A logical device for processing nautical data

• Autorzy: Filipowicz W.

Identyfikatory

DOI

10.17402/246

• Języki publikacji: EN

Abstrakty

EN

Nautical measurements are randomly and systematically corrupted. There is a rich scope of knowledge regarding the randomness shown by results of observations. The distribution of stochastic distortions remains an estimate and is imprecise with respect to their parameters. Uncertainties can also occur through the subjective assessment of each piece of available data. The ability to model and process all of the aforementioned items through traditional approaches is rather limited. Moreover, the results of observations, the final outcome of a quality evaluation, can be estimated prior to measurements being taken. This a posteriori analysis is impaired and it is outside the scope of traditional, inaccurate data handling methods. To propose new solutions, one should start with an alternative approach towards modelling doubtfulness. The following article focusses on belief assignments that may benefit from the inclusion of uncertainty. It starts with a basic interval uncertainty model. Then, assignments engaging fuzzy locations around nautical indications are discussed. This fragment includes transformation from density functions to probability distributions of random errors. Diagrams of the obtained conversions are included. The presentation concludes with a short description of a computer application that implements the presented ideas.

Słowa kluczowe

EN

probability density; probability distributions; nautical evidence; uncertainty model; nautical propositions; simple belief structure

• Wydawca: Wydawnictwo Naukowe Akademii Morskiej w Szczecinie
• Czasopismo: Zeszyty Naukowe Akademii Morskiej w Szczecinie
• Rocznik: 2017
• Tom: nr 52 (124)
• Strony: 65--73
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Filipowicz W.

• Gdynia Maritime University 81/83 Morska St., 81-225 Gdynia, Poland

Bibliografia

• 1. Ayoun, A. & Smets, P. (2001) Data Association in Multi-Target Detection Using the Transferable Belief Model. International Journal of Intelligent Systems 16, pp. 1167–1182.
• 2. Dempster, A.P. (1968) A generalization of Bayesian inference. Journal of the Royal Statistical Society B 30, pp. 205–247.
• 3. Denoeux, T. (2000) Modelling vague beliefs using fuzzy valued belief structures. Fuzzy Sets and Systems 116, pp. 167–199.
• 4. Filipowicz, W. (2009) Application of the Theory of Evidence in Navigation. In: Knowledge Engineering and Export Systems. Warsaw: Academic Editorial Board EXIT, pp. 599–614.
• 5. Filipowicz, W. (2009a) Belief Structures and their Applications in Navigation. Methods of Applied Informatics, Polish Academy of Sciences 3, pp. 53–82.
• 6. Filipowicz, W. (2010) Fuzzy Reasoning Algorithms for Position Fixing. Measurements Automatics Control 12, pp. 1491–1495.
• 7. Filipowicz, W. (2012) Evidence Representations in Position Fixing. Electrical Review 10b, pp. 256–260.
• 8. Filipowicz, W. (2014) Fuzzy evidence reasoning and navigational position fixing. In: Tweedale, J.W. & Jane, L.C. (Eds). Recent advances in knowledge-based paradigms and applications (Advances in intelligent systems and computing) 234. Heildelberg, New York, London: Springer, pp. 87–102.
• 9. Filipowicz, W. (2014a) Mathematical Theory of Evidence in Navigation, in Belief Functions: Theory and Applications. Third International Conference, BELIEF 2014 Oxford, UK, (Fabio Cuzzolin ed.) Springer International Publishing Switzerland, pp. 199–208.
• 10. Filipowicz, W. (2014b) Systematic errors handling with MTE. ELSEVIER Science Direct Procedia Computer Science 35, pp. 1728–1737.
• 11. Filipowicz, W. (2015) On nautical observation errors evaluation. GMU Gdynia TransNav 9/4, pp. 545–550.
• 12. Filipowicz, W. (2016) On Mathematical Theory of Evidence in Navigation. Scientific Journals of the Maritime University of Szczecin, Zeszyty Naukowe Akademii Morskiej w Szczecinie 45, pp. 159–167.
• 13. Jurdziński, M. (2014) Principles of Marine Navigation. Gdynia: Akademia Morska.
• 14. Lee, E.S. & Zhu Q. (1995) Fuzzy and Evidence Reasoning. Heidelberg: Physica-Verlag.
• 15. Shafer, G. (1976) A mathematical theory of evidence. Princeton: Princeton University Press.
• 16. Srivastava, R.P., Dutta, S.K. & Johns, R. (1996) An Expert System Approach to Audit Planning and Evaluation in the Belief-Function Framework. International Journal of Intelligent Systems in Accounting, Finance and Management 5(3), pp. 165–183.
• 17. Yager, R. (1996) On the normalization of fuzzy belief structures. International Journal of Approximate Reasoning 14.
• 18. Yen, J. (1990) Generalizing the Dempster–Shafer theory to fuzzy sets. IEEE Transactions on Systems, Man and Cybernetics 20 (3), pp. 559–570.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).