Volume 4(53)

CONTENTS

Sokolova O., Shvarckop N. Using of non-stationary networks in monitoring problems
Ivanova N. M., Vishnevsky V. M. On reliability of a tethered unmanned high-altitude platform using k-out-of-n system and machine learning methods
Kalimoldayev M. N., Mazakova A. T., Yashchenko R. V., Mazakov T. Zh., Abdildayeva A. A. Electronic database design for zoological collection of the republic of Kazakhstan
Artiukhov A. Active Knowledge Base prototype on the basis of computational models
Bredikhin S. V., Lyapunov V. M., Scherbakova N. G. Ranking authors of the weighted coauthorship network: Analysis of DB RePEc data

Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia

Higher College of Informatics NSU, 630058, Novosibirsk, Russia

USING OF NON-STATIONARY NETWORKS IN MONITORING PROBLEMS

DOI: 10.24412/2073-0667-2021-4-5-15

The study was supported by the RFBR, grant № 19-01-00562-a.

In recent years, researchers in many countries have been paying attention to the networks with a non-stationary topology with nodes on vehicles (VANET, Vehicle ad-hoc network), on flying vehicles (FANET, Flying ad-hoc networks). The method of collecting data using these networks is increasingly being used, especially in monitoring problems: transport monitoring, environmental monitoring, etc.

Smart sensors are used, which, in addition to collecting data, perform the function of processing information and transmitting it to the GPRS module for uploading to a web server. Similar projects are carried out in different countries - China, Canada, Sweden. For example, observations of the state of air in an urban environment using wireless sensors were carried out in Uppsala (Sweden). This work is part of the Swedish Green IoT project, which uses the Internet of Things to measure urban air pollution.

In publications about FANET, much attention is paid to the use of unmanned aerial vehicles (UAVs) for monitoring large territories, hard-to-reach areas. The main investigations are devoted to the development of algorithms for controlling the routes and optimizing the process of transmitting information collected by mobile nodes. The goal of similar projects is to collect information in real time and upload data to a web server for users to view. UAV are equipped with environmental monitoring sensors (gas sensor, air quality sensor, humidity sensor, temperature sensor), as well as a microcontroller and a GPRS module. The nodes are distributed in space and transmit data wirelessly. The microcontroller performs the function of processing the data received from the sensors and transmits the processed information to the base station, where it is open to users in real time.

The limited battery life of the UAV narrows their functioning, therefore, various possibilities forrecharging are used - for example, recharging stations. When the critical level of the remaining energy is reached, the path to the nearest station must be denoted for UAV. In the case of a swarm of drones, there should be a flight schedule for recharging, because with a very large number of UAV, queues to stations are possible. Thus, the problem of optimal placement of charging stations in a territory is urgent: the total number of stations is not more than a given one, and the lifetime of the network is not less than a given one.

To solve optimization problems, it is necessary to have convenient simulation tools in order to

test algorithms on various models. For the optimizing monitoring problem, it is convenient to use

a system to simulate the movement of nodes, the transfer of information between them, as well as to determine the possible parameters of devices for collecting and transmitting data. Well-known systems are used to simulate data transmission from nodes on moving objects for example, Network

Simulator, Any Logic. However, these systems mainly simulate the movement, as well as the process of collecting information by nodes. Such urgent problems as ensuring monitoring of the entire given territory, recharging drones during network operation are not in the attention of simulation.In order to complete the simulation possibilities, the authors developed a UAV-monitoring simulation system, in which some urgent problems were solved by adapting well-known algorithms.

Key words: non-stationary networks, unmanned aerial vehicles, models of networks with nodes on the UAV, simulation.

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Bibliographic reference: Sokolova O., Shvarckop N. Using of non-stationary networks in monitoring problems //journal “Problems of informatics”. 2021, № 4. P.5-15. DOI: 10.24412/2073-0667-2021-4-5-15

N. M.Ivanova , V. M. Vishnevsky

Institute of Control Sciences of Russian Academy of Sciences,Profsoyuznaya str. 65, Moscow, 117997, Russia

Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, Moscow, 117198, Russia

ON RELIABILITY OF A TETHERED UNMANNED HIGH-ALTITUDE PLATFORM USING K-OUT-OF-N SYSTEM AND MACHINE LEARNING METHODS

DOI: 10.24412/2073-0667-2021-4-16-39

The research is supported by the Russian Foundation for Basic Research, project no. 19-29-06043 and the

RUDN University Strategic Academic Leadership Program.

The article considers the reliability of tethered unmanned high-altitude platforms. These platforms have great potential in the eld of telecommunications. In remote and underdeveloped areas, they are one of the main means of communication, providing information interaction with mobile networks and the Internet. Moreover, the platforms are highly energy-e‑cient, since the long-term operation of these platforms is ensured by the electricity transportation from ground to board via a thin cable-rope. In this paper, the study of tethered high-altitude unmanned platforms is carried out using the so-called k-out-of-n systems. To assess reliability characteristics of such systems for arbitrary distributions of the life and repair time of system's elements new methods and algorithms have been developed. Moreover, to predict the reliability stationary characteristics of the k-out-of-n system, which adequately describes the operation of a tethered unmanned platform, machine learning methods were used for the first time.

The results obtained are illustrated by numerical examples.

Key words: tethered unmanned high-altitude platform, k-out-of-n system, system's reliability, Markov process, stationary probabilities, simulation modeling, machine learning, TensorFlow library.

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Bibliographic reference: Ivanova N. M., Vishnevsky V. M. On reliability of a tethered unmanned high-altitude platform using k-out-of-n system and machine learning methods //journal “Problems of informatics”. 2021, № 4. P.16-39. DOI: 10.24412/2073-0667-2021-4-16-39

M. N. Kalimoldayev*, A. T. Mazakova**, R. V. Yashchenko***, T. Zh. Mazakov**;*, A. A. Abdildayeva*

*Institute of Information and Computing Technologies, KN MES RK,

**KazNU named after al-Farabi,

***Institute of Zoology, MES RK

ELECTRONIC DATABASE DESIGN FOR ZOOLOGICAL COLLECTION OF THE REPUBLIC OF KAZAKHSTAN

DOI: 10.24412/2073-0667-2021-4-40-54

In recent years, research in biology and genetics has led to an increase in biological information

stored in databases. The same increase in the amount of information occurred in the eld of zoology, but the development of databases in this area was not considered. The article describes the developed electronic database for storing the information about zoological collection of the Institute of Zoology of the Ministry of Education and Science of the Republic of Kazakhstan.

The creation of a database and a data bank for the state scientic zoological collection will allow it

to be used for scientic, educational and applied purposes, which will be used for accounting, monitoring the status and long-term preservation of a single national zoological collection and managing valuable zoological collection materials.

The State Zoological Scientic Collection is the most important source of information for various

areas of biological research. It is not only the basis for conducting scientic research on systematics, molecular genetics of animals, but also documentary conrmation of the correctness of the faunistic works performed. When solving this problem, which is important in theoretical and practical terms, an inventory of specimens of species diversity is of particular relevance.

On the basis of MySQL, an electronic database has been developed with a convenient interface for entering data from the state zoological collection of the Republic of Kazakhstan. The developed EDB includes an information retrieval system and will ensure the further formation of a virtual scientic zoological collection.

The electronic database is intended for zoologists, as well as for specialists of other proles in need of zoological information.

Key words: databases, biological taxonomy, zoological collection, interface, DBMS, MySQL, PhpMyAdmin.

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Bibliographic reference: Kalimoldayev M. N., Mazakova A. T., Yashchenko R. V., Mazakov T. Zh., Abdildayeva A. A. Electronic database design for zoological collection of the republic of Kazakhstan //journal “Problems of informatics”. 2021, № 4. P.40-54. DOI: 10.24412/2073-0667-2021-4-40-54

A. Artiukhov

Novosibirsk State University, 630090, Novosibirsk, Russian Federation

ACTIVE KNOWLEDGE BASE PROTOTYPE ON THE BASIS OF COMPUTATIONAL MODELS

DOI: 10.24412/2073-0667-2021-4-55-66

The ever-growing volume of knowledge, for example, in the programming eld, requires a person to increase the speed of knowledge processing, to speed up knowledge mastering, and to use it more e‑ciently. One possible solution is to automate the process of knowledge application. But in many spheres of activity today humanity is accumulating knowledge in an informal way by using linguistic systems. Mastering the reading, understanding and correct use of the knowledge presented in this form, requires a long learning curve. Thus, knowledge accumulated in this form cannot be applied directly and automatically. Moreover, if the person who has mastered it does not use it very often, over time it will begin to be forgotten and subsequently can be completely lost.

In this work, the application of knowledge is considered as creation of a computer program that

utilizes it, and the automation of the knowledge application is understood as the automatic synthesis of such programs.

To solve mentioned problems e‑ciently the system must not only store the knowledge itself, but

also save the functional connections between individual concepts. In addition, it is necessary that the system, according to the task specication, is able to automatically construct a software application that solves it.

The system that allows one to automatically apply knowledge is called the Active Knowledge Base. This system is based on the theory of structural synthesis of programs. Knowledge in such a system is represented in the form of computational models, i.e. bipartite graphs in which the vertices in one set correspond to operations, and in the other - to variables. The edges of the graph determine whether a variable is the result of an operation or its input. Variables in such models correspond to some values of the described subject area, and operations are associated with certain program modules at the execution stage. The input and output parameters of program modules are associated with the variables of the computational model.

One of the key steps in constructing a software application is building a computation plan based

on the specification of the problem. A computation plan is a partially ordered set of operations, where the order relation is consistent with information dependencies and is defined as

"to compute F2, you need to compute F1\.

Within this prototype, knowledge in the form of computational models is stored in a special Sqlite database, and the system itself is divided into 3 parts. The rst part is a subsystem for storing computational models. It adds new computational models to the database and reads them from it.

The second part is a subsystem for constructing computation plans, which is engaged in building

computation plans according to the specification of the problem. The third part is an execution

subsystem. The computation plan is not a complete program that could be executed by the user's

OS. To execute it, one needs a system that interprets the plan. In this work it's done by the execution subsystem.

Computational models are described in special les, where each le is a description of one entity, for example, a variable of a computational model or an operation.

To construct a computation plan, a problem specication should be described rst. It consists of

the name of the computational model, a set of input variables, and a set of output variables. When the specication is completed, the le describing it is fed to the subsystem for constructing computationplans via command line arguments. Having received it, the system launches the construction algorithm, its result is a le with a computation plan.

The general operating principle of the execution subsystem is similar to that of the interpreter.

Operations are executed sequentially, as the variables on which they depend are ready. The execution subsystem supports the initialization of the input variables of the computation plan with the initial values passed to it; for this, it needs to create a special le with initialization parameters and send it to the execution subsystem via command line.

The process of operations execution continues until one of the conditions is met: all operations from the computation plan have already been calculated, or all output variables of the computation plan have been calculated. If in the course of calculations all operations from the calculation plan have been executed, and not all of the output variables have been calculated, then the calculations are completed with a corresponding warning message sent to the user. Otherwise, when the variables have already been calculated, and there are still operations in the computation plan, then the plan execution ends normally, the output variables are saved in accordance with their types, and the user is informed about the result of the computations through the standard output stream.

Results of the development and implementation of the system called Active Knowledge Base are

presented in the paper.

Key words: Active knowledge, Program synthesis, Active knowledge base, Structural synthesis of programs, Automatic programs construction, Knowledge base, Knowledge storage, Computational models.

References

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Models and Applied Computational Science. 2008. № 4.

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"Nauka\ Sibirskoe otd-e,1988.

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Bibliographic reference: Artiukhov A. Active Knowledge Base prototype on the basis of computational models //journal “Problems of informatics”. 2021, № 4. P.55-66. DOI: 10.24412/2073-0667-2021-4-55-66

S. V. Bredikhin, V. M. Lyapunov, N. G. Scherbakova

Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia

RANKING AUTHORS OF THE WEIGHTED CO-AUTHORSHIP NETWORK: ANALYSIS OF DB REPEC DATA

DOI: 10.24412/2073-0667-2021-4-67-83

This work was carried out under state contract with ICMMG SB RAS (0251-2021-0005).

In the previous paper [12] we investigated the co-authorship network N represented by an unweighted graph: nodes correspond to authors, and two authors are considered connected if they are coauthors of at least one publication. Basic network properties are: existence of the giant component (includes 90% of authors), small worldness [24] and a power-law tting of the distribution of coauthors. In this paper we focus on centrality measures in order to identify key authors on the base of the weighted co-authorship network. Using co-authorship data from the distributed database RePEc [13] we construct two weighted networks that dier in the way of computing edge weights.

Let P (|P| = l) be the set of publications and assume that each publication in P has at least

two authors. Let V (|V | = n) be the set of authors of these publications. We consider two schemes For assigning weights to edge when constructing networks. When using the “complete” weighting scheme the strength of the collaborative tie (the edge weight) between two aytors is set equal to the number of joint papers. AND when using the “fractional” weighting scheme the edge weight between two authors depend not only on the number of coauthored papers, but also on the number of other coauthors of these papers [7].

The raw data processing procedure is presented in [12], as a result the number of authors |V| = 32 434 and the number of coauthored publications |P| = 91 113. For each of the network

(unweighted and weighted according two schemes) four measures of centrality such as degree, closeness, betweenness and eigenvector have been calculated and the tables (tabs. 24) containing the names of the authors with the highest ranks are provided. It should be noted that these authors have high h-index values (according to Google Scholar search engine or IDEAS ranking system [25] based on all publications of the authors).

In order to study the dependence of author ranks on the method of calculating the contributions of authors to publications we calculated Pearson's correlation coefficients and Spearman's rank correlation coefficients for the same centrality measures for the networks under consideration. It was shown that regardless of how the edge weights are calculated the same centrality measures have signicant correlation with each other. The most signicant correlation according to both coefficients is fixed for the betweenness centrality, the least - for the eigenvector centrality, which determines the prestige of the network actor.

To illustrate the studied ways of calculating edge weights and the dependence of node ranks on the method and a node location, we considered the 12-node component of N and applied four centrality measures to its weighted representations. We see that the ranks of authors differ depending on the method of edge weights calculating. On the base of node ranks we calculated node weights and presented new ranks of authors (tab. 10) within any component representation and centrality measure used. It is noted that the high ranked authors are the influential persons with a large number of citations.

The purpose of further research is to identify the relationship between key authors and the number of citations of coauthored publications. The question of interest is whether collaborative publications receive more citations than single author publications.

Key words: bibliometry, co-authorship network, centrality measures, key authors.

References

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Bibliographic reference: Bredikhin S. V., Lyapunov V. M., Scherbakova N. G. Ranking authors of the weighted coauthorship network: Analysis of DB RePEc data //journal “Problems of informatics”. 2021, № 4. P.67-84. DOI: 10.24412/2073-0667-2021-4-67-84

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