metadata of articles for the last 2 years
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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 use of an artificial neural network in thermal diagnostics of the printed node of the on-board take-off control device of an aircraft

2022. T.10. № 3. id 1218
Uvaysov S.U.  Chernoverskaya V.V.  Dang N.V.  Tuan N.V. 

DOI: 10.26102/2310-6018/2022.38.3.012

Technical diagnostics and monitoring of an electronic device are integral parts of its life cycle since they help to assess not only the technical condition of components and modules in real time, but also make it possible to identify hidden defects that have arisen during the production or operation of the device, and make a forecast about the residual life of the product. It can be said that in the process of technical diagnostics, the reliability indicators of the device under study and the compliance degree of the embedded and implemented functionality are evaluated, which is inextricably linked with the qualitative characteristics of the product. Modern radio-electronic devices characterized by high circuit, structural and technological complexity require additional study of the existing diagnostic methods and the search for new approaches to increasing the resolution, reliability, and effectiveness of diagnostic procedures. In this area, achievements from the field of artificial intelligence, machine learning, and neural networks along with traditional, proven methods have been actively used recently. In addition, the use of modeling and computational experiment in design made it possible to combine design and diagnostic procedures, conduct diverse studies of the virtual twin of the device and make the necessary changes in a timely manner, thereby preventing the manifestation of negative effects in the finished product at the early stages of development even before the production of a prototype. The article presents the results of a study aimed at creating a thermal model of the designed node and developing an artificial neural network for recognizing structural defects of the device by its thermal field. In this research, specialized computer-aided design systems were actively employed, including engineering analysis and calculation tools, as well as the high-level Python programming language. The findings have a practical importance and can be utilized by developers of radio-electronic devices in order to achieve high reliability and operational characteristics of the product at all stages of its life cycle.

Keywords: radio-electronic device, on-board device, control of aircraft take-off dynamics, thermal mode, thermal modeling, artificial neural network, fault database, computer-aided design system, technical diagnostics

Modified genetic algorithm for project scheduling

2022. T.10. № 3. id 1214
Korotkov V.V. 

DOI: 10.26102/2310-6018/2022.38.3.007

The paper describes a modified genetic algorithm for solving resource-constrained project scheduling problem. The relevance of the study is due to the widespread prevalence of project organization of activities and the extremely high computational complexity of the problem under consideration. Further improvement of existing heuristic algorithms is needed to enable efficient planning of large projects. The available genetic algorithms are based on activity order encoding methods and implementations of genetic operators, which does not fully take into account the specifics of the problem. Therefore, the paper proposes an alternative encoding method and the corresponding crossover operator, which, unlike classical approaches, highlights relative rather than absolute positions of activities as inherited features. The study regards the main properties of such encoding which can be represented as square Boolean matrices. A mapping operator that helps to reduce Boolean matrices to a canonical row form is also introduced. The resulting genetic algorithm and classical implementations were compared using a test set of tasks. The suggested approach has shown potential efficiency, especially with large projects. The findings can be of practical importance in the development of decision support systems for project management.

Keywords: genetic algorithm, crossover operator, project planning, combinatorial optimization, scheduling theory

The impact of coronavirus infection on the socio-economic indicators of the region

2022. T.10. № 3. id 1213
Pecherina A.V. 

DOI: 10.26102/2310-6018/2022.38.3.028

The new coronavirus infection (COVID-19) which emerged in Wuhan, China, in early December 2019 quickly spread to almost every country in the world and shocked the global economy. This article highlights the most important problems that are caused by the coronavirus pandemic. The author discusses the impact of the new coronavirus infection Covid-19 on some socio-economic indicators of a particular region of the Russian Federation as well as the Russian Federation as a whole. In order to do that, an analytical procedure was developed using Knime Analytics Platform (the free and open source data analysis platform), which, in turn, greatly simplified data processing and visualization of results. The platform makes it possible to develop reproducible and scalable workflows by integrating a wide range of analysis tools. The analysis was based on the data extracted from the website of the Center for Spatiotemporal Innovation at Harvard University (NSF Spatiotemporal Innovation Center) and the statistical data extracted from the website of the Federal State Statistics Service. We visualized the data and drew conclusions about COVID-2019 incidence rate and the cost of a constant set of consumer products and services for the purposes of inter-regional comparisons of purchasing power.

Keywords: data analysis, data mining, covid-19, coronavirus infection, socio-economic indicators

Detection of information security threats using deep neural networks in computer networks in real time

2022. T.10. № 3. id 1212
Trunov E.E.  Klyuev S.G. 

DOI: 10.26102/2310-6018/2022.38.3.011

Currently, the issue of detecting information security threats in computer networks is becoming a problem when it comes to preventing such threats in real time. The number of subscribers of almost any computer network is growing and so does the number of threats that can create a potential danger to the functioning of the network. In this regard, modern mechanisms that will help to respond to emerging information security threats in a timely manner are required. In this paper, the analysis of possible mechanisms of protection against security threats in computer networks is carried out and a methodology for implementing such protection using neural networks is proposed. In addition, a control example is implemented with a trained deep neural network which is able to detect information security threats with high accuracy and minimal delays. The materials of the article are of practical value when incorporating such a neural network into an intrusion detection system. By means of the method proposed in the article, it is possible to achieve a near-real-time response to information security threats and, as a result, prevent possible information security accidents.

Keywords: computer network, neural network, security threat, deep learning, protection mechanism

Ensemble methods for detecting outliers in the preparation of a training data set

2022. T.10. № 3. id 1210
Dorofeev V.S.  Volosatova T.M. 

DOI: 10.26102/2310-6018/2022.38.3.013

Most machine learning methods are most effective when working with data that satisfies a nor-mal distribution. On the other hand, the training set often contains “outliers” of various nature, which can significantly reduce the accuracy of machine learning methods. Thus, in any machine learning task, there is a problem of detecting outliers. The article provides a classification of the main types of emissions. Various methods for detecting one-dimensional outliers are considered: the method using the Grubbs criterion; Z-score method; robust Z-score (RZ-score) method; in-terquartile range (IQR) method; Winsorization method. The methods for detecting one-dimensional outliers are compared. For the automated detection of outliers, an ensemble method has been proposed that combines various methods for detecting one-dimensional outliers. The ensemble method helps to configure an automated outlier detection procedure according to the rule of the required severity. The suggested method is applied to analyze and detect outliers in data on sales of goods during the promotion in a large retail network. The applicability of using outlier detection method ensemble to stratification of the training sample is shown. At the same time, the absolute and relative forecasting error of the final model decreased by 5% compared to the initial one.

Keywords: outliers, machine learning, training sample, ensemble method, z-score, interquartile range method

Vessels route planning under ice conditions

2022. T.10. № 3. id 1209
Grinyak V.  Akmaykin D.  Ivanenko Y. 

DOI: 10.26102/2310-6018/2022.38.3.006

This paper is devoted to the problem of navigation safety in ice-covered sea areas. The route planning is examined as a means of lessening the impact of ice upon a vessel as it follows its course, taking into consideration the type of ice and the ice class of the vessel. Special information services based on satellite monitoring as well as reports from vessels and polar stations, presenting information on ice cover in tables and diagrams, can be used as the source of data on the ice situation along the route. The current paper proposes a pattern concept of route planning and notes the complexity of its implementation. Simplifying the problem by finding the shortest way of the route in the weighted graph is suggested, which is a conventional strategy in ship navigation. Possible approaches to developing a set of graph nodes and edges as well as weighing the graph edges are discussed. Some recommendations for reducing computational complexity of tasks are given. The paper is accompanied with calculations of vessel routes using the data on ice situation in the sea of Okhotsk. The given examples show that the ship's track is formed in such a manner that the traffic in the sea areas covered by ice is decreased. Following on from the results of calculating routes under various ice conditions, a conclusion is made about the possibility of solving the problem in this way.

Keywords: navigation safety, route planning, ice conditions, vessel ice class, graph algorithms, shortest path

Synthesis of cognitive-constructive process management in human-technical-natural systems

2022. T.10. № 3. id 1208
Beltiukov A.P.  Maslov S.G. 

DOI: 10.26102/2310-6018/2022.38.3.005

The paper deals with the problems of synthesizing emotionally oriented management of cognitive-constructive processes in human-technical-natural systems with respect to the influence of heterogeneous illusions and misconceptions. The general problem statement is examined, logical models that reflect the task are proposed. The basic foundations of the synthesis of various factors for solving problems are identified. Emotions, illusions, and misconceptions are considered among such factors. This is done to overcome emerging contradictions and address the problems with due regard for the positive and negative aspects of the listed factors. Another goal of this is the formation of an adequate environment and means of cognitive and constructive activity in terms of complex system specifics, including humans, technology, and natural objects. The foundations for a new systematization of factors and adequate means of cognitive-constructive activity are being created. These basics take into account the influence of emotions, illusions and misconceptions. Individual and collective characteristics of intellectual and information resources are analyzed along with the features of objective and subjective aspects of control synthesis. New abstract and specific models are being designed that improve cognitive and constructive activity, the development and use of knowledge for the problems being solved.

Keywords: emotionally oriented management, synthesis, human-technical-natural system, cognitive-constructive process, emotions, illusions, misconceptions

Fractal calculus application for analysis of the results of an associative-verbal experiment

2022. T.10. № 3. id 1206
Barinov V.R.  Philippovich Y.N. 

DOI: 10.26102/2310-6018/2022.38.3.018

The paper supports the hypothesis on the use of fractal calculus to process the results of an associative-verbal experiment. The results of associative-verbal experiments are unstructured data with a large volume. The relevance of the study is due to the fact that the existing methods of processing the results of associative-verbal experiments cannot be unified because the data obtained have significant differences in formats. In turn, this limits the possibilities of creating and applying typical data processing algorithms for various associative-verbal experiments. One of the main problems of computational linguistics is the heterogeneity of hierarchical structures describing human consciousness and speech, as well as their constant modification. One of the possible solutions to this problem is to employ the method of fractal calculus that can help develop a model of linguistic consciousness having not only a complex structure of its own but is in constant interaction with other structures of the real world. Leading Russian scientists have substantiated means for addressing similar issues with unstructured data in other academic fields, which is analyzed in this article. Based on the presented research of scientists, a comparative analysis of existing fractal models for solving problems in various fields of activity is given, and a hypothesis about the possibility of using this method to model linguistic consciousness is suggested.

Keywords: fractal, calculus, associative-verbal experiment, artificial intelligence, big data, language consciousness, information search

Methods of user identification in the information and telecommunication environment based on the analysis of account attributes

2022. T.10. № 3. id 1203
Romanov A.G. 

DOI: 10.26102/2310-6018/2022.38.3.002

The relevance of the study is due to the problem of the growing number of unidentified persons who have committed crimes on the Internet and beyond. In this regard, the aim of the article is to demonstrate the means for personal identification by identifying users in the virtual space in order to convict them of criminal offence. The improvement of information technologies and the development of services in the information and telecommunications space provide an opportunity to analyze numerous data, including those left by users about themselves in social networks. Thus, the leading method to investigate the problem is the techniques to determine the similarity of alphanumeric objects created by users in the attributes of social network profiles. This article presents a possible algorithm of actions to deanonymize the identity of a criminal. The development and application of methods for identifying users in the virtual space will allow us to comprehensively consider the existing problem and accomplish one of the main tasks assigned to the internal affairs bodies and related to crime solving and charging perpetrators with a criminal offence. The materials of the article may be of practical value to the internal affairs bodies in the terms of enhancing the efficiency and effectiveness of law enforcement activities.

Keywords: user identification, internet, data analysis, social networks, crimes

On the approach to forecasting indicators of socio-economic development of the region based on indirect indicators

2022. T.10. № 3. id 1202
Rusanov M.A.  Abbazov V.R.  Baluev V.A.  Burlutsky V.V.  Melnikov A.V. 

DOI: 10.26102/2310-6018/2022.38.3.004

Economic and social development requires constant modernization of the regional management system based on the system of key socio-economic indicators of the region's development and methods of their analysis and forecasting. The article proposes a comprehensive approach to forecasting based on the application of classical forecasting methods for existing time series of statistical indicators and by identifying and analyzing indirect semantically close indicators to a new indicator in the absence of the necessary time series for forecasting. The article provides a general methodology for obtaining a forecast and describes in detail the method for constructing a forecast estimate of the change dynamics in the estimated indicator as well as a description of the AutoML library with open source FEDOT, which was used to build forecasts. The issue of constructing and optimizing a combined forecast with the aid of automatic machine learning tools is considered. At the end of the article, the result of an experiment on predicting the indicators “Population of the subject of the Russian Federation” and “Life expectancy at birth” according to the proposed approaches and a comparison of the findings is presented. It can be concluded that the suggested approach to making a predictive assessment of the change dynamics in the estimated indicator by identifying indirect indicators can be applied to socio-economic indicators of the development of the region.

Keywords: socio-economic indicators, forecasting, incompleteness, autoML, indicator of senior official activity effectiveness

Development of a method for determining the dominant type of human breathing pattern based on computer vision technologies, motion capture systems and machine learning

2022. T.10. № 4. id 1200
Zubkov A.V.  Donskaya A.R.  Busheneva S.N.  Orlova Y.A.  Rybchits G.M. 

DOI: 10.26102/2310-6018/2022.39.4.016

The study raises the problem of the absence of methods for determining the dominant type of breathing pattern that can be used in the implementation of software products that contribute to the support of patients with respiratory insufficiency and their rehabilitation at the stage of inpatient and outpatient treatment. Existing methods are either too labor-intensive to implement due to the excessive amount of markers utilized by motion capture systems or economically unprofitable due to the cost of the equipment itself or developed only for research purposes and are not applicable in clinical practice. In this regard, this article is aimed at developing a method for determining the type of breathing, which could later be employed for automated rehabilitation of patients with respiratory insufficiency. As part of the study, computer vision and machine learning methods were applied as well as methods based on motion capture technologies. The article presents methods for determining the position of markers in space and analyzing the type of human breathing (thoracic, abdominal, mixed) in real time based on the data obtained by means of motion capture system markers. The materials of the article are of practical value in the field of medical rehabilitation of patients with respiratory insufficiency; they make it possible to optimize labor processes within the field of medical rehabilitation, i.e. reducing labor and time costs of rehabilitologists.

Keywords: computer vision, neural networks, motion capture systems, patient rehabilitation systems, detection of breathing patterns

Modified Elman neural network with dynamic learning rate for tracking and motion prediction of a nonholonomic three-wheeled mobile robot

2022. T.10. № 3. id 1199
Berezina V.A.  Mezentseva O.S.  Mezentsev D.V. 

DOI: 10.26102/2310-6018/2022.38.3.003

This article proposes to track and predict the trajectory of a non-holonomic three-wheeled mobile robot using a modified Elman neural network. An algorithm for calculating the learning rate of a neural network is suggested, which improves the efficiency and speed of learning and also reduces the number of iterations required for learning. The modified Elman algorithm with dynamic learning rate (MENN) is compared with the classical Elman neural network (ENN) and the PSO algorithm (PSO-ENN). Training of a neural network is evaluated according to two criteria: the number of iterations required for training and the average training time. In addition, the deviation from the given trajectory of movement is checked: movement along a straight line, in a square and in a circle for each algorithm. The simulation results showed that the modified Elman neural network with a dynamic learning rate is more efficient (by 32.4% on average) and accomplishes the learning objective faster (by 66.4% on average) and has the least deviation from the given motion trajectory. The relative measurement error ranges from 7.8% to 20.2% at 95% reliability and five tests for each group of measurements.

Keywords: recurrent neural network, elman neural network, learning rate, nonholonomic three-wheeled robot, motion trajectory prediction

Modeling of artificial intelligence system for early detection of emergency situations at vital facilities

2022. T.10. № 3. id 1197
Borovskoy I.G.  Shelmina E.A.  Afanasyeva I.G.  Matolygin A.A. 

DOI: 10.26102/2310-6018/2022.38.3.001

The article presents the results of modeling an artificial intelligence system for early detection of undesirable situations of various types at objects of particular national economic importance. Pipeline transport or any other production system, in which continuous monitoring of operability parameters of critical components and mechanisms is carried out, can be specified as such object. This model can be applied by various oil and gas production companies. The results of modeling and subsequent development of the information system will provide the basis for industrial implementation of highly effective systems of accident detection and prevention in reliance on neural network analysis of continuously received streaming data. As a part of this research, the possibility of using modern neural network architectures for the problem under consideration is examined, namely, convolutional neural networks – TCN, direct propagation neural networks – MLP, recurrent neural networks – LSTM. It was proposed to abandon the activation function for LSTM which helps to provide the neural network with "long-term memory" of stored values, which is crucial to this problem. In addition, a cross-comparison of the error reduction rate during network training was performed to detect an architecture capable of "self-learning". All models were tested with the aid of the training data from the "Vostochny kupol" wells. Acceptable coincidence of test and extrapolation data was obtained for all models.

Keywords: artificial intelligence, time series, artificial neural network, emergency, neural network architecture, convolutional neural networks, direct propagation neural networks, recurrent neural networks

Development of a steganalysis system for digital images based on a neural network classifier

2022. T.10. № 2. id 1196
Minaychev A.A.  Mezentsev A.O.  Yandashevskaya E.A. 

DOI: 10.26102/2310-6018/2022.37.2.020

The article discusses an approach to the implementation of a system for steganographic analysis of digital images based on a neural network classifier. It is used as a part of an integrated system for monitoring information security events of corporate infocommunication systems. As a basic structure for the neural network classifier, it is proposed to use a modified version of the convolutional neural network. Its preprocessing module implements the histogram method for analyzing the color and brightness characteristics of digital images. To automate the learning process of the neural network classifier, it is suggested to introduce a module for mass generation of stegocontainers with predefined values for the type and size of a digital image as well as for the size of the payload into the structure of the system being developed. Based on the developed structure of the steganalysis system for digital images, a factorial experiment was planned and conducted to evaluate the quality of the described neural network classifier in comparison with the known solutions of binary statistical classifiers. The choice of the area under the error curve (AUC ROC) as a metric for assessing the quality of classification is the main feature of the experiment. The results show that it is possible to use neural network classifiers to solve steganalysis problems, including their implementation in advanced information security tools.

Keywords: digital steganography, digital images, convolutional neural network, binary classification, steganographic container, classification accuracy

Support decision-making for analyzing the effectiveness of a website using Web Usage Mining methods

2022. T.10. № 2. id 1191
Zelenina A.N.  Kokorina A.I.  Petrosov D.A. 

DOI: 10.26102/2310-6018/2022.37.2.019

In the modern world, one of the most effective methods to maintain the functioning of an organization or business with a view to facilitating development is to design a website and then to employ it to communicate with users and customers. The website helps to systematize all information about the organization, provides a means of e-commerce and gives the opportunity for representatives of the organization and users to communicate with each other to exchange ideas or feedback on products or services. Thus, effectiveness analysis of the website and appropriate decision-making, regarding its optimization and changes to the design, which will allow the company subsequently to achieve its goals, becomes more relevant. In this article, a decision support system was implemented to analyze the effectiveness of a website using Web Usage Mining methods. Statistical methods, which enable performance improvement of the website based on the information received, were chosen as well as data mining methods, in particular, clustering and association rules that are utilized to personalize content and, in the case of selling websites, purchasing offers, which will significantly increase the loyalty of users and customers.

Keywords: decision support system, web Usage Mining, website, log file, machine learning, clusterization, association rules

Optimization of a discrete-time system for transferring a continuous medium over a network carrier

2022. T.10. № 2. id 1190
Tran D.  Gunkina A.S. 

DOI: 10.26102/2310-6018/2022.37.2.029

The technologies for transferring continuous media (gas, oil, petroleum products, etc) use carriers (main pipelines) with a topological structure similar to that of a geometrical graph. A large volume of literature is devoted to the issues of mathematical modeling of transfer processes along such carriers as well as to the analysis of various kinds of optimization problems related to them, but the mathematical justification of the findings is not sufficient from the standpoint of the general mathematical theory of heat and mass transfer. The paper considers the problem of a differential-difference system optimization, which determines the discrete-time equivalent of a differential system for the transport equation on a graph (in applications, on a network). E. Rote's method is employed, which is based on semi-discretization with respect to the time variable of the initial-boundary value problem, which helps to establish not only the conditions for the solvability of the specified problem, but also to obtain an optimization problem for the differential-difference system. Moreover, the coercive property of the elliptic operator bilinear differential form and the continuity of the quadratic functional being minimized are necessary and sufficient conditions for the unique solvability of the optimization problem. The findings are applicable in modeling network-like processes of continuum transport by formalisms of differential-difference systems with a spatial variable fluctuating on a network-like multidimensional domain. The conditions that determine the solution of the optimization problem or the set of such solutions are presented. Concurrently, approaches to the analysis of the optimization problem for a system defined on a multidimensional network-like domain are outlined. The findings underlie the analysis of optimal control problems for differential systems with distributed parameters on a graph, which have interesting analogies with multiphase problems of multidimensional hydrodynamics.

Keywords: differential-difference system, spatial variable on a graph, optimization problem, initial-boundary value problem, network (directed graph)

Human resource management and extracting information about research activity in the field

2022. T.10. № 2. id 1189
Zhuravleva K.I.  Smetanina O.N.  Yusupova N.I. 

DOI: 10.26102/2310-6018/2022.37.2.016

The article deals with the issues of human resources management and the extraction of information about research activity in this field using the functionality of scientific electronic library eLibrary. The article reflects the analysis results of modern ideas about human resources and their management, defines the problems of extracting information about research activity and the problem statement; analyzes the known approaches to extracting information about research activity; offers a methodology for data processing for information extraction; provides quantitative characteristics obtained from the research and their interpretation; reviews the results of information extraction about the main trends in human resources and interpretation of these results. The proposed definition of the problem involves selecting from a set of scientific articles D a set of documents relevant to the query: the ranking of authors by research activity in the field of human resources; the ranking of journals with publications in the field of human resources; the ranking of organizations whose authors do research in the field of human resources; the ranking of authors of the most cited publications in the field; a set of major trends in human resources at the present time. The results from the analysis of the content of the selected articles showed that the greatest interest in the field of human resource management is associated with both the requirements imposed on the personnel in connection with the digitalization of the economy and the implementation of digitalization in companies.

Keywords: human resources, human resource management, information extraction, data processing methodology, digital platform, research activity, digital economy

Methods and models of resource allocation service in load balancing clusters for data centers

2022. T.10. № 2. id 1188
Mochalov V.  Linets G.  Bratchenko N.  Palkanov I. 

DOI: 10.26102/2310-6018/2022.37.2.030

The object of the research is computing clusters of cloud data centers, containing many servers, data storage systems, an input-output system interconnected by a communication network. The goal of this research is to develop methods and models for improving the performance of a data center cluster by reducing the processing time of service requests as well as reducing equipment costs due to the efficient allocation of its resources. Therefore, it is necessary to implement optimization algorithms for placing virtual machines (VMs) on physical servers in real time based on load balancing. The proposed method of resource allocation is based on an iterative greedy algorithm and a limited search procedure. Reduction in the computation time is achieved by introducing restrictions on the permissible search depth. The paper puts forward a mathematical model of resource allocation, built using the Erlang model in the form of a multi-line m-node queuing system (QS) of the M|M|m|n type with an n-seat buffer, which makes it possible to determine the main indicators of service request quality in the form of QS parameters. The efficiency of this approach was tested on a simulation model built on the basis of the system functioning statistical analysis. Its experimental study was also carried out.

Keywords: computing clusters, virtual machines, physical servers, resource allocation model, heuristic algorithms, model experiment

Situation-Oriented Databases: Processing Office Documents

2022. T.10. № 2. id 1187
Mironov V.V.  Gusarenko A.S.  Yusupova N.I. 

DOI: 10.26102/2310-6018/2022.37.2.021

This article discusses the application of a situation-oriented approach to the problem of extracting semantic information from office documents. Office documents created by vector graphics editors and word processors are reviewed. The ability to extract semantic information is due to the fact that such documents are based on open XML formats that can be processed by external programs. Processing of documents based on a situational database where word documents are programmatically loaded as XML files extracted from zip-archives is considered. In the situation-oriented database, it is possible to present an office document as a virtual document that is mapped both on XML files and the ZIP archive with XML files. This applies not only to text documents, but also to graphic documents that have an internal XML representation. This enables processing of documents in Office Open XML and Open Document Format. The article discusses various aspects of identifying and finding the necessary information during document processing by means of special standard definitions as bookmarks, key phrases and text labels. Models and algorithms for extracting the required information are examined. Examples of the practical use of this approach in the field of distance learning of students at the university are given. In addition, an example of extracting metadata of scientific publications in the Open Journal Systems publishing system is regarded.

Keywords: situation-oriented database, built-in dynamic model, office Open XML, open Document Format

Oil and gas well repair analysis technique based on data mining in management

2022. T.10. № 2. id 1186
Nurgalieva Z.D.  Latypova V.A. 

DOI: 10.26102/2310-6018/2022.37.2.017

One of the most important steps to increase profits in oil production is not only investment in equipment, exploration and discovery of new fields, but also analytics. The efficiency of oil and gas production in existing fields can be improved through a comprehensive analysis of the existing data stream. Monitoring of oil and gas production and preventive maintenance of wells involve the collecting and processing of data on the functioning of wells. Such data are not always sufficient for making accurate decisions on well repair management. A number of problems cannot be identified due to the scarcity of information, and therefore the efficiency of the decisions is reduced. Well repair monitoring using data mining performs a number of functions. Firstly, it determines the status of critical well repair conditions for which an action plan will be developed. Secondly, it provides management with feedback by identifying the causes of past positive and negative results. The article proposes an oil and gas well repair analysis technique based on data mining with the aid of repair sequential pattern mining in management. The technique was tested in the oil and gas company Gazpromneft on oil and gas well repair data of Gazpromneft-Noyabrskneftegaz community field.

Keywords: sequential pattern mining, oil and gas well repair, data mining, oil and gas field, well repair analysis

Using SERVQUAL methodology with HR benchmarking to assess the satisfaction of the organization's staff

2022. T.10. № 3. id 1185
Korovin E.N.  Krivonosova M.V. 

DOI: 10.26102/2310-6018/2022.38.3.021

In the world today with its dynamic environment, employees are the main asset of a company. Thanks to the staff, competitiveness and full development of an entire organization are ensured. The key to a company's success lies in meeting the needs of its employees. The issue of assessing the quality of customer service and the quality of employee performance appears to be a concern for many company executives. Additionally, management often consider the option of conducting such assessments. The relevance of the study is due to the fact that nowadays there is no universal means of assessing staff work satisfaction in the organization. The leading method for studying this problem is HR benchmarking using the SERVQUAL methodology and the CSI satisfaction index, which helps to comprehensively examine the problem of assessing staff satisfaction. The inquiry as to whether the organization has the necessary personnel to function effectively and whether employees can operate in accordance with the chosen strategy is the goal of HR benchmarking. The article explores the peculiarities of customers' perception of the services offered by the company, which reflects the concept of "SERVQUAL". It provides an opportunity to achieve a standard that is convenient for customers. Owing to this concept, the company will be able to identify the level of employee satisfaction focusing on the results. The study of employees' opinions about work, including their satisfaction with working conditions, is the goal of further improvement of the company's business processes. The materials of the article are of practical value for modern companies with any type of activity.

Keywords: technique, benchmarking, HR benchmarking, SERVQUAL, personnel, organization, CSI, perception, expectation, satisfaction index

Analysis and risk management of ICS information security risks based on cognitive modeling

2022. T.10. № 2. id 1184
Vasilyev V.I.  Vulfin A.M.  Kirillova A.D. 

DOI: 10.26102/2310-6018/2022.37.2.022

The paper considers the problem of optimizing cognitive model parameters in the analysis of information security risks of industrial control systems (ICS), reflecting the optimal distribution of costs for the realization, implementation, and maintenance of countermeasures, taking into account their functional limitations. A genetic algorithm for optimizing the weight coefficients of cognitive models is used, which makes it possible to determine the optimal configurations of protection measures in the process of assessing ICS information security risks under the conditions of complex multi-step attacks. On the example of the oil delivery ICS and receipt point, the optimization of the countermeasure configuration is carried out to select the most effective options for the allocation of resources of means and information security systems to minimize information security risks. The proposed approach enabled the reduction of information security risk assessment by 85%, increase the assessment of the countermeasure operating efficiency, and reduce the assessment of the countermeasure operating cost. Analysis of the correlation between the obtained information security risk assessments within the allocated ICS zones and the costs of measures to reduce them helps to determine the mechanisms for managing the security of the system target resources and maintain its required level of security as well as to assess the costs required for the integration and maintenance of countermeasures. The result testifies to the effectiveness of the proposed approach to optimizing the configuration of the selected countermeasures with due regard for the multicriteria risk optimization and assessing the economic aspects of ensuring the information security of the object.

Keywords: cybersecurity, risk management, fuzzy gray cognitive maps, genetic algorithm, countermeasures

Evaluation of the efficiency of oil and gas field development using expert methods

2022. T.10. № 2. id 1183
Khafizova A.U.  Latypova V.A. 

DOI: 10.26102/2310-6018/2022.37.2.018

Nowadays, the problems of assessing the development of oil and gas fields are becoming increasingly important. When monitoring the implementation of the strategy for the development of the oil industry, several indicators are used in the areas of development of the industry. Since different indicators of objects relate to different problems of the oil industry, it appears to be impossible to summarize these data without special tools. In the development of oil and gas fields for decision-making, individual assessments of efficiency experts will not help due to the fact that specialists who evaluate performance have different expectations. Therefore, it can be concluded that such situation is the cause of a conflict of interest when considering the development of deposits. In this situation, there is a need to obtain an integral estimate. The article describes the proposed model for evaluating the efficiency of field development based on the calculation of an integral indicator by means of expert methods. The developed model enables the increase of decision-making efficiency in the management of oil and gas fields. The model was tested on the example of Severo-Ingolsky, Zimny, Orekhovo-Ermakovskoye, Alexander Zhagrin fields at Gazprom Neft, further implementation is expected as a module in the Integration system for long-term development system.

Keywords: field development, efficiency assessment, integrated performance indicator, expert method, oil and gas field

Support of functional safety management of software and hardware complexes based on system archetypes

2022. T.10. № 2. id 1181
Gvozdev V.E.  Bezhaeva O.Y.  Guzairov M.B.  Vasilyev V.I. 

DOI: 10.26102/2310-6018/2022.37.2.025

This paper describes an approach focused on the construction of mathematical models that illustrate from different angles typical situations arising in the implementation of software projects. The basis of the approach is the analysis of projects for creating hardware and software complexes as a kind of subject-centric systems. This lays the groundwork for scientific adaptation of well-known approaches, used for researching complex systems of a different nature, to the field of functional safety of hardware and software complexes. In the publications, typical problem situations that occur in managing complex systems of different nature are regarded at the declarative level and called system archetypes. From a practical point of view, the limitation of system archetypes is that they represent situations only at a qualitative level. They do not depict the structure of the control system and the parametric dependencies of direct and cross-links that take place in the control system. In this paper, several examples of constructing structural models corresponding to different system archetypes are considered. For the generation and analysis of alternatives for resolving situations, methods for converting archetypes to the form of structural and mathematical models are proposed. The range of applicability of the proposed approach includes projects of medium scale, i.e. mass-produced projects.

Keywords: project management, problem situation, functional safety, hardware and software complex, system archetype

Control of mass transfer processes in sorption gas emissions cleaning

2022. T.10. № 2. id 1179
Merentsov N.A.  Persidskiy A.V.  Golovanchikov A.B. 

DOI: 10.26102/2310-6018/2022.37.2.023

Equipping absorption devices for cleaning gas emissions with automatic control systems is the most effective and promising way to improve the quality of their operation and increase energy efficiency. However, the systems for automatic control of mass transfer apparatuses, known today, do not have the ability to maintain an extremely unstable hydrodynamic emulsification mode while it has the highest efficiency. The object of industrial gas emission sorption purification control system is a mass transfer apparatus where the gas phase flow being purified contacts with a liquid absorbent. The purpose of the control is to intensify the processes of mass transfer during absorption refining of gas emissions under disturbing influences and program recognition of the desired hydrodynamic modes of the mass transfer apparatus operation according to the actual values measured during the process of technological characteristics. The constructed mathematical model is based on the approximation of the points of adjacent filtration curves on which the range of the hydrodynamic emulsification mode is isolated. An indicator of the desired emulsification mode emergence is the appearance of "bursts" in the value of the turbulence index as the flow rate of the gas phase increases. When using the proposed mathematical model in real ACS, the coefficients determined during experimental studies can be identified automatically and used subsequently in the calculation of control actions. Identification of the mathematical control model on a real mass transfer apparatus is advised to be carried out automatically during auto-calibration of technological parameters.

Keywords: mathematical model, control, automation, mass transfer, absorption, gas purification, hydrodynamics, turbulence index, emulsification mode

Identification of mathematical control model aimed at controlling absorption devices for selective gas emissions cleaning

2022. T.10. № 2. id 1178
Merentsov N.A.  Persidskiy A.V.  Golovanchikov A.B. 

DOI: 10.26102/2310-6018/2022.37.2.024

Equipping gas absorption apparatuses with automated control systems for the hydrodynamic mode of their operation is by far the most effective means of improving the quality and efficiency of their operation. At the same time, the most time-consuming task in commissioning such devices is to configure the parameters of the automated control system. The purpose of the study, considered in this paper, is to enhance the quality of operation and increase the energy efficiency of systems for gas emission sorption purification by maintaining the most intensive hydrodynamic modes of their operation. The main goal is to create an automated control system and an algorithm for mathematical control model identification program. The automated control system and algorithm, regarded in this article, make it possible to identify the mathematical control model (also called auto-calibration) by testing the apparatus in an automated mode. The paper gives a description of the mechanism for recognizing hydrodynamic modes and searching for an emulsification mode to identify a mathematical model for automatic control of a packed absorption apparatus. A diagram of the system for identification and control of a packed absorption apparatus operating modes is suggested. An algorithm for the identification program for the mathematical control model (auto-calibration) of a mass-exchange absorption system is presented. The proposed automated control system and auto-calibration algorithm enables the reduction of the commissioning time by up to 8 times and helps to improve the quality and energy efficiency of the gas absorption purification process.

Keywords: automated control system, identification of process parameters, mass exchange, gas absorption, sorption mass exchange apparatus, hydrodynamics, turbulent mode, emulsification

Development of a web-application to predict biological age by functional indicators

2022. T.10. № 2. id 1177
Zotov A.O.  Limanovskaya O.V.  Gavrilov I.V.  Meshchaninov V.N. 

DOI: 10.26102/2310-6018/2022.37.2.015

The rate of aging is a complex indicator of human health which depends on many factors that include external and internal effects on the body (disease and its correction processes), which is reflected in the biomedical indicators of the body (functional, biochemical, hematological and others). To determine the rate of aging, the concept of bio-age is widely used, which is a complex parameter based on ascertaining the degree of human body aging (wear, damage) in reliance on its biomedical parameters. This article presents the development of a client-server web-application for determining the bio-age of a user by evaluating their functional indicators - systolic blood pressure, diastolic blood pressure, breathing delay time on inhalation, breathing delay time on exhalation, the value of lungs vital capacity, hearing acuity, the state of eye lens accommodation, static balancing time, body weight, height. The web-application allows doctors and administrators to determine the patient's bio-age, drawing on the user's functional data entered in the application, taking into account the influence of geroprophylactic therapy. The web-application displays data in the form of a list and a graph and enables one to send reports to the patient's email and to upload them. The server part of the application is written in the C# programming language and ASP.NET framework. The TypeScript programming language and the React framework with the Antd user interface component library were employed to design the client part of the application. PostgresSQL is utilized as a database. As a module for predicting biological age, a previously developed mathematical model, trained on a data sample of 650 records and having an accuracy of 5.87 years, is applied. The ability to predict the patient's bio-age with consideration to the duration and a type of geoprophylactic exposure makes the developed application a suitable tool to identify the leading mechanism of a patient’s aging.

Keywords: bio-age, biological age, aging mechanisms, web-application for determining bio-age, machine learning in medicine

Generation of genre musical compositions according to the emotional state of a person

2022. T.10. № 2. id 1175
Nikitin N.A.  Orlova Y.A.  Rozaliev V.L. 

DOI: 10.26102/2310-6018/2022.37.2.026

The aim of this article is research and development of algorithms and software for automation and support of technical creativity process by automated generation of musical compositions of different genres, based on the emotional state of a person. It relies on the method of generating musical material with the aid of artificial neural networks. To generate music, a recurrent neural network with long-short term memory is chosen because this is the type of neural networks that helps to take into account the hierarchy and codependency of musical data. The paper contains a detailed description of training data collection process, the process of neural network training, its use for generating musical compositions as well as an illustration of the network architecture. In addition, it outlines a generalized method for obtaining the emotional state of a person by analyzing an image by utilizing the principles of the Luscher test. For the synthesis of sounds with the help of the prefabricated musical material, the sampling method is applied. It is this method that makes it possible to emulate the realistic sound of musical instruments, which is also relatively easy to implement. Furthermore, the article includes a description of the software design and development process with a view to confirming the algorithms and methods under review, namely a website for generation musical composition by analyzing an image.

Keywords: automated musical generation, spotify API, sampling, recurrent neural network, correlation schemes between color and pitches

Express method for determining the spectral composition of a signal based on extreme filtering

2022. T.10. № 2. id 1173
Myasnikova N.V.  Lysova N.V. 

DOI: 10.26102/2310-6018/2022.37.2.027

To date, one of the most common systems are those based on the results of measurement experiments. The processing of experimental data is widely used in information-measuring systems, technical control and diagnostic systems as well as in automated control systems. Spectral methods are a powerful and most widely used tool for data processing and analysis. Spectral characteristics are employed extensively in engineering due to their high informative value and reversibility, which makes it possible to perform signal compression and restoration with high calculation accuracy. Questions of spectral signal analysis and descriptions of the main methods for spectrum extraction are examined. An express method for determining the spectral composition of a signal through extreme filtering is considered. The results of processing experimentally registered signals from the paper machine scanner are presented. A method for quick spectrum extraction through extreme filtering is described, which provides the means for analyzing the spectral composition of a signal with the aid of available software tools and obtain visual representations of a wide range of characteristics that help to compile a complete description of the signal under study. The results show the convergence with the minimization of computational effort and simplification of the algorithm. These factors enable the application of this method for quick analysis in technical systems.

Keywords: spectral analysis, signal digital processing, discrete spectrum, prony filtering, extremal filtering, fourier transform

Application of queueing theory methods for estimating synchronization parameters of distributed computing systems

2022. T.10. № 2. id 1171
Polukhin P.V. 

DOI: 10.26102/2310-6018/2022.37.2.028

The paper discusses the approach to estimating the synchronization parameters of distributed computing systems, based on the application of mass queueing theory algorithms. The proposed approach is built upon the use of statistical approaches by means of the maximum likelihood method as well as a number of numerical algorithms to find optimal parameters of synchronization systems. The application of mass queueing theory methods and the Ricart-Agraval model helps to efficiently adapt a distributed system in terms of an optimal solution to the synchronization problem. The employment of statistical approaches in reliance on the calculation of the likelihood function allows one to obtain statistical estimates of the input and output flow intensities of resource synchronization requirements, which enables optimization of the synchronization system with a heterogeneous hardware configuration and makes it possible to determine the maximum allowable flow of requirements for this system. A computational experiment was conducted utilizing Spark as a basic distributed computing system. When conducting an experiment, the algorithm analyzed in the article is used instead of the standard synchronization algorithm included in the Spark assembly. Relations between synchronization time and volume of data transmitted between units of the analyzed system are obtained, which provides a means of calculating parameters of the synchronization system as well as selecting optimal values for the given system. The practical results presented in the scientific study prove the correctness of the theoretical approaches used in the process of creating effective systems for synchronizing distributed resources for the Spark platform in question.

Keywords: distributed computing system, synchronization, queueing system, conditional likelihood function, ricart-Agraval model, maximum posterior method, intensity of demand flows, accident punishment algorithm