ISSN 1991-2927
 

ACP № 2 (60) 2020

Author: "Nina Sergeevna Ageeva"

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Sergei Aleksandrovich Ageev, Doctor of Sciences in Engineering, Associate Professor; graduated from the Faculty of Radioengineering of Ulyanovsk Polytechnic Institute; Head of a research department at Radioavionica JSC in St. Petersburg; specializes in the design of telecommunication systems, automated communication management systems, and the development of methods and algorithms for managing complex systems; an author of articles and patents in the field of telecommunication and multiservice systems and communication networks. e-mail: serg123_61@mail.ruS. A. Ageev

Nina Sergeevna Ageeva, graduated from the Faculty of Engineering and Physics of the Saint Petersburg National Research University of Information Technologies, Mechanics, and Optics (ITMO University); Scientific Associate the Marshal Budjonny Military Academy of Signal Corps; specializes in building the automated service quality management systems in multiservice communication networks; an author of articles and patents in the field of data compression methods and algorithms in multiservice communication networks. e-mail: nine11ia@yandex.ru.N. S. Ageeva

Vladimir Vladimirovich Karetnikov, Doctor of Sciences in Engineering, Professor; graduated from the Saint Petersburg State University of Waterway Communications; Head of the Department of Inland Navigation at Admiral Makarov State University of Maritime and Inland Shipping; specializes in the design and development of systems, communication networks and radio navigation systems; an author of articles and patents in this subject area. e-mail: spguwc-karetnicov@yandex.ruV. V. Karetnikov

Andrei Andreevich Privalov, Doctor of Military Sciences; graduated from the Marshal Budjonny Military Academy of Signal Corps; Professor of the Department of Electrical Communication of the Emperor Alexander I St. Petersburg State Transport University; specializes in the field of mathematical modeling and development of methods, algorithms, and control systems of infocommunication systems; an author of articles and patents in the field of operational management of communication networks and large cyber-physical systems. e-mail: aprivalov@inbox.ruA. A. Privalov

The real-time assessing method for the state of network elements to provide for quality parameters in corporate high-speed multiservice communication networks60_3.pdf

The article suggests an intelligent method and algorithms of assessing the state of network elements to provide for quality parameters of communication services in corporate multi-service communication networks, which operate in near real- time mode. The distinctive feature of corporate multi-service networks is the rapid change of their state. And this is an automated network management system, which provides for the specified quality of communication services. In this way, the relevance of this research is highlighted by the need to implement network management processes with specified quality in near real-time mode. The real-time assessing method for network element state is based on the intelligent agent concept. The suggested approach assumes the intelligent agents are created as hierarchical situational fuzzy networks, where management solutions are made on the basis of solution of hierarchical system of optimization problems, which relies on the methods of fuzzy mathematical programming, unlike common case-based methods. The main paradigm of their operation lies in ‘situation – action.’ This allows to reduce significantly the dimension of solving task and to obtain Pareto-optimal solutions for quality management of communication services. The intelligent agent is able to implement management solutions automatically provided that the network manager designate such capabilities to it. The method of estimating traffic parameters is based on the concept of conventional nonlinear Pareto-optimal filtering developed by V.S. Pugachev. The core of this method is in the two-stage procedure. In the first stage the parameter estimations are predicted, and in the second one their values are corrected, after next observations have been received. Adaptation is implemented on the basis of pseudogradient procedures, which parameters are adjusted with Takagi-Sugeno fuzzy logical conclusion. The mean relative error of traffic parameters estimation does not exceed 10%, which is sufficient for real-time quality management of communication services.

Hierarchical situational fuzzy network, intelligent agent, pseudogradient procedures, conventional nonlinear Pareto-optimal filtering, Takagi-Sugeno fuzzy logical conclusion, fuzzy rule base, fuzzy knowledge base.

2020_ 2

Sections: Automated control systems

Subjects: Automated control systems.

Nina Sergeevna Ageeva, S.M. Budyonny Military Communication Academy, Junior Research Assistant of the Research Laboratory at S.M. Budyonny Military Communication Academy; graduated from the Faculty of Engineering and Physics of St. Petersburg National Research University of Information Technologies, Mechanics and Optics; an author of articles and patents in the field of coding and decoding of mobile image. [e-mail: n.4geeva@gmail.com]N. Ageeva

Developing an Interconnected System of Quality Metrics of Video Data Compression Methods for Real-time Systems 000_6.pdf

The paper is concerned with an interconnected system of quality metrics (sQM) of video data compression methods. The system was developed on the basis of the analysis of the main existing methods and algorithms for video data coding. The system of quality parameters is important for formation and transmission of video data in systems working in time close to real. For example, such systems could be systems of data transmission from aboard an unmanned aerial vehicle (UAV) to a ground control post (Gcp). The paper presents the research results of mathematical modeling methods and video data compression algorithms enabling analysis of the mutual impact of quality metrics and also their impact on the quality of video data obtained at the Gcp.

Unmanned aerial vehicles, video data compression, video data reconstruction, non-orthogonal transformation, orthogonal transformation, fractal video data transformation, cosine transform, wavelet decomposition, identification of mobile objects, system of quality metric of video data compression, telecommunication channels, entropy coding, entropy decoding.

2016_ 3

Sections: Mathematical modeling

Subjects: Mathematical modeling, Automated control systems, Architecture of ship's system.


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