• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.933-944
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• 2019

This study analyzes the tide deformation of land boundary regions on the east (Region A) and west (Region B) sides of the Ross Ice Shelf in Antarctica using Double-Differential Interferometric Synthetic Aperture Radar (DDInSAR). A total of seven Sentinel-1A SAR images acquired in 2015-2016 were used to estimate the accuracy of tide prediction model and Young's modulus of ice shelf. First, we compared the Ross Sea Height-based Tidal Inverse (Ross_Inv) model, which is a representative tide prediction model for the Antarctic Ross Sea, with the tide deformation of the ice shelf extracted from the DDInSAR image. The accuracy was analyzed as 3.86 cm in the east region of Ross Ice Shelf and it was confirmed that the inverse barometric pressure effect must be corrected in the tide model. However, in the east, it is confirmed that the tide model may be inaccurate because a large error occurs even after correction of the atmospheric effect. In addition, the Young's modulus of the ice was calculated on the basis of the one-dimensional elastic beam model showing the correlation between the width of the hinge zone where the tide strain occurs and the ice thickness. For this purpose, the grounding line is defined as the line where the displacement caused by the tide appears in the DDInSAR image, and the hinge line is defined as the line to have the local maximum/minimum deformation, and the hinge zone as the area between the two lines. According to the one-dimensional elastic beam model assuming a semi-infinite plane, the width of the hinge region is directly proportional to the 0.75 power of the ice thickness. The width of the hinge zone was measured in the area where the ground line and the hinge line were close to the straight line shown in DDInSAR. The linear regression analysis with the 0.75 power of BEDMAP2 ice thickness estimated the Young's modulus of 1.77±0.73 GPa in the east and west of the Ross Ice Shelf. In this way, more accurate Young's modulus can be estimated by accumulating Sentinel-1 images in the future.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.69-100
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• 2006

We examine the effect of controlling shareholders ownership structure and related-party transactions(hereafter 'RPT') of publicly traded companies on their firm values during the post-IMF period. In the multivariate regression analysis using control variables such as firm size, capital structure, investment, dividend, profitability and industry dummy that might affect firm values, we find that there exists a significant negative relation between the controlling shareholders ownership structure and firm values proxied by Tobin's Q, and also find that there is a significant negative relation between RPT and the firm values. Those evidences seem to support the controlling shareholders' expropriation hypothesis. Additionally, we investigate the relation between ownership structure and rim value through the piecewise regression analysis. We find a significant 'inverse' U-shape pattern between the controlling shareholders ownership structure and firm values. This result is quite different from the existing literatures that have usually reported an U-shape pattern. In conclusion, the findings in this study do not support the notion that the ownership concentration to the controlling shareholders does negatively affect the firm values monotonically.

• Journal of Chest Surgery
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• v.53 no.3
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• pp.104-113
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• 2020

Background: Accurate intraoperative assessment of mediastinal lymph nodes is a critical aspect of lung cancer surgery. The efficacy and potential for upstaging implicit in these dissections must therefore be revisited in the current era of uniportal video-assisted thoracoscopic surgery (VATS). Methods: A retrospective study was conducted in which 544 patients with stage I (T1abc-T2a, N0, M0) primary lung cancer were analyzed. To assess risk factors for nodal upstaging and to limit any imbalance imposed by surgical choices, we constructed an inverse probability of treatment-weighted (IPTW) logistic regression model (in addition to non-weighted logistic models). We also evaluated risk factors for early locoregional recurrence using IPTW logistic regression analysis. Results: In the comparison of uniportal and multiportal VATS, the resected lymph node count (14.03±8.02 vs. 14.41±7.41, respectively; p=0.48) and rate of nodal upstaging (6.5% vs. 8.7%, respectively; p=0.51) appeared similar. Predictors of nodal upstaging included tumor size (odds ratio [OR], 1.74; 95% confidence interval [CI], 1.12-2.70), carcinoembryonic antigen level (OR, 1.11; 95% CI, 1.04-1.18), and histologically confirmed pleural invasion (OR, 3.97; 95% CI, 1.89-8.34). The risk factors for locoregional recurrence within 1 year were found to be number of resected N2 nodes, age, and nodal upstaging. Conclusion: Uniportal and multiportal VATS appear similar with regard to accuracy and thoroughness, showing no significant difference in the extent of nodal dissection.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.2
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• pp.127-140
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• 2015

A fault is one of the critical factors that may lead to a possible ground collapse occurring in construction site. A fault core, however, possibly acting as a failure plane in whole fault zone, is composed of fractured rock and gouge nonuniformly distributed and thus can be characterized by its wide range of shear strength which is generally acquired by experimental method for stability analysis. In this study, we performed direct shear test and grain size distribution analysis for 62 fault core samples cropped from 12 different spots located in the vicinity of Kyongju and Ulsan, Korea. As a result, the range of shear strength representing the characteristics of fault cores in the study regions is determined with regard to vertical stress using a regression analysis for experiment data. The weight ratio of gravels in the samples is proportional to the shear strength and that of silt and clay is in inverse proportion to the shear strength. For most samples, the coefficient of determination is over 0.7 despite of inhomogeneity of them and consequently we determined the lower limit and upper limit of the shear strength with regard to the weight ratio by setting the confidence interval of 95%.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.5
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• pp.563-574
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• 2007

There seems to be a consensus among most people that visibility impairment is the most obvious indicator of air pollution. While considerable evidence on the association between air pollution and health outcomes including death and disease have been established, based on industrial complex areas or huge urban cities, time-series, case-crossover and cohort studies, scarce literature exists on the direct evidence for the association between visibility and adverse health outcomes. Our study is assessed the effect of air pollution measured by visibility impairment on respiratory mortality over a period of six years. Relative risks in respiratory deaths were estimated by a Poisson regression model of daily deaths between $1999{\sim}2004$. Daily counts of respiratory deaths as dependent variable was modelled with daily 24-hr mean visibility measurements (kilometers) as independent variable by means of Poisson regression. This model is controlled for confounding factors such as day of weeks, weather variables, seasonal variables and $PM_{10}$. The results in this study is observed the statistically significant association between an inverse health effect and visibility during the study period for respiratory mortality (percentage change in the relative risk for all aged -0.57%, 95% Cl, $-1.01%{\sim}-0.12%$; for $0{\sim}15$ aged -7.12%, 95% Cl, $-13.29%{\sim}-0.51%$; for 65+ aged -0.43%, 95% Cl, $-0.93%{\sim}-0.06%$ per 1 km increased in visibility). The effect size was much reduced during warm season. Visibility impairment resulting from air pollution is strongly associated with respiratory mortality, especially for children may be spent at outdoor. Our result provides a quick and useful indicator for eliciting the contribution of air pollution to the excess risk of respiratory mortality in Seoul, Korea.

• Journal of Preventive Medicine and Public Health
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• v.30 no.4 s.59
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• pp.729-740
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• 1997

To determine the relationship between hypertension and nutrient intake cross-sectional study were performed in a rural area. Adult resident over 30-year-old age were measured blood pressure and body mass index(BMI), and interviewed about food in-take for the previous 24 hours. 250 men and 297 women participated the survey. Significant correlation was showen in men between mean systolic blood pressure and protein density. Significant correlation with mean diastolic blood pressure was showen on protein density, protein energy(%), calcium density and energy-adjusted protein in men. We analysed risk factor for hypertension adjust the effect of age, BMI, sex and family history by multiple logistic regression. Protein density(odds ratio=3.18), fat density(odds ratio=1.94) and energy-adjusted protein(odds ratio=1.01) intake were positively associated with hypertension but sodium density(odds ratio=0.73) was showen to have inverse relationship.

• Korean Journal of Food Science and Technology
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• v.20 no.3
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• pp.310-316
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• 1988

The gelatinized model food system were prepared by combining moisture, starch and protein, and the textural properties of their gels of different temperatures and times of heating were investigated by the use of Instron Universal Testing Machine. The hardness, springiness, cohesiveness, gumminess and chewiness of model foods had a high correlation with solid content and the regression equations between the hardness of model foods and moisture content heated for 20min. at $80{\circ}C$ were as follows; $H(PS_4)=18.6405-3.8201M+0.1959M^2,\;H(P_1S_1)=244.7933-5.692M+0.0332M^2,\;H(P_4S)=693.0292-16.6884M+0.1005M^2$, The correlation coefficients were $0.996^{**},\;0.998^{**}\;and\;0.998^{**}$, respectively. Total correlations between textural parameters and temperature and heating times were different according to model foods. The correlation between textural parameters was proportional to protein foods, but the hardness and cohesiveness of starch foods showed the relationship of inverse proportion. Under low solid content, the parameters of model foods appeared to decrease as protein content increased. Under high solid content, the parameters of protein foods were higher than those of starch foods above some level of protein content. The regression equation between the hardness and protein content heated for 20min. at $80^{\circ}C$ was as follows; Hardness(20%)=5.6858-13.5670P+$9.7758P^2$ and the correlation coefficient was $0.95^{**}$.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.1
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• pp.35-40
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• 2011

While high-definition precipitation maps with a 270 m spatial resolution are available for South Korea, there is little information on geospatial availability of precipitation water for the famine - plagued North Korea. The restricted data access and sparse observations prohibit application of the widely used PRISM (Parameter-elevation Regressions on Independent Slopes Model) to North Korea for fine-resolution mapping of precipitation. A hybrid method which complements the PRISM grid with a sub-grid scale elevation function is suggested to estimate precipitation for remote areas with little data such as North Korea. The fine scale elevation - precipitation regressions for four sloping aspects were derived from 546 observation points in South Korea. A 'virtual' elevation surface at a 270 m grid spacing was generated by inverse distance weighed averaging of the station elevations of 78 KMA (Korea Meteorological Administration) synoptic stations. A 'real' elevation surface made up from both 78 synoptic and 468 automated weather stations (AWS) was also generated and subtracted from the virtual surface to get elevation difference at each point. The same procedure was done for monthly precipitation to get the precipitation difference at each point. A regression analysis was applied to derive the aspect - specific coefficient of precipitation change with a unit increase in elevation. The elevation difference between 'virtual' and 'real' surface was calculated for each 270m grid points across North Korea and the regression coefficients were applied to obtain the precipitation corrections for the PRISM grid. The correction terms are now added to the PRISM generated low resolution (~2.4 km) precipitation map to produce the 270 m high resolution map compatible with those available for South Korea. According to the final product, the spatial average precipitation for entire territory of North Korea is 1,196 mm for a climatological normal year (1971-2000) with standard deviation of 298 mm.

• Korean Journal of Biological Psychiatry
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• v.27 no.1
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• pp.18-26
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• 2020

Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.391-400
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• 2017

Italian ryegrass (IRG) is one of the fastest growing grasses available to farmers. It offers rapid establishment and starts growing early in the following spring and has fast regrowth after defoliation. So, IRG can be utilized as the dominant/single species of grass used in a farming system, or to play a role as a large producing pasture and sacrificial paddock. The objective of this study was to develop the use of unmanned aerial vehicle (UAV) for the evaluation of feed value of IRG. For this study, UAV imagery was taken on the Nonsan regions two times during the IRG growing season. We analyzed the relationships between $NDVI_{UAV}$ and feed value parameters such as fresh matter yield, dry matter yield, acid detergent fiber (ADF), neutral detergent fiber (NDF), total digestible nutrient (TDN) and crude protein at the season of harvest. Correlation analysis between $NDVI_{UAV}$ and feed value parameters of IRG revealed that $NDVI_{UAV}$ correlated well with crude protein (r = 0.745), and fresh matter yield (r = 0.655). According to the relationship, the variation of $NDVI_{UAV}$ was significant to interpret feed value parameters of IRG. Eight different regression models such as Linear, Logarithmic, Inverse, Quadratic, Cubic, Power, S, and Exponential model were used to estimate IRG feed value parameters. The S and exponential model provided more accurate results to predict fresh matter yield and crude protein than other models based on coefficient of determination, p- and F-value. The spatial distribution map of feed values in IRG plot was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when $NDVI_{UAV}$ was applied to regression equation. These lead to the result that the characteristics of variations in feed value of IRG according to $NDVI_{UAV}$ were well reflected in the model.