• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.2
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• pp.282-297
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• 1996

The effective construction for ready-made clothes is one of the central concerns of both consumers and manufactuers in today's apparel industry. In order to reduce the burden of stocks and increase clothing fitness, systematic information on typical body sizes and somatotypes is essential. The purpose of this study i-: to provide basic data on young women's somatotypes for form designers and pattern makers. The subjects of the survey were 310 women of 18 to 26 years old. The study collected 84 anthropometric data for each Person. The data was analyzed by using of the multivariate method. The factor analysis was utilized in regard to the 65 items obtained from anthropometric measurement respectively. The principal component analysis was applied to the data with orthogonal rotation after extraction. The factor scores used in the factor analysis became the basis of determining the value of each variable of the cluster analysis. The cluster analysis was applied for identifying typical somatotypes. Ward's minimum variance method was applied for the purpose of extracting distance metrix by the standardized Euclidean distance. The element forming each cluster can be subdivided into several sets by crosstabulation which is obtained by the fastclus of the SAS. This research has demonstrated 3 distinctive types of silhouette contour of the trunk. Incidentally it also identified 4 of the lower body from the waistline to thigh contour respectively. The discriminant analysis showed that the most significant discriminant factor of the trunk classification were side neck point -1 scapular -1 waistiline length and waist girth. In Korea, the average somatotype of female college students tends to be tall, slim and straight. Reviewing the relationship between the classifications of three parts of body, they are related to each other to some extent but their distribution are not constant. Therefore, in view of clothing construction, a proper separation of the body surface is a necessity.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.9
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• pp.197-206
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• 2020

Collaborative research in science applications based on HPC service needs rapid transfers of massive data between research colleagues over wide area network. With regard to this requirement, researches on enhancing data transfer performance between major superfacilities in the U.S. have been conducted recently. In this paper, we deploy multiple data transfer nodes(DTNs) over high-speed science networks in order to move rapidly large amounts of data in the parallel filesystem of KISTI's Nurion supercomputer, and perform transfer experiments between endpoints with approximately 130ms round trip time. We have shown the results of transfer throughput in different size file sets and compared them. In addition, it has been confirmed that the DTN cluster with three nodes can provide about 1.8 and 2.7 times higher transfer throughput than a single node in two types of concurrency and parallelism settings.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.80-90
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• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• Food Science and Biotechnology
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• v.17 no.1
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• pp.144-150
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• 2008

The main objective of this study was to understand the factors affecting the acceptance of fermented soybean products. Seventy-six consumers rated the acceptance and perceived intensity of 4 types of Korean and 4 types of Japanese style fermented soybean products. The consumer's food variety seeking tendency and the general attitude toward various fermented soybean products were measured. Ten descriptive analysis panelists evaluated the sensory characteristics of the 8 samples. Univariate and multivariate statistical analyses were applied to the data sets. Fermented soybean products consisting of sweet and moist sensory characteristics were preferred the most. The variety seeking tendency was not an effective predictor for understanding the acceptance of the products tasted in the experiment. K-means cluster analysis identified 3 sub-consumer segments sharing a common preference pattern for the 8 samples within each group. These 3 groups somewhat differed in the consumption frequency, acceptance, and familiarity of various fermented soybean products in general.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1541-1544
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• 2003

In This Paper, alternative method fur data pattern estimation is proposed and its numerical experiment is carried out. Proposed method gives candidates cluster numbers of given data set between n-2 and 2 by means of movement of the center of gravity. To observe the performance of proposed method, Test sample data sets are offered. Finally, this method is applied to Box and Jenkins's gas furnace data to verify the performance with previous researches.

• 한국사회복지학회:학술대회논문집
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• 2002.10a
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• pp.227-263
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• 2002

The Scandinavian cluster of welfare societies has for many years been considered a realisation of Richard Titmuss' institutional redistributive model of social policy. Recent reforms have, however challenged this assumption. The paper sets out to evaluate whether recent major changes in welfare provision are merely modifying the model or whether the Scandianian states are converging towards some kind of European social model. It is concluded that besides very many first order changes, such as reducing benefits, an number of second and third order changes have occurred; i.e. the institutional setting and the objective of the welfare states have changed during the 1990s. The Scandinavian welfare states are still distinct, but less so than a decade or two ago. The new elements are features usually associated with welfare models at play within the European Union. It is, hence, concluded that welfare in Scandinavia is undergoing a process of Europeanisation.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.79.1-79.1
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• 2013

We present new integrated spectroscopy of 24 Galactic globular clusters, observed with the Isaac Newton Telescope in La Palma. Spectra have been extracted from one core radius for each cluster, achieving high wavelength resolution of FWHM ${\sim}2.0^{\circ}A$. In combination with two previous data sets from Puzia et al. 2002 and Schiavon et al. 2005, we construct the largest database of the Lick indices for total 53 Galactic globular clusters. The empirical metallicity.index relations are given for the 20 Lick indices for the use of deriving metallicities of remote, unresolved stellar systems.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.393-398
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• 2002

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. forthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.253-257
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• 2004

Neural networks are works are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division(corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.