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논문 기본 정보

자료유형
학술저널
저자정보
Van-Suong Nguyen (Vietnam Maritime University) Van-Cuong Do (Vietnam Maritime University) Nam-Kyun Im (Mokpo National Maritime University)
저널정보
한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.18 No.1
발행연도
2018.3
수록면
41 - 49 (9page)
DOI
10.5391/IJFIS.2018.18.1.41

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In the studies on an berthing control of ship, an artificial neural network (ANN) model is commonly employed as the main controller to control the rudder and the propeller. The existing ANN controllers that use the parameters consisting of the ship position and the ship heading as inputs cannot be applied to control automatically the ship into berth in different ports. To deal with this problem, the parameters, such as relative bearing and distance from ship to berth calculated by radar can be used as inputs for the controller. However, the calculation of these factors is not accurate because some errors arise on using radar for berthing process. This leads to the lack of confidence in ship berthing system using the parameters determined by radar. In this research, the neural network based-automatic berthing system is developed for ship by using the parameters which are measured by distance measurement system. By this proposed system, the ship is brought automatically into berth in different ports without retraining the neural network. In addition, this system guarantees that the parameters used for inputs of the neural network is measured exactly and continually. To validate the proposed algorithm, numerical simulations are carried out to two imaginary ports and a real port, and result showed the good performance of the proposed system for automatic ship berthing.

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Abstract
1. Introduction
2. Ship Dynamic Model for Ship Berthing
3. The Current Automatic Berthing System for Ship Using ANN
4. Development of Automatic Ship Berthing System Using ANN and Distance Measurement System
5. Numerical Simulation for Validation
6. Conclusions
References

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UCI(KEPA) : I410-ECN-0101-2018-003-001906781