• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.314-324
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• 2020

Due to the nonlinearity and environmental uncertainties, the design of the ship's steering controller is a long-term challenge. The purpose of this study is to design an intelligent autopilot based on Extended Kalman Filter (EKF) trained Radial Basis Function Neural Network (RBFNN) control algorithm. The newly developed free running model scaled surface vessel was employed to execute the motion control experiments. After describing the design of the EKF trained RBFNN autopilot, the performances of the proposed control system were investigated by conducting experiments using the physical model on lake and simulations using the corresponding mathematical model. The results demonstrate that the developed control system is feasible to be used for the ship's motion control in the presences of environmental disturbances. Moreover, in comparison with the Back-Propagation (BP) neural networks and Proportional-Derivative (PD) based control methods, the EKF RBFNN based control method shows better performance regarding course keeping and trajectory tracking.

• Journal of Ocean Engineering and Technology
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• v.32 no.2
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• pp.138-142
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• 2018

The marine industry is rapidly developing as a result of the increase in various needs in the marine environment. In addition, accidents involving ship fires and explosions and the resulting casualties are increasing. Generally, manpower and safety problems exist in fire fighting. A fire fighter in the form of an autonomous surface robot would be ideal for marine fire safety, because it has no manpower and safety problems. Therefore, an autonomous surface robot with the abilities of fire recognition and tracking, nozzle selection, position and attitude control, and fire fighting was developed and is discussed in this paper. The test and evaluation results of this robot showed the possibility of real-size applications and the need for additional studies.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.257-269
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• 2023

This paper presents a method for detecting faults in data obtained from the Automatic Identification System (AIS) of surface vessels. The data include latitude, longitude, Speed Over Ground (SOG), and Course Over Ground (COG). We derive two methods that utilize two models: a constant state model and a derivative augmented model. The constant state model incorporates noise variables to account for state changes, while the derivative augmented model employs explicit variables such as first or second derivatives, to model dynamic changes in state. Generally, the derivative augmented model detects faults more promptly than the constant state model, although it is vulnerable to potentially overlooking faults. The effectiveness of this method is validated using AIS data collected at a harbor. The results demonstrate that the proposed approach can automatically detect faults in AIS data, thus offering partial assistance for enhancing navigation safety.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1463-1478
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• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.5
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• pp.532-541
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• 2014

The ship-borne mobile down range telemetry antenna system having 4.6 m reflector antenna and 3-axis mounting structure at S-band requires the precise pointing accuracy at sea for the launch mission. Using the LEO satellites tracking, a method to determine and verify the antenna pointing and tracking performance, and to find the pointing bias which dominantly contributes to the pointing inaccuracy, is presented. Based upon the tests conducted on the Jeju sea and Pacific sea, the pointing bias is determined and its origin is also identified as the drift of the heading angle of the gyrocompass. The applied systematic correction taking into account the pointing bias, and the achieved improvement of the pointing accuracy are shown. Thanks to the correction, it is presented that this antenna tracked the launcher(KSLV-I) stably within the required pointing accuracy in the following KSLV-I third launch.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.343-347
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• 2020

This paper presents the development of a software for tracking the location of objects in the water and for creating a three-dimensional hull map. The objective of this software, as a software for underwater hull cleaning robot, is to map the location of underwater hull cleaning robot and to locate the position of sensor by identifying the shaded area of acoustic communication. For the software designed for mapping the location of cleaning robot in the water, the height and intensity were applied as variables for underwater ultrasonic communication. The software for creating a three-dimensional hull was developed by OpenGL using scanned lines from a blueprint of a ship. This software can help identifying the location of underwater hull cleaning robot without actual visibility and can be used to maintain a stable communication status by locating the position of sensor by easily spotting the shaded area of acoustic communication caused by the curved area of the bottom of the ship.

• Journal of Navigation and Port Research
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• v.33 no.1
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• pp.35-41
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• 2009

A waypoint tracking algorithm(WTA) is designed for Unmanned Surface Vehicle(USV) in which water-jet system is installed for propulsion To control the heading of USV for waypoint tracking, the steering nozzle of water-jet need, to be controlled. Firstly, target heading is calculated by using the position information of waypoints input from the land control center. Secondly, the command for the steering nozzle of water-jet is calculated in real time by using the heading and the rate-of-turn( ROT) from magnetic compass, In this study, in order to consider the drift angle due to external disturbance such as wind and wave, the course of ground( COG) can be used instead of heading at higher speed than a certain value, To test the performance of newly-designed WTA, the tests were carried out in actual sea area near Gwang-an bridge of Busan. In this paper, the sea trial test results from WTA are analyzed and compared with those from manual control and those from commercial controller.

• Proceedings of KOSOMES biannual meeting
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• 2006.11a
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• pp.277-282
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• 2006

The purpose in having a control surface on a ship is to control the motion of the ship. The control surface may be composed entirely of a single movable surface or of a combination of fixed and movable portion A control surface has one sole function to perform in meeting its purpose, and that is to develop a control force in consequence of its orientation and movement relative to the water. The forces and moments generated as a result of this rotation and angle of attack then determine the maneuvering characteristics of the ship. In this paper the study of flapped rudder's 2-dimensional section was accomplished. Model tests had been carried out with different angles of attack of a main foil and flap's deflection angles to predict the performance of the flapped rudder and the 2 frame particle tracking method had been used to obtain the velocity distribution in the flow field $Re=2.8\times10^4$ had been used during the whole experiments and measured results had been compared with each other.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.278-287
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• 2013

Modern information technologies continue to provide industries with new and improved methods. With the rapid development of Machine to Machine (M2M) communication, a smart container supply chain management is formed based on high performance sensors, computer vision, Global Positioning System (GPS) satellites, and Globle System for Mobile (GSM) communication. Existing supply chain management has limitation to real time container tracking. This paper focuses on the studies and implementation of real time container chain management with the development of the container identification system and automatic alert system for interrupts and for normal periodical alerts. The concept and methods of smart container modeling are introduced together with the structure explained prior to the implementation of smart container tracking alert system. Firstly, the paper introduces the container code identification and recognition algorithm implemented in visual studio 2010 with Opencv (computer vision library) and Tesseract (OCR engine) for real time operation. Secondly it discusses the current automatic alert system provided for real time container tracking and the limitations of those systems. Finally the paper summarizes the challenges and the possibilities for the future work for real time container tracking solutions with the ubiquitous mobile and satellite network together with the high performance sensors and computer vision. All of those components combine to provide an excellent delivery of supply chain management with outstanding operation and security.