Journal archive

The clusterization problem on incomplete data is considered with its application to classification of the partly defined objects. The problem may arise in different practical areas including diagnosis making, forecasting, face recognition and so on. An original approach is outlined which involves a solution technique based on maximum independent set (maximum clique) definition in fuzzy graph (with evident interpretation of a cluster as some maximum clique in a fuzzy graph). An input partly defined object then gets to one of the cliques (clusters) with a decision undertaken specific to that clique. The entire approach subsequently uses a modal logical system, providing necessary formalization to find the maximum independent set (maximum clique) in fuzzy graph. This formalization is based on the transition from modal logic to Lukasewich multi-valued logics accordingly to Tarski theoretical results. The next step consists in transformation of the Lukasewich multi-valued logic to a classical boolean-valued system accordingly to suggested scheme. It is then shown how to formulate a pseudo-boolean optimization (pbo) problem and solve it by means of the suggested simple heuristic method which delivers a solution to a multipple kanpsack problem which we use instead of pbo. It is also noticed that there is a possibility to use different multi-valued Lukasewich logics to interpret a modal system in order to increase an accuracy of the solution. We give also a Python code realizing a standard imputation of the missing data by means of using average values and show that this technique gives incorrect results while the suggested method provides a right solution. Together with acceptable computational complexity of the suggested approach this gives good reasons to recommend the entire rechnique to practical usage.

The description of the mathematical model for the analysis of the state and forecasting the national security of the state is presented. The model is developed based on the system dynamics model. As model variables, the model uses national security indicators of various countries of the world (BRICS,UN). The model also uses external factors selected on the basis of correlation analysis and analysis of interrelated time series that affect the modeled variables. To illustrate the cause-effect relationships between the model variables, a digraph is constructed. The procedure for the formation of a mathematical forecast model for a specific country using structural coefficients is shown. The adequacy of the developed model was verified using retrospective data. The results of a computational experiment with the developed model for the Russian Federation are presented: the process of setting up the model is analyzed, the working areas of the model are highlighted, and the national security indicators of the Russian Federation are forecasted until 2020. For the accumulation, processing, analysis of statistical information necessary for the development of a forecast model and directly for forecasting the national security indicators of the state, a computer simulation system was developed. The basis of the computer simulation system is the «Program for modeling and forecasting the main indicators of national security of the Russian Federation», developed in GUIDE MatLab. The methodology of using the developed mathematical and software for the training of specialists in the field of national security on the basis of the educational resources of the RANEPA is presented. The situational training center, functioning on the basis of the Institute of Law and National Security of the RANEPA and the expertanalytical center of the RANEPA, is designed to conduct an express analysis of the socio-economic, political, demographic situation in the region / country; development of scenarios of socio-economic and political development of the country; modeling of critical situations arising in the real economic, social, political systems of the country.

Issues which make engineers choose and use work time organizing systems are reviewed in this article. In addition, an overview of modern applications from this segment is provided and a notes system is proposed as a simple and effective tool to increase workflow effectiveness. And finally, requirements, model and development principles for an application to solve all these issues are described. The application was developed on the basis of the Spring Boot framework with partial use of the Domain Driven Development ideas. It has a good level of code coverage by autotests due to using the Test Driven Development. For posting of source codes in the Internet, the GitHub repository and the free open source software GPL v3 license have been chosen. The developed organizer helps to avoid waste of time for searching old data in case of work resumption under tickets suspended some time ago due to systematization of all required information, files and sub tasks. Regular using of the organizer makes it possible to increase efficiency of a software engineer’s and the whole development team’s work through minimization of time taken by routine operations connected with search for and use of project related information.

A recognized direction of improving the efficiency of the use of organizational and technical systems is management automation, which provides increased efficiency and reasonableness of decisions made. A significant role in increasing the efficiency of any automated system is played by its software. First, this thesis refers to the application or special software. Development of application programs is fraught with certain difficulties, including those associated with the efficiency of user interfaces created in its process. The analysis carried out by the authors showed that there are a number of problems in this field, which are determined by the fact that it is located at the intersection of scientific disciplines: control theory, ergonomics, technical aesthetics, and psychology. The article analyzes the factors affecting the effectiveness of the development of user interfaces. Based on the analysis, proposals for solving problems based on the use of standardization, unification and prototyping tools were synthesized. Prototyping methods are divided into evolutionary and rapid. The analysis showed that for the conditions of developing application software of automated decision support systems, the latter approach provides the greatest efficiency, namely, the use of specialized prototyping systems. The article proposes to refine the regulatory documentation, which defines the development of automated control systems, to be implemented in the structure of the process of creating such systems, an obligatory stage of interface prototyping

Currently, work is underway on the development of promising portal tracklayer instead of the item on the supply tracklayer. Accordingly, there is a task to evaluate the application of newly developed technology on the manufacturing capabilities of military units RT when performing regular tasks. Stochastic modeling methods can be applied to solve this problem. This approach will allow to form a sample sufficient to assess the main probabilistic temporal characteristics of the system under study. To test this approach in solving this problem was developed a simulation model of the technological process for building the top structure of tracks. The model was created using simulation software. The results obtained in the course of carrying out numerical experiments using the developed «Simulation model structure the upper structure of railway tracks with the use of tractor tracklayers» allowed to justify the basic tactical and technical characteristics of the perspective of the tracklayers. These requirements were included in the tactical and technical task for the ROC to create a «Portal tracklayer» and «tractor tractor-dispenser». On the basis of the results obtained, during the model experiment, the main tactical and technical requirements for a promising tractor tracklayer machine were formulated.

The article discusses the risk management of investment, innovative and import substitution projects. Since the implementation of such projects requires investors and investments, innovative projects can sometimes be considered as a kind of investment projects, and import substitution projects as a kind of innovative ones. However, these projects have significant differences from each other in viewpoint of emerging risks. Moreover, a systemic effect may arise of a sharp increase in the complexity in the total project risk during the implementation of complex system projects. Therefore, the use of simulation with elements of artificial intelligence, including fuzzy logic and artificial life algorithms, is becoming relevant in the management of project risks. As a result, a new method is proposed for identifying the need to create hybrid fuzzy-logical simulation models or the absence of such a need based on identifying synergistic effects when analyzing the risk rubricator in a specific project. The analysis of the investment attractiveness of the Russia’s regions using the evolutionary-simulative methodology on the example of the Smolensk and Tambov regions. A new method is proposed for selecting innovation clusters and promoting innovative projects in the regions on the basis of integrated bacteria swarm-simulation modeling with detection of contours selected territories borders. A new economic and mathematical toolkit for managing the risk of import substitution has been created.

Usage of automated informational systems in the daily activities of a financial institution is the basis of its successful existence and development. The most common ways of automation in the financial sector based on own development or standard of automated banking systems (ABS) are no longer able to meet the increasing needs. That is why the use of successfully operating in other industries ERP systems becomes very attractive. For today, many researches has been done on the existing market of banking systems. There are also works describing the advantages of using ERP systems for automation of economic activities of the bank. Nevertheless, it is quite difficult to make a choice about strategy of automation. The aim of the study is to identify and assess the strengths and weaknesses of each of the ways of automation and their ability to withstand the opportunities and threats of environmental factors. The method of quantitative SWOT analysis based on expert assessments was used for this study. The quantitative SWOT analysis matrices had been obtained because the study allow decision-makers in the field of IT strategy to have a more balanced approach to the choice of the development path. Comparison of SWOT matrices for analysis of automation paths allows making a conclusion about the preference of using ERP system as a basic system in the bank.

The results of the study of the influence of the characteristics of convolution and subsampling layers (sub-sampling layer) at the input of a deep convolutional neural network on the quality of pattern recognition are presented. For the convolution layer, the variable parameters were the size of the convolution kernel; the varied parameters of the architecture of the down sampling layer were the size of the receptive field, which determines which region of the input feature will be processed to form the output of the layer. All the parameters listed that determine the architecture of the input layers of convolution and subsampling, the neural network developers have to select, based on their experience, known good practices. This choice is influenced by a preliminary analysis of the parameters of the processed images: image size, number of color channels, features of signs determining the classification of recognizable objects in different classes (recognition of silhouette, texture) and more. To take into account the noted factors when creating the architecture of the input convolution and subsampling layers, it is proposed to use numerical characteristics calculated based on the analysis of histograms of input images and pixel color intensity dispersions. A histogram of both the entire image and the fragments is constructed, as well as the calculation of the total variance and local variances of the fragments, compared with the total dispersion. Based on these comparisons, recommendations were developed for choosing the size of the convolution kernel, which will reduce the time needed to search for a suitable neural network architecture. A study of the influence of the above parameters on the quality of image recognition by a convolutional neural network was carried out experimentally, using a network created in Python using the Keras and Tensorflow libraries. To visualize and control the learning process of the neural network, the TensorBoard cross-platform solution was used. Network training was carried out on the Nvidia GeForce GTX 1060 GPU, supporting CUDA technology for hardware and software parallel computing architecture.

Paper presents the results of the study of the possibility and efficiency of computational procedures for constructing autoregressive statistical models and their close derivatives, as well as their ability to solve practical problems of constructing a forecast of electricity prices. Sufficiently detailed results of numerical construction of ARIMA-models are presented and supplied with options for preprocessing the initial data, taking into account the regularities characterizing the functioning of the energy complex. Adequacy verification of forecast mathematical models with reference to historical natural data in the form of time series was carried out on the basis of numerical estimation of the standard error. The achieved accuracy level for the designed predictive models for electrical energy day-ahead market which were found through Russian Belgorod region data 2016-2018 matches already published results over international energy markets in Europe. America and Australia. Comparative analysis and interpretation of mathematical models for prediction of the accuracy and adequacy of the field data, both published and obtained in this work leads to the conclusion that increasing complexity of statistical autoregressive forecast models (complexity of structures, the number of unknown parameters, the combination of heterogeneous components, the introduction of correction coefficients) only in individual cases and slightly increases the prediction accuracy. It is concluded that it is expedient to introduce additional information about significant factors affecting the full-scale time series of the predicted variable into the mathematical models of the forecast. Note for more information about influencing factors by the introduction of appropriate computing method algorithm changes and the use of somehow combined prediction mathematical model structure supposed to be possible directions for further research.