• 한국정보디스플레이학회:학술대회논문집
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• 2005.07a
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• pp.506-510
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• 2005

As FPD technology is getting improved, there are a lot of issues on signal processing and analysis, and its relative importance has been increasing day by day. In particular, response time sad in the evaluation item of FPD has been measured by oscilloscope. In this paper, we propose an effective measurement method of response time in FPD. The proposed method is to calculate the rising/ falling time by using statistical techniques of histogram and analyzing an energy distribution. Ultimately, the method has proved the utility and reliability by comparison of oscilloscope

• The Korean Journal of Pain
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• v.23 no.1
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• pp.35-41
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• 2010

Background: Statistical analysis is essential in regard to obtaining objective reliability for medical research. However, medical researchers do not have enough statistical knowledge to properly analyze their study data. To help understand and potentially alleviate this problem, we have analyzed the statistical methods and errors of articles published in the Korean Journal of Pain (KJP), with the intention to improve the statistical quality of the journal. Methods: All the articles, except case reports and editorials, published from 2004 to 2008 in the KJP were reviewed. The types of applied statistical methods and errors in the articles were evaluated. Results: One hundred and thirty-nine original articles were reviewed. Inferential statistics and descriptive statistics were used in 119 papers and 20 papers, respectively. Only 20.9% of the papers were free from statistical errors. The most commonly adopted statistical method was the t-test (21.0%) followed by the chi-square test (15.9%). Errors of omission were encountered 101 times in 70 papers. Among the errors of omission, "no statistics used even though statistical methods were required" was the most common (40.6%). The errors of commission were encountered 165 times in 86 papers, among which "parametric inference for nonparametric data" was the most common (33.9%). Conclusions: We found various types of statistical errors in the articles published in the KJP. This suggests that meticulous attention should be given not only in the applying statistical procedures but also in the reviewing process to improve the value of the article.

• Journal of the Korean Chemical Society
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• v.55 no.4
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• pp.620-625
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• 2011

A simple, rapid potentiometric back titration of Isoniazid (INH) in the presence of Rifampicin (RIF) in tablets and syrups is described. The method is based on the oxidation of INH by a known excess of copper (II) ion and the back titration of unreacted copper (II) ion potentiometrically with ascorbic acid using a lab-made Copper Based Mercury Film Electrode (CBMFE). The titration conditions have been optimized for the determination of 1.0-10.0 mg of INH in pure and dosage forms. The precision and accuracy of the method have been assessed by the application of lack-of-fit test and other statistical methods. Interference was not caused by RIF and other excipients present in dosage forms. Application of the method for INH assay in tablets and syrups was validated by comparison of the results of proposed method with that of the British Pharmacopoeia (BP) method using F- and t- statistical tests of significance.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.127-139
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• 2021

Independent Component Analysis is a popular statistical method to separate independent signals from the mixed data, and Group Independent Component Analysis is an its multi-subject extension of Independent Component Analysis. It has been applied Functional Magnetic Resonance Imaging data and provides promising results. However, classical Group Independent Component Analysis works poorly when outliers exist on data which is frequently occurred in Magnetic Resonance Imaging scanning. In this study, we propose a robust version of the Group Independent Component Analysis based on ROBPCA. Through the numerical studies, we compare proposed method to the conventional method, and verify the robustness of the proposed method.

• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.225-234
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• 2014

We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.38
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• pp.117-129
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• 1996

This Study intends to analysis what 3 factors, which are indices of Capital, Labor and Distribution, really affect to Value-Added Productivity through Statistical Analysis. For this, We selected 12 indices of Value-Added from the edition of 'Annual report of Korean companies' published in 'Korea Investors Service., Inc', especially in parts of Chemicals and Chemical products of total 85 companies. Using this data, Multivariate Statistical Analysis such as Principal Component Analysis, Factor Analysis, Covariance Structure Analysis is taken for modeling the effect of 3 factor(Labor Productivity, Capital Productivity and the Index of Distribution) on Value-Added Productivity.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.1
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• pp.33-39
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• 2003

Interior parts of a vehicle are getting important to reduce interior noise. Therefore, prior analysis of cabin noise related with interior parts are necessary at first design stage. Recently, Statistical Energy Analysis(SEA) has been suggested as a possible way for high frequency range noise analysis of interior parts. The validity of noise analysis with SEA to interior parts has been preyed by comparing with experimental result, and the developed method with SEA has been applied in finding optimized interior parts package.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.357-367
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• 2009

According to the development of modern medical science, one can find many other related researches in various fields. In order to get correct research results, research design, research process and analysis of results should be done in objective and reasonable manner. Therefore, various statistical analysis approaches are widely used. In this paper, we investigate the usage of statistical methods in research papers published in four medical journals between 2004 and 2007.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.37-46
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• 2020

Recurrent event data frequently occur in clinical studies, demography, engineering reliability and so on (Cook and Lawless, The Statistical Analysis of Recurrent Events, Springer, 2007). Sometimes, two or more different but related type of recurrent events may occur simultaneously. In this study, our interest is to estimate the covariate effect on bivariate recurrent event times with zero inflations. Such zero inflation can be related with susceptibility. In the context of bivariate recurrent event data, furthermore, such susceptibilities may be different according to the type of event. We propose a joint model including both two intensity functions and two cure rate functions. Bivariate frailty effects are adopted to model the correlation between recurrent events. Parameter estimates are obtained by maximizing the likelihood derived under a piecewise constant hazard assumption. According to simulation results, the proposed method brings unbiased estimates while the model ignoring cure rate models gives underestimated covariate effects and overestimated variance estimates. We apply the proposed method to a set of bivariate recurrent infection data in a study of child patients with leukemia.