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Improvement of good seamanship using specialized processes and algorithms onboard ships, in fleet operation centers, and in simulations

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The recent rapid improvement of nautical equipment functionality allows one to better observe and predict the dangers related to seamanship. However, these new features come with added complexity, and large amounts of information can overwhelm vessel crews and fleet operation centers, and the current state-of-the-art tools cannot filter out only the most important data for a given time and location. This paper presents the concepts and the algorithms of a software suite that provides a user with problem-oriented advice about a particular risk endangering a vessel and its crew. Based on the calculated navigational dangers and their predicted development, actionable guidance is proposed in an easy-to-understand human language. The quality of good seamanship is improved by a holistic approach to vessel installation, automated fleet operation center priority queuing, and the evaluation of crew performance during simulator training and daily operations. Both the software user interface, as well as the insights provided by the algorithm, are discussed.
Rocznik
Strony
83--88
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
  • Westpomeranian University of Technology, Electrical Engineering Faculty Department of Power Systems and Electrical Drives
  • MacGregor Germany GmbH & Co. KG, Germany
autor
  • MacGregor Germany GmbH & Co. KG, Germany
Bibliografia
  • 1. Åström, K.J. & Hägglund, T. (2006) Advanced PID control. ISA – The Instrumentation, Systems, and Automation Society.
  • 2. Baker, C.C. & McCafferty, D.B. (2005) Accident database review of human-element concerns: what do the results mean for classification? London: The Royal Institution.
  • 3. Bolton, W. (2015) Mechatronics: Electronic Control Systems in Mechanical and Electrical Engineering. 6th Edition. Pearson Educational Limited.
  • 4. Dekker, S. (2014) The Field Guide to Understanding ‘Human Error’. 3th Edition. Routledge.
  • 5. Fortuna, Z., Macukow, B. & Wąsowski, J. (2015) Metody Numeryczne. Warszawa: WNT.
  • 6. Heald, G. (2017) The Similarities between Fuzzy Logic and Probability. ResearchGate, April.
  • 7. Ibrahim, A.M. (2004) Fuzzy Logic for Embedded Systems Applications. Elsevier Science (USA).
  • 8. Kosko, B. (1990) Fuzzines vs. Probability. International Journal of General Systems 17, 2–3, pp. 211–240.
  • 9. Łukasiewicz, D. (2011) On Jan Łukasiewicz’s many-valued logic and his criticism of determinism. Philosophia Scientiae, Studies in the history of Science and Philosophy, pp. 7–20.
  • 10. Manley, P. (2008) Practical Navigation for the Modern Boat Owner: Navigate Effectively by Getting the Most Out of Your Electronic Devices. 1st Edition. Fernhurst Books Limited.
  • 11. Merriam-Webster (2019) Difinition of ‘risk’. [Online] Available from: https://www.merriam-webster.com/dictionary/ risk [Accessed: October 20, 2019].
  • 12. Ross, T.J. (2004) Fuzzy Logic With Engineering Applications. 2nd Edition. John Wiley & Sons Ltd.
  • 13. Wang, L.-X. (1994) Adaptive fuzzy systems and control: design and stability analysis. Prentice Hall.
  • 14. Weintrit, A. (2013) Marine Navigation and Safety of Sea Transportation: Advances in Marine Navigation. 1st Edition. CRC Press.
  • 15. Zadeh, L.A. (1973) Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transactions on Systems, Man, and Cybernetics SMC-3, 1, pp. 28–44.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f07daa00-a286-4170-821b-a6bd96dd43ea
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