• Journal of Animal Science and Technology
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• v.54 no.6
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• pp.463-469
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• 2012

This study utilized a standardization and cluster analysis technique for the selection and classification of beneficial bacteria. A set of synthetic data consisting of 100 individual variables with three characteristics was created for analysis. The three characteristics assigned to each independent variable were designated to have different numeric scales, averages, and standard deviations. The variables were bacterial isolates at random, and the three characteristics were fermentation products, including cell yield, antioxidant activity of culture, and enzyme production. A standardization method utilizing a standard normal distribution equation to record fermentation yields of each isolate was employed to weight their different numeric scales and deviations. Following transformation, the data set was analyzed by cluster analysis. The Manhattan method for dissimilarity matrix construction along with complete linkage technique, an agglomerative method for hierarchical cluster analysis, was employed using statistical computing program R. A total of 100 isolates were classified into groups A, B, and C. In a comparison of the characteristics of each group, all characteristics in groups A and C were higher than those of group B. Isolates displaying higher cell yield were classified as group A, whereas those isolates showing high antioxidant activity and enzyme production were assigned to group C. The results of the cluster analysis can be useful for the classification of numerous isolates and the preparation of an isolation pool using numerical or statistical tools. The present study suggests that a simple technique can be applied to screen and select beneficial microbes using the freely downloadable statistical computing program R.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.441-442
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• 2008

• Journal of Korean Elementary Science Education
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• v.25 no.spc5
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• pp.495-506
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• 2007

This study investigated the differences in learning activity preferences according to different thinking styles between gifted and average students. A cluster analysis procedure was performed to classify students on the basis of thinking styles. Two clusters of different thinking styles were deduced: the gifted group with a high level thinking style (cluster 1), and the average group with a low level thinking style (cluster 2). The gifted group (cluster 1) preferred projects, simulations, discussions and game activities to other types of loaming activities. Gifted students and average students also were clustered into each three unique subgroups with respect to levels and patterns in thinking styles, and these subgroups also showed different learning preferences. The clusters of gifted students included the self-regulated learning type (cluster a), cooperative-learning type (cluster b), and the passive-learning type (cluster c). The clusters of average students included the independent learning type (cluster i), no-preference learning type(cluster ii), and the no-motivation & teacher-directed learning type (cluster iii). Theses clusters indicated significant differences not only in thinking styles but also in terms of preferences regarding learning activities. Theses findings are discussed in terms of their educational implications.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.8
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• pp.1386-1397
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• 2001

The purpose of this study was to classify and analyze the somatotype of early middle-aged women and to provide its total data for clothing construction, and to improve clothing culture. The subjects were 277 early middle-aged women between 35 and 44 years old. Data were collected through anthropometry and photometry and analyzed by factor analysis, cluster analysis and discriminant analysis. The results were as follows; 1. The result of factor analysis indicated that 10 factors were extracted through factor analysis and those factors comprised 86.13 percent of total variance. 2. Using factor scores, cluster analysis was carried out and the subject were classified into 4 cluster. Type 1 is tall, slim, and X type in front. Type 2 is standard height and weight, short upper body, and hip-protruded on the side. Type 3 is standard height, thin, H type in front, back and hip are clearly protruded, and lean-back type on the side. Type 4 is standard height, fat, and long upper body. 3. According to the stepwise discriminant analysis, the 8 important iems is classifying the somatotype of early middle-aged women are as follows : bust girth, back length hip breadth-waist breadth, back protruded point depth(back)-back waist depth(back), hip tangent tilt, hip depth(back) waist dapth(back), bust depth-waist depth, and cervical hight, The correct classification rate for these items is as exact as 83.20%.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.30.3-31
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• 2015

We present a kinematic analysis of 172 likely member galaxies of the Ursa Major Cluster. In order to understand the dynamical state of the cluster, we investigate the correlation of the cluster morphology with rotation, the velocity dispersion profile, and the rotation amplitude parallel to the global rotation direction. Both the minor axis and the rotation are very well-aligned with the global rotation axis in the outer region at half radius (> 0.5 $R_{max}$), but not in the inner region. The cluster exhibits low velocity dispersion and rotation amplitude profiles in the inner region, but higher in the outer. Both profiles exhibit outwardly increasing trends, suggesting an inside-out transfer of angular momentum of dark matter via violent relaxation, as revealed by a recent off-axis major-merging simulation. From Dressler-Schectman plots in the plane of galactic positions, and velocity versus position angle of galaxy, we are able to divide the Ursa Major Cluster into two substructures: Ursa Major South (UMS) and Ursa Major North (UMN). We derive a mass of $3.2{\times}10^{14}M_{\odot}$ for the cluster through the two-body analysis by the timing argument with the distance information (37 for UMN and 36 for UMS) and the spin parameter of ${\lambda}=0.049$. The two substructures appear to have passed each other 4.4 Gyr ago and are moving away to the maximum separation.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.45-70
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• 2012

There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2302-2324
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• 2014

Cluster-based wireless sensor network (WSN) can significantly reduce the energy consumption by data aggregation and has been widely used in WSN applications. However, due to the intrinsic many-to-one traffic pattern in WSN, the network lifetime is generally deteriorated by the unbalanced energy consumption in a cluster-based WSN. Therefore, energy efficiency and network lifetime improvement are two crucial and challenging issues in cluster-based WSNs. In this paper, we propose a Non-Uniform Node Distribution (NUND) scheme to improve the energy efficiency and network lifetime in cluster-based WSNs. Specifically, we first propose an analytic model to analyze the energy consumption and the network lifetime of the cluster-based WSNs. Based on the analysis results, we propose a node distribution algorithm to maximize the network lifetime with a fixed number of sensor nodes in cluster-based WSNs. Extensive simulations demonstrate that the theoretical analysis results determined by the proposed analytic model are consistent with the simulation results, and the NUND can significantly improve the energy efficiency and network lifetime.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.1
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• pp.29.4-30
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• 2017

Korea Astronomy and Space Science Institute We present a weak-lensing study of the galaxy cluster SPT-CL J2106-5844 at z=1.132 discovered in the South Pole Telescope Sunyaev-Zel'dovich (SPT-SZ) survey. The cluster is claimed to be the most massive system at z > 1 in the SPT-SZ survey. The inferred mass ($M_{200c}=(1.27{\pm}0.21){\times}10^{15}M_{sun}$) is somewhat unusual at such a high redshift given the current ΛCDM prediction. The mass estimates, however, may be biased because the hydrostatic assumption may not hold when the universe was about 40% of the current age. In this work, we reconstruct the dark matter distribution and measure the mass of this interesting cluster using weak-lensing analysis based on the images from the Advanced Camera for Surveys and Wide Field Camera 3 on-board the Hubble Space Telescope. We find that the mass distribution of the cluster is unimodal with no significant substructures. The centroid of the dark matter agrees with both galaxy luminosity and number density distributions, as well as the hot gas centroid. We confirm that the cluster is indeed extremely massive ($M_{200c}=(1.81{\pm}0.47){\times}10^{15}M_{sun}$) supporting the previous non-lensing measurements. We also discuss the rarity of the cluster in the ΛCDM cosmology, comparing with the expected abundance of similarly massive clusters.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.29.5-30
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• 2020

We present an improved weak-lensing (WL) study of the high-z (z=0.87) merging galaxy cluster ACT-CL J0102-4915 ("El Gordo"), the most massive system known to date at z > 0.6. El Gordo has been known to be an exceptionally massive and rare cluster for its redshift in the current ΛCDM cosmology. Previous multi-wavelength studies have also found that the cluster might be undergoing a merging event showing two distinctive mass clumps and radio relics. The previous WL study revealed a clear bimodal mass structure and found that the entire system is indeed massive (M200a = (3.13 ± 0.56) × 1015 Msun). This mass estimate, however, was obtained by extrapolation because the previous HST observation did not extend out to the virial radius of the cluster. In this work, we determine a more accurate mass estimate of the cluster using WL analysis utilizing a new set of WFC3/IR and wide-field ACS observations. While confirming the previous bimodal mass structure, we find that the new data yield a ~20% lower mass for the entire system (M200a = (2.37 ± 0.28) × 1015 Msun). We also discuss the rarity of the cluster in the ΛCDM paradigm and suggest an updated merging scenario based on our new measurement.

• Journal of Environmental Impact Assessment
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• v.28 no.4
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• pp.373-386
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• 2019

In The study, is to investigate the spatial characteristics of the Miho stream, which is the main tributary of the Geum River system, and to identify the main factors influencing the water quality using water quality analysis and multivariate analysis. The survey subjects were selected as 7 main sites in the Miho stream water system, From 2012 to 2017, 16 items including weather temperature and weather data were used for multivariate analysis. As a result of the water quality analysis, the average concentration of BOD and COD for 6 years was 3grade (normal) compared with the water quality environmental standard (river) of conditions. The concentrations of nitrogen and phosphorus were highest at th upstream site, then decreased and then increased again by the hydrogeological and geomorphological effect. Cluster analysis of spatial and water quality characteristics, it was evaluated as three clusters and the pollution sources is the greatest impact. As a result of principal component analysis and factor analysis on each cluster and mainstream, three to four major components were extracted. Main stream and the Cluster 1, Cluster 3 first principal factor included nitrogen and seasonal factors,first factor of Cluster 2 included nitrogen and water temperature. Nitrogen is the principal factor which affects water quality in Miho stream.