Data-driven detection and diagnosis on the navigation states of surface vessels

Li, Kuan; Tan, Tianyu; Shen, Xinrui

doi:10.1109/icist.2017.7926793

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…As for some conflicting or insufficient evidences, the standard D-S evidence theory may be invalid. At this time, the sum of all BPAs in equation (19) is less than 1, which brings about unstable combination results. We should introduce the BPA m j (u) of the uncertain element u in order to compensate the deficient component.…”

Section: Nonlinear Svm Classification Modelmentioning

confidence: 99%

“…[16][17][18] It is often considered as a new training algorithm for the polynomial, radial basis function (RBF), and multi-layer classifier by defining the kernel function. Aimed at the safety and reliability of surface vessels, Li et al 19 diagnosed the navigation state to give a warning of dangerous state, which took measures to make vessels free from hazard before it was late. In Wang and Wang, 20 some models were to make estimation in the Yangtze River.…”

Section: Introductionmentioning

confidence: 99%

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Innovative assessment scheme of navigation risk based on improved multi-source information fusion techniques

Pang

2018

International Journal of Distributed Sensor Networks

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To deal with highly time complexity and unstable assessments for conflicting evidences from various navigation factors, we put forward an innovative assessment scheme of navigation risk based on the improved multi-source information fusion techniques. Different from the existing studies, we first deduce the nonlinear support vector machine classification model for the general scenario. The slack variable is adaptively computed based on the Euclidean distance ratio. Considering the unsatisfactory characteristics of the standard Dempster-Shafer evidence theory, the optimal combination rule is derived step by step. What's more, the lowly dimensional Kalman filter is applied to forecast the navigation risk. Simultaneously, the time complexity of each technique is analyzed. With respect to the vessel navigation risk, the assessment results are provided to indicate the reliability and efficiency of the proposed scheme.

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Section: Nonlinear Svm Classification Modelmentioning

confidence: 99%