• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.5
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• pp.125-130
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• 2009

Storage function method which considers the non-linearity of the relationship between rainfall and runoff has been frequently used to predict runoff in a basin and a flood pattern. However, it is time-consuming to estimate appropriate parameters of every basin and rainfall event, which requires the empirical parameter equation applicable in Korea. In this study, multiple regression analysis is used to develop empirical equations to estimate parameters of Storage Function method using basin characteristics. The basin area, maximum stream length, and stream slope are considered as the basin characteristics as the result of the regression analysis. Collinearity is removed and trial-and-error method is used to choose the most descriptive parameters to the dependent variables in Han River basin which is divided into 30 subbasins. The developed equations are validated using the rainfall events in MunMak gauging station and named as 'Han River equation'. The equation could provide the useful information about Storage Function method parameter to calculate runoff from a basin and predict river stage.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.170-179
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• 2003

In order to classify Land Use/Land Cover using multispectral images, we have to give consequence to defining proper classes and selecting training sample with higher class separability. The process of satellite hyperspectral image which has a lot of bands is difficult and time-consuming. Furthermore, classification result of hyperspectral image with noise is often worse than that of a multispectral image. When selecting training fields according to the signatures in the study area, it is difficult to calculate covariance matrix in some clusters with pixels less than the number of bands. Therefore in this paper we presented an overview of feature extraction methods for classification of Hyperion data and examined effectiveness of feature extraction through the accuracy assesment of classified image. Also we evaluated the classification accuracy of optimal meaningful features by class separation distance, which is also a method for band reduction. As a result, the classification accuracies of feature-extracted image and original image are similar regardless of classifiers. But the number of bands used and computing time were reduced. The classifiers such as MLC, SAM and ECHO were used.

• Nutrition Research and Practice
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• v.13 no.2
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• pp.126-133
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• 2019

BACKGROUND/OBJECTIVES: The glycemic index (GI) is a measure of the postprandial glucose response (PPGR) to food items, and glycemic load (GL) is a measure of the PPGR to the diet. For those who need to maintain a healthy diet, it is beneficial to regulate appropriate levels of blood glucose. In reality, what influences the meal GI or GL depends on the macronutrient composition and the physical chemistry reactions in vivo. Thus, we investigated whether different macronutrients in a meal significantly affect the PPGR and the validity of calculated GI and GL values for mixed meals. SUBJECTS/METHODS: 12 healthy subjects (6 male, 6 female) were recruited at a campus setting, and subjects consumed a total of 6 test meals one by one, each morning between 8:00 and 8:30 am after 12 h of fasting. PPGR was measured after each consumed meal and serial finger pricks were performed at indicated times. Test meals included 1) 68 g oral glucose, 2) 210 g rice, 3) rice plus 170 g egg white (RE), 4) rice plus 200 g bean sprouts (RS), 5) rice plus 10 g oil (RO), and 6) rice plus, egg white, bean sprouts, and oil (RESO). The incremental area under the curve (iAUC) was calculated to assess the PPGR. Mixed meal GI and GL values were calculated based on the nutrients the subjects consumed in each of the test meals. RESULTS: The iAUC for all meals containing two macronutrients (RS, RO, or RE) were not significantly different from the rice iAUC, whereas, the RESO iAUC ($2,237.5{\pm}264.9$) was significantly lower (P < 0.05). The RESO meal's calculated GI and GL values were different from the actual GI and GL values measured from the study subjects (P < 0.05). CONCLUSIONS: The mixed meal containing three macronutrients (RESO) decreased the PPGR in healthy individuals, leading to significantly lower actual GI and GL values than those derived by nutrient-based calculations. Thus, consuming various macronutrient containing meals is beneficial in regulating PPGR.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.170-178
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• 2019

Recently, as the interest in energy savings has increased, there has been increasing use of LED lighting, which is an eco-friendly device that replaces high energy consuming fluorescent lamps and incandescent lamps. In the case of a high output LED, however, the life time is shortened due to deterioration caused by heat generation. As a solution to this problem, this paper evaluated the LED life extension effect by increasing the convective heat transfer coefficient of the heat sink surface for LED packaging. A roughing process was carried out using sandpaper and sand blasting. The changes in surface roughness and surface area after each surface treatment process were evaluated quantitatively and the convective heat transfer coefficient was measured. When sandblasting and sandpaper were used to roughen the aluminum surface, a higher convection heat transfer coefficient was obtained compared to the untreated case, and a high heat dissipation efficiency of 82.76% was obtained in the sandblast treatment. Therefore, it is expected that the application of heat dissipation to the heat sink will extend the lifetime of the LED significantly and economically by increasing the heat efficiency.

• International Area Studies Review
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• v.12 no.3
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• pp.367-390
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• 2008

The Chinese cellular phone markets growth has the character which is two-sided gives the chance and threat in Korean mobile communication industry. This also demands the localization of the international marketing strategies which establishes in grasp of consumer consuming propensity and purchase intention. It is the goal that provides a strategic current point to the Korean enterprise which enter to China. To observe a test of hypothesis results concretely from empirical analysis with after words is same. The hypothesis 1, 'corporate image really will plus affect(+) in purchase intention' the hypothesis was adopted. The hypothesis 2, 'brand image really will plus affect(+) in purchase intention' also the hypothesis was adopted. The hypothesis 3, 'product attributes really will plus affect(+) in purchase intention' also the hypothesis was adopted. The other research hypothesis which sex and age were rejected, school was adopted partially, others was adopted. When about the consumer who is used from empirical analysis measurement items in the center application presents the strategies which is possible in fact.

• Smart Structures and Systems
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• v.24 no.5
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).

• Korean Journal of Environmental Agriculture
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• v.38 no.2
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• pp.69-75
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• 2019

BACKGROUND: Measurement of electrical conductivity of saturated soil paste ($EC_e$) for assessment of soil salinity is time-consuming, and thus conversion of EC of 1:5 soil-water extract ($EC_{1:5}$) to $EC_e$ using a dilution factor may be of help to monitor salinity of huge number of soil samples. This study was conducted to evaluate the dilution factor for reclaimed tideland (RTL) soils of South Korea. METHODS AND RESULTS: Soil samples (n=40) were collected from four RTLs, and analyzed for $EC_{1:5}$, $EC_e$, and cation compositions of 1:5 soil-water extract. The dilution factor (8.70) was estimated by regression analysis between $EC_{1:5}$ and $EC_e$, and the obtained dilution factor was validated by applying to an independent data set (n=96) of $EC_{1:5}$ and $EC_e$. The $EC_e$ measured and predicted was strongly correlated ($r^2=0.74$, P<0.001), but $EC_e$ was overestimated by 16% particularly for the soils with high clay content and low sodium adsorption ratio (SAR). CONCLUSION: This study suggests that using the dilution factor to convert $EC_{1:5}$ to $EC_e$ is feasible method to monitor changes in the soil salinity of the study RTL. However, overestimation of $EC_e$ should be cautioned for the soils with high clay content and low SAR.

• Progress in Medical Physics
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• v.33 no.4
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• pp.142-149
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• 2022

Purpose: This study seeks to compare the dosimetric parameters of the bulk electron density (ED) approach and synthetic computed tomography (CT) image in terms of position variation of the air cavity in magnetic resonance-guided radiotherapy (MRgRT) for patients with pancreatic cancer. Methods: This study included nine patients that previously received MRgRT and their simulation CT and magnetic resonance (MR) images were collected. Air cavities were manually delineated on simulation CT and MR images in the treatment planning system for each patient. The synthetic CT images were generated using the deep learning model trained in a prior study. Two more plans with identical beam parameters were recalculated with ED maps that were either manually overridden by the cavities or derived from the synthetic CT. Dose calculation accuracy was explored in terms of dose-volume histogram parameters and gamma analysis. Results: The D_95% averages were 48.80 Gy, 48.50 Gy, and 48.23 Gy for the original, manually assigned, and synthetic CT-based dose distributions, respectively. The greatest deviation was observed for one patient, whose D_95% to synthetic CT was 1.84 Gy higher than the original plan. Conclusions: The variation of the air cavity position in the gastrointestinal area affects the treatment dose calculation. Synthetic CT-based ED modification would be a significant option for shortening the time-consuming process and improving MRgRT treatment accuracy.

• Journal of Korean Academy of Nursing
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• v.53 no.1
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• pp.101-114
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• 2023

Purpose: This study aimed to identify regional differences in problem drinking among adult males in single-person households and predict the determinants. Methods: This study used data from the 2019 Community Health Survey. Geographically weighted regression analysis was performed on 8,625 adult males in single-person households who had been consuming alcohol for the past year. The Si-Gun-Gu was selected as the spatial unit. Results: The top 10 regions for problem drinking among adult males in single-person households were located in the Jeju-do and Jeollanam-do areas near the southern coast, whereas the bottom 10 regions were located in the Incheon and northern Gyeonggi-do areas. Smoking, economic activity, and educational level were common factors affecting problem drinking among this population. Among the determinants of regional disparities in problem drinking among adult males in single-person households, personal factors included age, smoking, depression level, economic activity, educational level, and leisure activity, while regional factors included population and karaoke venue ratio. Conclusion: Problem drinking among adult males in single-person households varies by region, and the variables affecting each particular area differ. Therefore, it is necessary to develop interventions tailored to individuals and regions that reflect the characteristics of each region by prioritizing smoking, economic activity, and educational level as the common factors.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.9-18
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• 2024

Small streams, despite their rich ecosystems, face challenges in vegetation assessment due to the limitations of traditional, time-consuming methods. This study presents a groundbreaking approach, combining unmanned aerial vehicles(UAVs), convolutional neural networks(CNNs), and the vegetation differential vegetation index (VDVI), to revolutionize both assessment and management of stream vegetation. Focusing on Idong Stream in South Korea (2.7 km long, 2.34 km² basin area)with eight diverse revetment methods, we leveraged high-resolution RGB images captured by UAVs across five dates (July-December). These images trained a ResNeXt101 CNN model, achieving an impressive 89% accuracy in classifying vegetation cover(soil,water, and vegetation). This enabled detailed spatial and temporal analysis of vegetation distribution. Further, VDVI calculations on classified vegetation areas allowed assessment of vegetation vitality. Our key findings showcase the power of this approach:(a) TheCNN model generated highly accurate cover maps, facilitating precise monitoring of vegetation changes overtime and space. (b) August displayed the highest average VDVI(0.24), indicating peak vegetation growth crucial for stabilizing streambanks and resisting flow. (c) Different revetment methods impacted vegetation vitality. Fieldstone sections exhibited initial high vitality followed by decline due to leaf browning. Block-type sections and the control group showed a gradual decline after peak growth. Interestingly, the "H environment block" exhibited minimal change, suggesting potential benefits for specific ecological functions.(d) Despite initial differences, all sections converged in vegetation distribution trends after 15 years due to the influence of surrounding vegetation. This study demonstrates the immense potential of UAV-based remote sensing and CNNs for revolutionizing small-stream vegetation assessment and management. By providing high-resolution, temporally detailed data, this approach offers distinct advantages over traditional methods, ultimately benefiting both the environment and surrounding communities through informed decision-making for improved stream health and ecological conservation.