• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.09a
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• pp.155-159
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• 2001

A study on multivariate statistical classification of ground water hydrographs was conducted. The vast data of national ground water monitoring network (78 sites of alluvium) were used. 6 factors were selected to classify the ground water level change. Factor analysis was proved to be useful tool for classifying vast hydrogeological data.

• 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.

• Korean Journal of Optics and Photonics
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• v.31 no.3
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• pp.125-133
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• 2020

An innovative method for separating overlapping latent fingerprints, using laser-induced plasma spectroscopy (LIPS) combined with multivariate analysis, is reported in the current study. LIPS provides the capabilities of real-time analysis and high-speed scanning, as well as data regarding the chemical components of overlapping fingerprints. These spectra provide valuable chemical information for the forensic classification and reconstruction of overlapping latent fingerprints, by applying appropriate multivariate analysis. This study utilizes principal-component analysis (PCA) and partial-least-squares (PLS) techniques for the basis classification of four types of fingerprints from the LIPS spectra. The proposed method is successfully demonstrated through a classification example of four distinct latent fingerprints, using discrimination such as soft independent modeling of class analogy (SIMCA) and partial-least-squares discriminant analysis (PLS-DA). This demonstration develops an accuracy of more than 85% and is proven to be sufficiently robust. In addition, by laser-scanning analysis at a spatial interval of 125 ㎛, the overlapping fingerprints were separated as two-dimensional forms.

• Korean Journal of Clinical Oncology
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• v.14 no.2
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• pp.89-94
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• 2018

Purpose: To investigate the relationship between MUC expression and clinicopathologic factors in advanced gastric cancer. Methods: A total of 237 tumor specimens were assessed for MUC expression by immunohistochemistry. The clinicopathologic factors were investigated with MUC1, MUC2, MUC5AC, and MUC6. Results: MUC1, MUC2, MUC5AC, and MUC6 expression was identified in 148 of 237 (62.4%), 141 of 237 (59.5%), 186 of 237 (78.5%), and 146 of 237 (61.6%) specimens, respectively. MUC1 expression was correlated with age, human epidermal growth factor receptor 2 (HER2) status, lymphatic invasion, Lauren classification and histology. Further multivariate logistic regression analysis revealed a significant correlation between MUC1expression and lymphatic invasion, diffuse type of Lauren classification. MUC5AC expression was correlated with HER2 status, Lauren classification and histology. Further multivariate logistic regression analysis revealed a significant correlation between MUC5AC expression and HER2 status, diffuse and mixed type of Lauren classification. MUC2 and MUC6 expression were not correlated with clinicopathologic factors. The patients of MUC1 expression had poorer survival than those without MUC1 expression, but MUC2, MUC5AC or MUC6 were not related to survival. In an additional multivariate analysis that used the Cox proportional hazards model, MUC1 expression was not significantly correlated with patient survival independent of age, N-stage, and venous invasion. Conclusion: When each of these four MUCs expression is evaluated, in light of clinicopathologic factors, MUC1 expression may be considered as a prognostic factor in patients with advanced gastric cancer. Therefore, careful follow-up may be necessary because the prognosis is poor when MUC1 expression is present.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.131.2-131.2
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• 2011

We present performance of artificial neural network multivariate classifier in identifying non-astrophysical origin noise transients from the gravitational wave channel of Laser Interferometer Gravitational-wave Observatory (LIGO). LIGO has successfully conducted six science runs, achieving the sensitivity as planned and producing many fruitful scientific results. It has been well observed that the detector noise is non-Gaussian and non-stationary, which results in large excess of noise transients called glitches arising from instrumental and environmental artifacts. Great efforts have been committed to reduce the glitches by tuning the detector instruments and by vetoing them but further improvement is still needed. To this end, there have been efforts to incorporate data from hundreds of auxiliary, physical and environmental channels into identifying the glitches in the gravitational wave channel. We introduce a multivariate classification method using Artificial Neural Networks (ANNs) that efficiently handles large number of variables. In this poster, we present preliminary results of the application of our ANN algorithm to data from LIGO's Science Run 4 and compare its performance with conventional vetoing method.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.63-78
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• 1994

For discrimination with binary variables, reformulated full and first order Bahadur model with incomplete observations are presented. This allows prior probabilities associated with multiple population to be estimated for the sample-based classification rule. The EM algorithm is adopted to provided the maximum likelihood estimates of the parameters of interest. Some experiences with the models are evaluated and discussed.

• Korean journal of applied entomology
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• v.32 no.1
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• pp.30-41
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• 1993

In the present investigation, some multivariate discriminant analyses were done for each population of the native honeybee workers (Apis cerana), which were selected for 15 different localities in spring and 16 in summer form Korea. when the comparison of both seasons for different populations and regardless of seasons were conducted, all the classification results revealed that the differences were significantly prominent. And the length of fore tibia(FTL) was the best contributed character among the 47 morphometric characters used in the analysis.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1179-1189
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• 2017

In this study, we propose an alternative method for discriminant analysis using a multivariate empirical distribution function to express multivariate data as a simple one-dimensional statistic. This method turns to be the estimation process of the optimal threshold based on classification accuracy measures and an empirical distribution function of data composed of classes. This can also be visually represented on a two-dimensional plane and discussed with some measures in ROC curves, surfaces, and manifolds. In order to explore the usefulness of this method for discriminant analysis in the study, we conducted comparisons between the proposed method and the existing methods through simulations and illustrative examples. It is found that the proposed method may have better performances for some cases.

• Journal of the Korean Chemical Society
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• v.63 no.2
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• pp.78-84
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• 2019

High-resolution $^1H$ NMR spectroscopic technique has been widely used as one of the most powerful analytical tools in food chemistry as well as to define molecular structure. The $^1H$ NMR spectra-based metabolomics has focused on classification and chemometric analysis of complex mixtures. The principal component analysis (PCA), an unsupervised clustering method and used to reduce the dimensionality of multivariate data, facilitates direct peak quantitation and pattern recognition. Using a combination of these techniques, the various green teas and black teas brewed were investigated via metabolite profiling. These teas were characterized based on the leaf size and country of cultivation, respectively.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.169-174
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• 2013

The ROC curve is drawn with two conditional cumulative distribution functions (or survival functions) of the univariate random variable. In this work, we consider joint cumulative distribution functions of k random variables, and suggest a ROC curve for multivariate random variables. With regard to the values on the line, which passes through two mean vectors of dichotomous states, a joint cumulative distribution function can be regarded as a function of the univariate variable. After this function is modified to satisfy the properties of the cumulative distribution function, a ROC curve might be derived; moreover, some illustrative examples are demonstrated.