• Journal of Korean Society for Atmospheric Environment
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• v.9 no.3
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• pp.207-215
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• 1993

In order to statistically predict $O_3$ levels in Seoul, the study used the TMS (telemeted air monitoring system) data from the Department of Environment, which have monitored at 20 sites in 1989 and 1990. Each data in each site was characterized by 6 major criteria pollutants ($SO_2, TSP, CO, NO_2, THC, and O_3$) and 2 meteorological parameters, such as wind speed and wind direction. To select proper variables and to determine each pollutant's behavior, univariate statistical analyses were extensively studied in the beginning, and then various applied statistical techniques like cluster analysis, regression analysis, and expert system have been intensively examined. For the initial study of high level $O_3$ prediction, the raw data set in each site was separated into 2 group based on 60 ppb $O_3$ level. A hierarchical cluster analysis was applied to classify the group based on 60 ppb $O_3$ into small calsses. Each class in each site has its own pattern. Next, multiple regression for each class was repeatedly applied to determine an $O_3$ prediction submodel and to determine outliers in each class based on a certain level of standardized redisual. Thus, a prediction submodel for each homogeneous class could be obtained. The study was extended to model $O_3$ prediction for both on-time basis and 1-hr after basis. Finally, an expect system was used to build a unified classification rule based on examples of the homogenous classes for all of sites. Thus, a concept of high level $O_3$ prediction model was developed for one of $O_3$ alert systems.

• The korean journal of orthodontics
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• v.21 no.1 s.33
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• pp.171-183
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• 1991

This investigation was designed to categorize Angle's class II malocclusion groups through analyzing horizontal and vertical components of craniofacial skeleton in Angle's class II malocclusion. The material selected for this study consisted in standard lateral cephalogram of two hundred and twenteen children, eighty eight boys and one hundred twenty four girls, aged 6 through 18 years, having Angle's class II malocclusion. On the basis of findings of this study, the following results were obtained. 1. In horizontal skeletal classifications, 16 groups were classified according to FMN-A-B, SE-FMN-A, Ba-SE-Me, Ba-Se/Ra P. The sequences that have relatively high frequency are as follows: a) Horizontal Group 16 b) Horizontal Group 12 c) Horizontal Group 13 d) Horizontal Group 9 & 15 2. In vertical skeletal classification, 8 groups were classified according to the PMV/PP, PMV/Occ. P. PMV/Mn. P. The sequences that relatively high frequency are as follows; a) Vertical Group A b) Vertical Group D c) Vertical Group C d) Vertical Group H 3. In vertical and horizontal skeletal classifications, the sequence that relatively high frequency are as follows; a) Group13-A b) Group16-A & 9-A c) Group12-A & 15-A d) Group16-C

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4502-4518
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• 2015

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the "divide-conquer" strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.349-374
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• 2019

A class imbalance problem arises when one class outnumbers the other class by a large proportion in binary data. Studies such as transforming the learning data have been conducted to solve this imbalance problem. In this study, we compared resampling methods among methods to deal with an imbalance in the classification problem. We sought to find a way to more effectively detect the minority class in the data. Through simulation, a total of 20 methods of over-sampling, under-sampling, and combined method of over- and under-sampling were compared. The logistic regression, support vector machine, and random forest models, which are commonly used in classification problems, were used as classifiers. The simulation results showed that the random under sampling (RUS) method had the highest sensitivity with an accuracy over 0.5. The next most sensitive method was an over-sampling adaptive synthetic sampling approach. This revealed that the RUS method was suitable for finding minority class values. The results of applying to some real data sets were similar to those of the simulation.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.1005-1012
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• 2009

The area under the curve(AUC) is commonly used as a measure of the receiver operating characteristic(ROC) curve which displays the performance of a set of binary classifiers for all feasible ratios of the costs associated with true positive rate(TPR) and false positive rate(FPR). In the bipartite ranking problem where one has to compare two different observations and decide which one is "better", the AUC measures the quantity that ranking score of a randomly chosen sample in one class is larger than that of a randomly chosen sample in the other class and hence, the function which maximizes an AUC of bipartite ranking problem is different to the function which maximizes (minimizes) accuracy (misclassification error rate) of binary classification problem. In this paper, we develop a way to construct the unified framework for AUC maximizer including support vector machines based on maximizing large margin and logistic regression based on estimating posterior probability. Moreover, we develop an efficient algorithm for the proposed unified framework. Numerical results show that the propose unified framework can treat various methodologies successfully.

• Journal of the Korean Society for Library and Information Science
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• v.26
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• pp.75-112
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• 1994

The purpose of this study is to investigate the problems concerning 780 music division of DDC. The object is especially arrangement of 780 music in the 20th edition of DDC which is the complete revision. The result is summarized as follows : 1. Although music is an important subject in humanities, especially in arts, it was classified as one division (780) not class. 2. The arrangement of 780 music is severely west-oriented music theory, vocal music and instrumental music. 3. Classification number of 780 music becomes longer because of the limitation of decimal notation. 4. 780 music division of DDC neglects music theory and emphasizes music practicing, especially performance. 5. The assignment of classification number is unbalanced, especially between theory and practice, composition and performance, and among sub-sections of vocal and instrumental music. 6. Many important subject are omitted in DDC music schedule, for example, musicology and branches of musicology, composition and traditional instruments of many countries. 7. Employment of terminology is often improper and inconsistant.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.193-201
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• 2020

The main objective of this study is to investigate the impact of additional modalities on the performance of emotion recognition using speech, facial expression and physiological measurements. In order to compare different approaches, we designed a feature-based recognition system as a benchmark which carries out linear supervised classification followed by the leave-one-out cross-validation. For the classification of four emotions, it turned out that bimodal fusion in our experiment improves recognition accuracy of unimodal approach, while the performance of trimodal fusion varies strongly depending on the individual. Furthermore, we experienced extremely high disparity between single class recognition rates, while we could not observe a best performing single modality in our experiment. Based on these observations, we developed a novel fusion method, called parametric decision fusion (PDF), which lies in building emotion-specific classifiers and exploits advantage of a parametrized decision process. By using the PDF scheme we achieved 16% improvement in accuracy of subject-dependent recognition and 10% for subject-independent recognition compared to the best unimodal results.

• The korean journal of orthodontics
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• v.10 no.1
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• pp.55-64
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• 1980

The purpose of this study was to investigate the interrelationship of the experimental group and control group by analyzing case histories, intraoral radiographs, orthopantomographs, intraoral slide films and dental casts. The data for this study were complied from 654 outpatients of the Department of Orthodontics, Seoul National University Hospital. The following conclusions were obtained. 1. When one or more thins molar teeth were congenitally missing, the incidence of the other congenitally missing teeth was high. 2. The frequency of congenitally missing teeth was comparatively higher in male, maxilla, class II and class III. 3. The congenitally missing srea of the third molar by Angle's classification was not significant. 4. The order of frequency of congenitally missing teeth was the third molar, the second premolar, the lateral incisor, the first premolar, the central incisor, the canine, the first molar, the second molar.

• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.846-859
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• 2005

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

• Journal of Korean Neurosurgical Society
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• v.39 no.2
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• pp.114-119
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• 2006

Objective : The authors investigate appropriate evaluation and surgical methods in treatment of the cerebral paragonimiasis accompanying epilepsy. Methods : Thirteen patients with the cerebral paragonimiasis accompanying epilepsy were included for this study. Preoperative evaluation methods included history taking, skin and serologic tests for Paragonimus westermani, neurologic examinations, computerized tomography, magnetic resonance imaging, amytal test, PET or SPECT, and video-EEG monitoring with depth and subdural grid electrodes. Seizure outcome was evaluated according to Engel's classification. Results : Surgical methods were temporal lobectomy including lesions in six, lesionectomy in five, and temporal lobectomy plus lesionectomy in two. Postoperative neurological complications were not noticed, and seizure outcomes were class I in 12 patients [92%], class II in one [8%]. Conclusion : In patients with a cerebral paragonimiasis accompanying epilepsy, further evaluation methods must be done to define the epileptogenic zone, and complete resection of the epileptogenic zone with different surgical methods should be performed for seizure control.