasets and performance metrics (precision, recall, mAP); simulation modeling in real conditions using the libraries mediapipe, opencv-python, and ultralytics. The following results were obtained: a comparative analysis of the methods was conducted, where MediaPipe provided the highest accuracy (95% Detection Rate), OpenCV—the maximum speed (50.7 FPS), and YOLOv8—a balance for limited resources (73% Detection Rate); recommendations for hybrid approaches to optimization were proposed. Conclusions: The developed comparative analysis of real-time gesture recognition methods demonstrates that MediaPipe effectively eliminates interference from variable lighting and background, achieving a stable accuracy of 95%, while OpenCV optimizes processing speed to 50.7 FPS. Modeling was performed in Python with visualization of the results.
Copyright (c) 2025 Olha Yeroshenko, Vadym Tsipkovskyi http://creativecommons.org/licenses/by-nc/4.0
PRACTICAL APPLICATION AND VULNERABILITIES OF HILL CIPHER IN A MODERN CONTEXT
The article examines the Hill cipher as a classical example of applying linear algebra and modular arithmetic in cryptography. It elucidates the mathematical foundations of the algorithm, including the formation of the key matrix over finite fields, the conditions for its invertibility, and the implementation of encryption as the multiplication of plaintext vectors by the key matrix. This approach is presented as a distinctive “bridge between theory and practice,” combining formal mathematical constructions with practical data protection mechanisms. Particular attention is given to the vulnerabilities of the Hill cipher. It is shown that the algorithm is insecure in modern contexts due to the linearity of its transformations and the absence of nonlinear components. Examples of cryptanalytic attacks are provided, demonstrating that the cipher can be “broken apart” even with a minimal number of plaintext-ciphertext pairs. This makes it evident that, in the context of 21st-century cybersecurity, the Hill cipher is merely a “shadow of its era” and cannot serve as a fully reliable tool for data protection. At the same time, the educational and methodological value of the cipher is emphasized. It allows one to “see the mechanism from the inside,” clearly illustrating the transition from monoalphabetic ciphers to block transformations. The article highlights that the Hill cipher played the role of a “first step” in the development of structures that later evolved into DES and AES, where the use of block structures and matrix operations became a fundamental element of modern cryptography.
Copyright (c) 2025 Yevhen Zhyvylo, Yurii Kuchma http://creativecommons.org/licenses/by-nc/4.0
IMPROVING THE AI STOCK MARKET FORECASTING WITH CANDLESTICK PATTERNS
In the rapidly evolving digital economy, the application of Artificial Intelligence (AI) in financial forecasting has gained significant traction. This study investigates the effect of various candlestick patterns on the performance of Long Short-Term Memory (LSTM) models in predicting stock market movements. Experiments conducted on the stock price history data demonstrate that supplementing traditional input parameters (e.g., open price) with a range of candlestick patterns enhances the predictive accuracy of LSTM models. Although the initial model architecture lacked hyperparameter optimization for solving this kind of task, our findings suggest notable improvement in prediction performance when candlestick pattern flags are incorporated. Future work will focus on incorporating additional financial indicators into the model's training data and fine-tuning it through optimization algorithms to achieve greater robustness and accuracy.
Copyright (c) 2025 Oleksandr Zakovorotnyi, Nataliia Ausheva, Larysa Levchenko http://creativecommons.org/licenses/by-nc/4.0
SOLVING PRODUCTIVITY PROBLEMS USING ASYNCHRONOUS METHODS IN JAVASCRIPT
The Data-Driven approach, in which the interface is defined as a function of the current state (UI = f(state)), has become the dominant practice in front-end development. Despite the convenience of the declarative approach, the complexity of systems has led to the identification of a number of architectural shortcomings. The main ones are the loss of transparency of the sources of changes, asynchronous conflicts when updating the state, and the lack of semantic context of events. The article compares the Data-Driven and Event-Driven approaches, explores the key problems of the former, and offers practical solutions that can improve control over application logic. The purpose of this work is to identify typical architectural shortcomings inherent in the Data-Driven model in client-side development, analyze the causes of their occurrence, and develop conceptual and technical ways to minimize the corresponding risks. Special attention is paid to the problems of implicit state mutation, loss of change context, query synchronization, and dependence on the component life cycle in framework-oriented systems. The following results were obtained: As a result of the study, it was found that the Data-Driven approach in complex applications does not provide sufficient control over the sources of changes. It was also proven that even when using tools such as Redux DevTools or React Developer Tools, the developer often does not have a complete picture of state changes, since they occur at different points in the system without a single control path. It was found that the most effective compensatory approaches are the creation of a semantic event layer, centralization of mutations, and the combination of reactive modeling with declarative representation. Conclusions. Data-Driven architecture significantly simplifies UI construction in the conditions of simple or medium-complexity projects. However, with an increase in the number of state sources, the complexity of the relationships between components, and a high level of asynchrony, such a model demonstrates structural limitations. In such conditions, it is advisable to switch to hybrid solutions that combine Data-Driven rendering with Event-Driven semantics and control through single mutation points.
Copyright (c) 2025 Oleksandr Zakovorotnyi, Oleskandr Sapalskyi http://creativecommons.org/licenses/by-nc/4.0
ESTIMATIONS OF THE ASYMPTOTIC COMPLEXITY OF APPLIED MULTIPLE TRANSPORT ALGORITHMS WHEN DIVISIONING THEM INTO CLUSTERS
Applied multiple transport algorithms are usually heuristic and overly complex. Depending on the chosen heuristic of the algorithm, the real time of the software implementation depends on the programming language, the structure of the input data representation and the algorithmic complexity of the solution. The research is based on the use of the provisions of discrete mathematics and graph theory. It is shown that by analyzing asymptotic complexity it is possible to determine effective heuristics. Refinement of applied problem models also contributes to reducing complexity. Since the multiplicity of transport tasks is contained in a discrete coverage or in the functionality of multiple vehicles, it is possible to simplify the algorithm by decomposing them into clusters for which the conditions and constraints of the standard transport task apply. The features of geometric and combinatorial decomposition into clusters are presented. The formalization of cluster construction algorithms for multiple transport problems is provided. A comparative analysis of optimal and heuristic algorithms for solving multiple transport problems is carried out. The hierarchy of asymptotic complexity of heuristic algorithms is determined and the possibilities of their decomposition into clusters are analyzed, which simplify the solution of multiple applied transport problems. The notation of the complexity of the solution and the possibility of their evaluation by the objective function of the selected algorithm or by its approximation are provided. An analysis of the asymptotic complexity of heuristic algorithms for Big-O notation has been carried out. It has been shown that algorithms with a complexity not exceeding polynomial are suitable for implementation, and the use of algorithms with an exponential complexity estimate and higher is undesirable. It is noted that asymptotic estimates of algorithm complexity are suitable for large-dimensional tasks, and for tasks of small dimension, control runs are appropriate.
Copyright (c) 2025 Volodymyr Karlov, Oleksii Kolomiitsev, Oleksandr Kuznietsov, Oksana Biesova, Anna Biesova http://creativecommons.org/licenses/by-nc/4.0
ADAPTIVE METHOD FOR DYNAMIC RESOURCE CONTROL OF THE INDUSTRIAL INTERNET OF THINGS BORDER LAYER
In the process of functioning of the Industrial Internet of Things, the shortage of resources of the boundary layer manifests itself as an increase in processing delays, queues, data loss or service degradation. The purpose of this work is to develop an adaptive method for dynamic resource management of the IIoT boundary layer, which will allow for the effective use of computing resources.. The following results were obtained: A conceptual adaptive method for dynamic resource management of the boundary layer in IIoT using a multi-agent approach and horizontal and vertical scaling is proposed. Monitoring, evaluation, decision-making and learning methods are integrated into a single architecture. The developed mathematical model allows formalizing the balance of queues, resources, and task transfer between nodes. The method provides flexible adaptation to variable load and minimizes losses, delays, and also effectively uses limited resources. Conclusions. Adaptive management of computing resources of the edge layer of the IoT allows to increase the efficiency of the system and reduce the impact of resource shortages.
Copyright (c) 2025 Sergii Klivets, Alexander Kuleshov, Tetiana Kulieshova http://creativecommons.org/licenses/by-nc/4.0
ANALYTICAL MODEL OF A DISTRIBUTED REGISTRY BASED ON THE CONCEPT OF CROWDING
The main problem in the formation of a registry in distributed systems is to determine the dependence between the parameters of the request flow, the speed of transaction service and the general characteristics of the system performance. The purpose of the study: to build an analytical model of a distributed registry, which allows describing the transaction processing process in terms of the theory of queueing and determining the key indicators of the system's efficiency. Results. The article proposes an analytical model of a distributed registry, built on the basis of the theory of queueing. The model describes the processes of recording, verification and confirmation of transactions in a decentralized environment, taking into account the limitations of the node's bandwidth and the intensity of incoming requests. The use of a queuing apparatus allows us to formalize the time characteristics of the system, estimate the average waiting time for transaction processing, the load factor of nodes, and the probability of delay. An analytical study of the influence of network parameters on the productivity of the registry has been conducted and the conditions for system stability have been determined. The proposed approach can be used to optimize the architecture of distributed systems with increased requirements for speed and reliability of data processing.
Copyright (c) 2025 Andriy Kovalenko, Ivan Zamriy, Volodymyr Popov, Dmytro Zharikov http://creativecommons.org/licenses/by-nc/4.0
RESEARCH OF DISTRIBUTED DATA EXCHANGE TECHNOLOGIES IN THE CONTEXT OF INTELLIGENT TRANSPORT SYSTEMS
The article presents the results of a study of distributed data exchange technologies aimed at ensuring effective interaction between components of intelligent transport systems (ITS). Modern approaches to the organization of decentralized information transmission networks are considered, in particular, the use of the concepts of distributed registries, peer-to-peer protocols, and service-oriented architectures. An analysis of the requirements for reliability, latency, and bandwidth of communication channels, which are critical for data exchange scenarios between vehicles and infrastructure, is conducted. Based on a comparative analysis, the advantages and limitations of existing technological solutions in the context of ensuring security, scalability, and fault tolerance are determined. The results of the study can be used to design adaptive traffic management models and develop intelligent interaction modules in new generation transport networks.
Copyright (c) 2025 Nina Kuchuk , Maksym Tregubenko , Danylo Kovalenko , Dmytro Lysytsia , Oleksandra Bellorin-Herrera http://creativecommons.org/licenses/by-nc/4.0
STRUCTURAL AND FUNCTIONAL MODEL OF A CONVOLUTIONAL NEURAL NETWORK FOR PROCESSING, ANALYSING AND CLASSIFYING IMAGES OF VARYING COMPLEXITY
The article presents presents a structural and functional model of a convolutional neural network (CNN) designed for processing, analysing and classifying images of varying complexity. The model is based on a multi-level architecture using convolutional, residual and parallel computing blocks, which ensure high adaptability to different types of input data. The input tensor is normalised by the mean and standard deviation of the sample, which reduces data variability and stabilises the learning process. The first convolutional layer performs the initial extraction of image features with ELU activation, which ensures continuity of gradients and eliminates the problem of ‘dead neurons.’ The implementation of skip connections ensures the consistency of information flows and increases the stability of training. The results demonstrate increased classification accuracy and noise resistance compared to basic CNN architectures.
Copyright (c) 2025 Oleksandr Mozhaiev , Heorhii Kuchuk , Renat Safarov , Maksym Lavrovskyi , Kostiantyn Moroz http://creativecommons.org/licenses/by-nc/4.0
SHALLOW ANN MODELS TO CLASSIFY UKRAINIAN AI-GENERATED TEXT
In this study, we address the task of detecting AI-generated fragments within Ukrainian-language texts. The objective is to develop a tool capable of identifying content produced with the assistance of artificial intelligence, particularly in PDF documents related to the IT domain. The research explores and analyzes existing solutions and approaches currently available in this area. Several commercial AI-content detectors were evaluated using our custom datasets. The dataset was constructed by segmenting bachelor's theses from IT-related fields into fragments of approximately 1,000 characters each. Five artificial neural network models were tested using the custom dataset combined with a traditional NLP pipeline, achieving an accuracy of 87–88%. Given the complexity of the problem and the ethical considerations within the educational context, the classification results should be further validated by human experts. The current implementation can serve as a foundation for future improvements.
Copyright (c) 2025 Olena Peredrii http://creativecommons.org/licenses/by-nc/4.0
This research presents a client-side TinyML profiler of network in the web browser, designed for operation on resource-constrained devices and under unstable or low-bandwidth network conditions. The solution combines a threshold-based decision rule with a lightweight logistic (softmax) model executed locally to classify the quality of the network connection. Relevance. The rising share of mobile traffic, the heterogeneity of network environments, and the limited fidelity of existing browser indicators complicate accurate client-side decision-making. Object of study: client-side methods for profiling and classifying the quality of a browser-based network connection using standard Web Performance APIs and ML models. Aim: to analyze, design, and implement a profiler capable of classifying connection types and producing interpretable features without server support. Methods. Navigation/Resource Timing and PerformanceObserver are used to collect raw signals. A 14-dimensional feature vector is formed (medians/quantiles of RTT and throughput, variability measures, a loss-likelihood heuristic, and protocol/Service Worker indicators). The proposed a threshold rule with hysteresis and decision confidence, together with a softmax model. Results. The developed profiler requires no special permissions or third-party services. In controlled network-emulation scenarios it improves classification accuracy over a pure threshold baseline, while maintaining low overhead and decision explainability. Conclusions. The proposed client-side approach provides an effective, rapid, and interpretable assessment of network conditions in the browser and is suitable for resource-constrained settings. The results can be leveraged to further enhance adaptive content loading, expand the feature space, and deploy more compact ML models in web applications.
Copyright (c) 2025 Artem Prylipa, Anna Filatova http://creativecommons.org/licenses/by-nc/4.0
INTELLIGENT APPROACH TO PLANNING TAKING INTO ACCOUNT THE CONCEPT OF ACCEPTABLE WORK BALANCE
The article presents an intelligent approach to the problem of scheduling computing tasks in a distributed environment taking into account the concept of admissible load balance. The proposed model combines the principles of multi-criteria optimization and machine learning for adaptive resource allocation between system nodes. The introduction of an admissibility coefficient allows you to dynamically limit the set of machines available for performing a specific task, which increases the efficiency of capacity use and reduces task waiting time. The developed approach takes into account various efficiency criteria — productivity, load balance, and scheduling stability — and can be implemented in grid and cloud infrastructures. Experimental results demonstrate that the use of intelligent algorithms allows you to achieve an optimal compromise between system performance and uniform loading of computing resources.
Copyright (c) 2025 Viacheslav Radchenko, Yuliia Andrusenko http://creativecommons.org/licenses/by-nc/4.0
STRATEGIC PLANNING IN THE CONTEXT OF COMBINED SOFTWARE TESTING
Relevance. Given the rapid development of software engineering practices and the need for cost-effective quality assurance in competitive environments, the relevance of developing strategic planning approaches for combined testing is growing steadily. The object of research is the strategic planning process for combined software testing that integrates multiple testing methodologies through systematic framework implementation, risk assessment, and resource optimization algorithms. Purpose of the article. This study explores strategic planning approaches for combined software testing and assesses their effectiveness across various application domains. The article aims to provide a structured framework for testing strategy integration and evaluate optimization mechanisms for resource allocation in complex testing environments. Research results. A comprehensive Strategic Planning Framework for Combined Software Testing (SPF-CST) was developed, consisting of six interconnected components: context analysis, multi-dimensional risk assessment, AI-driven prioritization, strategy selection, resource optimization, and monitoring systems. Empirical validation across eight industry case studies demonstrates a 35% reduction in defect leakage rates, 28% improvement in testing efficiency, and 45% decrease in regression testing costs. The study revealed that strategic planning significantly enhances testing effectiveness through systematic methodology integration and adaptive resource management. Conclusions. The study demonstrates the effectiveness of risk-based prioritization and mathematical optimization in testing strategy selection. The proposed framework provides practical tools for organizations to implement comprehensive testing strategies while managing resource constraints and project timelines.
Copyright (c) 2025 Dmytro Rosinskiy, Vitalii Sitnikov, Daria Pyvovarova, Dmytro Vasylenko http://creativecommons.org/licenses/by-nc/4.0
MODELS AND METHODS OF ARTIFICIAL INTELLIGENCE FOR DATA PROCESSING IN COMPUTER NETWORKS
The article discusses modern models and methods of artificial intelligence for effective data processing in computer networks. It analyzes the main approaches to the application of machine learning, deep learning, and neural networks for optimizing network traffic, detecting anomalies, and improving the security of network systems. It investigates algorithms for classifying network traffic, methods for predicting load, and AI-based intrusion detection systems. The goal of this work is to develop and study smart ways to handle data in computer networks that are scalable, adaptable, and energy efficient. To do this, we plan to create traffic classification models, load balancing algorithms, and cyber threat detection systems based on machine learning and deep learning technologies. Results: The paper proposes a hybrid model for network traffic classification, an adaptive load balancing algorithm based on reinforcement learning, and a real-time cyber threat detection system. Experimental studies have confirmed the effectiveness of the methods: classification accuracy exceeds 94%, and network performance has increased by more than 20%. Conclusions: The use of machine learning and deep learning methods significantly improves the efficiency of computer network management. The results obtained are of practical importance for building scalable, energyefficient, and secure next-generation network systems.
Copyright (c) 2025 Oleg Slobodianyk, Igor Zykov, Denys Grynov http://creativecommons.org/licenses/by-nc/4.0
MICROCONTROLLER-BASED INTELLIGENT LIGHTING CONTROL SYSTEM WITH ADAPTATION TO ENVIRONMENTAL CONDITIONS
Relevance. The growing need for energy-efficient lighting solutions and the development of smart home technologies create the need to develop intelligent lighting control systems. Modern systems must adapt to changing environmental conditions, ensure user comfort and significantly reduce energy consumption. Research object: intelligent lighting control systems based on microcontrollers with functions of adaptation to environmental conditions. Purpose of the article: conducting an analytical review of existing intelligent lighting control solutions, determining optimal technical solutions and identifying promising development directions. Research results: A comprehensive analysis of modern intelligent lighting control systems was conducted, key technologies of sensors, microcontrollers and adaptation algorithms were considered. The advantages and disadvantages of the main commercial solutions were identified, and optimal hardware and software platforms were identified. Conclusions. Intelligent lighting control systems demonstrate significant potential for energy savings (up to 60%) and increased user comfort. The most promising solutions are based on 32-bit ARM Cortex-M microcontrollers with integration of multiple sensors and machine learning algorithms.
Copyright (c) 2025 Anton Sorokin, Mykola Chaikin http://creativecommons.org/licenses/by-nc/4.0
Relevance. The article is devoted to the analysis of the capabilities of the Cisco SecureX platform in the context of automating processes within Security Operations Centers (SOC). The study emphasizes the relevance of this research, which is driven by the growing number of cyber threats, the increasing complexity of attacks, and the shortage of highly qualified professionals in the field of cybersecurity. It is underlined that traditional SOC operation methods are insufficiently effective under conditions of multivector attacks, which objectively necessitates the implementation of orchestration and automation technologies. The research systematizes the core functional features of Cisco SecureX, including: integration with multi-component cybersecurity infrastructures (SIEM systems, EDR platforms, IDS/IPS solutions), which ensures the creation of a unified information space; orchestration of SOC processes through the use of response playbooks, enabling the automation of routine operations, reducing incident handling time, and minimizing the human factor; enhanced analytical capabilities, based on the use of machine learning mechanisms and event correlation from heterogeneous data sources; improved accuracy of threat detection through multi-layered data analysis, including the examination of user behavioral patterns and network activity. Particular attention is paid to the research aspect of SecureX’s impact on SOC efficiency. The study substantiates that the use of this platform makes it possible to reduce the Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR), which are critical indicators for evaluating SOC performance. It is demonstrated that integrating SecureX with Threat Intelligence systems provides a more comprehensive contextual understanding of attacks, increases the level of proactive defense, and contributes to the development of an adaptive security architecture. From the standpoint of scientific novelty, the article presents a systematization of approaches to SOC automation, in which Cisco SecureX is considered not only as a tool for practical implementation but also as a subject of research for evaluating the effectiveness of integrative security platforms. The paper identifies promising directions for further study, including: advancing the level of cognitive SOC automation based on AI, optimizing response playbooks through adaptive algorithms, and assessing the scalability of SecureX in diverse organizational environments. Thus, the implementation of Cisco SecureX is justified both from the perspective of theoretical research and practical application. The platform contributes to the formation of a new approach to information security management, based on integration, automation, and analytics, which determines its strategic significance for enhancing the cyber resilience of modern organizations.
Copyright (c) 2025 Tatiana Fesenko, Yuliia Kalashnikova http://creativecommons.org/licenses/by-nc/4.0
FUZZY ENSEMBLE OF DECISION TREES FOR THE COMPUTER SYSTEMS STATE IDENTIFICATION
The object of the research is the process of identifying the state of computer systems. The subject of the research is the methods of fuzzy ensemble decision trees with multidimensional decision nodes for identifying the state of computer systems. The goal of the research is to develop and evaluate the effectiveness of a fuzzy ensemble of decision trees to improve the accuracy of identifying computer system states under conditions of uncertainty, noise, and incomplete data. Methods used: machine learning methods, data preprocessing techniques, ensemble classifiers, stacking approaches, methods for feature selection and combination of computer system attributes. Results obtained: the effectiveness of both classical and newly developed methods for identifying the state of computer systems under complex conditions, including data imbalance and the presence of anomalous states, was investigated. A comprehensive approach using Fuzzy Stacking with MDT was proposed, providing high accuracy and stability of classification. The best results were achieved with the stacking approach, which combines base classifiers and fuzzy decision trees, minimizing both type I and type II errors and achieving high generalization ability (MCC, F1-score, TS, LN(DOR)). Conclusions. Based on the results of the study, an improved approach for identifying the state of computer systems is proposed, which combines the stacking method with Fuzzy MDT and feature selection optimization. The integrated use of these methods significantly enhances classification accuracy, result stability, and model robustness to data imbalance, while ensuring high-quality classification even in the presence of new anomalous states.
Copyright (c) 2025 Viktor Chelak, Oleksii Hornostal http://creativecommons.org/licenses/by-nc/4.0
USE OF VIRTUAL MEASURING DEVICES IN METROLOGY, ELECTRONICS AND ELECTRICAL MACHINES FOR THE TRAINING OF ELECTRICAL ENGINEERING SPECIALISTS
The work shows that a promising direction for the modernization of the educational laboratory base is the use of open systems methods together with the introduction of technologies for the use of virtual devices, based on the use of computer measurement methods. The purpose of the work is to analyze the possibilities of creating virtual laboratory work in various disciplines and their use in professional training of specialists in electrical engineering in higher educational institutions. As a result of the analysis of the most well-known application computer packages intended for the design of electronic units, the authors found that the ability to model both the simplest electrical circuits and power electronics circuits and rather complex control circuits for them, as well as various electrical machines is the main advantage of the Simulink package of the MATLAB environment in comparison with other software tools. Examples of constructing circuits for virtual laboratory work in the metrology course are given. It is shown that virtual laboratory work increases students' interest in the learning process, as well as the level of their skills and abilities.
Copyright (c) 2025 Оleksandr Shefer, Nataiia Yermilova, Оleksandr Dryuchko, Mariia Stepanko, Sergii Pasichko http://creativecommons.org/licenses/by-nc/4.0
STUDY OF THE PARAMETERS OF THE TOTAL HARMONIC DISTORTION OF A CONTROL STATION WITH AN INPUT PHASE-SHIFT AUTOTRANSFORMER
When developing minerals in Ukraine, such as oil, mechanized extraction methods are used, using submersible electric motors. Such extraction methods provide high productivity and the ability to adjust to the current parameters of the well, which in turn provides maximum flow rate. To ensure the optimal operation of the pump, frequency-controlled submersible electric motor control stations are used, which consume non-sinusoidal current from the power supply network during operation. Also, to reduce distortions of the consumed current, various methods of reducing the total harmonic distortion (THDi) of input currents are used. To solve this problem, a control station with a multi-pulse rectifier powered by a phaseshifting autotransformer was developed, which allows you to reduce the THDi of input currents. This solution allows you to improve the overall performance of the equipment and its cost by reducing the power of the phase-shifting transformer. The functional capabilities of the electric drive control station are considered based on experimental data of measuring THDi of input currents of the network +30% in the frequency range of 47.5-52.5Hz. This frequency range is due to the fact that in many cases, in the fields, work is carried out with power supply from diesel generators, in which the fluctuations of frequency and supply voltage are in a significant range. Additionally, the possibility of drive operation at input supply voltages of 460-480V and 430460V is investigated. The measurement results are analyzed, and recommendations are proposed for increasing the energy efficiency of the station.
Copyright (c) 2025 Dmytro Gapon, Petro Kachanov, Andrey Olshevskiy, Evgeniy Petrik http://creativecommons.org/licenses/by-nc/4.0
DETERMINATION OF OPTIMAL DIESEL TRAIN MOTION ON A SECTION OF A ROUTE WITH A KNOWN PROFILE
Relevance. Railway transport is one of the main consumers of energy resources, therefore, the development and implementation of energy-saving technologies is a relevant task, the solution of which has recently become one of the priority areas of activity for specialists in both fundamental and applied science in this field. Object of research: the processes of controlling the traction, dynamic and energy characteristics of multiple-unit rolling stock. Purpose of the article. To solve the problem of optimal control of a diesel train’s movement along a section of the track with a known profile, taking into account optimal fuel consumption and compliance with the traffic schedule. Research results. The article formulates and solves the problem of synthesizing a control system for the optimal movement of a diesel train with a known track profile using the terminal control method. The following were applied: the theory of train traction and traction electric drive, methods of system analysis and optimal control theory, mathematical modeling and computational experiments. Conclusions. An algorithm for optimal control of a diesel train has been developed, with the help of which program laws have been determined that establish the dependence of traction on the train’s mass, track parameters, and technical and economic conditions (speed, acceleration, traffic schedule) taking fuel consumption into account.
Copyright (c) 2025 Valentin Noskov, Maksym Lipchanskyi, Volodymyr Panchenko, Hennadii Heiko http://creativecommons.org/licenses/by-nc/4.0