Hao Cao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Hao Cao has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Guoyou Shi
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication - Kaige Zhu
Key Laboratory of Navigation Safety Guarantee of Liaoning Province, Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication - Lin Ma
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication - Shengyan Qin
Dalian Maritime University, Dalian 116026, Liaoning, China, China
1 shared publication - Weifeng Li
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication
Latest Publications
- Open Access
- 17 May 2024
- Published by MDPI AG in Preprints.org
A Ship State Recognition Method Based on Graph Convolutional Neural Network
To address the challenge of recognizing ship states accurately amidst the complexities of marine environments, this study proposes a novel ship state recognition approach leveraging a graph convolutional neural network (GCNN). Initially, the method extracts canonical and efficient ship motion trajectories from AIS data. Subsequently, a state recognition network tailored for ship motion trajectories is devised and implemented employing graph convolution. Notably, the accuracy of this model is enhanced through the introduction of novel weights and optimization of the Adj parameter. Experimental evaluations conducted on a ship state dataset demonstrate significant performance improvements. Specifically, the proposed recognition network achieves a recognition accuracy of 98.3% for regulated ship trajectories, marking an impressive 8.4% enhancement over traditional convolutional neural networks. This advancement holds promise for enhancing ship state recognition accuracy across diverse maritime applications including maritime supervision, navigation safety, and ship management.
- 17 May 2024
- Published by MDPI AG in Preprints.org
Open AccessA Ship State Recognition Method Based on Graph Convolutional Neural Network
To address the challenge of recognizing ship states accurately amidst the complexities of marine environments, this study proposes a novel ship state recognition approach leveraging a graph convolutional neural network (GCNN). Initially, the method extracts canonical and efficient ship motion trajectories from AIS data. Subsequently, a state recognition network tailored for ship motion trajectories is devised and implemented employing graph convolution. Notably, the accuracy of this model is enhanced through the introduction of novel weights and optimization of the Adj parameter. Experimental evaluations conducted on a ship state dataset demonstrate significant performance improvements. Specifically, the proposed recognition network achieves a recognition accuracy of 98.3% for regulated ship trajectories, marking an impressive 8.4% enhancement over traditional convolutional neural networks. This advancement holds promise for enhancing ship state recognition accuracy across diverse maritime applications including maritime supervision, navigation safety, and ship management.