• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.213-231
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• 2021

Purpose: This study intends to provide decision-making information to improve efficiency by analyzing the management efficiency of smart greenhouse business entities and identifying factors that affect the efficiency based on input and output. Methods: The subjects of analysis were business entities for cultivating strawberries in smart greenhouses in Jeolla region (northern and southern Jeolla provinces), and the analysis focused on the management performance of the 2019-2020 crop period (year). Data Envelopment Analysis(DEA) was applied as an analysis method for efficiency analysis, Quantile Regression(QR) analysis was applied as a factor affecting the efficiency. Results: The reason for the efficiency gap between business entities was that there were many business entities that did not minimize the input cost at the current level of output, and the area where the variance among business entities was large was the fixed cost per 10a. In the results of the affecting factor analysis, it was found that the seed-seedlings cost, fertilizer cost, other material cost, and employment and labor cost had a negative (-) effect on the efficiency, and that the repair and maintenance cost had a positive (+) effect. Conclusion: Therefore, to achieve the efficiency of scale, it is necessary to reduce the input scale to an appropriate level. In the case of business entities with low efficiency by quartile, the seed-seedlings, fertilizer, and other material costs reduce expenditures, and repair maintenance costs can improve efficiency by increasing expenditures.

• Journal of Korean Society of Rural Planning
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• v.28 no.2
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• pp.41-50
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• 2022

The primary purpose of this study is to identify various factors that affect farmland prices according to changes in the actual transaction price of farmland over the past decade, and to use this to derive policy implications for price stabilization. To this end, the farmland price model are constructed at the parcel level in the case area (Namwon-si, Jinju-si). The analysis method is based on the Hedonic price function, and the OLS and the quantile regression are used for the parcel level model. As a result of estimating the parcel level farmland price model in the case area, the larger the parcel area, the lower the farmland price, and the higher the farmland price outside the agricultural promotion area. It was found that there was a price difference according to the type of special purpose areas, and the location characteristics showed some differences across the cities. The farmland price models presented in this study are suitable for identifying the factors affecting farmland prices, and are expected to be highly utilized in that it is possible to construct flexible variables suitable for regional characteristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.130-130
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• 2022

기후변화로 인한 기상이변 현상으로 폭우와 홍수 등 수문학적 극치 사상의 출현 빈도가 잦아지고 있다. 따라서 이러한 기상이변 현상에 적응하기 위하여 보다 정확한 확률강우량 측정의 필요성이 증가하고 있다. 대장 지점의 미래 확률강우량 계산을 위해선 기후변화 시나리오의 비정상성을 고려해야 한다. 본 연구는 비정상적인 미래 기후에서 확률강우량이 어떻게 변화하는지 측정하는 것을 목표로 한다. Representative Concentration Pathway (RCP4.5)에 따른 우리나라의 확률강우량 계산에 인공신경망을 포함한 정상성, 비정상성 확률강우량 산정 모델들이 사용되었다. 지점빈도해석(AFA), 홍수지수법(IFM), 모분포홍수지수법(PIF), 인공신경망을 이용한 Quantile & Parameter regression technique(QRT & PRT)이 정상성 자료에 대해 확률강우량을 계산하는 모델로 사용되었으며, 비정상성 자료에 대해서는 비정상성 지점빈도해석(NS-AFA), 비정상성 홍수지수법(NS-IFM), 비정상성 모분포홍수지수법(NS-PIF), 인공신경망을 사용한 비정상성 Quantile & Parameter regression technique(NS-QRT & NS-PRT)이 사용되었다. Rescaled Akaike information criterion(rAIC)를 사용한 불확실성 분석과 적합도 검정을 통해서 generalized extreme value(GEV) 분포형 모델이 정상성 및 비정상성 확률강우량 산정에 가장 적합한 모델로 선정되었다. 이후, 관측자료가 GEV(0,0,0)을 따르고 시나리오 자료가 GEV(1,0,0)을 따르는 지점들을 선택하여 미래의 확률강우량 변화를 추정하였다. 각 빈도해석 모델들은 몬테카를로 시뮬레이션을 통해 bias, relative bias(Rbias), root mean square error(RMSE), relative root mean square error(RRMSE)를 바탕으로 측정하여 정확도를 계산하였으며 그 결과 QRT와 NS-QRT가 각각 정상성과 비정상성 자료로부터 가장 정확하게 확률강우량을 계산하였다. 본 연구를 통해 향후 기후변화의 영향으로 확률강우량이 증가할 것으로 예상되며, 비정상성을 고려한 빈도분석 또한 필요함을 제안하였다.

• Korean Journal of Social Welfare Studies
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• v.43 no.2
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• pp.345-366
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• 2012

This study analyze Family gaps(the wage gap between mothers and non-mothers) across the wages distribution in Korea using 2008 Korean Labor and Income Panel Study. Analysis models include Heckman's two stage estimation to control women's labor participation selection and Quantile regression method to examine the effects of children at different points of the wage distribution. The result indicates that first, comparing non-mothers, mothers with one child suffer statistically significant hourly wage losses at 25th, 50th, and 75th distribution, however not significant effects are found at lowest(10th) and highest(90th) distribution. Second, comparing non-mothers, mothers with two more children suffer statistically significant hourly wage losses at all distribution. Family gap differs across the wage distribution and highest family gaps are found at 25th distribution. With these results, the author suggests universal family policies to support mothers' labor participation and the reconciliation of work and family.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.117-124
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• 2021

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.133-153
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• 2020

Officially assessed land price has been the index of South Korea since 1989 throughout different sectors of tax and welfare. Officially assessed land price is used as a tax valuation for the tax on property holdings, and the equity of such is the most important factor in the fair taxation for the people of South Korea. On this wise, this research analyzed and verified the horizontal and vertical inequity of officially assessed land price in Seoul by using the real transaction data between 2016 and 2018. In fact, Seoul's assessment ratio for the entire three-year period was 60.64% and it showed to increase each year. Horizontal equity was found to be most favorable in 2017, and the horizontal equity of each borough of Seoul appeared to improve each year. Vertical inequity was found to have reverse inequality in most boroughs of Seoul, however, some parts of Gangnam districts such as Gangnam-gu, Seocho-gu, and Gangdong-gu presented progressive inequality. Such example showed the need for improvement in terms of balance by each borough. The use of quantile regression demonstrated reverse inequality in most quantile, but, the differences in the value of the coefficient by each quantile showed the need for improvement of officially assessed land price with the equity of each quantile. Through the equity verification of officially assessed land price, it was analyzed that the lack of equity was found by year, by borough, and by use district. In order to redeem the lack of equity, the government must systematically supplement the real-estate disclosure system by initiating ratio studies to verify horizontal and vertical equity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.607-614
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• 2020

This secondary analysis study used the 2015 Korea Health Panel data to identify the difference of factors affecting the quality of life (QoL) of Korean adults with diabetes mellitus (DM). The data from 1,343 subjects who met the criteria for screening was analyzed by multiple linear regression and quantile regression analyses. As a result, the average QoL of the subjects was .86 (±.14). The factors affecting the QoL of the subjects were age, gender, education level, spouse, economic activity, type of health insurance, and regular physical activity (all p<.05). However, there were differences of the results when dividing the subjects into three groups according to their QoL level. The factors for the lower 25% group were age, gender, education level, spouse, economic activity, type of health insurance, and regular physical activity, and those factors for the middle 50% group were age, gender, education level, spouse, type of health insurance, and regular physical activity, and those factors for the top 75% group were spouse, economic activity, household income, type of health insurance, and duration of DM. Based on these results, it is important to develop and provide a differentiated intervention strategy that considers the influential factors in order to improve the QoL of Korean adults with DM.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.2
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• pp.94-104
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• 2015

Population and urban development along the coast is growing in South Korea, and particularly sea level rise is likely to increase the vulnerability of coastal areas. This study aims to investigate the sea level rise through Mann-Kendall(MK) test, ordinary linear regression(OR) and quantile regression analysis(QRA) with sea level data at the 20 tide stations along the coast of Korean Peninsula. First, statistically significant long-term trends were analysed using a non-parametric MK test and the test indicated statistically significant trends for 18 and 10 stations at the 5% significance level in the annual mean value of sea level and the annual maximum value of sea level, respectively. The QRA method showed better performance in terms of characterizing the degree of trend. QRA showed that an average annual rise in mean sea level is about 1-6 mm/year, and an average rise in maximum sea level is about 1-20 mm. It was found that upward convergent and upward divergent were a representative change given the nine-category distributional changes. We expect that in future work we will address nonstationarities with respect to sea level that were identified above, and develop a nonstationary frequency analysis with climate change scenarios.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1253-1262
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• 2014

Regression tree is a tree-structured solution in which a simple regression model is fitted to the data in each node made by recursive partitioning of predictor space. There have been many efforts to apply tree algorithms to various regression problems like logistic regression and quantile regression. Recently, algorithms have been expanded to the panel data analysis such as RE-EM algorithm by Sela and Simonoff (2012), and extension of GUIDE by Loh and Zheng (2013). The algorithms are briefly introduced and prediction accuracy of three methods are compared in this paper. In general, RE-EM shows good prediction accuracy with least MSE's in the simulation study. A RE-EM tree fitted to business survey index (BSI) panel data shows that sales BSI is the main factor which affects business entrepreneurs' economic sentiment. The economic sentiment BSI of non-manufacturing industries is higher than that of manufacturing ones among the relatively high sales group.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1237-1246
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• 2021

The purpose of the research is to evaluate how education influences the income of household heads, who are young adult in rural Vietnam. In order to examine the impact of education on the households where their heads are young adults, in this paper, the authors employ two research methods. First, ordinary least squares (OLS) regression is used to study the impact of education on different groups of income; second, quantile regression is applied to find out how education influences the income of households. The dataset includes a survey of 800 young households aged between18 and 35 who are the head of agricultural farms in rural areas. The findings indicate that education has a positive impact on income of young households. Furthermore, the results prove that the longer schooling years, the higher income youth can attain. The results showed that, at the survey time (Sep 2019), the average monthly income of rural young adults who are joining the production process shows a big gap between low and high incomes. Moreover, the study has revealed that other factors positively affect the incomes, namely, joining job-related associations, land resource, hired labour, hi-tech application as well as extension of producing unit.