• Korean Chemical Engineering Research
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• v.48 no.4
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• pp.483-489
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• 2010

An image analysis-based soft sensor is designed and applied to automatic quality classification of product appearance with color-textural characteristics. In this work, multiresolutional multivariate image analysis (MR-MIA) is used in order to analyze product images with color as well as texture. Fisher's discriminant analysis (FDA) is also used as a supervised learning method for automatic classification. The use of FDA, one of latent variable methods, enables us not only to classify products appearance into distinct classes, but also to numerically and consistently estimate product appearance with continuous variations and to analyze characteristics of appearance. This approach is successfully applied to automatic quality classification of intermediate and final products in industrial manufacturing of engineered stone countertops.

• Bulletin of the Korean Chemical Society
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• v.31 no.4
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• pp.825-828
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• 2010

To classify Glycyrrhiza species, samples of different species were analyzed by $^1H$ NMR-based metabolomics technique. Partial least squares discriminant analysis (PLS-DA) was used as the multivariate statistical analysis of the 1H NMR data sets. There was a clear separation between various Glycyrrhiza species in the PLS-DA derived score plots. The PLS-DA model was validated, and the key metabolites contributing to the separation in the score plots of various Glycyrrhiza species were lactic acid, alanine, arginine, proline, malic acid, asparagine, choline, glycine, glucose, sucrose, 4-hydroxy-phenylacetic acid, and formic acid. The compounds present at relatively high levels were glucose, and 4-hydroxyphenylacetic acid in G. glabra; lactic acid, alanine, and proline in G. inflata; and arginine, malic acid, and sucrose in G. uralensis. This is the first study to perform the global metabolomic profiling and differentiation of Glycyrrhiza species using $^1H$ NMR and multivariate statistical analysis.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• The Korean Journal of Zoology
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• v.28 no.1
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• pp.9-20
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• 1985

Univariate and multivariate analyses of morphometric characters were performed with samples of striped field mice, Apodemus agrarius coreae Thomas, collected from Mt. Taebaek area, Mt. Wolak area, Mt. Palgong area, and Chongju area. It was confirmed that the samples of A. agrarius coreae were similar with one another to be grouped into a single subspecies. Moreover, clinal variation related with the altitude of localities where samples were collected was revealed in the first axis resulted from discriminant analysis and in the length of tail vertebrae.

• Korean Journal of Health Education and Promotion
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• v.21 no.2
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• pp.147-165
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• 2004

Injury is a leading cause of death in the children and adolescent populations. In particular, more than 80％ of unintentional injury was related to risk-taking behaviors involved in diverse accidents around school and home. Therefore, educational approaches should be provided for children and adolescent populations, and schools are the essential and appropriate sites to conduct safety education. This study was conducted to identify injury prevalence and safety education at schools among middle and high school students in Korea. About 1,034 middle and high students in 28 schools participated in a self-administered survey. The target schools were selected from the stratified random sampling method throughout schools of seven metropolitan cities in Korea. The questionnaires were delivered to the vice-principals by ground mailing service and the vice-principals administered survey data collection. The questionnaire asked about safety education provided in schools, injury experience in the last year, needs for injury prevention class in school, and demographics. All survey responses were entered into SPSS worksheet. Multivariate analysis of variance (MANOVA) and descriptive discriminant analysis (DDA) were used in statistical analysis with SPSS software 11.1. Multivariate analysis of variance was conducted as a preliminary analysis of DDA. According to the result of multivariate analysis of variance, gender (man), grade (poor), living with both parents, and displaying injury prevention messages on school news board were significantly different between the injured student group and the uninjured student group (p＝ .00). These four factors also had significant effects on students' injury experience in DDA, although correlation of the four factors with injury experience was weak overall based on their canonical function coefficients. All structure coefficients of the four factors were greater than .30, which means the four factors have discriminant effects on injury prevalence. The sizes of the discriminant effects, in order, were largly from gender, grade, living with both parents, and safety message display on school news boards.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.237-241
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• 2005

The physical and chemical characteristics of soils in a small watershed were investigated and the effect of geology and land use on soil quality were examined by using multivariate statistical methods, principal components analysis and discriminant analysis. It was considered that the accumulation of salts in the farmland soils indicated by electrical conductivity, contents of cations and anions and pH was caused by fertilizer input during cultivation. The contents of inorganic components are increased as following order: upland > orchard > paddy field > forest. The results of two discriminant analyses using water extractable inorganic components and their ratios by land use were also clearly classified by discriminant function 1 and 2. In discriminant analysis by components, discriminant function 1 indicated the effect of fertilizer application and increased as following order: upland > orchard > paddy field > forest soil.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• Asia pacific journal of information systems
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• v.15 no.2
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• pp.51-63
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• 2005

There has been substantial research into the bankruptcy prediction. Many researchers used the statistical method in the problem until the early 1980s. Since the late 1980s, Artificial Intelligence(AI) has been employed in bankruptcy prediction. And many studies have shown that artificial neural network(ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance, it has some problems such as overfitting and poor explanatory power. To overcome these limitations, this paper suggests a relatively new machine learning technique, support vector machine(SVM), to bankruptcy prediction. SVM is simple enough to be analyzed mathematically, and leads to high performances in practical applications. The objective of this paper is to examine the feasibility of SVM in bankruptcy prediction by comparing it with ANN, logistic regression, and multivariate discriminant analysis. The experimental results show that SVM provides a promising alternative to bankruptcy prediction.

• Proceedings of the KSRS Conference
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• v.2
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• pp.706-709
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• 2006

Cancer is one of the major causes of death; however, the survival rate can be increased if discovered at an early stage for timely treatment. According to the statistics of the World Health Organization of 2002, breast cancer was the most prevalent cancer for all cancers occurring in women worldwide, and it account for 16.8% of entire cancers inflicting Korean women today. In order to classify the type of breast cancer whether it is benign or malignant, this study was conducted with the use of the discriminant analysis and the decision tree of data mining with the breast cancer data disclosed on the web. The discriminant analysis is a statistical method to seek certain discriminant criteria and discriminant function to separate the population groups on the basis of observation values obtained from two or more population groups, and use the values obtained to allow the existing observation value to the population group thereto. The decision tree analyzes the record of data collected in the part to show it with the pattern existing in between them, namely, the combination of attribute for the characteristics of each class and make the classification model tree. Through this type of analysis, it may obtain the systematic information on the factors that cause the breast cancer in advance and prevent the risk of recurrence after the surgery.

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