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 studies possibilities for analyzing geopolitical processes within the framework of situational analysis methodology using cognitive modeling. Situational analysis description is given, and scenario for developing events is presented where two stages are distinguished: a preparatory stage (a pre-scenario stage) which is essential for performing descriptive and explanatory functions of predictive research, and a scenario stage intended for substantive and formal research as well as for description of predicted processes, construction of system models and preparation of all significant information for scenario synthesis. Furthermore, a method for applying situational analysis is proposed to be used within the framework of the cognitive modeling toolkit of a “future scenario” option and its analysis with account of new “main” factors, relationships, feedbacks and dynamics of their alterations. When forming a scenario for a specific geopolitical situation within the framework of cognitive modeling, this method can be presented by causal (functional) and logical-semantic relation between the elements/agents of actions and counteractions. By interpreting the logical-semantic as structural, and the causal as dynamic, we obtain a structural-dynamic systemic description of geopolitical confrontation using the language of cognitive graphs, i.e. presenting a graphical expression of causal relationships between the concepts (factors) that characterize a particular geopolitical process. Thus, within the framework of a scenario stage the following procedures are conducted: analyzing the initial geopolitical situation, namely: determining key factors that build up the scheme of internal connections and external relationships, and their structuring; defining factors that make an impact; determining impact directions and force (positive and negative effect); choosing basic stereotypes or generalized models of interactions that correspond to the initial situation; constructing cognitive models of the current state of a situation; studying trends for the situation’s development and its dynamics analysis; transferring a scenario onto a practical basis.

Keywords: geopolitical processes, situational analysis, cognitive modeling, methodology for constructing predictive scenarios

Mathematical modeling of complex systems often requires the use of variable grouping methods to build effective models. This paper considers the problem of constructing a homogeneous nested piecewise linear regression with variable grouping for modeling the staffing of information protection units. A corresponding model for the Social Fund of Russia is constructed using spatial data for the year 2022. The data on the number of employees of the organization, electronic signatures, protected nodes, protected resources, the total number of structural units, individual buildings and IT service specialists are used as independent variables.

Keywords: information protection, regression model, homogeneous nested piecewise linear regression, parameter estimation, least modulus method, linear-Boolean programming problem, index set, set power, social fund

Differential-algebraic equations for describing the motion of a plane-parallel robot-manipulator are investigated. The dynamic model is constructed using the Lagrange equation and the substructure method. The design of a control system regulator using the sliding mode method is considered. The control accuracy is tested on a model of a 3-RRR plane-parallel robot . It consists of three kinematic chains, each of which has two links with three rotational joints. To study the efficiency of the controller, a circular trajectory is used as the target motion for the multibody system. The considered control system for a plane-parallel robot is capable of solving problems of movement and ensuring high positioning accuracy.

Keywords: control, plane-parallel robot, kinematic characteristics, dynamic model, differential-algebraic equations, constraint equation, controller, sliding mode, Lyapunov function, program trajectory

This article presents a structured approach to deploying and integrating Grafana, Loki, and Alloy in Kubernetes environments. The work was performed using a cluster managed via Kubespray. The architecture is focused on ensuring external availability, high fault tolerance, and universality of use.

Keywords: monitoring, ocestration, containerization, Grafana, Loki, Kubernetes, Alloy

The paper is about special questions in simulation of controlled electro drive with speed feedback. The incremental encoder, that is an angle sensor in fact, is widely used as a speed feedback sensor in such a drives. It has same special features as speed sensor because of discrete operation and this features are to be taken in account in control system development and simulation. The simulation model of incremental encoder and speed signal decoder is present. Model is realized in SimInTech simulation system using visual modeling and programming language based description approach.

Keywords: Incremental encoder, speed sensor, quadrature decoder, electro drive simulation, incremental encoder simulation, SimInTech

Changes in external conditions, parameters of object functioning, relationships between system elements and system connections with the supersystem lead to a decrease in the accuracy of the artificial intelligence models results, which is called model degradation. Reducing the risk of model degradation is relevant for electric power engineering tasks, the peculiarity of which is multifactor dependencies in complex technical systems and the influence of meteorological parameters. Therefore, automatic updating of models over time is a necessary condition for building user confidence in forecasting systems in power engineering tasks and industry implementations of such systems. There are various methods used to prevent degradation, including an algorithm for detecting data drift, an algorithm for updating models, their retraining, additional training, and fine-tuning. This article presents the results of a study of drift types, their systematization and classification by various features. The solution options that developers need to make when creating intelligent forecasting systems to determine a strategy for updating forecast models are formalized, including update trigger criteria, model selection, hyperparameter optimization, and the choice of an update method and data set formation. An algorithm for forming a strategy for automatic updating of artificial intelligence models is proposed and practical recommendations are given for developers of models in problems of forecasting time series in the power industry, such as forecasting electricity consumption, forecasting the output of solar, wind and hydroelectric power plants.

Keywords: time series forecasting, artificial intelligence, machine learning, trusted AI system, model degradation, data drift, concept drift

This study presents an effective vision -based method to accurately identify predator species from camera trap images in protected Uganda areas. To address the challenges of object detection in natural environments, we propose a new multiphase deep learning architecture that combines extraction of various features with concentrated edge detection. Compared to previous approaches, our method offers 90.9% classification accuracy, significantly requiring fewer manual advertising training samples. Background pixels were systematically filtered to improve model performance under various environmental conditions. This work advances in both biology and computational vision, demonstrating an effective and data-oriented approach to automated wildlife monitoring that supports science -based conservation measures.

Keywords: deep learning, camera trap, convolutional neural network, dataset, predator, kidepo national park, wildlife

The paper examines the case of IntraService incident management system implementation in an organization operating in the digital infrastructure segment. The study focuses on the assessment of changes that occurred in the functioning of the support service based on quantitative and qualitative indicators. The method of comparative analysis of operational parameters before and after the launch of the system is used, accompanied by expert interpretation of internal processes.

Keywords: implementation, system, incident, support, automation, platform, organization, infrastructure, process, integration

The article provides an overview of modern approaches to the study of digital twins and assesses the state of their implementation in transport logistics. The authors show features of the digitalization formation and identify barriers and prospects for the development of digital twins in the transport and logistics sector. The analysis and systematization of methods used to define the concept of a digital twin, the structure and typology of digital twins in logistics are carried out. Certain promising areas and links in product supply chains, in which digital twins are being implemented especially actively, are highlighted. The paper concludes that the implementation of digital technologies and digital twins in transport logistics can become an effective tool for its transformation in modern conditions if the development and implementation of digital twins is carried out within the framework of product supply chains based on cooperation between industrial companies and related companies, with the active support of the state.

Keywords: digital twins, transport and logistics systems, supply chains, intralogistics, digital chain

The article considers the assessment of the suitability of solar radiation data from ERA5 atmospheric reanalysis for forecasting problems in the northern territories. The experimental site of the Mukhrino station (Khanty-Mansiysk Autonomous Okrug), equipped with an autonomous power supply system, was chosen as the object of analysis. A statistical analysis of the annual array of global horizontal insolation data obtained using the PVGIS platform has been carried out. Seasonal and diurnal features of changes in insolation are considered, distribution profiles are constructed, and emissions are estimated using the interquartile range method. It is established that the data are characterized by high variability and the presence of a large number of zero values due to polar nights and weather conditions. The identified features must be taken into account when building short-term forecasting models. The conclusion is made about the acceptable quality of ERA5 data for use in forecasting energy generation and consumption in heating systems.

Keywords: ERA5, solar radiation, horizontal insolation, the Far North, statistical analysis, forecasting, emissions analysis, renewable energy sources, energy supply to remote areas, time series, intelligent generation management

Processing of results the unequal measurements presented by a binary code and the rests is considered. The technique of increase of accuracy of results of telemeasurements is resulted at data transmission by a series from measurement by the rests together with a binary code. The rests are duplicated in half-words in a word of data. Results of application of a technique are shown at single distortions of bats of data for a series from three measurements: measurement by the rests, then measurement in a binary code and one more measurement by the rests. At processing a series from three measurements which is received with step on a scale, equal from unit up to half of module of comparison, accuracy of measurements raises at a single mistake in a bat in a word with a binary code and a word with the rests in comparison with transfer by a binary code.

Keywords: telemeasurements, unequal the measurements, the rests of data, a dispersion of an error, accuracy of measurements

This paper examines the application of Bidirectional Long Short-Term Memory (Bi-LSTM) networks in neural source code generation. The research analyses how Bi-LSTMs process sequential data bidirectionally, capturing contextual information from both past and future tokens to generate syntactically correct and semantically coherent code. A comprehensive analysis of model architectures is presented, including embedding mechanisms, network configurations, and output layers. The study details data preparation processes, focusing on tokenization techniques that balance vocabulary size with domain-specific terminology handling. Training methodologies, optimization algorithms, and evaluation metrics are discussed with comparative results across multiple programming languages. Despite promising outcomes, challenges remain in functional correctness and complex code structure generation. Future research directions include attention mechanisms, innovative architectures, and advanced training procedures.

Keywords: code generation, deep learning, recurrent neural networks, transformers, tokenisation

The article focuses on developing data clustering algorithms using asymmetric similarity measures, which are relevant in tasks involving directed interactions. Two algorithms are proposed: stepwise cluster formation and a modified version with iterative center refinement. Experiments were conducted, including a comparison with the k-medoids method. The results showed that the fixed-center algorithm is efficient for small datasets, while the center-recalculation algorithm provides more accurate clustering. The choice of algorithm depends on the requirements for speed and quality.

Keywords: clustering, asymmetric similarity measures, clustering algorithms, iterative refinement, k-medoids, directed interactions, adaptive methods

The article addresses the challenges and proposes mathematical models for optimizing container freight transportation within complex logistics systems, emphasizing the growing importance of digital technologies and artificial intelligence in logistics by 2025. It highlights key industry issues such as decentralized global supply chains, environmental risks, infrastructure deficiencies, safety concerns, and notably, the costly problem of transporting empty containers, which accounts for a significant portion of operational expenses worldwide and in Russia. The core contribution is a modified three-dimensional transport optimization model that incorporates container types, cargo volumes, and transportation costs, including the cost variations due to partially filled or empty containers. The model extends classical transportation problem formulations by introducing a potentials method that accounts for the contributions of suppliers, recipients, and container costs to determine an optimal transport plan minimizing total costs. Constraints ensure that supply and demand conditions, container capacities, and route feasibility are respected. The model uniquely integrates the degree of container filling into cost calculations using a coefficient to adjust transportation costs accordingly. This approach enables more accurate and cost-effective freight planning. Additionally, the article discusses the development of a simulation model and a client-server application to automate the search for optimal transport plans, facilitating practical implementation. The proposed framework can be expanded to include various container types, cargo characteristics, and transport modes, offering a comprehensive tool for improving logistics efficiency in container freight transportation.

Keywords: diversification of management, production diversification, financial and economic purposes of a diversification, technological purposes of ensuring flexibility of production