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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.

Because the maneuverability of ship at low speed is reduced significantly, ship berthing is one of most difficult duties that ship master usually has to face as arriving at the port. In order to make sure the safe accomplishment of ship berthing, all steps need to be performed properly. In fact of maritime safety, the tugboats and thrusters are commonly utilized to support crabbing motion of ship in final stage of berthing process before mooring. In addition, because of the nonlinear of ship dynamic on berthing, theories of artificial intelligence are suitable to mimic the actions of ship master in controlling ship berthing automatically. In this article, a support system is proposed to bring ship into berth automatically after using the maneuvering control system. Three fuzzy controllers are established for different tasks of the berthing algorithm. The first controller is designed to control the movement of ship in longitudinal direction toward to the normal of wharf with the ship's propeller, while the task of second controller is to stabilize the relative bearing error with tugboat. The final controller is responsible for bring the ship into wharf according to crabbing motion with bow thruster and tugboat simultaneously. Finally, numerical simulations are carried out to validate the performance of the proposed system. The results show that the berthing support system has good performance for ship.

: The interpolation of missing AIS data can be used for recovering the lost data of a ship's state which is then able to produce useful information for VTS stations or other ships. Previous research has introduced some interpolating methods however there are some problems with regard to missing AIS data. This paper proposes one new method which includes linear interpolation, cubic Hermit interpolation and an identification mechanism to overcome some of those limitations, first AIS data regarding ship position, COG, SOG and HDG is divided into separate time series, then the characteristic of the missing data is investigated into through using an identification mechanism, an appropriate interpolation is selected to fit all the time series which matches the characteristics. Numerical experiments are carried out using real AIS data to validate the algorithm of this approach and the results are compared with the previous method, after which the actual missing area is suggested to be interpolated by the proposed method. The interpolation results show this approach can be applied well in practice.

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