• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.1-8
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• 2016

We propose an adaptive histogram stretching algorithm for application to a car's personal recorder. The algorithm was used for pre-processing to detect the license plate region in an image from a personal recorder. The algorithm employs a Probability Density Function (PDF) and Cumulative Distribution Function (CDF) to analyze the distribution diagram of the images. These two functions are calculated using an image obtained by sampling at a certain pixel interval. The images were subjected to different levels of stretching, and experiments were done on the images to extract their characteristics. The results show that the proposed algorithm provides less deterioration than conventional algorithms. Moreover, contrast is enhanced according to the characteristics of the image. The algorithm could provide better performance than existing algorithms in applications for detecting search regions for license plates.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.187-198
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• 2019

Extensive literature exists on how to estimate optimal thresholds based on various accuracy measures using receiver operating characteristic (ROC) and cumulative accuracy profile (CAP) curves. This paper now proposes an alternative measure to represented the specific partial area under the ROC and CAP curves. The relationship between ROC and CAP functions is examined using differential equations of the new defined partial area under curves. In addition, the relationship with the optimal thresholds under conditions of various accuracy measures for the ROC and CAP functions is also derived. We assume there are two kinds of distribution functions composing the mixed distribution as various normal distributions before finding the optimal thresholds. Corresponding type 1 and 2 errors are also explored and discussed under various conditions for accuracy measures.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.415-420
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• 1996

The main objective of the present study is to propose a new measure of uncertainty importance based on distributional sensitivity analysis. The new measure is developed to utilize a metric distance obtained from cumulative distribution functions (cdfs). The measure is evaluated for two cases: one is a cdf given by a known analytical distribution and the other given by an empirical distribution generated by a crude Monte Carlo simulation. To study its applicability, the present measure has been applied to two different cases. The results are compared with those of existing three methods. The present approach is a useful measure of uncertainty importance which is based on cdfs. This method is simple and easy to calculate uncertainty importance without any complex process. On the basis of the results obtained in the present work, the present method is recommended to be used as a tool for the analysis of uncertainty importance.

• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.83-94
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• 1996

The test statistic in ANOVA tests has a single or doubly noncentral F distribution and the noncentral F distribution is applied to the calculation of the power functions of tests of general linear hypotheses. In this paper. the evaluation of the cumulative function of the single noncentral F distribution is applied to the neural network theory. The neural network consists of the multi-layer perceptron structure and learning process has the algorithm of the backpropagation. Numerical comparisons are made between the results obtained by neural network theory and the Patnaik's values.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.163-175
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• 2020

We consider a wide class of general weakly-dependent processes, called ψ-weak dependence, which unify almost all weak dependence structures of interest found in statistics under natural conditions on process parameters, such as mixing, association, Bernoulli shifts, and Markovian sequences. For detecting the tail behavior of the weakly dependent processes, change point tests are developed by means of cumulative sum (CUSUM) statistics with the empirical distribution functions of sample extremes. The null limiting distribution is established as a Brownian bridge. Its proof is based on the ψ-weak dependence structure and the existence of the phantom distribution function of stationary weakly-dependent processes. A Monte-Carlo study is conducted to see the performance of sizes and powers of the CUSUM tests in GARCH(1, 1) models; in addition, real data applications are given with log-returns of financial data such as the Korean stock price index.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.255-268
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• 2020

A mixture of multivariate canonical fundamental skew t-distribution (CFUST) has been of interest in various fields. In particular, interest in the unsupervised learning society is noteworthy. However, fitting the model via EM algorithm suffers from significant processing time. The main cause is due to the calculation of many multivariate t-cdfs (cumulative distribution functions) in E-step. In this article, we provide an approximate, but fast calculation method for the in univariate fashion, which is the product of successively conditional univariate t-cdfs with Taylor's first order approximation. By replacing all multivariate t-cdfs in E-step with the proposed approximate versions, we obtain the admissible results of fitting the model, where it gives 85% reduction time for the 5 dimensional skewness case of the Australian Institution Sport data set. For this approach, discussions about rough properties, advantages and limits are also presented.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.2
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• pp.131-140
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• 2019

Diagonally reinforced concrete coupling beams (DRCBs) have been widely adopted in reinforced concrete (RC) bearing wall systems. DRCBs are known to act as a fuse element dissipating most of seismic energies imparted to the bearing wall systems during earthquakes. Despite such importance of DRCBs, the damage estimation of such components and the corresponding consequences within the knowledge of performance based seismic design framework is not well understood. In this paper, drift-based fragility functions are developed for in-plane loaded DRCBs. Fragility functions are developed to predict the damage and to decide the repair method required for DRCBs subjected to earthquake loading. Thirty-seven experimental results are collected from seventeen published literatures for this effort. Drift-based fragility functions are developed for four damage states of DRCBs subjected to cyclic and monotonic loading associated with minor cracking, severe cracking, onset of strength loss, and significant strength loss. Damage states are defined in a consistent manner. Cumulative distribution functions are fit to the empirical data and evaluated using standard statistical methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.163-169
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• 1997

Due to the increasing of the number of cars and bad road conditions, car accidens are increasing every you in Korea. When a person meets a car accident, it is necessary for him to analyze and determine whether applying insurance or not, because standard discount rate and special increasing rate change with accident types and the amount of accident expenditure. When we consider insurance rate that includes more then ten elements, we need a decision making, In this paper, S insurance company investigated previous car causers in 1988, 1989, 1990 to 1996 with 600,000 real data. We investigate probability density functions and cumulative distribution functions for each year using ARENA software. We can apply the results of this study to various accidents that occur under uncertainty in our life. I hope that this paper contribute to strengthening competitive power of companies and developing new insurance rate systems in future.

• The Journal of Korea Robotics Society
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• v.11 no.1
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• pp.19-25
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• 2016

In this paper, we propose a new method for improving the accuracy of localizing a robot to find the position of a robot in indoor environment. The proposed method uses visible light for indoor localization with a reference receiver to estimate optical power of individual LED in order to reduce localization errors which are caused by aging of LED components and different optical power for each individual LED, etc. We evaluate the performance of the proposed method by comparing it with the performance of traditional model. In several simulations, probability density functions and cumulative distribution functions of localization errors are also obtained. Results indicate that the proposed method is able to reduce localization errors from 7.3 cm to 1.6 cm with a precision of 95%.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.415-433
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• 1999

This paper considers the design of step-stress accelerated life tests for the Weibull distribution with a nonconstant shape parameter under Type I censoring. It is assumed that scale and shape parameters are log-linear functions of (possibly transformed) stress and that a cumulative exposure model holds for the effect of changing stress. The asymptotic variance of the maximum likelihood estimator of a stated quantile at design stress is used as an optimality criterion. The optimum three step-stress plans are presented for selected values of design parameters and the effects of errors in pre- estimates of the design parameters are investigated.