The article explores the application of the residue number system in text information processing. The residue number system, based on the principles of modular arithmetic, represents numbers as sets of residues relative to pairwise coprime moduli. This approach enables parallel computation, potential data compression, and increased noise immunity. The study addresses issues such as character encoding, parallel information processing, error detection and correction, computational advantages in implementing polynomial hash functions, as well as practical limitations of the residue number system.

Keywords: residue number system, modular arithmetic, text processing, parallel computing, data compression, noise immunity, Chinese remainder theorem, polynomial hashing, error correction, computational linguistics

Ontological modeling is a promising direction in the development of the scientific and methodological base for developing intelligent information systems in the power industry. The article proposes a new approach to using ontological models in creating artificial intelligence systems for forecasting time series in electrical engineering problems. Formal metrics are introduced: the ontological distance between a feature and a target variable, as well as the semantic relevance of a feature. Using examples of domain ontologies for wind energy and electricity consumption of an industrial enterprise, algorithms for calculating these metrics are demonstrated and it is shown how they allow ranking features, implementing an automated selection of the most significant features, and providing semantic regularization of training regression models of various types. Recommendations are given for choosing coefficients for calculating metrics, an analysis of the theoretical properties of metrics is carried out, and the applicability limits of the proposed approach are outlined. The results obtained form the basis for further integration of ontological information into mathematical and computer models for forecasting electricity generation and consumption in the development of industry intelligent systems.

Keywords: ontology, ontological distance, feature relevance, systems analysis, explainable artificial intelligence, power industry, generation forecasting, electricity consumption forecasting

In the modern world, when technology is developing at an incredible rate, computers have gained the ability to "see" and perceive the world around them like a human. This has led to a revolution in visual data analysis and processing. One of the key achievements was the use of computer vision to search for objects in photographs and videos. Thanks to these technologies, it is possible not only to find objects such as people, cars or animals, but also to accurately indicate their position using bounding boxes or masks for segmentation. This article discusses in detail modern models of deep neural networks used to detect humans in images and videos taken from a height and a long distance against a complex background. The architectures of the Faster Region-based Convolutional Neural Network (Faster R-CNN), Mask Region-based Convolutional Neural Network (Mask R-CNN), Single Shot Detector (SSD) and You Only Look Once (YOLO) are analyzed, their accuracy, speed and ability to effectively detect objects in conditions of a heterogeneous background are compared. Special attention is paid to studying the features of each model in specific practical situations, where both high-quality target object detection and image processing speed are important.

Keywords: machine learning, artificial intelligence, deep learning, convolutional neural networks, human detection, computer vision, object detection, image processing

This article presents the development of a combined method for summarizing Russian-language texts, integrating extractive and abstractive approaches to overcome the limitations of existing methods. The proposed method is preceded by the following stages: text preprocessing, comprehensive linguistic analysis using RuBERT, and semantic similarity-based clustering. The method involves extractive summarization via the TextRank algorithm and abstractive refinement using the RuT5 neural network model. Experiments conducted on the Gazeta.Ru news corpus confirmed the method's superiority in terms of precision, recall, F-score, and ROUGE metrics. The results demonstrated the superiority of the combined approach over purely extractive methods (such as TF-IDF and statistical methods) and abstractive methods (such as RuT5 and mBART).

Keywords: combined method, summarization, Russian-language texts, TextRank, RuT5

The article considers the parameter identification issues of linear non-stationary dynamic systems adaptive models using the example of a linearized adjustable model of a DC motor with independent excitation. A new method for estimating the parameters of adjustable models from a small number of observations is developed based on projection identification and the apparatus of linear algebra and analytical geometry. To evaluate the developed identification method, a comparison of the transient processes of the adaptive model of a DC motor with independent excitation with the obtained parameter estimates with reference characteristics was carried out. The efficiency of the proposed identification method in problems of DC electric drive control is shown.

Keywords: DC motor, projection identification, dynamic system parameter estimation, adaptive model of non-stationary dynamic system

Modern computer systems for controlling chemical-technological processes make it possible to programmatically implement complex control algorithms, including using machine learning methods and elements of artificial intelligence. Such algorithms can be applied, among other things, to complex non-stationary multi-product and flexible discrete productions, which include such low-tonnage chemical processes as the production of polymeric materials. The article discusses the production of fluoroplastic in batch reactors. This process occurs under constantly changing parameters such as pressure and temperature. One of the important tasks of the control system is to stabilize the quality of the produced polymer, and for these purposes it is important to predict this quality during the production process before the release of fluoroplastic. The quality of the product, in turn, strongly depends on both the quality of the initial reagents and the actions of the operator. In non-stationary process conditions, typical virtual quality analyzers based on regression dependencies show poor results and are not applicable. The article proposes the architecture of a virtual quality analyzer based on mathematical forecasting methods using such algorithms as: random forest method, gradient boosting, etc.

Keywords: polymerization, multi-product manufacturing, low-tonnage chemistry, quality forecasting, machine learning

The article focuses on the development of a web portal for monitoring and forecasting atmospheric air quality in the Khabarovsk Territory. The study analyzes existing solutions in the field of environmental monitoring, identifying their key shortcomings, such as the lack of real-time data, limited functionality, and outdated interfaces. The authors propose a modern solution based on the Python/Django and PostgreSQL technology stack, which enables the collection, processing, and visualization of air quality sensor data. Special attention is given to the implementation of harmful gas concentration forecasting using a recurrent neural network, as well as the creation of an intuitive user interface with an interactive map based on OpenStreetMap. The article provides a detailed description of the system architecture, including the backend, database, and frontend implementation, along with the methods used to ensure performance and security. The result of this work is a functional web portal that provides up-to-date information on atmospheric air conditions, forecast data, and user-friendly visualization tools. The developed solution demonstrates high efficiency and can be scaled for use in other regions.

Keywords: environmental monitoring, air quality, web portal, forecasting, Django, Python, PostgreSQL, neural networks, OpenStreetMap

Analysis of a digital data transmission system through a noisy communication channel based on the Huffman compression method and encoding using cyclic Bose-Chowdhury-Hockingham codes This article examines the effectiveness of a digital data transmission system through a noisy communication channel using the Huffman compression method and cyclic BCH encoding (Bose-Chowdhury-Hockingham). Huffman compression reduces data redundancy, which increases the effective transmission rate, while BCH codes detect and correct errors caused by channel noise. The analysis likely includes evaluating parameters such as compression ratio, data transmission rate, error probability after decoding, and computational complexity of the algorithms. The results demonstrate the effectiveness of this combination of techniques in improving data transmission reliability in noisy environments.

Keywords: " digital transmission system, cyclic coding, compression ratio, decoding, encoding"

This article discusses the implementation features of named entity recognition models. In the course of the work, a number of experiments were conducted with both traditional models and well-known neural network architectures, a hybrid model, the features of the results, their comparison and possible explanations are considered. In particular, it is shown that a hybrid model with the addition of a bidirectional long short-term memory can give better results than the basic bidirectional representation model based on transformers. It is also shown that, improved by adding a thinning layer for regularization, a weighted loss function and a linear classifier on top of the outputs, a bidirectional representation model based on transformers can give high metric values. For clarity, the work provides graphs of model training and tables with metrics for comparison. In the process of work, conclusions and recommendations were formed.

Keywords: text analysis, artificial intelligence, named entity recognition, neural networks, deep learning, machine learning

To ensure the stable and reliable operation of isolated power systems, models based on rapid processing and analysis of non-Gaussian data are needed, which contributes to increased energy efficiency and improved energy management. Within the framework of the theory of optimal control of power consumption, based on a comprehensive ranking analysis procedure, a model of regime rationing was developed, which differs from the known ones in that for the first time an R-distribution device based on rank analysis was used, as well as a device and method of regime rationing, which automatically ensures the necessary stable power consumption of the regional electrical complex in conditions of resource constraints.

Keywords: Regime normalization, rank analysis, OLAP data cube, half division method, entropy, topological data, rank topological measure, resource constraints plan, approximation, regional electric complex, power consumption.

The article examines the influence of the data processing direction on the results of the discrete cosine transform (DCT). Based on the theory of groups, the symmetries of the basic functions of the DCT are considered, and the changes that occur when the direction of signal processing is changed are analyzed. It is shown that the antisymmetric components of the basis change sign in the reverse order of counts, while the symmetric ones remain unchanged. Modified expressions for block PREP are proposed, taking into account the change in the processing direction. The invariance of the frequency composition of the transform to the data processing direction has been experimentally confirmed. The results demonstrate the possibility of applying the proposed approach to the analysis of arbitrary signals, including image processing and data compression.

Keywords: discrete transforms, basic functions, invariance, symmetry, processing direction, matrix representation, correlation

This paper is devoted to the construction of a visual-inertial odometry system for an unmanned vehicle using both binocular cameras and inertial sensors as an information source, which would be able to simultaneously determine the vehicle's own position and the relative position of other road users. To ensure accurate and continuous localization, it is proposed to use an inertial navigation system and two types of image keypoints. Deep learning models are used to accurately and reliably track keypoints. To achieve efficient and reliable matching of objects between two frames, a multi-level data association mechanism is proposed that takes into account possible errors of various system components. The experimental results demonstrate the feasibility and application potential of the proposed system.

Keywords: multi-object visual-inertial odometry, localization, data association, tracking of 3D dynamic objects

The article is devoted to the description and mathematical justification of the U-shaped distribution of topic shares that arises in the latent Dirichlet allocation model with symmetric hyperparameters. It is shown that the bimodal shape is due to the reduction of the Dirichlet vector to a beta distribution, which makes traditional unimodal approximations incorrect. A composite probability model is proposed that combines beta, gamma, and Poisson components, as well as covariate accounting for semantic connectivity. The model parameters are determined by the differential evolution method using a criterion that includes the Wasserstein distance and the Jensen–Shannon and Kulbak–Leibler divergences. Based on the corpus of texts from the information field of the Rosatom State Corporation, it has been established that the new model is more accurate than lognormal, Pareto, exponential, and normal approximations, allowing for reliable characterization of thematic flows and supporting decisions in large text data monitoring tasks.

Keywords: system analysis, latent Dirichlet allocation, topic modeling, Dirichlet latent distribution, topic signal intensity, beta distribution, gamma distribution, Poisson process, Jensen–Shannon divergence, Wasserstein distance, Kulbak–Leibler divergence

In this paper, methods for estimating one's own position from a video image are considered. A robust two-stage algorithm for reconstructing the scene structure from its observed video images is proposed. In the proposed algorithm, at the feature extraction and matching stage, a random sample based on the neighborhood graph cuts is used to select the most probable matching feature pairs. At the nonlinear optimization stage, an improved optimization algorithm with an adaptive attenuation coefficient and dynamic adjustment of the trust region is used. Compared with the classical Levenberg-Marquard (LM) algorithm, global and local convergence can be better balanced. To simplify the system's decisions, the Schur complement method is used at the group tuning stage, which allows for a significant reduction in the amount of computation. The experiments confirmed the operability and effectiveness of the proposed algorithm.

Keywords: 3D reconstruction，graph-cut, Structure-from-Motion (SfM)，RANSAC，Bundle Adjustment optimization，Levenberg-Marquardt algorithm，Robust feature matching

This paper presents the design and experimental validation of an external load-balancing mechanism for server clusters that support a distributed educational network. A hybrid strategy is proposed that merges classical policies (Round Robin, Least Connections) with an evolutionary search based on a genetic algorithm. At the modeling level the user-session assignment problem is formulated as a minimization of the maximum node load under latency constraints. The solution is implemented entirely on a domestic technology stack— “1C:Enterprise” server clusters, Docker containers, and the “1C:Bus” integration middleware. Experimental results show that the new scheduling logic improves system resilience under traffic fluctuations, lowers user response times, and utilizes spare resources more efficiently, while imposing no substantial overhead on the control nodes. The study confirms the practical viability of evolutionary approaches for real-time load balancing.

Keywords: load balancing, server clusters, genetic algorithm, simulation modeling, 1C:Bus middleware

The paper considers a lightweight modified version of the YOLO-v5 neural network, which is used to recognize road scene objects in the task of controlling an unmanned vehicle. In the proposed model, the pooling layer is replaced by the ADown module in order to reduce the complexity of the model. The C2f module is added as a feature extraction module to improve accuracy by combining features. Experiments using snowy road scenes are presented and the effectiveness of the proposed model for object recognition is demonstrated.

Keywords: road scene object recognition, YOLOv5, Adown, C2f, deep learning, pooling layer, neural network, lightweight network, dataset

The purpose of the article is to study the possibility of the influence of various factors affecting the process of eliminating a water pipeline accident based on its modeling using fuzzy logic methods. The article discusses various options for managing the process of eliminating a water pipeline accident and, during the analysis, determines a set of qualitative parameters that are used in the fuzzy inference model based on the Mamdani method. To build a mathematical model, 37 products were formulated with the help of a group of experts, so that the model can work with selected qualitative variables as with quantitative ones and track the changes that occur in the process. The result of the inference cycle is a clear value of the parameters describing the possible actions necessary to eliminate the accident. The resulting mathematical model allows you to analyze the input parameters at a qualitative level, gives a qualitative representation of the result at the output, which will increase the effectiveness of actions aimed at eliminating a water pipeline accident. The quality of functioning of the described model is verified by a group of experts.

Keywords: fuzzification, defazziification, Mamdani method, system analysis, fuzzy logic, qualitative parameters, water pipe accident, mathematical model

The article is devoted to the study of the possibilities of automatic transcription and analysis of audio recordings of telephone conversations of sales department employees with clients. The relevance of the study is associated with the growth of the volume of voice data and the need for their rapid processing in organizations whose activities are closely related to the sale of their products or services to clients. Automatic processing of audio recordings will allow checking the quality of work of call center employees, identifying violations in the scripts of conversations with clients. The proposed software solution is based on the use of the Whisper model for speech recognition, the pyannote.audio library for speaker diarization, and the RapidFuzz library for organizing fuzzy search when analyzing strings. In the course of an experimental study conducted on the basis of the developed software solution, it was confirmed that the use of modern language models and algorithms allows achieving a high degree of automation of audio recordings processing and can be used as a preliminary control tool without the participation of a specialist. The results confirm the practical applicability of the approach used by the authors for solving quality control problems in sales departments or call centers.

Keywords: call center, audio file, speech recognition, transcription, speaker diarization, replica classification, audio recording processing, Whisper, pyannote.audio, RapidFuzz

This work presents the concept of an automated system for urban planning decisions to support balanced integrated residential development. The system aims to ensure developer profitability while maximizing socio-economic benefits for the city and its residents. The study focuses on analyzing stakeholder interactions, creating a multi-criteria optimization model to balance interests, and formalizing land selection processes. Methods include domain-driven design (DDD), hierarchy analysis for investment assessment, and multi-criteria optimization for financial modeling. The outcome is a conceptual taxonomic model, a quantitative land assessment method, and a financial model forming the basis for strategic urban development decisions.

Keywords: аutomated decision-making system, conceptual taxonomic model, mathematical model, economic model, weighting coefficients, integrated residential development, profitability, descriptive logic, domain driven design, investment attractiveness models

The article addresses the issues of integration and processing heterogeneous data within a single company as well as during interaction between various participants of business processes under conditions of digital transformation. Special attention is given to collaboration between equipment manufacturers and industrial enterprises, emphasizing the importance of aligning and transforming data when interacting with heterogeneous information systems. The problem of integrating historical data, challenges arising from transitioning to new infrastructure, and a solution based on principles similar to those used by open standards such as OpenCL are discussed. Particular emphasis is placed on providing complete and consistent datasets, developing effective mechanisms for semantic integration, and using ontological approaches to address difficulties in comparing and interpreting diverse data formats. It highlights the necessity of continuously updating metadata dictionaries and establishing connections between different data sources to ensure high-quality and reliable integration. The proposed methods aim at creating sustainable mechanisms for exchanging information among multiple business entities for making informed management decisions.

Keywords: digital transformation, heterogeneous systems, erp/mes systems, ontology, semantic integration, metadata, data mapping

The article reflects the basic principles of the application of artificial intelligence (AI) and machine learning (ML) technologies at oil refineries, with a particular focus on Russian industrial enterprises. Modern oil refineries are equipped with numerous sensors embedded in technological units, generating vast volumes of heterogeneous data in real time. Effective processing of this data is essential not only for maintaining the stable operation of equipment but also for optimizing energy consumption, which is especially relevant under the increasing global demand for energy resources. The study highlights how AI and ML methods are transforming data management in the oil industry by enabling predictive analytics and real-time decision-making. Python programming language plays a central role in this process due to its open-source ecosystem, flexibility, and extensive set of specialized libraries. Key libraries are categorized and discussed: for data preprocessing and manipulation (NumPy, SciPy, Pandas, Dask), for visualization (Matplotlib, Seaborn, Plotly), and for building predictive models (Scikit-learn, PyTorch, TensorFlow, Keras, Statsmodels). In addition, the article discusses the importance of model validation, hyperparameter tuning, and the automation of ML workflows using pipelines to improve the accuracy and adaptability of predictions under variable operating conditions. Through practical examples based on real industrial datasets, the authors demonstrate the capabilities of Python tools in creating interpretable and robust AI solutions that help improve energy efficiency and support digital transformation in the oil refining sector.

Keywords: machine learning (ML), artificial intelligence (AI), intelligent data analysis, Python, Scikit-learn, forecasting, energy consumption, oil refining, oil and gas industry, oil refinery

This paper is devoted to the construction of a robust visual-inertial odometry system for an unmanned vehicle using binocular cameras and inertial sensors as information sources.The system is based on a modified structure of the VINS-FUSION system. Two types of feature points and matching methods are used to better balance the quantity and quality of tracking points. To filter out incorrect matches of key points, it is proposed to use several different methods. Semantic and geometric information are combined to quickly remove dynamic objects. Keypoints of static objects are used to complement the tracking points. A multi-layer optimization mechanism is proposed to fully utilize all point matchings and improve the accuracy of motion estimation. The experimental results demonstrate the effectiveness of the system.

Keywords: robust visual-inertial odometry, localization, road scene, multi-level optimization mechanism

This article presents a method for adapting the TPL-1 telescopic rangefinder for photometric observations of artificial Earth satellites (AES). By integrating a ZWO ASI294MM Pro camera and a ""Jupiter-21M"" lens onto the telescope’s mount while retaining its original tracking capabilities, the system achieves high-precision photometric measurements without requiring expensive astronomical equipment. The custom-designed mounting mechanism ensures stable alignment and minimizes vibrations, allowing for prolonged observation sessions with reliable data acquisition. The study demonstrates the system’s effectiveness through observations of several satellites, including ERS-2, ADEOS-II, and ALOS, each exhibiting distinct photometric signatures. The results reveal periodic brightness variations, rotational dynamics, and reflective properties of these objects, with measurement accuracy comparable to professional setups. The adapted setup proves particularly valuable for educational purposes, space debris monitoring, and satellite behavior analysis, offering a cost-effective alternative to specialized instruments. This work highlights the potential of repurposing military-grade optics for scientific applications, bridging the gap between amateur and professional astronomy. Future enhancements could focus on automation and advanced data processing techniques to further expand the system’s capabilities.

Keywords: photometry, artificial satellites, TPL-1 telescope, equipment adaptation, light curves, space monitoring

The article is devoted to the consideration of key issues related to the use of machine and deep learning methods in agriculture. Particular attention is paid to the areas of application of these technologies in various processes of farming and growing crops. In addition, the features of using deep learning in practice are considered using the example of developing a recommender system, which is designed to generate proposals for the most suitable crops for growing in a certain region in the next season.

Keywords: agriculture, harvest, artificial intelligence, deep learning, forecasting, model, season, accuracy

The article discusses a software module developed by the authors for automatic generation of program code based on UML diagrams. The relevance of developing this module is due to the limitations of existing foreign code generation tools related to functionality, ease of use, support for modern technologies, as well as their unavailability in Russian Federation. The module analyzes JSON files obtained by exporting UML diagrams from the draw.io online service and converts them into code in a selected programming language (Python, C++, Java) or DDL scripts for DBMS (PostgreSQL, Oracle, MySQL). The Python language and the Jinja2 template engine were used as the main development tools. The operation of the software module is demonstrated using the example of a small project "Library Management System". During the study, a series of tests were conducted on automatic code generation based on the architectures of software information systems developed by students of the Software Engineering bachelor's degree program in the discipline "Design and Architecture of Software Systems". The test results showed that the code generated using the developed module fully complies with the original UML diagrams, including the structure of classes, relationships between them, as well as the configuration of the database and infrastructure (Docker Compose). The practical significance of the investigation is that the proposed concept of generating program code based on visual models of UML diagrams built in the popular online editor draw.io significantly simplifies the development of software information systems, and can be used for educational purposes.

Keywords: code generation, automation, python, jinja2, uml diagram, json, template engine, parsing, class diagram, database, deployment diagram