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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

The model of optimal distribution of renewable resources in the management of the criminal threat prevention project and the methodology of its program realization

2024. T.12. № 1. id 1500

Zhirnov A.A. Ovchinskiy A.S. Makarov V.F. Gurlev I.V.

DOI: 10.26102/2310-6018/2024.44.1.010

The paper considers the applicability of a project approach to managing the activities of criminal investigation units. The problem of developing project management models for countering criminal threats is underscored. The disadvantages that do not make it possible to use the existing models of resource allocation in the subject area under study without significant adaptation are indicated. The criminal threat prevention project has been defined. The general parameters of such a project are given, which helps to determine the problem of renewable resource allocation. The approach to calculating the minimum amount of renewable resources required for the implementation of the k-th operation of the project. The problem of allocating renewable resources of the criminal threat prevention project, the purpose of which is to solve a crime, is defined. To solve this problem, an appropriate model of integer programming is proposed. A numerical example of the problem solution and a method of software implementation of the proposed model in a tabular processor are given. The features that the authors believe should be taken into account when applying the proposed model and methodology in practice and possible areas of future research are considered: the study of the project duration dependence type on the number of performers and other factors; the development of a knowledge base on the parameters of the conducted operations.

Keywords: criminal threat prevention project, allocation of renewable resources, project approach, critical path method, CPM, PERT

Forecasting and evaluation of energy generation at solar power plants: the state of the problem and development trends

2024. T.12. № 1. id 1499

Azhmukhamedov I.M. Loba I.S. Machueva D.A.

DOI: 10.26102/2310-6018/2024.44.1.008

The paper considers the relevant issues related to the problem of calculations and forecasting in the production of solar electricity as a renewable energy source. To detect problems, the initial data for modeling and their sources have been identified. Renewable energy sources are systematized and an example is given for each. An analysis of the state of the global energy market and the state of government policy in the field of energy in Russia has underscored the need to address solar energy issues and solve the problems of forecasting electricity generation. This is important not only due to the availability of resources, but also to environmental friendliness. The classification of existing models and methods for forecasting SES energy generation is examined. Existing methods allow calculations to predict the power generation capacity, but they give average figures for the year. New technological and innovative methods are required to solve the existing problem. The key factors and aspects of the introduction and operation of a solar power plant are presented. The main difficulty in forecasting is taking into account a variety of nonlinear characteristics. An attempt to solve this problem is proposed. An overview of the state of the problem and trends in the development of solar energy is made, among which the main problems are identified and solutions are outlined.

Keywords: solar energy, renewable energy sources, aspects and operation of a solar power plant implementation, forecasting solar energy generation, forecasting methods

Optimal control of an organizational and technical system taking into account the intensity of control actions application

2024. T.12. № 1. id 1497

Akhmedyanova G.F.

DOI: 10.26102/2310-6018/2024.44.1.019

Predictive management with all its errors and difficulties is still an effective means of providing an organizational and technical system with time to increase its readiness for changes in the situation. To formulate and solve the problem of optimal control of this process, the Fokker-Planck-Kolmogorov equation was used, which is the first approximation in the probabilistic description of random processes. To formulate the optimal control problem, the Letov criterion was modified, a coordinate-parametric approach was applied, and the obvious fact of an increase in management costs with a decrease in the time to improve the readiness of the organizational and technical system was taken into account in the form of the square of change rate in the probability density. The Euler-Ostrogradsky-Poisson equations are applied to the final Lagrangian. The resulting nonlinear equations were solved using the small parameter method. The study of the resulting solution proves that even with optimal control, the magnitude of control actions increases in proportion to the target value and duration of control (increasing the planning horizon), the increase occurs according to the cube of the exponential, that is, very slowly at the beginning of control and very sharply at the end, and a similar pattern of increase demonstrates the dependence of the control influences from the demand for management results, but it is expressed through hyperbolic functions.

Keywords: optimal control, fokker-Planck-Kolmogorov equation, probabilistic quality criteria, intensity of application of control actions, small parameter method

Integrated data storage system for geological laboratory experiments

2024. T.12. № 1. id 1495

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

DOI: 10.26102/2310-6018/2024.44.1.007

The article examines the development of a new approach to storing and organizing the results of laboratory experiments with consideration to the features of their subsequent processing. To solve this problem, laboratory experiments are considered as structured data with unstructured parts. During the development of the system, the features of storing and processing laboratory test data were analyzed, after which the basic requirements for the system were formulated. The main data models were defined as well as the database entities. A standard relational data model has been chosen for storing structured data, and the storage of unstructured information such as experiment results or experiment parameters is implemented through the BJSON field. To solve the problem of providing secure access and creating an API for the system, the asynchronous FastAPI framework was chosen. The implementation of storing additional experiment files, which are located in the object storage and are associated with the experiment in the relational model through an additional entity, is also considered. The presented approach is notable for its flexibility to the structure of stored laboratory experiments, takes into account the features of geological laboratory experiments and also provides opportunities for complex meta-analysis of large volume of data. The system was tested and implemented into the technological process of the geotechnical laboratory at JSC MOSTDORGEOTREST.

Keywords: storage of geological laboratory experiment data, unstructured data, experiment results storage system, geoinformation system, database, geological environment, information resource, engineering geology

Acoustic emission diagnostics of hidden defects of multilayer printed circuit boards in electronic devices

2024. T.12. № 1. id 1493

Chernoverskaya V.V. Nguyen H.D. Lu N.T. The H.V. Uvaysov S.U.

DOI: 10.26102/2310-6018/2024.44.1.004

The article presents the results of the acoustic emission method application (AE) and machine learning algorithms in the problem of diagnosing defects in the stratification of a multilayer printed circuit board structure (MPB). A combination of physical and computational experiments is used to solve the problem. To conduct full-scale tests, the study uses a vibration stand to generate a load on the test object and receive acoustic emission signals. The computational experiment is carried out using mathematical modeling in a specialized ABAQUS environment. In order to obtain the best solution to the problem, an optimization problem is solved during the experiment to determine the frequency of the harmonic signal generated by the vibration stand with a view to receiving the maximum response of the MPB under review and unambiguous identification of the bundle defect. When conducting the numerical experiments, the effects and reactions (AE signals) of MPB were modeled at different frequencies of input vibration signals ranging from 100 to 2000 Hz. Full-scale experiments were conducted in the laboratory of control and testing of radioelectronic devices at the Department of KPRES of RTU MIREA. The results of the study have shown that the vibration frequency most effective for detecting a delamination defect equals 1500 Hz (a defect of almost rectangular shape with a size of 30×37 mm). Subsequently, this was confirmed by correlation analysis, which made it possible to identify the maximum differences between the acoustic emission signals of a suitable MPB sample and a sample with a delamination defect for the input vibration of a given frequency. The second part of the study deals with processing of the physical and computational experiment results, establishing the degree of adequacy of the obtained mathematical models to real samples of MPB and the processes occurring in them, as well as the use of machine learning algorithms for more reliable diagnosis of MPB defects. In the presented study, the random forest and the support vector machine learning (SVM) methods were employed as machine learning algorithms. Based on the results of their execution, the accuracy of the two algorithms was evaluated.

Keywords: acoustic emission, multilayer printed circuit board, hidden defects, structure stratification, modeling, physical experiment, machine learning algorithm, support vector machine method, random forest method, non-destructive testing

Functional content and structure of a software package for modeling the temperature of a hot-rolled strip with interval parameters

2023. T.11. № 4. id 1490

Dabas M.R. Saraev P.V.

DOI: 10.26102/2310-6018/2023.43.4.032

The article considers the structure and functional content of a software package for modeling the temperature regime in the strip and working rolls during hot rolling of steel. Software with real input parameters and interval input parameters has been developed for modeling of temperature. This article discusses software with interval input parameters, its content and design. The article presents the structure of software for modeling temperature in interval values, thermal conductivity equations for solving problems of modeling temperatures in the deformation zone, on the interstand gap and intermediate roller with interval input parameters. The scheme of the temperature modeling algorithm is considered, taking into account the configurations of cooling systems at a hot rolling mill, and an algorithm for finding friction coefficients taking into account interval input parameters is separately presented. The main functions of the implemented library of classical interval arithmetic are described with an indication of the mathematical description. The description of the interface for software modeling the temperature of the strip with interval input parameters and some functions is given. Groups of software input parameters are presented with a description of possible values and their units of measurement.

Keywords: equation of thermal conductivity, software structure, interval arithmetic, hot rolling, energy-power calculations

Feature selection methods for authorship attribution in cybersecurity context

2024. T.12. № 1. id 1489

Romanov A.S.

DOI: 10.26102/2310-6018/2024.44.1.001

This paper considers methods for authorship attribution of natural-language and artificially generated texts, which are important in the context of cybersecurity and intellectual property protection to prevent misinformation and fraud. The use of authorship methods is justified by the findings on the fastText and support vector method (SVM) effectiveness discussed in past studies. The feature selection algorithm is chosen based on the comparison of five different methods: genetic algorithm, forward and backward sequential methods, regularization selection and Shapley's method. The considered selection algorithms include heuristic methods, game theory elements and iterative algorithms. The regularisation-based algorithm is found to be the most efficient method, while methods based on complete brute-force selection are found to be inefficient for any set of authors. The regularization-based and SVM-based selection accuracy averaged 77 %, outperforming the other methods by between 3 and 10 % for an identical number of features. For the same tasks, the average accuracy of fastText is 84 %. A study was conducted to examine the robustness of the developed approach to generative samples. SVM proved to be more robust to model confounding. The maximum loss of accuracy for fastText was 16 % and for SVM was 12 %.

Keywords: feature selection, authorship attribution, machine learning, neural networks, text analysis, information security

Decision support method in reviewer multicriteria choice using integrated assessment and natural language processing methods in a scientific journal

2023. T.11. № 4. id 1487

Latypova V.A.

DOI: 10.26102/2310-6018/2023.43.4.035

A reviewer multicriteria choice task appears when defining reviewers for manuscripts submitted to the scientific journal. This is related to the fact that it is necessary not only choose reviewers, whose publications are most similar to manuscripts on the specifics of the research, but also take into account and other, not less significant, reviewers’ features. In the existing works, it is suggested to use different criteria, mainly involving reviewers’ expertise and authority. However, such criteria as quality of work in a reviewer role has not gained proper attention. The experience of reviewing, the quality of manuscript assessing and reviewer’s activity can significantly affect the result of the reviewing and its time. In the paper, it is suggested a decision support method in reviewer multicriteria choice using integrated assessment, taking into account the quality of work in a reviewer role, and natural language processing methods in scientific journal, which will allow to solve the described issue. Testing of the method on data on reviewers of scientific journal “Information technologies” showed its validity. Taking into account such criteria as the quality of work in a reviewer role in addition to the generally accepted features has a substantial impact on the reviewer choice for manuscripts.

Keywords: decision support, multicriteria choice, scientific journal, reviewer, integrated assessment, natural language processing

Using artificial neural networks to perform segmentation of hip radiographs in the treatment of osteoarthritis

2024. T.12. № 1. id 1486

Akutin A.S. Goriakin M.V. Zubavlenko R.A. Pechenkin V.V. Solopekin D.A.

DOI: 10.26102/2310-6018/2024.44.1.011

Today, the X-ray analysis procedure makes it possible to detect osteoarthritis (OA) in the early stages of the disorder. The presence or absence of the disorder is detected only when it has already manifested, and X-ray diagnostics have been carried out. The use of automated procedures for analyzing X-ray images and the availability of archives of such information with a long history can improve the results of predicting complications in patients. The article describes the experience of developing an application for computer analysis of radiographs, which, based on deep learning methods, allows us to identify the risks of developing osteoarthritis of the hip joint. The archive of a specialized medical institute is used as a training sample. In order to increase the size of the training set of radiographs, a data augmentation method is used, which increases the variability of the original data and, in some cases, increases the recognition efficiency. The research uses a convolutional network (U-net) designed for image segmentation, which is trained on X-ray images of a specific medical institution. As part of a project to segment and analyze the geometric characteristics of X-ray images of the hip joints, the software to automate the recognition of the joint space size was developed, which helps to clarify the patient’s diagnosis and prognosis for the development of the pathology.

Keywords: convolutional neural network, image segmentation, machine learning, osteoarthritis, hip joint

Placing on-board equipment in the fuselage space of an unmanned aerial vehicle using a genetic algorithm

2024. T.12. № 1. id 1484

Gainutdinov R.R. Chermoshentsev S.F.

DOI: 10.26102/2310-6018/2024.44.1.021

The current stage of unmanned aircraft system development is characterized by the widespread introduction of automated and intelligent electronic systems. One of the most difficult and critical stages in the development of unmanned aerial vehicles is determining the optimal locations for placing on-board equipment in the fuselage space. To solve this problem, the approach for determining the optimal installation locations for on-board equipment in the fuselage space of an unmanned aerial vehicle is proposed. The approach is based on the use of a genetic algorithm. A meaningful and mathematical formulation of the problem of determining the optimal installation locations for on-board equipment in the fuselage space of an unmanned aerial vehicle is given. Criteria and restrictions have been developed. As optimization criteria, first of all, electromagnetic compatibility criteria are considered, which are characterized by minimizing the sensitivity of on-board equipment above the level of electromagnetic field strength at the installation sites of on-board equipment, as well as limiting the excess of the threshold level of susceptibility of on-board equipment over the electromagnetic environment resulting from electromagnetic influences or interactions. Additionally, criteria for minimizing the total weighted length of cable connections are considered, and the maximum load-carrying capacity of the fuselage compartments of an unmanned aerial vehicle is limited. The plan has been developed for the installation of on-board equipment in the fuselage space using a developed program that implements a genetic algorithm.

Keywords: placement, optimization, on-board equipment, genetic algorithm, unmanned aerial vehicle

An approximate evaluation of the conditions for the termination of a computer virus epidemic in connected networks associated with random graphs

2023. T.11. № 4. id 1483

Nikiforova A.Y.

DOI: 10.26102/2310-6018/2023.43.4.034

Mathematical modeling of computer virus epidemics is the most important area of theoretical research in the field of information security. This paper examines a Markov model of the computer virus spread based on the Reed–Frost model. The main aim of the article is to analyze the applicability of the modified Reed-Frost model to the class of networks associated with random Erdos-Renyi graphs. In particular, the effect of the ratio of the probability of cure to the probability of infection on stopping the spread of a computer virus was tested. The results of this model are compared with ones obtained via the simulation modeling for different values of epidemic parameters and network characteristics. In the calculations and experiments carried out, the following parameters changed: the probability of infection, the probability of cure, as well as the connectivity of the network. The Wolfram Mathematica symbolic computing system was used for calculations. A C++ program written earlier by the author and their supervisor was used to conduct the computational experiment. The studies show that, under certain parameters, the condition for ending the epidemic is confirmed by both theoretical calculations and experimental results. However, the epidemic vanishes before the threshold value calculated is reached. In the future, the author plans to give a more accurate theoretical assessment of the conditions for ending the epidemic.

Keywords: computer virus, probability of infection, probability of cure, random graph, reed-Frost model, susceptible node

Quick search for anomalies in number series using the modified Hampel method

2023. T.11. № 4. id 1482

Gilmullin M.F. Gilmullin T.M.

DOI: 10.26102/2310-6018/2023.43.4.030

The article discusses and formally introduces the concepts of a number series anomaly and an anomaly filter function. The relevance of the research is due to the absence of a unified approach to understanding the concept of anomaly. At the same time, they play a key role in solving many practical problems. The study uses a method for measuring the stability of the selected method of statistical assessment for outliers using breakdown points and sliding windows. The method of filtering a number series for outliers is based on a combination of the median and the median absolute deviation. In relation to solving a wide range of issues in IT automation а modification of the Hampel method is proposed for determining outliers in a sample. Functions for filtering a number series for anomalies and determining the index of the first anomalous element are developed in Python. As an example, a script was developed using the Jupyter Notebook platform to solve the problem of quick search for anomalies in stock prices by means of the modified Hampel method. To obtain a sample with outliers, the author's library is used to generate test stock data. The experimental results confirm that the proposed algorithms can clearly filter anomalies for different values of adjustable parameters. The advantages and disadvantages of this method are noted. The Hampel filter is easy to optimize and parallelize. The article has practical application for solving the problem of automation and identifying anomalies in number series.

Keywords: number series, anomalies, outliers, filtering, hampel

Mathematical model for estimating the probability of pedestrians crossing a street at some random location

2023. T.11. № 4. id 1481

Arutiunian M.A.

DOI: 10.26102/2310-6018/2023.43.4.036

This article presents one of the scientific results obtained by the author during the dissertation study. The problem of forming a convenient and safe pedestrian infrastructure, which is one of the urgent issues of the development of a modern city, is revealed. An analysis of Russian and foreign experience in organizing pedestrian infrastructure was carried out. It was revealed that in the Russian experience of organizing effective and safe pedestrian infrastructure, problems are often observed that occur in neighborhoods with any types of development, including such as the absence or irrational location of technical means of organizing traffic. A mathematical model has been developed that makes it possible to assess the probability of a pedestrian crossing a street in a particular place along the entire length. The parameters of the proposed model are defined. It is also proposed to apply the results of studies of human behavior in situations in which similar or the same psychoemotional motivators work, prompting the intersection of the street in a particular place, as well as to use dependencies that obviously correlate with the statistical dependencies necessary for conducting this study. The obtained results are proposed to be used in subsequent works in the development of a simulated model of traffic management, which allows assessing the congestion of the road network and subsequent optimization of transport and pedestrian flows in order to ensure the required road safety.

Keywords: model, parameters, traffic and pedestrian flows, probability, road network, road accident, road safety

Development of a concept and tools for modeling web application testing processes using fuzzing using dynamic Bayesian networks

2023. T.11. № 4. id 1479

Azarnova T.V. Polukhin P.V.

DOI: 10.26102/2310-6018/2023.43.4.031

Ensuring the sustainability of web applications with respect to various security threats plays a crucial role in the development of modern information support technologies for industrial enterprises, financial structures and service organizations. This explains the high relevance of the development of new scientifically sound effective computational methods, algorithms and problem-oriented programs for testing web applications with a complex functional structure of internal and external interaction, which implement the capabilities of streaming data generated from the results of each of the test steps, and the application of the results in the process of managing the testing of web applications. The article describes the concept of modeling testing processes, research of the obtained models and development of analysis and prediction algorithms, based on a formalized apparatus of dynamic Bayesian networks. The Bayesian models proposed in the paper, built on the basis of statistical training, help to determine time relationships for each of the parameters determined during the test procedure, provide the opportunity to predict test results by performing simulations using probabilistic inference methods.

Keywords: web application vulnerabilities, bayesian network, probabilistic inference problems, testing process, monte Carlo method using Markov circuits, particle filtering algorithm

A method for constructing a logical model for interpreting the decisions of a trained neural network

2023. T.11. № 4. id 1477

Lyutikova L.A.

DOI: 10.26102/2310-6018/2023.43.4.037

In this paper, we propose a method for interpreting neural network solutions based on the use of Boolean integro-differential calculus. This method allows you to investigate the logic of decision-making by neural networks and determine the most important signs that affect their decisions. The method can be applied to classification problems, especially in cases where each feature can be represented as a k-valued variable. The paper considers local and global interpretations of solutions. At the first stage, each input vector is associated with the corresponding output of the neural network. Then, by solving a Boolean equation, logical functions are found that adequately reflect the input data and their corresponding outputs. At the second stage, global interpretation, functions are constructed that combine previously found logical functions. This choice of functions is based on their ability to most accurately reflect the decisions of the neural network and the study area. At the second stage, global interpretation, functions are constructed that combine previously found logical functions. This choice of functions is based on their ability to most accurately reflect the decisions of the neural network and the study area. The resulting function has interpretability, modifiability and the ability to represent a complete set of solutions corresponding to a given query. It also highlights the most significant features for each solution. The paper considers the practical implementation of the method on the example of a neural network trained on the basis of the structure and input data consisting of answers to questionnaire questions, with an output node predicting the diagnosis. In parallel with the development of the neural network, an interpretive model is being built, which allows identifying the most important signs for each diagnosis based on the decisions of the neural network. In addition, in cases with boundary solutions, when the neural network provides only one possible solution, the interpretative model is able to find all possible solutions with a predetermined accuracy, which helps to avoid mistakes in decision-making.

Keywords: neural networks, interpreter, connections, boolean differentiation, input data, analysis, hidden patterns

Intelligent decision support system for assessing information security risks of ICS

2023. T.11. № 4. id 1476

Kirillova A.D. Vulfin A.M. Vasilyev V.I. Guzairov M.B.

DOI: 10.26102/2310-6018/2023.43.4.029

The relevance of the article is due to the need to ensure information security of industrial control systems (ICS). Loss of control over industrial facilities can lead to undesirable consequences in a particular subject of the state or affect the economic indicators of the country as a whole as well as compromise the safety of the population. In this regard, this article aims to improve the procedure for quantitative assessment of information security risks as a necessary component of an integrated approach to ensuring information security, which helps to assess the feasibility of information security violation scenarios and identify their possible consequences for building an effective protection system. The architecture of a research prototype of an intelligent decision support system and a software implementation of tools for automating the modeling of attack scenarios and assessing the information security risks of ICS have been developed, the use of which makes it possible to increase the reliability and efficiency of information security risk assessment and, consequently, the choice of effective countermeasures at all stages of an industrial facility life cycle and its complex protection systems. The materials of the article are of practical value for information security specialists at all stages of the life cycle of distributed information and control systems of industrial facilities.

Keywords: information security risk assessment, intelligent decision support system, cognitive modeling, scenario modeling, graph models

Systematization and management of access to data in a multifunctional digitalized system

2023. T.11. № 4. id 1474

Gusev P.Y.

DOI: 10.26102/2310-6018/2023.43.4.025

The article is devoted to the formation of principles for constructing a data access system for a multifunctional digitalized system (MDS). The features of data generation and storage in the MDS are described. The importance of the unified information space of the MDS is shown and the peculiarity of the formation of a unified information space is described. Taking into account the peculiarities of the functioning of the MDS, the main tasks have been identified, the solution of which forms the model of access to data in the system. The dependence of the relevance of data on the assignment of those responsible for the data is indicated. The task has been set to ensure information security of a single information space. It is shown that the task of securing responsibility for data in the MDS is related to the task of disaggregating resources by type of activity attracted through functional areas. The dependence of MDS performance indicators on types of activities, functional areas and types of resources is shown. The paper examines mandatory, discretionary and role-based models of access to data. The shortcomings of access models in relation to the features of the MDS are identified. An architecture for regulating access to data using software modules and services is proposed. It is suggested to create a monitoring environment based on the construction of data sets determined through the performance indicators of the MDS.

Keywords: multifunctional digital environment, management, data, access rights, common information space

State of affairs and long-term trends in the field of neonatal incubator research and development

2023. T.11. № 4. id 1473

Frolov S.V. Korobov A.A. Savinova K.S. Potlov A.Y.

DOI: 10.26102/2310-6018/2023.43.4.016

The history of neonatal incubator development and the evolution of its design were described. A generalized structural and functional diagram of a modern neonatal incubator was presented. Airflow patterns have been studied in detail, including illustration of typical airflow paths in double-walled incubators. A classification of neonatal incubators was given. Information about manufacturers of modern incubators was presented in a table that includes 51 manufacturers from 17 countries with the addresses of web sites that contain specifications of medical products they manufacture. Publications that discuss modeling heat and mass transfer processes in incubators for newborns were analyzed. It was concluded that modern computational aerodynamics packages are usually used for numerical modeling with consideration to the infant’s thermoregulation, their 3D-model, air circulation, convective, radiant and conductive heat transfer. Numerical modeling research is usually combined with physical modeling. The movement of air flows is analyzed using visible and infrared video cameras. The use of anatomically correct neonatal phantoms created by means of additive manufacturing was demonstrated. The thermoregulation process is simulated with the help of electric heaters, temperature sensors and control systems based on microcontrollers. The methods for monitoring the physiological parameters of an infant placed inside a neonatal incubator were reviewed. The advantages of non-contact monitoring methods using video cameras and thermometry has been illustrated. Modern neonatal incubator control systems were examined. The proportional integral derivative controllers are the basis of almost all control algorithms in neonatal incubation systems. The studies on the application of fuzzy logic control and various types of adaptive control in neonatal incubators were presented. It has been concluded that the structural and functional diagram of a neonatal incubator needs to be improved with a view to protecting from noise, electromagnetic radiation, infections, and harmful airborne contaminants. Potential approaches to improving the efficiency of maintaining neonatal-appropriate environmental conditions in neonatal incubators have been demonstrated.

Keywords: neonatal incubator, neonatal tissue-like phantom, numerical model, heat and mass transfer, system for monitoring physiological parameters, microclimate control, environmental neonatology

Review of neural network models for solving the problems of predicting emergency situations and ensuring the safe operation of oil and gas wells

2024. T.12. № 1. id 1472

Sulavko A.E. Vasilyev V.I. Klinovenko S.A. Lozhnikov P.S. Suvyrin G.A. Guzairov M.B.

DOI: 10.26102/2310-6018/2024.44.1.017

An analytical study was carried out on the problem of preventing emergency situations and predictive diagnostics of equipment during hydrocarbon production in oil and gas fields as well as the ways to solve this problem by means of artificial intelligence based on deep neural networks. One of the key factors hindering the development of predictive equipment diagnostic systems is the lack of data describing pre-emergency situations, which is necessary for high-quality training of neural network models. An analysis of recent publications and research on the subject of telemetry data analysis and emergency recognition is provided. Neural network models are considered that can be used to predict the failure of pumping and compressor equipment and other units. Cases of the use of neural network models specially trained to solve this problem, as well as neural network models used in other tasks but analyzing similar data structures, were studied. The issue of transfer learning is raised to adapt neural network models originally developed and trained for other areas to use in the area under consideration in order to reduce the sample size when training industrial artificial intelligence. A comparison of the achieved results was carried out, and the advantages and disadvantages of existing technical solutions were identified.

Keywords: artificial neural networks, predictive diagnostics, machine learning, time series, telemetry, maintenance, data sets

Dysarthria speech recognition by phonemes using hidden Markov models

2024. T.12. № 1. id 1471

Bredikhin B.A. Antor M. Khlebnikov N.A. Melnikov A.V. Bachurin M.V.

DOI: 10.26102/2310-6018/2024.44.1.002

The relevance of the paper is due to the difficulties of oral interaction between people with speech disorders and normotypic interlocutors as well as the low quality of abnormal speech recognition by standard speech recognition systems and the inability to create a system capable of processing any speech disorders. In this regard, this article is aimed at developing a method for automatic recognition of dysarthric speech using a pre-trained neural network for recognizing phonemes and hidden Markov models for converting phonemes into text and subsequent correction of recognition results using a search in the space of acceptable words of the nearest Levenshtein word and a dynamic algorithm for splitting the output of the model into separate words. The main advantage of using hidden Markov models in comparison with neural networks is the small size of the training data set collected individually for each user, as well as the ease of training the model further in case of progressive speech disorders. The data set for model training is described, and recommendations for collecting and marking data for model training are given. The effectiveness of the proposed method is tested on an individual data set recorded by a person with dysarthria; the recognition quality is compared with neural network models trained on the data set used. The materials of the article are of practical value for creating an augmented communication system for people with speech disorders.

Keywords: hidden Markov models, dysarthria, automatic speech recognition, phonemes recognition, phoneme correction

A mathematical model of the decision-making process by a command group in the case of conflict interaction of the requirements for the efficiency and validity of the decision

2023. T.11. № 4. id 1470

Malyshev V.A. Mitrofanov D.V. Sidelev M.N.

DOI: 10.26102/2310-6018/2023.43.4.019

The article presents a mathematical model of the decision-making process by a command group in the context of conflict interaction of the requirements for the efficiency and validity of the decision. The proposed mathematical model reflects the physical and practical features of the decision-making process by the command group and takes into account the main factors influencing it. To model the decision-making process, the following are defined: stages of decision-making; the indicator characterizing the qualification of the officials included in the group; the indicator characterizing the quality of information support; an indicator characterizing the novelty of the task; the indicator characterizing the scale of activities, the indicator characterizing process automation. The model introduces a decision-making efficiency coefficient, which helps to link two main indicators of an optimal solution: validity (through the average time of development and analysis of several decision options) and efficiency (through the ratio of the total average decision-making time and the allocated time); this makes it possible to account for the influence of the quality of the decision made for the subsequent planning process. A coefficient of adopted decision obsolescence has been introduced, which helps to assess the relevance of the adopted decision after a certain period of time. Using a mathematical model, analytical expressions are obtained that make it possible to evaluate the effectiveness of decision-making while taking into account the average time of the decision-making stages and the quality factor of the command group.

Keywords: command group, decision-making process, validity of decision-making, efficiency of decision-making, quality factor of the combat control group, probability of making a well-founded and prompt decision, decision-making efficiency factor

Using the laser-induced spectroscopy method to analyze electrolytes in blood serum

2023. T.11. № 4. id 1469

Magomedsaidova S.Z. Magomedova S.V.

DOI: 10.26102/2310-6018/2023.43.4.027

The analysis of electrolytes in the blood serum helpsto identify the pathological state of the human body by means of indicators of calcium, sodium and potassium. It is possible to quantify the indicated indicators by means of spectroscopy (LIBS), which makes it possible to identify sufficiently accurate data in numerical values. The aim of the study is to form the possibility of a more accurate prediction of probable diseases by means of indicators of sodium, calcium and potassium in the blood. The research process was carried out by using the available biomaterials presented by the INVITRO laboratory of Makhachkala, the Republic of Dagestan, which were subsequently used for the analysis of electrolytes in blood serum on filter paper and slides. In order to predict the concentrations of potassium, sodium and calcium under consideration by means of LIBS, the method of partial least squares regression was applied. For serum samples, higher prediction accuracy with excellent linearity was achieved both on slides and on filter paper. For blood serum on slides, the prediction accuracy of K, Na, Ca was 1,45 %, 0,61 % and 3,80 %. Moreover, for blood serum on filter paper with the existing errors were 7,47 %, 1,56 % and 0,52 %. Results. The results of the study suggest that LIBS portable tools will be an excellent tool for clinical practice in real time.

Keywords: blood serum, clinical practice, analytical method, real time

Development of hybrid atmospheric-underwater optical communication system

2024. T.12. № 1. id 1468

Ali M. Saklakov V.M.

DOI: 10.26102/2310-6018/2024.44.1.032

Underwater optical wireless communications are promising and future-oriented wireless carriers to support underwater activities focused on 5G and beyond (5GB) wireless systems. The main challenges for the deployment of underwater applications are the physicochemical properties and strong turbulence in the transmission channel. Therefore, this paper analyzes the end-to-end performance of a hybrid free space optics (FSO) and underwater wireless visible light communication (UVLC) system under intensity modulation or direct detection (IM/DD) in a method considering a pulse amplitude modulation (PAM) scheme. In this study, a fading model with Gamma-Gamma (GG) distribution is used to deal with channel conditions with moderate and strong turbulence, and the links are designed by combining plane wave modeling in the corresponding links, respectively. The proposed performance methods excel in higher achievable data rates with minimal delay response and improves network connectivity in real-time monitoring scenarios compared to conventional underwater wireless communication techniques. The simulation results provide reliable estimates of system performance metrics such as average bit error rate (ABER) and probability of failure (Pout) in the presence of pointing errors. Finally, this paper uses a Monte Carlo approach for best curve fitting and validate the numerical expression with simulation results.

Keywords: 5G and 5GB networks, cooperative communication, optical communication, underwater communication, underwater sensor networks (USNs), VLC light communication

Reducing redundancy of laser scanning data for building digital terrain models

2023. T.11. № 4. id 1467

Zaytseva E.V. Kochneva A.A. Katuntsov E.V.

DOI: 10.26102/2310-6018/2023.43.4.033

The main subtle aspects of airborne laser scanning (ALS) involve a large level of density of laser reflection points (LRP) within a certain unit area. This results in the need to process a large amount of information while building digital terrain models (DTMs). Such processing is computationally intensive. For this reason, the main task which is solved during DTM building is to create an accurate description of terrain features required for geodetic works. At the same time, it is necessary to observe the minimum number of LRPs related to the characteristic landforms in the considered location to minimize the use of computing power. Currently available algorithms of information distribution for DTMs built on standard coordinate grids do not allow to successfully resolve data arrays while preserving the proper detalisation level of certain locations. New software, which is used in geodesy and makes it possible to create sparse data arrays during DTM building, is based on a closed code. The paper proposes an algorithm for finding unknown intermediate data obtained with laser scanning of terrain relief, which allows effective thinning of laser reflection points that are insignificant when describing the terrain relief. An automatic technique of DTM building is developed. An algorithm for searching unknown intermediate LRP arrays is formed. Displot is available for sloped areas as well. At the same time detailisation in the quality of structure lines and special points is preserved.

Keywords: digital terrain model (DTM), digital relief model (DRM), airborne laser scanning, quality assessment of digital terrain models, digital mine model

Adaptive configuration of the fuzzy logic controller of unmanned aerial vehicle flight dynamics

2023. T.11. № 4. id 1466

Potudinskiy A.V.

DOI: 10.26102/2310-6018/2023.43.4.021

Along with the rapidly growing demand for unmanned aerial vehicles for surveillance and reconnaissance, advanced controllers are needed for these critical systems. This article proposes a design of a flight dynamics controller that takes into account various uncertainties for a medium-range unmanned aerial vehicle. In addition to the nonlinearities of flight dynamics, three main sources of uncertainties caused by unknown controller parameters, simulation errors and external interference are considered. A reliable adaptive fuzzy logic controller responsible for nonlinear flight dynamics under the conditions of many uncertainties has been developed. Nonlinear flight dynamics relies on a soft association of local linear models. When constructing the controller, the optimal reference model is defined, which is stabilized using the linear quadratic controller procedure. Then a fuzzy logic controller is developed for the nonlinear model. In order to eliminate uncertainties, the gain coefficients of the fuzzy logic controller are reconfigured and constantly adjusted for reliable adaptation. The performance of a reliable adaptive fuzzy logic controller is evaluated in terms of stabilizing the transverse and longitudinal flight dynamics and tracking the state variables of the reference model under the conditions of various uncertainties.

Keywords: controller, fuzzy logic, flight dynamics, nonlinearity, uncertainty, model, control, parameter, unmanned aerial vehicle

Authentication of information system users by facial image

2023. T.11. № 4. id 1465

Guzairov M.B. Ismagilova A.S. Lushnikov N.D.

DOI: 10.26102/2310-6018/2023.43.4.017

Authentication belongs to the classical means of information security management of enterprise computer systems, the quality of which determines the security of the information system. This paper describes the authentication procedure of information system users by facial image. The architecture of an artificial neural network has been developed, biometric personal data sets have been formed and trained based on the recognition of information system users by facial image. As part of this research, the functionality of the artificial neural network architecture has been evaluated using international data banks (Dataset). Descriptors such as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) were extracted when recognizing information system users by facial image. A neural network-training model based on categorical cross-entropy was compiled, and the configuration of the compilation model (mini-sample size, number of epochs, activation function, and optimization function) was generated. The developed software module authenticates users of the information system on “friend-or-for” basis. The use of these image descriptors allows increasing the accuracy of user authentication in the information system (accuracy) and reducing the value of loss function (loss). The program code of the multimodal biometric authentication system has been implemented. To assess the efficiency of the software module, the first and second type error rates are given.

Keywords: authentication, biometrics, facial image, identity recognition, information system

Application rules for destructive ability of genetic algorithm operators in the problem of structural and parametric synthesis of business process simulation models

2023. T.11. № 4. id 1463

Petrosov D.A. Surova N.Y. Polyakov A.V.

DOI: 10.26102/2310-6018/2023.43.4.013

This study proposes the application rules for destructive ability of genetic algorithm operators in the problem of structural and parametric synthesis of business process simulation models. The aim of the research is to confirm the hypothesis that it is possible to influence the performance of a genetic algorithm by changing the operating parameters of its operators, which allows increasing the convergence of this evolutionary procedure and helps the intelligent algorithm overcome “bottlenecks”. The “bottleneck” of a genetic algorithm is understood as attenuation of the algorithm, finding the population at local extrema of the fitness function, etc. Based on this hypothesis, it is proposed to use an add-on in the form of an artificial neural network to intervene in the process of finding solutions as a control model. It is planned to simulate this process using the mathematical apparatus of Petri nets theory. When implementing such an approach to solving the problem, it is necessary to consider the influence of the destructive ability of operators on the behavior of the population and determine the order of actions that need to be performed to control the evolutionary search for solutions in the problem of structural and parametric synthesis of dynamic business process simulation models. The paper discusses examples of population states of a genetic algorithm as well as the results of applying the proposed rules for making adjustments to operator activities. The main operators that significantly influence the state of the population are considered: the selection operator, the crossing operator, and the mutation operator; the influence of the reduction operator was not regarded in this study.

Keywords: genetic algorithm, genetic algorithm operators, artificial neural network, structural and parametric synthesis, simulation models, business processes

Query cost estimation and development of an application for processing resource-intensive queries

2023. T.11. № 4. id 1462

Nurmatova E.V.

DOI: 10.26102/2310-6018/2023.43.4.026

Rapid growth of stored data volume necessitates integration of tools for monitoring, analysis and optimization of database queries for timely and correct identification of the most resource-intensive queries. These circumstances determine the relevance of developing software tools for assessing the causes of slow queries with the formation of various optimization options. This paper examines the reasons influencing the resource-intensive queries of data sampling. The reasons for slow queries are shown, such as the quality of collected statistics, use of indexes, hints, query structure, correctness of database initialization parameter settings, as well as possible solutions to the identified causes. The study is interesting from the point of view of explaining the basics of the physical operations provided by the query execution subsystem, which interprets the procedural plan of query execution to optimize the cost. To solve the problem of speeding up slow queries based on a correct procedural plan, we propose the development of an application that takes into account the composition of the analyzed cost, volume and time characteristics of queries to optimize them. The results of testing the developed system, which helps to improve the performance of queries, are described. The speed of query execution was evaluated by the following metrics: data access operation, expression cost, I/O operation cost, CPU time, time spent on processing the whole sample. Performing experiments to evaluate the correctness of identifying slow queries confirms the feasibility of applying in practice the results of the conducted research and the developed application.

Keywords: relational systems, query optimization, select, index efficiency, statistics, cost estimation

Environmental factors and bad habits in models of the malignancy prevalence in the municipalities of Altai Krai and other regions

2023. T.11. № 4. id 1460

Stepanov V.S. Rybkina I.D. Orlova E.S.

DOI: 10.26102/2310-6018/2023.43.4.022

The article proposes a regression relationship that connects the prevalence of malignant neoplasm cases in Altai Krai with a complex of explanatory variables, some of which are taken with lags. This complex consists of the purchasing power of the average wages for employees in large and medium-sized organizations, the average volume of alcoholic beverages consumption (per adult capita with age 15+), the share of the elderly population and several dummy variables. The latter characterize the level of technogenic pollution of the municipality territory due to various reasons, as well as the quality of its highways. As a result, a regression model with the variable structure was built. The model has a coefficient of determination of about 98 % and a mean absolute percentage error of 1,2 %. Its parameters were estimated using the ordinary least squares method on panel data for 2018-2019. After assessing the accuracy of the model, it predicts the prevalence of cancer within the municipality with a planning horizon of one year. Such forecasts were made both for a number of urban districts and for municipal districts of Altai Krai, as well as for many districts of Orel and Kurgan Oblasts and a number of other regions in Russia. Additionally, forecasting is performed by means of a roughly similar model that has been created using regional data for 2017-2018. Based on these two models, it is possible to make science-based management decisions aimed at the primary prevention of general cancer morbidity in the population of the municipality and development of specific activities against the morbidity.

Keywords: variable structure regression model, malignant neoplasm, technogenic pollution of territory, atmospheric air pollution, smoking, consumption of alcohol beverages, municipality, region

Functional near-infrared spectroscopy (fNIRS) as a hybrid system: a review

2024. T.12. № 1. id 1459

Samandari A.M.

DOI: 10.26102/2310-6018/2024.44.1.005

Sensor devices and biomedical imaging technologies used in clinical application scenarios are essential for providing a comprehensive portrait of patients’ state, but these technologies, despite their outstanding advantages, have their inherent disadvantages. Beginning with the principle of complementary images of medical imaging techniques, this review examines the functional near- infrared spectroscopy (fNIRS) technique and its use as a hybrid system. The fNIRS technology delivers impressive results in terms of the biological signal classification accuracy, but its use as a hybrid system with electroencephalography (EEG) and electromyography (EMG) achieved better results because it has become a complementary tool to fill the deficit of the common technology with it, and this has been highlighted in this review. The results show that the superiority in the biological signal classification accuracy provided by hybrid systems from fNIRS with EEG and EMG would provide a comprehensive and objective assessment of the patients’ state from the stage of illness to healing. In conclusion, we have no indication from the scientific studies of the previous four years (2020–2023) that demonstrate which of the hybrid systems is better than others when used in clinical practice, and this encourages further in-depth studies to validate the combination of methods to prove their success and preference.

Keywords: HBCIs, fNIRS, fMRI, EEG, EMG, MEG