• 원예과학기술지
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• 제32권2호
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• pp.241-251
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• 2014

본 연구는 남부 지방에서 널리 사용되고 있는 상록활엽수인 돈나무의 내한성 예측을 위한 편리하고 신뢰성 있는 평가 모델 개발을 목적으로 전해질 용출법을 통한 내한성 평가에서 나타나는 실험방법 상의 오차를 최소화하는 내한성 평가 모델을 도출하고자 수행되었다. 평가 모델링은 저온 처리된 식물체에 대한 재생검사와 전해질 용출 평가로 구성되었고, 전해질 용출법에서 표본조직 선택, 최대 전해질 용출 측정을 위한 온도 처리법, 치사 온도 예측을 위한 통계 분석법에 의한 방법적 조합들로부터 예측된 치사 온도들이 재생검사 결과와 비교되었다. 재생 검사 결과 돈나무의 저온 치사 온도는 50% 미만의 생존율을 보이는 최고온도인 $^-10{\circ}C{\sim}-5^{\circ}C$로 분석되었고, 이 결과를 바탕으로 전해질 용출법에 의해 예측된 저온 치사 온도를 분석한 결과, 잎을 표본 조직으로 하여 냉각치사법으로 최대 전해질 용출을 측정한 방법적 조합에서 재생 검사 결과와 가장 근접한 예측 저온 치사 온도가 나타났다. 저온 치사온도 예측을 위한 통계 모델 평가에서는 선형보간법이 비선형회귀에 비하여 내한성을 과대평가하는 경향이 상대적으로 높았다. 결론적으로 돈나무 내한성 예측을 위한 내한성 평가 모델은 잎을 표본 조직으로 사용하고, 최대 전해질 용출 측정을 위한 온도 처리 방법으로 냉각치사법을 적용하며, 치사온도 예측을 위한 통계 분석 기법으로 비선형회귀를 활용하는 방법적 구성이 가장 적합한 것으로 나타났다.

• 생태와환경
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• 제38권4호통권114호
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• pp.445-453
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• 2005

전남 주암호에서의 식물플랑크톤 동태를 파악하기 위해 2003년 2월부터 10월까지 식물플랑크톤 생물량 (클로로필 a)의 크기별 시 ${\cdot}$ 공간적 변동과 제반 환경요인에 대해 조사하였다. 본 논문에서는 주암호와 같은 담수호에서 식물플랑크톤의 크기 구조가 계절적, 공간적으로 나타나는 변동에 대한 영양염들의 영향을 회귀분석을 통해 파악하고자 하였다. 또한 인공지능망을 이용하여 전체 식물플랑크톤의 생물량(클로로필 a)에 대한 상향식, 하향식 조절인자들에 대한 상대적인 중요도를 정량적으로 파악하고자 하였다. 비록 동물플랑크톤 포식압을 나타내는 포식율이나 동물플랑크톤 생체량 대신 포식압의 간접 지수인 chlorophyll a: pheopigments ratio를 활용하였지만 회귀분석결과, 영양염 특히 인산염과 식물플랑크톤의 생물량이 양의 상관관계를 갖는 것으로 나타났고chlorophyll a: pheopigments ratio도 결정계수가 다소 낮기는 하지만 양의 상관관계를 보여 주었다. 인공지능망 시뮬레이션 결과에서는 주암호 식물플랑크톤의 생물량은 수온, 영양염 특히 인산염과 같은 상향식 조절이 우세한 것으로 나타났다.

• JSTS:Journal of Semiconductor Technology and Science
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• 제11권3호
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• Journal of the Korean Data and Information Science Society
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• 제25권2호
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• pp.403-410
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• 2014

이분형 로지스틱 회귀분석은 양적 혹은 질적 설명변수를 이용해서 이분형 반응변수를 설명하는 하나의 통계적인 기법이다. 이 모형에서는 반응변수가 1이 될 확률을 설명변수들의 선형결합의 변환(혹은 함수)으로 설명하고자 한다. 이 개념에 대한 이해가 비통계학자들이 이분형 로지스틱 회귀모형을 이해하는데 있어서 넘어야 할 커다란 장벽 중의 하나이다. 이 연구에서는 이분형 로지스틱 회귀모형의 필요성을 엑셀 VBA를 이용하여 설명하는 교육도구를 개발하고자 한다. 반응변수가 1이 될 확률을 설명변수의 선형함수로 모형화 할 때의 문제점과 선형결합에 대한 변환을 통해 이 문제점이 어떻게 해소되는지 보여준다.

• Journal of Dairy Science and Biotechnology
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• 제26권2호
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• pp.51-56
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• 2008

Using central composite design, we have designed optimization of the manufacturing of processed butter. And response surface analysis by least-square regression was used Statistical Analysis System(SAS). Central composite design can be achieved by response surface techniques that allow flexibility in modeling and analysis. Response surface methodology(RSM) was used to optimize hardness(%) using as independent variables; the content of butter($X_1$), ranging from 50 to 90(%), the content of soybean oil($X_2$), from 0 to 20(%), and the hydrogenated soybean oil($X_3$) from 0 to 4(%). The results on the regression coefficients calculated for overrun by response surface by least-square regression(RSREG) were followed. It was considered that the linear regression was significant(p<0.01). As for the processed butter, the regression model equation for the hardness(Y, %) to the change of an independent variable could be predicted as follow: $Y=60.88-8.92X_2-{29.3X_2}^2$. The optimal for the manufacturing of processed butter were determined at the content of butter of 88.22%, soybean oil of 6.71% and hydrogenated soybean oil of 2.36%, respectively. Optimum compositions were resulted in hardness of 65.78 N. Finally the reference sample(Butter in the morning, Seoul Dairy Co-op.) and processed butter manufacturing under the optimal conditions were compared with spreadability test. The spreadability scores result from reference sample and butter under optimal conditions was not found a significant difference.

• 한국대기환경학회지
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• 제24권6호
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• pp.649-661
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• 2008

The impact of the detailed surface data on regional meteorological fields in complex coastal area is studied using RAMS. Resolutions of topography and land use data are very important to numerical modeling, because high resolution data can reflect correct terrain height and detail characteristics of the surface. Especially, in complex coastal region such as Gwangyang area, southern area in Korean Peninsula, high resolution topography and land use data are indispensable for accurate modeling results. This study investigated the effect of resolutions of terrain data using SRTM with 3 second resolution topography and KLU with 1 second resolution land use data. Case HR was the experiment using high resolution data, whereas Case LR used low resolution data. In Case HR, computed surface temperature was higher than Case LR along the coastline and wind speed was $1{\sim}2m/s$ weaker than Case LR. Time series of temperature and wind speed indicated great agreement with the observation data. Moreover, Case HR indicated outstanding results on statistical analysis such as regression, root mean square error, index of agreement.

• 한국전기전자재료학회논문지
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• 제18권1호
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• pp.1-5
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• 2005

A new model of plasma etch process was constructed by using a radial basis function network (RBFN). This technique was applied to an etching of silicon carbide films in a NF$_3$ inductively coupled plasma. Experimental data to train RBFN were systematically collected by means of a 2$^4$ full factorial experiment. Appropriateness of prediction models was tested with test data consisted of 16 experiments not pertaining to the training data. Prediction performance was optimized with variations in three training factors, the number of pattern units, width of radial basis function, and initial weight distribution between the pattern and output layers. The etch responses to model were an etch rate and a surface roughness measured by atomic force microscopy. Optimized models had the root mean-squared errors of 26.1 nm/min and 0.103 nm for the etch rate and surface roughness, respectively. Compared to statistical regression models, RBFN models demonstrated an improvement of more than 20 % and 50 % for the etch rate and surface roughness, respectively. It is therefore expected that RBFN can be effectively used to construct prediction models of plasma processes.

• Smart Structures and Systems
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• 제15권5호
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• pp.1329-1344
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• 2015

The stiffness of a structure is one of several structural signals that are useful indicators of the amount of damage that has been done to the structure. To accurately estimate the stiffness, an equation of motion containing a stiffness parameter must first be established by expansion as a linear series model, a Taylor series model, or a power series model. The model is then used in multivariate autoregressive modeling to estimate the structural stiffness and compare it to the theoretical value. Stiffness assessment for modeling purposes typically involves the use of one of three statistical model refinement approaches, one of which is the efficient Akaike information criterion (AIC) proposed in this paper. If a newly added component of the model results in a decrease in the AIC value, compared to the value obtained with the previously added component(s), it is statistically justifiable to retain this new component; otherwise, it should be removed. This model refinement process is repeated until all of the components of the model are shown to be statistically justifiable. In this study, this model refinement approach was compared with the two other commonly used refinement approaches: principal component analysis (PCA) and principal component regression (PCR) combined with the AIC. The results indicate that the proposed AIC approach produces more accurate structural stiffness estimates than the other two approaches.

• Computers and Concrete
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• 제3권6호
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• pp.389-400
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• 2006

This paper presents the investigations towards developing a better understanding on the contribution of steel fibers on the tensile strength of high-performance fiber reinforced concrete (HPFRC). An extensive experimentation was carried out with w/cm ratios ranging from 0.25 to 0.40 and fiber content ranging from zero to 1.5 percent with an aspect ratio of 80. For 32 concrete mixes, flexural and splitting tensile strengths were determined at 28 days. The influence of fiber content in terms of fiber reinforcing index on the flexural and splitting tensile strengths of HPFRC is presented. Based on the test results, mathematical models were developed using statistical methods to predict 28-day flexural and splitting tensile strengths of HPFRC for a wide range of w/cm ratios. The expressions, being developed with strength ratios and not with absolute values of strengths and are applicable to wide range of w/cm ratio and different sizes/shapes of specimens. Relationship between flexural and splitting tensile strengths has been developed using regression analysis and absolute variation of strength values obtained was within 3.85 percent. To examine the validity of the proposed model, the experimental results of previous researchers were compared with the values predicted by the model.

• 한국의상디자인학회지
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• 제23권2호
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• pp.93-104
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• 2021

The worldwide coronavirus pandemic has brought to light the importance of having a reliable supply of masks for each person. This study aims to understand the effect of personal awareness (including community, others', and safety awareness) on consumption conformity and the repurchase intention of masks. The research method used the SPSS 22.0 and AMOS 22.0 statistical systems to analyze descriptive statistics in terms of reliability, validity, structural equation modeling, and moderated regression analysis. A total of 272 Chinese participants were recruited via an online survey website (www.sojump.com) from May 1 to May 14, 2020. Findings indicated that mask users' awareness can be categorized into three distinct types: community, others', and safety awareness. The more community and safety awareness is perceived, the higher the level of consumption conformity. In contrast, others' has no statistical effect on consumption conformity or repurchase intention. The positive influence of consumption conformity on the repurchase intention of masks is also weaker than price perception. However, another moderating variable, mask quality, has no moderating effect. The results of this study can help mask manufacturers and distributors retain their customers, resulting in reasonable protective measures while maintaining market order. Theoretical and managerial implications for mask suppliers are also provided.