Authors

Building an economic monitoring information system

Structural and functional modeling of business processes

The problem of the structural and functional description of the business environment which provides the requirements for information systems implementation is considered. Basic terms definitions are proposed followed by comparative analysis of concepts and tools of the two most elaborated modeling methodologies.

Modeling monitoring processes

The efficient information-gathering system is a crucial point for control procedures implementation. Developing the monitoring environment assumes that the performance parameters of the system to be built must be evaluated beforehand. The application-oriented models suit suggested in the paper may support this task.

A simulation technique for sophisticated models

Building simulation model is normally a rather smooth process when implemented in simulation software environment. However some real object features to be introduced into model may cause con¬cern while expressing them directly by means of the instrumental simulation system facilities and need a special treatment.

Web-based Information Use Technologies

Simulating investment processes

A method for investment processes analysis based on system approach is discussed. We analyze the dynamics of mutual investor and investment object influences. A decision-making supporting tool is composed of collaborative simulation and analytical models which in conjunction with application methods and techniques form the virtual modeling workbench. To illustrate the method an investment process associated with the municipal land use is considered. Read more...

Modeling publishing processes in scientific periodicals

Simulation technology and model management in «Actor Pilgrim» system

The paper presents the concepts and methodological basics of design, building and application of simulation models in the environment of Actor Pilgrim simulation system. The key features of the system compared to its earlier version are discussed as well as system capabilities to meet the challenges of managing complex economic processes, training and research tasks. The methods and techniques of practical work on the creation of and running the models using the system Actor Pilgrim are considered.

Enterprise value and enterprise price: concepts and derivability of financial indicators

The notion of and the connection between the book value and the market value are discussed. Analyzed are the relationships existing in Russian market environment between these values and the enterprise value as well as their derivability of conventional financial indicators. The indicators affecting enterprise value and enterprise price and their influence on company growth forecasting accuracy and value based management decisions are identified.

Using profit and cash flow factors in value based analysis and financial management

Impact of issuer’s significant facts disclosure on the share price

Resource saving knowledge testing based on cloud computing

Foreign approaches to M & S industrial-level acquisition

The concept and principles of adaptive monitoring

The concept and principles of adaptive monitoring (part 2)

Implementing and using teacher’s personal electronic organizer

Building a multilingual system for cataloging and using the library fund

Approach to creating applications for non-core teacher functions

Issues of methodological and instrumental support of workshops on modeling and simulation

The effect of learning depends on many factors, among the most important of which are the suitability of a plan for conducting practical exercises and laboratory works that most accurately reflects the purposes and didactic units of the course being studied, and a sufficient level of provision of practical training with methodological guidelines and software tools. Decisions regarding the choice of all these elements depend on a number of conditions, such as the rules and standards in force of a particular educational environment, the format of training, the students’ stuff, and others. The paper proposes a framework generalized plan for conducting laboratory workshops on modeling and simulation courses, summarizing the author's methodological experience. The main prerequisites and principles underlying the composition of the workshops are presented. The set of tools used to perform practical tasks is characterized. Brief characteristics of the goals of each practical task of the generalized plan, the setting of tasks and the software tools used to solve them are given. The ideas and principles presented in the paper can be useful for teachers planning practical lessons on modeling and simulation as well as for developing their methodological and instrumental support. Read more...

Fuzzy cognitive modeling of heterogeneous electromechanical systems

The article presents a method of fuzzy cognitive modeling for heterogeneous electromechanical systems (HEMSs) in the management of innovative design solutions. During the operation of the HEMSs, as a result of their operational aging, the properties of the windings parametric matrices and the HEMSs vector space properties change. Periodic testing of the HEMSs vector space allows obtaining reliable information about the current technical condition of the HEMSs, about its changes during operation and about the risks of operating capability loss. At the same time (I) the presence of proportional changes in signals during sequential testing indicates the homogeneous operational aging of the HEMSs and its rate; (II) a disproportionate change in one of the signals indicates the damage or the development of a heterogeneous aging process; (III) a change in signals with a change in the angular position of the rotor indicates worn bearings or damage of the HEMSs rotor. The article presents the HEMSs model, describes the method for the topological research of the vector space and the method for forming the diagnostic matrices. The deviations of their elements are fuzzy due to the uncertainty of the load, influencing environmental factors and unstable supply voltages. Therefore, for predictive estimation of the HEMSs state, it is proposed to use fuzzy relational cognitive models that allow implementing a completely fuzzy approach to modeling problem situations in these systems. The presented data confirm the growth of the HEMSs heterogeneity under conditions of uncertainty of external influences. The proposed method for predictive estimation of the HEMSs state, based on fuzzy relational cognitive models, provides resistance to an increase in the uncertainty of the estimation results for various models of system dynamics due to a reasonable set of fuzzy vector-matrix operations. Read more...

Rationalization of university teacher's work in atypical conditions

The educational sphere is one of the most influenced by global processes greatly accelerated during the last period, the consequences caused by them are becoming more and more radical. This paper is devoted to solving the task of reducing the load on the teacher at the expense of information to a minimum of routine operations increased, in particular, by introducing into the practice of the educational process a distant mode, subject to minimizing a reduction in high-quality level. The focus is on such an element of the work of the teacher as a summary of the final measures, for the conducting of which a unified scheme is proposed, covering the basic actions to plan and realizing activities in remote mode, evaluating and logging the responses of examinations, questions of the information interaction of the student and the teacher. The execution of a number of operations in the proposed scheme is automated, the description of the functionality and design principles of software application is given in the article. The approach is focused on achieving the highest possible level of reliability of exams, simplifying operations and transparency of the grades issued by teacher. In the proposed decision, the experience collected by the author during its practical work within the proposed approach is taken into account. Read more...

A computer program for electromechanical system operational diagnostics based on the topological approach

The paper presents a method, a mathematical model, and a computer program for the operational diagnostics of an electromechanical system (EMS). During EMS operation, service aging changes the properties of the parametric matrices of the windings and, as a consequence, the characteristics of the EMS vector space. Periodic testing of the vector space offers relevant and reliable data on the current health of the EMS, its changes during operation, and the risk of loss of function. The object of the study is an asynchronous electric motor (AEM). It is urgent to automate the process of assessing the current health of an AEM and to organize the storage of information on its states at different stages of its life cycle. To solve the problem, software (SW) for accumulation of information on AEM operation and for evaluation of its basic performance metrics has been developed in the Python programming language. The SW is based on the topological approach to diagnostics, which implies the analysis of the current responses of motor rotor windings to phase voltage pulses. The SW enables one to determine the rate of the service aging of an item, the probability of its survival and residual life, to obtain access to the history of previous diagnostics, and to visualize the in-service history of the above-mentioned performance metrics. The developed SW can be used to increase the AEM operation efficiency and to plan engineering or repair work; it can also be used as an information source for re- engineering and modification of existing AEMs. The described SW can be extended to perform operational diagnostics based on the topological approach of devices of various types. Also, this SW can be considered as a separate information component of the digital twin of a complex EMS, which will allow us to study the main indicators of its reliability, fault tolerance and operational efficiency at all stages of the life cycle. Read more...

Modeling the impact of significant facts on the level of capitalization of Russian companies

The article 30 of the Federal Law "On the Securities Market" establishes that significant facts are information that, if disclosed, may have a significant impact on the value or quotations of the issuer's securities and (or) on the decision to acquire or alienate the issuer's securities by any interested person acting reasonably and in good faith. At the same time, there are very few empirical studies proving the influence of significant facts on the value or quotations of securities of Russian issuers. Modeling of such influence in order to manage the value and capitalization level of Russian issuers is advisable for investors, owners and management of Russian public joint stock companies to study the possibilities of managing the value and capitalization level of companies. The solution to this problem is based on measuring the results of the joint-stock company's activities and publicly disclosing the information necessary for making managerial decisions, which in the Russian Federation includes, among other things, significant facts. Significant facts are events that can change the current state of the company, in particular, affect such an indicator of its activity as the level of capitalization, therefore, affect the decisions made by the interested person. A few studies on the impact of significant facts on the level of capitalization of Russian companies have already been conducted earlier, however, since their implementation, a number of regulatory documents have come into force that change the legislative environment and affect, among other things, the composition of mandatory registration and disclosure of significant facts. This paper presents the results of a study aimed at establishing a list of the most significant events characteristic of companies' activities, identifying events that are universal in terms of the frequency of occurrence and significance of consequences, and finding out whether there is a connection between their occurrence and the effects caused in the form of changes in the values of financial indicators. For the research, data from 20 large Russian companies representing the oil and gas, metallurgical, chemical and energy industries were used, and received in the form of reports for the period following the introduction of changes in the regulatory environment. Read more...

Computer program for modeling of technical state indicators of electromechanical systems

The article is aimed at solving the problem of scientific justification of criteria and methods for assessing the technical state of electromechanical systems based on the topological diagnostic method. Mathematical model and computer program for simulation of technical state indices of asynchronous electric motors (AEM) are presented. Functions and Green matrices, as well as deviation matrices, are considered as such indicators. The basis of the program is the mathematical model of the AEM with a non-accelerated rotor and non-homogeneous windings. AEM is supplied from pulse voltage source. The action is carried out in different directions of the vector space of the motor in order to determine its characteristics and degree of homogeneity. Based on the reactions of the object, the program calculates and analyzes technical indicators for intact and damaged states of the AEM. A computer program for mathematical modeling of the technical state indicators of the AEM was carried out using the Maple package of symbolic and numerical calculations, which provides extensive opportunities for mathematical studies of various levels. A description of a software implementation of the proposed mathematical model is given. An example of using a program to model the performance of a serial motor with specified technical characteristics is given. The article presents the results of modeling the object indicators corresponding to the object different operational states. A reference state, a damaged state characterized by a change in the properties of the vector space during long-term operation, as well as a limit state, which corresponds to a break in one of the phases of the rotor winding, were defined as these states. Conclusions on each of the given electric motor states are given. Read more...

Application of bacterial optimization algorithms for selecting a site to construct a tank park on the main oil pipeline

The oil industry is the leading sector of the Russian economy, that makes the largest contribution to the country’s budget, creates a huge number of jobs and fully meets the domestic needs for oil and its products. In Russia, transportation of crude oil from fields to consumers (primarily refineries) is carried out by 5 modes of transport. Pipeline transport has received the greatest distribution. It provides transportation for 83% of crude oil and 30% of oil products. The most important element of the pipeline system is tank parks, which are used to collect and store oil at the junctions of technological pipeline sections and transshipment to other modes of transport. They are especially dangerous industrial objects. Therefore, they are subject to extremely stringent design and construction requirements. The most important stage in the construction of a tank park is the site selection, which is carried out on the basis of economic criteria and engineering requirements. In order to reduce the number of options for its location, where the survey party will travel, it is proposed to conduct a preliminary selection of the most promising territories by solving the task of multi-criteria optimization. The presence of a huge number of criteria leads to the need to use heuristic methods, among which swarm optimization algorithms based on modeling the collective behavior of various living organisms are widely used. To solve this problem, it is proposed to use bacterial optimization algorithms that allow taking into account both favorable and negative factors. Fuzzy logic elements can be added to the classical algorithm (it is proposed to set the initial positions of bacteria using fuzzy-logical inference systems, where the available statistics and expert assessments will be input parameters). In general, the proposed approach can be used to select sites for the construction of various hazardous industrial facilities, for which a large number of parameters must be taken into account. Read more...

Multilevel algorithms for evaluating and making decisions on the optimal control of an integrated system for processing fine ore raw materials

The results of studies aimed at developing multi-level decision-making algorithms for management of energy and resource efficiency, technogenic and environmental safety of a complex multi-stage system for processing fine ore raw materials are presented (MSPFORM). A distinctive feature of such a system is its multidimensionality and multiscale, which manifests itself in the presence of two options for implementing technological processes for processing finely dispersed ore raw materials, the need to take into account the interaction of the aggregates included in the system, as well as the hierarchy of describing the processes occurring in them - mechanical, thermophysical, hydrodynamic, physical and chemical. Such a variety of processes characterizes the interdisciplinarity of research and the complexity of obtaining analytical, interconnected mathematical models. This situation inspired the analyze use of artificial intelligence methods, such as deep machine learning and fuzzy logic, to describe and analyze processes. The scientific component of the research results consists in the developed generalized structure of the MSPFORM, the conceptual basis of multilevel algorithms for evaluating and making decisions on the optimal control of this system, the proposed composition of the parameters and the form of the optimization criterion. The task of the study was to analyze possible options for the processing of ore raw materials, to develop a concept for the construction of the MSPFORM allowing the possibility of optimizing its functioning according to the criterion of energy and resource efficiency while meeting the requirements of environmental safety. The application of evolutionary algorithms for solving the problem of optimizing the MSPFORM according to the criterion of minimum energy consumption is announced and its stages are specified. The structure of the block of neuro-fuzzy analysis of information about the parameters of processes in MSPFORM is presented, which is based on the use of deep recurrent and convolutional neural networks, as well as a fuzzy inference system. The results of a simulation experiment on approbation of the software implementation of this block in the MatLab environment are presented. Read more...

An intelligent model for managing the risks of violation of the characteristics of electromechanical devices in a multi-stage system for processing ore raw materials

The results of studies on the development of the structure of an intelligent model for managing the risks of violation of the characteristics of electromechanical devices in a multi-stage system for processing ore raw materials are presented. Such devices are involved in all cycles of the technological process, so the assessment of this risk for them is an urgent task. A method for assessing such risks is proposed, which is based on the assessment of the useful life of equipment, performed on the basis of the prediction of characteristics by a deep recurrent neural network, with further generalization of the results of such an assessment in a fuzzy inference block. Recurrent neural networks with long short-term memory were used, which are one of the most powerful tools for solving time series regression problems, including predicting their values for long intervals. The use of deep neural networks to predict the characteristics of electromechanical devices made it possible to obtain a high prediction accuracy, which made it possible to apply a relatively less accurate recurrent least squares method for the iterative process of estimating the useful life of equipment. This approach made it possible to build a computational evaluation process with its constant refinement as new results of measurements of the characteristics of electromechanical devices become available. The results of a model experiment with a software implementation of the proposed method, performed in the MatLab 2021a environment, are presented, which showed the consistency of the program modules and obtaining a risk assessment result that is consistent with the expected dynamics of its change. Read more...

Algorithm for identifying threats to information security in distributed multiservice networks of government bodies

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. Read more...

Fuzzy model of a multi-stage chemical-energy-technological processing system fine ore raw materials

The results of the study, the purpose of which was to build a software model of a multi-stage integrated system for processing finely dispersed ore raw materials, are presented. The role of such raw materials can be processed waste at mining and processing plants of apatite-nepheline and other types of ores, which accumulate in large volumes in tailing dumps. They create a significant environmental threat in the territories adjacent to the plants due to weathering, dust formation, penetration into the soil and aquifers of chemical compounds and substances hazardous to human health. Therefore, the improvement of existing production processes, the development of new technological systems for mining and processing plants, including the application of the principles of the circular economy, waste recycling, justifies the relevance of the chosen research area. The proposed program model is based on the use of trainable trees of systems (blocks) of fuzzy inference of the first and second types. This approach made it possible to avoid unnecessary complication of the bases of fuzzy inference rules when using only one fuzzy block when building a multi-parameter model of the entire multi-stage complex system. The use of several fuzzy inference blocks that describe the behavior of individual units of the system and their configuration in accordance with the physical structure of the system allows the use of relatively simple sets of rules for individual blocks. The joint selection of their parameters when training a tree of fuzzy blocks makes it possible to achieve high accuracy of the solutions obtained. The novelty of the research results is the proposed software fuzzy model of an integrated system for processing finely dispersed ore raw materials. The results of a simulation experiment conducted in the MatLab environment using a synthetic data set generated in Simulink are presented. The results showed that the trained fuzzy model provides good fidelity of the parameters and variables from the test part of the synthetic set. Read more...

Neuroregulator of the complex technological system for processing ore waste

The study is devoted to improving the management system of a complex technological system for processing ore waste. Such waste accumulates in large volumes in the territories adjacent to the mining and processing plants, posing a great environmental threat to both the population and the environment due to dust formation and the penetration of harmful compounds into the soil and groundwater. Therefore, the task of improving the management systems for the processing of ore waste, as one of the priorities, is on the current agenda of the management of mining and processing plants. The complexity of the technological system is manifested in the presence of two processing lines that differ in the set of units, and the choice of line depends on the granulometric composition of ore waste. The scientific novelty of the research results is the proposed structure of the neural network controller based on the reference model for the technological system, which is used as deep recurrent neural networks. The general structure of the neuroregulator includes several local neurocontrollers for each of the units of the technological system. Recurrent neural networks make it possible to create high-precision digital copies of individual units of two processing lines and use them to simulate the response of control objects when setting up controllers. Approbation of the proposed structure of the neuroregulator was carried out in the MatLab-Simulik environment, neural networks were designed using the Deep Network Designer tool. The results of testing showed that the speed of the control system is increased compared to other architectures of neuroregulators available in the Simulik environment, which can positively affect the operation of the entire technological system in transient conditions, in particular, reduce technological losses. Read more...

Analysis and testing of neural network TCP/IP packet routing algorithms in private virtual tunnels

One of the most important components of the global Internet are traffic control and management systems. In order to achieve uninterrupted information and communication interaction, the organization of the process is constantly changing, covering not only individual subnets, but also p2p network architectures. The dominant areas for improving the network structure include 5G, IoT and SDN technologies, but their implementation in practice leaves the issue of ensuring the information security of networks built on their basis without a satisfactory solution. Current virtual tunnel deployment topologies and intelligent traffic distribution components provide only partial solutions, particularly in the form of access control based on user traffic and security through dedicated user certificates. The deployment of a tunnel is of particular importance in cases where it is necessary to ensure consistency and coordination of the work of complex socio-economic systems, an example of which is the information and communication exchange between participants in scientific and industrial clusters formed to implement projects for the creation of innovative products. However, existing solutions have disadvantages such as the need to purchase a license for full-featured access to the software product and specialized configuration of client-server authentication that provides secure access to a remote network route. The approach proposed by the authors, based on neural network distribution of traffic between clients of a private dedicated network, allows us to eliminate the noted shortcomings. Based on this principle, a multi-module system for intelligent packet routing was created and tested through unit testing. An analysis of the effectiveness of using a trained network address distribution model is presented in comparison with the use of a DHCP server based on the isc-dhcp-server package, distributed as the dhcpd service. Read more...

A method for predicting bank customer churn based on an ensemble machine learning model

The results of research are presented, the purpose of which was to develop a method for predicting the outflow of clients of a commercial bank based on the use of machine learning models (including deep artificial neural networks) for processing client data, as well as the creation of software tools that implement this method. The object of the study is a commercial bank, and the subject of the study is its activities in the B2C segment, which includes commercial interaction between businesses and individuals. The relevance of the chosen area of research is determined by the increased activity of banks in the field of introducing digital services to reduce non-operating costs associated, in particular, with retaining clients, since the costs of attracting new ones are much higher than maintaining existing clients. The scientific novelty of the research results is the developed method for predicting the outflow of commercial bank clients, as well as the algorithm underlying the software that implements the proposed method. The proposed ensemble forecasting model is based on three classification algorithms: k-means, random forest and multilayer perceptron. To aggregate the outputs of individual models, it is proposed to use a learning tree of fuzzy inference systems of the Mamdani type. Training of the ensemble model is carried out in two stages: first, the listed three classifiers are trained, and then, based on the data obtained from their outputs, a tree of fuzzy inference systems is trained. The ensemble model in the proposed method implements a static version of the forecast, the results of which are used in a dynamic forecast performed in two versions – based on the recurrent least squares method and based on a convolutional neural network. Model experiments carried out on a synthetic dataset taken from the Kaggle website showed that the ensemble model has a higher quality of binary classification than each model individually. Read more...