Vol 21, No 3 (2023)

The application of interval methods for queue analysis in queuing systems with group Poisson flows is considered. It is shown that, for a given load, the average value of queue sizes in systems with such flows is determined by the dispersion of the numbers of requests arriving during the processing time intervals of one request, and the specified dispersion depends linearly on the load factor. Relations are obtained that determine the dispersion of queue sizes in queuing systems with group Poisson flows. It is shown that this dispersion depends on the third central moment of the numbers of applications arriving during the time intervals of processing one application. The dispersions of the queues of two different group flows, which have the same dependencies of the average values of the queues, differ in the values of the indicated central moments.

This article explores the possibility of using reinforcement learning to dynamically assign priorities in time-sensitive networks. The presented approach purposefully optimizes the process of managing time constraints in the network. Using reinforcement learning techniques, the system independently adjusts priorities depending on the requirements of network traffic. To achieve this goal, two configuration schemes based on TSN standards are proposed: centralized and distributed. Having considered these schemes, we will identify their limitations necessary in meeting requirements close to real time and ensuring strict quality of service guarantees, taking into account the restrictions applied to a time-sensitive environment. The work also reveals the need to use additional equipment, a centralized controller, to reallocate priorities.

The task of identifying current security threats is becoming increasingly important, due to the increase in the volume of processed information of limited access, an increase in the number of all kinds of threats, and an increase in the potential of violators. At enterprises and organizations in various sectors of the economy, specialists regularly conduct information security audits in order to identify vulnerabilities and ultimately to prevent possible negative consequences. To build a rational information security system at the facility, it is important to organize protection against actual threats, since it is impossible to build protection against all threats, and it is impractical. The article proposes models and algorithms for determining current threats based on an expert approach, which can become the basis for the development of an automated system for making reliable decisions in the tasks of protecting informatization objects.

Currently, the development of computer vision makes it possible to solve many problems of detecting defects at various industrial facilities. One of the promising areas of application of these technologies is to identify inconsistencies in geometric parameters on cylindrical products. The purpose of this work is to review and systematize modern computer vision methods used to solve the problem of detecting defects on vertical cylindrical surfaces. The study analyzed existing approaches to the extraction of spatial characteristics of objects, including methods of stereo vision, spatial filtering and 3D reconstruction. Algorithms for identifying the main landmarks on a cylindrical surface were considered, which makes it possible to bind the coordinate system and localize areas of possible defects. Methods for estimating geometric deviations on the surface, which can act as criteria for detecting defects, have also been studied. As a result of the analysis, a classification of computer vision methods applicable to the problem of detecting defects on cylindrical objects was proposed. Promising directions for further research in the field of improving the accuracy of defect detection through a combination of various image processing algorithms have been identified.

This article explores the objectives of optimal resource allocation in metro construction organizations, including improving work efficiency, reducing project duration, and minimizing costs. The primary optimization methods, such as mathematical modeling, critical path analysis, and the use of network graphs, are discussed in the context of achieving these goals. The role of project management software in providing comprehensive control over planning, resources, and execution is emphasized. Additionally, the article underscores the significance of utilizing the Python programming language and its libraries, such as Pandas, Scikit-learn, and Matplotlib, in analyzing and optimizing resource allocation in metro construction. The paper contains a case study of a software implementation of risk analysis of delivery delays in metro construction using the Python programming language and its key libraries. Pandas for data processing and analysis, NumPy for performing complex mathematical calculations, and Matplotlib for visualizing the results were used.

This article introduces the development of an innovative application aimed at analyzing comments on English-language YouTube videos to identify and systematize the most frequently used English abbreviations. This application not only facilitates the process of learning English, but also provides a deep understanding of the use of abbreviations in the context of real dialogues, contributing to a more effective use of them in everyday communication. It provides a native speaker’s perspective on the English language. Details the features of developing an application, such as using the YouTube Data API, analyzing comment text using regular expressions and saving data for further study. This paper focuses on the challenges of working with the API (Application Programming Interface), text processing techniques and shows the possibilities of exploring English in an online environment.

The paper presents the first results obtained by modeling a routed inter-satellite network based on the parameters of the ReshUCube-3 space mission. ReshUCube-3 is a mission in which it is planned to launch a satellite constellation of four CubeSats. A network stack was proposed to provide the concept of a space integrated network. It is planned to use LoRa technology on the physical layer, and RPL as a routing protocol on the network layer. The model takes into account the orbital parameters of the satellite constellation, the antenna systems of spacecraft and ground station, and the parameters of radio modules. Based on this information, the analysis of inter-satellite communication channels was carried out. The maximum distance between the satellites, the energy properties of the channels, the average daily coverage time, etc. are determined. An important feature is the consideration of existing routes between satellites. It is planned that the proposed model will serve as a basis in the process of designing and operating an inter-satellite network.

In today’s world, technology continues to play an increasingly important role in all spheres of human life. Education is no exception and keeps pace with the times. One of the more promising directions in this area is the use of machine learning methods to analyze data on graduates and predict their future employment, based on the accumulated information about students. Such information not only helps to determine the future profession, but also will allow educational institutions to co-ordinate their activities more effectively and improve the quality of education. In addition, the use of machine learning in education can lead to the creation of new, more effective teaching methods that take into account the individual characteristics of each student. The result of this work is an in-telligent system of big data processing, capable of adapting to the current state of the labor market and helping graduates in an earlier determination of their future profession. Data analysis was con-ducted on a sample of students of Tomsk State Pedagogical University of 2021-2023 academic years.