Design Support System of Fishing Vessel Through Simulation Approach

  • Stefano Filippi
  • Piero Giribone
  • Roberto Revetria
  • Alessandro Testa
  • Guido Guizzi
  • Elpidio Romano
Conference paper


The objective of this work is to create a module for a ship maneuvering simulator, which will allow the training of the crews of vessels in deep water. In this work we developed a system of “artificial intelligence” to model marine biological entities: this system can simulate the movement of a school of fish in a realistic manner. In addition, we developed an analysis of the main instruments on board and problems relating to the virtual simulation.


Boids Echo-sounder Fish Modeling Ship Simulation Training 


  1. 1.
    M.V. Abrahams, P.W. Colgan, Risk of predation, hydrodynamic efficiency and their influence on school structure, Environ. Biol. Fishes 13, 195–202 (1985)Google Scholar
  2. 2.
    A. Alvarez, Z. Ye, Effects of fish school structures on acoustic scattering. CES J. Mar. Sci. 56(3), 361–369 (1999)Google Scholar
  3. 3.
    A.G. Bruzzone, P. Giribone, R. Revetria, Operative Requirements and Advances for the New Generation Simulators. in Multimodal Container Terminals, Winter Simulation Conference Proceedings, 2, pp. 1243–1252 (1999)Google Scholar
  4. 4.
    D. Chiocca, G. Guizzi, T. Murino, R. Revetria, E. Romano, A methodology for supporting lean healthcare. Stud. Comput. Intell. 431, 93–99 (2012)CrossRefGoogle Scholar
  5. 5.
    R. Di Micco, D.R. Montella, G. Naviglio, E. Romano, Design of experiments in a single stage multi product kanban system. Frontiers Artif. Intell. Appl. 246, 518–537 (2012)Google Scholar
  6. 6.
    M. Furusawa, K. Amakasu, Exact Simulation of Fish School Echoes and its Applications, OCEANS 2008—MTS/IEEE Kobe Techno-Ocean, no., pp.1, 6 (2008)Google Scholar
  7. 7.
    M. Furusawa, Prolate spheroidal models for predicting general trends of fish target strength. J. Acoust. Soc. Jpn. (E) 9, 13–24 (1988)CrossRefGoogle Scholar
  8. 8.
    D.A. Demera, M. Barangeb, A.J. Boydb, Measurements of three-dimensional fish school velocities with an acoustic Doppler current profiler. Fish. Res. 47, 201–214 (2000)CrossRefGoogle Scholar
  9. 9.
    C. Dai, D. Pi, Z. Fang, H. Peng, Wavelet transform-based residual modified GM(1,1) for hemispherical resonator gyroscope lifetime prediction. IAENG Int. J. Comput. Sci. 40(4), 250–256 (2013) ISSN 1819-656XGoogle Scholar
  10. 10.
    S. Filippi, P Giribone, R Revetria, A Testa, An Integrated Model for Supporting Better Fishering Vessel Design by Modeling Fish Schools Dynamics Ready for HLA, Lecture Notes in Engineering and Computer Science. in Proceedings of The World Congress on Engineering and Computer Science, WCECS 2013, 23-25 October, 2013, San Francisco, USA, pp. 1009-1016 (2013)Google Scholar
  11. 11.
    A. Barbaro, B. Einarsson, B. Birnir, S. Sigurðsson, H. Valdimarsson, O.K. Pa´lsson, S. Sveinbjo¨rnsson, P. Sigurðsson, Modelling and simulations of the migration of pelagic fish. ICES J Mar. Sci. 66(5), 826–838 (2009)Google Scholar
  12. 12.
    K.J. Benoit-Bird, W.L. Whitlow, Acoustic backscattering by Hawaiian lutjanid snappers. Acoust. Soc. Am. 114(5), 2757–2766 (2003)Google Scholar
  13. 13.
    J.W. Hannon, Image based computational fluid dynamics modeling to simulate fluid flow around a moving fish, University of Iowa Iowa Research Online (2011)Google Scholar
  14. 14.
    A.J. Holmin, Simulations of multi-beam sonar echos from schooling individual fish in a quiet environment. Acoust. Soc. Am. 132(6), 3720–3734 (2012)Google Scholar
  15. 15.
    J.K. Horne, J.M. Jech, Multi-frequency estimates of fish abundance: constraints of rather high frequencies. ICES J Mar. Sci. 56(2), 184–199 (1999)Google Scholar
  16. 16.
    G. Guizzi, D. Miele, L.C. Santillo, E. Romano, New formalism for production systems modeling, 25th European Modeling and Simulation Symposium. EMSS 2013, 571–576 (2013)Google Scholar
  17. 17.
    P. Holimchayachotikul, R. Derrouiche, K. Leksakul, G. Guizzi, B2B supply chain performance enhancement road map using data mining techniques, in International conference on System Science and Simulation in Engineering—Proceedings, pp. 336–341 (2010)Google Scholar
  18. 18.
    X.H. Li, R.G. Jianli, W. Hongru, An Adaptive Meta-cognitive Artificial Fish School Algorithm, International Forum on Information Technology and Applications, vol. 01 (IEEE Computer Society, Washington DC, 2009). ISBN 978-0-7695-3600-2Google Scholar
  19. 19.
    H.M. Manik, Underwater acoustic detection and signal processing near the seabed. Sonar Syst. 68(9): 1973–1985 (2011) ISBN: 978-953-307-345-3, chapter 12Google Scholar
  20. 20.
    A.D. Rijnsdorp, W. Dol, M. Hoyer, M.A. Pastoors, Effects of fishing power and competitive interactions among vessels on the effort allocation on the trip level of the Dutch beam trawl fleet, ICES J. Mar. Sci. 57(4), 927–937 2000Google Scholar
  21. 21.
    R. Samborski, M. Ziolko, Filter-based model of multimicrophone array in an adverse acoustic environment, Eng. Lett. 20(4), 336–338 (2012) ISSN 1816-093XGoogle Scholar
  22. 22.
    D.J.T. Sumpter, J. Krause, R. James, Consensus decision making by fish. Curr. Biol. 18(22), 1773–1777 (2008)CrossRefGoogle Scholar
  23. 23.
    R.H. Towler, J.M. Jech, J.K. Horne, Visualizing fish movement, behavior, and acoustic backscatter. Aquat. Living Resour. 16(3), 277–282 (2003) ISSN 0990-7440Google Scholar
  24. 24.
    Y. Tang, Y. Nishimori, M. Furusawa, The average three-dimensional target strength of fish by spheroid model for sonar surveys. ICES J. Mar. Sci. 66, 1176–1183 (2009)Google Scholar
  25. 25.
    M. Soria, P. Fréon, F. Gerlotto, Analysis of vessel influence on spatial behaviour of fish schools using a multi-beam sonar and consequences for biomass estimates by echo-sounder. ICES J. Mar. Sci. 53, 453–458 (1996)CrossRefGoogle Scholar
  26. 26.
    R. Revetria, A. Catania, L. Cassettari, G. Guizzi, E. Romano, T. Murino, G. Improta, H. Fujita, Improving healthcare using cognitive computing based software: an application in emergency situation, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7345 LNAI, pp. 477–490 (2012)Google Scholar
  27. 27.
    T.C. Weber, H. Pena, J.M. Jech, Consecutive acoustic observations of an Atlantic herring school in the Northwest Atlantic. ICES J. Mar. Sci. 66, 1270–1277 (2009)Google Scholar
  28. 28.
    E. Briano, C. Caballini, M. Mosca, R. Revetria, A system dynamics decision cockpit for a container terminal: The case of voltri terminal europe. Int. J. Math. Comput. Simul. 3(2), 55–64 (2009)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Stefano Filippi
    • 1
  • Piero Giribone
    • 1
  • Roberto Revetria
    • 1
  • Alessandro Testa
    • 1
  • Guido Guizzi
    • 2
  • Elpidio Romano
    • 2
  1. 1.DIMEUnivesity of GenoaGenoaItaly
  2. 2.DICMAPIUnivesity of Naples “Federico II”NaplesItaly

Personalised recommendations