• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.768-783
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• 2020

Typically, a Dynamic Positioning System (DPS) uses a PID feed-back system, and it often adopts a wind feed-forward system because of its easier implementation than a feed-forward system based on current or wave. But, because a ship's drifting motion is caused by wind, current, and wave drift loads, all three environmental loads should be considered. In this study, a motion predictive control for the PID feedback system of the DPS is proposed, which considers the three environmental loads by utilizing predicted drifted ship positions in the future since it contains information about the three environmental loads from the moment to the future. The prediction accuracy for the future drifted ship position is ensured by adopting deep learning algorithms and a replay buffer. Finally, it is shown that the proposed motion predictive system results in better station-keeping performance than the wind feed-forward system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.313-314
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• 2022

The intelligent maritime traffic information service provides a service to prevent collisions and stranding of ships based on the location information of ships periodically collected from ship equipment such as LTE-Maritime transceivers and AIS installed on ships. provided in real time. However, the above service may reduce the reliability of ship location information because GPS location information for measuring the ship's location may be cut off during transmission through LTE-Maritime or AIS networks, or phenomena such as location jumps and delays may occur. This study aims to estimate reliable position information to some extent even in an abnormal section through ship position prediction based on the existing received position information using the Kalman filter, which is an optimal estimation filter based on probability.

• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.78-85
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• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.145-147
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• 2017

Tracking filter plays a key role in accurate estimation and prediction of maneuvering vessel's dynamics. The third-order ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. Fading memory algorithm performs a better performance in numerous of ${\alpha}-{\beta}-{\gamma}$ filter algorithms. This study aims to optimize the fourth-order fading memory algorithm ${\alpha}-{\beta}-{\gamma}-{\eta}$ filter, which is extended form ${\alpha}-{\beta}-{\gamma}$ filter, to get much more accurate position of high dynamic target on the condition that the own ship is also high dynamic.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.18-25
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• 2023

In these days, high dimensional data prediction technology based on neural network shows compelling results in many different kind of field including engineering. Especially, a lot of variants of convolution neural network are widely utilized to develop pixel level prediction model for high dimensional data such as picture, or physical field value from the sensors. In this study, velocity vector field of ideal flow on ship surface is estimated on pixel level by Unet. First, potential flow analysis was conducted for the set of hull form data which are generated by hull form transformation method. Thereafter, four different neural network with a U-shape structure were conFig.d to train velocity vectors at the node position of pre-processed hull form data. As a result, for the test hull forms, it was confirmed that the network with short skip-connection gives the most accurate prediction results of streamlines and velocity magnitude. And the results also have a good agreement with potential flow analysis results. However, in some cases which don't have nothing in common with training data in terms of speed or shape, the network has relatively high error at the region of large curvature.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.44 no.3
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• pp.229-238
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• 2008

This paper describes on the consolidation of AIS and ARPA radar positions by comparing the AIS and ARPA radar information for the tracked ship targets using a PC-based ECDIS in Busan harbor, Korea. The information of AIS and ARPA radar target was acquired independently, and the tracking parameters such as ship's position, COG, SOG, gyro heading, rate of turn, CPA, TCPA, ship s name and MMSI etc. were displayed automatically on the chart of a PC-based ECDIS with radar overlay and ARPA tracking. The ARPA tracking information obtained from the observed radar images of the target ship was compared with the AIS information received from the same vessel to investigate the difference in the position and movement behavior between AIS and ARPA tracked target ships. For the ARPA radar and AIS targets to be consolidated, the differences in range, speed, course, bearing and distance between their targets were estimated to obtain a clear standards for the consolidation of ARPA radar and AIS targets. The average differences between their ranges, their speeds and their courses were 2.06% of the average range, -0.11 knots with the averaged SOG of 11.62 knots, and $0.02^{\circ}$ with the averaged COG of $37.2^{\circ}$, respectively. The average differences between their bearings and between their positions were $-1.29^{\circ}$ and 68.8m, respectively. From these results, we concluded that if the ROT, COG, SOG, and HDG informations are correct, the AIS system can be improved the prediction of a target ship's path and the OOW(Officer of Watch) s ability to anticipate a traffic situation more accurately.

• Journal of the Korean Society for Marine Environment & Energy
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• v.2 no.2
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• pp.78-85
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• 1999

Tracking model of a drifted ship lot the search and rescue mission in southern sea of Korea is studied. In this model, search area is determined by considering standard deviation of position around reference point. The reference point is estimated for a given type of ship when marine environmental conditions such as wind and current are given. A database for environmental data, which is necessary for the real-lime tracking of drilled ship, is gel)elated on southern sea and western sea of Korea. Using this database, the real-time prediction of wind and current is possible. The simulated trajectories and search area of our model ate validated by comparing with reported real data.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.53-54
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• 2015

The maritime industry is expanding at an alarming rate and as such there is a perpetual need to improve situation awareness in the maritime environment using new and emerging technology. Tracking is one of the numerous ways of enhancing situation awareness by providing information that may be useful to the operator. The tracking system described herein comprises determining existing states of own ship, state prediction and state compensation caused by random noise. The purpose of this paper is to analyze the process of tracking and develop a tracking algorithm by using ${\alpha}-{\beta}-{\gamma}$ tracking filter under a random noise or irregular motion for use in a warship. The algorithm involves initializing the input parameters of position, velocity and course. The actual positions are then computed for each time interval. In addition, a weighted difference of the observed and predicted position at the nth observation is added to the predicted position to obtain the smoothed position. This estimation is subsequently employed to determine the predicted position at (n+1). The smoothed values, predicted values and the observed values are used to compute the twice distance root mean square (2drms) error as a measure of accuracy of the tracking module.

• Journal of the Korean Society for Marine Environment & Energy
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• v.8 no.1
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• pp.17-22
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• 2005

The movement of drifting ships on the sea is closely related to marine environmental forces such as waves, currents, winds, etc. To develop a prediction model for trajectories oi drifting ships, an experiment on the movement of drifting ships was carried out in the Southeastern Sea of Korea. Five types of ships including a lire raft and tour ships with G/T 10tons, G/T 2o tons, G/T 50 tons, and G/T 80 tons, were considered in the experiment. The G/T 50 ton class ship was used as a base ship for obtaining the currents, winds and heading angles of ship following the trajectory. The trajectory of each ship was measured by DGPS(Differential Global Positioning System) and collected using APRS(Automatic Position Reporting System) installed on the base ship. The error range in position fix of DGPS are approximately ±1 m. The drift speed of ship in the experiment was between 3％ to 5％ of wind speed and drift direction of ship was deflected by ±90° from wind direction. Also, the heading of drifting ship was normal to wind direction.

• Journal of Ocean Engineering and Technology
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• v.37 no.1
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• pp.38-48
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• 2023

To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems.