• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.851-859
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• 2019

The COMS satellites take image of the Korean Peninsula every 15 minutes, but due to the limitations of the observational channels, they tend to underestimate when estimating rainfall. In this study, we developed satellite-based rainfall estimation technology using COMS and GPM that can be used in the heavy rain on the Korean Peninsula. The time resolution and spatial resolution of COMS satellites and GPM satellites were matched to improve accuracy using GPM IMERG data. As a result, it showed that the number of correlations with the ASOS observations was more than 0.7, enabling the estimation of rainfalls that are more accurate than the estimates of rainfall by COMS satellites. It is believed that the application of the subsequent satellite(GK-2A) will provide more accurate rainfall estimation information in the future. Therefore, we expect greater utilization in disaster management for the ungauged areas.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.4
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• pp.283-288
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• 2019

In this paper, there is summarized the validation of ground test results through the telemetry acquired during on-orbit initial activation on solar-array drive assembly(SDA) of GK2A launched at Dec-5, 2018. Especially, the decision logic of SDA initial position and the compensation logic are validated and confirmed. The SDA initial position is needed when GK2A enter to geostationary orbit from transfer orbit and the compensation logic is for the accumulated position error due to the open-loop control. Up to now, it is normal operating. Also the periodic offset between the geostationary orbit and Sun position is found that it is not checked on design phase, and then the proper threshold value is applied.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.777-788
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• 2021

Since aerosol has a relatively short duration and significant spatial variation, satellite observations become more important for the spatially and temporally continuous quantification of aerosol. However, optical remote sensing has the disadvantage that it cannot detect AOD (Aerosol Optical Depth) for the regions covered by clouds or the regions with extremely high concentrations. Such missing values can increase the data uncertainty in the analyses of the Earth's environment. This paper presents a spatial gap-filling framework using a univariate statistical method such as DCT-PLS (Discrete Cosine Transform-based Penalized Least Square Regression) and FMM (Fast Matching Method) inpainting. We conducted a feasibility test for the hourly AOD product from AHI (Advanced Himawari Imager) between January 1 and December 31, 2019, and compared the accuracy statistics of the two spatial gap-filling methods. When the null-pixel area is not very large (null-pixel ratio < 0.6), the validation statistics of DCT-PLS and FMM techniques showed high accuracy of CC=0.988 (MAE=0.020) and CC=0.980 (MAE=0.028), respectively. Together with the AI-based gap-filling method using extra explanatory variables, the DCT-PLS and FMM techniques can be tested for the low-resolution images from the AMI (Advanced Meteorological Imager) of GK2A (Geostationary Korea Multi-purpose Satellite 2A), GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI2 (Geostationary Ocean Color Imager) of GK2B (Geostationary Korea Multi-purpose Satellite 2B) and the high-resolution images from the CAS500 (Compact Advanced Satellite) series soon.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.247-251
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• 2012

Tree-based Group Diffie-Hellman (TGDH) is one of the efficient group key agreement protocols to generate the GK. TGDH assumes all members have an equal computing power. As one of the characteristics of distributed computing is heterogeneity, the member can be at a workstation, a laptop or even a mobile computer. Therefore, the group member sequence should be reordered in terms of the member's computing power to improve performance. This research proposes a reordering of members in the group key generation tree to enhance the efficiency of the group key generation.

• Microbiology and Biotechnology Letters
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• v.18 no.5
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• pp.471-475
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• 1990

A thermostable adenylate kinase isolated from the sonic extracts of Thermus caldophilus cells revealed higher substrate-specificity to the nucleoside monophosphate than to the nucleoside triphosphate. A $P', P^5$-di(adenosine-5') pentaphosphate was acted as a competitive inhibitor to the various substrates. Various divalent cations were activated the enzyme activity following orders: $Mg^{2+}, Ca^{2+}, Mn^{2+}, Ba^[2+}, $ and $Fe^{2+}$-. The enzyme activity was not affected by the sulfurhydryl reagent, p-chloromeric uribenzoic acid and activated by addition of the sodium chloride or phosphoenol pyruvate to the reaction mixture.

• Journal of the Korean Applied Science and Technology
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• v.30 no.3
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• pp.394-399
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• 2013

This study was performed to investigate the antidiabetic study effect in ethanol extract of Ailanthicortex(A.C) radici in Streptozotocin(STZ)-induced diabetic rats. Diabetes was induced by intravenous injection of STZ at a dose of 45mg/kg dissolved in citrate buffer. The ethanol extract of A.C radici was orally administrated once a day for 7 days at a dose of 500mg/kg. The content of serum glucose, triglyceride(T.G), total cholesterol were significantly decreased in treated group compared to the those of STZ-control group. The content of hepatic glycogen and activity of glucokinase(GK) were significantly increased in treated group compared to the those of STZ-control group. but activity of glucose-6-phosphate dehydrogenase(G-6-PDH) was significantly increased. These results indicated that ethanol extract of A.C would have antidiabetic effect in STZ-induced diabetic rats.

• The Korea Genome Conference
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• 2002.08a
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• pp.49.1-49.1
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• 2002

• Journal of the Korean Applied Science and Technology
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• v.27 no.4
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• pp.494-500
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• 2010

This study was carried to investigate the antidiabetic and lipid metabolism of water extract paecilomyces japonica(PJ) in Streptozotocin (STZ) induced diabetic rats. Diabetes were induced by intravenous injection of STZ at a dose of 42mg/kg dissolved in citrate buffer. The water extract of paecilomyces japonica were orally administrated once a day for 7 days at a dose of 500mg/kg or 1,000mg/kg. The contents of serum glucose, triglyceride(TG), total cholesterol were significantly decreased in PJ treated group compared to the those of STZ-control group. The content of hepatic glycogen and activities of glucose-6-phosphate dehydrogenase(G-6-PDH), glucokinase(GK) were significantly increased, but activity of glucose-6-phoshatase(G-6-Pase) was significantly decreased in PJ treated group compared to the those of STZ-control group. These results indicated that water extract of paecilomyces japonica would have antidiabetic and lipid metabolism effect in STZ-induced diabetic rats.