Mr. Yongtao Xi Profile

Yongtao Xi

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Proceedings Article | 9 April 2024 Paper

Risk early warning method of ship anchor dragging based on simulation experiments

Pengjie Chen, Yongtao Xi, Jiang Han

Proceedings Volume 12989, 129890Z (2024) https://doi.org/10.1117/12.3023906

KEYWORDS: Fuzzy logic, Wind speed, Safety, Matrices, Computer simulations, Probability theory, Motion analysis, Data processing, Artificial intelligence, Water

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In response to the issue of limited timeliness in the current monitoring of ship anchor dragging, a method of early warning of ship anchor dragging risk utilising fuzzy set theory was proposed. Based on sailing simulator, ship anchoring simulation experiments were conducted to collect trajectory data of ship anchoring under various environmental conditions, and the characteristics of the trajectory of normal anchored ships were analyzed. During a yawing motion, the yawing width, ground speed and heading change rate of were established as early warning indicators for ship anchor dragging risk. Triangular fuzzy numbers and the distribution of early warning indicators obtained from simulation experiments were combined to determine the membership functions of each indicator, and the entropy weight method was used to determine indicator weights. Then ship anchor dragging risk was comprehensively evaluated and the anchor dragging probability was quantified. Warning levels were set according to actual conditions, and the warnings will be alarmed if the anchor dragging probability reaches the corresponding warning level. The results indicate that during the yawing motion of anchored ships, larger yawing width, higher ground speed, and smaller ship heading change rate correspond to higher anchor dragging probability. Finally, sailing simulators were utilized for simulation experiments ,and the feasibility of the proposed method was validated. The method exhibits practical operability and demonstrates favorable timeliness in early warning capabilities.

Proceedings Article | 21 March 2023 Paper

Cognitive method of spatial-temporal differentiation characteristics to collision risk

Qinghua Zhu, Yongtao Xi, Shenping Hu, Jianjun Wu

Proceedings Volume 12609, 126090N (2023) https://doi.org/10.1117/12.2672140

KEYWORDS: Radar, Interpolation, Windows, Artificial intelligence, Mixtures, Engineering, Cognitive modeling, Oceanography, Statistical analysis, Data modeling

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With the disclosure of more and more Marine investigation accident reports, it becomes a new way to study ship collision characteristics through big data of ship collision accidents. In this paper, based on the big data of ship collision accidents, a radar window risk of ship collision (ROC) cognitive model based on Kriging interpolation and the Gaussian mixture method (KGMM) is constructed to study ROC characteristics. First of all, the spatial and temporal feature perception model of collision risk perception is established under the radar window. Secondly, a method based on KGMM is constructed. The method includes two parts which were data set interpolation using the Kriging interpolation method and the Gaussian mixture clustering method to cluster the interpolation results. Finally, different collision scenarios were set for 101 cases, and the spatial-temporal differentiation characteristics of ROC are identified by the ROC cognitive model in the radar window. The results show that the ROC presents a risk-sensitive area under the radar window. The characteristics of the risk-sensitive areas of different ships in the encounter situation are consistent with the overall characteristics. The research results can provide a reference for the ship officers to choose the low-risk and favorable position to avoid the ship under Convention on the International Regulations for Preventing Collisions at Sea (COLREGS).

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