Special Issue No. – 8, April, 2020

Abstract:

Due to depletion of such natural non-renewable energy sources, as oil and gas, the problem of using renewable resources is especially acute nowadays. Utilising river flows as a source of energy is one of the directions for solving the problem. The paper reflects the objectives and problems, that can be resolved through a sustainable use of the river flow energy. An impact of various factors on interaction between energy and extraction systems has been shown. The essence of the new approach to making hydroelectric units using the quantity-related component of the flow, and the prospects for creating hydroelectric units in this direction are represented. The presented analysis of some developments incorporates the specific features of the utilised working organs of free-flow micro HPSs in their interacting with the river flow. It serves a basis for suggesting a new trend in their developing and the use of a quantity-related flow component, i.e. the momentum, in particular.

Keywords:

Systems,hydraulic flow,momentum, energy extraction,hydraulic gradient,generator,polymers,

Refference:

I. Balkibekov C.E. The Development of non-traditional renewable energy sources (RES) and malingas in the Kyrgyz Republic. CRT, proceedings of the int. scientific-technical. Conf. April 22-24, 2011, S. 3.
II. BezrukikhР., Vissarionov V. Basic characteristics of Energy Renewed Resources and Prospects of their Utilization in Fuel and Energetical Balance of Russia Perspective of Renewable energy sources utilization in Karelian Fuel Energy Balance: Proceed, of the First Intern, seminar Eds P. Pelkonen, G. Sidorenko. 1993, Vol. LP.11-21.
III. Chugaev R.R. Hydraulic Engineering. Rev.3, M. – L.: “Energy”, 1975/ Чугаев Р.Р. Гидравлика. Изд.3., М.- Л.: «Энергия», 1975 г.
IV. http://abratsev.ru/hydrosphere/mechanizm.html – River flow mechanism (according to L.K. Davydov)/ Механизм течения рек (по Л. К. Давыдову)
V. http://miniges.com/variant – Mini HPS advantages and disadvantages/ Преимущества и недостатки мини ГЭС.
VI. http://www.inset.ru/r/predm.htm – Equipment for mini HPSs /Оборудование для мини ГЭС.
VII. http://www.solarhome.ru/ru/hydro/index.htm – Energy sources for small hydroelectric engineering/ Источники энергии для малой гидроэнергетики.
VIII. International workshop ISTC – expert Meeting on renewable energy Alternative energy and energy security concerns, Issyk-Kul, Kyrgyz Republic, 21-24.04.2013.
IX. Krasnov V.G. Free-flow hydropower installations [Text] V.G. Krasnov// Innovations and Investments. – 2015. – №4. – Pp.128–131 /Краснов В.Г. Свободнопоточные гидросиловые установки [Текст] / В. Г. Краснов // Инновации и инвестиции. – 2015. – № 4. – С. 128–131.
X. Krasnov V.G. Power engineering facilities for micro HPSs [Text] / V.G. Krasnov, U.K. Zhenishbek // Proceedings of the All-Russian applied research conference of students, postgraduates, and scientists. – Tyumen: TIU, 2017. – Pp. 211–216. /Краснов В. Г. Энергетическое обеспечение микроГЭС [Текст] / В. Г. Краснов, У. К. Женишбек // Материалы Всероссийской научно-практич. конференции обучающихся, аспирантов и учёных. – Тюмень: ТИУ, 2017. – С. 211–216.
XI. Krasnov V.G. Renewable energy sources for micro HPSs: [Text]: monography/ Krasnov V.G. – Cheboksary: NOU DPO “Expert-methodological centre”, 2017. – 56p. /Краснов В.Г. Возобновляемые источники энергии микроГЭС: [Текст]: монография / В.Г. Краснов. – Чебоксары: НОУ ДПО «Экспертно-методический центр», 2017. – 56 с.
XII. Markeev A.P. Theoretical mechanics: Textbook for universities. – Izhevsk: R&D Centre “Regular and chaotic dynamics”, 2001, p.59 /Маркеев А.П. Теоретическая механика: Учебник для университетов. – Ижевск: НИЦ «Регулярная и хаотическая динамика», 2001, 59.
XIII. Revue general nuclear, 1977, 1. 123. O. Eckstein. Water Resource Development. The Economics of Projects Evaluation. Harvard University Press, 1958.
XIV. Rusakova, T.I. and Karpliuk, V.I. (2002), “Numerical research of features of a flow with a separation of the varying cylinder”, Visnyk Dnipropetrovskoho universytetu, vol. 2, issue. 6, pp. 115-123.
XV. Sadovnichiy V.A., Samsonov V.A. (Moscow). On dynamics of fireballs// IX All-Russian conference on theoretical and applied mechanics. Abstract of papers. V.I (Nizhny Novgorod-2006) Nizhny Novgorod: Publ. house of N.I. Lobachevsky State University of Nizhny Novgorod, 2006. p.103/ Садовничий В.А., СамсоновВ.А. (Москва) К вопросу о динамике болидов// IX Всероссийский съезд по теоретической и прикладной механике. Аннотация докладов. т. I (Нижний Новгород-2006) Нижний Новгород: Изд-во Нижегородского госуниверситета им. Н. И. Лобачевского, 2006. С. 103

XVI. Samsonov V.I. Studying mechanics of arterial vessels // IX All-Russian conference on theoretical and applied mechanics. Abstract of papers. V.I (Nizhny Novgorod-2006) Nizhny Novgorod: Publ. house of N.I. Lobachevsky State University of Nizhny Novgorod, 2006. p.165 /Самсонов В. И. Исследование механики артериальных сосудов // IX Всерос сийский съезд по теоретической и прикладной механике. Аннотация докла дов. т. I (Нижний Новгород-2006) Нижний Новгород: Изд-во Нижегородского госуниверситета им. Н. И. Лобачевского, 2006. С. 165.
XVII. Satybaldyev A.B., Matisakov T.K., Attokurov A.K. Determination of optimal angle of water wheel paddle //International Journal of applied and fundamental research. – 2015. – Pp.413-416; URL: http://applied-research.ru/ru/article/view?id=6915 (date of reference: 17.08.2019)./ Сатыбалдыев А.Б., Матисаков Т.К., Аттокуров А.К. определение оптимального угла лопасти водяного колеса // Международный журнал прикладных и фундаментальных исследований. – 2015. – № 6-3. – С. 413-416; URL: http://applied-research.ru/ru/article/view?id=6915 (дата обращения: 17.08.2019).
XVIII. Survey of Energy Resources. WEC/1992 16. The European Renewable Energy Study. Commission of the European Communities. DGXVII. Annex 2. 1
XIX. V.G. Krasnov, A.D. Obozov, O.R. Nurislamov. Analysis of efficiency of use of the longitudinal-flow hydropower plant unit of a micro hydropower station without a dam for small rivers. Journal of Environmental Management and Tourism 9(3):439-451pp., July 2018
XX. Victor G. Krasnov, Petr M. Kosyanov and Nikolai P. Dmitriev, 2016. To the Study of the Motion of a Cylinder with Variable Mass in Flow: The Dynamics of a Free-Flow Micro Hydropower Plant. Journal of Engineering and Applied Sciences, 11: 3136-3141

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Abstract:

The paper summarizes the author’s research of topologies of parallel computation systems and the tasks solved with them, including the relevant tools of their modeling. The original topological model of such systems is presented, based on the modified Amdahl’s law. It allowed formalizing the dependence of the necessary number of processors and the maximal distance between informational adjacent nodes in the graph on directive values of acceleration or efficiency. The dependences of these values on the topology of the system interconnection and on the informational graph of the parallel task are also formalized. The tools for comparative evaluation of these dependences, the topological criteria and the functions of scaling and fault-tolerant functioning of parallel systems are based on the author’s technique of projective description of graph and the algorithms using it.

Keywords:

Interconnect topology,parallel computation systems,projective description of graphs,topological scalability ,fault-tolerance functions,

Refference:

I. Xian-He Sun, Yong Chen. Reevaluating Amdahl’s law in the multicore era, Journal of Parallel and Distributed Computing, Volume 70, Issue 2 (2010) 183–188. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2009.05.002 (accessed 20 June 2019).
II. Abd-El-Barr, M., Gebali, F. Reliability analysis and fault tolerance for hypercube multi-computer networks, Information Sciences, Volume 276 (2014) 295–318. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2013.10.031 (accessed 20 June 2019).
III. Caselli, S., Conte, G., Malavolta, U. Topology and process interaction in concurrent architectures: A GSPN modeling approach, Journal of Parallel and Distributed Computing, Volume 15, Issue 3 (1992) 270–281. ISSN 0743-7315. https://doi.org/10.1016/0743-7315(92)90008-B (accessed 20 June 2019).
IV. Chechina, N., Huiqing Li, Ghaffari, A., Thompson, S., Trinder, Ph. Improving the network scalability of Erlang, Journal of Parallel and Distributed Computing, Volumes 90–91 (2016). 22–34. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2016.01.002 (accessed 20 June 2019).
V. Das, Ch. R., Bhuyan, L. N. Dependability evaluation of interconnection networks, Information Sciences, Volume 43, Issues 1–2 (1987) 107–138. ISSN 0020-0255. https://doi.org/10.1016/0020-0255(87)90033-8 (accessed 20 June 2019).
VI. Dutt, Sh., Hayes, J. P. Designing fault-tolerant systems using automorphisms, Journal of Parallel and Distributed Computing, Volume 12, Issue 3 (1991) 249–268. ISSN 0743-7315. https://doi.org/10.1016/0743-7315(91)90129-W (accessed 20 June 2019).
VII. Emmert-Streib, F., Dehmer, M.,Yongtang Shi. Fifty years of graph matching, network alignment and network comparison, Information Sciences, Volumes 346–347 (2016) 180–197. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2016.01.074 (accessed 20 June 2019).
VIII. Grout, V. M., Sanders, P. W. Communication network optimization, Computer Communications, Volume 11, Issue 5 (1988) 281–287. ISSN 0140-3664. https://doi.org/10.1016/0140-3664(88)90039-4 (accessed 20 June 2019).
IX. HaoChe, Minh Nguyen. Amdahl’s law for multithreaded multicore processors, Journal of Parallel and Distributed Computing, Volume 74, Issue 10 (2014) 3056–3069. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2014.06.012 (accessed 20 June 2019).
X. Hayter, Th., Brookes, G. R. Approach to the simulation of various LAN topologies, Computer Communications, Volume 12, Issue 4 (1989) 204–212.
ISSN 0140-3664, https://doi.org/10.1016/0140-3664(89)90197-7 (accessed 20 June 2019).
XI. Hutchison, D., Sterbenz, J. P.G. Architecture and design for resilient networked systems, Computer Communications, Volume 131 (2018) 13–21. ISSN 0140-3664. https://doi.org/10.1016/j.comcom.2018.07.028 (accessed 20 June 2019).

XII. Jianxi Fan, XiaohuaJia, Xin Liu, Shukui Zhang, Jia Yu. Efficient unicast in bijective connection networks with the restricted faulty node set, Information Sciences, Volume 181, Issue 11 (2011) 2303–2315. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2010.12.011 (accessed 20 June 2019).
XIII. Jianxi Fan, XiaohuaJia. Edge-pancyclicity and path-embeddability of bijective connection graphs, Information Sciences, Volume 178, Issue 2 (2008) 340–351. ISSN 0020-0255. https://doi.org/10.1016/j.ins.2007.08.012 (accessed 20 June 2019).
XIV. Ke Huang, Jie Wu. Fault-tolerant resource placement in balanced hypercubes, Information Sciences, Volume 99, Issues 3–4 (1997) 159–172. ISSN 0020-0255. https://doi.org/10.1016/S0020-0255(96)00270-8 (accessed 20 June 2019).
XV. Kornushk, V. F., Bogunova, I. V., Flid, A. A., Nikolaeva, O. M., Grebenshchikov, A. A. Information-algorithmic support for development of solid pharmaceutical form. Fine Chemical Technologies, 13(5) (2018) 73–81. https://doi.org/10.32362/2410-6593-2018-13-5-73-81 (accessed 20 June 2019).
XVI. Levi, G., Luccio, F. A technique for graph embedding with constraints on node and arc correspondences, Information Sciences, Volume 5 (1973) 1-24. ISSN 0020-0255. https://doi.org/10.1016/0020-0255(73)90001-7 (accessed 20 June 2019).
XVII. Lloret, J., Palau, C., Boronat, F., Tomas, J. Improving networks using group-based topologies, Computer Communications, Volume 31, Issue 14 (2008) 3438–3450. ISSN 0140-3664. https://doi.org/10.1016/j.comcom.2008.05.030 (accessed 20 June 2019).
XVIII. Melent’ev ,V.A., Shubin, V.I., Zadorozhny, A.F. Topological scalability of hypercubic parallel systems and tasks. ISJ Theoretical & Applied Science 11 (31) (2015) 122–129. Doi: http://dx.doi.org/10.15863/TAS.2015.11.31.19 (accessed 20 June 2019).
XIX. Melent’ev, V. A. An analytical approach to the synthesis of regular graphs with preset values of the order, degree and girth, Prikl. Diskr. Mat., 2(8), (2010) 74–86. http://mi.mathnet.ru/eng/pdm178 (accessed 20 June 2019).
XX. Melent’ev, V. A. Bracket form of the graph description and its use in the structural investigations of enduring computer systems, Avtometriya, 4 (2000) 36–51. https://elibrary.ru/item.asp?id=14954075 (accessed 20 June 2019).
XXI. Melent’ev, V. A. Embedding of subsystems limiting length and number of paths between vertexes of computing system graph, UBS, 47 (2014) 212–246. http://mi.mathnet.ru/eng/ubs749 (accessed 20 June 2019).
XXII. Melent’ev, V. A. Fault-tolerance of hypercubic and compact topology of computing systems. ISJ Theoretical & Applied Science, 12 (44) (2016) 98–105. Doi: http://dx.doi.org/10.15863/TAS.2016.12.44.20 (accessed 20 June 2019).
XXIII. Melent’ev, V. A. On approach to the configuring of fault-tolerant subsystems in case of scarce topological fault-tolerance of the computing system. ISJ Theoretical & Applied Science, 10 (54) (2017) 101–105. Doi: https://dx.doi.org/10.15863/TAS.2017.10.54.20 (accessed 20 June 2019).
XXIV. Melent’ev, V. A. On topological fault-tolerance of scalable computing systems, UBS, 70 (2017) 58–86. URL: https://doi.org/10.25728/ubs.2017.70.3 (accessed 20 June 2019).
XXV. Melent’ev, V. A. On topological scalability of computing systems, UBS, 58 (2015) 115–143. http://mi.mathnet.ru/eng/ubs844 (accessed 20 June 2019).
XXVI. Melent’ev, V. A. The metric, cyclomatic and synthesis of topology of systems and networks (2012). https://elibrary.ru/download/elibrary_22249411_42044045.pdf (accessed 20 June 2019).
XXVII. Melent’ev, V. A. Use of Melentiev’s graph representation method for identification and enumeration of circuits of the given length. ISJ Theoretical & Applied Science, 11 (67) (2018) 85–91. Doi: https://dx.doi.org/10.15863/TAS.2018.11.67.16 (accessed 20 June 2019).
XXVIII. Melent’ev, V. A. Use of Melentiev’s graph representation method for detection of cliques and the analysis of topologies of computing systems. ISJ Theoretical & Applied Science, 12 (68), (2018) 201–211. Doi: https://dx.doi.org/10.15863/TAS.2018.12.68.28 (accessed 20 June 2019).
XXIX. Melentiev, V. A. Formalnyeosnovyskobochnyhobrazov v teoriigrafov [Formal bases of bracket images in graph theory]. 2nd International Conference “Parallel computations andy management tasks” PACO’2004: In-t problem upravleniya RAN im. V.A. Trapeznikova, (2004) 694–706.
XXX. Melentiev, V. A. Formalnyypodkhod k issledovaniyustrukturvychislitelnykh system [Formal approach to researching the structures of computation systems]. VestnikTomskogogosudarstvennogo universiteta,14 (2005) 167–172.
XXXI. Melentiev, V. A. The bracket pattern of a graph. 6th International Conference on Pattern Recognition and Image Analysis: New Information Technologies, PRIA-6-2002, October 21-26 (2002) Proceedings, Velikiy Novgorod, Russian Federation, 57–61.
XXXII. Melentiev, V. A. The limiting paralleling in the computing system with a hypercube topology under length restriction of interprocess connections. https://elibrary.ru/item.asp?id=23316608 (accessed 20 June 2019).
XXXIII. Melentiev, V. A., Limit configuring of subsystems in hypercubic computing systems, Journal of Information Technologies and Computing Systems, 2 (2015) 20–30. http://www.jitcs.ru/images/documents/2015-02/20_30.pdf (accessed 20 June 2019).
XXXIV. Melentiev, V. Edge scaling of computing systems, UBS, 64 (2016) 81–11. http://mi.mathnet.ru/eng/ubs898 (accessed 20 June 2019).
XXXV. Nutaro, J., Zeigler, B. How to apply Amdahl’s law to multithreaded multicore processors, Journal of Parallel and Distributed Computing, Volume 107 (2017) 1–2. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2017.03.006 (accessed 20 June 2019).
XXXVI. Subharthi, P., Jianli Pan, Raj Jain. Architectures for the future networks and the next generation Internet: A survey, Computer Communications, Volume 34, Issue 1 (2011) 2–42. ISSN 0140-3664. https://doi.org/10.1016/j.comcom.2010.08.001 (accessed 20 June 2019).
XXXVII. Volkova, A. A Technical Translation of Melentiev’s Graph Representation Method with Commentary. University Honors Theses. Paper 503 (2018). http://dx.doi.org/10.15760/honors.507 (accessed 20 June 2019).
XXXVIII. Wu A. Y. Embedding of tree networks into hypercubes, Journal of Parallel and Distributed Computing, Volume 2, Issue 3 (1985) 238–249. ISSN 0743-7315, https://doi.org/10.1016/0743-7315(85)90026-7 (accessed 20 June 2019).
XXXIX. Xian-He Sun, Yong Chen. Reevaluating Amdahl’s law in the multicore era, Journal of Parallel and Distributed Computing, Volume 70, Issue 2 (2010) 183–188. ISSN 0743-7315. https://doi.org/10.1016/j.jpdc.2009.05.002 (accessed 20 June 2019).
XL. Yavits, L., Morad, A., Ginosar, R. The effect of communication and synchronization on Amdahl’s law in multicore systems, Parallel Computing, Volume 40, Issue 1 (2014) 1–16. ISSN 0167-8191. https://doi.org/10.1016/j.parco.2013.11.001 (accessed 20 June 2019).

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Abstract:

The article explores symbolic systems as a special type of complex systems. The relationship between the concepts of “sign” and “symbol” and symbolic means are analyzed. Four research methods of analyzing a language as a symbolically symbolic system are investigated: linguistic, semiotic, systemic and informational. The article describes the symbolic system using the information approach and demonstrates the presence of emergence in sign-symbolic systems. The article shows that a sign-symbolic system is best described as an informational construction. The main functions of sign-symbolic systems are the following: representation, communication, information and externalization of implicit knowledge.

Keywords:

Complex systems,sign,symbol,symbolically symbolic systems,linguistics,information approach,

Refference:

I. Amaglobeli G. Semantic triangle and linguistic sign //Journal in Humanities. – 2012. – Т. 1. – №. 1. – С. 37-40.

II. Chang H. H., Ying Z. A global information approach to computerized adaptive testing //Applied Psychological Measurement. – 1996. – Т. 20. – №. 3. – С. 213-229.

III. Chowdhury M. F. Interpretivism in aiding our understanding of the contemporary social world //Open Journal of Philosophy. – 2014. – Т. 4. – №. 03. – С. 432.

IV. De Man P. Sign and Symbol in Hegel’s Aesthetics //Critical Inquiry. – 1982. – Т. 8. – №. 4. – С. 761-775

V. Fischer K. Linguistic methods for investigating concepts in use //Methodologie in der Linguistik. – 2003. – С. 39-62.

VI. Folke C. Resilience: The emergence of a perspective for social–ecological systems analyses //Global environmental change. – 2006. – V. 16. – №. 3. – р. 253-267.

VII. Giffin M. et al. Change propagation analysis in complex technical systems //Journal of Mechanical Design. – 2009. – Т. 131. – №. 8. – С. 081001.

VIII. Goldstein J. Emergence as a construct: History and issues //Emergence. – 1999. – V. 1. – №. 1. – р. 49-72.
IX. I. N. Rozenberg. Information Construction and Information Units in the Management of Transport Systems // European Journal of Technology and Design, 2016, 2(12), pp. 54-62, DOI: 10.13187/ejtd.2016.12.54 www.ejournal4.com

X. Koppenjan J., Groenewegen J. Institutional design for complex technological systems //International Journal of Technology, Policy and Management. – 2005. – Т. 5. – №. 3. – С. 240-257.

XI. Leontiev A. A. Sign and activity //Journal of Russian & East European Psychology. – 2006. – Т. 44. – №. 3. – С. 17-29.

XII. Luhmann N., Baecker D., Gilgen P. Introduction to systems theory. – Cambridge : Polity, 2013.

XIII. Markus M. L., Majchrzak A., Gasser L. A design theory for systems that support emergent knowledge processes //MIS quarterly. – 2002. – С. 179-212.

XIV. Mesarovic M. D., Takahara Y. Abstract systems theory. – 1989.

XV. Plummer K. A world in the making: Symbolic interactionism in the twentieth century //A Companion to Social Theory. 2nd ed.[Web document], Blackwell [Cited 15.12. 2011]. – 2000. – С. 193-222.

XVI. Po-An Hsieh J. J., Wang W. Explaining employees’ extended use of complex information systems. – 2007.

XVII. Reis D. S. et al. Applicability of the semiotic inspection method: A systematic literature review // Proceedings of the 10th Brazilian Symposium on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction. – Brazilian Computer Society, 2011. – С. 177-186.

XVIII. Toren C. Sign into symbol, symbol as sign: Cognitive aspects of a social process //Cognitive aspects of religious symbolism. – 1993. – С. 147-264.

XIX. Tsvetkov V. Ya. Information Units as the Elements of Complex Models // Nanotechnology Research and Practice. – 2014, № 1(1), р.57-64.

XX. Tsvetkov V. Yа. Dichotomic Assessment of Information Situations and Information Superiority // European Researcher.. 2014, № 11-1 (86), pp. 1901-1909. DOI: 10.13187/er.2014.86.1901
XXI. Tsvetkov V. Yа. Information interaction // European researcher. 2013. № 11-1 (62). С. 2573-2577

XXII. TsvetkovV.Ya. Information field // Life Science Journal. – 2014 – Т.11. №5. -с.551-554.

XXIII. V. Ya. Tsvetkov System Information // European Journal of Economic Studies, 2018, 6(1): 18-22. DOI: 10.13187/ejtd.2018.1.18

XXIV. V. Ya. Tsvetkov. Information Relations // Modeling of Artificial Intelligence. 2015. № 4(8). – р.252-260/

XXV. Williams M. Interpretivism and generalisation //Sociology. – 2000. – Т. 34. – №. 2. – С. 209-224

XXVI. Цветков В.Я. Теория систем: Монография. – М.: МАКС Пресс, 2018. – 88 с. ISBN 978-5-317-05718-3

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Abstract:

The implementation of ‘Smart Objects’ is an important part of the development of adaptive Smart Grid structures. For this class of objects, poorly for malizable factors, such as microclimate parameters, environmental indicators, and consumer load, acquire a significant influence. To solve this, PID controllers are usually used in Smart Objects; however, their accuracy is limited. Fuzzy neural controllers are an alternative solution for the integrated optimization of Smart Objects. This article proposes a scalable model of Smart Object equilibrium by the example of basic utility systems (heating, air conditioning/ventilation and illumination). It was found that the use of fuzzy neural controllers in such systems makes it possible to improve their efficiency by increasing the accuracy of energy consumption forecasts. Control systems based on PID controllers and fuzzy neural controllers in Smart Object were comparted only to find that the latter have a higher accuracy.

Keywords:

Smart Objects, distributed objects,PID controllers,fuzzy neural network,fuzzy neural controller,mathematical modeling,

Refference:

I. Alanis, A. Y., Ricalde, L. J., Simetti, C., Odone, F. (2013). Neural model with particle swarm optimization kalman learning for forecasting in smart grids. Mathematical Problems in Engineering, 2013, 1–9.

II. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L. (2016). Realizatsiyaprogrammnogokompleksaprognozirovaniyaurovnyaregionalnogoenergopotrebleniya [Sales program complex: Prediction of the regional level of energy consumption]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 12(3), 41–47.

III. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L., Shukur, O.M. (2016). Realizatsiyaelementovprogrammnogokompleksaprognozirovaniyaregionalnogoenergopotrebleniyanabazeneyronnoyseti [Implementation of elements of program complex for prediction of the regional level of energy consumption based on neural network]. Proceedings of the 4th International Conference on modern methods of applied mathematics, control theory and computer technology (PMUKT-2016), Voronezh, Russia, 59–63.
IV. Çevik, H.H., Çunkaş, M. (2015). Short-term load forecasting using fuzzy logic and ANFIS. Neural Computing and Applications, 26(6), 1355–1367.
V. Cheng, Z., Juncheng, T. (2015). Adaptive combination forecasting model for china’s logistics freight volume based on an improved PSO-BP neural network. Kybernetes, 44(4), 646.
VI. Danilov, A.D., Shukur, O.M., Rutskov, A.L. (2016). Analizprimeneniyanechotkikhneyronnykhseteydlyaprognozirovaniyaenergopotrebleniyapromyshlennykhpredpriyatiy [Analysis of the use of fuzzy neural networks for predicting power consumption of industrial enterprises]. Aktualnyyenauchnyyeissledovaniya XXI veka: teoriya i praktika = Topical scientific research of the 21st century: Theory and practice, 4, 6(26), 59–63.

VII. Gusev, K.Yu.,Burkovsky, V.L. (2012). Neyrosetevoyemodelirovaniyedinamikinelineynykhsistem [Neural network modeling of nonlinear dynamics]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 8(12.1), 51–56.

VIII. Hopfield, J.J. (1982). Neural networks and physical systems with emergent computations abilities. Proceedings of the National Academy of Sciences, 79, 2544–2558.

IX. Jiang, X., Ling, H., Yan, J., Li, B., & Li, Z. (2013). Forecasting electrical energy consumption of equipment maintenance using neural network and particle swarm optimization. Mathematical Problems in Engineering, 2013.
X. Komartsova, L.G. (2004). Neyrokompyutery [Neurocomputers], 2nd ed. Moscow, Bauman Moscow State Technical University, 400 p.
XI. Krysanov, V.N., Gamburg, K.S., Rutskov, A.L. (2014). Problemyprognozirovaniyapotrebleniyaelektroenergiinapredpriyatiyakh s odnostavochnymtarifom [Problems of forecasting electricity consumption in enterprises having straight-line rate]. Proceedings of International Scientific and Technical Conference “Power Supply Industry and Electrical Engineering”, International Institute of Computers Technology, May 15–21, Voronezh, Russia.
XII. Krysanov, V.N., Rutskov, A.L. (2013). Matematicheskoyemodelirovaniyesistemupravleniyaraspredelonnoynasosnoynagruzki s primeneniyemiskusstvennykhneyronnykhsetey [Mathematical modeling of distributed pump load control systems using artificial neural networks]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, May 14–15, Voronezh, Russia.
XIII. Krysanov, V.N., Rutskov, A.L. (2014). Primeneniyemetodovneyronnykh i neyro-nechotkikhsetey v system akhupravleniyastaticheskimipreobrazovatelyami v elektroprivode [Application of methods of neural and neuro-fuzzy networks in static converter control systems in electric drive]. Proceedings of the International (19th All-Russian) Conference on Automated Electric Drive (AEP-2014), Saransk, Russia.

XIV. Krysanov, V.N., Rutskov, A.L. (2014). Prognozirovaniyepotrebleniyaelektroenergiipromyshlennymipredpriyatiyami s ispolzovaniyemmetodoviskusstvennykhneyronnykh i neyro-nechotkikhsetey [Forecasting power consumption by industrial enterprises using artificial neural and neuro-fuzzy networks]. Proceedings of the International (19th All-Russian) Conference on Automated Electric Drive (AEP-2014), Saransk, Russia.

XV. Krysanov, V.N., Rutskov, A.L., Sharapov, Yu.V. (2015). Prostranstvennyy 3-d interpolyator s ispolzovaniyemnechotkoylogiki [Spatial 3-d interpolator with fuzzy logic]. Proceedings of the 15th International Seminar “Physical and Mathematical Modeling of Systems” (FMMS-15), November 27–28, Voronezh, Russia.

XVI. Krysanov, V.N., Rutskov, A.L., Shukur, O., Shukur M. (2015). Prognozirovaniyepotrebleniyaelektroenergii v razvivayushcheysyaregionalnoysistemeelektrosnabzheniya [Forecasting of electricity consumption in a developing regional power supply system]. Proceedings of the 15th International Seminar “Physical and Mathematical Modeling of Systems” (FMMS-15), November 27–28, Voronezh, Russia.

XVII. Krysanov, V.N., Shukur, O., Shukur M., Rutskov, A.L. (2015). Otsenkaeffektivnostiprimeneniyaiskusstvennykhneyronnykh i neyro-nechotkikhseteydlyakontseptsii Smart Grid v elementakhtransporta i potrebleniyaelektroenergii [Evaluation of the effectiveness of use of artificial neural and neuro-fuzzy networks for the Smart Grid concept in the elements of transport and electricity consumption]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, October 25–28, Voronezh, Russia.
XVIII. Li, P., Li, Y., Xiong, Q., Chai, Y., Zhang, Y. (2014). Application of a hybrid quantized elman neural network in short-term load forecasting. International Journal of Electrical Power & Energy Systems, 55, 749–759.
XIX. Mamdani, E.H. (1977). Advances in the linguistic synthesis of fuzzy controllers. IEEE Trans. on Computer, C-26, 1182–1191.

XX. Monteleoni, C., Schmidt, G.A., Saroha, S., Asplund, E. (2011). Tracking climate models. Statistical Analysis and Data Mining, 4(4), 372–392.
XXI. Nedellec, R., Cugliari, J., Goude, Y. (2014). Gefcom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting, 30(2), 375–381.
XXII. Panklib, K., Prakasvudhisarn, C., Khummongkol, D. (2015). Electricity consumption forecasting in Thailand using an artificial neural network and multiple linear regression. Energy Sources, Part B: Economics, Planning, and Policy, 10(4), 427–434.

XXIII. Pierrot, A., Goude, Y. (2011). Short-term electricity load forecasting with generalized additive models. Proceedings of ISAP Power, 593–600.

XXIV. Rutskov, A.L., Gagarinov, N.V., Romanov, A.V. (2015). Analizeffektivnostiupravleniyarezhimamisetey 220 kV [Efficiency analysis of mode control in 220 kV networks]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, October 25–28, Voronezh, Russia.

XXV. Rutskov, A.L., Myazin, D.S., Romanov, A.V. (2015). Povysheniyetekhnologicheskoy i energeticheskoyeffektivnostinaklonnykhdiffuzionnykhustanovokputomoptimizatsiiparametrov s primeneniyemneyro-nechotkikhprintsipov [Improving the technological and energy efficiency of inclined diffusion plants by optimizing their parameters using neuro-fuzzy principles]. Proceedings of All-Russian Scientific and Technical Conference “Scientific technologies in scientific research, design, management, production”, October 25–28, Voronezh, Russia.

XXVI. Shi B., Yu-Xia L I., Xin-Hua Y.U. (2009). Short-term load forecast based on modified particle swarm optimizer and back propagation neural network model. Journal of Computer Applications, 29(4), 1036–1039.

XXVII. Zadeh, L.A. (1974). Outline to a new approach to the analysis complex systems and decision processes. IEEE Trans. on Systems, Man, and Cybernetics, 3, 28–44.

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Abstract:

The article addresses optimization of power supply systems by using fuzzy neural networks to increase the accuracy of operational forecasts and implementactive control systems in the power supply grids. As a practical example, the article considers the optimization of parameters of the 220 kV Yuzhnaya Substation operated by the Regional Dispatching Office of the Voronezh Region Electric Power System (Voronezh, Russia). The obtained results indicate an increase in the energy efficiency of the studied equipment by 4.38% (in terms of real power loss),as compared to the existing control mode, through the use of fuzzy neural controllers that improve the accuracy of forecasts of the relevant technological parameters. The developed solutions can be used in electrical power systems and load nodes as a part of control modules. The economic effect is achieved by taking into account the poorly for malizablefactors and compensating for their impact on real power loss in the transformer equipment.

Keywords:

Optimization of power supply systems,energy efficiency,energy efficiency,distributed objects,fuzzy neural networks,adaptive control systems,

Refference:

I. Aiolfi, M., Capistran C., Timmermann, A. (2010). Forecast combinations. Working Papers 2010-04, Banco de México.

II. Al Rashidi, M. R., El-Hawary, M. E. (2009). A survey of particle swarm optimization applications in electric power systems. IEEE Transactions on Evolutionary Computation, 13(4), 913–918.

III. Antoniadis, A., Brossat, X., Cugliari, J., Poggi, J. (2013). Clustering functional data using wavelets. International Journal of Wavelets, Multiresolution and Information Processing, 11(01).

IV. Burkovsky, V.L., Gusev, K.Yu. (2010). Neyrosetevaya model prognozirovaniyadinamikiekonomicheskikhpokazateley [Neural network simulation for forecasting the dynamics of economic indicators]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 6(4), 80–82.

V. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L. (2014). Prognozirovaniyepotrebleniyaelektroenergiipromyshlennymipredpriyatiyami s ispolzovaniyemmetodoviskusstvennykhneyronnykh i neyro-nechotkikhsetey [Forecasting power consumption by industrial enterprises using artificial neural and neuro-fuzzy networks]. Proceeding of the International (19th All-Russian) Conference on Automated Electric Drive (AEP-2014), Saransk, Russia.

VI. Burkovsky, V.L., Krysanov, V.N., Rutskov, A.L. (2016). Realizatsiyaprogrammnogokompleksaprognozirovaniyaurovnyaregionalnogoenergopotrebleniya [Sales program complex: Prediction of the regional level of energy consumption]. VestnikVoronezhskogogosudarstvennogotekhnicheskogouniversiteta = Bulletin of the Voronezh State Technical University, 12(3), 41–47.

VII. Çevik, H.H., Çunkaş, M. (2015). Short-term load forecasting using fuzzy logic and ANFIS. Neural Computing and Applications, 26(6), 1355–1367.

VIII. Cheng, Z., Juncheng, T. (2015). Adaptive combination forecasting model for china’s logistics freight volume based on an improved PSO-BP neural network. Kybernetes, 44(4), 646.

IX. Cho, H., Goude, Y., Brossat, X., Yao, Q. (2013). Modeling and forecasting daily electricity load curves: a hybrid approach. Journal of the American Statistical Association, 108, 7–21.

X. Danilov, A.D., Shukur, O.M., Rutskov, A.L. (2016). Analizprimeneniyanechotkikhneyronnykhseteydlyaprognozirovaniyaenergopotrebleniyapromyshlennykhpredpriyatiy [Analysis of the use of fuzzy neural networks for predicting power consumption of industrial enterprises]. Aktualnyyenauchnyyeissledovaniya XXI veka: teoriya i praktika = Topical scientific research of the 21st century: Theory and practice, 4, 6(26), 59–63.

XI. Devaine, M., Gaillard, P., Goude, Y., Stoltz, G. (2013). Forecasting electricity consumption by aggregating specialized experts. Machine Learning, 90(2), 231–260.

XII. Devaine, M., Gaillard, P., Goude, Y., Stoltz, G. (2013). Forecasting electricity consumption by aggregating specialized experts. Machine Learning, 90(2), 231–260.

XIII. Eban, E., Birnbaum, A., Shalev-Shwartz, S., Globerson, A. (2012). Learning the experts for online sequence prediction. Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK.

XIV. Gamm, A.Z., Gerasimov, L.N., Golub, I.I., et al. (1983). Otsenivaniyesostoyaniya v elektroenergetike [State estimation in power generation industry]. Moscow, Nauka.

XV. Krysanov, V.N., Rutskov, A.L., Myazin, D.S. (2015). Optimizatsiyaparametrovtsikladiffuziisveklosakharnogoproizvodstva s primeneniyemneyro-nechotkikhprintsipov [Optimization of diffusion cycle parameters in beet-sugar production using neuro-fuzzy principles]. Elektrotekhnicheskiyekompleksy i sistemyupravleniya = Electrotechnical Complexes and Control Systems, 2, 65–70.

XVI. Li, P., Li, Y., Xiong, Q., Chai, Y., Zhang, Y. (2014). Application of a hybrid quantized elman neural network in short-term load forecasting. International Journal of Electrical Power & Energy Systems, 55, 749–759.

XVII. Monteleoni, C., Schmidt, G.A., Saroha, S., Asplund, E. (2011). Tracking climate models. Statistical Analysis and Data Mining, 4(4), 372–392.

XVIII. Nedellec, R., Cugliari, J., Goude, Y. (2014). Gefcom2012: Electric load forecasting and backcasting with semi-parametric models. International Journal of Forecasting, 30(2), 375–381.

XIX. Order of the Ministry of Industry and Energy of the Russian Federation No. 380 of June 23, 2015 “On the procedure for calculating the ratio of the consumption of real and reactive power for certain power receiver (groups of power receivers) of electrical energy consumers.” Available at: https://normativ.kontur.ru/document?moduleId=1&documentId=256534

XX. Rosenblatt, R. (1961). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Book, Washington D.C.

XXI. Russian National Standard GOST 32144-2013 (2013). Electric energy. Electromagnetic compatibility of technical equipment. Power quality limits in the public power supply systems.

XXII. Shi, B., Yu-Xia, L.I., Xin-Hua, Y.U. (2009). Short-term load forecast based on modified particle swarm optimizer and back propagation neural network model. Journal of Computer Applications, 29(4), 1036–1039.

XXIII. Taylor, J. (2003). Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing. Journal of Operational Research Society, 54, 799–805.

XXIV. Vorotnitsky, V.E., Zaslonov, S.V., Kalinkina, M.A., Parinov, I.A., Turkina, O.V. (2006). Metody i sredstvarascheta, analiza i snizheniyapoterelektricheskoyenergiipriyeyeperedachepoelektricheskimsetyam [Methods and means of calculating, analyzing and reducing electric power losses when it is transmitted through electrical networks]. Moscow.

XXV. Zadeh, L.A. (1974). Outline to a new approach to the analysis complex systems and decision processes. IEEE Trans. on Systems, Man, and Cybernetics, 3, 28–44.

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Abstract:

This paper discusses the multi-loop adaptation of a telecommunication network and shows how this problem can solved in the network’s variable operation conditions by applying generalized indices of information exchange efficiency evaluation (information efficiency indices), tensor methodology, spectral theory of graphs, and coherent models and considering accepted assumptions. The model representation of multi-loop adaptation is structured by levels, proceeding from the commonality of the problems being solved. The elaborated generalized algorithm and tensor orthogonal and imitative models for the telecommunication network of various topologies allow deriving information efficiency functions and evaluate this efficiency on the basis of generalized indices, including information transmission performance coefficient, inflow bandwidth, and band efficiency angle tangent. The modeling results confirm the feasibility and functionality of the suggested methodological tools for organization adaptation on the basis of the integral approach and the system of generalized indices in the context of inflow changes and under destabilizing influences.

Keywords:

Telecommunication network,multi-loop adaptation,integrated approach,information efficiency,performance coefficient,bandwidth,

Refference:

I. Anwar Hossain Md. Tarique M. Effect of multipath fading and multiple access interference on broadband code division multiple access systems. Int J Commun Sys. 2011; 25 (7); 874-886.
II. Arslan H, Chen ZhN, Benedetto M. Ultra Wideband Wireless Communication. New-York, London: John Wiley & Sons; 2005.
III. August NJ, Thirugnanam R, Ha DS. An adaptive UWB modulation scheme for optimization of energy, BER, and data rate. IEEE Conf UWB Sys Tech. 2004; 182 – 186.
IV. Baidas MW, Alsusa Em, Khairi A. Hamdi Power allocation over time varying multiple access interference channels. Int J Commun Sys. 2016; 29 (13); 2041-2058.
V. Bambos N. Toward power-sensitive network architectures in wireless communications: Concepts, issues and design aspects. IEEE Pers. C. 1998; 5(3); 50-59.
VI. Basagni S, Turgut D, Das SK. Mobility-adaptive protocols for managing large ad hoc networks. Proc IEEE Int Conf Commun. 2001; 1539 – 1543.
VII. Cuomo F, Martello C, Baiocchi A., Capriotti F. Radio resource sharing for ad hoc networking with UWB. IEEE J Sel Ar Commun. 2002; 20 (9); 1722 – 1732.
VIII. Cuomo F, Martello C. A distributed power regulated algorithm based on SIR margins for adaptive QoS support in wireless networks. Proc Pers Wirel Commun 2003; 2775 of Lecture Notes in Computer Science. Heidelberg: Springer; 114 – 127.
IX. Cvetkovic D,Doob M, Sachs H.Spectra of Graphs. Theory and Application. New York, San Francisco, London: Academic Press; 1980.
X. Goldsmith A. Wireless Communications. Cambridge: Cambridge University Press; 2005.
XI. Golovin OV, Prostov SP. Shortwave Radio Communication Devices and Systems [Sistemy i ustroystvakorotkovolnovoyradiosvyazi]. Moscow: GoryachayaLiniya; 2006.
XII. Gupta P, Kumar PR. Towards an information theory of large networks: An achievable rate region. IEEE Trans. Inform. Theory 2003; (49)8; 1877 – 1894.

XIII. Julian D, Chiang M, O’Neill D, Boyd S. QoS and fairness constrained convex optimization of resource allocation for wireless cellular and ad hoc networks. Proc. IEEE Infocom Conf. 2002.
XIV. Kawadia V, Kumar PR. Principles and protocols for power control in wireless ad hoc net works. IEEE J. Sel. Areas Commun. 2005; 23(1); 76 – 88.
XV. Kron G. Tensor Analysis of Networks. New-York, London: John Wiley & Sons; 1965.
XVI. Mezhuev AM, Korennoy AV, Pasechnikov II. Method of forming structurally sus-tainable and information efficient network information systems [Metodformirova-niyastrukturnoustoychivykh i informatsionnoeffektivnykhsetevykhinformatsion-nykhsistem]. Radiotekhnika 2019; 4; 84 – 94.
XVII. Mezhuev AM, Pasechnikov II, Korennoy AV. Analyzing efficiency function of in-formation network and algorithm of evaluating information exchange modes on the basis of derivatives of generalized indicator [Analizfunktsiieffektivnostiinfor-matsionnoyseti i algoritmotsenkirezhimovinformatsionnogoobmenanaosnove-proizvodnykhobobshchennogopokazatelya]. Electromagnetic Waves and Electronic Systems 2017; 5; 12-22.
XVIII. Mezhuev AM. Tensor orthogonal model, taking account of interference conditions in evaluating information communication networks for information efficiency [Tenzornayaortogonal’nayamodel’ suchetomvliyaniyapomekhovoyobstanovki-priotsenkeinformatsionnoyeffektivnostiinfokommunikatsionnykhsetey]. Radio-tekhnika 2018; 10; 50-59.
XIX. Mirhakkak M, Schult N, Thomson D. Dynamic bandwidth management and adaptive applications for a variable bandwidth wireless environment. IEEE J. Sel. Areas Commun. 2001; 19(10); 1984 – 1997.
XX. Nazarov AN, Sychev KI. Models and Methods of Calculating Performance Indicators of Nodal Equipment and Structural Network Parameters of Next-Gen Communication Networks [Modeli i metody rascheta pokazateley kachestva funktsionirovaniya uzlovogo oborudovaniya i strukturno-setevykh parametrov setey svyazi sleduyushchego pokoleniya]. Krasnoyarsk: Polikom; 2010.
XXI. Peterson LL, Davie BS. Computer Networks: A Systems Approach, Second Edition (The Morgan Kaufmann Series in Networking): 2nd ed.San Fransisco: Morgan Kaufmann; 2000.
XXII. Porcino D. Ultra-wideband radio technology: potential and challenges ahead. IEEE Commun Mag. 2003; 41(7); 66 – 74.
XXIII. Radunovic B, Le Boudec JY. Optimal power control, scheduling and routing in UWB networks. IEEE J Sel Ar Commun. 2004; 22 (7); 1252 – 1270.
XXIV. Subramanian L, Katz RH. An architecture for building self-configurable systems. Proc. Mobile Ad Hoc Network Comput. Workshop 2000; 63 – 73.
XXV. Yuh Shyan Chen, Chih Shun Hsu, Po Ta Chen. A multiple relay based medium access control protocol in multirate wireless ad hoc networks with multiple beam antennas. Int J Commun Sys. 2009; 23 (5); 596-632.

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Abstract:

One of the most important economic tasks is the provision of the population with safe high-quality food products. Milk and dairy products are socially significant products in the population diet. Social-economical programs of the development of food processing industry target primarily small-scale processing industry. Taking into account geographical peculiarities ant climate conditions in Russia, the studies on the improvement and development of new technologies of milk and dairy-containing preservatives with enhanced nutritional value and long shelf life have high priority. The first aspect provides the main properties of products, the second – preserves them during all the period of storage with minimum risk of quality reduction.    The evaluation of the quality and safety of preserved dairy products is performed by a number of organoleptic, physicochemical, and microbiological parameters. Taking into account constant improvement of the existing and the development of new innovative technologies, the increase in the range of the produced products, the strengthening of the requirements to shelf life, and other factors, the evaluation criteria of the quality and safety are constantly expanding and new methods are developed, which are introduced into the official registration documentation for new types of products. The analysis of the grounds for the development of dairy products with an extended shelf life showed that there are still ways to improve the traditional technologies for the increase in their effectiveness. Besides, the problem of the improvement of the stability of preserved dairy products is acute because of the increase in the volume of milk-containing preservatives and the increase in the self-cost and the deficit of natural dairy sources.     The development of new products provided the required conditions of their production and storage: a fine method of dehydration, optimization of the content and technological modes, stabilization of fatty bases with antioxidants, correction of fatty acids content, and sealed packaging. The developed technologies of the production of preserved milk and milk-containing products allow for the adaptation of the chosen method for the development of safe preserved milk products and the prognosis of their economic, social, and strategic significance.  

Keywords:

High-fat dry cream powder,flavonoid antioxidants,antioxidant complexes,inhibition,quality parameters,secondary products of lipid oxidation,shelf life,

Refference:

I. Abaturova N.A., Gavrilova N.B., Kusmanov K.K. et al. Natural antioxidants in the production of dairy products. Issues of stabilization and development of the production industry in Siberia, Mongolia and Khazakhstan in XXI century. International scientific-practical conference. Novosibirsk, 1999. p. 133-134.
II. Gemili S., Yemenicioglu A., Altinkaya A. Development of antioxidant food packaging materials with controlled release properties. J. Food Eng. 2010. № 3. p. 325-332.
III. Halliwell B. Antioxidant defense mechanisms: From the beginning to the end (of the beginning). B. Halliwell. Free Radic Res. 1999. Vol. 31. p. 261-272.
IV. Ivkova I.A., Batukhtin A.N. Antioxidant effect of different inhibitors. Technological education and stable development of the region. All-Russian scientific and practical conference. Novosibirsk, 2010, p. 25-27.
V. Ivkova I.A., Batukhtin A.N., Pilyaeva A.S. The ways of preservation of quality of dry dairy and milk-containing preservatives: monography. Omsk. OmSAU Press, 2013. p. 156.
VI. Ivkova I.A., Pilyaeva A.S. Modern technology in the production of the storage of fat containing products. Agrarian science. Materials of VI international scientific-practical conference. Barnaul, 2010. p. 56-57.
VII. Ivlova I.A., Batukhtin A.N. Antioxidant activity of different inhibitors. Journal of OmSAU. Omsk. 2011. № 2. p. 54-56.
VIII. Kharitonov V.D., Petrova L.V., Petrova S.V. Thermo destructive changes in dry milk during the spray dehydration: monography. Omsk. OmSAU Press, 2009. p. 233.
IX. Knipschildt M.E., Andersen G.G. Drying of milk and milk products. Modern Dairy Technology. Volume 1. Advances in Milk Processing. London: Chapman and Hall, 1994. p. 159-254.
X. Kobanov L.A., Mizeretskiy N.N., Kharitonov V.D. Influence of some processes parameters on physical structure of dry milk. Journal of the Universities: food technology, 1983. № 5. p. 136-138.
XI. Mischarina T.A. Antioxidant properties of laurel and fennel ether oils in combination with coriander ether oil. Biotechnology: status and perspectives of the development: materials of the congress. P. 1. М., 2005. p. 132.
XII. Mitaseva L.F., Degtyarev P.S., Selischeva A.A. Antioxidant plant extracts. Meat processing industry. 2002. № 12. p. 28-29.
XIII. Nakhost Z, Karel M. Measurement of oxidations. Related changes in proteins153 of freeze-dried meats. J Food Sci. 1984. Vol. 49. p. 1171-1173.
XIV. Nekrasova T.E. Vitamins and antioxidants for oil and fat products. Food processing industry. 2002. № 10. p. 68-70.
XV. Palagina I.A., Kasyanov G.I. Food antioxidant additives. М.: Issues of the Universities. Food processing technology. 2002. № 2. p. 117.
XVI. Saprygin G.P., Khotsko Y.A. Comparative storage quality in dry cream powder, whole and skimmed milk. Publications of OmAU, 1982. Т. 3. p. 46-48.
XVII. Stirton A.J., Turner J., Stirton J., Riemenschneider W. Oxygen absorption of methyl esters of fat acids and the effect of antioxidants. Oil and Soap. 1965. T. 22. p. 81-83.
XVIII. Tokaev E.S. Natural antioxidant dihydroquercetin. Meat processing industry. 2003. № 6. p. 27-28.
XIX. Tolkunova N.N., Bidyuk A.Y. The study of antioxidant properties of the extracts of mealberry, hypericum, and oak cortex. Fat and oil industry. 2002. № 3. p. 34-35.
XX. Vladimirov Y.A. Free radicals and antioxidants. Issues of RAMN. 1998. № 7. p. 43-51.
XXI. Vladimirov Y.A., Archakov A.I. Lipid peroxidation in biological membranes. M.: Science, 1972. p. 252.

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Abstract:

The present research is devoted to developing a method of structural adaptation of network information systems (NIS) in conditions of high unstable input flow and the influence of destabilizing factors (interference) based on the current values ​​of the generalized parameter of evaluating the effectiveness of information exchange (information efficiency) in the basic and reserve structures. Applying the abovementioned method allows determining the boundary value of the input traffic for implementing structural adaptation, as well as forming an unambiguous condition for the transitions between the basic and reserve topologies of the system. The method can significantly increase the efficiency of information transfer and significantly expand the bandwidth of the NIS. Simulation and analytical models for evaluating the effectiveness of information exchange in NIS using the obtained method of structural adaptation were developed. During the simulation, the feasibility of the developed method and the reliability of the results obtained on its basis were confirmed, as well as recommendations were given on its practical application as algorithmic software for the monitoring controller of the system.

Keywords:

Algorithmicadaptation,informationefficiency,informationloss,adaptationcriteria,adaptationprocedures,lanetransmission,

Refference:

I. Bertsekas, D. Data Networks: 2nd ed. / D. Bertsekas, R. Gallager // Prentice-Hall, Englewood Cliffs. NJ, 1992. – 556 р.
II. Borshchev, A. The Big Book of Simulation Modeling. Multimethod modeling with AnyLogic 6 / A. Borshchev // AnyLogic North America, 2013. – 614 p.
III. C. Chaabane, A. Pegatoquet, M. Auguin and M. Ben Jemaa, “A Joint Mobility Management Approach and Data Rate Adaptation Algorithm for IEEE 802.15.4/ZigBee Nodes,” Wireless Sensor Network, Vol. 6 No. 2, 2014, pp. 27-34.
IV. Diestel, R. GraphTheory, ElectronicEdition / R. Diestel // Springer-Verlag, NY 2016. – 447 p.
V. Goldsmith, A. Wireless Communications. – M.: Technosphere, 2011. 904 p.
VI. Golovin, O. V. Systems and devices of short-wave radio communication / O. V. Golovin, S. P. Prostov. – M.: Hotline – Telecom, 2006. 600 p.
VII. Julian, D. QoS and fairness constrained convex optimization of resource allocation for wireless cellular and ad hoc networks / D. Julian, M. Chiang, D. O’Neill, S. Boyd // Proc. IEEE Infocom Conf. – June 2002. – pp. 477 – 486.
VIII. Korennoy, A. V. Applied problems of navigation, communication and control. Methods of analysis and synthesis. – M.: Radio Engineering, 2015.– 161 p.
IX. M. Ahmed, A. Abdullah and A. El-Sayed, “A Survey of MANET Survivability Routing Techniques,” International Journal of Communications, Network and System Sciences, Vol. 6 No. 4, 2013, pp. 176-185.
X. M. Chen, L. Zhou, T. Hara, Y. Xiao and V. Leung, “Advances in Multimedia Communications,” International Journal of Communication Systems, Vol. 24, No. 10, 2011, pp. 1243-1245.
XI. Mezhuev, A. M. Analysis of the efficiency function of the information network and the algorithm for evaluating the modes of information exchange based on derivatives of a generalized parameter / A. M. Mezhuev, I. I. Pasechnikov, A. V. Korennoy // Electromagnetic waves and electronic systems. – M .: Radio engineering, 2017. – No. 5. – P. 12 – 22.
XII. Mezhuev, A. M. Joint solution of the problems of algorithmic and structural adaptation in infocommunication systems // High technology in space research of the Earth. – S.-Pb. : Media Publisher, 2015. – V. 7, No. 6. – P. 36 – 43.
XIII. Mezhuev, A. M. Methodological foundations of the organization of multi-curev adaptation in network information systems / A. M. Mezhuev, I.I. Pasechnikov, A.V. Korennoy // Electromagnetic waves and electronic systems. – M.: Radio engineering, 2019. No. 4. – P. 35 – 45.
XIV. Mezhuev, A. M. Tensor orthogonal model taking into account the influence of the noise situation when evaluating the information efficiency of infocommunication networks / A. M. Mezhuev, I. I. Pasechnikov, A. V. Korennoy // Radio engineering. – M.: Radio engineering, 2018. – No. 10. – P. 96 – 108.
XV. Miguel López-Benítez, Javier Gozálvez. Link adaptation algorithms for improved delivery of delay- and error-sensitive packet-data services over wireless networks. // Wireless Networks. – April 2010, Volume 16, Issue 3, pp 593–606.
XVI. Pasechnikov, I. I. Methodology of analysis and synthesis of extremely loaded information networks. – M.: Mechanical Engineering-1, 2004. 216 p.
XVII. Ramanathan R. Topology control of multihop wireless networks using transmit power adjustment / R. Ramanathan, R. Rosales-Hain // Proc. IEEE Infocont Conf. – March 2000. – pp. 404 – 413.
XVIII. Renato E. N. Moraes, Welber W. F. dos Reis, Helder R. O. Rocha, Daniel J. C. Coura Power-efficient and interference-free link scheduling algorithms for connected wireless sensor networks, 1–20 JUN 2019, Wireless Netw / https://doi.org/10.1007/s11276-019-02045-z.
XIX. Saâd Biaz, Yawen Dai. Basestation flow control for wired to wireless networks. // Wireless Networks. – April 2010, Volume 16, Issue 3, pp 775–791.
XX. Schriber, T. J. Simulation using GPSS / T. J. Schriber // John Wiley & Sons, New York, 1974. – 533 p.
XXI. Y. Qu and S. Georgakopoulos, “An Average Distance Based Self-Relocation and Self-Healing Algorithm for Mobile Sensor Networks,” Wireless Sensor Network, Vol. 4 No. 11, 2012, pp. 257-263.

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