• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.38.2-38.2
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• 2010

We present ultraviolet (UV) color-magnitude relations (CMRs) of early-type dwarf galaxies in the Virgo cluster, based on Galaxy Evolution Explorer (GALEX) UV and Sloan Digital Sky Survey (SDSS) optical imaging data. We find that dwarf lenticular galaxies (dS0s), including peculiar dwarf elliptical galaxies (dEs) with disk substructures and blue centers, show a surprisingly distinct and tight locus separated from that of ordinary dEs, which is not clearly seen in previous CMRs. The dS0s in UV CMRs follow a steeper sequence than dEs and show bluer UV-optical color at a given magnitude. We also find that the UV CMRs of dEs in the outer cluster region are slightly steeper than that of their counterparts in the inner region, due to the existence of faint, blue dEs in the outer region. We explore the observed CMRs with population models of a luminosity-dependent delayed exponential star formation history. We confirm that the feature of delayed star formation of early-type dwarf galaxies in the Virgo cluster is strongly correlated with their morphology and environment. The observed CMR of dS0s is well matched by models with relatively long delayed star formation. Our results suggest that dS0s are most likely transitional objects at the stage of subsequent transformation of late-type progenitors to ordinary red dEs in the cluster environment, In any case, UV photometry provides a powerful tool to disentangle the diverse subpopulations of early-type dwarf galaxies and uncover their evolutionary histories.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.41-47
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• 2008

Cluster based fuzzy model tree has certain drawbacks to decrease performance of testinB data when over-fitting of training data exists. To reduce the sensitivity of performance due to over-fitting problem, we proposed a modified cluster based fuzzy model tree with node information. To construct model tree, cluster centers are calculated by fuzzy clustering method using all input and output attributes in advance. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. In the prediction step, membership values are calculated by using fuzzy distance between input attributes and all centers that passing the nodes from root to leaf nodes. Finally, data prediction is performed by the weighted average method with the linear models and fuzzy membership values. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional cluster based fuzzy model tree.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• The Korean Journal of Ecology
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• v.10 no.2
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• pp.91-107
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• 1987

This study is concerned with some methods and applications, used as a basis on information and entropy analysis of vegetation data. These methods are adopted for the evaluating the effect of sampling intensity on information, which repersnets the departure of observed variable from standard component. Classes on the data matrix are caluculated by using marginal dispersion array for rank and weighting information program. Finally the information and entropy are computed by applying seven options. On the application of vegetation studies, two models for cluster analysis and analysis of concentration are explained in detail. Cluster analysis is based on use of equivocation information and Rajski's metrics. The analysis of concentration utilizes coherence coefficience being transformed values, which has been adjusted from blocks and entropy values. The relationship btween three begetation clusters and four stands of Naejangsan data is highly significant in 79% of total variance. Cluster A relatively tends to prefer north side, and cluster C south side.

• Journal of The Korean Astronomical Society
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• v.28 no.2
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• pp.177-196
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• 1995

We present UBV CCD photometry of the double cluster NGC 1850 located at the NW edge of the bar of the Large Magellanic Cloud. The color-magnitude diagram shows that NGC 1850 has a prominent population of massive core-He burning stars which is incomparably richer than any other known star clusters. The reddening is estimated from the (U-B) - (B-V) diagram to be E(B - V) = $0.15{\pm}0.05$. We have estimated the ages of NGC 1850 and a very compact blue star cluster (NGC 1850A) located at ${\sim}30''$ west of NGC 1850 using isochrones based on the convective overshooting models: $80{\sim}10$ Myrs and $5{\sim}2$ Myra, respectively. Several evidence suggest that it is probably the compact cluster NGC 1850A that is responsible for the arc-shaped nebulosity (Henize N 103B) surrounding the east side of NGC 1850.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.38.1-38.1
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• 2019

The formation and evolution of galaxies is known to be fundamentally linked to the local environment in which they reside. In the highest-density cluster environments, galaxies tend to be more massive, have lower star formation rates and dust content, and a higher fraction have elliptical morphologies. The stellar populations of these cluster galaxies are older implying that they formed the bulk of their stars much earlier and have since evolved passively. Quantifying the specific environmental factors that contribute to shaping cluster galaxies over the Hubble time and measuring their early evolution can only be accomplished by directly tracing the galaxy growth in young clusters and forming porto-clusters. In this talk, I will present a novel technique designed to map out the total dust obscured star formation relative to where existing stars lie. I will demonstrate that this technique can be used 1) to determine if/where/when the activity is heightened or suppressed in dense cluster environment; 2) to measure the total mass and spatial distribution of stellar populations; and 3) to better inform theoretical models. Our ongoing work to extend this analysis out to protoclusters (z~2-4) will be discussed.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.72.2-72.2
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• 2013

We present a UBVI and $H{\alpha}$ photometric study of the young open cluster NGC 1893 in the outer Galaxy as part of "Sejong Open cluster Survey (SOS)" project. Using the properties of early-type stars in the photometric diagrams 65 early-type members were selected. More than 120 $H{\alpha}$ emission stars and candidates were found by $H{\alpha}$ photometry. Together with the published young stellar object catalogue and X-ray source list for this cluster, a total of 837 pre-main sequence (PMS) stars were identified in our photometric data. We obtained the mean reddening of < E(B-V) > = $0.56{\pm}0.08$ mag from the (U-B, B-V) diagram and confirmed the normal reddening law ($R_V=3.1$) toward NGC 1893 based on color excess ratios from optical to mid-infrared wavelengths. The zero-age main sequence fitting to the reddening-corrected color-magnitude diagrams gives a distance modulus of $V_0-M_V=12.8{\pm}0.1$. The age of the cluster inferred from stellar evolution models is about 1-2 Myr. We also found the Salpeter/Kroupa type initial mass function for this cluster. Finally, the mass accretion rate of 80 PMS stars with UV excess emission was estimated for the stars with masses from $0.6M_{\odot}$ to $5M_{\odot}$.

• Journal of The Korean Astronomical Society
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• v.29 no.2
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• pp.181-194
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• 1996

We present a study of the metallicity of the old open cluster NGC 1245 , based on the Washington CCD photometry obtained using the 0.6 m telescope at the Sobaeksan Observatory, Korea. NGC 1245 has been known to be a unique cluster among the known open clusters in the sense that the previous metallicity estimates for this cluster are much larger $(by\;\sigma)$ than the value expected from the radial metallicity gradient of the old open clusters in Our galaxy. We have estimated the metallicity of the cluster red giants using the four color-color diagrams, obtaining a value for the mean metallicity of $[Fe/H] = -0.04\pm0.05$ dex. The total error including the error of the metallicity calibration, 0.15 dex, is 0.16 dex. The metallicity estimate of NGC 1245 we have obtained in this study is smaller than previous estimates, and is consistent with the radial metallicity gradient of the old open clusters, showing that the mean metallicity of NGC 1245 is not abnormally high. The reddening, distance, and age of the cluster have also been derived using the isochrones based on the convective overshooting models: the reddening $E(B-V) = 0.28\pm0.03$; the distance $d = 2.5\pm0.2 kpc$ (the corresponding galactocentric distance is RGC = 10.7 kpc, and the distance from the galactic plane is z = -0.4 kpc); and the age $t = 1.1\pm0.1 Gyrs$.

• Smart Structures and Systems
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• v.26 no.1
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.47-61
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• 1996

We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.