• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.581-593
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• 2006

Various geological thematic maps have been generated by interpolating sparsely sampled ground survey data and geostatistical kriging that can consider spatial correlation between neighboring data has widely been used. This paper applies multi-variate geostatistical algorithms to integrate secondary information with sparsely sampled ground survey data for geological thematic mapping. Simple kriging with local means and kriging with an external drift are applied among several multi-variate geostatistical algorithms. Two case studies for spatial mapping of groundwater level and grain size have been carried out to illustrate the effectiveness of multi-variate geostatistical algorithms. A digital elevation model and IKONOS remote sensing imagery were used as secondary information in two case studies. Two multi-variate geostatistical algorithms, which can account for both spatial correlation of neighboring data and secondary data, showed smaller prediction errors and more local variations than those of ordinary kriging and linear regression. The benefit of applying the multi-variate geostatistical algorithms, however, depends on sampling density, magnitudes of correlation between primary and secondary data, and spatial correlation of primary data. As a result, the experiment for spatial mapping of grain size in which the effects of those factors were dominant showed that the effect of using the secondary data was relatively small than the experiment for spatial mapping of groundwater level.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1083-1091
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• 2010

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. However, widely used estimation schemes fail to describe the realistic variability of daily precipitation field. We compare and contrast the performance of statistical methods for the spatial estimation of precipitation in two hydrologically different basins, and propose a two-step process for effective daily precipitation estimation. The methods assessed are: (1) Inverse Distance Weighted Average (IDW); (2) Multiple Linear Regression (MLR); (3) Climatological MLR; and (4) Locally Weighted Polynomial Regression (LWP). In the suggested simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before applying IDW scheme (one of the local scheme) to estimate the amount of precipitation separately on wet days. As the results, the suggested method shows the better performance of daily rainfall interpolation which has spatial differences compared with conventional methods. And this technique can be used for streamflow forecasting and downscaling of atmospheric circulation model effectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.733-735
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• 2014

Example based Super Resolution(SR) is using the correspondence between the low and high resolution image from a database. This method uses only one image to estimate a high resolution image and can get the larger image than 2 times. Example based SR is proposed to solve the problem of classical SR. 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 advanced NE baesd on SVR having an excellent generalization ability to solve this problem. Given a low resolution image, we estimate a pixel in its high resolution version by using SVR based NE. Through experimental results, we quantitatively and qualitatively confirm the improved results of the proposed algorithm when comparing with conventional interpolation methods and NE.

• Sleep Medicine and Psychophysiology
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• v.24 no.1
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• pp.55-61
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• 2017

Objectives: Suicide is one of the leading causes of death among young adults. We investigated whether anxiety level and sleep quality were related to suicide ideation among university students. Methods: Questionnaires were distributed to 1094 students at a local college. The scale for suicide Ideation, the Hospital Anxiety-Depression scale, the Pittsburgh Sleep Quality Index, and Morningness-eveningness questionnaires were used. Multiple linear regression analysis was conducted to examine the relationship between these variables and suicide ideation. Results: Among the 292 students who answered the suicide ideation questionnaire, 31 students had a high suicide ideation score and 261 patients had a low suicide ideation score. Demographic variables that showed significant differences between the two groups were gender, exercise, chronotype, sleep quality, depression and anxiety. The results of multiple linear regression analysis showed that suicidal ideation increased as the level of sleep quality decreased. There was no significant relationship between depression and suicidal ideation. Another multiple linear regression analysis was performed to evaluate the relationship between sleep quality sleep related factors. This suggested the quality of sleep decreased as weekend oversleep increased. Conclusion: The results of this study showed that when anxiety was higher and the quality of sleep was lower, the more suicide ideation increased. Therefore, improving sleep quality and reducing anxiety are important strategies for reducing suicidal ideation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of the Korean Regional Science Association
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• v.31 no.3
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• pp.55-77
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• 2015

The purpose of this research is to analyze the influence of the regional location factors to the lifecycle of manufacturing firms in the Seoul Metropolitan Area. A firm has a lifecycle in common like a household. The firm's lifecycle is divided into 4 sections such as formation, dissolution, growth and decline for the manufacturing firms as light industry, heavy industry and high-tech industry. In addition, the regional location factors are divided into 4 categories. As a result of this research, there are differences for the statistically significant location factors. In addition, the value for the explanation ability of each multiple linear regression model (adj. $R^2$) was high in the formation and growth sections than in dissolution and decline sections. It means that the local governments need differentiated policies considering their regional characteristics for the location factors by firm's lifecycle when they established policies for industry or job. From the view point of the public sectors, it is much important to focus on formation and growth of firms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.7
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• pp.4758-4767
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• 2015

This study investigates the relationship between occupational stress, depression, drinking among heavy industrial male workers. The participants of this study were 312 workers in a local heavy industry. The data were collected by self-report using questionnaires from May to June, 2014. The data were analyzed using descriptive statistics. t-test, ANOVA, and pearson correlation coefficient, scheffe test, stepwise multiple linear regression with the SPSS/WIN 20.0 program. The total mean scores of occupational stress on the subjects were $53.77({\pm}6.33)$, depression were $12.10({\pm}7.44)$, drinking were $10.32({\pm}7.55)$. The study showed that drinking is positively correlated with occupational stress, depression. Also drinking explained 15.9% of occupational stress in heavy industrial male workers. This study provides baseline data for the preparation of management strategies that can address the occupational stress, depression, drinking of heavy industrial male workers.

• Korean Journal of Health Education and Promotion
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• v.32 no.3
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• pp.23-31
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• 2015

Objectives: The contribution of health behavior is high in the mortality variation. Mortality variation can be decreased through the policies and programs for improving health behavior. We investigated that health behaviors effected with standardized mortality in community. Methods: We examined the distribution of health determinant factors and correlation analyzed between factors and performed multiple linear regression. Data were collected from 2012 Community Health Survey in 253 communities, annual regional statistics, and statistics from Statistics Korea. Results: This study defined that the variation of standardized mortality and there are exist inequality level of health determinant factors in 253 communities. This study showed that the higher standardized mortality explained through health behavior factors of the current smoking rate, walking exercise rate and diagnosis of hypertension or diabetes rate after adjusted other factors(adjusted $R^2=0.709$, p<0.001). Conclusions: Smoking, walking exercise and diagnosis chronic disease affecting on the regional variation of standardized mortality. These factors can be improved by the local residents themselves.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.3 s.49
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• pp.1-11
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• 2006

This paper presents a seismic fragility analysis method for a cable-stayed bridge with energy dissipation devices. Model uncertainties represented by random variables include input ground motions, characteristics of energy dissipation devices and the stiffness of cable-stayed bridge. Using linear regression, we established demand models for the fragility analysis from the relationship between maximum responses and the intensity of input ground motions. For capacity models, we considered the moment and shear force of the main tower, longitudinal displacement of the girder, deviation of the stay cables tension and the local buckling of the main steel tower as the limit states for cable-stayed bridge. As a numerical example, fragility analysis results for the 2nd Jindo bridge are presented. The effect of energy dissipation devices is also briefly discussed.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.406-417
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• 2001

With the advances in multimedia databases on the World Wide Web, it becomes more important to provide users with the search capability of distributed multimedia data. While there have been many studies about the database selection and the collection fusion for text databases. The multimedia databases on the Web have autonomous and heterogeneous properties and they use mainly the content based retrieval. The collection fusion problem of multimedia databases is concerned with the merging of results retrieved by content based retrieval from heterogeneous multimedia databases on the Web. This problem is crucial for the search in distributed multimedia databases, however, it has not been studied yet. This paper provides novel algorithms for processing the collection fusion of heterogeneous multimedia databases on the Web. We propose two heuristic algorithms for estimating the number of objects to be retrieved from local databases and an algorithm using the linear regression. Extensive experiments show the effectiveness and efficiency of these algorithms. These algorithms can provide the basis for the distributed content based retrieval algorithms for multimedia databases on the Web.