• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.164-170
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• 2024

The light buoy, a floating structure at sea, is prone to drifting due to external factors such as oceanic weather. This makes it imperative to monitor for any loss or displacement of buoys. In order to address this issue, the Ministry of Oceans and Fisheries aims to issue alerts for buoy displacement by analyzing historical buoy position data to detect patterns. However, periodic lifting inspections, which are conducted every two years, disrupt the buoy's location pattern. As a result, new patterns need to be analyzed after each inspection for location monitoring. In this study, buoy position data from various periods were analyzed using convex hull and distance-based clustering algorithms. In addition, the optimal data collection period was identified in order to accurately recognize buoy location patterns. The findings suggest that a nine-week data collection period established stable location patterns, explaining approximately 89.8% of the variance in location data. These results can improve the management of light buoys based on location patterns and aid in the effective monitoring and early detection of buoy displacement.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.7
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• pp.1003-1010
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• 2013

In this paper, TCP/IP based Machine-to-Machine (M2M) communication uses CDMA/GSM network for data communication. This communication method is widely used by offshore moored buoy for data transmission back to the system server. Due to weather and signal coverage, the TCP/IP M2M communication often experiences transmission failure and causing data losses in the server. Data losses are undesired especially for meteorological and oceanographic analysis. This paper discusses a software approach to minimize M2M data losses by handling transmission failure and re-attempt which meant to transmit the data for recovery. This implementation was tested for its performance on a meteorological buoy placed offshore.

• Journal of Ocean Engineering and Technology
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• v.12 no.2 s.28
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• pp.139-146
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• 1998

This paper introduces a network system for monitoring coastal oceanographic data. The network system consists of three parts such as the buoy to observe oceanographic data, the local site to collect data transferred from buoys, and the host site to construct the oceanographic data-base and to share the information for monitoring coastal oceanographic data. The buoy has a one-board microcomputer to manage and to acquire coastal environment data in real-time. A wireless and wire communication technique is employed in order to transfer data measured by buoys and to link local and host sites, respectively. In measuring coastal environment data, this system shows more cost-effective way than the presents conventional. In addition, the realtime monitoring system continuously from various sites with the network systems.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.313-320
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• 2022

The light buoy installed on the sea is always flexible, because it is affected by the weather as well as passing vessels. The position of the light buoy can be cached through the AtoN AIS (Automatic Identification System) and RTU (Remote Terminal Unit). This study analyzed the position data of the light buoys for the last five years (2017-2021), as well as the distribution of the light buoys within the maximum separated position. As a result, there was a basic error of 17.9% in the position data. Additionally, the separated position error of 197 light buoys to be analyzed was 70.64%, and the AtoN RTU was worse than the AtoN AIS by equipment. On the other hand, as a result of the plotting the position data of the light buoy, it was classified into four types. The most common percussion center type, the percussion center dichotomous type in which the position is divided into two zones based on the chimney, the central movement type with a fluctuating center, and the drag type, in which the position is deviated from the center for a certain period. Except for Type-1, the type was determined according to the position at which the light buoy was installed. This study is the first to analyze the position data of the light buoy, and it is expected that it will contribute to the improvement of the quality of the position data of the light buoy.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.47-53
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• 2002

Ocean surface wind speed was estimated using TRMM (Tropical Rainfall Measurement Mission) TMI (TRMM Microwave/Imager) data. It is used the TRMM TMI brightness temperature and National Data Buoy Center's buoy winds speed dataset near North-America to estimate by the algorithm of the ocean surface wind speed retrieval over North America. Comparing with the buoy data by D-matrix equation, the result that RMSE, BIAS, and correlation coefficient are 2.19 $ms^{-1}$, 1.10 $ms^{-1}$, and 0.81, respectively. Therefore the estimated oceanic surface wind speed by TRMM TMI brightness temperature data show that available to ocean research over upper ocean.

• Ocean and Polar Research
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• v.33 no.4
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• pp.507-516
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• 2011

Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) Array is the series of buoys for the international ocean research project, which is mostly supported by National Ocean and Atmosphere Administration (NOAA) and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We can determine the effect of the equatorial and Pacific Ocean conditions on global climate change from buoy array measurement data. The TAO/TRITON array comprises around 70 measurement buoys from $10^{\circ}$ north to $10^{\circ}$ south in the tropics and between Galpagos and New Guinea. NOAA maintains ATLAS buoys in the central and eastern Pacific between $165^{\circ}E$ and $95^{\circ}W$, and JAMSTEC maintains the 12 buoys in the western Pacific along $137^{\circ}E$, $147^{\circ}E$, and $156^{\circ}E$. The KA-10-03 cruise excursion provided us with a good opportunity to obtain knowledge on oceanic buoy operation and maintenance. Further, we learned advanced techniques and know-how on buoy operation and maintenance. Once we are confident with our buoy management and maintenance techniques, both KORDI and NOAA technicians may be able to help each other when needed and share available resources.

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.4
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• pp.614-624
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• 2013

The resonance power buoy is a convincing tool that can increase the extraction efficiency of wave energy. The buoy needs a corresponding draft, to move in resonance with waves within the peak frequency band where wave energy is concentrated. However, it must still be clarified if the buoy acts as an effective displacement amplifier, when there is insufficient water depth. In this study, the vertical displacement of a circular cylinder-type buoy was calculated, with the spectrum data observed in a real shallow sea as the external wave force, and with the corresponding draft, according to the mode frequency of normal waves. Such numerical investigation result, without considering Power Take-Off (PTO) damping, confirmed that the area of the heave responses spectrum can be amplified by up to about tenfold, compared with the wave energy spectrum, if the draft corresponds to the peak frequency, even with insufficient water depth. Moreover, the amplification factor of the buoy varied, according to the seasonal changes in the wave spectra.

• Korean Journal of Computational Design and Engineering
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• v.16 no.3
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• pp.227-235
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• 2011

Ship handling simulator should have capabilities of calculating ship motions (heave, pitch, and roll) at given sea state and displaying the calculated motions through a real-time 3D visualization system. Motion solver of a ship handling simulator generally calculates those motions in addition to position for an own ship, a main simulation target, but provides only position information for traffic ships. Therefore, it is required to simulate real-time traffic ship and buoy motions coupled with ocean waves in a ship handling simulator for the realistic visualization. In the paper, the authors propose a simple dynamics model by which ship and buoy motions are calculated with the input data of wave height and discuss a method for the implementation of a ship and buoy motions calculation module.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.109-116
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• 2017

In this paper, we propose an Internet of Things (IoT) platform for ocean observation buoys. The proposed system consists of various sensor modules, a gateway, and a remote monitoring site. In order to integrate sensor modules with various communications interfaces, we propose a controller area network (CAN)-based sensor data packet and a protocol for the gateway. The proposed scheme supports the registration and management of sensor modules so as to make it easier for the buoy system to manage various sensor modules. Also, in order to extend communication coverage between ocean observation buoys and the monitoring site, we implement a multi-hop relay network based on a mesh network that can provide greater communication coverage than conventional buoy systems. In addition, we verify the operation of the implemented multi-hop relay network by measuring the received signal strength indication between buoy nodes and by observing the collected data from the deployed buoy systems via our monitoring site.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.221-226
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• 1999

A data assimilation system for a 1-dimensional mixed layer model has been constructed using the adjoint method. The classical adjoint method does not work well for the mixed layer variabilities due to the occurrence of spikes in the gradient of the cost function. To solve this problem, the two techniques of scaling the cost function and optimization in the frequency space are used. As a result, the heat flux can be reliably estimated with an accuracy of 8Wm$^{-2}$ rms error in the identical twin experiments. We then applied this system to the tropical Pacific TOGA-TAO buoy data. The air-sea heat flux as well as the mixed layer variability were estimated in close approximation to the buoy data, particularly on time scales longer than the seasonal one.