• Health Policy and Management
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• v.16 no.2
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• pp.21-48
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• 2006

As prior studies indicate that chronic diseases are mainly attributed to health behavior, preventive health care rather than treatment for illness needs to improve health status. Since chronic conditions require long-term therapy, health care expenditures to treat chronic diseases have been substantial burden at national level. In this point of view, this study suggests that the health promotion program should be based on Knowledge Based System Using Data Mining Technique, we developed a predictive model for preventive healthcare management on diabetes mellitus. Generally, in the outbreak of diabetes mellitus there is a difference in lifestyle and the risk factors according to gender. So we developed a predictive model in accordance with gender difference and applied the Logistic Regression Model based on Data Mining process. The result of the study were as follow. The lift of the last predictive model was an average 2.23 times(male model : 2.13, female model 2.33) more improved than in the random model in upper 10% group. The health risk factors of diabetes mellitus are gender, age, a place of residence, blood pressure, glucose, smoking, drinking, exercise rate. On the basis of these factors, we suggest the program of the health promotion.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.118-123
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• 2015

There have been various studies on measurements of flood risk and forecasting models. For river and dam region, PDF and FVI has been proposed for measurement of flood risk and regression models have been applied for forecasting model. For Bo region unlikely river or dam region, flood risk would unexpectedly increase due to outgoing water to keep water amount under the designated risk level even the drain system could hardly manage the water amount. GFI and general linear model was proposed for flood risk measurement and forecasting model. In this paper, FVI with the consideration of duration on GFI was proposed for flood risk measurement at Bo region. General linear model was applied to the empirical data from Bo region of Nadong river to derive the forecasting model of FVI at three different values of Base High Level, 2m, 2.5m and 3m. The significant predictor variables on the target variable, FVI were as follows: ground water level based on sea level with negative effect, difference between ground altitude of ground water and river level with negative effect, and difference between ground water level and river level after Bo water being filled with positive sign for quantitative variables. And for qualitative variable, effective soil depth and ground soil type were significant for FVI.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.138-147
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• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.453-464
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• 2018

Purpose: The purpose of this study is to propose a solution to the case where $V_{50}$ calculation is impossible in the process of bulletproof test. Methods: In this study, we proposed a $V_{50}$ estimation method using logistic regression analysis. Six scenarios were applied by combining the homogeneity of the sample and the speed range. Then, 1,000 simulations were performed per scenario and six assumptions reflecting the reality were applied. Results: The result of the study, it was confirmed that there was no statistical difference between the $V_{50}$ value calculated by the conventional method and the $V_{50}$ value calculated by the improvement method. Therefore, in situations where $V_{50}$ can not be calculated, it is reasonable to use logistic regression analysis. Conclusion: This study develops a methodology that is easy to use and reliable by using statistical model based on actual data.

• Korean Journal of Veterinary Research
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• v.44 no.4
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• pp.669-676
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• 2004

To evaluate the diagnostic accuracy of two commercial ELISA tests (Allied- and CSL-ELISA) for the diagnosis of Mycobacterium paratuberculosis in cattle, Meta-analysis using English language papers published during 1990-2001 was performed. Diagnostic odds ratios (DOR) were analyzed using regression analysis together with summary receiver operating characteristic (ROC) curves. The difference in diagnostic performance between the two ELISA systems was evaluated by using linear regression. Publication bias was assessed by funnel plot and linear regression. The pooled sensitivity and specificity were 44% (95% CI, 38 to 51) and 98% (95% CI, 96 to 99) for the random-effect model. The DOR between studies was heterogeneous. The area under the fitted ROC curve (AUC) was 0.72 for the unweighted and 0.77 for the weighted model. Maximum joint sensitivity and specificity for the unweighted and weighted model from their summary ROC curve were 70% and 75%, respectively. Based on the fitted model, at a specificity of 95%, sensitivity was estimated to be 52% for the unweighted and 57% for the weighted model. From the final multivariable model study characteristic, the country was the only significant variable with an explained component variance of 13.3%. There were no significant differences in discriminatory power, sensitivity, and specificity between the two ELISA tests. The overall diagnostic accuracy of two commercial ELISA tests was moderate, as judged by the AUC, maximum joint sensitivity and specificity, and estimates from the fitted model and clinical usefulness of the tests for screening program is limited because of low sensitivity and heterogeneous of DOR. It is, therefore, recommended to use ELISA tests as a parallel testing with other diagnostic tests together to increase test sensitivity in the screening program.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.6
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• pp.545-550
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• 2012

Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.93-101
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• 2007

To jointly optimize the spatial registration and the exposure compensation, an iterative registration algorithm, the Lucas-Kanade algorithm, is combined with an exposure compensation algorithm, which is based on the histogram transformation function. Based on a simple regression model, a nonparametric estimator, the empirical conditional mean, and its polynomial fitting are used as histogram transformation functions for the exposure compensation. Since the proposed algorithm is composed of separable optimization phases, the proposed algorithm is more advantageous than the joint approaches of Mann and Candocia in the aspect of implementation flexibility. The proposed algorithm performs a better registration for real images than the case of registration that does not consider the exposure difference.

• Proceedings of the Korean Printing Society Conference
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• 2000.04a
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• pp.25-34
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• 2000

Recently, color proofing in printing industry grow rapidly. If an order decide color from known color information in the case of color reproduction, we can reduce expenses and time. In color proofing the best important point must be closed proofed color to primary color and secondary color. Model-based approaches have the advantages of faster recharacterization and the opportunity of simulating product enhancements such as changes in ink properties and halftoning. In this paper, we transformed the dot area of CMYK to CIELAB color space using color reprodution models. Firstly, we measured spectral reflectance of primary color printed by Matchprint II and the data was used to find tone reproduction curve using regression equation, and than we applied at primary color model, such as Murray-Davies, Yule-Nilsen, and mixed color model, such as Kubelka--Munk, relaxed version of spectral Neugebauser. In such results, the Kubleka-Munk model resulted in the best spectral reconstruction accuracy followed by relaxed version of spectral Neugebauer model, color difference is 2.8401.

• Journal of the Korean Graphic Arts Communication Society
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• v.18 no.2
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• pp.29-40
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• 2000

Recently. color proofing in printing industry grow rapidly. If an order decide color from known color information in the case of color reproduction, we can reduce expenses and time. In color proofing the best important point must be closed proofed color to primary color and secondary color. Model-based approaches have the advantages of faster recharacterization and the opportunity of simulating product enhancements such as changes in ink properties and halftoning. In this paper, we transformed the dot area of CMYK to CIELAB color space using color reproduction models. Firstly, we measured spectral reflectance of primary color printed by Matchprint II and the data was used to find tone reproduction curve using regression equation, and than we applied at primary color model, such as Murray-Davies, Yule-Nilsen, and mixed color model, such as Kubelka-Munk, relaxed version of spectral Neugebauer. In such results, the Kubelka-Munk model resulted in the best spectral reconstruction accuracy followed by relaxed version of spectral Neugebauer model, color difference is 2.8401.