• International Journal of Automotive Technology
• /
• v.4 no.2
• /
• pp.101-108
• /
• 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 Korean Operations Research and Management Science Society
• /
• v.33 no.2
• /
• pp.153-174
• /
• 2008

There have been two streams of the studies on development structure : conceptual model approach and statistical analysis approach. But In these days, the latter has been becoming the main approach owing to the development of multivariate statistical methods and statistical packages. In this study, we examine methodologies and results of the leading researches related to development structure based on statistical analysis and propose the future research directions. This analysis would be expected to contribute toward the construction of long-range development policies on each country.

• The Korean Journal of Applied Statistics
• /
• v.30 no.5
• /
• pp.747-757
• /
• 2017

We investigate multivariate volatilities based on high frequency time series. The PCA (principal component analysis) method is employed to achieve a dimension reduction in multivariate volatility. Multivariate realized volatilities (RV) with various frequencies are calculated from high frequency data and "optimum" frequency is suggested using PCA. Specifically, RVs with various frequencies are compared with existing daily volatilities such as Cholesky, EWMA and BEKK after dimension reduction via PCA. An analysis of high frequency stock prices of KOSPI, Samsung Electronics and Hyundai motor company is illustrated.

• Korean Journal of Food Science and Technology
• /
• v.48 no.5
• /
• pp.418-423
• /
• 2016

This study was conducted to identify the geographical origin of soybeans in Cheonggukjang by analyzing its organic components and inorganic elements with Fourier transform near-infrared spectroscopy (FT-NIRS) and with energy dispersive X-ray fluorescence (ED-XRF) coupled with multivariate statistical analysis. For method development, 280 samples from various regions were collected and analyzed. The discriminant accuracy for the developed methods was 97.5% for FT-NIRS and 98.0% for ED-XRF with multivariate statistical analysis. A validation test confirmed the discriminant accuracy to be 96.3% for FT-NIRS and 95.0% for ED-XRF. Overall, the results showed that methods using FT-NIRS and ED-XRF could be used to identify the geographical origin of Cheonggukjang.

• Journal of the Ergonomics Society of Korea
• /
• v.26 no.4
• /
• pp.103-111
• /
• 2007

The purpose of this study is to categorize and analyze the body shape of obese Korean men that are needed for industrial design. Using the anthropometric data that were surveyed through the 5^th Size Korea project, this study was conducted in four steps mostly through the multivariate statistical analysis. In the first step, Broca, BMI, WHR indices are used to define obesity and select obese men from Korean adults and teens. After 34 human anthropometric variables are supposed to be related to obesity were extracted through an expect survey. In the second step, a factor analysis was executed for those human anthropometric variables. Through this analysis, we obtained the human body factors that are related to the representation of obesity. Then the third step, we used a cluster analysis from the result of the factor analysis. And ANOVA analysis was also conducted to obtain the critical obese human anthropometric variables. In the final step, we found the characteristics of the body types of obese men according to clusters and ages. The body types of obese men classified in the study are expected to be applied to product design for clothing, furniture, automobile packaging, etc.

• Biomolecules & Therapeutics
• /
• v.17 no.2
• /
• pp.133-137
• /
• 2009

The root of Astragalus membranaceus is a traditional folk medicine that has been used for many therapeutic purposes in Asia. It reportedly acts as an immunostimulant, tonic, hepatoprotective, diuretic, antidiabetic, analgesic, expectorant, sedative, and anticancer drug. In this study, metabolomic profiling was applied to the roots of A. membranaceus of different ages using NMR coupled with two multivariate statistical analysis methods: such as principal components analysis (PCA) and canonical discriminant analysis (CDA). This allowed various metabolites to be assigned in NMR spectra, including $\gamma$-aminobutyric acid (GABA), aspartic acid, succinic acid, glutamic acid, glutamine, N-acetyl aspartic acid, acetic acid, arginine, alanine, threonine, lactic acid, and valine. The score plot from PCA and also CDA allowed a clear separation between samples according to age.

• Korean Journal of Medicinal Crop Science
• /
• v.24 no.2
• /
• pp.93-100
• /
• 2016

Background: A set of logical criteria that can accurately identify and verify the cultivation region of raw materials is a critical tool for the scientific management of traditional herbal medicine. Methods and Results: Volatile compounds were obtained from 19 and 32 samples of Angelica gigas Nakai cultivated in Korea and China, respectively, by using steam distillation extraction. The metabolites were identified using GC/MS by querying against the NIST reference library. Data binning was performed to normalize the number of variables used in statistical analysis. Multivariate statistical analyses, such as Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) were performed using the SIMCA-P software. Significant variables with a Variable Importance in the Projection (VIP) score higher than 1.0 as obtained through OPLS-DA and those that resulted in p-values less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Among the 19 variables extracted, styrene, ${\alpha}$-pinene, and ${\beta}$-terpinene were selected as markers to indicate the origin of A. gigas. Conclusions: The statistical model developed was suitable for determination of the geographical origin of A. gigas. The cultivation regions of six Korean and eight Chinese A. gigas. samples were predicted using the established OPLS-DA model and it was confirmed that 13 of the 14 samples were accurately classified.

• The Korean Journal of Applied Statistics
• /
• v.11 no.2
• /
• pp.389-401
• /
• 1998

This paper introduces CybeRClass(Cyber Regression Class). CybeRClass uses the technique of animation arid voice to teach regression analysis. The structure of this system make it possible to extend to multivariate analysis methods such as discriminant analysis and cluster analysis. Tools for multimedia is Multimedia ToolBook, and Xlisp-Stat is used for statistical computation and statistical graphics.

• Environmental Engineering Research
• /
• v.11 no.2
• /
• pp.63-76
• /
• 2006

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.

• YAKHAK HOEJI
• /
• v.60 no.1
• /
• pp.29-35
• /
• 2016

Traditional Korean medicines may be managed more scientifically, through the development of logical criterion to verify their cultivation region. It contributes to advance the industry of traditional herbal medicines. Volatile compounds were obtained from 14 samples of domestic Taeksa and 30 samples of Chinese Taeksa by steam distillation. The metabolites were identified by NIST mass spectral library in the obtained gas chromatography/mass spectrometer (GC/MS) data of 35 training samples. The multivariate statistical analysis, such as Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), were performed based on the qualitative and quantitative data. Finally trans-(2,3-diphenylcyclopropyl)methyl phenyl sulfoxide (47.265 min), 1,2,3,4-tetrahydro-1-phenyl-naphthalene (47.781 min), spiro[4-oxatricyclo[5.3.0.0.(2,6)]decan-3-one-5,2'-cyclohexane] (54.62 min), 6-[7-nitrobenzofurazan-4-yl]amino-morphinan-4,5-epoxy (54.86 min), p-hydroxynorephedrine (55.14 min) were determined as marker metabolites to verify candidates for the origin of Taeksa. The statistical model was well established to determine the origin of Taeksa. The cultivation areas of test samples, each 3 domestic and 6 Chinese Taeksa were predicted by the established OPLS-DA model and it was confirmed that all 9 samples were precisely classified.