• Science of Emotion and Sensibility
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• v.5 no.1
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• pp.9-16
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• 2002

This study was designed to cluster the preferred sensibilities of college students, and to distinguish the most preferred sensibility according to gender and their inter-subject differences. 98 of sensibility adjectives were composed into questionnaire with SD method and the data of 176 students were analyzed in ANOVA, Cluster and Correspondence analysis using SAS package. College students were divided into 10-sensibility clusters: the Casual(no-decoration casual, cool-dynamic casual and elaborate-clean casual), the Romantic Modem, the Simple Natural, the Classic(craft-romantic classic and monotonous classic), the Expressionless, the Gorgeous, and the Traditional Folklore. The adjectives and the sensibility clusters of students were visualized together into 2-D with two axis of static vs. dynamic and light vs. heavy.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.577-586
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• 2005

Many algorithms have been proposed for (ace recognition that is one of the most successful applications in image processing, pattern recognition and computer vision fields. Research for what kind of attribute of face that make harder or easier recognizing the target is going on recently. In flus paper, we propose method to improve recognition performance using relevance of face data and applied algorithms, because recognition performance of each algorithm according to facial attribute(illumination and expression) is change. In the experiment, we use n-tuple classifier, PCA and Gabor wavelet as recognition algorithm. And we propose three vectorization methods. First of all, we estimate the fitnesses of three recognition algorithms about each cluster after clustering the test data using k-means algorithm then we compose new clusters by integrating clusters that select same algorithm. We estimate similarity about a new cluster of test data and then we recognize the target using the nearest cluster. As a result, we can observe that the recognition performance has improved than the performance by a single algorithm without clustering.

• Journal of KIISE:Information Networking
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• v.32 no.1
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• pp.68-79
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• 2005

The popularity of Web service is ever increasing. As the number of services and clients continue to increase, the problem of providing a system that scales with this increase is becoming more difficult. A costly and ineffective method is to buy a new system that is more powerful every time the load becomes unbearable. h more cost effective solution is to expand the system as the need arises. This is the approach taken in Web cluster systems. However, providing effective scalability in a Web cluster system is stil1 an open issue. In this study, we implement a Web cluster system based on Layer 7 switching technique on Linux. The implementation is based on a design proposed and implemented by Aron et al., but on the FreeBSD. Though the design li the same, due to the vast difference between the FreeBSD and Linux, the implementation presented in this paper is totally new. We also propose the Dual Scheduling (DS) load distribution algorithm that distributes the requests to the system resources by observing the variations in the request rate. We show through measurement on our implementation that the DS alorithm performs considerably bettor than previous algorithms.

• Journal of the Korean association of regional geographers
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• v.6 no.3
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• pp.139-152
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• 2000

The regional characteristics of summer precipitation in Korea are analyzed with the data observed in 66 stations from 1973 to 1997, using the cluster analysis method. In the phenomena of summer precipitation, the rain-rich regions lie in the south coast region, the northern part of Kyonggi Province, and Yongdong region. The monthly precipitation is mostly influenced by Changma fronts and cyclons in June, Changma fronts in July, typhoons in August, and all of typhoons, Changma, and cyclons in September. The increasing and decreasing trends of the monthly precipitation are equally divided with regard to both regional groups and monthly distribution in the cluster analysis. Especially such trends are considerably clear in the rain-rich regions. The increasing tendency is predominant in the northern part of Kyonggi Province and Yongdong region, while the decreasing trend and the periodicity are noted in the south coast region and Cheju Island. The variation of the monthly precipitation is shown to be great in the rain-rich regions, while it is not much associated with the rain-scare regions. Also, the variation is the greatest in September, while the least variation is shown in July.

• Development and Reproduction
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• v.18 no.4
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• pp.287-292
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• 2014

In this study, seven oligonucleotides primers were shown to generate the shared loci, specific loci, unique shared loci to each species and shared loci by the three species which could be obviously calculated. Euclidean genetic distances within- and between-species were also calculated by complete linkage method with the sustenance of the hierarchical dendrogram program Systat version 13. The genomic DNA isolated from herring (Clupea pallasii), Korean anchovy (Coilia nasus) and large-eyed herring (Harengula zunashi), respectively, in the Yellow Sea, were amplified several times by PCR reaction. The hierarchical dendrogram shows three chief branches: cluster 1 (PALLASII 01, 02, 03, 04, 06 and 07), cluster 2 (NASUS 08, 09, 10, 11, 12, 13 and 14), and cluster 3 (ZUNASHI 15, 16, 17, 18, 19, 20, 21 and PALLASII 05). In three clupeid species, the shortest genetic distance displaying significant molecular difference was between individual PALLASII no. 03 and PALLASII no. 02 (0.018). Individual no. 06 of PALLASII was most distantly related to NASUS no. 11 (genetic distance = 0.318). Individuals from herring (C. pallasii) species (0.920) exhibited higher bandsharing values than did individuals from Korean anchovy (C. nasus) species (0.872) (P<0.05). As a result, this PCR analysis generated on the genetic data displayed that the herring (C. pallasii) species was widely separated from Korean anchovy (C. nasus) species. Reversely, individuals of Korean anchovy (C. nasus) species were a little closely related to those of large-eyed herring (H. zunashi) species.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.135-142
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• 2012

In the present study, a parallel processing method running on a multi-core PC-Cluster is introduced to produce digital surface model (DSM) and digital terrain model (DTM) from huge airborne laser scanning data. A hybrid model using both message passing interface (MPI) and OpenMP was devised by revising a conventional MPI model which utilizes only MPI, and tested on a multi-core PC-Cluster for performance validation. In the results, the hybrid model has not shown better performances in the interpolation process to produce DSM, but the overall performance has turned out to be better by the help of reduced MPI calls. Additionally, scheduling function of OpenMP has revealed its ability to enhance the performance by controlling inequal overloads charged on cores induced by irregular distribution of airborne laser scanning data.

• Journal of The Korean Society of Grassland and Forage Science
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• v.24 no.4
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• pp.293-300
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• 2004

This study was carried out to investigate the ecological behaviour forage value and similarity among the plant communities of the grazing pasture near Witzenhausen in middle part of Germany. Sixteen plant communities of the different grazing pasture were mostly the Molinio-Arrhenatheretea and Festuco-Brometea, and those were named the class of plant sociological nomenclature. The ecological behaviour and forage value of the communities except mesobromion(half dry grassland community) were relatively good for forage production. The correlation coefficient between class No. 14 and 12 of plant communities was highest, and the similarity among the communities were greatly affected by botanical composition. The resemblance measure of the cluster-analysis by complete-linkage-method for the similarity among plant communities was better the euclidean distance than those of others. The clustering analysis showed that the communities of relatively similar botanical composition were closely grouped.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.8 s.145
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• pp.1079-1091
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• 2005

The purpose of this study was to point out the unification of the size of swimming headgear by measuring detailed measurement of head ferm and systematizes the head form. A total of 300 male and female Korean adults aged from 18 years old to 35 years old were measured through the direct measurement method. This study attempted to identify the head form of male and female adults using measurement data and the head form was classified through factor analysis and cluster analysis. (1) Based on the fact that this study especially focuses on the size of swimming headgear, factor analysis was performed far both direct measurements with hair and without hair. For the measurements with hair, seven factors were extracted. They were head thickness factor, head width factor, width of side head factor, height of back head factor, length of front head factor, circumference of front head factor, thickness of front head factor and head height factor. They explained the $70.95\%$ of the measurements. (2) The cluster analysis was executed to classify the somatotype of the korean head form on the basis of similarity. According to the cluster analysis result, the measurements with hair categorized types, 'Woman's long-hair type', 'Small type', 'Long and flat type', 'Large type'. (3) Head circumference B and Left tragion - Vertex - Right tragion circumference which are generally length and width in choosing swimming headgear size were selected as measurement items far size of swimming headgear. Direct measurements and sizes of four swimming headgear with different materials taking into account their elongation rate were also compared.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.367-376
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• 2015

Today, big data has become a hot keyword in that big data may be defined as collection of data sets so huge and complex that it becomes difficult to process by traditional methods. Clustering method is to identify the information in a big database by assigning a set of objects into the clusters so that the objects in the same cluster are more similar to each other clusters. The similarity measures being used in the cluster analysis may be classified into various types depending on the nature of the data. In this paper, we computed upper and lower limits for probability interestingness measure based similarity measures without marginal probability such as Yule I and II, Michael, Digby, Baulieu, and Dispersion measure. And we compared these measures by real data and simulated experiment. By Warrens (2008), Coefficients with the same quantities in the numerator and denominator, that are bounded, and are close to each other in the ordering, are likely to be more similar. Thus, results on bounds provide means of classifying various measures. Also, knowing which coefficients are similar provides insight into the stability of a given algorithm.

• The Transactions of the Korea Information Processing Society
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• v.4 no.5
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• pp.1221-1230
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• 1997

In this study, the performance of a parallel file system(N-PFS), which is inplemented using conventional disks as disk arrays on a Workstation Cluster, is analyzed by using analytical method and adtual values in experiments.N-PFS can be used as high-performance file sever in small-scale server systems and effciently pro-cess massive data I/Os such as multimedia and scientifid data. In this paper, an analytical model was suggested and the correctness of the suggested was verified by analyzing the experimental values on a system.The result of the appropriate stping unit for processing massive data of the Workstation Cluster with 8 disks is 64-128Kbytes and the maximum throughput on it is 15.8 Mbytes/ses.In addition, the performance of parallel file system on massive data is bounded by the time required to copy data between buffers.