• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.337-343
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• 2004

Spatial information could be obtained from spaceborne high resolution optical and synthetic aperture radar(SAR) images. However, some satellite images do not provide physical sensor information instead, rational polynomial coefficients(RPC) are available. The objectives of this study are: (1) 3-dimensional ground coordinates were computed by applying rational function model(RFM) with the RPC for the stereo pair of Ikonos images and their accuracy was evaluated. (2) Interferometric SAR(InSAR) was applied to JERS-1 images to generate DEM and its accuracy was analysis. (3) Quality of the DEM generated automatically also analyzed for different types of terrain in the study site. The overall accuracy was evaluated by comparing with GPS surveying data. The height offset in the RPC was corrected by estimating bias. In consequence, the accuracy was improved. Accuracy of the DEMs generated from InSAR with different selection of GCP was analyzed. In case of the Ikonos images, the results show that the overall RMSE was 0.23327", 0.l1625" and 13.70m in latitude, longitude and height, respectively. The height accuracy was improved after correcting the height offset in the RPC. i.e., RMSE of the height was 1.02m. As for the SAR image, RMSE of the height was 10.50m with optimal selection of GCP. For the different terrain types, the RMSE of the height for urban, forest and flat area was 23.65m, 8.54m, 0.99m, respectively for Ikonos image while the corresponding RMSE was 13.82m, 18.34m, 10.88m, respectively lot SAR image.

• Journal of Korean Academy of Nursing
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• v.46 no.5
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• pp.619-629
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• 2016

Purpose: This study was a systematic review to evaluate the effects of aromatherapy on menopausal symptoms, perceived stress and depression in middle aged-women. Methods: Eight databases were searched from their inception September 8, 2015. Two reviewers independently performed the selection of the studies, data abstraction and validations. The risk of bias was assessed using Cochrane criteria. For analysis of the data, a meta-analysis of the studies was performed. Results: From the electronic databases, 73 articles were selected, and 19 removed due to duplication. After two reviewers read the abstracts of 54 studies, 34 studies were selected. Complete papers for 34 s were read and, 12 studies which met selection criteria were reviewed and the effects of aromatherapy on menopausal symptoms, stress and depression analyzed using meta-analysis with RevMan. In the 2 studies which included Randomized Controlled Trials testing of aromatherapy on menopausal symptoms and comparison of control and placebo groups were done. Aromatherapy massage was favorably effective in reducing the menopausal symptoms compared to the control group (n=118, MD=-6.33; 95% CI -11.51 to -1.15), and compared to the placebo group (n=117, MD=-4.14; 95% CI -7.63 to -0.64). Also aromatherapy was effective in reducing stress (n=72, SMD=-0.64; 95% CI -1.12 to -0.17) and depression (n=158, MD=-5.63; 95% CI -10.04 to -1.22). Conclusion: There is limited evidence suggesting that aromatherapy for middle-aged women may be effective in controlling menopausal symptoms, perceived stress and depression.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.41-61
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• 2004

The purpose of this paper is to construct $\ulcorner$CIDEAR(Cross Impact-DEA-AR)$\lrcorner$ model which evaluates proposed R&D projects considering cross impact among them and selects proper projects to utilize resources efficiently as well as to maximize efficacy of investments. For this purpose, $\ulcorner$CIDEAR$\lrcorner$ model is designed as the following six steps. $\ulcorner$Decision Theory Evaluation Model$\lrcorner$ is for setting and selecting the evaluation items according to the structured procedure of evaluation system. The priority of items is decided at $\ulcorner$AR Decision Model$\lrcorner$$\ulcorner$Cross Impact Estimation Model$\lrcorner$ is for computing the final probability of success and the result is used to revise the evaluation results of $\ulcorner$Decision Theory Evaluation Model$\lrcorner$. $\ulcorner$Resource Performance Analysis Model$\lrcorner$ classifies the proposed R&D projects on the basis of required resources and expected performance. Consequently, the possibility of bias of project selection can be prevented. $\ulcorner$Priority Oder Decision Model$\lrcorner$ is for computing the efficacy of proposed projects. Finally, $\ulcorner$Efficacy-Efficiency Cause Analysis Model$\lrcorner$ analyzes the structure of efficacy and efficiency of the projects. The major findings and significances of this study are summarized as follows: (1) $\ulcorner$CIDEAR$\lrcorner$ model can deal with the affairs of R&D projects having the characteristics of mutual independence as well as mutual dependence in the point of efficacy and efficiency. Hence, it is possible to evaluate and select R&D projects more accurately. (2) It can be possible to raise the possibility of projects success. R&D manager can use the information for project management because the efficacy-efficiency structure of selected projects can be analyzed. (3) We proved the usefulness of the constructed $\ulcorner$CIDEAR$\lrcorner$ model using an case about twenty-one R&D projects of a leading company of electronic industry in Korea.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.205-224
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• 2012

One of the limitations of BOW method is that each term is recognized only by its form, failing to represent the term's meaning or thematic background. To overcome the limitation, different profiles for each term were defined by thematic categories depending on contextual characteristics. In this study, a specific term was used as a classification feature based on its meaning or thematic background through the process of comparing the context in those profiles with the occurrences in an actual document. The experiment was conducted in three phases; term weighting, ensemble classifier implementation, and feature selection. The classification performance was enhanced in all the phases with the ensemble classifier showing the highest performance score. Also, the outcome showed that the proposed method was effective in reducing the performance bias caused by the total number of learning documents.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.533-545
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• 2017

Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.5
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• pp.401-406
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• 2001

The applicability of non-destructive near infrared reflectance spectroscopic (NIRS) method was tested to determine the protein and oil contents of intact soybean [Glycine max (L.) Merr.] seeds. A total of 198 soybean calibration samples and 101 validation samples were used for NIRS equation development and validation, respectively. In the developed non-destructive NIRS equation for analysis of protein and oil contents, the most accurate equation was obtained at 2, 8, 6, 1(2nd derivative, 8 nm gap, 6 points smoothing, and 1 point second smoothing) and 2, 1, 20, 10 math treatment conditions with Standard Normal Variate and Detrend (SNVD) scatter correction method and entire spectrum (400-2500 nm) by using Modified Partial Least Squares (MPLS) regression, respectively. Validation of these non-destructive NIRS equations showed very low bias (protein: 0.060%, oil: -0.017%) and standard error of prediction (SEP, protein: 0.568 %, oil : 0.451 %) as well as high coefficient of determination ($R^2$, protein: 0.927, oil: 0.906). Therefore, these non-destructive NIRS equations can be applicable and reliable for determination of protein and oil content of intact soybean seeds, and non-destructive NIRS method could be used as a mass screening technique for selection of high protein and oil soybean in breeding programs.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.647-659
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• 2019

The purpose of this study was to investigate the influence of children's elementary school entrance to working conditions of employed mothers. The data from 4th to 8th wave of Panel Study on Korean Children (PSKC) were used for analysis. Specifically, we examined changes in wages, working hours and regular employment of employed mothers after their children entered elementary schools. We adopted Heck selection model for unbalanced panel data after controlling sample selection bias, and compare results of analysis for unbalanced and balanced panel data. The results showed that children's elementary school entrance reduces employed mothers' wage, working hours and regular employment. These results indicate that mother tend to leave regular job and could not entry into decent job when their children are in elementary school.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.221-229
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• 2017

In the international businesses human resource elements acquired in different countries might have different values in varied industries due to the different quality of education and experiences in the original countries. Using selection models to evaluate expected values in earnings equation of human resource elements such as education and experiences etc. acquired in sending countries, system equations are expanded to examine also the values of science and engineering degrees in technology jobs with selectivity bias correction. This paper used the US census survey data of 2015 on earnings, academic degrees, occupations etc. The US has long maintained the policy of accepting more STEM workers than any other countries and helped maintaining own technological leadership. Assuming per capita GDP gap between the sending country and the US downgrades immigrant human resource quality, it rarely affects occupational selection but depresses earnings on average by two or more years' worth of education. Immigrant quality index in the sense of GDP gap appears to be a valid tool to assess the expected earnings of the worker with. Engineering degrees increase significantly the probability of selecting not only engineering jobs but also general management jobs, as well as increasing the expected earning additionally over nine years'worth of education. Getting a technology job is additionally worth about four years of education. Economics and business degrees are worth additionally almost six years of education but humanities degrees depress expected earnings. Since years after immigration does not very fast enhance earnings capacity, education level and English language ability might be more useful criteria to expect better future earnings by.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.199-211
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• 2019

This paper is concerned with issues in the finite mixture of regression modeling as well as the simultaneous selection of the number of mixing components and relevant predictors. We propose a penalized likelihood method for both mixture components and regression coefficients that enable the simultaneous identification of significant variables and the determination of important mixture components in mixture of regression models. To avoid over-fitting and bias problems, we applied smoothly clipped absolute deviation (SCAD) penalties on the logarithm of component probabilities suggested by Huang et al. (Statistical Sinica, 27, 147-169, 2013) as well as several well-known penalty functions for coefficients in regression models. Simulation studies reveal that our method is satisfactory with well-known penalties such as SCAD, MCP, and adaptive lasso.

• The Journal of Internal Korean Medicine
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• v.42 no.3
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• pp.323-339
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• 2021

Objectives: This study investigates the effect of electro-acupuncture on aphasia after stroke. Methods: A search of OASIS, NDSL, PubMed, Cochrane, and CNKI was executed between 4 January 2021 and 4 February 2021, with no limitation on publication year. Extraction and selection from the studies were made by 3 authors. The quality of the studies was evaluated using Cochrane's risk of bias (RoB) tool. Results: 10 studies met the selection criteria. As the treatment site for electro-acupuncture, GV20 (Baihui) was used the most. In all studies, the region located on the head was used for treatment without distinguishing between acupoints and areas of scalp acupuncture, and the stimulation was organized into 3 conditions: speed, intensity, and time. The outcome indicators used before and after treatment focused on the evaluation of language function and the degree of aphasia. The results showed that using electro-acupuncture with speech rehabilitation therapy for aphasia after stroke was more effective than using speech rehabilitation therapy alone. Conclusions: In this review, electro-acupuncture for aphasia after stroke was found to have a significant effect compared to the previous treatment alone. However, because of limitations, information was not reliable enough. Additional research is needed to produce more objective evidence.