Summary of Sequential Modeling Of Complex Marine Navigation: Case Study on a Passenger Vessel (student Abstract), by Yimeng Fan et al.
Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)
by Yimeng Fan, Pedram Agand, Mo Chen, Edward J. Park, Allison Kennedy, Chanwoo Bae
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning approach to reduce vessel fuel consumption in the maritime industry is presented, using a real-world dataset from a ferry in western Canada. The focus is on creating a time series forecasting model that predicts dynamic states based on actions and disturbances. This model can be used as an evaluative tool to assess the effectiveness of the ferry’s operation under the captain’s guidance. Additionally, it provides a foundation for future optimization algorithms, offering valuable feedback on decision-making processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers is trying to make ships use less fuel by using a special kind of artificial intelligence called machine learning. They used data from a real ferry in Canada over two years to train the AI model. The goal is to create a system that can predict what will happen to the ship based on its actions and any problems it might face. This could help captains make better decisions and even reduce fuel consumption. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Time series
