A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification
Abstract
Safety analysis of navigation over a given area may cover application of various risk measures for ship collisions. One of them is percentage of the so called near- miss situations (potential collision situations). In this article a method of automatic detection of such situations based on the data from Automatic Identification System (AIS), is proposed. The method utilizes input parameters such as: collision risk measure based on ship’s domain concept, relative speed between ships as well as their course difference. For classification of ships encounters, there is used a neuro-fuzzy network which estimates a degree of collision hazard on the basis of a set of rules. The worked out method makes it possibile to apply an arbitrary ship’s domain as well as to learn the classifier on the basis of opinions of experts interpreting the data from the AIS.
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- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
Polish Maritime Research
no. 25,
edition S1(97),
pages 14 - 21,
ISSN: 1233-2585 - Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Niksa-Rynkiewicz T., Szłapczyński R.: A Framework of A Ship Domain-Based Near-Miss Detection Method Using Mamdani Neuro-Fuzzy Classification// Polish Maritime Research. -Vol. 25, iss. S1(97) (2018), s.14-21
- DOI:
- Digital Object Identifier (open in new tab) 10.2478/pomr-2018-0017
- Bibliography: test
-
- Chai, Y., L. Jia, Z. Zhang: Mamdani Model based Adaptive Neural Fuzzy Inference System and its Application. open in new tab
- Cpałka, K.: Design of Interpretable Fuzzy Systems, Springer, 2017. open in new tab
- Cpałka, K., L. Rutkowski: On Designing of Flexible Neuro- Fuzzy Systems for Classification. open in new tab
- Driankov, D., H. Hellendoorn, M. Reinfrank: An Introduction to Fuzzy Control, Springer Berlin Heidelberg, 1996. open in new tab
- Goerlandt, F., J. Montewka: Maritime transportation risk analysis: Review and analysis in light of some foundational issues, Reliab. Eng. Syst. Saf. 138 (2015), pp. 115-134. open in new tab
- Hansen, M.G., T.K. Jensen, F. Ennemark: Empirical Ship Domain based on AIS Data, (2013), pp. 931-940. open in new tab
- van Iperen, E.: Classifying ship encounters to monitor traffic safety on the North Sea from AIS data, TransNav -Int. J. Mar. Navig. Saf. Sea Transp. 9 (2015), pp. 53-60. open in new tab
- Lazarowska, A.: Multi-criteria ACO-based Algorithm for Ship's Trajectory Planning, TransNav, Int. J. Mar. Navig. Saf. Sea Transp. 11 (2017), pp. 31-36. open in new tab
- Lisowski, J.: Game control methods in avoidance of ships collisions, Polish Marit. Res. 19 (2012), pp. 3-10. open in new tab
- Lisowski, J., A. Lazarowska: The radar data transmission to computer support system of ship safety, Solid State Phenom. 196 (2013), pp. 95-101. open in new tab
- Nowicki, R.K.: Fuzzy decision systems in issues of limited knowledge (in Polish), Akademia Oficyna Wydawnicza EXIT, 2009.
- Pietrzykowski, Z., P. Wo, P. Borkowski: Decision Support in Collision Situations at Sea, (2017), pp. 447-464. open in new tab
- Rutkowska, D.: Neuro-Fuzzy Architectures and Hybrid Learning, Physica-Verlag HD, Heidelberg, 2002. open in new tab
- Rutkowska, D., R. Nowicki: Implication-Based Neuro- Fuzzy Architectures, Int. J. Appl. Math. Comput. Sci. 10 (2000), pp. 675-701. open in new tab
- Rutkowski, L., K. Cpalka: Flexible neuro-fuzzy systems, IEEE Trans. Neural Networks. 14 (2003), pp. 554-574. open in new tab
- Szlapczynski, R.: A new method of planning collision avoidance manoeuvres for multi-target encounter situations, J. Navig. 61 (2008). open in new tab
- Szlapczynski, R., J. Szlapczynska: Customized crossover in evolutionary sets of safe ship trajectories, Int. J. Appl. Math. Comput. Sci. 22 (2012). open in new tab
- Szłapczynska, J.: Multi-objective Weather Routing with Customised Criteria and Constraints, J. Navig. 68 (2015), pp. 338-354. open in new tab
- Szłapczyński, R., R. Smierzchalski: Supporting navigator's decisions by visualizing ship collision risk, Polish Marit. Res. 16 (2009). open in new tab
- Wang, Y., H. Chin: An Empirically-Calibrated Ship Domain as a Safety Criterion for Navigation in Confined Waters, (2015). open in new tab
- Van Westrenen, F., J. Ellerbroek: The Effect of Traffic Complexity on the Development of Near Misses on the North Sea, IEEE Trans. Syst. Man, Cybern. Syst. 47 (2017), pp. 432-440. open in new tab
- Wu, X., A.L. Mehta, V.A. Zaloom, B.N. Craig: Analysis of waterway transportation in Southeast Texas waterway based on AIS data, Ocean Eng. 121 (2016), pp. 196-209. open in new tab
- Zadeh, L.A.: The Concept of a Linguistic Variable and its Application to Approximate Reasoning-I, (1975), pp. 199-249. open in new tab
- Zhang, W., F. Goerlandt, P. Kujala, Y. Wang: An advanced method for detecting possible near miss ship collisions from AIS data, Ocean Eng. 124 (2016), pp. 141-156. open in new tab
- A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems, Int. J. Approx. Reason. 52 (2011) pp. 894-913. open in new tab
- CONTACT WITH THE AUTHORS Rafał Szłapczyński e-mail: rafal@pg.edu.pl open in new tab
- Tacjana Niksa-Rynkiewicz e-mail: tacniksa@pg.edu.pl open in new tab
- Gdansk University of Technology Faculty of Ocean Engineering and Ship Technology 11/12 Narutowicza St. 80 -233 Gdańsk Poland open in new tab
- Sources of funding:
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- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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