• Journal of Ocean Engineering and Technology
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• v.38 no.3
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• pp.103-114
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• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.654-659
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• 2018

In this study, continuous long-term observation is implemented with an ocean radar. Ocean radar conducts remote observation (combined) with ground-based radars, which enable a series of simultaneous observations of an extensive range of the coast with high frequency. An ocean radar for continuous long-term observation is operated at Samcheok on the east coast of Korea. Samcheok experienced tsunami damage in recent years and is the location of a nuclear power plant. In order to examine the reliability of the ocean radar, a pressure-type wave gauge, ultrasonic wave gauge, and ocean buoy are installed for the purpose of data comparison and verification. The ocean radar used in this study is an array-type HF-RADAR named WERA (WavE RAdar). The analysis of the data obtained from continuous long-term observations showed that the radar observations were in agreement with more than 90% of the wave data collected within a 25 km range from the center of two sites. Less than 1% of the entire observation data was unmeasured by the time series analysis. As a result of comparing the radar data with the direct observations made by the wave gauge, it was inferred that the RMS deviation is less than 20cm and the correlation coefficient was in the range of 0.84 ~ 0.87. Moreover, supported by such observations, a comprehensive monitoring system is being developed to provide the public with real-time reports on waves and currents via the internet.

• Journal of Ocean Engineering and Technology
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• v.36 no.6
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• pp.380-389
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• 2022

In the Haeundae area of Busan, South Korea, damage has continued to occur recently from building wind from caused by dense skyscrapers. Five wind observation stations were installed near LCT residential towers in Haeundae to analyze the effect of building winds during typhoon Omais. The impact of building wind was analyzed through relative and absolute evaluations. At an intersection located southeast of LCT (L-2), the strongest wind speed was measured during the monitoring. The maximum average wind speed for one minute was observed to be 38.93 m/s, which is about three times stronger than at an ocean observation buoy (12.7 m/s) at the same time. It is expected that 3 to 4 times stronger wind can be induced under certain conditions compared to the surrounding areas due to the building wind effect. In a Beaufort wind scale analysis, the wind speed at an ocean observatory was mostly distributed at Beaufort number 4, and the maximum was 8. At L-2, more than 50% of the wind speed exceeded Beaufort number 4, and numbers up to 12 were observed. However, since actual measurement has a limitation in analyzing the entire range, cross-validation with computational fluid dynamics simulation data is required to understand the characteristics of building winds.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.5
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• pp.296-306
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• 2020

It has been reported that the wave heights measured by Wave and Tide gauges (WTG) have been underestimated, and thus it is important to improve its measuring accuracy for enhancing estimation of harbor tranquility. In this study, the significant wave heights from WTG were calibrated using measured data from AWAC and Waverider buoys moored at the same four locations with the WTG. It was observed that the product of significant wave height and peak wave period, HT, was not underestimated but linearly proportional between the measurements by two instruments. This linearity was applied to develop 3^rd order polynomial functions that best represented the relationship between HT and significant wave heights measured by WTG. These functions were then applied to calibrate the WTG significant wave heights that were lower than 0.7 m, the critical value established for the low waves in this study. The results showed that the linearity between the AWAC (or Waverider buoy) and calibrated wave heights were improved, and the magnitude of underestimated WTG wave heights were increased to be more realistic. The results of this study are expected to be effectively applied for other data sets obtained by WTG only, to increase the observation accuracy of WTG and to improve the estimation of harbor tranquility.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1261-1272
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• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.275-285
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• 2017

National Meteorological Satellite Center(NMSC) has produced Sea Surface Temperature (SST) using Communication, Ocean, and Meteorological Satellite(COMS) data since April 2011. In this study, we have developed a new regional COMS SST algorithm optimized within the North-West Pacific Ocean area based on the Multi-Channel SST(MCSST) method and made a composite SST using polar orbit satellites as well as the COMS data. In order to retrieve the optimized SST at Northwest Pacific, we carried out a colocation process of COMS and in-situ buoy data to make coefficients of the MCSST algorithm through the new cloud masking including contaminant pixels and quality control processes of buoy data. And then, we have estimated the composite SST through the optimal interpolation method developed by National Institute of Meteorological Science(NIMS). We used four satellites SST data including COMS, NOAA-18/19(National Oceanic and Atmospheric Administration-18/19), and GCOM-W1(Global Change Observation Mission-Water 1). As a result, the root mean square error ofthe composite SST for the period of July 2012 to June 2013 was $0.95^{\circ}C$ in comparison with in-situ buoy data.

• Journal of Ship and Ocean Technology
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• v.10 no.4
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• pp.34-48
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• 2006

The visual observation of wave condition depends on the observer's skill and experience. Also, the environmental conditions such as light and cloud heavily influence the visual measurement. In the speed test of sea trial, the wave measurement should be objective and accurate. In this paper, the problems of visual measurement and their effects on speed test are described. To overcome those problems, we developed the wave measurement system using commercial marine X-band radar, WaveFinder. The system installed at inland base was calibrated by waverider buoy and then the system's operability was defined. Onboard tests had also been performed three times for formal wave measurement to correct the ship speed. The results illustrated very good agreement with visual observation by experts. It can be concluded that the system would be useful to measure wave and swell information for the sea trial, irrespective of day and night.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1377-1386
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• 2017

We propose a smart ocean meteorological observation system which is capable of real-time measurement of vulnerable marine climate and oceanographic conditions. Besides, imported products have several disadvantages such that they can't be measured for a long time and can't transmit data in real time. In the proposed system, smart ocean observation digging system, it observes real-time ocean weather with data logger methods. Furthermore, we also use existing dataloggers functions with various sensors which are available in the ocean at the same time. Also, we applied the Kalman-filter algorithm to the ocean crest measurement to reduce the noise and increase the accuracy of the real-time wave height measurement. In the experiment, we experimented the proposed system with our proposed algorithms through calibration devices in the real ocean environment. Then we compared the proposed system with and without the algorithms. As a result, the system developed with a lithium iron phosphate battery that can be charged by a system used in the ocean and minimized power consumption by using an RTC based timer for optimal use. Besides, we obtained optimal battery usage and measured values through experiments based on the measurement cycle.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.4
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• pp.588-594
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• 2009

The maximum power point operation point(MPPOP) of photovoltaic(PV) power generation systems changes with varying atmospheric conditions such as temperature, solar radiation. For achieving a high efficiency in PV system, it is very important for PV system to track the MPPOP correctly according to operation condition. Although the MPPT(maximum power point tracking) algorithm which applied P&O(Perturbation & Observation) or IncCnd(Incremental Conductance) algorithm tracks the MPPOP efficiently, its efficiency drops noticeably in case that the incidence angle of PV panel on buoy changes rapidly. To solve this problem, this paper proposes maximum power point searching and tracking algorithm(MPPST). The proposed algorithm set the specific area and measures the PV voltage at the same interval. The proposed algorithm have been obtained high efficiency than P&O algorithm through ocean experiment.