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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology

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issn 2310-6018

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metadata of articles for the last 2 years

A system for analyzing images of nucleated bone marrow cells for the formation of a diagnostic conclusion in oncohematology

2025. T.13. № 1. id 1796

Polyakov E.V. Popov V.V. Dmitrieva V.V.

DOI: 10.26102/2310-6018/2025.48.1.016

The paper presents a system for analyzing images of nucleated bone marrow cells to form a diagnostic conclusion in oncohematology, aimed at solving the problem of constructing a data processing pipeline in automatic analyzers of biomedical images. The relevance of the study is due to the need to improve the reliability of theof automatic microscopic analysis of biomedical samples, which is aa difficult task due to high variability and morphological complexity of the investigated objects. One solution to this problem is to develop a web service that uploads, processes and describes images, then classifies them into categories of confirmed and unconfirmed cases. This web service provides cross-platform and accessibility, builds an open database of verified images and providestools for processing and analyzing images, as well as tools for correcting by the physician of the processing results. The system does not prescribe treatment and does not make diagnoses in dependently, but serves as an intelligent tool for processing, analyzing and transmission of research results in real time. The testing results showed high accuracy of the system: 91% for neural network methods and up to 97% for classical algorithms. The developed system allows for the analysis of data processing modules for computer microscopy systems.

Keywords: analysis of biomedical images, selection of objects, classification of nucleated cells, pattern recognition, oncohematology

Application of elitist ant system and Max-Min ant system algorithms for path optimization in quantum key distribution networks

2025. T.13. № 1. id 1792

Razdyakonov E.S. Korchagin S.A. Timoshenko A.V. Bulatov M.F.

DOI: 10.26102/2310-6018/2025.48.1.012

This study focuses on route optimization in quantum key distribution (QKD) networks, whose features are a number of physical constraints and strong topology dependence. This paper examines the application of two variations of the ant colony algorithm, the elitist ant system (EAS) and Max-Min ant system (MMAS) algorithms, to construct optimal routes in QKD networks. A metric for the communication efficiency of a route in QKD networks has been presented to evaluate the quality of a route according to given capacity and security requirements. The peculiarity of this metric is its non-additive capacity component, which depends on the minimum link efficiency in the route. A series of experiments were conducted on a randomly generated planar graph for long and short routes with EAS and MMAS algorithms, which resulted in MMAS being significantly more efficient for long routes, but in the case of short routes, EAS found the route faster without significant loss in solution quality. The results obtained in this study can be applied in solving problems of dynamic routing, as well as optimization of the topology of quantum key distribution networks.

Keywords: quantum key distribution, metaheuristics, ant algorithm, elitist ant system, max-Min ant system, pathfinding

Simplified physical models of the mediastinum of newborns for electrical impedance tomography

2025. T.13. № 1. id 1791

Konko M.A. Aleksanyan G.K. Gorbatenko N.I. Elkin N.S. Temnyakov N.S.

DOI: 10.26102/2310-6018/2025.48.1.011

The article presents the results of the development and experimental study of two simplified physical models of the neonatal mediastinum for electrical impedance tomography. The created models are based on spiral computed tomography data and take into account the anatomical features of the infant chest organs. The designs were implemented using 3D printing technologies, which made it possible to achieve high accuracy of geometric parameters. The models are equipped with a controlled air filling system for the lungs and three rows of electrodes, which makes it possible to conduct experiments on modeling global and regional ventilation. Experimental studies have demonstrated that the developed models make it possible to record respiratory volumes in the range from 2 to 120 ml, which corresponds to the physiological parameters of newborn breathing. The data obtained confirmed the operability of the models, their sensitivity to changes in air volumes, as well as their suitability for research and testing of new algorithms and methods in the field of electrical impedance tomography. It was found that the proposed models provide adequate reproduction of ventilation processes and can be used to develop diagnostic solutions in the field of neonatology. The results of the work are of practical value for scientific research aimed at improving methods for diagnosing respiratory disorders in newborns, and can be used in educational practice.

Keywords: simplified physical model of mediastinum, electrical impedance tomography, newborns, process of global and regional ventilation, lungs

Determination of the optimal geometric parameters of the metal seal of the tubing hanger

2025. T.13. № 1. id 1789

Timofeev E. Godenko A. Tarasova I.

DOI: 10.26102/2310-6018/2025.48.1.017

Operation of underwater complex hydrocarbon production systems is accompanied by increased risks of emergency situations, in particular, leaks and emissions due to loss of sealing between the underwater fountain fittings and the tubing hanger. Emissions and leaks during operation of underwater wells can lead to such irreversible consequences as loss of produced products and harm to the environment, as well as damage to expensive equipment, which requires expensive and technically complex repairs. For this reason, in the process of experimental design work on the design of this type of equipment, it is necessary to carry out high-quality and effective calculation support for the developed metal seals, allowing for determining their optimal geometry. The authors have developed a mathematical model for determining the stress-strain state of a metal seal of the tubing hanger, taking into account its rigidity characteristics. To determine the optimal geometry of a metal seal, the theory of qualities was used, based on which the overall quality of a metal seal was assessed by its strength and tightness. For the tightness and strength of a metal seal, particular quality functions were constructed, which are combined into an objective function using the Kolmagorov-Nagumo functional average. The results of optimizing the standard geometric parameters of a metal seal of a prototype of a tubing hanger according to the proposed method are presented. The information contained in this publication is useful to engineering and scientific professionals involved in the development and research of methods for ensuring the tightness of underwater connections using metallic seals.

Keywords: subsea production system, metal seal, stress-strain state, tubing hanger, underwater fountain fittings, contact pressure, quality theory, optimization

Biotechnical system of personalized rehabilitation of patients with limited motor functions

2025. T.13. № 1. id 1787

Filist S.A. Petrunina E.V. Pshenichny A.E. Ermakov D.A. Krupchatnikov R.A. Serebrovskiy V.V.

DOI: 10.26102/2310-6018/2025.48.1.002

The article considers a rehabilitation biotechnical system with an adaptable virtual reality intended for rehabilitation of patients with impaired motor functions of the lower limbs in rehabilitation complexes with combined feedback. The biotechnical system has the following functional modules: formation of controlled effects on the patient, control of controlled effects, rehabilitation management and information support. During rehabilitation, the patient's muscle fatigue and its dynamics are monitored. This made it possible to make adjustments to the rehabilitation block program during a rehabilitation session and manage the procedure for adapting virtual reality to the patient's functional state, as well as to carry out mathematical modeling of rehabilitation course scenarios. A model for planning a rehabilitation course using biofeedback intended for a biotechnical system with virtual reality is proposed. An experimental group was formed to assess the effectiveness of rehabilitation of post-stroke patients with paretic lower limbs. The rehabilitation results in this group showed that the choice of virtual reality content adapted to the patient allows increasing the effectiveness of rehabilitation according to the LEFS scale by 11%. Experimental studies of the effectiveness of muscle fatigue control during rehabilitation have been conducted. It is confirmed by testing the statolocomotor sphere according to the Tinetti scale, the indicators of which, on average, exceeded the indicators in the comparison group by 10%. Inclusion of adaptive virtual reality and muscle fatigue monitoring in the rehabilitation process leads to earlier restoration of impaired balance function, motor activity and social rehabilitation.

Keywords: persons with limited mobility, biotechnical system, impaired mobility, virtual reality, biofeedback, muscle fatigue

IIntelligent system for managing physiological load based on IoT devices and data processing methods

2025. T.13. № 1. id 1786

Mikhalev A.S. Podolyak A. Golovenkin S.E. Savitsky I.V. Antamoshkin O.A.

DOI: 10.26102/2310-6018/2025.48.1.029

With the growing popularity of wearable IoT devices for cardiovascular monitoring, their use faces the problem of measurement accuracy, especially during physical activity. This paper focuses on developing a methodology for detecting and eliminating anomalies in heart rate (HR) data collected from IoT devices to assess myocardial workload. As part of the work, an experiment was conducted in which HR data collected from wearable IoT devices (smart watches) were compared with the readings of certified medical equipment (Holter monitor). An algorithm for time series analysis is proposed, including the stages of data preprocessing, anomaly detection and correction them. Isolation forest algorithm was used to detect anomalies. The results of the study demonstrated that the proposed approach can reduce measurement error and achieve acceptable accuracy in the range of HR 90–120 beats per minute, which is critical for cardiac rehabilitation tasks. Based on the cleaned data, a model for classifying physical activity levels was developed, including recommendations for optimizing the patient's activity. The proposed methodology combines elements of system analysis, control and information processing, which makes it universal for application in intelligent health monitoring systems. The obtained results emphasize the prospects of IoT devices as a basis for building remote cardiac rehabilitation systems that can improve the quality of life of patients and reduce the burden on healthcare.

Keywords: ioT devices, heart rate, anomaly detection, intelligent data analysis, load management, cardiorehabilitation

Structural modeling for management in organizational systems with a heterogeneous structure of spatial elements

2024. T.12. № 4. id 1785

Linkina A.V.

DOI: 10.26102/2310-6018/2024.47.4.041

The article discusses the process of structural modeling in management in organizational systems with a heterogeneous structure of spatial elements. It is shown that the objects under study belong to territorially distributed organizational systems. Their peculiarity is the variety of spatial elements within a limited territory, which significantly influence the indicators of the effective functioning of the activity environment. Three classes of structural models are identified: object, system and process levels. The processes of structural modeling of the organizational system, its management, and management decision-making are examined in detail. It has been determined that the structural model of the organizational system reflects the interaction of the control center, objects with a heterogeneous structure of spatial elements, and a geographically distributed environment that unites objects into an organizational whole. The structural model of the management system is focused on the balanced interaction of the traditional management subsystem based on expert opinions and the decision support subsystem based on optimization of resource distribution, transformation and variation processes. The structural model of the process level is formed as an invariant sequence of algorithmic actions when making management decisions. It is substantiated that in order to meet the requirements of the control center it is necessary to combine algorithmic actions within the following stages: identification of classification signs of heterogeneity in the structure of spatial elements of objects, expert assessment of quantitative parameters for the formation of a multivariate choice model, generation of options for management decisions, expert selection on the generated set.

Keywords: organizational system, management, heterogeneity of the structure of spatial elements, structural modeling, expert optimization modeling

Optimization of corporate learning process management: a mathematical model and its application

2025. T.13. № 1. id 1782

Kharitonov I.A.

DOI: 10.26102/2310-6018/2025.48.1.006

This article considers the problem of optimization of the employee training process control at an enterprise, including the distribution of teachers, students, lessons and training rooms under multiple constraints. The relevance of the work is determined by the need for effective management of training processes in organizations, considering the qualifications of teachers, skills of employees, time constraints and the sequence of skill acquisition. To formalize the problem, a mathematical model was developed that allows for a linear description of the key aspects of training. The model includes multidimensional constraints such as training time, teacher employment, availability of training places and the sequence of skill acquisition. However, due to the combinatorial nature of the problem and discrete variables, its solution requires the use of specialized methods. To solve the problem, optimization approaches are used that include: formalization of the problem in a linear manner to identify subtasks that can be solved separately (for example, determining available classes for teachers); application of heuristic methods and dynamic programming for the final distribution of classes and resources. The proposed model demonstrates its effectiveness in personnel training management scenarios where both cost minimization and fulfillment of all specified constraints are important. Despite the limitations of the linear description, the model provides a simplification of the solution due to the structured approach to resource allocation. This makes it a universal tool applicable to the management of employee training in companies of various types. The materials of the article can be useful for the development of adaptive learning management systems, as well as for further research aimed at improving resource allocation algorithms.

Keywords: in-house training, mentoring, training optimization, resource planning, skills sequencing, human resources management, employee qualifications, training simulation

Mathematical models and software complex for intelligent analysis and forecasting the performance of government contracts

2025. T.13. № 1. id 1778

Rubtsov D.Y.

DOI: 10.26102/2310-6018/2025.48.1.010

The paper proposes mathematical models and a software package for intellectual analysis and forecasting of the execution of government contracts, based on a neural network and classical machine learning methods trained on a retrospective database of counterparties and contracts. A set of mathematical models and programs allows you to calculate the probabilities and risks of non-fulfillment of government contracts, thereby reducing budget losses and positively influencing the stability of the real sector of the economy. A comparative analysis of machine learning methods was carried out: logistic regression, decision tree, support vector machine and neural network model. A model has been developed that allows forecasting with an accuracy of 97.89%. For each mathematical model, a separate module has been developed, which together constitute a software package. The neural network model showed a result of 87.65%, which is associated with a relatively small set of data for training; however, this model allows us to reveal the further potential of the system in connection with continuous training in real time on new contracts, for the evaluation of which the proposed software package will be used. The results of the study can be used to further improve decision support systems in the field of procurement and its application in order to improve the overall quality of analysis and forecasting of the implementation of government contracts.

Keywords: mathematical modeling, software package, data analysis, government contracts, machine learning, intelligent system, forecasting

Dynamic model of coordinateometry of nearby signal sources using the super-resolution algorithm MUSIC

2025. T.13. № 1. id 1775

Glushankov E.I. Kondrshov Z.K. Syrovetnik D.S. Rylov E.A.

DOI: 10.26102/2310-6018/2025.48.1.004

In recent years, the problem of radar and radio direction finding has become very relevant due to the rapid development of microelectronics for small-sized unmanned aerial vehicles and communication terminals. The article is devoted to determining the coordinates of signal sources. In particular, closely spaced, uncorrelated emitters and receiving antenna arrays on several mobile devices spaced apart in space are investigated. To resolve such signal sources, the MUSIC super-resolution algorithm is used, followed by solving the coordinate measurement problem using the least squares method. A software model was created in the MATLAB environment, implementing a dynamic system in which each radio device has its own trajectories and speed indicators. A comparative analysis of the accuracy of the results obtained in various situations from the point of view of geometry and dynamics was carried out. It was found that the algorithm works most effectively in the case of targets located inside the zone formed by the scanning objects. In this case, the accuracy of determining coordinates is comparable to the distance between the signal sources. Based on the obtained results, it is possible to construct radar receivers for direction finding of nearby signal sources in the main lobe of the directional pattern of the receiving antenna of a location station, which is required when solving a number of location and monitoring problems in a complex electronic environment.

Keywords: direction finding, coordinateometry, super-resolution, MUSIC, MATLAB

A comprehensive analysis of the impact of G1 Garbage Collector parameters on JVM performance and stability

2024. T.12. № 4. id 1774

Zolotukhina D.Y.

DOI: 10.26102/2310-6018/2024.47.4.039

The relevance of thе study is determined by the necessity of improving memory management efficiency in high-load Java applications, where minimizing garbage collection pauses and maintaining high throughput are critically important tasks. This article aims to systematically investigate the parameter space of the G1 Garbage Collector (G1 GC) and develop practical recommendations for its optimization under high-load conditions. The primary research method is an empirical approach, which involves the development of a multithreaded Java application capable of generating sustained memory and CPU loads. The study utilized a control and six experimental G1 GC configurations, differing in parameters such as heap region size, heap occupancy threshold, maximum pause duration, young region size, the number of GC threads, and the activation of Periodic GC. Key metrics, including pause durations, GC frequency, throughput, and freed memory volume, were measured, visualized, and systematically analyzed using the GCViewer tool. The article presents recommendations for G1 GC optimization, highlights the advantages of reallocating memory toward young regions and enabling Periodic GC, and identifies the limitations of the MaxGCPauseMillis parameter in aggressive configurations. The findings have practical value for developers of high-load applications requiring low latency and high system stability. The conclusions contribute to a deeper understanding of G1 GC functionality and can serve as a foundation for further research in JVM memory management.

Keywords: g1 garbage collector, memory management, jvm optimization, high-load applications, gc parameter tuning, throughput, pause duration, garbage collection, young memory regions, java application performance

The role of machine learning algorithms in optimizing agricultural production: a review of international experience and adaptation to Ethiopian conditions

2025. T.13. № 1. id 1773

Mekecha B. Gorbatov A.V.

DOI: 10.26102/2310-6018/2025.48.1.005

The relevance of this study is driven by the need to enhance the efficiency of agricultural production in response to the growing demand for food security, particularly in economically underdeveloped countries such as Ethiopia. The main objective of the research is to explore the potential application of machine learning algorithms to optimize agricultural processes and adapt international practices to the specific conditions of Ethiopia. The methodological approach includes an analysis of contemporary scientific literature on the use of machine learning in agriculture and the systematization of successful practices involving algorithms such as CNN, LSTM, RNN, and Q-Learning. The study investigates the characteristics of Ethiopia's agricultural sector, including existing barriers to the adoption of advanced technologies. The results highlight that machine learning algorithms hold significant potential for increasing crop yields, improving soil and crop monitoring, and forecasting climate risks. Specifically, utilizing data from drones and sensors enables the creation of precise models for managing agricultural processes. However, key challenges such as insufficient funding, a lack of specialized data processing infrastructure, and limited access to technology have been identified. The study concludes by emphasizing the importance of attracting governmental and international investments, developing tailored databases, and creating models that account for local conditions. The findings provide practical value for developing strategies to digitize agriculture and prevent food crises in countries facing similar challenges.

Keywords: machine learning, artificial intelligence, agricultural production, precision agriculture technologies, ethiopian conditions

Method of intelligent decision support for forming tutor’s assistant group for checking free response works in online learning management

2024. T.12. № 4. id 1769

Latypova V.A.

DOI: 10.26102/2310-6018/2024.47.4.031

Despite the attractiveness of massive online courses for learners, only a small part of the latter reaches the finish line. This situation arises due to the effects of different adverse factors on the learning process. Additional staff and “smart” assistants (educational chatbots) are used for reducing the impact of these factors by helping tutors in online learning management. Tutor’s assistants are engaged to perform checking free response works and identifying course problems connected with its content, and educational chatbots to guide students through a course and organize interactions between them. On every launch of the online course, a tutor faces a choice of a group of the most suitable assistants. In the existing research, different assistants’ features such as: scores, motivation, way of communicating, etc. are taken into consideration in this selection. However, in the former, the ability of assistants to properly assess and comment on the performance of free response works are not taken into account. To close the gap, a method of intelligent decision support for forming tutor’s assistant group for checking such works is proposed in the paper. The method was tested on one of the laboratory works on the course “Modeling” and allowed to form a group of assistants capable of assessing the work correctly.

Keywords: intelligent decision support, tutor’s assistant, feedback mining, online learning management, free response work, online course

About a decentralized solution to the problem of simultaneous arrival of autonomous mobile objects to the final point using large data analysis

2024. T.12. № 4. id 1768

Chernoivanenko I.A. Kravets O.Y.

DOI: 10.26102/2310-6018/2024.43.4.036

The need to switch to more advanced control methods when using a conventional autonomous mobile facility (AMO) to control simultaneous arrivals arises due to excessive deviation. An innovative solution to this problem is the use of a decentralized management method to control the simultaneous arrival of the AMO to the final point, which is based on the analysis of big data. A solution was proposed to combine decentralized information through the use of filtering, on the basis of which the decentralized coordination of formations is managed. The article presents the main characteristics of AMO, shows the parameters of combining information about AMO, describes decentralized coordination management of formation and calculates the optimal path and convergence rate for decentralized management, and also takes into account restrictions on communication delay. An experimental study of errors in the x direction by the proposed method was carried out and compared with errors in the experiment without using this control method. Graphs comparing the convergence rate are also presented. The results of the experiment showed that the decentralized management method has a significant impact on the definition of the aim of AMO and the convergence of errors. Thanks to the proposed approach, it was possible to increase the efficiency of management and reduce errors, thereby proving the expediency of using this management method.

Keywords: large data analysis, autonomous mobile objects, decentralized management, information filtering, coordination management of formation

The role of user identification in predicting target actions on a website

2024. T.12. № 4. id 1767

Svyatov R.S.

DOI: 10.26102/2310-6018/2024.47.4.037

The relevance of study lies in the need to improve the accuracy of predicting users' target actions on websites, which is a key aspect of optimizing marketing strategies and personalizing user experiences. The complexity of the task is exacerbated by the lack of stable identifiers, leading to data fragmentation and reduced prediction accuracy. This paper aims to analyze the impact of user identification methods and develop approaches to segmentation, which will help to eliminate existing gaps in this area. The primary research method involves applying machine learning algorithms to evaluate the influence of different identifiers, such as client_id and user_id, on prediction accuracy. Segmentation of users was carried out based on the gradient boosting method, as well as an analysis of the effectiveness of retargeting campaigns in the Yandex.Direct system based on conversion rates, customer acquisition costs, and the share of advertising expenses using the example of a client specializing in the sale of e-books. The findings reveal that utilizing the user_id identifier improves purchase prediction accuracy by 8%, recall by 6%, and the F1-score by 7%. Segmenting users into targeted groups demonstrated a 67% reduction in customer acquisition cost, a decrease in advertising expense share to 5.87% compared to Yandex auto-strategies, and an increase in conversion rate to 34%. The article's materials are of significance for specialists in the field of e-commerce and marketing, providing a scientific basis for the implementation of personalized advertising campaigns. The proposed methods also offer potential for further enhancement of analytics and data integration in multichannel environments.

Keywords: machine learning, user behavior analysis, user identification, user segmentation, e-commerce, target action prediction

Systematization of CT reconstruction filters for artificial intelligence technologies: a retrospective study for chest and brain

2025. T.13. № 1. id 1766

Vasilev Y.A. Blokhin I.A. Gonchar A.P. Kodenko M.R. Reshetnikov R.V. Arzamasov K.M. Omelanskaya O.V.

DOI: 10.26102/2310-6018/2025.48.1.003

The selected convolution kernel in computed tomography (CT) directly affects the results of artificial intelligence (AI) algorithms. The formation of uniform requirements for this parameter is complicated by the fact that such filters are unique to equipment developers. The aim of the work is to create a table of correspondence of reconstruction filters between different equipment manufacturers to direct to the AI algorithms the series of images on which, in CT of the chest organs and the brain, the quantitative analysis will be most reproducible. DICOM tags 0018,1210 (Convolution Kernel), 0008,0070 (Manufacturer), 0018,0050 (Slice Thickness) of CT images from the Unified Radiology Information Service of Moscow were downloaded and analyzed. Inclusion criteria: age older than 18 years; slice thickness ≤ 3 mm. The data analysis is presented in the form of summary tables comparing reconstruction filters from different manufacturers for chest and brain CT, a number of clinical tasks, as well as descriptive statistics of their distribution by scanning area and manufacturer. 1905 chest ("CHEST" and "LUNG") and brain ("HEAD", "BRAIN") CT studies were included in the analysis. In chest CT, reconstructions to evaluate pulmonary parenchyma and mediastinal structures were common. Reconstructions for brain parenchyma and bone structures were common in brain CT. Systematization of reconstruction filters for chest and brain CT was performed. The obtained data will allow correct image series selection for quantitative AI analysis.

Keywords: reconstruction filters, computed tomography, artificial intelligence, chest organs, brain, data systematization

Analysis of the stability of water-saturated soils under cyclic influence: mathematical models and forecasts

2025. T.13. № 1. id 1765

Tishin N.R. Ozmidov O.R. Proletarsky A.V.

DOI: 10.26102/2310-6018/2025.48.1.001

The article discusses the mathematical modeling of soil liquefaction under the influence of dynamic loads, such as seismic, storm, or technogenic cyclic impacts. The liquefaction process, in which soil loses strength and bearing capacity, is critical for assessing the safety of construction objects, especially in areas with increased seismic activity or water-saturated soils. Several approaches were used for modeling, including the following functions: the exponential function from the work of H. Bilge et al. (2009), the logarithmic function from the work of V. Lentini et al. (2018), the power function (polynomial) proposed by C. Guoxing et al. (2018), an additional logarithmic function from the study of E. Meziane et al. (2021), and a hyperbolic function proposed by the authors of this article, which approximated the soil's resistance to cyclic impacts. The study analyzed laboratory test data for various soil types, combined into engineering-geological elements. Each function was analyzed in terms of approximation accuracy using the least squares method, which minimized the deviations between experimental and theoretical values. When evaluating the functions, consideration was given to how each behaves under a large number of loading cycles, which is important for predicting liquefaction under intense and prolonged loads. The selection of the optimal function was made by comparing the MSE and R2 metrics presented in the results tables. The application of the research results has practical significance in geotechnical design, especially for calculating foundations and underground structures in conditions of potentially liquefiable soils. Choosing the most suitable function for modeling soil liquefaction allows predicting soil stability under long-term and intense cyclic loads, minimizing the risk of deformation and destruction of structures.

Keywords: soil liquefaction, mathematical modeling, geotechnical engineering, dynamic loads, soil liquefaction function, liquefaction potential

Evaluation of the quality of intelligent text paraphrasing in Russian

2024. T.12. № 4. id 1763

Dagaev A.E. Popov D.I.

DOI: 10.26102/2310-6018/2024.47.4.038

The study focuses on the development of an integral metric for evaluating the quality of text paraphrasing models, addressing the pressing need for comprehensive and objective evaluation methods. Unlike previous research, which predominantly focuses on English-language datasets, this study emphasizes Russian-language datasets, which have remained underexplored until now. The inclusion of datasets such as Gazeta, XL-Sum, and WikiLingua (for Russian) as well as CNN Dailymail and XSum (for English) ensures the multilingual applicability of the proposed approach. The proposed metric combines lexical (ROUGE, BLEU), structural (ROUGE-L), and semantic (BERTScore, METEOR, BLEURT) evaluation criteria, with weights assigned based on the importance of each metric. The results highlight the superiority of ChatGPT-4 on Russian datasets and GigaChat on English datasets, whereas models such as Gemini and YouChat exhibit limited capabilities in achieving semantic accuracy regardless of the dataset language. The originality of this research lies in the integration of multiple metrics into a unified system, enabling more objective and comprehensive comparisons of language models. The study contributes to the field of natural language processing by providing a tool for assessing the quality of language models.

Keywords: natural language processing, text paraphrasing, gigaChat, yandexGPT 2, chatGPT-3.5, chatGPT-4, gemini, bing AI, youChat, mistral Large

Dynamic pricing for real estate

2025. T.13. № 1. id 1761

Razumovskiy L.G. Gerasimova M.A. Karenin N.E.

DOI: 10.26102/2310-6018/2025.48.1.008

The paper considers a mathematical model for dynamic price adjustment in real estate. The model is characterized by a finite number of real estate objects, a fixed sales horizon, and the presence of intermediate goals for sales and revenue. The model developed in this work addresses the case of variable total demand, incorporating the time value of money and the increase in real estate objects value as construction progresses. The general structure of pricing policy is studied, and an algorithm for determining prices under variable total demand is presented. Similar constructions are carried out for a model that accounts for the time value of money and the rising property value during construction. The case of a linear elasticity function is also examined as a basic but widely used practical scenario. Rigorous mathematical proofs of the results are provided, along with numerical simulations based on real estate data from a specific city over 3.5 years to compare different approaches to pricing policy formulation. The obtained results can be applied to effectively manage real estate pricing.

Keywords: dynamic pricing, real estate, price adjustment, variable total demand, cost of money, price increase, stages of construction

Multi criteria decision-making with the use of ELECTRE methods group

2024. T.12. № 4. id 1760

Latypova V.A.

DOI: 10.26102/2310-6018/2024.47.4.030

Solving semi-structured problems is an essential part of the organizational system management. To simplify addressing these problems, different methods of multi-criteria decision-making are used. Among the basic widespread methods can be distinguished ELECTRE family methods. A large number of scientific works are devoted to the latter, but nevertheless they do not give enough coverage to the following problem: when using different ELECTRE methods for solving the same task, you can get an unequal result. The reason is that these methods possess their own specific features along with the common basis. A method of multi criteria decision-making with the use of a group of ELECTRE methods: ELECTRE I, ELECTRE Iv, ELECTRE Is, ELECTRE II, ELECTRE III, ELECTRE IV; considering results of each method and applying integral scores of alternatives in defining an overall task solution, is suggested in the paper to eliminate the problem. The proposed method has been validated on a test case of multi-criteria selection of a candidate for a vacant position in the hiring process in human resource management. The former allowed to smooth out the discrepancies in the results each of the methods of the group and identify a comprehensive solution.

Keywords: decision-making, ELECTRE, multicriteria choice, integral score, expert assessment

Mathematical formalization of agent conflict in achieving local goals

2024. T.12. № 4. id 1757

Rossikhina L.V. Toropov B.A. Makarov V.F. Ovchinsky A.S.

DOI: 10.26102/2310-6018/2024.47.4.035

The article presents a mathematical formalization of the conflict interaction of active agents focused on achieving their local goals in the process of achieving the common goal of the organizational system. The conflict is considered as a clash of active agents over a single resource, the possession of which will allow achieving a local goal. Three types of relations of an active agent to a given resource (possession, non-distinction, opposition) are presented, taking into account their usefulness in achieving a local goal. Mathematically, the conflict between agents is determined by the establishment of links between the elements of the set of active agents with the elements of the set of resources that caused the conflict. An algorithm for evaluating the mutual impact of active agents due to a resource in the core of the conflict is proposed, based on the construction of a bipartite graph "active agent - resource" and a graph of conflict in the organizational system. The weights of the arcs of a bipartite graph are defined as the values of the utility functions of the resource that caused the conflict in achieving local goals by active agents. The implementation of the algorithm allows to obtain an assessment of the degree of collision of active agents due to a single resource and an assessment of the interaction of active agents in the core of the conflict. An example of the algorithm execution is given.

Keywords: agent, conflict, resource, conflict core, local target, graph, graph weight matrix, organizational system

Using neural networks to determine dust pollution near open-pit coal mining areas based on Earth remote sensing data

2025. T.13. № 1. id 1756

Ozaryan Y.A. Kozhevnikova T.V. Tsygulev K.S. Okladnikov V.Y.

DOI: 10.26102/2310-6018/2025.48.1.007

The article examines the use of neural networks for detecting dust pollution near open-pit coal mining areas based on remote sensing data. The study involved coal mining sites located in various regions of the Russian Federation. Satellite images from the Sentinel-2 mission served as the primary data source and were processed using Quantum GIS software. An algorithm for forming the training dataset was developed, utilizing the visible and near-infrared spectral bands from the satellite imagery. The mask creation technology in the developed algorithm is based on the Enhanced Coal Dust Index and its subsequent clustering. U-Net is used as a neural network model. The trained model was tested on a validation dataset. The recognition accuracy was 59.3% for the Intersection over Union metric, 78.9% for the Precision metric, 80.6% for the F1 metric, and 95.5% for the Accuracy metric. This level of accuracy is attributed to the limited volume of training data. The potential for improving accuracy through increasing the sample size in conjunction with optimizing the parameters of the neural network is discussed. The results obtained provide a basis for assessing the environmental impacts of coal mining activities and for developing measures to ensure ecological safety based on these findings.

Keywords: dust pollution, earth remote sensing, machine learning, clustering, neural network

Model of stochastic electrical load in the residential sector using the Weibull probability density function

2024. T.12. № 4. id 1755

Borovskiy A.V. Yumenchuk A.A.

DOI: 10.26102/2310-6018/2024.47.4.034

The article proposes a method for simulating daily schedules of electrical loads in the residential sector based on convolution theory. The authors consider models using the Weibull probability density and the probability density of the normal distribution for shifts in the time of switching on household appliances. The goal is to select a model, the results of which most accurately correspond to the actual energy consumption in the residential sector. First, the energy consumption of household appliances is considered, and the results are compared without a shift and with a shift in the Weibull probability density. The correct variant of comparing the results of simulation modeling using the Weibull probability density with the results of modeling using the probability density of the normal distribution is determined. Next, the energy consumption of households in rural areas is considered, which takes into account the use of electric heating devices by the population. This makes it possible to carry out simulation modeling of energy consumption of settlements or their individual areas. The results are compared with real data on the energy consumption of the village. Based on the results of the work, a model was selected that most accurately reflects the real dynamics of changes in energy consumption levels in the residential sector. The reasons why the choice was made in favor of one of the models are described. Sufficient accuracy of simulation modeling using the selected model has been demonstrated.

Keywords: stochastic energy consumption models, simulation modeling, daily energy consumption schedule, weibull probability density, normal distribution

Influence of the TensorFlow library’s version on the quality of code generation from an image

2024. T.12. № 4. id 1754

Nikitin I.V.

DOI: 10.26102/2310-6018/2024.47.4.040

This study compares the efficiency of training models that implement two different approaches: complicating the original neural network architecture, or maintaining the architecture while improving the tools used in the training framework. Attempts to complicate the architecture of the solution for generating source code based on an image lead to solutions that might be difficult to support in the future. At the same time, such improvements do not use more modern tools and libraries that such systems are built upon. The relevance of the study is due to the lack of attempts to use more modern and relevant libraries. In this regard, during the experiment to compare the indicators of models of three versions of image-based source code generation systems: the original pix2code system, its complex version, and the version with a modern version of the TensorFlow library - in the process of their training, it was revealed that approaches with a complex architecture and the current TensorFlow have the same indicators, higher quality than the original pix2code. Based on the experiment, we can conclude that updating the TensorFlow library can provide an additional increase in the quality of results that the image-based source code generation system can predict.

Keywords: code generation, image, machine learning, tensorFlow, keras, domain-specific language

Integration of the processes of digital transformation of enterprises and training of specialists in the field of automation of industrial and agricultural production

2024. T.12. № 4. id 1753

Lishilin M.V. Pryakhin V.N. Karapetyan M.A.

DOI: 10.26102/2310-6018/2024.47.4.032

Digital transformation in industry and agriculture is aimed at driving intensive economic growth and revolutionizing project management at the state level. It involves changing management strategies and operational models through the integration of information systems. Key challenges include increasing the digital maturity of agricultural enterprises, involving stakeholders in the digital transformation process, improving access to technological information, and expanding the use of technologies like the Industrial Internet of Things and digital twins. To achieve strategic goals, it is crucial not only to enhance the digital maturity of enterprises but also to prepare qualified personnel. The integration of university and enterprise information systems, the use of virtual computer labs in education, and data-driven management are essential elements of successful digital transformation. A critical factor is the systematic use of tools such as virtual computer labs as a technological foundation for integrating production data into the educational process. However, further development of methodological and regulatory frameworks for university and enterprise information systems is needed. This will improve the quality of specialist training and actively involve universities in the processes of industrial digital transformation.

Keywords: digital transformation, virtual computer lab, information technology, training of personnel, knowledge management

Development and research of a software system for biometric authentication of a user based on the dynamics of a handwritten signature using fuzzy features

2024. T.12. № 4. id 1750

Anisimova E.S. Anikin I.V.

DOI: 10.26102/2310-6018/2024.47.4.027

The complexity of reliable biometric user authentication based on the dynamics of handwritten signatures is due to their high intra-class variability associated with changes in the physical and emotional state of a person, as well as writing conditions. Existing approaches do not always provide sufficient accuracy and resistance to these variations. This work is devoted to the study and development of a software system for biometric authentication using the apparatus of fuzzy set theory to improve the reliability of recognition. In this work, we proposed an original feature model of a dynamic handwritten signature, including a set of static and dynamic features, including fuzzy ones, taking into account the uncertainty and variability of handwriting. As a signature standard, we used a set of membership functions built on the basis of the components of the feature model. We proposed an architecture of the recognition system consisting of training subsystems, creating a test signature model, and making a decision on authenticity. We developed a software system that implements the proposed approach using the SciLab mathematical package and the C++ programming language. The system provides the functionality of user registration and formation of a training sample based on signatures entered using a graphic tablet, as well as recognition of test signatures. We conducted an experimental study based on the MCYT Signature 100 signature collection. During the study, we experimentally determined the optimal value of the cluster compactness degree for constructing feature membership functions that minimizes the equal error rate coefficient. The experimental results demonstrate a decrease in the equal error rate coefficient compared to known methods, which indicates the effectiveness of the proposed approach. The use of fuzzy features helps to increase the system's resistance to variations in signatures and, as a result, increase the reliability of biometric authentication in various applications that require identity verification. The results of the study can be used to improve the security of authentication systems and protect confidential information.

Keywords: biometric authentication, handwritten signature, graphic tablet, signature input dynamics, fuzzy set theory, fuzzy logic, signature model, signature standard, pressure pattern, writing rhythm

Method of preparing data on scientific publications for intelligent decision-making support in evaluating expertise of peer reviewers

2024. T.12. № 4. id 1748

Latypova V.A.

DOI: 10.26102/2310-6018/2024.47.4.026

One of the main factors in assigning a peer reviewer is his expertise on the manuscript topic (the existence of the relevant publicatios). Decision-making support, based on the usage of mining scientometric base data on scientific publications, speeds up the process of evaluating the expertise of peer reviewers and makes it less time-consuming. However, the critical point in this case is the correctness of the data on scientific publications subject to intellectual analysis. At present, researchers actively deal with the question of defining the scientometric base data correctness and means of ensuring it, conducting different procedures of cleaning within data preparation. Yet in the existing works, the specifics of the task, for which data on scientific publications are gathered, is not taken into account. To address the problem, a method of preparing data on scientific publications for intelligent decision-making support in evaluating expertise of peer reviewers, considering features associated with the need to define the semantic similarity of text of data on publications, is suggested in the paper. The method was successfully tested when preparing data on scientific publications of members of the academic journal “Systems Engineering and Information Technologies” editorial board, involving the content of their profiles in scientometric bases “RISC” and “Google Scholar”.

Keywords: data preparation, decision-making support, data mining, peer reviewer, scientific publication

Analysis of mudflow characteristics with limited data using machine learning models

2024. T.12. № 4. id 1747

Лютикова Л.А.

DOI: 10.26102/2310-6018/2024.47.4.029

In paper, a combined method for analyzing incomplete and distorted information is proposed, demonstrated by the example of mudflow forecasting. The main purpose of the study is to demonstrate the ability not only to create accurate forecasts, but also to analyze the decision-making mechanisms of the model, identifying significant parameters that affect predictions. To represent the identified sets of parameters affecting the volume of the mudflow in the form of logical rules, it was necessary to use data categorization. This made it possible to increase the reliability of models in the presence of emissions and noise, as well as to take into account non-linearities. Two approaches were used to form logical rules: the method of associative analysis and the original method of constructing a logical classifier. As a result of associative analysis, rules were identified that reflect certain patterns in the data, which, as it turned out, required significant correction. The use of a logical classifier made it possible to clarify and correct the patterns, ensuring the determination of a set of factors influencing the volume of mudflow. This approach made it possible to identify the most significant input variables and understand how the model processes data to generate a forecast, identify factors that play a key role in forecasting results, and ensure adequate accuracy and stability of forecasts, taking into account the specifics and complexity of mudflow data. The patterns deduced as a result of the study, reflecting the hidden principles of the subject area under study, and the methods of logical analysis used in the study helped to identify possible causes of the formation of different volumes of carried-out solid deposits. The results obtained can be used to improve monitoring systems and prevent the negative consequences of mudslides.

Keywords: machine learning, neural networks, cluster analysis, associative rules, mudflows, model

Organization of radio communication with remote mobile ground objects

2024. T.12. № 4. id 1743

Dorokhin S.V. Ivannikov V.A. Zhaivoronok D.A.

DOI: 10.26102/2310-6018/2024.47.4.028

The article considers the issues of improving the quality of information transmission on mobile objects by using modern equipment of the digital radio system DMR (Digital Mobile Radio) technology, corresponding to modern requirements for noise immunity, communication range, security of data transmission and reception. The equipment has all the advantages of digital technologies compared to analog ones, uses one channel with a frequency band of 12.5 kHz, divided in time into two logical channels. This allows you to work through a repeater with support for dual-frequency simplex technology with duplex diversity, in this mode two simultaneous independent voice connections are possible [2]. The structural diagrams of the radio interface of the proposed standard, its main advantages, characteristics, and advantages over currently used digital and analog radio systems are described. Structural schemes for the organization of communication between several subscribers have been developed, providing the possibility of simultaneous operation of two groups of users through one or more repeaters on the same channel. In order to effectively use the available data exchange resource, modern methods of channel multiplexing and their combinations are proposed. Statistical and time multiplexing using discrete multi-tone modulation allows minimizing the effects of signal attenuation with increasing frequency. The proposed technical solutions provide the possibility of gradual replacement of obsolete equipment due to the simultaneous use of analog and digital equipment, as well as effective use of the frequency range in conditions of its limited distribution.

Keywords: information transmission, system, equipment, standards, communication channels, radio signal, interference

Implementing dynamic pricing across multiple pricing groups in real estate

2025. T.13. № 1. id 1741

Razumovskiy L.G. Gerasimova M.A. Karenin N.E. Safro M.V.

DOI: 10.26102/2310-6018/2025.48.1.009

The article presents a mathematical model of dynamic pricing for real estate that incorporates multiple pricing groups, thereby expanding the capabilities of existing models. The developed model solves the problem of maximizing aggregate cumulative revenue at the end of the sales period while meeting the revenue and sales goals. The basic formulation of the problem of optimizing revenue from the sale of all real estate objects in inventory by the end of the sales period is considered. Theoretical results describing the general form of the pricing policy for this problem are presented. A method is proposed for distributing aggregate cumulative revenue goals across different for real estate pricing groups. The model is further modified to account for the time value of money and the real estate value increase as construction progresses. The algorithm for constructing a pricing policy for multiple pricing groups is described, and numerical simulations are performed to demonstrate how the algorithm operates. The relevance of the developed model lies in the need to account for multiple pricing groups when forming the pricing policy, as well as the time value of money and the value of real estate increase as construction progresses. The obtained results can be applied to price management of real estate objects in practice.

Keywords: dynamic pricing, real estate, pricing groups, revenue maximisation, even inventory absorption, value of money, real estate value