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Open AccessArticle

An Internet of Things Approach for Extracting Featured Data Using AIS Database: An Application Based on the Viewpoint of Connected Ships

by Wei He 1,2,3, Zhixiong Li 4,5,†, Reza Malekian 6,*, Xinglong Liu 3,7,* and Zhihe Duan 8
1
College of Marine Sciences, Minjiang University, Fuzhou 350108, China
2
The Fujian College’s Research Based of Humanities and Social Science for Internet Innovation Research Center (Minjiang University), Fuzhou 350108, China
3
Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou 350121, China
4
School of Mechatronic Engineering & Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining & Technology, Xuzhou 221116, China
5
Department of Mechanical Engineering, Iowa State University, Ames, IA 50010, USA
6
Department of Electrical, Electronic & Computer Engineering, University of Pretoria, Pretoria 0002, South Africa
7
Department of Physics and Electronic Information Engineering, Minjiang University, Fuzhou 350108, China
8
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710001, China
*
Authors to whom correspondence should be addressed.
Current address: Fuzhou 350108, China.
Symmetry 2017, 9(9), 186; https://doi.org/10.3390/sym9090186
Received: 19 July 2017 / Revised: 24 August 2017 / Accepted: 28 August 2017 / Published: 7 September 2017
(This article belongs to the Special Issue Applications of Internet of Things)
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. View Full-Text
Keywords: Automatic Identification System (AIS); Evidential Reasoning rule; likelihood modeling; belief distribution; non-linear optimization Automatic Identification System (AIS); Evidential Reasoning rule; likelihood modeling; belief distribution; non-linear optimization
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MDPI and ACS Style

He, W.; Li, Z.; Malekian, R.; Liu, X.; Duan, Z. An Internet of Things Approach for Extracting Featured Data Using AIS Database: An Application Based on the Viewpoint of Connected Ships. Symmetry 2017, 9, 186.

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