An internet of things approach for extracting featured data using AIS database : an application based on the viewpoint of connected ships

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dc.contributor.author He, Wei
dc.contributor.author Li, Zhixiong
dc.contributor.author Malekian, Reza
dc.contributor.author Liu, Xinglong
dc.contributor.author Duan, Zhihe
dc.date.accessioned 2017-10-25T07:29:03Z
dc.date.available 2017-10-25T07:29:03Z
dc.date.issued 2017-09
dc.description.abstract Automatic Identification System (AIS), as a major data source of navigational data, is widely used in the application of connected ships for the purpose of implementing maritime situation awareness and evaluating maritime transportation. Efficiently extracting featured data from AIS database is always a challenge and time-consuming work for maritime administrators and researchers. In this paper, a novel approach was proposed to extract massive featured data from the AIS database. An Evidential Reasoning rule based methodology was proposed to simulate the procedure of extracting routes from AIS database artificially. First, the frequency distributions of ship dynamic attributes, such as the mean and variance of Speed over Ground, Course over Ground, are obtained, respectively, according to the verified AIS data samples. Subsequently, the correlations between the attributes and belief degrees of the categories are established based on likelihood modeling. In this case, the attributes were characterized into several pieces of evidence, and the evidence can be combined with the Evidential Reasoning rule. In addition, the weight coefficients were trained in a nonlinear optimization model to extract the AIS data more accurately. A real life case study was conducted at an intersection waterway, Yangtze River, Wuhan, China. The results show that the proposed methodology is able to extract data very precisely. en_ZA
dc.description.department Electrical, Electronic and Computer Engineering en_ZA
dc.description.librarian am2017 en_ZA
dc.description.sponsorship The National Science Foundation of China (Grants No. 51479155), Fujian Province Natural Science Foundation (No. 2015J05108), Fuzhou Science and Technology Planning Project (No. 2016S117), the Fujian College’s Research Base of Humanities and Social Science for Internet Innovation Research Center (Minjiang University) (No. IIRC20170104), Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201727), and the Yingcai project of CUMT (YC170001). en_ZA
dc.description.uri http://www.mdpi.com/journal/symmetry en_ZA
dc.identifier.citation He, W., Li, Z., Malekian, R., Liu, X. & Duan, Z. 2017, 'An internet of things approach for extracting featured data using AIS database : an application based on the viewpoint of connected ships', Symmetry, vol. 9, no. 9, art. no. 186, pp. 1-15. en_ZA
dc.identifier.issn 2073-8994 (online)
dc.identifier.other 10.3390/sym9090186
dc.identifier.uri http://hdl.handle.net/2263/62926
dc.language.iso en en_ZA
dc.publisher MDPI Publishing en_ZA
dc.rights © 2017 by the authors. This work is licensed under the Creative Commons Attribution License. en_ZA
dc.subject Evidential reasoning rule en_ZA
dc.subject Likelihood modeling en_ZA
dc.subject Belief distribution en_ZA
dc.subject Non-linear optimization en_ZA
dc.subject Automatic identification system (AIS) en_ZA
dc.title An internet of things approach for extracting featured data using AIS database : an application based on the viewpoint of connected ships en_ZA
dc.type Article en_ZA


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