• Journal of Haehwa Medicine
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• v.9 no.1
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• pp.423-442
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• 2000

Objectives : The objectives of this study is to find out the classification methods of the cause of disease and the Acupuncture points on the cough(咳嗽, hae-soo) from the oriental medical literature. The results obtained as follows. Methods : We surveyed the oriental medical books from Hung-Ti-Nei-Ching $\ll$黃帝內經$\gg$ to recent books concerning the Acupuncture therapy for the cough(咳嗽, hae-soo). Results : 1. There are the classification methods of the cause of the cough(咳嗽, hae-soo) by affection by exopathogen and internal injury, by five zang-organs and six fu-organs, by time-belt, and by cold and heat. 2. The acupuncture points at P'yesu(肺兪, BL13), T'aeyon(太淵, LU9), Ch'okt'aek(尺澤, LU5), P'ungmun(風門, BL12), Yolgyol(列缺, LU7), Ch'ondol(天突, CV22), Taech'u(大椎, GV14), Hapkok(合谷, LI4), Kohwang(BL43), T'aegye(太谿, KI3), Chok-samni(足三里, ST36) are most frequently used on the acupuncture therapy for the cough(咳嗽, hae-soo). Conclusions : Among the classification methods of the cause of the cough(咳嗽, hae-soo), the classification methods by affection by exopathogen and internal injury may be most effective and the acupuncture points at Lung meridian(手太陰肺經, LU), Bladder mendian(足太陽膀胱經, BL) are most frequently used on the acupuncture therapy for the cough(咳嗽, hae-soo).

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.89-96
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• 2007

Various classification methods have been developed to reuse components. These classification methods enable the user to access the needed components quickly and easily. Conventional classification approaches include the following problems: a labor-intensive domain analysis effort to build a classification structure, the representation of the inter-component relationships, difficult to maintain as the domain evolves, and applied to a limited domain. In order to solve these problems, this paper describes a composite cluster analysis approach for component classification. The cluster analysis approach is a combination of a hierarchical cluster analysis method, which generates a stable clustering structure automatically, and a non-hierarchical cluster analysis concept, which classifies new components automatically. The clustering information generated from the proposed approach can support the domain analysis process.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.271-281
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• 2013

This paper considers a problem of classification of variable stars based on functional data analysis. For a better understanding of galaxy structure and stellar evolution, various approaches for classification of variable stars have been studied. Several features that explain the characteristics of variable stars (such as color index, amplitude, period, and Fourier coefficients) were usually used to classify variable stars. Excluding other factors but focusing only on the curve shapes of variable stars, Deb and Singh (2009) proposed a classification procedure using multivariate principal component analysis. However, this approach is limited to accommodate some features of the light curve data that are unequally spaced in the phase domain and have some functional properties. In this paper, we propose a light curve estimation method that is suitable for functional data analysis, and provide a classification procedure for variable stars that combined the features of a light curve with existing functional data analysis methods. To evaluate its practical applicability, we apply the proposed classification procedure to the data sets of variable stars from the project STellar Astrophysics and Research on Exoplanets (STARE).

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1329-1331
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• 2003

Improvement of the classification accuracy is one of the major concerns in the field of remote sensing application research in recent years. Previous research shows that the accuracy of the conventional classification methods based only on the original spectral information were usually unsatisfied and need to be refined by manual edit. This present paper describes a method of combining the image processing, ancillary data (such as digital elevation model) and expert knowledge (especially the knowledge of local professionals) to improve the efficiency and accuracy of the satellite image classification in hilly land. Firstly, the Landsat TM data were geo-referenced. Secondly, the individual bands of the image were intensitynormalized and the normalized difference vegetation index (NDVI) image was also generated. Thirdly, a set of sample pixels (collected from field survey) were utilized to discover their corresponding DN (digital number) ranges in the NDVI image, and to explore the relationships between land use type and its corresponding spectral features . Then, using the knowledge discovered from previous steps as well as knowledge from local professionals, with the support of GIS technology and the ancillary data, a set of conditional statements were applied to perform the TM imagery classification. The results showed that the integration of image processing and spatial analysis functions in GIS improved the overall classification result if compared with the conventional methods.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.39-51
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• 2022

Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.127-134
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• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

• Korean journal of medical education
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• v.30 no.4
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• pp.347-357
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• 2018

Purpose: The purpose of this study was to apply alternative standard setting methods for the Korean Medical Licensing Examination (KMLE), a criterion-referenced written examination, and to compare them to the conventional cut score used on the KMLE. Methods: The process and results of criterion-referenced standard settings (i.e., the modified-Angoff and bookmark methods) were evaluated. The ratio of passing and failing examinees determined using these alternative standard setting methods was compared to the results of the conventional criteria. Additionally, the external, internal and procedural evaluation of these methods were reviewed. Results: The modified-Angoff method yielded the highest cut score, followed sequentially by the conventional method and the bookmark method. The classification agreement between the modified-Angoff and bookmark methods was 0.720 measured by Cohen's ${\kappa}$ coefficient. The intra-panelist classification consistency of modified-Angoff method was higher than bookmark method. However, the inter-panelist classification consistency was vice versa. The standard setting panelists' survey results showed that the procedures of both methods were satisfactory, but panelists had more confidence in the results of the modified-Angoff method. Conclusion: The modified-Angoff method showed results that were more similar to those of the conventional method. Both new methods showed very high concordance with the conventional method, as well as with each other. The modified-Angoff method was considered feasible for adoption on the KMLE. The standard setting panelists responded positively to the modified-Angoff method in terms of its practical applicability, despite certain advantages of the bookmark method.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.141-146
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• 2005

Ensemble method has been known as one of the most powerful classification tools that can improve prediction accuracy. Ensemble method also has been understood as ‘perturb and combine’ strategy. Many studies have tried to develop ensemble methods by improving perturbation. In this paper, we propose two new ensemble methods that improve combining, based on the idea of pattern matching. In the experiment with simulation data and with real dataset, the proposed ensemble methods peformed better than bagging. The proposed ensemble methods give the most accurate prediction when the pruned tree was used as the base learner.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.125-133
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• 2004

In this paper, we present a new method that combines the characteristics of edge in-formation and second-order neural networks for the classification of structural textures. The edges of a texture are extracted using an edge detection approach. From this edge information, classification features called second-order features are obtained. These features are fed into a second-order neural network for training and subsequent classification. It will be shown that the main disadvantage of using structural methods in texture classifications, namely, the difficulty of the extraction of texels, is overcome by the proposed method.

• 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.