PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Marine Navigation Using Expert System

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A ship’s autopilot adjustment is a matter of utmost importance since it affects its safety, command as well as fuel and time efficiency. A number of methods have been developed in order to cope with this issue usually based on models that simulate the weather conditions and adjust the device accordingly. Some of them have a considerable degree of success but none dealt with the problem completely. The main obstacles are the difficulty of simulating the infinite weather and loading conditions and to properly represent them with mathematical equations or rules. This paper describes a method of selecting the best out of a pre-existing set of configurations, taking into account any weather situation, loading condition and type of ship. Moreover, the selected configuration can improve itself during the entire life cycle of the vessel, since it fine tunes its properties for better results. This approach uses Case Based Reasoning as its core technology and is a part of a hybrid system that analyses and solves prefixed problems of maritime interest.
Twórcy
autor
  • University of the Aegean, Chios, Greece
autor
  • University of the Aegean, Chios, Greece
Bibliografia
  • 1. Alter, S. L. (1980), Decision support systems: current practice and continuing challenges, Reading, Mass., Addison-Wesley Pub.
  • 2. Bain, W., (1986), Case Based Reasoning: A computer model of subjective assessment, PhD diss, Department of Computer Science, Yale
  • 3. Belur V Dasarathy, (1991), Nearest neighbour, Norms, Pattern Classification Techniques, IEEE Computer Society Press
  • 4. Birnbaum, L & Collins G, (1989), Reminding and en-gineering design themes: A case study in indexing vo-cabulary, Pensacola, Florida, Morgan Kaufman
  • 5. Bonczek R. H, C. W Holsapple & A. B Whinston. (1981), Foundations of Decision Support Systems, New York, Academic Press.
  • 6. Bowditch N, (2002), The American practical Naviga-tor, Maryland, USA, National Imagery and Mapping Agency
  • 7. Cheeseman P, Kelly J, Matthew, Stutz J, Taylor W, and Freeman, (1988), Autoclass, a Bayesian classifi-cation system. Proceedings of the Fifth International Machine Learning Workshop, San Mateo, Morgan Kaufmann
  • 8. International Maritime Organization, (1972), Conven-tion on the International Regulations for Preventing Collisions at Sea, Part B, Rule 9, Narrow Channels
  • 9. Dhar V, Stein R. (1997), Seven methods for trans-forming corporate data into business intelligence, Upper Sandle river, NJ, Prentice Hall,
  • 10. Domeshek E and Kolodner J., (1991), Toward a case based aid for conceptual design, International journal of Expert Systems, pp. 201 - 220
  • 11. Dutton B, (1958), Navigation and Piloting, Annapolis, Maryland, United States Naval Institute
  • 12. E. A. Feigenbaum and J. Feldman, (1963), The simu-lation of natural learning behaviour. Computers and thoughts, New York, McGraw-Hill
  • 13. Fischer D, (1987), Knowledge Acquisition via incremental conceptual clustering, Machine Learning Journal, Vol II, 139 – 172, Springer Netherlands publishers
  • 14. G Honderd, J. E. W Winkelman, (1972), An adaptive Autopilot for ships, Proceedings 3rd Ship Control Sys-tems Symposium, Bath, UK,
  • 15. Goel A, 1992, Representation of design functions in experience based design, Intelligent Computer Aided Design, ed. D Brown, M Waldron, H Yoshikawa, Amsterdam, North Holland
  • 16. Gorry and Scott Morton. (1971), A framework for management information systems, Sloan Management review, 13, pp 56 - 79
  • 17. Hammond K, (1987), Explaining and repairing plans that fail. Proceedings of IJCAI-87, San Mateo, Mor-gan Kaufmann
  • 19. Herther et al, (1971), A fully Automatic Marine Radar Data Plotter, Journal Inst. Navigation vol 24, pp 43 – 49
  • 20. Holsapple C. W & A. B. Whinston. (1996), Decision Support Systems, A knowledge based approach, Min-neapolis, West publishing company
  • 21. Holzhuter T, (1997), LQG Approach for the High pre-cision Track Control of ship IEE Proc Control Theory Application, 44, 121 – 127
  • 22. International Maritime Organization, (1995), Interna-tional Convention on Standards of Training, Certifi-cation and Watch keeping for Seafarers (STCW), Chapter VIII, Watch keeping
  • 23. J Van Amerongen, A. J. U Ten Cate, (1975), Model reference adaptive autopilots for ships, Automatica Vol 11, pp 441 – 449, Pergamon press
  • 24. J Van Amerongen, H. R Van Nauta Lemke, (1986) Recent developments in automatic steering of ships, Proc. of the meeting of the Royal Institute of Naviga-tion, Amsterdam, The Netherlands
  • 25. Keen, P. G. W. and Scott Morton M S., (1978), Deci-sion Support Systems: An Organizational Perspective, Reading, MA: Addison-Wesley
  • 26. Keeney, Raiffa, (1976), Decisions with multiple objec-tives, Preferences and value trade offs, Cambridge University Press
  • 27. KJ Astrom, (1977), Self tuning regulators, NASA Conference Publication
  • 28. Knowles T W, (1989), Management Science, Building and using models, pp. 28, Homewood, Illinois, Irwin pub.
  • 29. Kolodner. J, (1993), Case Based Reasoning, San Mateo, California, Morgan Kaufmann publ
  • 30. K Ohtsu, M Horigome, G Kitagawa, (1979), A new ship’s autopilot design through a stochastic model, Automatica, 15, pp. 255 - 268
  • 31. K. R Goheen, E. R Jeffreys, (1990), System Identifica-tion of Remotely Operated Vehicle Dynamics, Journal Offshore Mechanics and Arctic Engineering
  • 32. Leake D, Kolodner J, (2003), Learning through case analysis, Encyclopaedia of cognitive science, Nature publishing group, London
  • 33. Leake D. (1996), Case-Based Reasoning: Experienc-es, Lessons & Future Directions, Menlo Park Califor-nia, USA, American Association for Artificial Intelli-gence,
  • 34. Leontopoulos A (1979), Practical Applications of Ship‘s Stability, Piraeus, Hellenic Educational Center of Merchant Marine Executives (KESEN)
  • 35. Mark W, E. Simoudis & D. Hinkle, (1996), Case Based Reasoning, Expectations and results, Menlo Park, California, AAAI Press
  • 36. Michalski R S and Stepp R E, (1983), Learning from observation: Conceptual clustering, Machine Learn-ing: An Artificial Intelligence Approach, VOL I, Los Altos, California, Morgan Kaufman
  • 37. Moorman K and Ram A, (1992), A case based ap-proach to reactive control for autonomous robots. Proceedings of the AAAI Fall Symposium on AI for Real World Autonomous Robots, Cambridge, Massa-chusetts, MIT Press
  • 39. Polkinghorne M. N. Roberts G. N, Burns R. S, (1994) The implementation of a fuzzy logic marine autopilot, Proceedings IEE Control International Conference VOL II, p. 1572 – 1577, Warwick, UK
  • 40. Roberts G. N and Sutton R, (2006), Advances in Un-manned Marine Vehicles, IEE Control series, IEE Press
  • 41. Power, D. J. (2000). Web-based and model-driven de-cision support systems: concepts and issues Proceed-ings of the Americas Conference on Information Sys-tems, Long Beach, California.
  • 42. Redmond M A, (1992), Learning by observing and understanding expert problem solving. Georgia Insti-tute of technology, College of Computing, Atlanta
  • 43. Rissland E., Kolodner J. & Waltz D., (1989) Case Based Reasoning from DARPA: Machine learning program plan, CBR Workshop, Pensacola, Florida, Morgan Kauffman
  • 44. Simpson R L, (1985), A computer model of case based rea-soning in problem solving: An investigation in the domain of dispute mediation. Georgia Institute of technology, School of Information and computer science, Atlanta
  • 45. Schank, Kass, Riesbeck. (1994), Inside case based ex-planation, The Institute of the learning sciences, North-western University, Lawrence Erlbaum, publ. Hillsdale, New Jersey.
  • 46. Sprague, R. H. and E. D. Carlson (1982), Building ef-fective decision support systems. Englewood Cliffs, N.J., Prentice-Hall
  • 47. Stanfild C W and Waltz D, (1987), The memory based reasoning paradigm, Proceedings AAAI – 87, pp. 576 - 578
  • 48. Unar M. A and Murray Smith D. J, (1999), Automatic Steering of Ship using Neural Networks, International Journal of Adaptive Control and Signal Process 13, 203 – 218
  • 49. X. J Yang & X. R Zhao, (2006) Self organizing Neu-ral Net Control of Ship’s Horizontal Motion, Interna-tional Symposium on Instrumentation Science and Technology, Institute of Physics Publishing, Journal of Physics: Conference series 48, 1284 – 1288
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9aa78594-0cf9-4104-8757-b8a6879cce48
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.