Software & Systems

Articles of journal № 2 at 2022 year.

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11. Automating the assessment of the power grid state in remote areas of Russia using smart structures [№2 за 2022 год]
Author: Shevnina Yu.S.
Visitors: 2062
The paper discusses a method for automating the assessment of the power grid state in remote regions of Russia using smart structures. The proposed automation method is implemented as a mobile applica-tion. The smart structure underlying the described method of automating the assessment of the power grid state consists of modules for receiving and processing data from sensors, searching for patterns in the power grid characteristics and generating state classifiers, offering recommendations for repair and optimal operation of the power grid and substation. The scientific novelty of the proposed solution is in the method of analyzing and processing the power grid characteristics and their combinations. In addition, the external influence parameters in the form of natural and man-made factors are taken into account. The method of analyzing and processing information about the power grid and substation is based on the machine learning method – logical data analysis. Assessing the state of a power grid and a substation is important when studying and solving the problems of predicting changes in the power grid state, selecting recommendations and making de-cisions on repair and maintenance work. The method for assessing the power grid state is based on the search for patterns and the construc-tion of classifiers. It allows taking into account all the characteristics and parameters of a power grid, their totality and the relationship between them. In addition, the described method allows analyzing and obtaining patterns for incomplete and inaccurate data, which is a fairly common occurrence in real power networks. The method can be used in the design and maintenance of power grids and substations in hard-to-reach and remote regions of the Russian Federation. The proposed reduction of the characteristic regularities and their sets based on their recurrent con-junction makes it possible to obtain optimal classifiers of the states of a power grid and a substation with high interpretability and generalization. It increases the accuracy of assessing the power grid state, therefore, increases the accuracy of predicting behavior, recommendations and making decisions about repair work and the optimal mode operation.

12. Automated detection and classification of objects in the traffic flow on satellite images of the city [№2 за 2022 год]
Author: V.S. Tormozov
Visitors: 2437
The paper discusses the developed methods of detecting and classifying objects in a traffic flow on ul-tra-high spatial resolution space survey data. Due to appearing the large amounts of free access satellite data, the development of machine learn-ing methods based on geospatial data, in particular satellite data, is becoming increasingly urgent. The paper justifies the choice of a source of data on traffic flows – ultra-high resolution satellite images. It also describes the main problems and tasks associated with the recognition and classification of objects in traffic flows. The purpose of scientific work is to develop and study a chain of algorithms that allows detecting and classifying objects in traffic flows with high accuracy. The research is based on a numerical as-sessment of the quality of the algorithms. The work uses the methods of pattern recognition, machine learning and digital image processing. The scientific novelty of the completed work is based on: a unique algorithm for extracting images of local sections of the road network, an algorithm for determining the direction of object’s road movement, modernization of the selective search algorithm, which consists in filtering the extracted candidates. The work novelty is also confirmed by the fact that the used ultra-high resolution survey data have become accessible for private use relatively recently.

13. Software implementation of demographic data analysis based on the unified population register [№2 за 2022 год]
Authors: Yusifov F.F., Akhundova N.E.
Visitors: 1693
A unified population register is a key component of the e-demographic system. The register is based on the integrated databases exchanging both aggregated data and individual data between separate regis-ters. The paper examines the analysis of demographic data on the basis of a unified population register. Population registers play an important role in obtaining information about the population. It should be noted that the COVID-19 pandemic has once again emphasized the importance of using administrative data as e-registers for demographic research. The paper provides an experimental analy-sis of demographic characteristics in the context of the COVID-19 pandemic based on the data of indi-viduals integrated into a unified register. The data on individuals in the study are hypothetical data tak-en from two separate registers: the population and health registers. A database was taken for 1000 peo-ple integrated into the unified register. The paper presents the program implementation of demographic data analysis. Demographic analy-sis was implemented in Jupyter Notebook 6.1.4., Python 3.8.5. The results show that the establishment of an e-demographic system requires the integration of various state registers for more detailed analy-sis. This will allow processing and analyzing larger and more multidimensional structured data at dif-ferent time intervals. At the same time, the reliability of the information included in the register, the elimination of inconsistencies, and ensuring continuous updating of registration information for each individual are very important issues. Elimination of errors in registration data makes unified popula-tion registers a reliable source of information.

14. Developing universal framework design for federated learning [№2 за 2022 год]
Authors: Efremov M.A., I.I. Kholod
Visitors: 2333
The paper researches the technology of federated learning that allows collective machine learning on distributed training datasets without transferring them to a single central storage. The relevance of the technology is determined by the long growing trend towards using machine learning methods to solve many applied problems on the one hand, and by the growth of requests for privacy and data processing closer to the data source or directly at the source, including legislative ones, on the other hand. The main problems in creating federated learning systems are the lack of flexible frameworks for various federated learning scenarios: the majority of the existing solutions focus on training artificial neural networks in a centralized computing environment. The subject of the research is the common framework architecture for developing applied federated learning systems, which allows building systems for different scenarios, parameters and topologies of the computing environment, various models, and machine learning algorithms. The article considers the federated learning subject area, gives the main definitions, describes the process of federated learning, presents and analyzes various scenarios of possible applied tasks for federated learning. It contains the analysis of the most well-known federated learning frameworks at the time of writing, as well as their application for possible cases that were described previously. As a result, there is a description of the architecture of a universal framework that, unlike the existing ones, can be used to develop applied federated learning systems of various types and different cases.

15. Evaluation of the effectiveness of chemical reaction conditions [№2 за 2022 год]
Authors: N.V. Zvyagintsev, Billig V.A.
Visitors: 1786
The paper considers the problem of evaluating the effectiveness of chemical reaction conditions taking into account such factors as the presence of impurities, the cost of catalysts, and some other factors af-fecting the cost of a technological process. In order to evaluate the effectiveness of a chemical reaction, this paper proposes first to inde-pendently evaluate the effectiveness of each factor involved in a reaction, and then build a summary estimate that takes into account the effectiveness of each factor. Since the nature of factors is different, the authors introduce the concept of bonuses awarded to each factor in order to be able to compare the influence of factors. Bonuses are awarded for receiving the main product, as well as for minimizing a by-product. Using the example of such factors as pressure and temperature that affect the reaction condition, the paper shows the expediency of introducing a concept of “soft condition”. A soft condition value is a value at which the costs of its implementation are minimal. Taking into account these assumptions, the evaluation of the effectiveness of each factor is constructed as a fuzzy measure of efficiency – a mono-tone function with values from the interval [0,1]. One of the approaches for assessing the significance of a particular factor is based on the possibility of using data mining methods. This method assumes the possibility of accumulating a sufficiently rep-resentative database. The total efficiency score is constructed as a weighted sum of the estimates of each factor. The accuracy of the proposed approach was verified on the real experiment data while recording both factors affecting the course of the chemical reaction and the amount of target and by-product ob-tained as a result of the reaction.

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