• International Journal of Automotive Technology
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• v.4 no.2
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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.

• Journal of the Ergonomics Society of Korea
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• v.19 no.3
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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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• v.15 no.4
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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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• v.19 no.2
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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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.

• Korean Journal of Remote Sensing
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• v.23 no.3
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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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• v.45 no.1
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• pp.18-24
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• 2002

This study was conducted to find out optimum conditions for kanjang fermented with barley bran. The correlation between taste components and sensory evaluation score was analyzed with stepwise multiple regression analysis. It was revealed that the taste of kanjang was explained with the mix of free amino acids, free sugars and organic acids. The highest multiple correlation coefficient was obtained from absolute value transformed with logarithm. Thus, stepwise multiple regression analysis was conducted with absolute value transformed with logarithm, for which F-value was highest and standard error of estimation was lowest among the multiple regression models transformed with six variables. The stepwise multiple regression analysis showed that the taste components which most contribute to the quality of taste of kanjang fermented with barley bran was salty taste component followed by palatable taste component, and bitter taste component.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.452-456
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• 2002

The sensor based control system has some sensor fault while operating in the field. In this paper, a sensor fault detection and reconstruction system for a sun tracking controller has been researched by using polynomial regression and principle component analysis approach. The developed sun tracking system controls tow actuators with sensor based mechanism as on-line control and sun orbit information as off-line control, alternatively. To show the validity of the developed system, several experiments were illustrated.

• Journal of the Korean Chemical Society
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• v.47 no.1
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• pp.19-25
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• 2003

A spectrophotometric method for the simultaneous determination of anionic and nonionic surfactant based on the application of multivariate calibration method such as principal component regression(PCR) and partial least squares(PLS) has been studied. The calibration models in PCR and PLS were obtained from the spectral data in the range of 400~700 nm for each standard of a calibration set of 26 standards, each containing different amounts of two surfactants. The relative standard error of prediction(RSEP$_{\alpha}$) was obtained to assess the model goodness in quantifying each analyte in a 5 validation samples which containing different amounts of two surfactants.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4342-4348
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• 2013

The present study was intended to reveal the relationships between serum lipid levels and various factors of obesity and blood pressure. The study subjects were 1,838 adult women measured at a mass health screening during the period from January through December, 2011. TC, TG, HDL-C, LDL-C, SBP, DBP, degree of obesity, body fat rate were measured and the relation between these obesity and blood pressure measurements to serum lipid levels were studied. As a results, TC, TG, LDL-C, body fat rate and degree of obesity increased linearly with advancing age. TC, TG and LDL-C increased linearly with increasing blood pressure, and these values were higher in hypertension group than that of normal group. TC, TG and SBP increased linearly with increasing degree of obesity, and these values were higher in obesity group than that of normal group. HDL-C decreased linearly with increasing degree of obesity, and these values were lower in obesity group than that of normal group. TC, TG, HDL-C, degree of obesity, body fat rate was positive correlation with each others, but these values negatively correlated to HDL-C. Principal component analysis, showed that subjects could be divided into the group having the hypertensive group(1st principal component), the obesity group(2nd principal component), the hyperlipidemia group(3rd principal component), and HDL-C(4th principal component). In multiple regression analysis, age, TC, TG and body fat rate were affected to HDL-C. Above results suggest that higher the degree of obesity and blood pressure, the higher the serum lipid levels.