| Title |
A New Vessel Path Prediction Method using Long Short-term Memory |
| Authors |
김종희(Jonghee Kim) ; 정찬호(Chanho Jung) ; 강도근(Dokeun Kang) ; 이창진(Chang Jin Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.7.1131 |
| Keywords |
vessel path prediction; recurrent neural network (RNN); long-short-term memory (LSTM) |
| Abstract |
In this paper, we propose a new vessel path prediction method using long short-term memory (LSTM). LSTM is one of recurrent neural networks which contains memory cell in order to deal with long-term data. In order to fully utilize the advantage of LSTM, our proposed method employs 3-layer LSTM instead of a fully connected layer. We also propose new input and output vectors well suited for the vessel path prediction. In order to prove the effectiveness of the proposed method, we compare the proposed method with a baseline method which consists of a LSTM and a fully connected layer. In comparison between the proposed method and the baseline method, the proposed method outperforms the baseline method based on LSTM. |