• Journal of The Korean Astronomical Society
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• v.36 no.3
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• pp.189-212
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• 2003

I review the current status of understanding when, how long, and how giant elliptical galaxies formed, focusing on the globular clusters. Several observational evidences show that massive elliptical galaxies formed at z > 2 (> 10 Gyr ago). Giant elliptical galaxies show mostly a bimodal color distribution of globular clusters, indicating a factor of $\approx$ 20 metallicity difference between the two peaks. The red globular clusters (RGCs) are closely related with the stellar halo in color and spatial distribution, while the blue globular clusters (BGCs) are not. The ratio of the number of the RGCs and that of the BGCs varies depending on galaxies. It is concluded that the BGCs might have formed 12-13 Gyr ago, while the RGCs and giant elliptical galaxies might have formed similarly 10-11 Gyr ago. It remains now to explain the existence of a gap between the RGC formation epoch and the BGC formation epoch, and the rapid metallicity increase during the gap (${\Delta}t{\approx}$ 2 Gyr). If hierarchical merging can form a significant number of giant elliptical galaxies > 10 Gyr ago, several observational constraints from stars and globular clusters in elliptical galaxies can be explained.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.56.2-56.2
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• 2012

We present a study of the asymptotic giant branch (AGB) contribution to the total Ks band luminosity of star clusters in the Large Magellanic Cloud (LMC) as a function of age. AGB stars, a representative intermediate-age population, are a strong source of NIR to MIR emission so that they are a critical component for understanding the near-to-mid infrared observation of galaxies. Current calibration of IR emission in evolutionary population synthesis (EPS) models for galaxies is mainly based on a small number of LMC star clusters. However, each LMC star cluster with intermediate age contains only a few AGB stars so that it suffers from a stochastic effect. Therefore a large number of them are needed for solid calibration of the EPS models. We study physical properties of a large number of LMC star clusters to estimate the Ks band luminosity fraction of AGB stars in star clusters as a function of age. We discuss the stochastic effect in calibrating models, and the importance of this calibration for studying the evolution of not only nearby galaxies but also of high-z galaxies.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1537-1544
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• 2020

Molecular dynamics (MD) simulations were conducted to investigate the temperature effects on the primary damage in gallium nitride (GaN) material. Five temperatures ranging from 300 K to 900 K were studied for 10 keV Ga primary knock-on atom (PKA) with inject direction of [0001]. The results of MD simulations showed that threshold displacement energy (E_d) was affected by temperatures and at higher temperature, it was larger. The evolutions of defects under various temperatures were similar. However, the higher temperature was found to increase the peak number, peak time, final time and recombination efficiency while decreasing the final number. With regard to clusters, isolated point defects and little clusters were common clusters and the fraction of point defects increased with temperature for vacancy clusters, whereas it did not appear in the interstitial clusters. Finally, at each temperature, the number of Ga interstitial atoms was larger than that of N and besides that, there were other different results of specific types of split interstitial atoms.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.513-522
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• 2007

Clustering is to divide given data and automatically find out the hidden meanings in the data. It analyzes data, which are difficult for people to check in detail, and then, makes several clusters consisting of data with similar characteristics. On-Line Document Clustering System, which makes a group of similar documents by use of results of the search engine, is aimed to increase the convenience of information retrieval area. Document clustering is automatically done without human interference, and the number of clusters, which affect the result of clustering, should be decided automatically too. Also, the one of the characteristics of an on-line system is guarantying fast response time. This paper proposed a method of determining the number of clusters automatically by geometrical information. The proposed method composed of two stages. In the first stage, centers of clusters are projected on the low-dimensional plane, and in the second stage, clusters are combined by use of distance of centers of clusters in the low-dimensional plane. As a result of experimenting this method with real data, it was found that clustering performance became better and the response time is suitable to on-line circumstance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.772-780
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• 2008

K-means is one of the simplest unsupervised learning algorithms that solve the clustering problem. However K-means suffers the basic shortcoming: the number of clusters k has to be known in advance. In this paper, we propose extensions of X-means, which can estimate the number of clusters using Bayesian information criterion(BIC). We introduce two different versions of algorithm: modified X-means(MX-means) and generalized X-means(GX-means), which employ one full covariance matrix for one cluster and so can estimate the number of clusters efficiently without severe over-fitting which X-means suffers due to its spherical cluster assumption. The algorithms start with one cluster and try to split a cluster iteratively to maximize the BIC score. The former uses K-means algorithm to find a set of optimal clusters with current k, which makes it simple and fast. However it generates wrongly estimated centers when the clusters are overlapped. The latter uses EM algorithm to estimate the parameters and generates more stable clusters even when the clusters are overlapped. Experiments with synthetic data show that the purposed methods can provide a robust estimate of the number of clusters and cluster parameters compared to other existing top-down algorithms.

• Journal of The Korean Astronomical Society
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• v.35 no.2
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• pp.75-85
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• 2002

We present N-body simulations of globular clusters including gravitational field of the Galaxy, in order to study effects of tidal field systematically on the shape of outer parts of globular clusters using NBODY6. The Galaxy is assumed to be composed of central bulge and outer halo. We mvestigate the cluster of multi-mass models with a power-law initial mass function (IMF) starting with different initial masses, initial number of particles, different slopes of the IMF and different orbits of the cluster. We have examined the general evolution of the clusters, the shape of outer parts of the clusters, density profiles and the direction of tidal tails. The density profiles appear to become somewhat shallower just outside the tidal boundary consistent with some observed data. The position angle of the tidal tall depends on the location in the Galaxy as well as the direction of the motion of. clusters. We found that the clusters become more elongated at the apogalacticon than at the pengalacticon. The tidal tails may be used to trace the orbital paths of globular clusters.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.80-89
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• 2009

A fast and efficient search algorithm to determine an optimal number of clusters in clustering algorithms is presented. The method is based on cluster validity index which is a measure for clustering optimality. As the clustering procedure progresses and reaches an optimal cluster configuration, the cluster validity index is expected to be minimized or maximized. In this Paper, a fast non-exhaustive search method for finding the optimal number of clusters is designed and shown to work well in clustering. The proposed algorithm is implemented with the k-mean++ algorithm as underlying clustering techniques using CB and PBM as a cluster validity index. Experimental results show that the proposed method provides the computation time efficiency without loss of accuracy on several artificial and real-life data sets.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.21-30
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• 2004

In Ad-Hoc network, the cluster structure enables effective use of multiple channels, reduces the number of control messages, and increase the scalability of network, Also, it is employed for reducing the number of broadcast messages in an Ad-Hoc network. With the consideration of these advantages, it is desirable that a cluster structure keeps a few clusters in the network, Generally, the cluster formation scheme based on connectivity yields fewer clusters than the other schemes. However, the connectivity based scheme may yield even more clusters than the other schemes according to the network topology. In this paper, a cluster formation scheme dividing the cluster formation into two phases is proposed. In the first phase, the lowest connectivity host in neighborhood initiates the cluster formation. Then, an adjustment procedure for affiliating a lot of the lowest connectivity hosts is employed. In the second phase, the hosts which were not affiliated to the first phase clusters are grouped into one or more clusters through criterions of connectivity and host ID. As a result, the proposed scheme yields a fewer clusters compared with existing other schemes in fully distributed method. The simulation results proves that our scheme is better than LIDCP(3) and HCCP(3).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.1-8
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• 2018

K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, this method has the limitation to be used with fixed number of clusters because of only considering the intra-cluster distance to evaluate the data clustering solutions. Silhouette is useful and stable valid index to decide the data clustering solution with number of clusters to consider the intra and inter cluster distance for unsupervised data. However, this valid index has high computational burden because of considering quality measure for each data object. The objective of this paper is to propose the fast and simple speed-up method to overcome this limitation to use silhouette for the effective large-scale data clustering. In the first step, the proposed method calculates and saves the distance for each data once. In the second step, this distance matrix is used to calculate the relative distance rate ($V_j$) of each data j and this rate is used to choose the suitable number of clusters without much computation time. In the third step, the proposed efficient heuristic algorithm (Group search optimization, GSO, in this paper) can search the global optimum with saving computational capacity with good initial solutions using $V_j$ probabilistically for the data clustering. The performance of our proposed method is validated to save significantly computation time against the original silhouette only using Ruspini, Iris, Wine and Breast cancer in UCI machine learning repository datasets by experiment and analysis. Especially, the performance of our proposed method is much better than previous method for the larger size of data.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.38.4-38.4
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• 2019

Ultra-diffuse galaxies (UDGs) are intriguing in the sense that they are much larger than dwarf galaxies but have much lower surface brightness than normal galaxies. To date, UDGs have been found only in the local universe. Taking advantage of deep and high-resolution HST images, we search for UDGs in massive galaxy clusters in the distant universe. In this work, we present our search results of UDGs in three massive clusters of the Hubble Frontier Fields: Abell 2744 (z=0.308), Abell S1063 (z=0.348), and Abell 370 (z=0.375). These clusters are the most distant and massive among the host systems of known UDGs. The color-magnitude diagrams of these clusters show that UDGs are mainly located in the faint end of the red sequence. This means that most UDGs in these clusters consist of old stars. Interestingly, we found a few blue UDGs, which implies that they had recent star formation. The radial number densities of UDGs clearly decrease in the central region of the clusters in contrast to those of bright galaxies which keep rising. This implies that a large fraction of UDGs in the central region were tidally disrupted. These features are consistent with those of UDGs in nearby galaxy clusters. We estimate the total number of UDGs (N(UDG)) in each cluster. The abundance of UDGs shows a tight relation with the virial masses (M_200) of thier host systems: M_200 \propto N(UDG)^(1.01+/-0.05). This slope is found to be very close to one, indicating that efficiency of UDGs does not significantly depend on the host environments. Furthermore, estimation of dynamical masses of UDGs indicates that most UDGs have dwarf-like masses (M_200 < 10^11 M_Sun), but a few UDGs have $L{\ast}$-like masses (M_200 > 10^11 M_Sun). In summary, UDGs in distant massive clusters are found to be similar to those in the local universe.