Topic: Intelligent, Safe, and Green Shipping-oriented Maritime Data Exploitation and Knowledge Discovery
Guest Editor(s)
Assoc. Prof. Xinqiang Chen
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China.
Guest Editor Assistant(s)
Assoc. Prof. Shaorui Zhou
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong, China.
Special Issue Introduction
Economic globalization has fostered the development of intelligent navigation and automated container terminals. It is important to recognize that smart and green shipping will shape future shipping variation tendencies by considering safety, cost, and efficiency factors. Automatic identification system (AIS) data is commonly used to aid real-world ship navigation. Indeed, varied maritime data sources (ship-borne surveillance video, port surveillance video, Lidar, GPS, etc.) have been introduced to enhance shipping safety and efficiency. This approach facilitates the collection of sufficient data for conducting smart shipping-related studies.
It is noted that ships in the smart shipping era can find optimal traveling trajectories by exploiting historical yet large-scale AIS data. Moreover, energy consumption in the smart ship era is supposed to be significantly lower than in the traditional shipping era. For instance, cargo will be transmitted by autonomous guided vehicles (AGV) powered by green energy. Meanwhile, safety can be enhanced in the smart shipping era as potential accidents in sea and port areas will be easily identified with the help of AI techniques. It is necessary to exploit shipping-related knowledge from large-scale maritime data, which can be further used to optimize shipping energy consumption. With that aim in mind, we invite full paper contributions that match the general theme of "Intelligent, Safe, and Green Shipping-oriented Maritime Data Exploitation and Knowledge Discovery". We also encourage submissions from a broad range of research fields related to the relevant issues. Some potential exemplary topics are shown as follows (but not limited to):
● Energy efficiency-based ship trajectory optimization using large-scale AIS data;
● Maritime traffic safety-based shipping knowledge discovery;
● Ship autonomous navigation employing multiple maritime data sources;
● Analysis of energy consumption reduction in the smart shipping era;
● Optimization of scheduling tasks for AGVs, shore bridges, and yards in automated container terminals;
● Intelligent maritime data mining, etc.
It is noted that ships in the smart shipping era can find optimal traveling trajectories by exploiting historical yet large-scale AIS data. Moreover, energy consumption in the smart ship era is supposed to be significantly lower than in the traditional shipping era. For instance, cargo will be transmitted by autonomous guided vehicles (AGV) powered by green energy. Meanwhile, safety can be enhanced in the smart shipping era as potential accidents in sea and port areas will be easily identified with the help of AI techniques. It is necessary to exploit shipping-related knowledge from large-scale maritime data, which can be further used to optimize shipping energy consumption. With that aim in mind, we invite full paper contributions that match the general theme of "Intelligent, Safe, and Green Shipping-oriented Maritime Data Exploitation and Knowledge Discovery". We also encourage submissions from a broad range of research fields related to the relevant issues. Some potential exemplary topics are shown as follows (but not limited to):
● Energy efficiency-based ship trajectory optimization using large-scale AIS data;
● Maritime traffic safety-based shipping knowledge discovery;
● Ship autonomous navigation employing multiple maritime data sources;
● Analysis of energy consumption reduction in the smart shipping era;
● Optimization of scheduling tasks for AGVs, shore bridges, and yards in automated container terminals;
● Intelligent maritime data mining, etc.
Keywords
Ship autonomous navigation, maritime knowledge discovery, shipping energy consumption, intelligent maritime data exploitation, intelligent AGV path optimization
Submission Deadline
30 Nov 2024
Submission Information
For Author Instructions, please refer to https://www.oaepublish.com/ir/author_instructions
For Online Submission, please login at https://oaemesas.com/login?JournalId=ir&IssueId=IR231225
Submission Deadline: 30 Nov 2024
Contacts: Amber Ren, Assistant Editor, editorial@intellrobot.com
