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  8. 40-1

Electronic modeling

Vol 40, No 1 (2018)

https://doi.org/10.15407/emodel.40.01

CONTENTS

Mathematical Modeling and Computation Methods

  YALOVETS A.L.
About the Taxonomy of Autonomous Agents
3-30
  FEYZIYEV F.G., MEKHTIYEVA M.R.
Modification of Peterson-Gorenstein-Zierler Method, Bringing the Matrix to Triangular Form
31-46
  KRASILNIKOV A.I.
Modeling of Perforated Random Variables on the Basis of Mixtures of Shifted Distributions		 47-62

Computational Processes and Systems

  HAHANOV V.I., IEMELIANOV I.V., LIUBARSKYI M.M., CHUMACHENKO S.V., LITVINOVA E.I.
Quantum Memory-Driven Method for Test Synthesis Based on Qubit Data Structures
63-80

Application of Modeling Methods and Facilities

  FARHADZADEH E.M., MURADALIYEV A.Z., FARZALIYEV Y.Z., RAFIYEVA T.K., ABDULLAYEVA S.A.
Increase of Work Efficiency of Steam-Turbine Plants of HPS Units
81-92
  KOSENKO S.O.
The Main Statements of Ontology Theory and Its Implementation in the System of Legal Knowledge
93-114

Short Notes

  POLISSKY Yu.D.
Transformation of Pseudo-Numbers of the Residual Class System with All Even Modules into the Numbers of the System
115-120

ABOUT THE TAXONOMY OF AUTONOMOUS AGENTS

A.L. Yalovets, Dr Sc. (Eng.),
Institute of Program Systems, NAS of Ukraine
5 Bldg, 40 Acad. Glushkov Ave, Kyiv, 03187, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2018, 40(1):03-16
https://doi.org/10.15407/emodel.40.01.003

ABSTRACT

The problem of constructing taxonomy of autonomous agents has been investigated. The most well-known taxonomy of autonomous agents proposed by S. Franklin and A. Graesser has been analyzed and the contradictions in it have been considered. Based on this analysis results a new taxonomy of autonomous agents is proposed. This taxonomy realizes natural classification of autonomous agents and takes into account the current state of their research. The classes and subclasses of autonomous agents that are represented in the taxonomy are defined. Three main classes of computer agents are compared and the main differences between them are distinguished.

KEYWORDS

taxonomy, classification, autonomous agents, software agents, modeling agents, simulation agents. 

REFERENCES

1. Yalovets, A.L. (2013), “To problem definition on prosecuting in the plane”, Problemy programuvannya, no. 2, pp. 95-100.
2. Yalovets, A.L. (2013), “About one method of persecution on a plane”, Problemy programuvannya, no. 3, pp. 117-124.
3. Yalovets, A.L. (2013), “About the method of the nearest point as a method of strategies management of pursuit/escape of agents”, Problemy programuvannya, no. 4, pp. 94-99.
4. Yalovets, A.L. (2014), “Methods of modeling the behavior of agents in multi-agent system Navigation", Problemy programuvannya, no. 2-3, pp. 212-220.
5. Yalovets, A.L. (2014), “The problem of the formation of groups of agents in the tasks of pursuit/escape on a plane”, Problemy programuvannya, no. 1, pp. 108-118.
6. Yalovets, A.L. (2015), “The problem of modelling of maneuvering of agents in the tasks of pursuit/escape on a plane”, Problemy programuvannya, no. 2, pp. 86-100.
7. Yalovets, A.L. (2017), “The architecture and functionality of the multi-agent system Navigation”, Problemy programuvannya, no. 1, pp. 83-96.
8. Yalovets, A.L., Kondraschenko, V.Ya. and Aristov, V.V. (2014), 57880 Certificate of registration of copy-right in a product. Computer program — “Multi-agent systems «Navigation» version 2.5”. The State Service of Intellectual Property of Ukraine.
9. Franklin, S. and Graesser, A. (1996), Is it an agent, or just a program?: A taxonomy for autonomous agents, Proceedings of Workshop Intelligent Agents III : Agent Theories, Architectures, and Languages (ECAI’96), Springer, Budapest, Hungary, pp. 21-35.
10. Sanchez, J.A. (1997), A taxonomy of agents. Technical report ICT-97-1, Interactive and Cooperative Technologies Lab, Department of Computer Systems Engineering, Universidad de las Americas-Puebla, Mexico.
11. Huang, Z., Eliens, A., van Ballegooij, A. and de Bra, P. (2000), A taxonomy of web agents, Proceedings of the 11th International Workshop on Database and Expert Systems Applications, London, UK, pp. 765-769.
https://doi.org/10.1109/DEXA.2000.875111
12. Coninx, K. and Holvoet, T. A (2014), A microscopic traffic simulation platform for coordinated charging of electric vehicles, Proceedings of the 12th International Conference “Advances in Practical Applications of Heterogeneous Multi-Agent Systems” (PAAMS 2014), Springer, Salamanca, Spain, pp. 323-326.
https://doi.org/10.1007/978-3-319-07551-8_29
13. Garcia-Magarino, I. (2014), Practical multi-agent system application for simulation of tourists in Madrid routes with INGENIAS, Proceedings of the 12th International Conference “Advances in Practical Applications of Heterogeneous Multi-Agent Systems” (PAAMS 2014), Springer, Salamanca, Spain, pp. 122-133.
https://doi.org/10.1007/978-3-319-07551-8_11
14. Hajinasab, B., Davidsson, P., Persson, J.A. and Holmgren, J. (2016), Towards an agent-based model of passenger transportation, Proceedings of International Workshop “Multi-Agent-Based Simulation XVI” (MABS 2015), Springer, Istanbul, Turkey, pp. 132-145.
https://doi.org/10.1007/978-3-319-31447-1_9
15. Hassan, S., Antunes, L. and Pavón, J. (2010), Mentat: a data-driven agent-based simulation of social values evolution, Proceedings of International Workshop “Multi-Agent-Based Simulation X” (MABS 2009), Springer, Budapest, Hungary, pp. 135-146.
https://doi.org/10.1007/978-3-642-13553-8_12
16. Henein, C.M. and White, T. (2005), Agent-based modeling of forces in crowds, Proceedings of Joint Workshop “Multi-Agent and Multi-Agent-Based Simulation” (MABS 2004), Springer, New York, USA, pp. 173-184.
17. Jordan, R., Birkin, M. and Evans, A. (2011), Agent-based simulation modeling of housing choice and urban regeneration policy, Proceedings of International Workshop “Multi-Agent-Based Simulation XI” (MABS 2010), Springer, Toronto, Canada, pp. 152-166.
18. Molina, M., Martin, J. and Carrasco, S. (2014), An agent-based approach for the design of the future European air traffic management system, Proceedings of the 12th International Conference “Advances in Practical Applications of Heterogeneous Multi-Agent Systems” (PAAMS 2014), Springer, Salamanca, Spain, pp. 359-362.
https://doi.org/10.1007/978-3-319-07551-8_38
19. Monga, R. and Karlapalem, K. (2009), MASFMMS: Multi-agent systems framework for malware modeling and simulation, Proceedings of International Workshop “Multi-Agent-Based Simulation IX” (MABS 2008), Springer, Estoril, Portugal, pp. 97-109.
20. Niwa, T., Okaya, M. and Takahashi, T. (2015), TENDENKO: Agent-based evacuation drill and emergency planning system, Proceedings of International Workshop “Multi-Agent-Based Simulation XV” (MABS 2014), Springer, Paris, France, pp. 167-179.
21. Pezzulo, G. and Calvi, G. (2005), Designing and implementing MABS in AKIRA, Proceedings of Joint Workshop “Multi-Agent and Multi-Agent-Based Simulation” (MABS 2004), Springer, New York, USA, pp. 49-64.
https://doi.org/10.1007/978-3-540-32243-6_5
22. Vanëk, O., Jakob, M., Hrstka, O. and Pëchoucëk, M. (2012), Using multi-agent simulation to improve the security of maritime transit, Proceedings of International Workshop “Multi-Agent-Based Simulation XII” (MABS 2011), Springer, Taipei, Taiwan, pp. 44-58.
https://doi.org/10.1007/978-3-642-28400-7_4
23. Werlang, P., Fagundes, M.Q., Adamatti, D.F. and et al. (2014), Multi-agent-based simulation of mycobacterium tuberculosis growth, Proceedings of International Workshop “Multi-Agent-Based Simulation XIV” (MABS 2013), Springer, Saint Paul, USA, pp. 131-142.
https://doi.org/10.1007/978-3-642-54783-6_9
24. Georgakarakou, C.E. and Economides, A.A. (2009), Software agent Technology: An overview, Software applications: concepts, methodologies, tools, and applications, Edited by Tiako P.F., IGI Global, pp. 128-151.
https://doi.org/10.4018/978-1-60566-060-8.ch014
25. Macal, C.M. and North, M.J. (2014), Tutorial on agent-based modeling and simulation, Agentbased modeling and simulation, Edited by Taylor S.J.E., Palgrave MacMillan, pp. 11-31.
26. Meyers, R.A., editor (2009), Encyclopedia of complexity and systems science, Springer.
https://doi.org/10.1007/978-0-387-30440-3
27. Weiss, G., editor (1999), Multiagent systems: a modern approach to distributed artificial intelligence, The MIT Press, Massachusetts, USA.
28. Hadzic, M., Wongthongtham, P., Dillon, T. and Chang, E. (2009), Ontology-based multiagent systems, Springer.
https://doi.org/10.1007/978-3-642-01904-3
29. Padgham, L. and Winikoff, M. (2004), Developing intelligent agent systems. A practical guide, John Wiley & Sons Ltd.
https://doi.org/10.1002/0470861223
30. Wooldridge, M. (2002), An introduction to multiagent systems, JohnWiley & Sons, Ltd.
31. Wooldridge, M. and Jennings, N.R. (1995), Intelligent agents: Theory and practice, The Knowledge Engineering Review, Vol. 10, no. 2, pp. 115-152.
https://doi.org/10.1007/3-540-58855-8
32. Available at: https://www.collinsdictionary.com/dictionary/english/entity
33. Available at: https://en.wikipedia.org/wiki/Entity
34. Wooldridge, M. (2000), Reasoning about rational agents, The MIT Press, Massachusetts, USA.
35. Hexmoore, H., Castelfranchi, C. and Falcone, R., eds (2003), Agent autonomy, Springer Science + Business Media, LLC.
36. Nickles, M., Rovatsos, M. and Weiss, G. eds (2005), Agents and computational autonomy. Potential, risks and solutions, Springer Science + Business Media, Inc.
37. Mele, A.R. (1995), Autonomous agents. From self-control to autonomy, Oxford University Press, Oxford, USA.
38. Leitno, P. and Karnouskos, S., eds (2015), Industrial agents. Emerging applications of software agents in industry, Elsevier.
39. Odell, J. (2007), Agent technology: What is it and why do we care? Enterprise Architecture Advisory Service Executive Report. Cutter Consortium, Vol. 10, no. 3, pp. 1-25.
40. Weyns, D., Michel, F., Van Dyke Parunak, H. et al. (2015), Agent environments for multiagent systems – a research roadmap, Proceedings of the 4th International Workshop “Agent Environments for Multi-Agent Systems” (E4MAS 2014), Springer, Paris, France, pp. 3-21.
41. Weyns, D., Omicini, A. and Odell, J. (2007), Environment as a first class abstraction in multiagent systems, Vol. 14, Iss. 1, pp. 5-30.
42. Russell, S. and Norvig, P. (2007), Iskusstvennyi intellect: Sovremennyi podkhod [Artificial intelligence: Modern approach], 2nd ed., Transl. from English, Izd. dom “Vilyams”, Moscow, Russia.
43. Shatalkin, A.I. (2012), Takcsonomiya. Osnovy, printsipy i pravila [Taxonomy. Grounds, principles and rules], Tovarishchestvo nauchnykh izdaniy KMK, Moscow, Russia.
44. Yilmaz, L. and _ren, T. eds (2009), Agent-directed simulation and systems engineering, WILEY-VCH Verlag GmbH & Co.
45. Kondakov, N.I. (1976), Logicheskii slovar spravochnik [Logical reference-dictionary], Nauka, Moscow, USSR.
46. Bocharov, V.A. and Markin, V.I. (1998), Osnovy logiki [The basics of logic], INFRA-M, Moscow, USSR.
47. H`ppner, S. (2003), An agents’ definition framework and a methodology for deriving agents’ taxonomies, Proceedings of the 26th Annual German Conference on AI, Hamburg, Germany, pp. 618-632.
48. Poole, D.L. and Mackworth, A.K. (2010), Artificial intelligence: Foundations of computational agents, Cambridge University Press, Cambridge, USA.
https://doi.org/10.1017/CBO9780511794797
49. Wilson, R.A. and Keil, F.C. eds (1999), The MIT encyclopedia of the cognitive sciences, The MIT Press, Massachsetts, USA.
50. Siebers, P.-O. and Aickelin, U. (2008), Introduction to multi-agent simulation, Encyclopedia of decision making and decision support technologies, Ed. by Adam, F., Humphreys, P., IGI Global.
51. Brenner, W., Zarnekow, R. and Schubert, C. (1998), Intelligent software agents: foundations and applications, Springer-Verlag.
52. Green, S., Hurst, L., Nangle, B., Cunningham, P., Somers, F. and Evans, R. (1997), Software agents: A review. Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-1997-06.
53. Janca, P.С. and Gilbert, D. (1998), Practical design of intelligent agent systems. Agent technology:Foundations, applications, and markets, Edited by Jennings, N.R. and Wooldridge, M.J., Springer, pp. 73-89.
https://doi.org/10.1007/978-3-662-03678-5_4
54. Genesereth, M.R. and Ketchpel, S.P. (1994), Software agents, Communications of ACM, Vol. 37, no. 7, pp. 48-53.
55. Subrahmanian, V.S. et al. (2000), Heterogeneous agent systems, The MIT Press, Massachusetts, USA.
56. Zhang, Z. and Zhang, C. (2004), Agent-based hybrid intelligent systems: An agent-based framework for complex problem solving, Springer.
57. Bordini, R.H. et al. eds. (2005), Multi-agent programming: Languages, platforms and applications, Springer.
58. Yampolskiy, R.V. (2016), Artificial superintelligence: A futuristic approach, CRC Press.
59. Hibbard, B. (2012), Model-based utility functions, Journal of Artificial General Intelligence, Vol. 3, Iss. 1, pp. 1-24.
60. Orseau, L. and Ring, M. (2011), Self-modification and mortality in artificial agents, Proceedings of the 4th International Conference on Artificial General Intelligence (AGI 2011), USA, pp. 1-10.
61. Brazier, F.M., Wijngaards, N.J.E. (2001), Designing Self-Modifying Agents, Proceedings of International Conference of Computational and Cognitive Models of Creative Design V, Sydney, pp. 93-112.
62. Lin, L.-J. (1992), Self-reactive agents based on reinforcement learning, planning and teaching, Machine Learning, Vol. 8, pp. 293-321.
63. Schmidhuber, J. (1999), A general method for incremental self-improvement and multiagent learning in unrestricted environments, Evolutionary Computation: Theory and Applications, World Scientific Publishing Co, pp. 81-123.
64. Yampolskiy, R.V., Ashby, L. and Hassan, L. (2012), Wisdom of artificial crowds – a metaheuristic algorithm for optimization, Journal of Intelligent Learning Systems and Applications, Vol. 4, pp. 98-107.
65. Zimmermann, J., Henze, H.H. and Cremers, A.B. (2015), G`del agents in a scalable synchronous agent framework, Proceedings of the 8th International Conference on Artificial General Intelligence (AGI 2015), Germany, pp. 404-413.
66. Wang, S. (2016), Towards dynamic epistemic learning of actions for self-improving agents and multi-agent systems, Proceedings of IEEE International Conference on Autonomic Computing (ICAC 2016), Germany, pp. 292-299.
67. Mahoney, M. A model for recursively self improving programs, available at: http://mattmahoney.net/rsi.pdf.
68. Steunebrink, B.R., Thórisson, K.R. and Schmidhuber, J. (2016), Growing recursive selfimprovers, Proceedings of the 9th International Conference on Artificial General Intelligence
(AGI 2016), USA, pp. 129-139.
69. Yampolskiy, R.V. (2015), On the limits of recursively self-improving AGI, Proceedings of the 8th International Conference on Artificial General Intelligence (AGI 2015), Germany, pp. 394-403.
70. Ginodman, V.A., Obelets, N.V. and Pavlov, A.A. (2014), Ot pervykh virusov do tselevykh atak [From first viruses to target attacks], NIYaU MIFI, Moscow, Russia.
71. Kaspersky, K. (2005), Zapiski issledovatelya kompyuternykh virusov [The notes of the computer virus surveyor], Piter, St.Petersburg, Russia.
72. Klimentyev, K.E. (2013), Kompyuternyie virusy i antivirusy:vzglyad programmista [Computer viruses and anti-viruses: the programmer’s view], DMK Press, Moscow, Russia.
73. Gordon, Ya. (2004), Kompyuternyie virusy bez sekretov [Computer viruses without secrets], Novyi izdatelskii dom, Moscow, Russia.
74. Bonfante, G., Marion, J.-Y. and Reynaud-Plantey, D. (2009), A computability perspective on self-modifying programs, Proceedings of the Seventh IEEE International Conference on Software Engineering and Formal Methods, USA, pp. 231-239.
75. Rennard, J.-P. ed. (2007), Handbook of research on nature inspired computing for economics and management, Idea Group Inc.
76. Alkhateeb, F., Maghayreh, E.A. and Doush, I.A. (2011), Multi-agent systems – modeling, control, programming, simulations and applications, InTech.
77. Barnes, D.J. and Chu, D. (2010), Introduction to modeling for biosciences, Springer.
78. Meyer, R. (2015), Event-driven multi-agent simulation, Proceedings of International Workshop “Multi-Agent-Based Simulation” (MABS 2014), Springer, Paris, France, pp. 3-16.
79. Uhrmacher, A.M. and Weyns, D. (2009), Multi-agent systems: simulation and applications, CRC Press.
80. Reynolds, C. (1987), Flocks, herds and schools: A distributed behavioral model, Proceedings of the 14th annual conference on Computer graphics and interactive techniques (SIGGRAPH’87), pp. 25-34.
81. Aitken, M., Butler, G., Lemmon, D., Saindon, E., Peters, D. and Williams, G. (2004), The lord of the rings: the visual effects that brought middle earth to the screen, International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’04), Course Notes, Article № 11, Los Angeles, USA.
82. Burke, R., Isla, D., Downie, M., Ivanov, Y. and Blumberg, B. (2002), Creature smarts: The art and architecture of a virtual brain, Proceedings of the 3rd International Conference on Intelligent Games and Simulation (Game-On 2002), Westminster, UK, pp. 89-93.
83. Perlin, K. and Goldberg, A. (1996), Improv: A system for scripting interactive actors in virtual worlds, Proceedings of the ACM Computer Graphics Annual Conference, NY, USA, pp. 205-216.
https://doi.org/10.1145/237170.237258
84. Musse, S.R. and Thalmann, D. (2001), Hierarchical model for real time simulation of virtual human crowds, IEEE Transactions on Visualization and Computer Graphics, Vol. 7, no. 2, pp. 152-164.
85. Buckland, M. (2005), Programming game AI by example, Wordware Publishing, Inc.
86. Gemrot, J., Kadlec, R., BRda, M., Burkert, O., et al. (2009), Pogamut 3 can assist developers in building AI (not only) for their videogame agents, Agents for games and simulations. Trends in techniques, concepts and design, Edited by Dignum, F., Bradshaw, J., Silverman, B., van Doesburg, W. Springer, pp. 1-15.
87. Bedau, M.A. (2007), Artificial life. Handbook of the Philosophy of Biology, Edited by Matthen, M., Stephens, C., Elsevier.
88. Kumar, A. (2012), An overview of abstract and physical characteristics “Artificial Life Systems”, International Journal of Scientific and Research Publications, Vol. 2, Iss. 12, pp. 1-7.
89. Banzhaf, W. and McMullin, B. (2012), Artificial life. Handbook of Natural Computing, Edited
by Rozenberg, D., Springer.
90. Komosinski, M. and Adamatzky, A. eds. (2009), Artificial life models in software, Springer.

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MODELING OF PERFORATED RANDOM VARIABLES ON THE BASIS OF MIXTURES OF SHIFTED DISTRIBUTIONS

A.I. Krasilnikov, Cand. Sc. (Phys.-Math.),
Institute of Technical Thermal Physics,
2a Zhelyabov St, Kyiv, 03057, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2018, 40(1):47-62
https://doi.org/10.15407/emodel.40.01.047

ABSTRACT

The use of a family of mixtureû of shifted distributions for the modeling of perforated distributions and random variables has been justified. Peculiarities of simulation of perforated distributions are considered. The cumulant coefficients of mixtures of shifted distributions have been analyzed. The models of perforated random variables on the basis of a two-component mixture of shifted logistic distributions have been constructed.

KEYWORDS

cumulant coefficients, moment-cumulant models, cumulant analysis, perforated distributions, mixtures of distributions.

REFERENCES

1. Malakhov, A.N. (1978), Kumuliantnyi analiz sluchainykh negaussovykh protsessov i ikh preobrazovanii [Cumulant analysis of random non-Gaussian processes and their transformations], Sov. radio, Moscow, USSR.
2. Kunchenko, Yu.P. (2001), Polinomialnye otsenki parametrov blizkikh k gaussovskim sluchainykh velichin. Ch.I. Stokhasticheskie polinomy, ih svoistva i primenenie dlia nakhozhdeniia otsenok parametrov [Parameter polynomial estimations of random variables close to Gaussian. Part I. Stochastic polynomials, their properties and application for finding parameter estimations], ChITI, Cherkassy, Ukraine.
3. Kunchenko, Yu.P. and Zabolotnyi, S.V. (2001), Polinomialnye otsenki parametrov blizkikh k gaussovskim sluchainykh velichin. Ch. II. Otsenka parametrov blizkikh k gaussovskim sluchainykh velichin [Parameter polynomial estimations of random variables close to Gaussian. Part II. Parameters estimation of vandom variables close to Gaussian], ChITI, Cherkassy, Ukraine.
4. Alexandrou, D., De Moustier, C. and Haralabus, G. (1992), Evaluation and verification of bottom acoustic reverberation statistics predicted by the point scattering model, J. Acoust. Soc. Am., Vol. 91, no. 3, pp. 1403-1413.
https://doi.org/10.1121/1.402471
5. Karpov, I.G. (1999), “Approximate identification of distribution laws of hindrances in adaptive receivers with use of a method of the moments”, Radiotekhnika, no. 7, pp. 11-14.
6. Kuznetsov, V.V. (2009), “Use of the moments of the third order in calculations of electric loadings”, Vestnik Samarskogo GTU. Seriia “Tekhnicheskie nauki”, no. 2 (24), pp. 166-171.
7. Wang, H. and Chen, P. (2009), Fault diagnosis method based on Kurtosis wave and information divergence for rolling element bearings, WSEAS Transactions on Systems, Vol. 8, Iss. 10, pp. 1155-1165.
8. Kuznetsov, B.F., Borodkin, D.K. and Lebedeva, L.V. (2013), “Cumulant models of additional errors”, Sovremennye tekhnologii. Sistemnyi analiz.Modelirovanie, no. 1 (37), pp. 134-138.
9. Lukach, E. (1979), Kharakteristicheskie funktsii [Characteristic Functions], Translated by Zolotarev, V.M., Nauka, Moscow, USSR.
10. Kunchenko, Iu.P., Zabolotnii, S.V.,KovalV.V. and Chepynoha,A.V. (2005), “Simulation of excess random variables with a given cumulative description on the basis of the bi-Gaussian distribution”, Visnyk ChDTU, no. 1, pp. 38-42.
11. Zabolotnii, S.V. and Chepynoha, A.V. (2008), “Tetra-Gaussian symmetrically distributed probabilistic models on the basis of a moment description”, Zbirnyk naukovykh prats IPME im. G.Ie. Pukhova NAN Ukrainy, no. 47, pp. 92-99.
12. Chepynoha, A.V. (2010), “Areas of realization of bi-Gaussian models of asymmetric-excess random variables with a perforated moment-cumulant description”, Visnyk ChDTU, no. 2, pp. 91-95.
13. Krasilnikov, A.I. (2013), “Class of non-Gaussian distributions with zero skewness and kurtosis”, Izvestiia vysshikh uchebnykh zavedenii. Radioelektronika, Vol. 56, no. 6, pp. 56-63.
https://doi.org/10.3103/S0735272713060071
14. Krasilnikov, A.I. (2017), “Class of non-Gaussian symmetric distributions with zero coefficient of kurtosis”, Elektronnoe modelirovanie, Vol. 39, no. 1, pp. 3-17.
15. Krasilnikov, A.I. (2016), “Models of asymmetrical distributions of random variables with zero asymmetry coefficient”, Elektronnoe modelirovanie, Vol. 38, no. 1, pp. 19-33.
16. Korolev, V.Iu. (2008), Veroiatnostno-statisticheskii analiz khaoticheskikh protsessov s pomoshchiu smeshannykh gaussovskikh modelei. Dekompozitsiia volatilnosti finansovykh indeksov i turbulentnoi plazmy [Probabilistic-statistical analysis of chaotic processes using mixed Gaussian models. Decomposition of volatility of financial indices and turbulent plasma], Izdatelstvo Instituta problem informatiki RAN, Moscow, Russia.
17. Feller, V. (1984), Vvedenie v teoriiu veroiatnostei i ee prilozheniia [Introduction to probability theory and its applications], Vol. 2., Translated by Prokhorov, Yu.V., Mir, Moscow, USSR.
18. Vadzinskii, R.N. (2001), Spravochnik po veroiatnostnym raspredeleniiam [Reference book on probabilistic distributions], Nauka, St. Petersburg, Russia.
19. Kendall, M. and Stuart, A. (1966), Teoriia raspredelenii [Distribution theory], Translated by Sazonov, V.V. and Shiriaev, A.N., Ed Kolmogorov, A.N., Nauka, Moscow, Russia.

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QUANTUM MEMORY-DRIVEN METHOD FOR TEST SYNTHESIS BASED ON QUBIT DATA STRUCTURES

V.I. Hahanov, Dr Sc. (Eng.),  I.В. Iemelianov, post-graduate student, M.M. Liubarskyi, post-graduate student, S.V. Chumachenko, Dr Sc. (Eng.),   E.I. Litvinova, Dr Sc. (Eng.),
National University of Radioelectronics of Kharkov
Kharkov, 61166, Ukraine, This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2018, 40(1):63-80
https://doi.org/10.15407/emodel.40.01.063

ABSTRACT

One of the possible solutions to the problem of creating and testing the theory and methods of quantum memory-driven computing on the classical computers for their subsequent application in all fields of human activity is proposed. Engineering-focused definitions of computing types, including quantum ones, are used, including the notions of superposition and entanglement, and also memory-driven computing. The necessity of joint and parallel solution of the problem of creation of a market-accessible quantum computer and development of quantum-focused applications and cloud services is explained. Examples of quantum memory-driven design and test of digital circuit fragments are presented. A method for synthesizing and minimizing tests for black-box functionality is proposed, using a matrix of qubit derivatives and a sequencer for defining
a quasi-optimum coverage.

KEYWORDS

test synthesis, qubit coverage, memory-driven computing, digital circuit, Boolean qubit derivative, fault simulation.

REFERENCES

1. Almudever, C.G. et al. (2017), The engineering challenges in quantum computing, Design, Automation & Test in Europe Conference & Exhibition (DATE), Lausanne, pp. 836-845.
2. Nielsen, M.A. and Chuang, I.L. (2010), Quantum computation and quantum information, Cambridge University Press, Cambrige, UK.
https://doi.org/10.1017/CBO9780511976667
3. Williams, R.S. (2017), What’s Next? [The end of Moore’s law], Computing in Science & Engineering, Vol. 19, no. 2, pp. 7-13.
https://doi.org/10.1109/MCSE.2017.31
4. Available at: https://www.labs.hpe.com/next-next/mdc.
5. Singh, J. and Singh, M. (2016), Evolution in quantum computing, Int. Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, 2016, pp. 267-270.
https://doi.org/10.1109/SYSMART.2016.7894533
6. Shaikh, T.A. and Ali, R. (2016), Quantum computing in big data analytics: A Survey, IEEE Int. Conference on Computer and Information Technology (CIT), Nadi, 2016, pp. 112-115.
https://doi.org/10.1109/CIT.2016.79
7. Vandersypen, L. and van Leeuwenhoek, A. (2017), 1.4 Quantum computing - the next challenge in circuit and system design, IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, CA, 2017, pp. 24-29.
https://doi.org/10.1109/ISSCC.2017.7870244
8. Hahanov, V. et al. (2017), Qubit test synthesis of the functionality, The 14th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), Lviv, 2017, pp. 251-255.
9. Hahanov, I., Chumachenko, S., Iemelianov, I., Hahanov, V., Larchenko, L. and Daniyil, T. Deductive qubit fault simulation, The 14th Int. Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), Lviv, 2017, pp. 256-259.
10. Hahanov, V.I., Tamer Bani Amer, Chumachenko, S.V. and Litvinova, E.I. (2015), “Qubit technologies for analysis and diagnostics od digital devices”, Elektronnoe modelirovanie, Vol. 37, no. 3, pp. 17-40.
11. Hahanov, V.I., Iemelianov, I.V., Liubarskyi, M.M. et al. (2017), “Qubit method of deductive analysis of failures for logical schemes”, Elektronnoe modelirovanie, Vol. 39, no. 6,
pp. 59-91.

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INCREASE OF WORK EFFICIENCY OF STEAM-TURBINE PLANTS OF HPS UNITS

E.M. Farhadzadeh, Dr Sc. (Eng.),  A.Z. Muradaliyev, Dr Sc. (Eng.),  Y.Z. Farzaliyev,  Cand. Sc. (Eng.), T.K. Rafiyeva., Cand. Sc. (Eng.),  S.A. Abdullayeva, post-graduate student,
Azerbaidzhan Research and Design Institute of Energy Engineering
94 G.Zardabi Ave, Baku, Az1012, Azerbaidzhan Republic, е-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2018, 40(1):81-92
https://doi.org/10.15407/emodel.40.01.081

ABSTRACT

The automated system of the analysis and synthesis of TEI of power units of HPS developed by the authors is quick and faultless. The possibility of “manual” synthesis of TEI is practically excluded since it is connected with a high risk of wrong decisions. Along with information support the system provides also methodical support of personnel in the form of recommendations about the increase of work efficiency of both separate power units, and HPS in general. Objectivity of these recommendations is undoubted within the framework of the basic data.

KEYWORDS

efficiency, steam-turbine plants, heat power station, power units, knots, the analysis, synthesis, standards of aging, information and methodical support, the automated system.

REFERENCES

1. Rules of technical operation of power plants and networks of the Russian Federation (2003), Ministry of Energy of the Russian Federation, Russia.
2. Fursova, T.N. (2012), “Automation of definition and analysis of technical and economic indicators of power units”, Mashinobuduvannya, no. 9, pp. 233-244.
3. RD 34.09.454. (2017), Standard algorithm of calculation of technical and economic indicators of the 300, 500, 800 and 1200 MW condensation power units, VTN, p. 276.
4. Duel, M.A., Kanyuk, G.I. and Fursova, T.N. (2013), “Automation of definition of power characteristics of power units”, Energosberezhenie, energetika, energoaudit, no. 2, pp. 13-19.
5. Lisyanskiy, A.S. and Nazarov, V.V. (2000), “Steam turbine construction of LMZ in modern conditions”, Elektricheskie stantsii, no. 12, pp. 69-72.
6. Farhadzadeh, E.M., Farzaliyev, Y.Z. and Muradaliyev, A.Z. (2015), “Method and algorithm of ranging of boiler installations of block power plants by criterion of reliability and profitability of work”, Teploenergetika, no. 10, pp. 22-29.
7. Farhadzadeh, E.M., Farzaliyev, Y.Z., Muradaliyev, A.Z. and Rafiyeva, T.K. (2015), “Increase of reliability and profitability of boiler installations of block power plants”, Collected articles: Methodicheskie voprosy issledovaniya nadyozhnosti bolshikh system energetiki, Iss. 66, Urgent problems of reliability of power systems, Resp. editors N.I. Voropay, M.A. Korotkevich, A.A. Mikhalevich, BNTU, Minsk, Belarus.
8. Farhadzadeh, E.M., Farzaliyev, Y.Z., Muradaliyev, A.Z. and Rafiyeva, T.K. (2017), “The comparative analysis of methods of calculation of the integrated indicators characterizing overall performance of objects of EES”, Elektronnoe modelirovanie, Vol. 39, no. 2, pp. 75-89.
9. Farhadzadeh, E.M., Muradaliyev, A.Z., Rafiyeva, T.K. and Abdullayeva, S.A. (2017), “Method of algorithm of calculation of indicators of reliability for multidimensional data”, Energetika, no. 1, pp. 16-23.

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