Volume 4, Issue 6, December 2016, Page: 150-156
Feasibility Study on Intelligent Evaluation of Marine Traffic Congestion Degree for Restricted Water Using Fuzzy Expert System with AIS Report
Emmanuel Nartey, Department of Electrical/Electronic, Regional Maritime University, Accra, Ghana
Isaac Owusu-Nyarko, Department of Electrical/Electronic, Regional Maritime University, Accra, Ghana
Received: Nov. 22, 2016;       Accepted: Nov. 30, 2016;       Published: Jan. 3, 2017
DOI: 10.11648/j.jeee.20160406.12      View  3663      Downloads  182
In this paper we investigate the modification of feasibility study on Intelligent Evaluation Marine Traffic Congestion Degree for Restricted Water Area which has now become one of the numerous challenging factors to improve the safety of navigation. However, the observation of marine traffic using Fuzzy Expert with the aid of AIS acquired knowledge from the collected data enable the congestion degree to be visualize and possible to estimate Restricted Water Area base on traffic congestion degree with main flow velocity. The formation, aggregation, and decomposition of rules explained fuzzy mathematical tools and the calculus of IF-THEN rules provides a most useful paradigm for the automation and implementation of an extensive body of human knowledge. Therefore, the clarification includes a discussion of fuzzification and defuzzification strategies, the definition of fuzzy implication and an analysis of fuzzy reasoning mechanism. Now IMO is trying to standardize criterion of Universal AIS. As you know AIS is now studied and has been studied in many advanced country as a tools to exchange information in both ship-to-ship and ship-to-shore in congested waterways. The intention of standardization is to improve safety of VTS management and to support effective navigation of ships.
Marine Traffic, Expect Systems, Fuzzy Logic, Fuzzy Sets, AIS
To cite this article
Emmanuel Nartey, Isaac Owusu-Nyarko, Feasibility Study on Intelligent Evaluation of Marine Traffic Congestion Degree for Restricted Water Using Fuzzy Expert System with AIS Report, Journal of Electrical and Electronic Engineering. Vol. 4, No. 6, 2016, pp. 150-156. doi: 10.11648/j.jeee.20160406.12
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Deepa, S. N., Savanandam, S. N., and Sumathis, S. (2007). Introduction to fuzzy logic using MATLAB.
The Math Works Inc., (1995-2010). Fuzzy Logic Toolbox™ User’s Guide.
Lopez de Mantaras, R., Agusti, J. Plaza, E., Sierra, C. (1991). A fuzzy expert system shell. In: Kandel A (Ed) Fuzzy expert systems. CRC, Boca Raton, FL.
Zadeh, L. A. (1973). The calculus of fuzzy if–then rules.
Yasunobu, S., Myamoto, S. (1985). Automatic train operation by predictive fuzzy control. Industrial applications of fuzzy control. North-Holland, Amsterdam.
Dubois, D., Prade, H. (1990). An introduction to possibility and fuzzy logics. In: Shafer G, Pearl J (Eds) Readings in uncertain reasoning. Morgan Kaufmann, San Francisco.
Dubois, D., Prade, H. (1998). Soft computing, fuzzy logic, and artificial intelligence, soft computing a fusion of foundations, methodologies and applications.
Bauer, P., Bodenhofer, U., Klement, E. P. (1996). A fuzzy algorithm for pixel classification based on the discrepancy norm. In: Proceedings of 5th IEEE 8.international conference on fuzzy systems, New Orleans, LA, September vol 3, pp. 2007–2012.
Zadeh, L. A. (1965). Fuzzy set. Inform. Contr., vol. 8, pp. 338-353.
Chang, S. S. L., Zadeh, L. A. (1972). Fuzzy mapping and control. IEEE Trans. Syst., Man, Cybern., vol. SMC-2, pp. 30-34.
Bellman, R. E., Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Magement Sci., vol. 17, pp. B-141-B-164.
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