Applied Aspects of Information Technology
The publication focuses on the practical implementation of the latest information technologies in industry, energy, financial technologies, healthcare, sociology, education, project management, ecology, and human safety.
Design of modified Duffing pendulum for trajectory generation
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: Duffing pendulum, chaotic systems, distance-based state variables, nonlinear observability, multi-dimensional attractors Relevance. The article proposes a new approach to the construction and analysis of chaotic dynamical systems based on distance-based state variables. In contrast to traditional state-space models, where the state of the system is described directly by coordinate variables, the proposed method represents the system using distances between the current state and a set of predefined reference (base) points. These base points can remain fixed in space or move along given trajectories. Such a geometric representation of the system state offers additional flexibility for modeling nonlinear dynamics and allows the construction of new types of chaotic behavior. Purpose and objectives. In this framework, the evolution of the system is described in terms of distances to selected base points, rather than through initial coordinates. This transformation generalizes traditional state-space descriptions and allows the developer to influence the structure of the dynamics by choosing the number, configuration, and movement of the reference points. As a result, the proposed formulation provides a convenient way to introduce additional nonlinear interactions into the system while maintaining a clear geometric interpretation. Methods. To demonstrate the proposed approach, the Duffing pendulum is used as a representative example. The classical Duffing oscillator is a well-known nonlinear system that can exhibit periodic, quasi-periodic, and chaotic oscillations. In this work, the Duffing dynamics is reformulated using distance-based variables defined with respect to several reference points. Two scenarios are considered: systems with fixed reference points and systems where the reference points move in time. In the latter case, the motion of the reference points can be periodic or chaotic, which further enriches the behavior of the system as a whole. Both continuous and discrete formulations of the model are developed. The continuous representation allows for the analysis of theoretical properties of the system, while the discrete version is well suited for numerical simulation and implementation on digital platforms and embedded devices. Results. Numerical experiments show that the modified systems reproduce the main qualitative features of the classical Duffing oscillator, as well as create new types of attractors. In particular, the models can generate unipolar and more complex multidimensional chaotic attractors. Conclusions. These results indicate that distance-based observability equations can serve as an effective tool for the design of new chaotic systems. The proposed approach can be useful in applications such as secure communication, parallel generation of chaotic signals, and the study of complex nonlinear dynamics.
Applied information technologies in energy engineering and automation
Applied information technology in energyengineering and automation
Copyright (c) 2026 Applied Aspects of Information Technology
Diabetic retinopathy diagnosis using deep neural networks
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: Diabetic retinopathy, fundus images, deep learning, adaptive attention, image recognition, uncertainty estimation Relevance: is driven by the high prevalence of diabetic retinopathy as one of the leading causes of vision loss worldwide and the limited availability of ophthalmological diagnostic resources. In medical applications, predictive uncertainty assessment is critically important, since erroneous yet confident decisions may lead to serious clinical consequences. Despite significant advances in deep learning-based DR diagnostic systems, insufficient attention has been paid to the systematic interaction between data preprocessing strategies, class imbalance handling, and predictive uncertainty estimation, all of which directly affect model reliability. Aim and objectives: the aim of this study is to develop and experimentally validate an automated DR stage classification framework that achieves high diagnostic performance while systematically evaluating predictive reliability and uncertainty. Methods: the proposed framework integrates computer vision and deep learning methods based on convolutional neural networks with transfer learning and incorporates an adaptive attention mechanism that dynamically regulate channel and spatial attention strength according to feature variability. The approach also incorporates two preprocessing pipelines, class-balancing techniques, and uncertainty estimation through stochastic inference without modifying the training procedure. A systematic experimental study was conducted to evaluate different combinations of preprocessing and balancing strategies under fixed architectural conditions. Model performance was evaluated using standard classification metrics and an original integral reliability indicator, which jointly accounts for classification quality, prediction confidence, and predictive uncertainty. Results: combining advanced preprocessing, targeted balancing strategies, adaptive attention mechanisms, and uncertainty estimation significantly improves both classification effectiveness and predictive reliability. Proposed configuration achieved the highest scores, while uncertainty-aware models exhibited lower predictive variance and higher confidence, particularly in challenging or ambiguous cases. Conclusions: the proposed framework provides a structured methodology for reliability-aware DR classification and contributes an integrated evaluation approach that enhances the practical applicability of deep learning systems in clinical decision support.
Systems analysis, applied information systems and technologies
Systems analysis, applied information systems and technology
Adaptive and coordinated IT project management in dynamic environments: A multi-agent ai perspective
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: Multi-agent systems, adaptive project management, collective decision-making, agent coordination, risk management, artificial intelligence, dynamic role allocation, IT governance, decision support systems, project scheduling The increasing complexity and volatility of IT project environments expose the structural limitations of both plan-driven methodologies and human-centric Agile frameworks, which adapt at cadences incompatible with the tempo at which risks materialize and requirements shift in large-scale software development contexts. This paper develops an integrated theoretical and methodological basis for adaptive and coordinated IT project management using collectives of specialized AI agents, addressing three concurrent gaps in the literature: the absence of a unifying conceptual framework, the lack of formal agent coordination models for project management contexts, and the underdevelopment of collective decision-making and risk management methods for multi-agent governance systems. The proposed system is formally defined as a five-tuple comprising the dynamic project environment, a nine-agent collective organized across three functional layers, an adaptive policy set, and a human governance structure with explicit oversight constraints. Coordination relies on a conflict matrix and utility-based dynamic role reallocation; decisions are produced through weighted aggregation with confidence-driven dispatch; risk exposure is updated via multi-agent Bayesian assessment. Experimental validation against single-agent AI and human-only Agile baselines on a controlled six-sprint scenario demonstrates a 68% reduction in mean decision latency, a 2.4× improvement in risk detection lead time, a conflict resolution rate of 0.91, and an autonomous dispatch rate of 0.81 at final sprint, with a false escalation rate of 0.13. The results establish that distributed functional specialization and collective decision authority provide qualitative architectural advantages that monolithic AI systems and retrospective human governance cannot replicate, while preserving full human oversight accountability.
A method for operative sharing confidential images among a group of trusted users
Section: Computer engineering and cybersecurity Categories: Computer systems and cybersecurity Keywords: Chaotic Tent map, Vernam cipher, Diffie-Hellman protocol, Burmester-Desmedt protocol, Montgomery elliptic curve Encryption of high-resolution images plays a key role in ensuring their confidentiality and integrity. Given the rapid growth of traffic of such images in open communication channels, the development of new methods and means of their protection is becoming increasingly actual. The objective of this study is to develop and model a method for exchanging confidential images for a group of trusted users. In the process of the study, encryption methods with chaotic maps, cryptography on elliptic curves, and agreement group secret agreement with various protocols were used . One of the requirements for the method being developed is its operation on a time scale close to real. This is due to the rapid aging of information in images subject to analysis in the group. The study is based on the method of generating a pseudo-random sequence using a modified chaotic map Tent. A short key containing the parameters of this map is transmitted to trusted users who form the group. Then each user can expand the key into a pseudo-random sequence and encrypt/decrypt the image with the Vernam cipher . A group of trusted users is formed using the Diffie-Hellman protocol or Burmester-Desmedt on elliptic curves. The Weierstrass, Montgomery and Edwards curves were analyzed from the point of view of computational costs. The Montgomery curve was chosen as the most computationally efficient working elliptic curve. To transfer to a group of trusted users, the short key of Tent map is encrypted with a block cipher with a secret as the key. The developed method is modeled for the curve Montgomery Curve25519. Image security and confidentiality are ensured by five levels of protection. As a result, it was found that group formation according to the Burmester-Desmedt protocol ensures the operation of the method on a time scale close to real time and a high level of scalability.
Analytical modeling of OAM-based programmable hardware accelerators for data-invariant algorithms execution
Section: Computer engineering and cybersecurity Categories: Computer systems and cybersecurity Keywords: Programmable hardware accelerator, parallel architecture, ordered access memory, high-performance computing Relevance. The growing demand for high-performance computing in domains such as signal processing and scientific modeling necessitates efficient hardware accelerators capable of predictable and scalable execution of tasks. Algorithms with data-invariant structure, characterized by fixed execution schedules and static data dependencies, represent an important class of workloads that can benefit from specialized architectural solutions. Ordered Access Memory (OAM)-based programmable hardware accelerators offer deterministic and conflict-free parallel data access, making them a promising approach, however, systematic analytical methods for their performance and resource evaluation remain insufficiently developed. Aim and objectives. The aim of the scientific research highlighted in this paper is to develop a unified analytical framework for modeling and evaluation of OAM-based programmable hardware accelerators. The objectives include formalization of memory requirements, computational throughput, and execution time, as well as analysis of architectural trade-offs for different accelerator structures. Methods used. The study employs analytical modeling based on matrix representation of data, instructions, and indices, and derives formal expressions for basic system characteristics as functions of architectural parameters and algorithm properties. A unified execution time model is developed, incorporating preparation, computation, and output phases, with explicit consideration of memory access parallelism and overlapping operations. Comparative analysis is conducted for dual-memory and algorithm-adaptive memory structures. Results. The proposed framework provides models for estimating data, instruction, and index memory requirements, computational throughput, and execution time. It enables systematic exploration of the architectural parameter space and reveals trade-offs between memory organization, degree of parallelism, and performance. The analysis shows that adaptive memory structures reduce redundant data movement, leading to improved execution time and more efficient resource utilization compared to dual-memory designs. Conclusions. The novelty of the work lies in the development of a unified analytical modeling framework that jointly captures memory organization, computational structure, and execution dynamics of OAM-based accelerators. The practical value of the results consists in enabling early-stage, platform-independent evaluation and optimization of accelerator structures for data-invariant algorithms execution, supporting informed design decisions without the need for time-consuming simulation or prototyping.
Metrical loss functions for image segmentation based on convolutional neural networks
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Image segmentation, loss functions, convolutional neural networks, deep learning, metric, partition, computer vision Image segmentation remains a fundamental challenge in computer vision, with neural network training heavily dependent on appropriate loss functions. While common losses such as Dice are widely used, they lack rigorous mathematical foundations as proper distance metrics and do not fully capture the geometric structure of partitions. We introduce a weighted metric for comparing segmentation based on partition theory that satisfies all metric axioms, including the triangle inequality. The proposed metric compares partitions through symmetric difference and intersection operations, incorporating both spatial structure and semantic features via a weight function characterizing region properties, such as color, texture and other. We prove that the proposed functional forms a proper metric space on weighted partitions under specified conditions, with particular emphasis on establishing the triangle inequality. Experimental verification on synthetic segmentation tasks demonstrates feasibility, although practical implementation faces challenges, such as the need for differentiated segment extraction, which can be solved using the Straight-Through Estimator approximation. The triangle inequality property opens up opportunities for hierarchical approaches to segmentation and efficient partition search. This work bridges the gap between geometric clustering theory and deep learning-based segmentation, providing a theoretically grounded alternative to heuristic loss functions and also experimentally proves the possibility of using the proposed metric as a loss function when training convolutional neural networks.
Computer science and software engineering
Rational data sampling for surrogate modeling of the stress-strain state of plate elements
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Stress-strain state, surrogate modelling, regression analysis, machine learning, dataset, rational data sampling strategy, rapid diagnostics of engineering structures Background. Surrogate models based on machine learning are a promising alternative to computationally expensive finite element analysis for predicting the stress-strain state of structural elements. However, their efficiency critically depends on the quality of training data, while most existing approaches rely on abstract stochastic sampling methods that ignore the physical nature of engineering parameters. Aim. The aim of the study is to conduct a comparative analysis of the efficiency of stochastic versus physically-oriented data sampling strategies for the surrogate modelling of the stress-strain state of plate elements, with the focus shifted from increasing model complexity to developing an approach for rational dataset formation that integrates a priori engineering knowledge. Methods. Using the surrogate modelling of thin rigidly clamped plate deflection as an example, two fundamentally different sampling strategies are compared: the classical strategy, based on Sobol quasi-random sequences, and the proposed rational strategy. The latter is based on the real empirical properties of structural steels and accounts for the standardized plate thicknesses of sheet metal assortments. To evaluate and quantitatively compare the impact of the data structure on the precision of the prediction, an intelligent modelling procedure was developed. The following machine learning algorithms were used as test models for the regression task: K-Nearest Neighbours, Random Forest, XGBoost, Gaussian Process Regression, and Multilayer Perceptron. Results. It was found that switching to the rational strategy provides a consistent reduction in mean squared error across all evaluated models by three and a half to four times compared to the baseline strategy. Using the rational dataset increased the coefficient of determination by twenty-seven percent for k-nearest neighbors, twenty-three percent for random forest, and twenty-one percent for XGBoost. The highest precision was achieved by the models of gaussian process regression (with a coefficient of determination of ninety nine percent) and multilayer perceptron (with a coefficient of determination of ninety eight percent). It is proven that the low efficiency of traditional samples is due to the fact that more than fifty percent of the samples contain combinations of geometric dimensions and material properties that do not correspond to any real engineering standards. These unrealistic combinations create “information noise” during training. Conclusions. The proposed rational sampling approach serves as a basis for creating robust AI tools for the rapid diagnostics of engineering structures, providing high model generalisation capability with a smaller volume of input data.
rapid diagnostics of engineering structures
Development of a unified feature space for ensemble classification of polystructural heterogeneous small data
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Polystructural data, heterogeneous data, small data, ensemble classifiers, Gompertz function, audio classification Relevance. The paper addresses the problem of classification of polystructural heterogeneous small data, which include structured tabular information and unstructured audio signals. Objective. The study proposes a unified methodological framework for constructing an ensemble classification system based on a common feature space that ensures compatibility between different data types and machine learning models. Main Research Material. A unified feature space model is developed, incorporating a linear representation for structured data and a three-level representation for audio data, including tabular spectral features, spectrogram-based image representation, and temporal sequences of features. To improve the quality of input data, a Backward Feature Elimination procedure is applied to adapt feature subsets to the specifics of individual classifiers and the removal of non-informative features. The classification framework is based on a stacking ensemble architecture that combines multiple base models, including classical machine learning algorithms and deep learning models. Three aggregation strategies are considered: hard voting, soft voting, and soft voting with Gompertz fuzzy ranking, which enables nonlinear adjustment of classifier probabilities and improves robustness under uncertainty. Results. Experimental evaluation was conducted on five datasets from different domains, including healthcare, finance, audio signal analysis, and deepfake detection. The results demonstrate that the proposed approach consistently improves classification performance compared to individual models. The application of feature selection and the integration of ensemble methods provides significant gains for polystructural data. Conclusions. The proposed model offers a flexible and scalable solution for handling heterogeneous small data and can be effectively applied across multiple domains, providing improved generalization, robustness to noise, and adaptability to different data representations.
A system internals modeling and annotation language for large language model-driven software engineering
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: System modeling, software architecture, language models, token efficiency, code summarization, software engineering Rapid scaling of large language models has significantly disrupted traditional software engineering paradigms with unprecedented capabilities for code understanding, generation, and automated review. However, practical repository-level deployment is constrained by context limits and token cost. While retrieval-augmented generation is widely used to address this limit, it often splits a codebase into disconnected semantic chunks, omitting high-level structural dependencies. In contrast, alternative approaches that attempt to feed the entire repository structure into the context window typically rely on generic formats such as JSON. While widely recognized, these formats introduce redundant syntactic units that significantly influence the token budget, effectively reintroducing the bottleneck. This work introduces SiMAL (System internals Modeling and Annotation Language), a domain-specific language designed specifically for language-model-driven software engineering workflows, with the primary objective of providing a compact, human-readable, error-tolerant, yet highly structured representation of a software system, optimized for iterative interaction with language models. It combines both static and dynamic aspects of a software system, unifying architectural views, component and endpoint definitions, runtime deployment metadata, and other development artifacts into a single textual schema that can be converted to and from a normalized machine representation. This effort includes the definition of the language’s syntax and grammar, an open-source parser, and a visualizer that renders schemas into nested system diagrams. The proposed language is validated through a comprehensive protocol that assesses token efficiency alongside schema validity and semantic faithfulness. This includes deterministic structural checks (parsing integrity and annotation consistency), schema-to-code correspondence analysis, and an “LLM-as-a-judge” evaluation for repository coverage. The results indicate that prompt-efficient schema modeling reduces token overhead without systematically degrading structural usability or quality, making it a practical backbone for scalable autonomous software engineering.
Assessment of digital image quality for visual objects in Gamified Systems
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: visual perception, data informativeness, color difference, fractal dimension, integral quality indicator This paper devoted to the development and justification of a comprehensive approach to assessing the quality of digital images in gamification environments, taking into account the peculiarities of the perception of visual objects by users. The relevance of the study is due to the growing role of gamified visual interfaces in digital applications and the need for objective quality control of visual content, which directly affects the level of user engagement and motivation. The paper analyzes modern methods for assessing the informativeness and quality of digital images, in particular, approaches based on texture analysis, spatial structure, machine learning, and deep neural networks. A hybrid method is proposed that combines the evaluation of color differences according to the CIEDE2000 standard with fractal analysis of structural characteristics of images, which allows taking into account both color and geometric-textural properties of visual objects. The research methodology is based on the formulation and testing of hypotheses regarding the influence of image processing quality on the perception of objects in gamified environments, mathematical modeling of the integral quality indicator using weighting factors, as well as software implementation of the proposed approach using Python. Practical testing was carried out on a set of digital images of different levels of graphic complexity (simple, medium complexity, and complex), for which the CIEDE2000 indicators, fractal dimension, integral quality index, and MSE and SSIM metrics were calculated. The results obtained confirm that the combination of color and structural analysis provides a more complete and objective assessment of the quality of gamified images compared to the use of separate methods. The work shows that improving the quality of images is statistically associated with improving the perception of visual objects, and the use of a combined method allows for more correct differentiation of quality levels for images of different complexity. The proposed approach can be used for automated quality control of visual content in computer games, educational platforms and other gamified systems, and also creates a basis for further research in the direction of adaptive and intelligent assessment of visual data.
Models of semantic analysis of product descriptions for automatic determination of customs codes
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Customs classification, semantic analysis, machine learning, text processing, automation, harmonized system In today's global trade environment, accurate and fast classification of goods by Harmonized System (CUSTOMS/CUSTOMS) codes is crucial for the efficient functioning of customs processes. The increase in data volumes and the complexity of product descriptions create a need to implement intelligent methods for analyzing text information. The use of Natural Language Processing (NLP) technologies in combination with machine learning opens up new opportunities for automating the process of determining customs codes and reducing the human factor in customs classification. The aim of the study is to increase the accuracy of automated determination of Harmonized System codes by applying modern models of semantic analysis of text descriptions of goods. To achieve this goal, the following tasks were set: to analyze existing natural language processing models, to investigate their effectiveness for customs classification tasks, to form the optimal model architecture and to evaluate its advantages compared to traditional algorithms. The work uses semantic modeling and machine learning methods. An experimental approach was used to combine these models with classification algorithms, in particular, logistic regression, decision trees, and neural networks. The evaluation was carried out using accuracy, completeness, and measure indicators. The results of the study showed that the use of contextual embeddings, in particular the BERT model, provides a significant improvement in the accuracy of automated classification of goods compared to traditional statistical methods. The proposed generalized model, combining semantic analysis with machine learning, allows to increase the level of correct assignment of customs codes based on text descriptions, even in cases of ambiguous or incomplete data. The study confirmed the feasibility of integrating natural language processing technologies into customs classification systems. The scientific novelty lies in the development of a hybrid model that combines semantic text representations and classification algorithms, which increases the accuracy and efficiency of automated determination of customs codes. The practical significance of the work lies in the possibility of implementing the proposed approach in "smart customs" systems to optimize control processes and accelerate the clearance of goods.
An empirical evaluation of reasoning models for classifying information manipulation techniques
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Propaganda detection, artificial intelligence, reasoning models, large language models, information security, information space The rapid evolution of modern geopolitical conflicts has transformed information manipulation and propaganda techniques from a tool of persuasion into a sophisticated weapon of mass influence. As these operations become increasingly complex, relying on subtle psychological tactics rather than overt falsehoods, the development of advanced, automated detection mechanisms is a critical security challenge. This study aims to bridge the gap between theoretical capabilities and practical applications by empirically evaluating the performance of emerging reasoning models in the specific task of classifying information manipulation techniques. The primary objective is to benchmark these generative architectures against historical supervised baselines to determine if their internal chain-of-thought capabilities offer a tangible advantage over traditional pattern-matching approaches in identifying complex rhetorical strategies. The research methodology utilizes a standardized international benchmark dataset for propaganda detection (SemEval-2020 Task 11) to conduct a comparative analysis of frontier models without task-specific fine-tuning. The study employs an inference-only strategy, integrating role-playing, definition embedding, and structured reasoning instructions to simulate expert analysis. A key methodological contribution involves the systematic variation of the reasoning budget allocation during inference to measure the correlation between computational deliberation and classification accuracy. The investigation reveals a distinct semantic advantage where reasoning models significantly outperform previous supervised systems in detecting nuanced techniques that rely on cultural context, emotional weight, and indirect logic. However, the results also uncover a critical limitation where increased reasoning effort might degrade performance on structurally simple tasks, confirming the existence of an overthinking phenomenon in automated classification. The analysis further identifies a non-linear relationship between computational cost and performance, indicating that monolithic reasoning models often yield diminishing returns compared to lightweight architectures for high-volume processing. The paper concludes that while reasoning models represent a paradigm shift in semantic understanding, they are not yet a universal solution for all information manipulation types due to structural blind spots and economic inefficiencies. The study proposes moving away from single large models toward multi-agent systems. This proposed approach advocates for an adaptive system that assigns specialized tasks to a team of virtual experts, balancing precision with operational viability in the defense of the information space.
A fragment-based spatial-temporal video analysis method for detecting anomalous violent events
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: video analysis, spatio-temporal video processing, event detection, anomalous behavior detection, violent event detection, fragment-based representation, adaptive fragmentation, motion-based analysis, video anomaly detection In this work, we address the problem of violent event detection and classification in video streams under realistic computational constraints. Many safety-critical events, such as violent interactions or abnormal behavior, are characterized by short-term and spatially localized motion patterns, while the majority of video content remains static or irrelevant. Conventional deep learning approaches typically process full video frames or dense spatio-temporal representations, which leads to high computational cost and inefficient use of computational resources. We propose a fragment-based spatio-temporal video analysis method inspired by principles of video coding. Each video frame is divided into fragments, and motion activity is estimated using dense optical flow between consecutive frames. Only fragments exhibiting significant temporal changes are selected for further processing, while static regions are suppressed at an early stage. The fragment size is adaptively adjusted according to local motion intensity, allowing finer spatial resolution in dynamic regions and coarser representation in static areas. The selected fragments form a compact representation that is subsequently used for event classification via lightweight temporal aggregation. By reducing spatio-temporal redundancy prior to feature extraction, the proposed method significantly lowers computational complexity while preserving discriminative motion cues. The proposed method is evaluated on the UBI-Fights dataset, with additional training data augmentation using the Video Fight Detection (VFD2000) dataset. Experimental results demonstrate that the method achieves competitive performance with area under the receiver operating characteristic curve up to 0.72, area under the precision-recall curve up to 0.63, and binary F1-score up to 0.60, while maintaining efficient inference speed. These results indicate a favorable trade-off between accuracy and efficiency compared to dense frame-based baselines, making the method suitable for real-time and resource-constrained video analysis systems.
Improving the quality of mine detection by a mine-detecting drone by selecting an architecture for classifying objects in visible-light camera images
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Convolutional neural network, transformer, attention, weights, classification quality, tokenization The use of unmanned aerial vehicles (drones) is increasingly being used in humanitarian demining. Multiple sensors operating on different physical principles are used simultaneously to search for mines. This increases the probability of mine detection. To expedite the survey of the area, the initial processing of sensor signals is performed onboard the drone. Therefore, the requirement for sensor signal processing algorithms not only increases the probability of object detection but also improves the ratio of classification accuracy to the required computational effort. One of the sensors used is visible-light cameras. A large number of neural networks have been developed for classifying objects in images. However, a specific feature of their use for humanitarian demining is the lack of large datasets for training and testing. Therefore, the challenge arises of finding neural networks that offer a high ratio of classification accuracy to the required computational effort, while also being able to train on very small data sets. This paper compares convolutional neural networks and networks based on transformers. The networks were trained on a small dataset containing images of anti-tank mines, rocks, and various backgrounds. The study showed that convolutional neural networks train faster and are more resilient to image quality degradation. However, their potential is limited; increasing the number of layers does not significantly improve mine classification quality. Transformer-based neural networks offer greater flexibility and a wealth of options for architectural configuration, resulting in superior performance compared to convolutional networks. Although they are slower to train and potentially require an expanded training dataset, they are the preferred choice for implementing image processing on drones.
Architecture and implementation of a hybrid prototype system of a digital spectral twin of thin films
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: digital twin, spectroscopy, hybrid information system, thin-film structures, neural networks, parameter identification The relevance of this work is driven by the need to develop intelligent information systems for processing spectroscopic data and identifying material parameters in real time. The aim of the study is to develop a hybrid architecture prototype of a digital spectral twin for real-time identification of thin-film parameters. To achieve this aim, the tasks of integrating physical and neural network models, implementing an online self-adaptation mechanism, and evaluating the system’s performance on experimental data were addressed. A numerical scheme based on the transfer matrix method and physics-informed neural networks was implemented for modeling spectral responses and adaptive parameter estimation. The online self-adaptation mechanism, which continuously updates material parameters based on experimental spectral data under the presence of noise and slow drift, was investigated, along with the analysis of parametric identifiability using local spectral sensitivity. The results of the study is the development of an architecture that ensures stable convergence of thickness, refractive index, and absorption coefficient estimates over 100–200 iterations, and convergence of the overall spectrum up to 300 iterations. Relative errors below tree percent for thickness, hundredths of a percent for the refractive index, and tenths of a percent for the absorption coefficient were achieved. The model’s ability to track gradual parameter changes, maintain high predictive accuracy over long-term operation, and reduce spectral reconstruction errors was demonstrated. Robustness to noise and non-stationary conditions, as well as accurate reproduction of spectral maxima, relative intensities, and selective approximation, were confirmed. The developed hybrid architecture is an effective tool for intelligent spectroscopic monitoring and material identification. Further research will focus on extending the system to multilayer structures and industrial measurement applications.
Specialized recurrent U-Net architecture for immunohistochemistry image segmentation
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: U-net, deep learning, segmentation, immunohistochemistry, software architecture, biomedical images Microobject segmentation in biomedical images is a complex and time-consuming process, and current efforts are aimed at qualitatively separating the studied objects from the background in the image. Immunohistochemical images are used for breast cancer analysis. One of the types of images is estrogen samples. The specificity of immunohistochemical images requires a more thorough approach to segmentation, because standard algorithms or classical neural network models do not allow achieving the required accuracy. This article presents a new convolutional network architecture based on a recurrent U-network for segmentation of immunohistochemical images. The proposed architecture is specifically designed to process the unique features of immunohistochemical images. The principles of recurrent convolutional neural networks were used to develop the model and train it using open datasets. The architecture proposed in this work is based on the configuration of neural network layers, which allows to obtain the best result for a given type of image. In addition, a pipeline of continuous integration of the program code and the model was implemented to ensure continuous retraining in a cloud environment, which significantly simplified the workflow for researchers and engineers. Additionally, a mechanism for processing medical images and providing automation of the big data processing process using computer vision and deep learning was proposed. The proposed method achieved higher segmentation accuracy. The architecture allows for more accurate cell segmentation on immunohistochemical images, which is a crucial step in the diagnostic pipeline. High-quality cell segmentation increases the reliability of subsequent analyses and parameter extraction, thereby contributing to increased diagnostic accuracy.
Estimating the number of lines of code of the latest releases of open-source applications using nonlinear regression models
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: regression model, estimation, lines of code, open-source application, normalizing transformation, Mardia criterion, outlier, Mahalanobis distance, test statistic, support vector regression, machine learning The problem of early estimating the number of lines of code of the latest releases of software, including open-source applications, is important because it directly affects the effort prediction for their development and subsequent improvement. The research aim is to build several regression models for early estimating the number of lines of code of the latest releases of open-source applications. The research object is the process of early estimating the number of lines of code of the latest releases of open-source applications. The research subject is the regression models for early estimating the number of lines of code of the latest releases of open-source applications. For early estimating the number of lines of code of the latest releases of open-source applications, the models, confidence, and prediction intervals of two nonlinear regressions with two predictors were constructed using the Box-Cox three-variate normalizing transformation and specialized techniques. These techniques, relying on multiple nonlinear regression analyses incorporating the use of multivariate normalizing transformations, account for the presence of outliers in multidimensional non-Gaussian data. In the paper, we built two nonlinear regression models for early estimating the number of lines of code of the latest releases of open-source applications that depend on two predictors: both the number of classes of their latest and first releases. The first model has good quality, but the second one can only be used for estimating the conditional mean, and it has poor quality for predicting the response as the dependent random variable. We have compared the quality of two constructed regression models and two linear support vector regressions. The quality of the above models is similar. An analysis has been carried out to compare the constructed models with nonlinear regression models that only depend on the number of classes for their current releases, which were built based on the Box-Cox bivariate transformation. Compared to such nonlinear regression models, the constructed models demonstrate a larger multiple coefficient of determination, a smaller value of the mean magnitude of relative error, a larger percentage of predictions that fall within 25 percent of the actual values, and narrower confidence and prediction intervals. The comparison results indicate better quality of the constructed models with two predictors. The prospects for further research may include the use of other data sets to construct the nonlinear regression models for early estimating the number of lines of code of the latest releases of open-source applications, for other restrictions on predictors.
A machine vision system for real-time grain quality classification using machine learning
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Machine vision, grain classification, machine learning, support vector machine, image processing, grain sorting Grain quality control directly affects the efficiency of grain processing, the stability of product parameters, and the economic outcomes of storage and cleaning operations. In industrial practice, visual inspection and manual sampling remain widespread, yet these approaches are time-consuming and sensitive to human subjectivity, especially when large grain volumes must be assessed continuously. This study addresses the need for an automated, objective, and scalable solution for grain quality classification that can operate in real time under conveyor-based conditions. The purpose of the research is to develop and experimentally validate a machine vision system that classifies grain by quality and supports operational decisions for fractional separation and cleaning. An experimental test stand was built to simulate conveyor transportation of grain, enabling controlled variation of belt speed and illumination conditions. A dataset of wheat, corn, and barley was formed using laboratory image capture and manual labeling into three quality classes based on visible defects and damage severity. Image preprocessing and augmentation were applied to increase variability and improve robustness. Two supervised approaches were implemented for comparative evaluation: a deep learning image classifier and a kernel-based classifier using handcrafted visual descriptors. The experimental results demonstrate that the deep learning approach achieves higher classification accuracy, while the kernel-based classifier provides faster inference with a moderate reduction in accuracy. The most frequent misclassifications occur between adjacent quality categories, indicating the importance of borderline-class coverage and labeling consistency. Processing time measurements confirm the feasibility of real-time operation for moderate grain flow rates, with performance degradation at higher conveyor speeds due to motion-related image distortions. The scientific novelty lies in the integrated experimental assessment of classification accuracy and throughput under controlled conveyor conditions. The practical significance is the feasibility of deploying the system as a component of automated grain cleaning and separation lines.
Analysis of the total random error in simulation systems
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: Simulation modelling, forecasting, machine learning, information technologies, high-precision calculations, floating point representation, random error summation, simulation errors, improving accuracy of simulation systems, unequal-sized data, simulation system, representation as arrayslist skills, labour market analysis, unsupervised learning, clustering, academic program, specific subject competences The article presents an analytical assessment of error in computer simulation systems for cases of an unknown arbitrary type of symmetric distributions of the total random error of calculations. The study is based on the use of asymptotic properties of a sequence of random variables. The work includes the development of a mathematical framework for predicting a confidence estimate of random error. An innovative approach to selecting the initial approximation is proposed. The proposed approach increases the efficiency of the iterative error calculation process. It has been established that, for any type of symmetric distribution laws, the series contains only integer powers of n. Using the established pattern ensures better convergence and optimizes the calculation of the numerical value of the modeling error. Simplified formulas have been created for calculating confidence intervals at high probability levels. The applicability conditions of the methodology have been determined. It has been experimentally confirmed that for a uniform distribution, acceptable accuracy is achieved with at least two components. At the same time, for a sinusoidal distribution, at least three components are needed to achieve an error below the acceptable limit. For high levels of modeling reliability, the required accuracy is achieved with two components without the need to introduce additional adjustments. The reliability of the obtained results was verified for two cases of input data. In the first case, the results of studies on modelling accuracy using the representation of numbers in the binary-hexadecimal system as arrays were used. In the second case, input data from error calculations of solving linear GPU NVIDIA tasks with floating-point representation were used. The methodology is universal and can be applied to both random and systematic errors that occur in simulation systems.
improving accuracy of simulation systems
Copyright (c) 2025 Applied Aspects of Information Technology
A method for constructing GL-models of behavior under failure flow for complex non-basic fault-tolerant multiprocessor systems
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: Fault-tolerant multiprocessor systems, GL-models, non-basic systems, control systems, reliability evaluation, statistical experiments The paper proposes a method for constructing GL-models of the behavior of complex non-basic fault-tolerant multiprocessor systems under a failure flow. The aim of the study is to develop a universal approach that enables the formation of an integrated GL-model for systems characterized by multiple independent or weakly coupled operability conditions. Such models can be used, in particular, to evaluate the reliability parameters of the systems under consideration using statistical simulation methods. The study focuses on systems whose operability is determined by the simultaneous fulfillment of several relatively simple conditions, for each of which established methods for constructing GL-models are available (for example, a condition limiting the number of failures within a certain subset of processors). These include, in particular, hierarchical systems composed of multiple subsystems with their own levels of fault tolerance, as well as systems containing specialized processors of different types. The proposed method involves the preliminary construction of auxiliary GL-models for each operability condition; followed by their integration into a unified model through the sequential merging of their graphs via selected vertices (the merged vertices form a single vertex, while the remaining vertices and edges are copied). The order of model merging and the choice of corresponding vertices can be defined arbitrarily, providing flexibility in the structure of the resulting GL-model. Examples of the method’s application are presented, illustrating various options for determining the sequence of merging auxiliary models and selecting the connecting vertices of their graphs, as well as the use of different methods for constructing these models. The scientific novelty of the work lies in the generalization and formalization of the sequential GL-model merging procedure, which makes it possible to combine models of arbitrary structure and type into a unified model of a complex system without compromising the correctness of its behavior. Experimental results confirm that, despite structural differences in the graphs of the obtained models, their behavior on identical input vectors coincides completely and accurately reflects the operation of the fault-tolerant multiprocessor system under a failure flow. It is also shown that the method imposes no restrictions on the construction techniques of GL-models for individual conditions: models of different types can be combined, the conditions do not necessarily correspond to basic systems, and the model graphs may be other than a cycle graph. Furthermore, the paper provides complexity estimates for GL-models constructed by the proposed method, including the number of vertices and edges in their graphs and the overall complexity of edge functions, depending on the characteristics of the corresponding auxiliary models. The practical value of the method is that it enables automated construction of comprehensive models for systems with complex operability conditions and supports efficient reliability evaluation of real multiprocessor systems.
Structure of a model for scientific research of complex electrical objects
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: System analysis, system approach, methodology, principles, graph-analytical methods, graph, modeling, management, modern power supply One of the main directions of systems research is systems analysis and systems approach. Systems theory, as a subdivision, is one of its main concepts and, along with model theory, control theory, and information theory, belongs to the methodological base in scientific research. It is essential to examine the laws and principles that facilitate effective research into the structural transformation of research organization. In practice, a systems approach involves systems coverage, systems representation, and systems orientation in research. System coverage requires considering the problem from different aspects and perspectives. Systems representation is achieved by building a single model that can replace a real object and provide relevant information about the object being modeled. Using systems analysis, an assessment of the network topology is carried out - the number of substations, line lengths, voltage levels. The formation of systemology is based on a systems approach and is used in the construction of more intelligent control systems. Ensuring the organization of research is of no small importance, which means identifying the problem; internal analysis of the problem; external analysis of the problem; system identification: goals, constraints, trends, factors, properties; model synthesis; model behavior analysis – system operation modeling; system optimization. Planning an optimization experiment, experimental model identification. Analysis of the results of the experiment with the model. Interpretation of the results in relation to the system solving the problem P. Solution implementation. Within the framework of the system approach, the tasks of analysis and synthesis are interconnected, alternating with a given regularity and characterizing two sides of a single cycle in the research process. The described cycle of problem solving applies to both the quantitative and qualitative system approaches. In this case, identification and imitation are implemented at an intuitive, heuristic level. The main disadvantage of the qualitative approach is that counterintuitive behavior that manifests itself in complex socio-economic systems is overlooked and not analyzed. The advantage of the qualitative approach over the quantitative one is a significant saving of time and money for conducting research. The set of methodological tools used to prepare and justify decisions regarding complex, scientific, and technical issues includes system analysis. System analysis is defined as a set of methods for studying complex objects by presenting them in the form of systems and further analyzing such systems. The order of stages of system analysis includes: problem formulation (choosing the object of study, determining the goal and criteria for its study); isolation and structuring (or decomposition) of the system under study, i.e., dividing it into subsystems that are relatively clearly described; creation of a mathematical model of the system. The article presents graphical and analytical stages of making evaluative management decisions based on network modeling. The results obtained proved the feasibility of using a structured system-parametric approach in the study of power supply systems. This is the difference between system analysis and the local approach, which consists of studying the structure and functional features of autonomous, separately taken elements of the system. The inability to comprehensively cover all parties, connections and «mediation» allows, during the system analysis, on the one hand, to strive for maximum completeness of the description, and on the other hand, to carry out a reasonable simplification of the object. This is especially relevant and appropriate in the field of research on processes in power supply systems.
Vector logic structures and functions of computing
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Element structure, vector logic functionality, vector logics, logic vector, Cartesian logic, intelligent computing, test map, fault simulation, good-value simulation, in-memory modeling for simulation, prompt engineering Vector logic computing is a cost-effective mechanism for intelligent in-memory computing, utilizing read-write transactions to address practical problems in the analysis and management of physical, social, and business processes based on monitoring. The concept of a mechanism is introduced as a harmonious relationship between the model and the algorithm for computing. The investigation aims to significantly reduce time and energy costs associated with simulation processes in the physical, social, and digital worlds. The means to achieve this goal is using vector-logical in-memory computing mechanisms, which significantly simplify algorithms due to the exponential redundancy of data structures. The mechanisms for reducing the algorithm's computational complexity, which is typically exponential due to the use of data structures based on vector logic, are considered. An analysis of functions and structures is conducted from the perspective of algorithmic simplicity for modeling and simulation purposes. Several mechanisms based on Cartesian logic are proposed for modeling a logical vector from the structure of a digital circuit. A Boolean vector is used to create a test map in three matrix operations. A cloud-based MOSI service is offered that simulates the behavior and faults of digital circuits and their functionalities using truth table addresses.
A method for synthesizing index policies to ensure the survivability of a mobile platform-based information system
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Information system, mobile platform, survivability, index policy, dual-loop adaptation, method This paper proposes a method for synthesizing index policies to ensure the survivability of a mobile platform-based information system operating under intermittent connectivity and incomplete observability. The method aims to maintain the probability of violations within specified budgets by combining event-based index ranking with calibrated risk management and dual-loop parameter tuning. The fast loop adaptively adjusts the weight of the risk component of the index based on the magnitude of recorded excesses, while the slow loop periodically recalibrates feature normalization, base weights, and relevance factors, thereby enhancing robustness to non-stationarity and small sample sizes. Probabilistic constraints are interpreted through upper confidence bounds on a short sliding window, enabling controlled risk without rigid assumptions regarding disturbance distributions and without reliance on continuous telemetry or frequent global coordination. To maintain decision consistency, a computationally lightweight acceptability check and event logging are employed, facilitating independent reproducibility. The study presents the index rule formulation—including feature normalization, weight structure, and risk testing—along with a risk correction protocol for sampling per decision step and a procedure for detecting mode changes and safe reconfiguration. The computational complexity of the method is amortized linear-logarithmic with respect to the number of active objects and step capacity, ensuring suitability for real-time operation. Verification was performed on event logs featuring intermittent connectivity and surge load scenarios. The results demonstrate the convergence of the violation frequency upper bound to specified budgets, the stabilization of the risk component weight without fluctuations, and predictable service metric dynamics. A step-by-step ablation study highlighted the contribution of each component: disabling the fast loop delays the achievement of target risk levels, while the absence of the slow loop locks the risk component at an excessive weight, degrading the operational balance. Compared to heuristic rules lacking explicit risk control and model-predictive approaches sensitive to telemetry quality, the proposed method ensures the survivability of the information system through controllable probabilistic guarantees, event scalability, and reproducible verification procedures.
Vision-based GUI testing: a systematic review of computer vision advances in software quality assurance
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Computer vision, GUI testing, multimodal artificial intelligence, software quality assurance, deep learning, large language models, reinforcement learning, automation Ensuring the reliability and adaptability of graphical user interfaces (GUIs) has become a central challenge in contemporary software quality assurance, as modern applications increasingly rely on visually dense, dynamic, and cross-platform interaction patterns. Traditional script-based and DOM-dependent testing approaches are brittle and expensive to maintain, since even minor layout shifts can invalidate large portions of test logic. These limitations have accelerated interest in computer vision and artificial intelligence techniques that evaluate GUIs directly from rendered visuals, enabling more robust, flexible, and human-like validation. This study aims to systematize current research on vision-based GUI testing and to identify how emerging multimodal and language-guided methods reshape quality-assurance practices. Conducted as a structured review of academic and industrial work published between 2010 and 2025, the analysis synthesizes findings across five methodological families: classical image-processing and template-matching approaches, deep neural detectors, generative models for synthetic augmentation, reinforcement-learning agents for autonomous exploration, and transformer-based multimodal systems integrating large language models. The review incorporates an additional empirical-synthesis chapter that consolidates reported evaluation results for vision transformer-based perception, large language models -guided test reasoning, and multimodal vision–language pipelines. The results reveal a consistent technological trajectory: from early perceptual matching to high-fidelity object detection, interactive workflow exploration, and, most recently, semantic interpretation of GUI tasks through natural-language grounding. Experimental evidence shows that visual transformers improve structural perception, reinforcement learning agents enhance interaction coverage, and multimodal systems introduce reasoning capabilities that approach human-like test generation. At the same time, the review identifies persistent challenges, including dataset scarcity – particularly of multimodal screenshot-instruction-execution corpora – fragmented evaluation practices, cross-platform variability, and the computational overhead of multimodal inference. The study concludes that vision-based GUI testing is evolving into a cognitively informed quality-assurance paradigm that integrates perception, behavior, and semantic reasoning. Its novelty lies in providing the unified taxonomy of methods, a consolidated synthesis of empirical verification results, and a set of actionable future research directions. These contributions offer practical value for advancing reproducibility, robustness, and methodological coherence in next-generation GUI-testing systems.
Research on the impact of optimization algorithms on the accuracy of YOLOv11 neural networks
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Artificial intelligence, computer vision, convolutional neural networks, COCO dataset, hyperparameter optimization, SGD, Adam, AdamW, YOLOv11, mAP, IoU, precision, recall Visual inspection and positioning based on image detection results is a rapidly growing component of automation systems. Machine vision is increasingly used in production lines for various technological purposes, as well as in special equipment. Improving recognition accuracy in such applications can be a difficult task, especially in conditions of possible limitations, one of which may be size and weight restrictions, which in turn limit the power of computer devices that implement image detection and recognition. A possible solution to this problem is to improve recognition accuracy by automatically tuning the hyperparameters of detection models using various optimization methods. This article presents the results of a study of the effectiveness of algorithms for automatically tuning the hyperparameters of the YOLO (You Only Look Once) image detection model, built on the basis of the SGD (stochastic gradient descent), Adam (adaptive learning rate estimation), and AdamW (improved version of Adam) optimization methods. The models were trained on the COCO 2017 dataset, limited to eight classes and balanced in terms of the number of images. For each optimization algorithm, 10 iterations of automatic selection were used, followed by training for 30 epochs. The test results showed that in tasks where detection accuracy is important and there are sufficient computational resources for hyperparameter selection during model training, it is advisable to use the stochastic gradient descent optimization algorithm, since the detection model configured with its use provides a higher probability of successful image recognition in real time under conditions of limited computational resources.
Online anomaly detection in correlated data streams using robust Kalman filtering
Section: Computer science and software engineering Categories: Computer science and software engineering Keywords: Streaming data, inertial navigation, anomaly detection, Kalman filter, iForest The demand for online data analysis opens new challenges and research opportunities. The growing wave of IoT devices, low-cost sensors, and robotic systems generates vast amounts of high-frequency streaming data. Efficient online analysis of such data requires algorithms that operate under memory and latency constraints, often within a sliding-window framework. However, the reliability of these data streams critically affects the accuracy of the inference results.This study considers one of the tasks in streaming data analysis – anomaly detection in the smartphone sensors data streams. Our goal was to improve the quality of the geolocation by filtering out anomalies in the signal and then measure the accuracy of trajectory estimation for pedestrian navigation. Pedestrian navigation in urban environment is non-trivial because of global navigation sattelite system signal distortions. These distortions can be caused by various factors such as multipath effects, signal blockage from tall buildings, and interference, which are common in dense urban areas. The full data pipeline requires robust techniques for smartphone sensor data processing wich include low-pass or high-pass filtering of acceleration signal, synchronizing several streams by the timestamps, converting measurements from the device frame of reference to the global coordinate system, feature enrichments etc. When multiple data streams from device sensors are available, their fusion can be used to mitigate the limitations of individual sources. One of the adopted methods for this is the so-called robust Kalman filter. We compared this method with an ensemble anomaly detection method (iForest) applied to the geolocation data stream in the pedestrian navigation set up. We used orthogonal distance metric to compare predicted trajectories with groud truth coordinates and showed that robust Kalman filter achieves superior performance in the streaming setting. A mean deviation from the ground truth trajectory of one metre and eighty-three centimetres was achieved on the test dataset, with the total route length measuring one hundred eighty-four metres.
Parametric approach to improving significant indicators of thermoelectric systems for maintaining thermal conditions
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: reliability indicators, dynamic characteristics, thermoelectric material, efficiency, current mode The problem of improving the reliability of complex systems is fundamental, since it is impossible to achieve complete failure-free operation in real systems that interact with the external environment. In modern information systems, increasing the probability of failure-free operation by stabilizing the temperature of critical heat-loaded elements using a thermal control system remains a pressing issue. To solve this problem, a mathematical model of a thermoelectric cooler of a given design has been developed. The model is intended for comparative analysis and assessment of the influence of various combinations of parameters of the initial thermoelectric material of equal efficiency on the main performance indicators of the product under variable current load. The results of calculations of reliability indicators, dynamic and energy characteristics of single-stage thermoelectric coolers, and the geometry of thermoelectric element chains are presented for various combinations of parameters of the initial thermoelectric materials. It is shown that with a rational design of a single-stage thermoelectric cooler, it is possible to select a combination of parameters of the initial material with the same efficiency to reduce the operating current and increase the supply voltage at different thermal loads. Additional means of controlling the parameters of a thermoelectric cooler are the geometry of the thermocouple branches, the physical parameters of the initial thermocouple materials, the operating current, the supply voltage, and the failure rate. Analysis of the research results showed that it is possible to select a combination of raw material parameters that reduces the operating current by an average of 20% and reduces the relative failure rate by up to 40% under otherwise equal conditions.
Method for automated control of rotor engine blade synchronization
Section: Applied information technologies in energy engineering and automation Categories: Applied information technology in energyengineering and automation Keywords: automated control, computer modeling rotary-blade internal combustion engine, computer simulation, electrical synchronisation, torque control This study addresses the critical issue of synchronization accuracy in rotary-blade internal combustion engines, which directly influences their efficiency and operational reliability. Traditional mechanical synchronization methods suffer from significant drawbacks, including rapid wear, high maintenance costs, and limited modernization potential. To overcome these limitations, we propose an innovative electrical synchronization approach based on adaptive torque control of electric machines. The developed algorithm dynamically adjusts torque in real time according to the displacement of the bisector of the angle between the blades forming the combustion chamber, ensuring precise synchronization during each engine cycle. A mathematical model was formulated to describe the relationship between torque, bisector displacement, and engine dynamics. This model was implemented in a custom simulation environment developed in JavaScript with interactive visualization tools. Computer simulations were conducted under various operating conditions to evaluate the algorithm’s performance. Results demonstrate a substantial reduction in bisector displacement - from several degrees to thousandths of a degree and stabilization of cycle duration after only a few iterations. These findings confirm the algorithm’s ability to maintain synchronization accuracy and improve engine stability. The proposed approach offers practical benefits for the design of advanced rotary-blade internal combustion engines, reducing mechanical wear and enhancing energy efficiency. Future research will focus on optimizing the correction factor, adapting the algorithm for engines operating under variable loads and temperatures, and integrating PID-based control strategies for improved responsiveness.
computer modeling rotary-blade internal combustion engine
Technological platforms, tools, and standardization in the unified information space for automated unmanned aerial vehicle design
Section: Systems analysis, applied information systems and technologies Categories: Systems analysis, applied information systems and technology Keywords: model-based engineering, product lifecycle, digital thread, tool interoperability, unmanned aircraft, systems integration Unmanned aerial vehicle development entails highly interdisciplinary processes, often hampered by fragmented digital toolchains and siloed data. This paper addresses the critical need for an integrated unified information space across the unmanned aerial vehicle lifecycle. We present a review of modern platforms and standards that enable end-to-end data continuity – the “digital thread” – from initial requirements through design, manufacturing, and operations. The aim is to synthesize current approaches to achieving interoperability and an authoritative source of truth for unmanned aerial vehicle projects. Key objectives include mapping prevalent Model-Based Systems Engineering and Product Lifecycle Management platforms, comparing lifecycle standards (e.g., systems modeling language, Standard for the Exchange of Product Data, Open Services for Lifecycle Collaboration, Open platform communications unified architecture) across development phases, proposing an integration framework to connect heterogeneous tools, and identifying gaps in adoption. In terms of methods, a structured literature review was conducted using IEEE Xplore, Scopus, and engineering databases. Search strings targeted unmanned aerial vehicle tool integration, Model-Based Systems engineering and Product Lifecycle Management interoperability, and digital thread case studies. Inclusion criteria focused on publications and standards addressing cross-domain data integration; industry white papers and standards documentation were also analyzed. The results reveal a rich landscape of standards for requirements management, system architecture, product data exchange, simulation interoperability, and IoT-based operational feedback. We provide a comparative analysis (including tables) of these standards versus lifecycle stages, and of integration approaches (Application Programming Interfaces, service buses, Open Services for Lifecycle Collaboration links, industrial Internet of Things protocols) against factors like latency, scalability, and security. A conceptual integration blueprint is outlined, leveraging open standards to connect systems modeling language models, Computer-Aided Design/Product Lifecycle Management data, and real-time sensor information into a cohesive environment. Discussion highlights trade-offs such as proprietary Product Lifecycle Management suites versus openness, the maturity and portability of Model-Based Systems Engineering models (systems modeling language version 1.0 limitations and systems modeling language version 2.0 prospects), and cloud vs. on-premises deployment given aerospace security International Traffic in Arms Regulations constraints. Organizational readiness (business process reengineering, stakeholder buy-in) emerges as a key success factor. In conclusion, unifying the information space can dramatically improve unmanned aerial vehicle development efficiency, traceability, and innovation. However, realizing this vision requires not only technical solutions but also adoption of standards and cultural change in engineering practices. The paper’s synthesis provides practical insights and a roadmap for both researchers and practitioners aiming to implement a consistent digital thread for complex aerospace systems.
Research on improving a graph neural network model forcomputer network simulation
Section: Computer engineering and cybersecurity Categories: Computer engineering and cybersecurity Keywords: Сomputer network modeling, machine learning, graph neural network, network parameters, node characteristics, optimization, network topology Modern computer networks face increasing challenges due to the growing complexity of their structure, dynamic traffic fluctuations, and the need to maintain high performance. Traditional approaches to network modeling often fail to accurately predict parameters such as latency or packet loss, as they have limited capacity to capture the specific characteristics of individual network elements. This highlights the relevance of developing novel methods that can adapt to real operating conditions and ensure efficient resource management. The aim of this study is to enhance network modeling methods by developing a model that incorporates the individual properties of network elements to improve the accuracy of parameter prediction and to optimize routing processes. The research objectives include the analysis of current modeling approaches, the design of an improved model based on machine learning techniques, the refinement of training algorithms, and the execution of experiments to evaluate the model’s effectiveness. Machine learning methods were applied in the implementation, with particular emphasis on a graph neural network, which enables the modeling of complex interdependencies among network elements. The proposed model integrates node-specific characteristics into the data processing pipeline, thereby ensuring adaptability to heterogeneous conditions. Experiments were conducted on multiple datasets representing real-world network topologies; with prediction accuracy assessed using several evaluation metrics. The results demonstrate that the proposed model provides higher accuracy in predicting network parameters compared to baseline approaches, exhibiting the ability to generalize to unseen topologies. The scientific novelty of the work lies in the incorporation of element-level characteristics into the modeling process, allowing for a more precise reflection of real-world conditions. The practical significance is manifested in the potential application of the model in network management systems for routing optimization and infrastructure cost reduction. The findings open new prospects for further development of modeling and management methods in modern networked systems.