• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.421-439
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• 2022

Phase I dose-finding trials are essential in drug development. By finding the maximum tolerated dose (MTD) of a new drug or treatment, a Phase I trial establishes the recommended doses for later-phase testing. The primary toxicity endpoint of interest is often a binary variable, which describes an event of a patient who experiences dose-limiting toxicity. However, there is a growing interest in dose-finding studies regarding non-binary outcomes, defined by either the weighted sum of rates of various toxicity grades or a continuous outcome. Although several novel methods have been proposed in the literature, accessible software is still lacking to implement these methods. This study introduces a newly developed R package, UnifiedDoseFinding, which implements three phase I dose-finding methods with non-binary outcomes (Quasi- and Robust Quasi-CRM designs by Yuan et al. (2007) and Pan et al. (2014), gBOIN design by Mu et al. (2019), and by a method by Ivanova and Kim (2009)). For each of the methods, UnifiedDoseFinding provides corresponding functions that begin with next that determines the dose for the next cohort of patients, select, which selects the MTD defined by the non-binary toxicity endpoint when the trial is completed, and get oc, which obtains the operating characteristics. Three real examples are provided to help practitioners use these methods. The R package UnifiedDoseFinding, which is accessible in R CRAN, provides a user-friendly tool to facilitate the implementation of innovative dose-finding studies with nonbinary outcomes.

• Journal of The Korean Astronomical Society
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• v.49 no.5
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• pp.209-223
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• 2016

We present analyses of ~1250 variable sources identified in a 20 square degree field toward NGC 2784 by the KMTNet Supernova Program. We categorize the variable sources into three groups based on their B-band variability. The first group consists of 31 high variability sources with their B-band RMS variability greater than 0.3 magnitudes. The second group of medium variability contains 265 sources with RMS variability between 0.05 and 0.3 magnitudes. The remaining 951 sources belong to the third group of low variability with an RMS variability smaller than 0.05 magnitudes. Of the entire ~1250 sources, 4 clearly show periods of variability greater than 100 days, while the rest have periods shorter than ~51 days or no reliable periods. The majority of the sources show either rather irregular variability or short periods faster than 2 days. Most of the sources with reliable period determination between 2 and 51 days belong to the low-variability group, although a few belong to the medium-variability group. All the variable sources with periods longer than 35 days appear to be very red with B - V > 1.5 and V - I > 2.1 magnitudes. We classify candidates of 51 Cepheids, 17 semi-regular variables, 3 Mira types, 2 RV(B) Tauri stars, 26 eclipsing binary systems and 1 active galactic nucleus. The majority of long-term variables in our sample belong to either Mira or semi-regular types, indicating that long-term variability may be more prominent in post-main sequence phases of late-type stars. The depth of the eclipsing dips of the 26 candidates for eclipsing binaries is equivalent to ~0.61 as the average relative size of the two stars in the binary system. Our results illustrate the power of the KMTNet Supernova Program for future studies of variable objects.

• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.61-68
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• 2014

We propose binary classification methods by modifying logistic regression classification. We use variable selection procedures instead of original variables to select the principal components. We describe the resulting classifiers and discuss their properties. The performance of our proposals are illustrated numerically and compared with other existing classification methods using synthetic and real datasets.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.431-434
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• 2003

In this paper we introduce a modified objective function for fuzzy c-means clustering with logistic regression model in the presence of noise cluster. The logistic regression model is commonly used to describe the effect of one or several explanatory variables on a binary response variable. In real application there is very often no sharp boundary between clusters so that fuzzy clustering is often better suited for the data.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1731_1732
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• 2009

This paper presents a stability analysis of networked control systems with packet dropouts. The packet dropouts are modeled as a linear function of the stochastic variable satisfying Bernoulli random binary distribution and weighted moving average (WMA). The observer based controller scheme is designed to exponentially mean square stabilize the NCS. Simulation results is provided to show the applicability of the proposed method.

• Bulletin of the Korean Space Science Society
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• 1993.04a
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• pp.8-8
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• 1993

Long period eclipsing binary 32 Cygni was photometrically observed in 1992 at Yonsei University Observatory. Instrumental differential magnitude and standardized magnitude of this star show some intrinsic light variations. Some possible explanations will be discussed. BVRI photometric observations show that 30 Cyg may be a variable star. A further monitoring of 30 Cyg is therefore called for justification of such evidences.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.270-274
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• 2008

This paper proposes an advanced signal processing circuit for a fingerprint sensor based on ridge resistivity. A novel fingerprint integrated sensor using ridge resistivity variation resulting from ridges and valleys on the fingertip is presented. The pixel level simple detection circuit converts from a small and variable sensing current to binary voltage out effectively. The sensor circuit blocks were designed and simulated in a standard CMOS 0.35 ${\mu}m$ process.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.255-264
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• 2004

Due to a computerized data processing, there are many cases when we encounter a huge data set. On the other hand, advances in computing technologies make it possible to deal with a huge data set. One important area is the data mining. In this paper we consider data mining when the dependent variable is binary. The proposed method is to use the poly-class model when the independent variables consists of continuous and discrete variables. An example is provided.

• Publications of The Korean Astronomical Society
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• v.11 no.1
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• pp.243-250
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• 1996

Data acquisition system mounted on the Solar Flare Telescope at Bohyunsan Optical Astronomy Observatory is briefly described. The system is made up with CCD cameras, an image processor, a PCI-type PC and a SUN workstation. The image processor, MVC 150/40 comprises a variable scan acquisition module, an image manager and a binary correlator computational module. A typical polarization image of a sunspot is presented to demonstrate performance of the system.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.565-574
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• 2004

Classification tree algorithms including as CART by Brieman et al.(1984) in some aspects, recursively partition the data space with the aim of making the distribution of the class variable as pure as within each partition and consist of several steps. SUPPORT(smoothed and unsmoothed piecewise-polynomial regression trees) method of Chaudhuri et al(1994), a weighted averaging technique is used to combine piecewise polynomial fits into a smooth one. We focus on applying SUPPORT to a binary class variable. Logistic model is considered in the caculation techniques and the results are shown good classification rates compared with other methods as CART, QUEST, and CHAID.