• Publications of The Korean Astronomical Society
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• v.22 no.4
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• pp.141-149
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• 2007

Preliminary time-series observations for short-period variability survey (SPVS) were carried out using a 155mm refractor and a $2k{\times}3k$ CCD camera at Bohyunsan Optical Astronomy Observatory. We found 21 new variable stars in the $90'{\times}60'$ test field region : 9 eclipsing binary stars, $5{\delta}$ Scuti type stars, a ${\gamma}$ Doradus type star, and 6 long period variables. The observing field center is R.A. $05^h\;00^m\;00^s$, DEC. $50^{\circ}\;00'\;00"$ (J2000.0). The period and amplitude ranges for the short-period variables, i.e., ${\delta}$ Scuti stars, were 0.052day - 0.107day and 0.012mag - 0.064mag, respectively.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.481-483
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• 1998

In general, spatial filtering method was proposed to simplify measurement system through parallel Processing hardware. Spatial filtering is a method of detection that we can get a spatial pattern information, as we process a special space pattern, to say, as we process spatial parallel process by using the spatial weighting function. The important processing characteristics will be depended in according to how ire design a spatial weighting function, a spatial sensitive distribution. The form of the weighting function which is realized from the generally used spatial filtering is fixed and the weighting value was already became a binary-value. In this paper, we propose a new method in order to construct adaptive measurement systems. This method is a weighting function design to make multi-valued and variable.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.79-89
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• 2019

Classification models pertaining to receiver operating characteristic (ROC) curve analysis have been extended from univariate to multivariate setup by linearly combining available multiple markers. One such classification model is the multivariate ROC curve analysis. However, not all markers contribute in a real scenario and may mask the contribution of other markers in classifying the individuals/objects. This paper addresses this issue by developing an algorithm that helps in identifying the important markers that are significant and true contributors. The proposed variable selection framework is supported by real datasets and a simulation study, it is shown to provide insight about the individual marker's significance in providing a classifier rule/linear combination with good extent of classification.

• Steel and Composite Structures
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• v.48 no.5
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• pp.583-597
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• 2023

This paper introduces a novel approach to multi-material topology optimization (MTO) targeting in-plane bi-directional functionally graded (IBFG) non-uniform thickness Reissner-Mindlin plates, employing an alternative active phase approach. The mathematical formulation integrates a first shear deformation theory (FSDT) to address compliance minimization as the objective function. Through an alternating active-phase algorithm in conjunction with the block Gauss-Seidel method, the study transforms a multi-phase topology optimization challenge with multi-volume fraction constraints into multiple binary phase sub-problems, each with a single volume fraction constraint. The investigation focuses on IBFG materials that incorporate adequate local bulk and shear moduli to enhance the precision of material interactions. Furthermore, the well-established mixed interpolation of tensorial components 4-node elements (MITC4) is harnessed to tackle shear-locking issues inherent in thin plate models. The study meticulously presents detailed mathematical formulations for IBFG plates in the MTO framework, underscored by numerous numerical examples demonstrating the method's efficiency and reliability.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.203-212
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• 2024

The area under the ROC curve (AUC) is one of the most common criteria used to measure the overall performance of binary classifiers for a wide range of machine learning problems. In this article, we propose a L₁-penalized AUC-optimization classifier that directly maximizes the AUC for high-dimensional data. Toward this, we employ the AUC-consistent surrogate loss function and combine the L₁-norm penalty which enables us to estimate coefficients and select informative variables simultaneously. In addition, we develop an efficient optimization algorithm by adopting k-means clustering and proximal gradient descent which enjoys computational advantages to obtain solutions for the proposed method. Numerical simulation studies demonstrate that the proposed method shows promising performance in terms of prediction accuracy, variable selectivity, and computational costs.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.1-11
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• 2012

We present methods to study the log-density ratio of the conditional densities of the predictors given the response variable in the logistic regression model. This allows us to select which predictors are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. A simulation study shows that the linear and log terms are required in general. If the conditional distributions of xjy for the two groups overlap significantly, we need both the linear and log terms; however, only the linear or log term is needed in the model if they are well separated.

• Journal of the Korean Society of Visualization
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• v.13 no.3
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• pp.15-19
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• 2015

We developed a high-throughput microscopy (HTM) method which enabled us to replace a conventional phase contrast microscopy (PCM) method that has been used as a standard analytical method for airborne asbestos. We could obtain the concentration of airborne asbestos fibers under detection limit by automated image processing and analysis using HTM method. Here we propose an improved image processing algorithm with variable parameters to enhance the accuracy of the HTM analysis. Since the variable parameters that compensate the difference of the brightness are applied to the individual images in our new image processing method, it is possible to enhance the accuracy of the automatic image analysis method for sample slides with low asbestos concentration that caused errors in binary image processing. We demonstrated that enumeration of fibers by improved image processing algorithm remarkably enhanced the accuracy of HTM analysis in comparison with PCM. The improved HTM method can be a potential alternative to conventional PCM.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.70.1-70.1
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• 2018

The DEEP-South photometric census of small Solar System bodies is producing massive time-series data of variable, transient or moving objects as a by-product. To fully investigate unexplored variable phenomena, we present an application of multi-aperture photometry and FastBit indexing techniques to a portion of the DEEP-South year-one data. Our new pipeline is designed to do automated point source detection, robust high-precision photometry and calibration of non-crowded fields overlapped with area previously surveyed. We also adopt an efficient data indexing algorithm for faster access to the DEEP-South database. In this paper, we show some application examples of catalog-based variability searches to find new variable stars and to recover targeted asteroids. We discovered 21 new periodic variables including two eclipsing binary systems and one white dwarf/M dwarf pair candidate. We also successfully recovered astrometry and photometry of two near-earth asteroids, 2006 DZ169 and 1996 SK, along with the updated properties of their rotational signals (e.g., period and amplitude).

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.35.2-35.2
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• 2021

Blue straggler stars (BSs) are "rejuvenated" main sequence stars first recognized by Allan Sandage from his observation of the prominent northern globular cluster M3 in the year of 1953. BSs are now known to be present in diverse stellar environments including open clusters, globular clusters, dwarf galaxies, and even the field populations of the Milky Way. This makes them a very useful tool in a wide range of astrophysical applications: Particularly BSs are considered to have a crucial role in the evolution of stellar clusters because they affect on the dynamics, the binary population, and the history of the stellar evolution of the cluster they belong to. Here we report a part of the preliminary results from our ongoing research on the BSs in the two metal-poor globular clusters (GCs) in the Large Magellanic Cloud (LMC), Hodge 11 and NGC1466. Using the high precision multi-band images obtained with the Advanced Camera for Survey (ACS) onboard the Hubble Space Telescope (HST), we extract time-series photometry to search for the signal of periodic variations in the luminosity of the BSs. Our preliminary results confirm that several BSs are intrinsic "short period (0.05 < P < 0.25 days)" variable stars with either pulsating or eclipsing types. We will discuss our investigation on the properties of those variable BS candidates in the context of the formation channels of these exotic main sequence stars, and their roles in the dynamical evolution of the host star clusters.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.297-307
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• 2009

Korean household debt is one of the main sources of the current financial crisis. This paper studies the impact of household members' attributes such as a type of housing(self-own or rent), education, age, average monthly income of the head of household, and the area of residence, on the stress level of the household members due to household debt. We analyze a real data set collected by KB Kookmin Bank in 2004. We consider low and high stress level as a binary response variable and use a logistic regression model with the attributes of household members as explanatory variables. A simple but well-fitting model is selected by backward elimination method based on the likelihood statistic for goodness-of-fit test, and the impact of the attributes on the stress level is studied from parameter estimates of the selected model. We also perform the similar analysis on a binary response variable which distinguishes households with no debt from the rest. From the analysis, the stress level tends to be low for households with self-own houses, high average monthly income, low education level, and young members.