• 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.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.199-206
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• 2005

• Environmental and Resource Economics Review
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• v.14 no.1
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• pp.51-73
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• 2005

The purpose of this study is estimating the value of recreation of the eighteen national parks in Korea. A conditional logit model and a nested logit model have been estimated for the purpose. The data used for the study have been collected via a national level off-site survey. In addition, the annual aggregate data on the number of visitors to each park have been combined with the survey data to derive more reliable estimates. The paper finds that there are substantial differences in preferences for mountain and marine national parks. Not only the value of each park but also the values of the main characteristics of the parks are estimated.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.995-1005
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• 2009

Conditional correlation between financial time series plays an important role in risk management, asset allocation and portfolio selection and therefore diverse efforts for modeling conditional correlations in multivariate-GARCH processes have been made in last two decades. In particular, CCC (cf. Bollerslev, 1990) and DCC(dynamic conditional correlation, cf. Engle, 2002) models have been commonly used since they are relatively parsimonious in the number of parameters involved. This article is concerned with DCC modeling for multivariate GARCH processes in comparison with CCC specification. Various multivariate financial time series are analysed to illustrate possible advantages of DCC over CCC modeling.

• Management & Information Systems Review
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• v.24
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• pp.97-124
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• 2008

This study aimed to explore how the involvement of environment influenced eight subjects group. Thus, experiment was performed to clarify the role that the attitude of university student consumer plays in the communication process depending on the level of engagement of consumer in the environment and method to raise donation for preservation of environment. Analyzing as per the type of appeal, the mark in altruistic appeal type was higher in all variables than egoistic appeal type. Finally, checking the average mark of each variable as per the condition of donation, the value in unconditional donation was higher than in all variables than conditional donation. It was found 3 groups composed of 2 groups with high level of environmental engagement and 1 group with low level of environmental engagement were suitable to double mediation model among the 8 experimental groups. The group where double mediation model best corresponds than any other group was high level related to environment and the group that contacts altruistic appeal and the message in the form of conditional donation. It was also found that the group that has low level of environmental engagement and contacts egoistic appeal type and conditional donation shows the group that corresponds to double mediation model in the second place among the 8 groups. Finally, it was found that the group that has high level of environmental engagement and is stimulated by altruistic appeal and unconditional donation corresponds to double mediation model. Depending on the condition of message stimulation, unconditional donation is found to better correspond to double mediation model than conditional donation. However, opposite phenomena is observed when the level of environmental engagement is high and appeal type is egoistic. Namely, it was found that conditional donation better corresponds to double mediation model than unconditional donation.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.533-542
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• 2021

The main objective of this article is to investigate the existence of the lunar effect during the full moon period (FM period) and the new moon period (NM period) on the selected Islamic stock market returns and volatilities. For this purpose, the Ordinary Least Squares model, Autoregressive Conditional Heteroscedasticity model, Generalised Autoregressive Conditional Heteroscedasticity model and Generalised Autoregressive Conditional Heteroscedasticity-in-Mean model are employed using the mean daily returns data between January 2010 and December 2019. Next, the log-likelihood, Akaike Information Criterion and Schwarz Information Criterion value are analyzed to determine the best models for explaining the returns and volatility of returns. The empirical results have deduced that, during the NM period, excluding Malaysia, the total mean daily returns for all of the selected countries have increased mean daily returns in contrast to the mean daily returns during the FM period. The volatility shocks are intense and conditional volatility is persistent in all countries. Subsequently, the volatility behavior tends to have lower volatility during the FM period and NM period in the Islamic stock market, except Malaysia. This article also concluded that the ARCH (1) model is the preferred model for stock returns whereas GARCH-M (1, 1) is preferred for the volatility of returns.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.187-198
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• 2021

This study aims to understand the current status of the use of IoT-based products and services from the perspective of consumers and analyze the role and consumption value of each service-generated from the products and services. Their features and generated consumption value have been identified The decision-making process was divided into based on three stages (pre-purchase, purchase, and post-purchase) stages, and IoT services were classified in stages. In the pre-purchase stage, the IoT service provides information and alternatives, and is used for interaction and automatic payment systems in the purchasing stage. In the post-purchase stage, repetitive purchases are encouraged and after-sale services are provided. Throughout the decision-making process, smart retai application and servicel provides epistemic and functional value. In addition, it provides conditional and social value in the pre-purchase stage, and conditional value in the post-purchase stage. This study aims to provideprovides marketers and retailers an insight advice for the enhanced satisfaction of consumersimproving the satisfaction of consumers and the development of smart retail by examining the consumer-centered consumption value.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.669-681
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• 2010

Value-at-Risk(VaR) is an important part of risk management in the financial industry. This paper present a VaR forecasting for financial time series based on the quantile regression for GARCH models recently developed by Lee and Noh (2009). The proposed VaR forecasting features the direct conditional quantile estimation for GARCH models that is well connected with the model parameters. Empirical performance is measured by several backtesting procedures, and is reported in comparison with existing methods using sample quantiles.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.25-32
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• 1999

A single outlier can influence on the least squares estimators and can invalidate analysis based on these estimators. The Cook's statistic has been introduced to measure influence of individual data point on parameter estimation and the quantile of the F distribution is recommended as a reference value. but in practice subjective judgement is applied in the choice of appropriate quantile. A simple reference value is introduced in this paper which is developed by approximating conditional quantities of Cook's measure. The performance of the proposed criterion is evaluated through analysis of real data set.