• Journal of Climate Change Research
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• v.6 no.4
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• pp.357-366
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• 2015

This study was conducted to estimate carbon stocks of Quercus serrata with drawing volume of trees in each tree height and DBH applying the suitable stem taper equation and tree specific carbon emission factors, using collected growth data from all over the country. Information on distribution area, tree number per hectare, tree volume and volume stocks were obtained from the $5^{th}$ National Forest Inventory (2006~2010), and method provided in IPCC GPG was applied to estimate carbon storage and removals. Performance in predicting stem diameter at a specific point along a stem in Quercus serrata by applying Kozak's model,$d=a_1DBH^{a_2}a_3^{DBH}X^{b_1Z^2+b_2ln(Z+0.001)+b_3{\sqrt{Z}}+b_4e^Z+b_5({\frac{DBH}{H}})}$, which is well known equation in stem taper estimation, was evaluated with validations statistics, Fitness Index, Bias and Standard Error of Bias. Consequently, Kozak's model turned out to be suitable in all validations statistics. Stem volume tables of Quercus serrata were derived by applying Kozak's model and carbon stock tables in each tree height and DBH were developed with country-specific carbon emission factors ($WD=0.65t/m^3$, BEF=1.55, R=0.43) of Quercus serrata. As a result of carbon stock analysis by age class in Quercus serrata, carbon stocks of IV age class (11,358 ha, 36.5%) and V age class (10,432; 33.5%) which take up the largest area in distribution of age class were 957,000 tC and 1,312,000 tC. Total carbon stocks of Quercus serrata were 3,191,000 tC which is 3% compared with total percentage of broad-leaved forest and carbon sequestration per hectare(ha) was 3.8 tC/ha/yr, $13.9tCO_2/ha/yr$, respectively.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1719-1729
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• 2021

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-mean-square-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.533-544
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• 2018

Rainfall is one of the most important meteorological variables in meteorology, agriculture, hydrology, natural disaster, construction, and architecture. Recently, satellite remote sensing is essential to the accurate detection, estimation, and prediction of rainfall. In this study, the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) product, a composite rainfall information based on Global Precipitation Measurement (GPM) satellite was evaluated with ground observation data in the inland of Korea. The Automatic Weather Station (AWS)-based rainfall measurement data were used for validation. The IMERG and AWS rainfall data were collocated and compared during one year from January 1, 2016 to December 31, 2016. The coastal regions and islands were also evaluated irrespective of the well-known uncertainty of satellite-based rainfall data. Consequently, the IMERG data showed a high correlation (0.95) and low error statistics of Bias (15.08 mm/mon) and RMSE (30.32 mm/mon) in comparison to AWS observations. In coastal regions and islands, the IMERG data have a high correlation more than 0.7 as well as inland regions, and the reliability of IMERG data was verified as rainfall data.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.415-424
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• 2017

This study was conducted to estimate carbon stocks of Pinus densiflora with drawing volume of trees in each tree height and DBH applying the suitable stem taper equation and tree specific carbon emission factors, using collected growth data from all over the country. Information on distribution area, tree age, tree number per hectare, tree volume and volume stocks were obtained from the $5^{th}$ National Forest Inventory (2006~2010) and Statistical yearbook of forest (2016), and method provided in IPCC GPG was applied to estimate carbon stock and uptake. Performance in predicting stem diameter at a specific point along a stem in Pinus densiflora by applying Kozak's model, $d=a_{1}DBH^{a_2}a_3^{DBH}X^{b_{1}Z^2+b_2ln(Z+0.001)+b_3\sqrt{Z}+b_4e^z+b_5(\frac{DBH}{H})}$, which is well known equation in stem taper estimation, was evaluated with validations statistics, Fitness Index, Bias and Standard Error of Bias. Consequently, Kozak's model turned out to be suitable in all validations statistics. Stem volume table of P. densiflora was derived by applying Kozak's model and carbon stock tables in each tree height and DBH were developed with country-specific carbon emission factors ($WD=0.445t/m^3$, BEF = 1.445, R = 0.255) of P. densiflora. As the results of analysis in carbon uptake for each province, the values were high with Gangwon-do $9.4tCO_2/ha/yr$, Gyeongsandnam-do and Gyeonggi-do $8.7tCO_2/ha/yr$, Chungcheongnam-do $7.9tCO_2/ha/yr$ and Gyeongsangbuk-do $7.8tCO_2/ha/yr$ in order, and Jeju-do was the lowest with $6.8tC/ha/yr$. Total carbon stocks of P. densiflora were 127,677 thousands tC which is 25.5% compared with total percentage of forest and carbon stock per hectare (ha) was $84.5tC/ha/yr$ and $7.8tCO_2/ha/yr$, respectively.

• 한국노년학
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• v.37 no.2
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• pp.431-444
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• 2017

This study identified effect size of cognition improvement programs developed for normal elderly in Korea. By searching four databases, 9,624 studies published from 2000 to 2016 were identified. Finally, 14 studies which satisfied the inclusion criteria were selected for meta-analysis. Quality assessment of included studies was conducted using controlled trial checklist of Scottish Intercollegiate Guidelines Network. R version 3.3.2 (2016-10-31) with "meta" and "metafor" package was used to calculate effect sizes, identify the reasons of heterogeneity, and check publication bias. Total effect size of cognition improvement program was middle (SMD=0.759, p<.001, $I^2=34%$). For heterogeneity, moderator analysis was executed and combined program with exercise had the largest effect size (SMD=1.231, p<.001, $I^2=40.8%$). Publication bias was exist by funnel plot and Egger 's regression test, but trim and fill analysis showed that there was no serious error to reverse study results. Therefore, Combined program with exercise for the normal elderly was expected to improve the cognitive function of the normal elderly.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.381-397
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• 2023

The most representative design used in clinical trials is randomization, which is used to accurately estimate the treatment effect. However, comparison between the treatment group and the control group in an observational study without randomization is biased due to various unadjusted differences, such as characteristics between patients. Propensity score weighting is a widely used method to address these problems and to minimize bias by adjusting those confounding and assess treatment effects. Inverse probability weighting, the most popular method, assigns weights that are proportional to the inverse of the conditional probability of receiving a specific treatment assignment, given observed covariates. However, this method is often suffered by extreme propensity scores, resulting in biased estimates and excessive variance. Several alternative methods including trimming, overlap weights, and matching weights have been proposed to mitigate these issues. In this paper, we conduct a simulation study to compare performance of various propensity score weighting methods under diverse situation, such as limited overlap, misspecified propensity score, and treatment contrary to prediction. From the simulation results overlap weights and matching weights consistently outperform inverse probability weighting and trimming in terms of bias, root mean squared error and coverage probability.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.543-551
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• 2023

Snow is an essential climate factor that affects the climate system and surface energy balance, and it also has a crucial role in water balance by providing solid water stored during the winter for spring runoff and groundwater recharge. In this study, statistical analysis of Local Data Assimilation and Prediction System (LDAPS), Modern.-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and ERA5-Land snow depth data were used to evaluate the applicability in South Korea. The statistical analysis between the Automated Synoptic Observing System (ASOS) ground observation data provided by the Korea Meteorological Administration (KMA) and the reanalysis data showed that LDAPS and ERA5-Land were highly correlated with a correlation coefficient of more than 0.69, but LDAPS showed a large error with an RMSE of 0.79 m. In the case of MERRA-2, the correlation coefficient was lower at 0.17 because the constant value was estimated continuously for some periods, which did not adequately simulate the increase and decrease trend between data. The statistical analysis of LDAPS and ASOS showed high and low performance in the nearby Gangwon Province, where the average snowfall is relatively high, and in the southern region, where the average snowfall is low, respectively. Finally, the error variance between the four independent snow depth data used in this study was calculated through triple collocation (TC), and a merged snow depth data was produced through weighting factors. The reanalyzed data showed the highest error variance in the order of LDAPS, MERRA-2, and ERA5-Land, and LDAPS was given a lower weighting factor due to its higher error variance. In addition, the spatial distribution of ERA5-Land snow depth data showed less variability, so the TC-merged snow depth data showed a similar spatial distribution to MERRA-2, which has a low spatial resolution. Considering the correlation, error, and uncertainty of the data, the ERA5-Land data is suitable for snow-related analysis in South Korea. In addition, it is expected that LDAPS data, which is highly correlated with other data but tends to be overestimated, can be actively utilized for high-resolution representation of regional and climatic diversity if appropriate corrections are performed.

• Food Science and Biotechnology
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• v.17 no.3
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• pp.642-650
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• 2008

A mathematical model was developed for predicting the growth rate of Enterobacter sakazakii in tryptic soy broth medium as a function of the combined effects of temperature (5, 10, 20, 30, and $40^{\circ}C$), pH (4, 5, 6, 7, 8, 9, and 10), and the NaCl concentration (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10%). With all experimental variables, the primary models showed a good fit ($R^2=0.8965$ to 0.9994) to a modified Gompertz equation to obtain growth rates. The secondary model was 'In specific growth $rate=-0.38116+(0.01281^*Temp)+(0.07993^*pH)+(0.00618^*NaCl)+(-0.00018^*Temp^2)+(-0.00551^*pH^2)+(-0.00093^*NaCl^2)+(0.00013^*Temp*pH)+(-0.00038^*Temp*NaCl)+(-0.00023^*pH^*NaCl)$'. This model is thought to be appropriate for predicting growth rates on the basis of a correlation coefficient (r) 0.9579, a coefficient of determination ($R^2$) 0.91, a mean square error 0.026, a bias factor 1.03, and an accuracy factor 1.13. Our secondary model provided reliable predictions of growth rates for E. sakazakii in broth with the combined effects of temperature, NaCl concentration, and pH.

• Atmosphere
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• v.25 no.1
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• pp.19-30
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• 2015

The Ka-band cloud radar (KCR) has been operated by the National Institute of Meteorological Research (NIMR) of Korea Meteorological Administration (KMA) at Boseong National Center for Intensive Observation of severe weather since 2013. Evaluation of data quality is an essential process to further analyze cloud information. In this study, we estimate the measurement error and the sampling uncertainty to evaluate data quality. By using vertically pointing data, the statistical uncertainty is obtained by calculating the standard deviation of each radar parameter. The statistical uncertainties decrease as functions of sampling number. The statistical uncertainties of horizontal and vertical reflectivities are identical (0.28 dB). On the other hand, the statistical uncertainties of Doppler velocity (spectrum width) are 2.2 times (1.6 times) larger at the vertical channel. The reflectivity calibration of KCR is also performed using X-band vertically pointing radar (VertiX) and 2-dimensional video disdrometer (2DVD). Since the monitoring of calibration values is useful to evaluate radar condition, the variation of calibration is monitored for five rain events. The average of calibration bias is 10.77 dBZ and standard deviation is 3.69 dB. Finally, the statistical characteristics of cloud properties have been investigated during two months in autumn using calibrated reflectivity. The percentage of clouds is about 26% and 16% on September to October. However, further analyses are required to derive general characteristics of autumn cloud in Korea.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.393-402
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• 2001

A computational model representative of cardiovascular circulation was built using 12 standard lumped compartments. Especially, both the baroreceptor reflex and the cardiopulmonary reflex control model were implemented to explain the auto-regulation of cardiovascular system. Another important aspect of this model is to utilize the impulse-response curve of the nerve system in transferring the impulse error signals to autonomous nerve system. For the verification of this model, we have computed the normal hemodynamic conditions and compared those with the clinical data. Then. hemodynamic shock of 20% hemorrhage to cardiovascular system was simulated to test the effects of the control system model. The results of these two simulations were well matched with the experimental ones. The steady state LBNP simulation was also performed. The transient changes of hemodynamic variables due to ramp increase of bias pressure of LBNP showed good agreement with the physiological experiments. Numerical solution using only the baroreflex model showed relatively a larger deviation from the experimental data. compared with the one using the control model haying both the baroreflex and the cardiopulmonary reflex systems, which shows an important role of the cardiopulmonary reflex system for the simulation of the hemodynamic behavior of the cardiovascular system .