• Journal of Navigation and Port Research
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• v.48 no.2
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• pp.116-124
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• 2024

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 planning 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.7
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• pp.566-572
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• 2014

We propose a two-way multi-hop relaying scheme improving the throughput as well as enlarging the coverage for ship-to-ship communications in multi-ship marine networks. The proposed scheme reduces the time slots required for the data exchange by designing data transmission and network coding procedures in a sophisticated way based on two-phase digital network coding. Simulation results show that the proposed two-way multi-hop relaying scheme improves the throughput of the conventional one about 5/3 times.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10A
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• pp.760-768
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• 2009

In this paper, we propose the energy efficient transmission parameter selection method for Wireless Personal Area Network (WPAN) system which is applied to e-Navigation system considering various ship models environment. An appropriate selection of transmission parameters of HR-WPAN system is very essential to be considered for saving WPAN devices' energy consumption, when HR-WPAN system is applied to ship area network (SAN). Therefore, we propose an energy consumption model for a ship area network employing IEEE 802.15.3 based CSMA/CA HR-WPAN model and analyze the effect of transmission parameter selection on the performance of energy consumption. In particular, the path loss is the major performance decision parameter for the SAN employing HR-WPAN system, since it varies according to the material of shipbuilding such as steel(for large ship), FRP(for medium size ship) and compound wood(for small ship). Thus, we analyze and demonstrate that the proper transmission parameter selection of transmit power, PHY data rate and fragment size for each ship model could guarantee energy efficiency.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.3
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• pp.235-249
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• 2018

The Artificial Neural Network (ANN) model has been known as one of the most effective theories for automatic ship berthing, as it has learning ability and mimics the actions of the human brain when performing the stages of ship berthing. However, existing ANN controllers can only bring a ship into a berth in a certain port, where the inputs of the ANN are the same as those of the teaching data. This means that those ANN controllers must be retrained when the ship arrives to a new port, which is time-consuming and costly. In this research, by using the head-up coordinate system, which includes the relative bearing and distance from the ship to the berth, a novel ANN controller is proposed to automatically control the ship into the berth in different ports without retraining the ANN structure. Numerical simulations were performed to verify the effectiveness of the proposed controller. First, teaching data were created in the original port to train the neural network; then, the controller was tested for automatic berthing in other ports, where the initial conditions of the inputs in the head-up coordinate system were similar to those of the teaching data in the original port. The results showed that the proposed controller has good performance for ship berthing in ports.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1703_1704
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• 2009

Fuzzy neural ship controllers is used in ship steering control. It can make full use of the advantage of all kinds of intelligent algorithms. This provides an efficient way for this paper. An RBF neural network and GA optimization are employed in a fuzzy neural controller to deal with the nonlinearity, time varying and uncertain factors. Utilizing the designed network to substitute the conventional fuzzy inference, the rule base and membership functions can be auto-adjusted by GA optimization. The parameters of neural network can be decreased by using union-rule configuration in the hidden layer of the network. The ship control quality is effectively improved in case of appending additional sea state disturbance. The performance of controller is evaluated by the system simulation using simulink tools.

• Journal of Korea Port Economic Association
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• v.25 no.2
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• pp.1-24
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• 2009

Transportation decisions of ship liners are crucial for policy formulation in ports and shipping lines. Ship liners' port selection depends on the location characteristic of port. With network theory based, we empirically investigated determinants of global ship liners' port selection focused on major trade ports in China and Korea during 1995-2007. We present a detailed discussion on the related literatures about port selection, and develop hypothesis using network-based view. With conditional logit model, empirical results show that global liners select globally positioned ports rather than domestic oriented ones. Global ship liners select ports which have intra national network centrality, global ship network centrality and global network linkage.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.170-171
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• 2022

A ship's maneuvering motion model is important in a computer simulation, especially under the trend of intelligent navigation. This model is usually constructed by the hydrodynamic parameters of the ship which are generated by the principles of hydrodynamics. Ship's motion model is a nonlinear function. By using this function, ships' motion elements can be calculated, then the ship's trajectory can be predicted. Deeping neural networks can construct any linear or non-linear equation theoretically if there have enough and sufficient training data. This study constructs some kinds of deep Networks and trains this network by real ship motion data, and chooses the best one of the networks, uses real data to train it, then uses it to predict the ship's trajectory, getting some conclusions and experiences.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2001.10a
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• pp.89-99
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• 2001

Optimum Ship Routing can be defined as “The selection of an optimum track for a transoceanic crossing by the application of long-range predictions of wind, waves and currents to the knowledge of how the routed vessel reacts to these variables”. This paper treats the methodology how to serve optimum ship routing problem by network modeling and reveals the validity of the network model by some numerical experiments.

• Journal of the Korean Institute of Navigation
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• v.25 no.3
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• pp.211-223
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• 2001

Optimum Ship Routing can be defined as \"The selection of an optimum track for a transoceanic crossing by the application of long-range predictions of wind, waves and currents to the knowledge of how the routed vessel reacts to these variables\". This paper treats the methodology how to solve optimum ship routing problem by network modeling and reveals the validity of the network model by some numerical experiments.periments.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1994.04a
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• pp.55-67
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• 1994

Studies on the ship's automatic navigation & berthing control have been continued by way of solving the ship's mathematical model but the results of such studies have not reached to our satisfactory level due to its non-linear characteristics ar low speed. In this paper the authors propose a new berthing control system which can evaluate as closely as captain's decision-making by using the FNN(Fuzzy Neural Network) controller which can simulate captain's decision-making by using the FNN(Fuzzy neural Network) controller which can simulate captain's knowledge. This berthing controller consists of the navigation subsystem FNN controller and the berthing subsystem FNN controller. The learning data are drawn from Ship Handling Simulator (NavSim NMS90 MK III) and represent the ship motion characteristics internally According to learning procedure both FNN controllers can tune membership functions and identify fuzzy control rules automatically The verified results show the FNN controllers effective to incorporate captain's knowledge and experience of berthing.