• Journal of Korean Society on Water Environment
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• v.29 no.4
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• pp.504-513
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• 2013

Water quality trends for major tributaries (66 sites) in the Yeongsan River basin of Korea were examined for 12 parameters based on water quality data collected every month over a period of 12 months. The complex data matrix was treated with multivariate analysis such as PCA, FA and CA. PCA/FA identified four factors, which are responsible for the structure explaining 78.2% of the total variance. The first factor accounting 27.3% of the total variance was correlated with BOD, TN, TP, and TOC, and weighting values were allowed to these parameters for grade classification. CA rendered a dendrogram, where monitoring sites were grouped into 5 clusters. Cluster 2 corresponds to high pollution from domestic wastewater, wastewater treatment and run-off from livestock farms. For grade classification of tributaries, scores to 10 indexes were calculated considering the weighting values to 3 parameters as BOD, TN and TP which were categorized as the first factor after FA. The highest-polluted group included 10 tributaries such as Gwangjucheon, Jangsucheon, Daejeoncheon, Gamjungcheon, Yeongsancheon. The results indicate that grade classification method suggested in this study is useful in reliable classification of tributaries in the study area.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.53-66
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• 2008

This research is to the establishment of a conceptual framework that supports road characteristic classification from a new point of view in order to complement of the existing road functional classification and examine of traffic pattern. The road characteristic classification(RCC) is expected to use important performance criteria that produced a policy guidelines for transportation planning and operational management. For this study, the traffic data used the permanent traffic counters(PTCs) located within the national highway between 2002 and 2006. The research has described for a systematic review and assessment of how exploratory factor analysis should be applied from 12 explanatory variables. The optimal number of components and clusters are determined by interpretation of the factor analysis results. As a result, the scenario including all 12 explanatory variables is better than other scenarios. The four components is produced the optimal number of factors. This research made contributions to the understanding of the exploratory factor analysis for the road characteristic classification, further applying the objective input data for various analysis method, such as cluster analysis, regression analysis and discriminant analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.347-356
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• 2009

This paper is described clustering analysis of traffic characteristics-based highway classification in order to deviate from methodologies of existing highway functional classification. This research focuses on comparing the clustering techniques performance based on the total within-group errors and deriving the optimal number of cluster. This research analyzed statistical clustering method (Hierarchical Ward's minimum-variance method, Nonhierarchical K-means method) and Kohonen self-organizing maps clustering method for highway characteristic classification. The outcomes of cluster techniques compared for the number of samples and traffic characteristics from subsets derived by the optimal number of cluster. As a comprehensive result, the k-means method is superior result to other methods less than 12. For a cluster of more than 20, Kohonen self-organizing maps is the best result in the cluster method. The main contribution of this research is expected to use important the basic road attribution information that produced the highway characteristic classification.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.2
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• pp.83-92
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• 2009

As the innovative internet technologies and multimedia are being rapidly developed, malicious codes are a remarkable new growth part and supplied by various channel. This project presents a classification methodology for malicious codes in Windows OS (Operating System) environment, develops a test classification system. Thousands of malicious codes are brought in every day. In a result, classification system is needed to analyzers for supporting information which newly brought malicious codes are a new species or a variety. This system provides the similarity for analyzers to judge how much a new species or a variety is different to the known malicious code. It provides to save time and effort, to less a faulty analysis. This research includes the design of classification system and test system. We classify the malicious codes to 9 groups and then 9 groups divide the clusters according to the each property.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.3
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• pp.385-392
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• 2010

The breast types of middle-aged women of 80A (formerly 80B) size were classified through a 3D scanned nude body. Thirty seven measurements including the radius of curvature, surface area, volume, surface length, and breast displacements were used as input variables. We extracted five main factors through the factor analysis of the measurements and classified 36 subjects into 3 clusters through the cluster analysis. As a result of the factor analysis, the size of the breast, breast sag, the curvature of the inner and the outer breast curve, the width of the breast, and the nipple direction were found as the main factors. For the results of the classification of breast types, Cluster 1 was characterized by narrow breast width and unsymmetrical under the breast curve, whereas Cluster 2 was a wide and sagged shape. Cluster 3 was classified into big breast volume and symmetrical under-breast curve. The results are useful to the product development of high quality brassieres which reflect the 3D characteristics of breast types of middle-aged women.

• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.63-70
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• 2009

This project presents a classification methodology for malicious codes in Windows OS (Operating System) environment, develops a test classification system. Thousands of malicious codes are brought in every day. In a result, classification system is needed to analyzers for supporting information which newly brought malicious codes are a new species or a variety. This system provides the similarity for analyzers to judge how much a new species or a variety is different to the known malicious code. It provides to save time and effort, to less a faulty analysis. This research includes the design of classification system and test system. We classify the malicious codes to 9 groups and then 9 groups divide the clusters according to the each property. This system provides the similarity for analyzers to save time and effort. It is used prospect system of malicious code in the future.

• Archives of Pharmacal Research
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• v.27 no.12
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• pp.1270-1274
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• 2004

Random amplified polymorphic DNA (RAPD) analysis was used to determine the genetic relationships among seventeen species of the Acanthopanax species. The DNA isolated from the leaves of the samples was used as template in polymerase chain reaction (PCR) with twenty random decamer primers in order to distinguish plant subspecies at the level of their genomes. The RAPD patterns were compared by calculating pairwise distances using Dice similarity index, and produced to the genetic similarity dendrogram by unweighted pair-group method arithmetic averaged (UPGMA) analysis, showing three groups; a major cluster(twelve species), minor cluster (4 species) and single-clustering species. The results of RAPD were compatible with the morphological classification, as well as the chemotaxonomic classification of the Acanthopanax species. The Acanthopanax species containing 3,4-seco-lupane type triterpene compounds in their leaves corresponded to the major cluster, another species having oleanane or normal lupane type constituents to minor clusters, and one species not containing triterpenoidal compound to single-cluster.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.

• The Research Journal of the Costume Culture
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• v.13 no.2 s.55
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• pp.200-208
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• 2005

This study was aimed to provide the measurement data and shape classification of the head of the Korean female children for the headwear sizing systems. Four hundred nineteen female children, aged nine to twelve years, participated for this study. The 19 regions on the head and height, weight of the subjects were directly measured by the expert experimenters. Factor analysis, cluster analysis, GLM analysis and Tukey HSD test were performed using these data. Through factor analysis, five factors were extracted upon factor scores and those factors comprised $71.318\%$ for the total variances. Three clusters as their head shape were categorized using fiver factor scores by cluster analysis. Type 1 was characterized by the widest head width, Bitragion arc, and shortest head length, and medium height and weight. Type 2 had the longest head length and the widest side head width and the highest head circumference, and highest height and largest weight. Type 3 was characterized by the medium head length, smallest head circumstance, narrowest head width and side head width, and smallest height and weight.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.9-16
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• 2018

This study described partial discharge(PD) distribution analysis between the XLPE(Cross-Linked PolyEthylene)and EPDM(Ethylene Propylene Diene Monomer) interface with unsupervised classification. The ${\phi}-q-n$ patterns were analyzed using phase resolved partial discharge(PRPD). K-means cluster analysis forms a cluster based on similarities and distances among scattered individuals, and analyzes the characteristics of the formed clusters, dividing the multivariate data into several groups according to the similarity of each characteristic, Is a statistical analysis that makes it easier to navigate. It was confirmed that the phase angle of the cluster with the maximum discharge charge was concentrated around $0^{\circ}$ and $180^{\circ}$ at 30 kV after the initial phase distribution localized around $90^{\circ}$ and $300^{\circ}$ expanded to the whole phase angle according to the voltage rise. The Euclidean distance between the center of gravity and the discharge charge in the ${\Phi}-q$ cluster increased with increasing applied voltage.