• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
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• 2003.10a
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• pp.189.1-189
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• 2003

‘부유’단감은 국내에서는 일반화되어 있는 PE필름 밀봉 저장방식으로 과실의 호흡에 의해 산소농도의 감소와 이산화탄소의 증가로 호흡이 억제되고 이에 따라 노화가 지연됨으로 과실의 저장수명을 증가시키는 방식이며, 최적저장온도는 -0.5~$0^{\circ}C$라고 보고되고 있다. 이에 본 실험에서는 상온유통을 고려하여 2$0^{\circ}C$에서 0.03mm, 0.05mm LDPE필름으로 포장한 경우와 무포장한 경우를 비교하여, 중량, 수소이온농도, 가용성고형분, 경도를 측정하고 이를 외관품질검사 결과와 종합적으로 검토하였으며, 또한 환경기체조성의 범위를 설정하기 위하여 산소농도(1~5%), 이산화탄소농도(5~15%)를 독립변수로 중심합성계획법(central composite design)에 의해 3단계로 부호화하였고 산소소비농도, 이산화탄소 발생속도, pH 당도, 경도를 종속(반응)변수로 결과를 이용하여 독립변수와 종속변수간의 함수관계를 규명하며, 독립변수들의 값의 변화에 따라서 반응량(종속변수)이 어떻게 달라지는 가를 예측하며, 독립변수가 종속변수인 반응량을 최적화(Optimize) 하는가와 어떤 실험계획법을 쓰면 가장 좋은 정도를 얻을 수 있는지를 규명하고자 한다.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2006.05a
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• pp.206-210
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• 2006

본 논문에서는 HDPCVD를 이용한 실리콘 산화막 형성에서 산화막의 특성에 영향을 미치는 RF Power, Gas, 산소 등의 공정조건과 증착된 산화막의 특성을 나타내는 증착율, 균일성 및 굴절율에 관한 주효과와 교호작응을 정량적으로 규명하고, 산화막 증착에서 관심의 대상이 되는 여러가지의 반응변수를 모두 만족시키는 최적의 공정조건을 제시한다. 실험의 효율성을 높이기 위해 통계적인 실험계획법을 활용하여 실험의 회수를 줄이는 한편 반응모델링을 통하여 입력변수와 반응변수의 관계를 시각적으로 도식화 한다. 실험을 통하여 현재 사용되고 있는 공정조건에 대한 개선점을 발견하였으며, 수립된 모델을 바탕으로 한 반응최적화 알고리즘을 통하여 세 가지 반응변수 모두 만족시킬 수 있는 5가지의 입력조건을 제시한다.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.33-39
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• 2013

In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.

• Survey Research
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• v.11 no.2
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• pp.29-44
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• 2010

The aim of this study was to determine principal non-acoustical factors for noise annoyance in the vicinity of Kimpo Airport in Seoul, Korea. Noise annoyance was estimated using self-reported annoyance scale. We have conducted a social survey aiming to identify the main sound sources, evaluate the annoyance and analyse the main effects of noise on people. Acoustical and non-acoustical variables are expected to greatly affect annoyance responses. This study divided acoustical variables into aircraft, road traffic and neighboring noises, and non-acoustical variables into demographic, situational and attitudinal variables. The study performed multiple regression analysis to determine the influences each variable has on annoyance responses. Acoustical variables affect noise annoyance to aircraft and neighboring noise except road traffic noise. For road traffic and neighboring noise annoyance was affected by non-acoustical variable, insulation by housing type. For aircraft noise, main noise source of this area, annoyance was affected by acoustical variable and some non-acoustical variables, mainly exposure time.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.1-12
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• 2024

In many real-world data, multiple response variables are often dependent on the same set of explanatory variables. In particular, if several response variables are correlated with each other, simultaneous estimation considering the correlation between response variables might be more effective way than individual analysis by each response variable. In this multivariate regression analysis, least distance estimator (LDE) can estimate the regression coefficients simultaneously to minimize the distance between each training data and the estimates in a multidimensional Euclidean space. It provides a robustness for the outliers as well. In this paper, we examine the least distance estimation method in multivariate linear regression analysis, and furthermore, we present the penalized least distance estimator (PLDE) for efficient variable selection. The LDE technique applied with the adaptive group LASSO penalty term (AGLDE) is proposed in this study which can reflect the correlation between response variables in the model and can efficiently select variables according to the importance of explanatory variables. The validity of the proposed method was confirmed through simulations and real data analysis.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1021-1026
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• 2017

Mixture experiments are commonly encountered in many fields including food, chemical and pharmaceutical industries. In mixture experiments, measured response depends on the proportions of the components present in the mixture and not on the amount of the mixture. Statistical analysis of the data from mixture experiments has mainly focused on a continuous response variable. In the example of quantal response data in mixture experiments, however, the tumor incidence data have been analyzed in Chen et al. (1996) to study the effects of 3 dietary components on the expression of mammary gland tumor. In this paper, we compared the logistic regression models with linear predictors such as second degree Scheffe polynomial model, Becker model and Akay model in terms of classification accuracy.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.61-74
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• 1995

These days, many kinds of graphical methods have been developed, and it is possible to get information directly from data. Especially, R-code (Cook and Weisberg, 1994) make it possible to draw various kinds of two and three dimensional plots, and to rotate the axis of the plots. But the maximum dimensional of the plot is three, so we can not draw plot of one response variable with more than three explanatory variables. Li(1991, 1992) has developed a method to reduce the dimension of the explanatory variables, so it is possible to draw lower dimensional plots to get information of the full explanatory variables. One of the dimension reduction method developed by Li is pHd. In this paper, we have tried to apply the pHd method for the model with multivariate response.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.12
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• pp.689-697
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• 2017

As a fundamental study to apply the new flocculation method using ballast in water treatment process, the optimal conditions for general and ballast coagulant dosage, and pH, which are known to have a significant influence, were derived by response surface methodology. Poly aluminum chloride (PAC) and magnetite ballast were used as a general coagulant and ballast, respectively. Coagulation experiments were performed by jar-tester using the kaolin based synthetic water. The effects of three independent variables (pH, PAC, and ballast) on response variables (turbidity removal rate and average settling velocity of flocs) and the optimum condition of independent variables to induce the optimum flocculation were obtained by 17 experimental conditions designed by Box-Behnken procedure. After performing experiments, the quadratic regression model was derived for each of response variables, and the response surface analysis was conducted to explore the correlation between independent variables and response variables. The $R^2$ values for the turbidity removal rate and the average settling velocity were 0.9909 and 0.8295, respectively. The optimal conditions of independent variables were 7.4 of pH, 38 mg/L of PAC and 1,000 mg/L of ballast. Under these conditions, the turbidity removal rate was more than 97% and the average settling velocity exceeded 35 m/h.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.1-9
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• 1996

반응표면분석에서 다반응값의 최적화 문제는 다반응값 최적화 문제보다 복잡하다. 이런 다반응값 문제에서 반응변수들이나 설명변수 상호간의 곤계나 중요성 등을 평가하는 것은 중요하다. 이러한 평가를 위하여 biplot가 유용한 그림도구로 쓰일 수 있다.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.518-518
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• 2012

공정 수율 향상을 위한 웨이퍼의 대면적화는 공정 반응용기의 부피변화를 수반한다. 반응용기의 부피가 커지면 플라즈마 내의 전자와 이온이 손실되는 면적이 증가하게 되고, 그 결과 공정결과에 직접적으로 영향을 미치는 전자온도와 전자밀도가 떨어지게 된다. 이렇게 변화된 플라즈마 변수들을 원래의 값으로 되돌리기 위해서는 인가전력, 실험압력, 유량과 같은 외부변수들이 조절되어야 하는데, 공간 평균 모델(global model) 식을 이용하여 외부변수들의 변량을 계산할 수가 있다. 본 연구에서는 부피가 다른 두 반응용기에서의 플라즈마 변수 진단을 통해서 부피가 커진 환경에서의 전자온도와 전자밀도가 떨어지는 현상을 관찰하였고, 공간 평균 모델로 계산된 외부변수들의 변량을 적용하였을 때 원래의 값으로 가까워 지는 경향을 볼 수가 있었다. 이렇게 같은 공정 결과를 얻기 위한 외부변수들의 변량을 간단히 계산함으로써 대면적화가 되었을 때 외부변수들을 얼마나 변화시켜야 하는지에 대한 일반적인 방향을 제시해 줄 수 있다.