Prediction of Motion State of a Docking Small Planing Ship using Artificial Neural Network

• 영문명: Prediction of Motion State of a Docking Small Planing Ship using Artificial Neural Network
• 발행기관: 한국항해항만학회
• 저자명: Hoang Thien Vu Thi Thanh Diep Nguyen 윤현규(Hyeon Kyu Yoon)
• 간행물 정보: 『한국항해항만학회지』제48권 제2호, 116~124쪽, 전체 9쪽
• 주제분류: 공학 > 해양공학
• 파일형태: PDF
• 발행일자: 2024.04.30

영문 초록

Automatic docking of small planing ship is a critical aspect of maritime operations, requiring accurate prediction of motion states to ensure safe and efficient maneuvers. This study investigates the use of Artificial Neural Network (ANN) to predict motion state of a small planing ship to enhance navigation automation in port environments. To achieve this, simulation tests were conducted to control a small planing ship while docking at various heading angles in calm water and in waves. Comprehensive analysis of the ANN-based predictive model was conducted by training and validation using data from various docking situations to improve its ability to accurately capture motion characteristics of a small planing ship. The trained ANN model was used to predict the motion state of the small planing ship based on any initial motion state. Results showed that the small planing ship could dock smoothly in both calm water and waves conditions, confirming the accuracy and reliability of the proposed method for prediction. Moreover, the ANN-based prediction model can adjust the dynamic model of the small planing ship to adapt in real-time and enhance the robustness of an automatic positioning system. This study contributes to the ongoing development of automated navigation systems and facilitates safer and more efficient maritime transport operations.

목차

1. Introduction

2. Dockng problem and simulation model

3. Proposed approach

4. Result

5. Conclusion

References

키워드

Artificial Neural Network motion state small planing ship automatic ship docking real-time