• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.643-648
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• 2023

The Korea Augmentation Satellite System is an SBAS system being developed with the goal of providing SoL (Safety of Life) in accordance with ICAO (International Civil Aviation Organization) standards by December 2023. Monitoring the status of the system is essential for the continuous provision of KASS services, and a status monitoring tool should be developed for this purpose. The development of a status monitoring tool was divided into SYSRT (SYStem Real Time monitoring tool), SMSPP (Subsystem Monitoring Statistics tool for Post Processing) depending on the purpose. Tool development was completed through a series of procedures: requirements definition, design, development, and verification. To verify the status monitoring tool, the KASS system's real data (August 2023) were used to verify it, and the results were statistically analyzed to derive operating time and operating rate. It plans to use these tools to support continuous service provision for SoL service starting after this year.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.841-848
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• 2023

The Klobuchar ionospheric model included in global positioning system (GPS) navigation messages provides ionospheric correction information to single-frequency users. This ionospheric model accuracy has a significant impact on the accuracy of navigation solutions. We examine the GPS navigation messages from 1993 to 2022 and analyze their accuracy, presence of coefficients and accuracy of the Klobuchar model. Early GPS navigation messages often did not include ionospheric data, and even when they did include ionospheric models, the accuracy was often quite low. From 2002, when the accuracy of the ionospheric model was stabilized, until 2022, the accuracy of the ionospheric model is analyzed by comparing it with the ionospheric model of the International GNSS Service (IGS). Changes in accuracy per day and per year and accuracy differences along geomagnetic latitude are analyzed.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Analytical Science and Technology
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• v.29 no.4
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• pp.170-178
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• 2016

Accuracy and precision of ID methods for different spike isotopes of ⁷⁶Se, ⁷⁷Se, and ⁷⁸Se were compared for the analysis of Selenium using quadrupole ICP/MS equipped with Octopole reaction cell. From the analysis of Se inorganic standard solution, all of three spikes showed less than 1 % error and 1 % RSD for both short-term (a day) and long-term (several months) periods. They showed similar results with each other and ⁷⁸Se showed was a bit better than ⁷⁶Se and ⁷⁷Se. However, different spikes showed different results when NIST SRM 1568a and SRM 2967 were analyzed because of the several interferences on the m/z measured and calculated. Interferences due to the generation of SeH from ORC was considered as well as As and Br in matrix. The results showed similar accuracy and precisions against SRM 1568a, which has a simple background matrix, for all three spikes and the recovery rate was about 80% with steadiness. The %RSD was a bit higher than inorganic standard (1.8 %, 8.6 %, and 6.3 % for ⁷⁸Se, ⁷⁶Se and ⁷⁷Se, respectively) but low enough to conclude that this experiment is reliable. However, mussel tissue has a complex matrix showed inaccurate results in case of ⁷⁸Se isotope spike (over 100 % RSD). ⁷⁶Se and ⁷⁷Se showd relatively good results of around 98.6 % and 104.2 % recovery rate. The errors were less than 5 % but the precision was a bit higher value of 15 % RSD. This clearly shows that Br interferences are so large that a simple mathematical calibration is not enough for a complex-matrixed sample. In conclusion, all three spikes show similar results when matrix is simple. However, ⁷⁸Se should be avoided when large amount of Br exists in matrix. Either ⁷⁶Se or ⁷⁷Se would provide accurate results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.135-145
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• 2019

Analysis of a long cycle or a trend of time series data based on a long-term observation would require comparability between data observed in the past and the present. In the present study, we proposed an approach to ensure the compatibility among the instruments used for the long-term observation, which would allow to secure continuity of the data. An open-path gas analyzer (Model LI-7500, LI-COR, Inc., USA) has been used for eddy covariance flux measurement in the Gwangneung deciduous forest for more than 10 years. The open-path gas analyzer was replaced by an enclosed-path gas analyzer (Model EC155, Campbell Scientific, Inc., USA) in July 2015. Before completely replacing the gas analyzer, the carbon dioxide ($CO_2$) and latent heat fluxes were collected using both gas analyzers simultaneously during a five-month period from August to December in 2015. It was found that the $CO_2$ fluxes were not significantly different between the gas analyzers under the condition that the daily mean temperature was higher than $0^{\circ}C$. However, the $CO_2$ flux measured by the open-path gas analyzer was negatively biased (from positive sign, i.e., carbon source, to 0 or negative sign, i.e., carbon neutral or sink) due to the instrument surface heating under the condition that the daily mean temperature was lower than $0^{\circ}C$. Despite applying the frequency response correction associated with tube attenuation of water vapor, the latent heat flux measured by the enclosed-path gas analyzer was on average 9% smaller than that measured by the open-path gas analyzer, which resulted in >20% difference of the sums over the study period. These results indicated that application of the additional air density correction would be needed due to the instrument heat and analysis of the long-term observational flux data would be facilitated by understanding the underestimation tendency of latent heat flux measurements by an enclosed-path gas analyzer.

• Korean Journal of Construction Engineering and Management
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• v.15 no.5
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• pp.49-60
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• 2014

Electrical construction cost index has been applied fixed-weighted method. But fixed-weighted method can't faithfully reflect industrial structure changes. Because the weighted value and price index of fixed-weighted method is fixed on the basic period. Electrical construction cost index is composed of the cost of labor and material. So it fluctuates sharply whenever the construction association of korea announces the laborer's wage of electrical construction. And it depends on only the producer price index changes that is related to electrical construction since then. So a study is focused on developing electrical construction cost index applied chain-weighted method. Because chain-weighted method can reflect the realities of the electrical construction and alleviate the sudden changes of labor cost with link index. We verify that chain-weighted method relieves the step states of electrical construction cost index applied fixed-weighted method.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.6
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• pp.545-550
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• 2012

Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.493-505
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• 2020

The rainfall-runoff model has been generally adopted to obtain a consistent runoff sequence with the use of the long-term ground-gauged based precipitation data. The Thiessen polygon is a commonly applied approach for estimating the mean areal rainfall from the ground-gauged precipitation by assigning weight based on the relative areas delineated by a polygon. However, spatial bias is likely to increase due to a sparse network of the rain gauge. This study aims to generate continuous runoff sequences with the mean areal rainfall obtained from radar rainfall estimates through a PRMS rainfall-runoff model. Here, the systematic error of radar rainfall is corrected by applying the G/R Ratio. The results showed that the estimated runoff using the corrected radar rainfall estimates are largely similar and comparable to that of the Thiessen. More importantly, one can expect that the mean areal rainfall obtained from the radar rainfall estimates are more desirable than that of the ground in terms of representing rainfall patterns in space, which in turn leads to significant improvement in the estimation of runoff.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.443-459
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• 2013

Located on Java subduction zone, Merapi volcano is an active stratovolcano with a volcanic activity cycle of 1-5 years. Merapi's eruptions were relatively small with VEI 1-3. However, the most recent eruption occurred in 2010 was quite violent with VEI 4 and 386 people were killed. In this study, we have attempted to study the characteristics of Merapi's eruptions during 18 years using optical Landsat images. We have collected a total of 55 Landsat images acquired from July 6, 1994 to September 1, 2012 to identify pyroclastic flows and their temporal changes from false color images. To extract areal extents of pyroclastic flows, we have performed supervised classification after atmospheric correction by using COST model. As a result, the extracted dimensions of pyroclastic flows are nearly identical to the CVP monthly reports. We have converted the thermal band of Landsat TM and ETM+ to the surface temperature using NASA empirical formula and calculated time-series of the mean surface temperature in the area of peak temperature surrounding the crater. The mean surface temperature around the crater repeatedly showed the tendency to rapidly rise before eruptions and cool down after eruptions. Although Landsat satellite images had some limitations due to weather conditions, these images were useful tool to observe the precursor changes in surface temperature before eruptions and map the pyroclastic flow deposits after eruptions at Merapi volcano.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.40-49
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• 2013

Current service system of the Korea Meteorological Administration (KMA) for blooming date forecasting in spring depends on regression equations derived from long term observations in both temperature and phenology at a given station. This regression based system does not allow a timely correction or update of forecasts that are highly sensitive to fluctuating weather conditions. Furthermore, the system cannot afford plant responses to climate extremes which were not observed before. Most of all, this method may not be applicable to locations other than that which the regression equations were derived from. This note suggests a way to replace the location restricted regression equations with a thermal time based phenology model to complement the KMA blooming forecast system. Necessary parameters such as reference temperature, chilling requirement and heating requirement were derived from phenology data for forsythia, azaleas and Japanese cherry at 29 KMA stations for the 1951-1980 period to optimize spring phenology prediction model for each species. Best fit models for each species were used to predict blooming dates and the results were compared with the observed dates to produce a correction grid across the whole nation. The models were driven by the KMA's daily temperature data at a 5km grid spacing and subsequently adjusted by the correction grid to produce the blooming date maps. Validation with the 1971-2012 period data showed the RMSE of 2-3 days for Japanese cherry, showing a feasibility of operational service; whereas higher RMSE values were observed with forsythia and azaleas.