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Born in September 1987, I am a lecturer with a master's degree, mainly focused on maritime intelligent information processing. From September 2010 to June 2012, i obtained master degree of traffic information engineering and control from the school of merchant shipping in Shanghai Maritime University; Then, i has joined Zhejiang Ocean University and engaged in teaching of navigation technology from July 2012 to now; At last, i am studying for doctor degree of traffic information engineering and control from the school of merchant shipping in Shanghai Maritime University from September 2017. I has Published 13 papers and participated in 7 projects such as National Natural Science Foundation of China and Shanghai Science and Technology Commission, applied for 5 invention patents and authorized 5 software copyrights.
Project Goal: construct abnormal ship behavior detection model
Current Stage: analyzing data
Project Goal: intelligent maritime search and rescue
Current Stage: analyzing data
Project Goal: ship route planning optimization
Current Stage: completed
Project Goal: route planning based on maritime big data
Current Stage: completed
Since the spread of the coronavirus disease 2019 (COVID-19) pandemic, the transportation of cargo by ship has been seriously impacted. In order to prevent and control maritime COVID-19 transmission, it is of great significance to track and predict ship sailing behavior. As the nodes of cargo ship transportation networks, ports of call can reflect the sailing behavior of the cargo ship. Accurate hierarchical division of ports of call can help to clarify the navigation law of ships with different ship types and scales. For typical cargo ships, ships with deadweight over 10,000 tonnages account for 95.77% of total deadweight, and 592,244 berthing ships’ records were mined from automatic identification system (AIS) from January to October 2020. Considering ship type and ship scale, port hierarchy classification models are constructed to divide these ports into three kinds of specialized ports, including bulk, container, and tanker ports. For all types of specialized ports (considering ship scale), port call probability for corresponding ship type is higher than other ships, positively correlated with the ship deadweight if port scale is bigger than ship scale, and negatively correlated with the ship deadweight if port scale is smaller than ship scale. Moreover, port call probability for its corresponding ship type is positively correlated with ship deadweight, while port call probability for other ship types is negatively correlated with ship deadweight. Results indicate that a specialized port hierarchical clustering algorithm can divide the hierarchical structure of typical cargo ship calling ports, and is an effective method to track the maritime transmission path of the COVID-19 pandemic.
Hailin Zheng; Qinyou Hu; Chun Yang; Jinhai Chen; Qiang Mei. Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining. Sustainability 2021, 13, 1089 .
AMA StyleHailin Zheng, Qinyou Hu, Chun Yang, Jinhai Chen, Qiang Mei. Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining. Sustainability. 2021; 13 (3):1089.
Chicago/Turabian StyleHailin Zheng; Qinyou Hu; Chun Yang; Jinhai Chen; Qiang Mei. 2021. "Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining." Sustainability 13, no. 3: 1089.
Shipping containers are tokens of multimodal international transportation and rapid logistics. Container deliveries are scheduled to satisfy rapidly changing requirements. Unpredictable increases in costs and unforeseeable events such as pandemics compel ship owners and managers to adopt risk minimization measures. This study addresses one issue: how to determine an alternative port of call from massive data to offer a realistic destination change recommendation for a container vessel. Recommendation algorithms have become ubiquitous and are used effectively in other fields, but there is no such model for the port of call selection or recommendation. Large scale automatic identification system (AIS) data are readily available. We developed a computational framework based on a novel natural language programming algorithm that was tailored to support port recommendation rather than use a conventional adjacency matrix method. We mined large scale AIS data to construct sequential berth records for container vessels and mapped each port onto a vector in an embedded space. The natural language neural programming algorithm can suggest ports similar to the scheduled ports of call that were unable to offer service. The recommendations were validated with geo-analysis of sailing distance and could offer viable alternative ports to shipping managers.
Qiang Mei; Qinyou Hu; Chun Yang; Hailin Zheng; Zhisheng Hu. Port Recommendation System for Alternative Container Port Destinations Using a Novel Neural Language-Based Algorithm. IEEE Access 2020, 8, 199970 -199979.
AMA StyleQiang Mei, Qinyou Hu, Chun Yang, Hailin Zheng, Zhisheng Hu. Port Recommendation System for Alternative Container Port Destinations Using a Novel Neural Language-Based Algorithm. IEEE Access. 2020; 8 ():199970-199979.
Chicago/Turabian StyleQiang Mei; Qinyou Hu; Chun Yang; Hailin Zheng; Zhisheng Hu. 2020. "Port Recommendation System for Alternative Container Port Destinations Using a Novel Neural Language-Based Algorithm." IEEE Access 8, no. : 199970-199979.
Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.
Xinqiang Chen; Jun Ling; Yongsheng Yang; Hailin Zheng; Pengwen Xiong; Octavian Postolache; Yong Xiong. Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction. Mathematical Problems in Engineering 2020, 2020, 1 -9.
AMA StyleXinqiang Chen, Jun Ling, Yongsheng Yang, Hailin Zheng, Pengwen Xiong, Octavian Postolache, Yong Xiong. Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction. Mathematical Problems in Engineering. 2020; 2020 ():1-9.
Chicago/Turabian StyleXinqiang Chen; Jun Ling; Yongsheng Yang; Hailin Zheng; Pengwen Xiong; Octavian Postolache; Yong Xiong. 2020. "Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction." Mathematical Problems in Engineering 2020, no. : 1-9.
Yifan Jang; Hailin Zheng. Analysis of Impact Factors of Bulk Carrier Energy Efficiency Operation Index. Proceedings of the 2nd Symposium on Health and Education 2019 (SOHE 2019) 2019, 1 .
AMA StyleYifan Jang, Hailin Zheng. Analysis of Impact Factors of Bulk Carrier Energy Efficiency Operation Index. Proceedings of the 2nd Symposium on Health and Education 2019 (SOHE 2019). 2019; ():1.
Chicago/Turabian StyleYifan Jang; Hailin Zheng. 2019. "Analysis of Impact Factors of Bulk Carrier Energy Efficiency Operation Index." Proceedings of the 2nd Symposium on Health and Education 2019 (SOHE 2019) , no. : 1.
Linghui Yu; Hailin Zheng; Botao Fu. Comparative Analysis of Ship Carbon Emission Monitoring Methods Based on MRV Rules. Proceedings of the 2nd Symposium on Health and Education 2019 (SOHE 2019) 2019, 1 .
AMA StyleLinghui Yu, Hailin Zheng, Botao Fu. Comparative Analysis of Ship Carbon Emission Monitoring Methods Based on MRV Rules. Proceedings of the 2nd Symposium on Health and Education 2019 (SOHE 2019). 2019; ():1.
Chicago/Turabian StyleLinghui Yu; Hailin Zheng; Botao Fu. 2019. "Comparative Analysis of Ship Carbon Emission Monitoring Methods Based on MRV Rules." Proceedings of the 2nd Symposium on Health and Education 2019 (SOHE 2019) , no. : 1.