• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.467-468
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• 1998

Impulsive noise appears as black and/or white spots in an image. It is usually caused by errors during the image acquisition or transmission through communication channels. This paper presents a study on the impulsive noise reduction filter of digital image. A much more effective method for removing impulse noise is weighted median filtering. But it loses some information by changing center value with no condition. We propose some new technique to change center value with some conditions. In this paper, the performance of conditional weighted median filter is compared to the commonly used median filter, mean filter, max/min filter, and weighted median filter. A quantitative comparison is performed on MSE (Mean Square Error), RMSE (Root Mean Square Error), and SNR (Signal to Noise Ratio). Proposed conditional weighted median filter can yield better performance than regular filters.

• Journal of the Korean Mathematical Society
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• v.53 no.6
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• pp.1275-1292
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• 2016

The Marcinkiewicz-Zygmund strong law of large numbers for conditionally independent and conditionally identically distributed random variables is an existing, but merely qualitative result. In this paper, for the more general cases where the conditional order of moment belongs to (0, ${\infty}$) instead of (0, 2), we derive results on convergence rates which are quantitative ones in the sense that they tell us how fast convergence is obtained. Furthermore, some conditional probability inequalities are of independent interest.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.6
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• pp.301-309
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• 2019

A methodology to assess seismic fragility of a nuclear power plant (NPP) using a conditional mean spectrum is proposed as an alternative to using a uniform hazard response spectrum. Rather than the single-scenario conditional mean spectrum, which is the conventional conditional mean spectrum based on a single scenario, a multi-scenario conditional mean spectrum is proposed for the case in which no single scenario is dominant. The multi-scenario conditional mean spectrum is defined as the weighted average of different conditional mean spectra, each one of which corresponds to an individual scenario. The weighting factors for scenarios are obtained from a deaggregation of seismic hazards. As a validation example, a seismic fragility assessment of an NPP containment structure is performed using a uniform hazard response spectrum and different single-scenario conditional mean spectra and multi-scenario conditional mean spectra. In the example, the number of scenarios primarily influences the median capacity of the evaluated structure. Meanwhile, the control frequency, a key parameter of a conditional mean spectrum, plays an important role in reducing logarithmic standard deviation of the corresponding fragility curves and corresponding high confidence of low probability of failure (HCLPF) capacity.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.33-40
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• 1994

The conditional expectation of a random variable in a multivariate normal random vector is a multiple linear regression on its predecessors. Using this fact, the least median of squares estimation method developed in a multiple linear regression is adapted to a multivariate data to identify influential observations. The resulting method clearly detect outliers and it avoids the masking effect.

• Journal of Korean Society of Transportation
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• v.19 no.5
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• pp.109-117
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• 2001

The median encroachment accident model proposed in this paper is the first step to develop cost-effective criteria about installing facilities preventing traffic accidents by median encroachment. This model consists of expected annual number of median encroachment on roadway and conditional probability to collide with vehicles on opposite lane after encroachment. Expected encroachment number is related to traffic volume and quote from a study of Hutchinson & Kennedy(1966). The probability of vehicle collision is composed of assumed headway distribution of opposite directional vehicles (negative exponential distribution), driving time of encroaching vehicle and Gap & Gap acceptance model. By using expected accident number yielded from the presented model, it will be able to calculate the benefit of reduced accident and to analyze the cost of installing facilities. Therefore this will help develop cost-effective criteria of what, to install in the median.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.27-37
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• 2010

This article concerns the forecasting in binomial AR(p) models which is proposed by Wei$\ss$ (2009b) for time series of binomial counts. Our method extends to binomial AR(p) models a recent result by Jung and Tremayne (2006) for integer-valued autoregressive model of second order, INAR(2), with simple Poisson innovations. Forecasts are produced by conditional median which gives 'coherent' forecasts, and we estimate the forecast distributions of future values of binomial AR(p) models by means of a Monte Carlo method allowing for parameter uncertainty. Model parameters are estimated by the method of moments and estimated standard errors are calculated by means of block of block bootstrap. The method is fitted to log data set used in Wei$\ss$ (2009b).

• Communications for Statistical Applications and Methods
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• v.30 no.5
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• pp.453-465
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• 2023

The prediction problem of univariate records, though not addressed in multivariate records, has been discussed by many authors based on records values. There are various definitions for multivariate records among which depth-based records have been selected for the aim of this paper. In this paper, by means of the maximum likelihood and conditional median methods, point and interval predictions of depth values which are related to the future depth-based multivariate records are considered on the basis of the observed ones. The observations derived from some elements of the elliptical distributions are the main reason of studying this problem. Finally, the satisfactory performance of the prediction methods is illustrated via some simulation studies and a real dataset about Kermanshah city drought.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3718-3726
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• 1999

Color image is more effective than gray one in human visual perception. Therefore, color image processing becomes important area. Color images are often corrupted by noises due to the input sensor, channel transmission errors and so on. Some filtering techniques such as vector median, mean filter, and vector $\alpha-trimmed$ mean filter have been used for color noise removal. Among them, vector $\alpha-trimmed$ mean filter gave the best performance in the mixed color noise. But, there are edge shift and blurring effect because vector $\alpha-trimmed$ mean filter is uniformly processed across the image. So, we proposed a conditional fuzzy cluster filter to improve this problems. Simulation results showed that the proposed scheme improves the NCD measure and visual quality over the conventional vector $\alpha-trimmed$ mean filter in the mixed color noise.

• Korean Journal of Community Nutrition
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• v.27 no.4
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• pp.309-320
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• 2022

Objectives: This study aimed to analyze the consumption values of fast foods among American consumers and compare the consumption values according to the levels of health consciousness. Methods: An online survey using a self-administered questionnaire was conducted on 351 American consumers. Based on the median health consciousness score (3.83 out of 5 points), the subjects were classified into the low health-conscious group (Low group) and the high health-conscious group (High group). Factor analysis was used to extract factors for the five consumption values (functional, social, emotional, conditional, and epistemic values). The differences in the consumption values between the two groups were analyzed. Results: A total of 14 factors were extracted for the five consumption values and 9 factors among them (convenience, healthiness, and taste in functional values; health-conscious people, young, busy, obese people, low class, and budget restricted people in social values; guilt in emotional values; accidental situations in conditional values) showed significant differences between the two groups. The Low group had a higher perception of the factor of healthiness (P < 0.001) than the High group. The High group had a relatively higher perception of the factors of convenience (P < 0.001), taste (P < 0.001), and guilt (P < 0.001). In addition, the High group perceived the social values of fast foods more negatively. The High group consumed fast foods less frequently than the Low group and perceived their health status and healthiness of eating habits more positively. Conclusions: The results reveal that the health consciousness level significantly influences consumption value perceptions about fast foods in American consumers. Policymakers and marketers can develop effective strategies based on the results of this study.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.815-826
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• 2012

The underestimating trend using existing ordinary regression (OR) based trend analysis has been a well-known problem. The existing OR method based on least squares approximate the conditional mean of the response variable given certain values of the time t, and the usual assumption of the OR method is normality, that is the distribution of data are not dissimilar form a normal distribution. In this regard, this study proposed a quantile regression that aims at estimating either the conditional median or other quantiles of the response variable. This study assess trend in annual daily maximum rainfall series over 64 weather stations through both in OR and QR approach. The QR method indicates that 47 stations out of 67 weather stations are a strong upward trend at 5% significance level while OR method identifies a significant trend only at 13 stations. This is mainly because the OR method is estimating the condition mean of the response variable. Unlike the OR method, the QR method allows us flexibly to detect the trends since the OR is designed to estimate conditional quantiles of the response variable. The proposed QR method can be effectively applied to estimate hydrologic trend for either non-normal data or skewed data.