• Journal of Oral Medicine and Pain
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• v.8 no.1
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• pp.121-126
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• 1983

This study is conducted with a view to make correct sexual differentiation by the utilization of discriminant functions. For that purpose were randomly sampled out 148 young adults testes, comprising 67 males and 81 females, ranging from 15 through 18 years fo age. Based on the values made available from the measurement of 6 items corresponding to the maxillary cast models, a statistical analysis was made to abstract feasible discriminant functions. The results findings are as follows: 1. The mean value by sex indicates, in all items, higher one in male group than in female group. 2. Through the measurement were defined as singnificant items in sexual differentiation the bucco-lingual dimensions of canine, 1st-molar, 2nd molar, and 1st bimolat width. 3. Derived from the value from measurement items were discriminant functions with the intention of applying them to sexual differentiation, as follows: 1) Y=-25.4112+0.7513BL3+0.3298BL4-0.2854BL5+0.7350BL6-0.3482BL7+0.2893AW (as tested by Method I) 2)Y=-25.0628+0.7737BL3+0.7468BL6-0.3885BL5+0.2951AW(as tested by Method II) BL3 : Bucco-lingual dimension of upper canine BL4 : Bucco-lingual dimension of upper first prmolar BL5 : Bucco-lingual dimension of upper second premolar BL5 : Bucco-lingual dimension of upper first molar BL6 : Bucco-lingual dimension of upper second molar AW : Upper first bimolar width 4. Sexual defferentiation in terms of descriminant functions represented a probility of 74.6%.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.6
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• pp.1938-1944
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• 2004

In this study, when we make a diagnosis of constitution using QSCC Ⅱ(Questionnaire of Sasang Constitution Classification). data mining techniques are applied to seek the classification function for improving the accuracy. Data used in the analysis are the questionnaires of 1051 patients who had been treated in Dong Eui Oriental Medical Hospital and Kyung Hee Oriental Medical Hospital. The criteria for data cleansing are the response pattern in the opposite questionnaires and the positive proportion of specific questionnaires in each constitution. And the criteria for variable selection are the test of homogeneity in frequency analysis and the coefficients in the linear discriminant function. Discriminant analysis model and decision tree model are applied to seek the classification function into Sasang constitution. The accuracy in learning sample is similar in two models, the higher accuracy in test sample is obtained in discriminant analysis model.

• Natural Product Sciences
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• v.24 no.3
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• pp.164-170
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• 2018

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

• Journal of Korean Society of Transportation
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• v.23 no.5 s.83
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• pp.65-71
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• 2005

Area type is often used in freight demand analysis and logistics planning models. For example, in freight transportation planning. area type variable is most often commonly used in freight generation (attraction) model. Yet a reliable, forecastable and measurable definition or area type is generally not documented. In fact, there is little literature on the subject of predicting area type in the context of freight planning models. This can be troublesome when applying models to long-range logistics planning where significant changes in population and employment result in changes in the general character of an area. Through the use of Discriminant Model, GIS (Geographic Information System) analysis and Delphi methods, this paper presents the successful exploration for a quantifiable means of determining area type.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.622-625
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• 2004

This paper presents multi-source spatial data integration models based on probability theory for landslide hazard assessment. Four probabilistic models such as empirical likelihood ratio estimation, logistic regression, generalized additive and predictive discriminant models are proposed and applied. The models proposed here are theoretically based on statistical relationships between landslide occurrences and input spatial data sets. Those models especially have the advantage of direct use of continuous data without any information loss. A case study from the Gangneung area, Korea was carried out to quantitatively assess those four models and to discuss operational issues.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.783-788
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of the variability in scale, location, orientation and pose. The difficulties in visual detection and recognition are caused by the variations in viewpoint, viewing distance, illumination. In this paper, we present an efficient linear discriminant for multi-view face detection and face location. We define the training data by using the Fisher`s linear discriminant in an efficient learning method. Face detection is very difficult because it is influenced by the poses of the human face and changes in illumination. This idea can solve the multi-view and scale face detection problems. In this paper, we extract the face using the Fisher`s linear discriminant that has hierarchical models invariant size and background. The purpose of this paper is to classify face and non-face for efficient Fisher`s linear discriminant.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.757-764
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• 2009

In discriminant analysis, we consider a special pattern which contains a block of missing observations. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider the bootstrap confidence intervals of the error rate in the circular models when the covariance matrices are equal and not equal.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.909-917
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• 2003

Although many classification models have been used to classify binary data, none of the classification models dominates all varying circumstances depending on the number of variables and the size of data(Asparoukhov and Krzanowski (2001)). This paper proposes a classification model which uses information on marginal distributions of sub-variables and its maximum entropy distribution. Classification experiments by using simulation are discussed.

• 전자공학회논문지 IE
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• v.45 no.2
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• pp.34-39
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• 2008

This study established the efficient system that retrieves the 3D model by using a statistical technique called the function of discriminant analysis. This method was suggested to search index, which was formed by the statistics of 128 feature vectors including those scope, minimum value, average, standard deviation, skewness and scale. All of these were sampled with Osada's D2 method and the statistics as a factor effecting a change turned the value of discriminant analytic function into that of index. Through the primary retrieval on the model of query, the class above the top 2% was drawn out by comparing the query with the index of previously saved class from the group of same models. This method was proved an efficient retrieval technique that saved its procedural time. It shortened the retrieval time for 3D model by 57% faster than the existing Osada's method, and the precision that similar models were found in the first place was recorded 0.362, which revealed it more efficient by 44.8%.

• Journal of Biosystems Engineering
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• v.42 no.4
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• pp.293-300
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• 2017

Purpose: This study aims to propose a method for fast geographical origin discrimination between domestic and imported rice using a visible/near-infrared (VNIR) hyperspectral imaging technique. Methods: Hyperspectral reflectance images of South Korean and Chinese rice samples were obtained in the range of 400 nm to 1000 nm. Partial least square discriminant analysis (PLS-DA) models were developed and applied to the acquired images to determine the geographical origin of the rice samples. Results: The optimal pixel dimensions and spectral pretreatment conditions for the hyperspectral images were identified to improve the discrimination accuracy. The results revealed that the highest accuracy was achieved when the hyperspectral image's pixel dimension was $3.0mm{\times}3.0mm$. Furthermore, the geographical origin discrimination models achieved a discrimination accuracy of over 99.99% upon application of a first-order derivative, second-order derivative, maximum normalization, or baseline pretreatment. Conclusions: The results demonstrated that the VNIR hyperspectral imaging technique can be used to discriminate geographical origins of rice.