Journal archive

In this paper, the authors conduct a comparative analysis of instrumental methods used in modeling stochastic processes, namely, component analysis of time series, fractal modeling and modeling using p-adic mathematics. As an object of study, the authors chose the dynamics of the MICEX index. At the first step of the work, the authors carry out a detailed component analysis of the time series, which made it possible to identify the main development trend in the form of a quadratic function; periodic fluctuations with a period of six levels and a cyclical component describing fluctuations in the world economy with a period of fifty-five levels. At the second step of the work, the authors simulate the dynamics of the MICEX index using a fractal theory based on the self-similarity of the development of the economic process, which showed the ergodicity of the series under study with a stable influence of only the last twenty-four levels. The third step of the work was the p-adic modeling of the patterns existing in the series under study, which allowed the authors to reduce the model error to 6.8%. As a result of the work, a forecast of the dynamics of the MICEX exchange rate at four levels is presented, presented in three scenarios: optimistic, realistic and pessimistic. As conclusions of the work, an analysis was made of the possibility of using the considered methods for multiple, medium and long-term forecasts; the complexity of the methods and the need to use special software products are evaluated.

Justification for the relevance of developing an individual learning path in the field of online learning. The problems of forming an individual learning trajectory are analyzed. The main problem of personalization of learning from the point of view of the student is highlighted – the difficulty in finding the most appropriate sequence of studying educational objects that best suit their skills and preferences. It is concluded that the existing practices and methods of organizing a personalized educational process of courses in online learning are focused on the statistical characteristics of students that do not change during the study of an online course. Therefore, there is a need to develop a methodology for the formation of an individual learning path. The proposed approach allows us to consider the formation of recommendations as a dynamic process. An algorithm for the formation of an individual learning trajectory has been developed, which consists of a multi-criteria choice of a sequence of online courses at each moment of decision-making according to a given set of criteria and sequential mastering of skills. The choice of online courses is carried out using the cluster analysis method – k-means. Groups of clusters that meet the criteria of online courses have been identified. Each cluster consists of the closest objects – online courses. Based on these results, a sequential selection of online courses is made, using the available information about the user»s requirements and the skills that the learner needs to acquire. The purpose of developing for the formation of an individual learning trajectory is to provide students with the most appropriate sequence of learning objects in accordance with their skills and preferences.

The article discusses the possibility of applying a precedent approach to improve the efficiency of control of thermophysical and chemical-energy-technological processes of processing ore raw materials. As an example, one of the variants of such processes is considered – heat treatment of pelletized phosphate raw materials. To form the knowledge base of an intelligent system, it is proposed to jointly use a compositional ontological model, which includes two ontologies, each of which is focused on describing one of the subject areas under consideration: thermophysical and chemical-energy-technological processes of heat treatment of pelletized phosphate ore processing plants. The use of this model makes it possible to take into account both the specific properties and characteristics of the processes under consideration, as well as unique tasks and management indicators, avoiding the need to form a generalized holistic ontology that would reflect these subject areas in a simplified form. The use of a compositional ontological model also makes it possible to store information not only in quantitative but also in qualitative form. To form solutions to provide support for the processes of managing the processing of ore raw materials, it is proposed to use a new modified case-based approach, which consists in the possibility of working with the proposed compositional ontological model in determining the closest solution to the current situation, as well as the formation of quantitative values of these decisions based on the information presented in linguistic form. It is possible to take into account the degree of significance of each of the ontologies when developing solutions for each individual current situation that arises when managing the processing of ore raw materials.

The article presents a solution for the development of an automated functional testing system for the electronic services portal. The proposed solution takes into account the features of the architecture of the electronic services portal and integrated information systems with it. To obtain the result, methods of emulating the actions of real users of the electronic services portal and methods of organizing work with automated tests, checkers, user accounts were used. The implementation of the functionality of the schedule and queue of automated test launches is presented. The implementation of the functional testing component is supplemented with a description of the process of creating new automated tests. Using the automated functional testing system, the operator of the electronic services portal will be able to carry out this process as often as it deems necessary. Verification of the obtained development results was achieved by presenting the structure and content of the screen forms of the system interface. The practical significance of the results lies in the fact that an approach has been proposed to create simple and cost-effective methods for conducting functional testing of electronic portals, with the help of which system users, including those who do not have special programming skills, will be able to create automated tests and update existing automated test scripts. To ensure these capabilities, the article presents solutions for creating a pseudo-language and an interpreter for it. All the solutions presented are scalable and can be used when working with other electronic portals.

The article is devoted to the issues of controlling the operational risks of a credit institution arising in the process of using IT technologies. Among banking risks, operational risk occupies a special place, primarily due to the fact, that it affects various areas of banking activity and is difficult to separate from other types of risk. Operational risks arise, among other things, as a result of downtime or incorrect operation of technical systems and equipment. Due to the constant growth in the degree of automation of banking business processes, new IT risk groups are emerging that can have a significant impact on the activities of a credit institution. The aim of the work is to create an artificial neural network using the high-level Keras library in Python, which automatically controls the level of criticality of the IT risk that has arisen. In the article, based on the analysis of risk events associated with the use of IT technologies, the data flows entering the input of the neural network is identified and its structure is determined. The paper also presents the results of training a neural network created by the authors based on the generated data sets. The use of intelligent methods for assessing the level of criticality of operational IT risk allows you to quickly take measures to minimize the consequences, and thus reduce direct and indirect losses. In connection with the above, the automation of operational risk management based on the use of neural network technologies is currently one of the most urgent tasks for credit institutions. The results obtained are new and can be used by credit institutions in the process of building automated systems for monitoring and managing operational risks.

The article analyzes the operation of thyristor automatic switching devices for uninterruptible power supply units of nuclear power plants. They are part of the emergency power supply system for auxiliary electrical equipment with a rated voltage of 0.4 kV. In such a reliable power supply system for especially responsible consumers, alternative networks and backup sources are necessarily used. Typically, groups of consumers for auxiliary needs of nuclear power plants are powered from the inverter network, so that in the event of a shutdown of the backup bypass network, these loads continue to be powered by the uninterruptible power supply unit. It incorporates a charger – a controlled rectifier, a battery pack and a transistor inverter. The transition from one network to another in any direction must be “shockless” in order to avoid the operation of the protections of uninterruptible power units and other electrical protections of the reliable power supply system. If there are failures in the algorithms or their irrational organization, the processes of transition between networks may be accompanied by a violation of uninterrupted power supply or phase-to-phase short circuits. A structural simulation model has been created in the MatLab computer mathematics system for testing transition algorithms for various phase shifts of networks and transition directions. The algorithm of transition between networks for uninterruptible power units of one of the manufacturing companies that supplied equipment to nuclear power plants was analyzed. A safer optimal algorithm for controlling network switching with phase-by-phase control of the current drop in the disconnected network is proposed. The proposals are supported by the results of computer simulations.

The results of studies are presented, the purpose of which was to develop an algorithm for identifying information security threats in distributed multiservice networks that provide information interaction of regional government bodies, as well as their communication with the population of the region. The relevance of the research topic is due to a significant increase in various types of cyber attacks on the computer networks of public authorities and the need to increase the level of security of these networks by intellectualizing methods for combating information security threats. The algorithm is based on the use of machine learning methods to analyze incoming traffic in order to identify events that affect the state of information security of public authorities. The algorithm provides for input traffic preprocessing, as a result of which a set of images (signatures) obtained from Wasm binary files is formed, and then the image classifier is launched. It contains a sequential inclusion of deep neural networks – a convolutional neural network for signature classification and a recurrent network that processes the sequences obtained at the output of the convolutional network. Features of the formation of signatures in the proposed algorithm, as well as sequences at the input to the recurrent network, make it possible to obtain the resulting assessment of information security, taking into account the history of its current state. The output of the recurrent network is aggregated with the result of comparing the actual signatures with those available in the database. The aggregation is performed by the fuzzy inference system of the second type, using the implication according to the Mamdani algorithm, which generates the final assessment of information security threats. Software was developed that implements the proposed algorithm, experiments were carried out on a synthetic data set, which showed the efficiency of the algorithm, confirmed the feasibility of its further improvement.

The article analyses the issues of the simulation models application in the large systems management. The background of the issue, specifically, the gradual increase of the models application domain in the management, is described. Quite successful results of the simulation models application in the strategic management of complex systems in different branches are noticed, for example, the works described in [6, 7]. It is mentioned that quite long execution time of most simulation models make it difficult to use them in the operational management, especially for complex systems. The possible solutions of this problem related to the synergy of multiple factors, primarily the emergence of the fundamentally new computational capabilities, use of contemporary concepts of simulation investigations, a combination of the simulation modeling with the multi-factor optimization, the use of the model as a solver, are proposed. The ability to conduct optimizing experiments with the model allows to find and recommend the best ways of the system development. The effectiveness of the simulation experiments application is shown in this article for the optimal planning of the oil refining company output. The technique of the optimal solution finding in the modeling environment by means of the connection of the IOSO multiparameter optimization software to the GPSS Studio modeling environment was successfully tested. Based on the results of the work, it was concluded that in the future the technique of optimal solution finding will allow use of the simulation model as an automatic “intellectual solver” in automatic production planning processes. The analysis and estimations performed showed that the integrated use of all new possibilities ensures the synchronization of the model execution time and the required time bounds of the management solution production. The conclusion about the beginning of active use of the simulation modeling method for the complex systems operational control is made.

The process of creating promising technical means is always based on the results of previous studies devoted to the definition and justification of the technical requirements for the samples being developed. The use of modern methods of simulation (computer) modeling at the stage of research works helps to analyze the many available options for the implementation of the developed systems, assess the degree of load on their individual elements, form reasonable proposals for the methods of functioning and composition. The article describes one of the ways of applying the simulation modeling method, developed in order to assess the effect obtained due to a change in the number of track railway trolleys used to feed the links of the rail grid to the place of their laying. The assessment is carried out on the basis of the tactical and technical characteristics of a promising portal tractor track-laying machine. The result of the simulation is the statistical data of the time indicators of the process of laying the rail grating. The developed simulation model makes it possible to calculate the production time of individual technological cycles at the stage of research work devoted to the creation of mobile means for laying railway tracks, in conditions of a shortage of initial data and the complexity of conducting a full-scale experiment with real objects. To analyze the degree of utilization of production facilities and mathematically substantiate the optimal number of transportation means involved, in order to reduce the downtime of technical means involved in loading and track laying works. As initial data, the main technical characteristics of the promising PB-5 tractor track-laying machine, PT-13 track railway trolleys, universal devices for placing automotive and pneumatic wheeled vehicles on the railway track are taken. The production process under study was modeled based on the accepted options for the organization and technology of work according to the technological map “Laying a track panels by links” included in the “Collection of technological maps of track work”. The developed model can act as a source of statistical data for organizationally related multi-node, multi-channel models of the functioning of transport construction formations, which include several levels of representation of the functioning of the system.

Modern economic conditions are characterized by a high degree of uncertainty and complexity, which is difficult to formalize. Fuzzy cognitive maps make it possible to solve this problem – to cope with complexity, but when cognitive maps are built on the basis of expert opinions, this sometimes causes distrust due to the subjectivity of the judgments of individual specialists and doubts about compliance with the examination procedure. Therefore, the task of developing analytical tools to increase the awareness of decision makers about the real state of affairs in the organization and in the external environment is relevant, because contributes to the growth of their efficiency. The article proposes and tests a procedure for automated construction of a cause-and-effect diagram using statistical methods, as well as methods and models of machine learning. With the help of modern methods of topic modeling, key topics (concepts) are identified in the area under consideration for the considered period of time. The Doc2Vec model is then used to derive a fixed length numeric vector from the identified topics. The Granger test is then used to establish the possibility of a causal relationship between the topics found. The constructed cause-and-effect diagram allows you to describe the current situation and understand the key concepts of the area under consideration. According to the Russian media for 20 years (from 2002 to 2021), a cause-and-effect diagram was built that reflects the problems of strategic management in Russia. The analysis of the diagram made it possible to conclude that the topic of Russian projects is the most significant in the area under consideration