An efficient Meta-VSW method for ship behaviors recognition and application

作     者：Cai, Zhiyuan Fan, Qidong Li, Lecheng Yu, Long Li, Congbo 

作者机构：Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Deep Sea Explorat Collaborat Innovat Ctr Adv ship Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Ocean & Civil Engn Shanghai 200240 Peoples R China Marine Design & Res Inst China Shanghai 200011 Peoples R China 

出 版 物：《OCEAN ENGINEERING》 (海洋工程)

年 卷 期：2024年第311卷第Part2期

核心收录：

学科分类：07[理学] 0707[理学-海洋科学] 0824[工学-船舶与海洋工程] 0814[工学-土木工程] 

基　　金：Marine Design and Research Institute of China  (MS202108) 

主　　题：Ship behavior recognition Unsupervised algorithm Massive unknown data Meta-trajectory Operational efficiency Fuel consumption 

摘      要：Ship behaviors refer to the operational process such as sailing, entering into port/departure, etc., which indicate by their position, speed, and so on. The collected big data normally have been treated by unsupervised Machine Learning methods. However, the process is time consuming and lacks consideration of time continuity. From the unknown data to recognize and recur the ship behaviors is still a complex problem. Hence, this study proposes a universal Meta-trajectory Variable Sliding Window (Meta-VSW) method to provide an efficient and high-fidelity solution. In this method, the ship data were connected into the smallest units by the meta-trajectory coding, and combines with variable sliding windows to achieve fast, continuous and accurate recognition of ship behaviors. Taking an inland-water ship and a marine transport ship as examples, the validity of the method was fulfilled and compared with two commonly used algorithms, Affinity Propagation (AP) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It has the fastest computational speed and can effectively classify the behaviors of massive unknown data from different ships. And it has good performance in capturing behavior boundaries, with the recognition accuracy up to 0.9. Then, the method was applied to analyze the operational effectively and fuel consumption.