• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.614-614
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• 2015

하천수는 하천의 지표면에 흐르거나 하천 바닥에 스며들어 흐르는 물 또는 하천에 저장되어 있는 물을 말하며, 인구의 증가나 산업화로 인하여 물 사용이 증대되고 있는 실정이다. 이에 따라 국가에서는 하천법 제50조에 의거하여 생활 공업 농업 환경개선 발전 주운 등의 용도로 하천수를 사용하려는 자는 국토교통부장관(홍수통제소장)의 허가를 받아야 한다. 우리나라 수문특성상 우기에 집중되어 있는 물을 갈수기에 이용할 수 있도록 분배하고 관리하는 것이 어려운 실정이다. 하천법 제51조에 의하면 하천유지유량은 생활, 공업, 농업, 환경개선, 발전, 주운 등의 하천수 사용을 고려하여 하천의 정상적인 기능과 상태를 유지하기 위한 최소한의 유량을 말하며, 2006년 고시된 낙동강 수계의 하천유지유량은 대부분 국가하천을 기준지점으로 산정되었고, 산정방법은 평균갈수량 13개, 기준갈수량 2개, 하천생태계 2개 지점이다. 또한, 하천관리유량은 하천유지유량과 이수유량의 합으로 산정된다. 본 연구에서는 2013년도 말 기준으로 하천수 사용허가 현황을 정리하였으며, 용도별, 수계별, 행정구역별, 하천등급별로 하천수 사용허가 건수와 허가량을 분석하였다. 낙동강본류를 대상으로 하천시설물을 고려하여 하천관리유량을 산정하였으며, 하천유지유량의 적절성을 검토함으로써 향후 낙동강권역의 물관리방안을 모색하였다. 낙동강본류구간은 하천관리유량이 기준갈수량에 비하여 큰 형태로써 하천관리유량 확보를 위한 노력이 필요한 것으로 나타났으며, 향후 하천수 사용허가 시설물의 회귀율에 대한 실제적인 자료 수집과 함께 정확한 분석을 통한 하천의 회귀유량을 산정하고, 하폐수 방류시설을 고려하여 산정한 결과와 비교하여 보다 정확한 물수지 분석 체계를 확립하여야 할 것이다.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.23-34
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• 2017

This paper is to study the simulation model of depot maintenance system that analyzes logistics supportability such as component availability and cost of target equipment. A depot maintenance system could repair or maintain multiple components simultaneously. The key performance indicators of this system are component availability, repair cycle time, and maintenance cost. The simulation model is based on the engine maintenance process of army aviation depot. This study combines the NOLH(Nearly Orthogonal Latin Hypercube) experimental design method, to composes 33 scenarios, with a multiple regression analysis to find out major factors that influence on key performance indicators. This study is significant in providing a cost-effectiveness analysis on depot maintenance system that is capable of maintaining multiple components at the same time.

• Journal of Korean Society for Atmospheric Environment
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• v.7 no.3
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• pp.180-188
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• 1991

기후변화를 나타내는 연구 결과들은 기온이나 강수량을 대상으로 하여 두 기간 평균치의 비교, 5년 이동평균치의 圖示, 회귀분석 등의 방법들을 사용하여 발표되지만 대부분의 경우 밑바탕이 되는 모집단의 통계적 특성을 엄격히 검토하지 않는 정성적인 분석인 경우가 많다. 서울, 부산, 인천, 목포 등 4개 지점의 80년 동안의 연강수량과 연평균기온자료를 분석한 결과 기온은 정규분포를 나타내므로 전통적인 t-검정과 회귀분석을 적용할 수 있다. t-검정 결과 두 기간(1920 $sim$ 1949, 1961 ~ 1990)은 연평균기온에서 0.3$^{\circ}$C에서 $0.8^{\circ}$C의 의의있는 차이를 보였다. 최근 30년의 연평균기온자료를 회귀분석한 결과 1년에 $0.02^{\circ}$C의 증가경향을 나타내었다. 연강수량은 정적인 왜곡도를 보이므로 정규분포를 가정하는 母數的 통계분석을 적용해서는 안되며 비모수적 통계분석법을 적용해야 한다. 연강수량의 분석에는 t-검정에 상응하는 Mann-Whitney 검정을 적용해 본 결과 두 기간(1920 ~ 1949, 1961 ~ 1990)의 평균은 통계적으로 의의있는 차이가 없었다. 회귀분석에 상응하는 Mann's 검정과 이 연구에서 새로이 제안된 Median Slope Change Estimator를 적용해 본 결과 역시 통계적으로 의의있는 변화가 없었다. Median Slope Change Estimator는 비모수적 통계치로서 회귀분석의 기울기에 해당하는 연변화를 나타낼 수 있는데 분석하려는 자료의 정규분포성을 요구하지 않으며 異常點(outlier) 영향에 덜 민감하다는 장점이 있으므로 주목하여 연구할 만한 가치가 있다.^{\circ}$~40$^{\circ}$ 구간에서는 ${\sigma}_c$와 I$_{sa}$, 40$^{\circ}$~90$^{\circ}$ 구간에서는 ${\sigma}_c$와 I$_{sd}$가 각각 양호한 상관관계를 보여준다. 또한 상관비(K=${\sigma}_c$/I$_s$)는 약 13정도로서 일반적으로 적용되는 비, 24와 상당한 차이가 있다. 이러한 현상은 호상편마암의 구조적 및 역학적 이방성 특성으로 인한 결과라고 판단된다. 한편 맥암류에서 K가 약 23정도로서 일반적인 비 24에 상당히 접근한다. 따라서, 이방성 구조가 뚜렷한 암석에서 상관비 24는 항상 적용할 수는 없으며 일축압축강도시험과 병행 실시하여 적용하는 것이 바람직하다.다. 한편, 감작감염후 77일과 도전감염후 7일의 시점에 있어서 sRBC에 대한 면역능에 미치는 영향은 전자의 양상과 비슷하였는데 대조에 비하여 지연형 과민반응과 로제트 형성능이 현저하게 저하되었다. 이 시점에 있어서의 유충회수율은 대조가 10.5% 이었는데 비하여 8.3%이었다.e also compared for the cases of Kim et al, which again gives promising agreement.면적 306~453$\textrm{cm}^2$, 유색계의 경우 수당면적 340~453$\textrm{cm}^2$ 일 때 경제능력을 제대로 발휘할 수 있고 경제성이 있는 것으로 나타났다. 첨가구가 높은 경향이 있었다8.4%. 79.7% 그리고 80.2%였다.

• KDI Journal of Economic Policy
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• v.36 no.1
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• pp.1-42
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• 2014

This study analyzes how heterogeneous across firms' productivity level the effects of foreign direct investment (FDI) on the productivity of firms in a host country are. The study uses firm level data over 2000~2009 in South Korea and takes a quantile regression approach to estimate FDI's heterogeneous effects on the invested firm ('direct effects') and other domestic firms in the industry to which the invested firm belongs ('intra-industry spillover effects'). Major empirical results are as follows. In manufacturing sector, FDI has positive and statistically significant direct effects on the invested firm. In addition, the higher the quantiles of firms' productivity level are, the larger the positive productivity effects are. FDI also has positive and statistically significant intra-industry spillover effects on domestic firms in low quantiles of productivity while it has negative and statistically significant or insignificant spillover effects on those in high productivity quantiles. In service sector, on the other hand, Sufficient evidence is not found that FDI has statistically significant direct effects or intra-industry spillover effects. Taken together, the study suggests that FDI has heterogeneous effects on the productivity of firms in host country, depending on the firms' productivity level and sector.

• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.79-88
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• 2012

Recently, the study in efficient operation, maintenance, and equipment-design have been growing rapidly in military industry to meet the required missions. Through out these studies, the importance of Concurrent Spare Parts(CSP) are emphasized. The CSP, which is critical to the operation and maintenance to enhance the availability, is offered together when a equipment is delivered. Despite its significance, th responsibility for determining the range and depth of CSP are done from administrative decision rather than engineering analysis. The purpose of the paper is to optimize the number of CSP per item using simulation and multiple regression. First, the result, as the change of operational availability, was gained from changing the number of change in simulation model. Second, mathematical regression was computed from the input and output data, and the number of CSP was optimized by multiple regression and linear programming; the constraint condition is the cost for optimization. The advantage of this study is to respond with the transition of constraint condition quickly. The cost per item is consistently altered in the development state of equipment. The speed of analysis, that simulation method is continuously performed whenever constraint condition is repeatedly altered, would be down. Therefore, this study is suitable for real development environment. In the future, the study based on the above concept improves the accuracy of optimization by the technical progress of multiple regression.

• Food Science and Preservation
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• v.13 no.1
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• pp.71-76
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• 2006

We monitored the characteristics of soybean hydrolysate prepared under various hydrolysis condition using response surface methodology. The yield was affected by protease content but 1be effect of hydrolysis time to yield gradually increased at over $0.4\%$ of protease, while the $R^2$ of polynomial equation was 0.978 (p<0.01). The soluble solid enlarged by increase of both variables and the $R^2$ of polynomial equation was 0.954 (p<0.01). The degree of hydrolysis was affected by protease content at low (under $0.4\%$) protease and maximized at $0.57\%$ protease and 5.49 hrs. The $R^2$ of polynomial equation for the degree of hydrolysis was 0.916 (P<0.05). The calcium intolerance capacity showed similar pattern like yield but the effect of hydrolysis time was rapidly increased at over $0.4\%$ protease. The $R^2$ of polynomial equation for calcium intolerance capacity was 0.932 (p<0.05). The total phenolic compounds increased in proportion to protease content and hydrolysis time, while the $R^2$ of polynomial equation was 0.920 (p<0.05). According to the results of this study, the optimal conditions for soybean hydrolysis were predicted to be $0.51\~0.66\%$ of protease and $6.5\~9.0\;hrs$, and the predicted values and actual values of each response variable were similar to each other when the hydrolysis was performed at a random point within the optimal range.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.80-90
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• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.218-236
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• 2023

This study attempted to analyze the environmental equity of fine dust(PM₁₀) in Daegu using MGWR(Multi-scale Geographically Weighted Regression) and KT(Korea Telecom Corporation) sensor data. Existing national monitoring network data for measuring fine dust are collected at a small number of ground-based stations that are sparsely distributed in a large area. To complement these drawbacks, KT sensor data with a large number of IoT(Internet of Things) stations densely distributed were used in this study. The MGWR model was used to deal with spatial heterogeneity and multi-scale contextual effects in the spatial relationships between fine dust concentration and socioeconomic variables. Results indicate that there existed an environmental inequity by land value and foreigner ratio in the spatial distribution of fine dust in Daegu metropolitan city. Also, the MGWR model showed better the explanatory power than Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) models in explaining the spatial relationships between the concentration of fine dust and socioeconomic variables. This study demonstrated the potential of KT sensor data as a supplement to the existing national monitoring network data for measuring fine dust.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.207-218
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• 2010

Generalized additive model(GAM) is the statistical model that resolves most of the problems existing in the traditional linear regression model. However, overfitting phenomenon can be aroused without applying any method to reduce the number of independent variables. Therefore, variable selection methods in generalized additive model are needed. Recently, Lasso related methods are popular for variable selection in regression analysis. In this research, we consider Group Lasso and Elastic net models for variable selection in GAM and propose an algorithm for finding solutions. We compare the proposed methods via Monte Carlo simulation and applying auto insurance data in the fiscal year 2005. lt is shown that the proposed methods result in the better performance.