Browsing by Author "Xu, Sheng"

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A Bayesian network risk model for predicting ship besetting in ice during convoy operations along the Northern Sea Route
(2022-07) Xu, Sheng; Kim, Ekaterina; Haugen, Stein; Zhang, Mingyang
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
To facilitate shipping in ice and to meet the increasing requirements of icebreaker services, convoy operations are the most effective alternative. However, convoy operations are among the most dangerous operations as they can result in ship-ship collisions and/or ship besetting in ice. To safeguard the assisted ships and improve the efficiency of convoy operations, predicting the besetment event is a paramount proactive measure. In this study, a Bayesian Network model is developed to predict the probability of ship besetting in ice in a convoy operation along the Northern Sea Route (NSR). The model focuses on the first-assisted ship and is based on expert elicitation. Correspondingly, four scenarios that may result in the first assisted ship besetting in ice have been identified. Further, the applicability of the model is evaluated through 12 scenarios derived from the real NSR voyage of ‘TIAN YOU’ assisted by the icebreaker ‘VAYGACH’ in August 2018. The results of the model evaluation and validity studies indicate that the developed model is feasible and can adequately predict the besetment event of the first assisted ship in convoy operations. The most important factors contributing to besetting in ice were found to be ice concentration, distance between icebreaker and ship, and navigation experience.
A probabilistic analytics method to identify striking ship of ship-buoy contact at coastal waters
(2022-12-15) Liu, Lei; Zhang, Mingyang; Hu, Yue; Zhu, Wei; Xu, Sheng; Yu, Qing
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The identification of the ship that contact with the buoy can provide evidence for accident accountability. To this aim, the paper develops a probabilistic analytics method to evaluate the ship-buoy contact risk for the striking ship identification at the coastal areas by combining buoy domain and bounding box models. The method makes use of Automatic Identification System (AIS) data and navigational buoy data. Firstly, an AIS-based probabilistic buoy domain model is adopted for the determination of the safety boundary of the buoy to detect potential striking ships with a higher contact probability. Then, the bounding boxes of the navigational buoy and the detected potential striking ships are developed to detect the real striking ship by analyzing the interaction be-tween the ship bounding box and the buoy bounding box. Finally, the probabilistic analytics method is demonstrated in the South China Sea and validated using historical ship-buoy contact records. Results indicated that, from a probabilistic perspective, the safety buoy domain (critical boundary) existed with diverse distances dynamically. The proposed method could assist the identification of striking ships while aiding the definition of the safety buoy domain for preventing ship-buoy contacts. As a result, it has the potential to support the development of ship-buoy contact risk management and assist surveillance operators and master on board by improving their cognitive abilities in dangerous traffic scenarios.