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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

Modeling of energy transition in a stress-strain geological environment for seismic risk assessment (Part 1)

2022. T.10. № 1. id 1061

Minaev V.A. Faddeev A.O. Stepanov R.O.

DOI: 10.26102/2310-6018/2022.36.1.007

The article considers the model for solving the problem of seismic risk quantitative correlation, calculated on the basis of modeling, with seismic impacts regulated in the Seismic building design code. The paper is the first part in a series of scientific publications on the subject. For the first time, it substantiates the criteria for selecting test territories, describes the methodology for verifying the adequacy of seismic risk assessment models, characterizes a probabilistic model of energy transitions in a stress-strain geological environment, and presents an approach to evaluating model parameters through the potential energy transformation indicators of the stress-strain geological environment. The content of the other two parts of the series is indicated. Armenia and neighboring states meet the criteria for choosing a region for practical testing of the model: high seismicity, the necessary information base and proven adequacy of the seismic risk model application at all deep levels of the earthquake epicenter locations. The method of the adequacy verification of the seismic risk assessment mathematical model, using the Student's criterion, is examined in detail. It is shown that when estimating the parameters of transitions between states of the model, described by the Kolmogorov equations, it is important to take into account both the influence of regional fields (anomalous gravitational field) and local fields (modern tectonic movements). Thus, a rationale is provided for employing two deterministic models – regional and local - for practical evaluation of stresses and displacements in the geological environment.

Keywords: model, seismic risk, stress-deformed geological environment, test territory, criterion, regional and local geophysical fields

Conducting numerical experiments to evaluate detection characteristics with the aid of a mathematical radar model

2022. T.10. № 1. id 968

Shchukin A.A. Pavlov A.E.

DOI: 10.26102/2310-6018/2022.36.1.016

The task of detecting and observing targets has always been relevant. One of the most important objectives of radar development is to improve target recognition. There are two ways to achieve this – firstly, the installation of more powerful radar systems, which is very expensive and hard to implement under the conditions of limited space, for example, on airplanes; secondly, the quality of the received signal can be enhanced with the aid of mathematical methods, which allows to considerably save on setting up additional equipment. One of the main problems of recognition is the fact that the number and angular location of targets can be difficult to determine from the signal received by the radar system. This problem can be addressed by employing a wavelet transform. This method enables to overcome the Rayleigh criterion, that makes it possible to obtain an angular super-resolution (to surmount the classical diffraction limit of the spatial resolution of an image focused by a lens that is less than half the radiation wavelength). The article uses a mathematical model of a radar station to present the results of numerical experiments to achieve super-resolution by means of algebraic methods at a significant noise level. We examine the suitability of utilizing different types of wavelets, namely the Haar wavelet, the symmetric Haar wavelet, and the Wave wavelet.

Keywords: wavelet transform, computer modeling, super-resolution, target search, simulation model

Analysis of methods for machine learning system security

2022. T.10. № 1. id 935

Bobrov N.D. Chekmarev M.A. Klyuev S.G.

DOI: 10.26102/2310-6018/2022.36.1.006

The employment of machine learning systems is an effective way to achieve goals, operating with large amounts of data, which contributes to their widespread implementation in various fields of activity. At the same time, such systems are currently vulnerable to malicious manipulations that can lead to a violation of integrity and confidentiality, which is confirmed by the fact that these threats were included in the Information Security Threats Databank by the Federal Service for Technical and Expert Control (FSTEC) in December 2020. Under these conditions, ensuring the safe use of machine learning systems at all stages of the life cycle is an important task. This explains the relevance of the study. The paper discusses the existing security methods, proposed by various researchers and described in the scientific literature, their shortcomings, and prospects for further application. In this respect, this review article aims to identify research issues, relating to machine learning system security, with a view to subsequent development of technical and scientific solutions, regarding the matter. The materials of the article are of practical value for information security specialists and developers of machine learning systems.

Keywords: machine learning, malicious impact, integrity, confidentiality, security