• Journal of Digital Contents Society
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• v.15 no.6
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• pp.737-743
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• 2014

For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2946-2952
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• 2014

Among the Example based Super Resolution(SR) techniques, Neighbor embedding(NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the poor generalization of NE decreases the performance of such algorithm. The sizes of local training sets are always too small to improve the performance of NE. We propose the Learning Sparse-Neighbor Image Representation baesd on SVR having an excellent generalization ability to solve this problem. Given a low resolution image, we first use bicubic interpolation to synthesize its high resolution version. We extract the patches from this synthesized image and determine whether each patch corresponds to regions with high or low spatial frequencies. After the weight of each patch is obtained by our method, we used to learn separate SVR models. Finally, we update the pixel values using the previously learned SVRs. Through experimental results, we quantitatively and qualitatively confirm the improved results of the proposed algorithm when comparing with conventional interpolation methods and NE.

• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Journal of Korean Society of Forest Science
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• v.107 no.4
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• pp.436-445
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• 2018

This study aims to find out factors to affect forest area of public sector such as state forest and estimate optimum area of state forest in Korea. This study was carried out with the rate of public forest and public forest per capita as dependent variables and 15 independent variables to the 35 countries in OECD countries using analysis of linear regression. From research, optimum area of the public forests of Korea was estimated from to minimum 2,136,000 hectares to maximum 2,667,000 hectares, based on OECD countries. The public forest areas of Korea were 1,984,000 hectares in 2010. To reach the average level of OECD countries, it is required that public forest areas of Korea are expended from minimum 152,000 hectares to maximum 683,000 hectares. It is hard to expect that enhancing the areas of public forest in Korea through expanding local government owned forest areas. Therefore, it required that state forest areas are expanded by Korea government.

• Water Engineering Research
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• v.6 no.2
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• pp.39-48
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• 2005

The Brazos River is one of the longest rivers contained entirely in the state of Texas, flowing over 700 miles from northwest Texas to the Gulf of Mexico. Today, the Brazos River Authority and Texas Commission on Environmental Quality interest in drought protection plan, waterpower project, and allowing the appropriation of water system-wide and water right within the Brazos River Basin to meet water needs of customers like farmers and local civilians in the future. Especially, this purpose of this paper primarily intended to provide the data for the engineering guidelines and make easily geological mapping tool. In the Brazos River basin, many stream-flow gage station sites are not working, and they can not provide stream-flow data sets enough for development of the Probable Maximum Flood (PMF) for use in the evaluation of proposed and existing dams and other impounding structures. Integrated GIS-Spatial Interpolation (GIS-SI) tool are composed of two parts; (1) extended GIS technique (new making interface for hydrological regionalization parameters plus classical GIS mapping skills), (2) Spatial Interpolation technique using weighting factors from kriging method. They are obtained from the relationship among location and elevation of geological watershed and existing stream-flow datasets. GIS-SI technique is easily used to compute parameters which get drainage areas, mean daily/monthly/annual precipitation, and weighted values. Also, they are independent variables of multiple linear regressions for simulation at un gaged stream-flow sites. In this study, GIS-SI technique is applied to the Brazos river basin in Texas. By assuming the ungaged flow at the sites of Palo Pinto, Bryan and Needville, the simulated daily/monthly/annual time series are compared with observed time series. The simulated daily/monthly/annual time series are highly correlated with and well fitted to the observed times series.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.15 no.2
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• pp.165-173
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• 1997

The combined bundle block adjustment with projection center coordinates determined by kinematic DGPS-positioning has reached a high level of accuracy. Standard deviations of the ground coordinates of $\pm{10cm}$ or even better can be reached. On this accuracy level also smaller error components are becoming more important. One major point of this is the interpolation of the projection centers as a function of time between the GPS-antenna locations. A just linear interpolation is not respecting the not linear movement of the aircraft. Based on a least squares polynomial fitting the aircraft maneuver can be estimated more accurate and blunders of the GPS-positions caused by loss of satellite and cycle slips are determinable. The interpolation with a time interval of 3sec in the study area RHEINKAMP is quite different to the interpolation with a time interval of 6-7sec in the study area MAAS. The GPS-positions of the study area are identified as blunders based on a local polynomial regression. This cannot be neglected for precise block adjustment.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.3
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• pp.221-230
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• 2022

Objectives: The aim of this study is to evaluate the association between shiftwork and skeletal muscle mass index in a single university health check-up. Methods: We used data from 98,227 workers who answered in a special interview on health check-up at a local university hospital from 2014 to 2020. Pearson correlation analysis was conducted for comparing the association between skeletal muscle mass index and demographic and hematological variables in shiftwork and non-shiftwork groups. Mixed linear model analysis after controlling demographic and hematological variables was used to analyze the difference of skeletal muscle mass index between groups at every visit for seven years. Results: In linear regression analysis, the variables most significantly correlated with skeletal muscle index in both groups were shiftwork(p=0.049), BMI(p<0.001), hypertension(p=0.024), platelet(p<0.001), total protein (p<0.001), AST(p=0.028), ALT(p=0.003), ALP(p<0.001), total cholesterol(p=0.002), triglyceride(p=0.019), BUN (p=0.001), creatinine(p<0.001), and uric acid(p=0.002). After the adjustment for demographic and hematologic variables, the skeletal muscle mass index at every visit was decreased both in the shiftwork group and non-shiftwork group. The slope of the shiftwork group was -0.240 and non-shiftwork group -0.149, showing a significant difference (p<0.001). Conclusions: In the shiftwork group, the skeletal muscle mass index showed a tendency to decrease markedly over time compared to the non-shiftwork group. It is presumed that shift workers' skeletal muscle health was adversely affected by changes in the biological clock due to changes in wake-up and sleep patterns, and changes in food intake.

• Journal of Service Research and Studies
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• v.10 no.1
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• pp.55-67
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• 2020

Fine dust has recently become one of the greatest concerns of Korean people and has been a target of considerable efforts by governments and local governments. In the academic world, many researches have been carried out in relation to fine dust, but the research on the economic field has been relatively few. So we wanted to know how fine dust affects the economy. Big data of PM10 concentration for fine dust and fine dust theme stock price were collected for five years from 2013 to 2017. Regression analysis was performed using the linear regression model, the generalized least squares method. As a result, the change in the fine dust concentration was found to have a effect on the related theme stocks' price. When the fine dust concentration increased compared to the previous day, the fine dust theme stocks' price also showed a tendency to increase. Also, according to the analysis of stock price change from 2013 to 2017 based on fine dust theme stocks, companies with large regression coefficients were changed every year. Among them, the regression coefficients of Monalisa were repeatedly high in 2014, 2015, 2017, Samil Pharmaceutical in 2015, 2016 and 2017, and Welcron in 2016 and 2017, and the companies were judged to be sensitive to the concentration of fine dust. The companies that responded the most in the past 5 years were Wokong, Welcron, Dongsung Pharmaceutical, Samil Pharmaceutical, and Monalisa. If PM2.5 measurement data are accumulated enough, it would be meaningful to compare and analyze PM2.5 concentration with independent variables. In this study, only the fine dust concentration is used as an independent variable. However, it is expected that a more clear and well-explained result can be found by adding appropriate additional variables to increase the explanatory power.

• Journal of agricultural medicine and community health
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• v.45 no.4
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• pp.183-193
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• 2020

Objectives: As an ecological study, this study was performed to identify the community-level variation of health related quality-of-life (HRQOL), and to explore the factors that explain the variation, using 2017 Korean Community Health Survey (KCHS) data. Methods: Community health indicators of KCHS, which are correlated with the EQ-5D index of Si-gun-gu districts, were selected as independent variables. Multiple linear regression model was used to derive factors that explain regional variations in the EQ-5D index. Results: The EQ-5D index variation in 229 districts nationwide was 1.1 times for extremal quotient (EQ) and 1.0 for coefficient of variance (CV). The Si-gun-gu districts with the EQ-5D index in the lower 25% were more distributed in the province (27.7%) than in the metropolitan area (20.3%). As a result of multiple linear regression analysis, the depressed mood experience rate, perceived stress rate, suicide ideation rate, and physician diagnosed arthritis rate were derived as major factors of the variation. Conclusions: In order to reduce the gap in HRQOL between the districts, the priority of local health policies should be placed on the above factors including psychosocial factors.

• Korea Journal of Hospital Management
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• v.12 no.3
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• pp.47-67
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• 2007

This paper aims at suggesting several ways lo change financial vulnerability and to improve managerial capability of local public hospitals (LPHs) in Korea through the identification of factors affecting profitability. Several findings of the research are as follows: To begin with, LPHs exhibited a statistically significant difference in their profitability from one another, according to tile analyses of their profitable margins from tile general characteristics. It depends on the number of hospitals in the area, the population of the hospital-built area, the number of competing hospitals, the number of staff per 100 beds, the opening of special clinic, the educational function, and the capacity of rooms. However, there was no variable in the managerial characteristics, presenting a significant difference, in contrast with hospitals which have been managed by private companies and made a great amount of profits. Second, according to the analyses of profit differences in behavioral effort-characteristics, a statistically significant difference was revealed upon the basis of the efforts to improve the clinic service, invite special patients, and shorten the period of being hospitalized. Third, the result of analyses about the difference of profitability from medical care and finance is statistically significant in the rate of labor cost, the rate of management cost, bed-occupancy rate, and the period of being hospitalized. Fourth, according to the analyses of the factors influencing the net profit ratio of the entire capital, Adjusted explanatory power(Adjusted $R^2$) was shown up to 65.2%, which is high. To compare the adjusted explanatory power stage by stage, the first stage model applying only two variables such as structural and strategic characteristics exhibited 23.8%, and the second stage model adding financial characteristics showed 51.5%. The explanatory power was much improved up to 65.2% when the third stage model incorporated the outcome of medical care performance. When the return on investment(ROI) was examined by using the multi-variate linear regression analysis at the final model of third stage, it was found that ROI had a positive relationship with the increase rate of patients, labor costs per doctor, and medical care rate of socially protected inpatients. However, it revealed that ROI had a negative relationship with the ratio of labor costs, the number of patients per managerial staff, and occupancy rate of rooms, respectively. The research suggests that in order for LPHs to increase profitability, LPH, should make efforts not only to attract patients to the hospitals without any discrimination of the patients depending on their financial status, but also to develop efficient management methods to reduce labor costs.