Advertisement

Unsupervised classification of NPPs transients based on online dynamic quantum clustering

  • Khalil Moshkbar-Bakhshayesh
  • Esmaiel PourjafarabadiEmail author
Regular Article
  • 2 Downloads

Abstract.

In this study, we propose a new method for identification of nuclear power plants (NPPs) transients based on online dynamic quantum clustering (DQC). In this unsupervised learning algorithm, the Gaussian kernel is the eigen-state of the Schrödinger equation and the minimums of the Schrödinger potential are the cluster centers of patterns. For clustering of transients, data of each event are given to the DQC and form a cluster independent of other transients. This process is done for all target plant conditions. The formed clusters are labeled according to the name of their related transients. Afterwards, to test the proposed identifier, as time goes by, new data points move toward the formed potential wells. Finally, each new datum falls into an appropriate cluster and, therefore, the type of transient is identified online. The DQC, unlike previously developed unsupervised learning algorithms, is not dependent on the geometric proximity of data. The developed identifier is examined by the Iris flower dataset and typical WWER-1000 plant transients. Results show a reasonable performance of DQC. We use singular value decomposition (SVD) and bipolar representation of real data to reduce the dimensions of data and to show explicitly positive and negative sides of information. The major novelty of this identifier is the development of a technique for online transient identification of NPPs utilizing the DQC without any preliminary information about the input patterns. The developed method is a step forward for practical recognition of NPPs transients.

References

  1. 1.
    K. Moshkbar-Bakhshayesh, M.B. Ghofrani, Prog. Nucl. Energy 67, 23 (2013)CrossRefGoogle Scholar
  2. 2.
    R.E. Uhrig, L.H. Tsoukalas, Prog. Nucl. Energy 34, 13 (1999)CrossRefGoogle Scholar
  3. 3.
    K. Moshkbar-Bakhshayesh, M.B. Ghofrani, IEEE Trans. Nucl. Sci. 61, 2383 (2014)ADSCrossRefGoogle Scholar
  4. 4.
    J.A.C.C. Medeiros, R. Schirru, Ann. Nucl. Energy 35, 576 (2008)CrossRefGoogle Scholar
  5. 5.
    A. dos Santos Nicolau, R. Schirru, A.A. de Moura Meneses, Prog. Nucl. Energy 53, 86 (2011)CrossRefGoogle Scholar
  6. 6.
    K. Moshkbar-Bakhshayesh, M.B. Ghofrani, IEEE Trans. Nucl. Sci. 63, 1882 (2016)ADSCrossRefGoogle Scholar
  7. 7.
    J. Ma, J. Jiang, Nucl. Eng. Technol. 47, 176 (2015)CrossRefGoogle Scholar
  8. 8.
    M. Sirola, J. Talonen, Advances Artificial Neural Systems 2012, 3 (2012)CrossRefGoogle Scholar
  9. 9.
    P. Baraldi, F. Di Maio, E. Zio, Int. J. Comput. Intell. Syst. 6, 764 (2013)CrossRefGoogle Scholar
  10. 10.
    K.C. Kwon, J.H. Kim, P.H. Seong, Int. J. Intell. Syst. 17, 791 (2002)CrossRefGoogle Scholar
  11. 11.
    O. Chapelle, B. Scholkopf, A.I. Zien, IEEE Trans. Neural Netw. 20, 542 (2009)CrossRefGoogle Scholar
  12. 12.
    G.A. Carpenter, S. Grossberg, IEEE Comput. 21, 77 (1988)CrossRefGoogle Scholar
  13. 13.
    G. Rozenberg, T. Bäck, J.N. Kok, Handbook of Natural Computing (Springer, 2012)Google Scholar
  14. 14.
    D. Horn, A.J.P.r.l. Gottlieb, Phys. Rev. Lett. 88, 018702 (2001)ADSCrossRefGoogle Scholar
  15. 15.
    D. Horn, A. Gottlieb, The method of quantum clustering, in Advances in Neural Information Processing Systems 14, edited by T.G. Dietterich, S. Becker, Z. Ghahramani (MIT Press, 2002) pp. 769--776Google Scholar
  16. 16.
    K. Moshkbar-Bakhshayesh, Ann. Nucl. Energy 132, 87 (2019)CrossRefGoogle Scholar
  17. 17.
    M. Weinstein, Analyzing big data with dynamic quantum clustering, arXiv:1310.2700 (2013)Google Scholar
  18. 18.
    G. Friesecke, M.J.J.o.M.P. Koppen, J. Math. Phys. 50, 082102 (2009)ADSMathSciNetCrossRefGoogle Scholar
  19. 19.
    BNPP, Final safety analysis report (FSAR), chapt. 15, Rev. 0 (2003)Google Scholar
  20. 20.
    I.K. Fodor, A survey of dimension reduction techniques (Lawrence Livermore National Lab., CA, 2002)Google Scholar
  21. 21.
    S.D. Villalba, P. Cunningham, Artif. Intell. Rev. 27, 273 (2007)CrossRefGoogle Scholar
  22. 22.
    IAEA, Development and review of plant specific emergency operating procedures (IAEA, 2006)Google Scholar

Copyright information

© Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Khalil Moshkbar-Bakhshayesh
    • 1
  • Esmaiel Pourjafarabadi
    • 2
    Email author
  1. 1.Department of Energy EngineeringSharif University of TechnologyTehranIran
  2. 2.Department of PhysicsShiraz UniversityShirazIran

Personalised recommendations