• Journal of Navigation and Port Research
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• v.44 no.2
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• pp.136-143
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• 2020

Maritime autonomous surface ships (MASS) has been developed over the years. But, there are many unresolved problems. To overcome these problems, this study proposes connected ship navigation system. The system comprises a slave ship and a master ship that leads the slave ship. To implement this system, communication network, route planning algorithms, and controllers are designed. The communication network is built using the transmission control protocol/Internet protocol (TCP/IP) socket communication method to exchange data between ships. The route planning algorithms calculate the course and distance of the slave ship using the middle latitude sailing method. Nomoto model is used as the mathematical model of the slave ship maneuvering motion. Then, the autoregressive with exogenous variables (ARX) model is used to estimate the parameters of Nomoto model. Based on the above model, the automatic steering controller is designed using a proportional-derivative (PD) control. Also, the speed controller is designed for the slave ship to maintain constant distance from the master ship. Sea experiments are conducted to verify the proposed system with two remodeled boats.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.671-672
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• 2008

Hybrid intelligent technique 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 Matlab.

• Journal of Navigation and Port Research
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• v.31 no.4
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• pp.289-293
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• 2007

In this paper, new application of adaptive neural network to design a ship's Rudder-Roll Damping(RRD) control system is presented Firstly, the ANNAI neural network controller is presented. Secondly, new RRD control system using this neural network approach is developed. It uses two neural network controllers for heading control and roll damping control separately. Finally, Computer simulation of this RRD control system is carried out to compare with a linear quadratic optimal RRD control system; discussions and conclusions are provided. The simulation results show the feasibility of using ANNAI controller for RRD. Also, the necessity of mathematical ship model in designing RRD control system is removed by using NN control technique.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.5 s.149
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• pp.619-629
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• 2006

The safety assessment of the structure related to people has become the most important part in the process of the design. Especially, in the case of a ship, as regulations about the safety of passengers and the pollution in the ocean are strictly reinforcing, the safety assessment has become the most important part in the process of the design. In this paper, we suggested network-based simulation system framework using HLA (High Level Architecture) among many kind of simulations to assess the safety of the ship. In addition, the visualization method which is composed of graphic user interface for ship modeling and the visualization of ship motion is presented.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.3
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• pp.329-336
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• 2008

Basic performances of wireless sensor network (WSN) and power line communication (PLC) confirmed in the test bed ashore are investigated in the 3000-ton class training ship of Korea Maritime University. The ubiquitous technologies can be considered for the provision of safety and convenience in a ship. We employed WSN and PLC, as the ubiquitous technologies, at the two areas within the training ship to estimate the realization of the ubiquitous environments in a ship. The experiments show rather good results in terms of data transfer rate. However, more detailed studies concerning the connection between WSN and PLC, noises induced to power line and fading effects are required to improve the quality and the stability of the communication for the ubiquitous environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.437-444
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• 2014

The unmanned ship control system controls the unmanned ship at a distance in ocean. Expecially, in order to control the unmanned ship, it needs the wireless communication networks and we use the IEEE 802.11 based WLAN. The IEEE 802.11 based WLAN technology for supporting a wide bandwidth is suitable for unmanned ship control system which has to transmit the multimedia data. First, we design the system structure for controlling the unmanned ship. Then, in order to overcome the limited communication area, we design the network structure for the unmanned ship communication network which can use a various communication network. we implemented and evaluated the unmanned ship system based on WLAN. We controlled the unmanned ship by use the WLAN and confirmed the signal feature of WLAN in the ocean.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of Navigation and Port Research
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• v.47 no.2
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• pp.75-84
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• 2023

Ships need to ensure safety during their navigation, which makes route determination highly important. It must be accompanied by a route following controller that can accurately follow the route. This study proposes a method for automatically generating the ship route based on deep reinforcement learning algorithm and following it using a route following controller. To generate a ship route, under keel clearance was applied to secure the ship's safety and navigation chart information was used to apply ship navigation related regulations. For the experiment, a target ship with a draft of 8.23 m was designated. The target route in this study was to depart from Busan port and arrive at the pilot boarding place of the Ulsan port. As a route following controller, a velocity type fuzzy P ID controller that could compensate for the limitation of a linear controller was applied. As a result of using the deep Q network, a route with a total distance of 62.22 km and 81 waypoints was generated. To simplify the route, the Douglas-Peucker algorithm was introduced to reduce the total distance to 55.67 m and the number of way points to 3. After that, an experiment was conducted to follow the path generated by the target ship. Experiment results revealed that the velocity type fuzzy P ID controller had less overshoot and fast settling time. In addition, it had the advantage of reducing the energy loss of the ship because the change in rudder angle was smooth. This study can be used as a basic study of route automatic generation. It suggests a method of combining ship route generation with the route following control.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.175-182
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• 2022

The new commercial maritime law in the Kingdom came comprehensive and detailed for all topics related to commercial maritime navigation, thus responding to most of the problems that arise in the field, specifically regarding the ship as the focus of the rules of maritime law. This system defines the ship in law, regulates its civil status, determines how to name it, determine its domicile, and the conditions for acquiring Saudi nationality. It also contained a regulation of the rights granted to ships by ownership, as well as their lease and mortgage, the mechanism of attachment to them to settle debts and the rights in kind dependent on them and controlling the rights of third parties on ships and the procedures for forcibly selling them from precautionary seizure and executive seizure and then forced sale in public auction. Until this research was an effort to present a clear picture about the legal system of the ship in the new Saudi commercial maritime system and confirming the extent of the success of the Saudi legislator with the ship system in highlighting the legal frameworks for this facility prepared for maritime navigation.

• Journal of the Korea Society of Computer and Information
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• v.24 no.7
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• pp.125-133
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• 2019

Because military weapons systems for special purposes are small and complex, they are not easy to maintain. Therefore, it is very important to maintain combat strength through quick maintenance in the event of a breakdown. In particular, naval ships are complex weapon systems equipped with various equipment, so other equipment must be considered for maintenance in the event of equipment failure, so that skilled maintenance personnel have a great influence on rapid maintenance. Therefore, in this paper, we analyzed maintenance data of defense equipment maintenance information system through text mining and social network analysis(SNA), and tried to identify the naval ship maintenance expertise. The defense equipment maintenance information system is a system that manages military equipment efficiently. In this study, the data(2,538cases) of some naval ship maintenance teams were analyzed. In detail, we examined the contents of main maintenance and maintenance personnel through text mining(word cloud, word network). Next, social network analysis(collaboration analysis, centrality analysis) was used to confirm the collaboration relationship between maintenance personnel and maintenance expertise. Finally, we compare the results of text mining and social network analysis(SNA) to find out appropriate methods for finding and finding naval ship maintenance expertise.