Electronic Modeling

VOL 36, NO 4 (2014)

СОДЕРЖАНИЕ

Mathematical Modeling and Computation Methods

	SAUKH S.E., BORISENKO A.V., DZHIGUN E.N. Model of the Trunk Transmission Lines Network in the Problems of Planning of the Electric Power Systems Development	3-14
	KULAKOV Yu.A., VOROTNIKOV V.V. Clusterization of Associative Network Based on Polynomially Computable Spectral Invariants of Graphs	15-24
	SIDORENKO S.I., ZAMULKO S.A., KONOREV S.I. Generalized Algorithm of Computational Material Design	25-32

Computational Processes and Systems

ROMANYUK V.V.
Admissibility of Approximate Solution of the Infinite Antagonistic Game on Unit Hypercube at Specific Sampling in Each of Its Dimensions

33-50

Parallel Computations

	PALAGIN V.V., GONCHAROV A.V., UMANETS V.M. Polynomial Algorithms of Joint Discrimination of Signals and Estimation of Their Parameters against a Background of Asymmetrical Non-Gaussian Interferences	51-66
	KUBITSKYI V.I., ZHUKOV I.A. Implementation of Parallel Method of Encoding the Lagrange Method	67-80

Application of Modelling Methods and Facilities

	APARTSIN A.S., SIDLER I.V. Integral Models of Development of Electric Power Systems with Allowance for Ageing of Equipment of Electric Power Plants	81-88
	VYNNYCHUK S.D., SAMOILOV V.D. Determination of Current Ribs of Graphs of Commutation Structures Based on Analysis of the Fundamental System of Cycles	89-100
	ZAKHARZHEVSKY O.A., AFONIN V.V. Allowance for the Construction of Windings of Asynchronous Machine in Matlab Modeling	101-116

Short Notes

POLISSKIY Yu.D.
Algorithm of Implementation of Complex Operations in the System of Residual Classes with the Help of Numbers Presentation in Reverse Codes

117-123

Model of the Trunk Transmission Lines Network in the Problems of Planning of the Electric Power Systems Development

SAUKH S.E., BORISENKO A.V., DZHIGUN E.N.

ABSTRACT

Algorithms of formation and parameter identification of a mathematical model of the equivalent network of high-voltage transmission lines have been proposed. These algorithms provide a significant reduction in the number of model lines and nodes and allow the model to adequately reflect the basic properties of the network of trunk lines.

KEYWORDS

trunk transmission lines, equivalent network, model intersystem power flows, PTDF-matrix.

REFERENCES

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2. Voropay, N.I. and Trufanov, V.V. (2002), “Mathematical modeling of electrical power systems in modern conditions”, Elektrichestvo, no. 10, pp. 6-12.
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8. Purchala, K., Meeus, L., Van Dommelen, D. and Belmans, R. (2005), “Usefulness of DC power flow for active power flow analysis”, IEEE Power Engineering Society General Meeting, Vol. 1, available at: http://www.esat.kuleuven.be/electa/publicaions/_Hlt390410574_Hlt390410575_Hlt3904105760_Hlt3904105770BM_1_BM_2_BM_3_BM_4_fulltexts/pub_1456.pdf
9. Cheng, Xu. (2005), “PTDF-based power system equivalents”, IEEE Transactions on Power Systems, Vol. 20, no. 4, pp. 1868-1876.
10. Duthaler, C., Kurzidem, M., Emery, M. and Andersson, G. (2008), “Analysis of the Use of PTDF in the UCTE Transmission Grid”, 16th Power Systems Computation Conference, Glasgow, 2008, available at: http://infoscience.epfl.ch/record/153995/files/0807_PSCC_PTDF-Duthaler.pdf
11. Chong Suk Song, Chang Hyun Park, Minhan Yoon, Gilsoo Jang (2011), “Implementation of PTDFs and LODFs for Power System Security”, Journal of International Council on Electrical Engineering, Vol. 1, no. 1, pp. 49-53.
12. Saukh, S.E. (2013), “Methods of computer simulation of competitive equilibrium in the electric power markets”, Elektronnoe modelirovanie, Vol. 35, no. 5, pp. 11-26.
13. Saukh, S.E. (2011), “Mathematical modeling of power chains”, Ibid., Vol. 33, no. 3, pp. 3-12.
14. Saukh, S.E. (2007), “CR factorization large-scale sparse matrixes method”, Ibid., Vol. 29, no, 6, pp. 3-22.

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Clusterization of Associative Network Based on Polynomially Computable Spectral Invariants of Graphs

KULAKOV Yu.A., VOROTNIKOV V.V.

ABSTRACT

Application of polynomial invariants of graphs is considered as basic information for breaking up of a graph. The use of the objective function — a self-weighted sum of squares of distances between the network nodes is offered for clusterization of the network nodes. The method of the Lagrange indefinite multipliers was used for minimization of the objective function, the condition of symmetry and positive definiteness of the Laplace matrix.

KEYWORDS

clusterization, associative network, spectral analysis, graph invariant, eigenvalues, characteristical polynomial.

REFERENCES

1. Andreev, A.M. and Mozharov, G.P. “Analysis of the main characteristics of computer systems using spectral graph theory”, available at: http://www.technomag.edu.ru/doc/ 233774.html
2. Vorotnikov, V.V. and Kulakov, Yu.A. (2013), “Determination of the structural complexity of decentralized telecom networks of special control systems by methods of spectral graph theory”, Elektronika i svyaz, no. 1, pp. 110-117.
3. Tsvetkovich, D., Dub, M. and Zakhs, Kh. (1984), Spektry grafov: Teoriya i primenenie. Per. s angl. Korolyuka, V.S.; pod red. Korolyuka, V.S., Izd. 2-e [Spectra of graphs: Theory and application. Trans. from English. Korolyuk, V.S.; ed. Korolyuk, V.S., Ed. 2], Naukova dumka, Kiev, Ukraine.
4. Aiello, M., Andreozzi, F., Catanzariti, E. and et al. (2007), “Fast Convergence for Spectral Clustering”, 14th International Conf. on Image Analysis and Processing (ICIAP-2007), pp. 641-646.
5. Buvaylo, D.P. and Tolok, V.A. (2002), “Quick high-performance algorithm for separating non-regular graphs”,Visnyk Zaporizkogo derzhavnogo universytetu, no. 2, p. 10.
6. Ryashko, L.B. and Bashkyrtseva, I.A. (2013), “Spectral criterion of stochastic stability of invariant manifolds”, Kibernetika i sistemnyy analiz, no. 1, pp. 82-90.
7. Yakobovskiy, M.V. (2004), “Processing of the data grid on distributed computing systems”, Voprosy atomnoy nauki i tekhniki. Ser. Matematicheskoe modelirovanie fizicheskikh protsessov, Vol. 2, pp. 40-53.
8. Barabasi, A. and Albert, R. (1999), “Emergence of Csaling in Random Networks”, Science, Vol. 286, pp. 500-512.
9. Parlett, B. (1983), Simmetrichnaya problema sobstvennykh znacheniy. Chislennye metody. Per. s angl. [Symmetric problem of eigenvalues. Numerical Methods. Transl. from English], Mir, Moscow, Russia.

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Generalized Algorithm of Computational Material Design

SIDORENKO S.I., ZAMULKO S.A., KONOREV S.I.

ABSTRACT

KEYWORDS

computational materials design, new materials with predetermined properties, direct problem, inverse problem.

REFERENCES

1. Ogorodnikov, V.V., Pokropivnyy, V.V. and Shtern, M.B. (2008), Kompyuternoe modelirovanie v materialovedenii. Neorganicheskoe materialovedenie: Entsikloped. izd. V 2 t. Pod red. Skorokhoda, V.V., Gnesina, G.G. T. 1: Osnovy nauki o materialakh [Computer simulations in materials science. Inorganic materials. Encyclopedias. Ed. In 2 v. Ed. Skorokhod, V.V., Gnesin, G.G. Vol. 1. The Basics of materials science], Nauk. dumka, Kiev, Ukraine, pp. 1092-1145.
2. Billinge, S.J.L., Rajan, K. and Sinnott, S.B. (2006), “From Cyberinfrastructure to Cyberdiscovery in Materials Science: Enhancing outcomes in materials research, education and outreach”, Report from a workshop held in Arlington, August 3-5, 2006, Virginia.
3. Iwata, S., Ohsawa, Y., Tsumoto, Sh. and et al. (2008), Communications and Discoveries from Multidisciplinary Data, Springer.
4. Rajan, K. (2005), “Materials informatics”, Materials Today, Vol. 8, pp. 38-45.
5. Raabe, D. (1998), Computational Materials Science, Wiley-VCH, Weinheim.
6. Sydorenko, S.I. and Zamulko, S.O. (2013), “Direct and inverse problems in computer construction materials”, Naukovi visti NTUU «KPI», no. 4, pp. 148-151.
7. Toyohiro, Chikyow (2006), “Trends in Materials Informatics in Research on Inorganic Materials”, Quarterly Review, no. 20, pp. 59-71.
8. Changwon Suh, Arun Rajagopalan, Xiang Li and Krishna Rajan (2002), “The Application of Principal Component Analysis to Materials Science Data”, Data Science Journal, Vol. 1, pp. 19-26.
9. Frenkel, D. and Smit, B. (1996), Understanding Molecular Simulations: From Algorithms to Applications, Academic Press, San Diego.
10. Yamamoto, T., Ohnishi, S., Chen, Ying and Iwata, S. (2009), “Effective Interatomic Potentials Based on The First-Principles Material Database”, Data Science Journal, Vol. 8, pp. 62-69.
11. Gang Yu, Jingzhong Chen and Li Zhu. (2009), “Data mining techniques for materials informatics: datasets preparing and applications”, Second International Symposium on Knowledge Acquisition and Modeling, IEEE, Vol 2, pp. 189-192.
12. Abicht, L., Freikamp, H. and Schumann, U. (2006), “Identification of Skills Needs in Nanotechnology”, CEDEFOP Panorama Series, 2006, available at: http://www.trainingvillage.gr/etv/Information_resources/Bookshop/publication_details.asp?pub_id=426
13. Lei Liu, Hui Zhang, Jianhui Li and et al. (2012), “Building a Community of Data Scientists: an Explorative Analysis”, Data Science Journal, Vol. 8, pp. 201-207.
14. Tan, P.-N., Steinbach, M. and Kumar, V. (2000), Introduction to Data Mining, Addison-Wesley, NY.
15. Ramalhete, P.S., Senos, A.M.R. and Aguiar, C. (2010), “Digital Tools for Material Selection in Product Design”, Materials and Design, Vol. 31, pp. 2275-2287.
16. Deng, Y.-M. and Edwards, K.L. (2007), “The role of Materials Identification and Selection in Engineering Design”, Ibid., Vol. 28, pp. 131-139.
17. Nong Zhi-sheng, Zhu Jing-chuan, Yu Hai-ling and Lai Zhong-hong (2012), “First Principles Calculation of Intermetallic Compounds in Fe Ti Co Ni V Cr Mn Cu Al System High Entropy Alloy”, Transactions of Nonferrous Metals Society of China, Vol. 22, Issue 6, pp. 1437-1444.
18. Shaoqing Wang and Hengqiang, Ye. (2011), “First-Principles Studies on the Component Dependences of High-Entropy Alloys”, Advanced Materials Research, Vol. 338, pp. 380-383.
19. Akai, H., Ogura, M. and Long, N.H. (2009), “Computational Materials Design and its Application to Spintronics”, Japan—Germany Joint Workshop, Kyoto, Jan. 21-23, 2009, available at: http:// www.jst.go.jp/sicp/ws2009_ge3rd/ presentation/29.pdf
20. Sydorenko, S.I., Zamulko, S.O., Voloshko, S.M. and Konorev, S.I.(2012), AS No 45279 Ukraine ”The algorithm of computer design of new materials”, publication date August 22, 2012. Bulletin no. 27.

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Admissibility of Approximate Solution of the Infinite Antagonistic Game on Unit Hypercube at Specific Sampling in Each of Its Dimensions

ROMANYUK V.V.

ABSTRACT

A three-stage method is presented for obtaining the approximate solution of the infinite antagonistic game on unit hypercube with specified uniform sampling of the game kernel along each of the cube dimensions. After the sampling in accordance with the suggested requirements is accomplished, mutually invertible reshaping of the game multidimensional matrix into the two-dimensional matrix is implemented, whereupon the approximate solution results. Admissibility of this solution is estimated over the consistency of the players’ optimal strategies supports, where its variation is restricted within minimal neighborhood of the sampling steps.

KEYWORDS

infinite zero-sum game, the unit hypercube, multidimensional matrix, the spectrum of the optimal strategy, the consistency of the approximate solution.

REFERENCES

1. Mushik, E. and Myuller, P. (1990), Metody prinyatiya tekhnicheskikh resheniy: Per. s nemetskogo [Methods of making technical decisions: Trans. from German], Mir, Moscow, Russia.
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3. Vorobyev. N.N. (1985), Teoriya igr dlya ekonomistov-kibernetikov [Game theory for economists and computer scientists], Nauka, Moscow, Russia.
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9. Romanuke, V.V. (2011), “Coming up at the Left Off-bound Projector Optimal Strategy in Support Construction with Four Props under Unit-normed Uncertain Squeezes”, Zbirnyk naukovykh prats In-tu problem modelyuvannya v energetytsi im. Pukhova, G.E. NAN Ukrayiny , Vol. 60, pp. 76-82.
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