• International Journal of Automotive Technology
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• 제4권2호
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• pp.101-108
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• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• 대한인간공학회지
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• 제19권3호
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• pp.23-37
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• 2000

Usability is one of the most important factors that affect customers' decision to purchase a product. Several studies have been conducted to model the relationship between the product design variables and the product usability. Since there could be hundreds of design variables to be considered in the model, a variable screening method is required. Traditional variable screening methods are based on expert opinions (Expert screening) in most Kansei engineering studies. Suggested in this study are statistical methods for screening important design variables by using the principal component regression(PCR), cluster analysis, and partial least squares(PLS) method. Product variables with high effect (PCR screening and PLS screening) or representative variables (Cluster screening) can be used to model the usability. Proposed variable screening methods are used to model the usability for 36 audio/visual products. The three analysis methods (PCR, Cluster, and PLS) show better model performance than the Expert screening in terms of $R^2$, the number of variables in the model, and PRESS. It is expected that these methods can be used for screening the product design variables efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.

• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.609-620
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• 2008

There may be several parallel equipments having the same function in a multi-stage manufacturing process, which affect the product quality differently and have significant differences in defect rate. The product quality may depend on what equipments it has been processed as well as what process variable values it has. Applying one model ignoring the presence of different equipments may distort the prediction of defect rate and the identification of important quality variables affecting the defect rate. We propose a procedure for data segmentation when constructing models for predicting the defect rate or for identifying major process variables influencing product quality. The proposed procedure is based on the principal component analysis and the analysis of variance, which demonstrates a better performance in predicting defect rate through a case study with a PDP manufacturing process.

• 한국자동차공학회논문집
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• 제11권1호
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• pp.25-31
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• 2003

There are a lot of factors influencing on the automobile fuel economy such as average speed, average acceleration, acceleration sum per kilometer, and so on. In this study, various driving data were recorded during road tests. The accumulated road test mileage in Seoul metropolitan area is around 1,300 kilometers. The data were analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis. The analyzed results show that the average trip time per kilometer is one of the most important factors to fuel consumption and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• 대한원격탐사학회지
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• 제23권3호
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• pp.161-169
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• 2007

A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images, $R^2$= 0.82 for SS and $R^2$ = 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.

• Applied Biological Chemistry
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• 제45권1호
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• pp.18-24
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• 2002

본 연구는 보리등겨로 제조한 간장 맛의 특성을 찾기 위해서 수행되었다. 맛성분은 기기분석으로, 관능검사는 panel로, 그 외 통계적 처리의 방법 등을 이용하였다. 보리간장 맛성분은 유기산, 유리당 및 유리아미노산으로 분류하였으며, 이들과 관능검사 성적과의 단순상관으로 보리간장 맛의 품질을 결정하는 것은 불가능하였다. 중상관계수는 절대값의 대수 변환에서 가장 높게 나타났으며, 따라서 단계적 중회귀분석은 가장 설명력이 높으며, 표준오차가 적은 절대값의 대수 변환을 이용하여 실시하였다. 단계적 중회귀분석 결과, 보리간장 맛의 좋고 나쁨에 기여를 하는 성분은 짠맛, 구수한 맛 및 쓴맛을 내는 성분 순이었다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2002년도 추계종합학술대회
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• pp.452-456
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• 2002

자연환경에 노출된 센서기반의 제어장치는 센서오류가 발생하게 된다. 본 논문에서는 센서의 오류 보정기능을 갖는 고정밀 태양추적장치를 개발하였다. 다항식회귀분석 (Polynomial Regression)과 주성분 분석(Principal Component Analysis)을 응용하였으며 태양추적장치의 센서를 모델링하고 자체 진단하고 복구하는 방법을 연구하였다. 시스템의 정상동작시의 센서간의 상호관계를 이용한 모델링과 센서 표본값의 주분포 모델인 PCA 모델이 이루어지면 이를 기준으로 센서의 여러 가지 오류를 점검하고 오류센서 신호를 재건을 한다.

• 대한화학회지
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• 제47권1호
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• pp.19-25
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• 2003

흡수 분광법에 의해 얻은 스펙트럼을 주성분분석(principal analysis, PCA) 으로 자료를 요약하여 주성분 회귀분서(principal component regression, PCR)과 부분 최소자승법(partial least squares, PLS)으로 음이온과 비이온 계면활성제(anionic and nonionic surfactant)를 동시에 정량하는 방법에 대하여 연구하였다. 두 가지 계면활성제가 서로 다른 농도로 혼합되어 있는 26개의 시료용액을 400~700 nm 범위에서 스펙트럼을 얻었고, 이를 이용하여 PCR과 PLS회귀모델을 얻었다. 두 가지 계면활성제가 서로 다른 농도로 포함된 5개의 외부검정용 시료들의 스펙트럼들을 이용해서 회귀모델의 적합성을 검정하기 위하여 외부검정용 시료의 농도를 계산하였다. 계산된 농도를 이용하여 relative standard error of prediction(RSEP$_{\alpha}$)를 구하여 회귀모델의 적합성을 검정하였다.

• 한국산학기술학회논문지
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• 제14권9호
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• pp.4342-4348
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• 2013

본 연구는 혈청지질과 비만 및 혈압과의 관련성을 검토하기 위하여 2011년 1월부터 12월까지 1년 동안에 대전광역시의 한 대학병원에서 종합건강검진을 받았던 30세에서 69세의 여성 1,381명을 대상으로 TC, TG, HDL-C, LDL-C, SBP, DBP, 비만도, 체지방률을 측정하여 혈청지질과 비만 및 혈압과의 관련성을 분석하였다. 연구결과, TC, TG, LDL-C, 비만도, 체지방률은 30대에서부터 60대에 걸쳐 단계적으로 상승하는 경향을 보였다. TC, TG 및 LDL-C는 혈압이 높아짐에 따라 상승하였으며, 정상혈압군에 비해 고혈압군에서 유의하게 높은 값을 보였다. TC, TG, SBP 는 비만도가 높아짐에 따라 단계적으로 상승하였고, 정상군에 비해 비만군에서 유의하게 높았으며, HDL-C는 비만도가 높아짐에 따라 감소하는 경향을 보였고, 정상군에 비해 비만군에서 유의하게 낮았다. TC, TG, LDL-C, 체지방률 및 비만도는 상호간에 유의한 정상관을 보인 반면, HDL-C와는 음의 상관을 보였다. 주성분분석 결과 제1주성분은 고혈압 인자, 제2주성분은 비만관련 인자, 제3주성분은 연령과 고지혈증 인자, 제4주성분은 고단백지콜레스테롤 인자가 선정되었다. HDL-C와 관련된 요인을 다중회귀분석을 사용하여 검토한 결과 HDL-C에 영향을 미치는 변수로는 연령, TC, TG 및 체지방률이 선정되었다. 위와 같은 결과는 비만도가 높고 혈압이 높은 군일수록 혈청지질치가 높아짐을 시사하고 있다.