• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.657-663
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• 2012

There are very small values and/or very big values which get out of the normal range for survey data in various fields. The reasons of occurrence for outlier are two. One of them is the error in process of data input and the other is the strange response of the respondent. If the data has outliers, then the summary statistics such as the mean and the variance produce misleading information. Therefore, researcher should be careful in detecting the outlier in data. In particular, it is very important problem for clinical fields because the cost of experiment is very high. This article introduce the Grubb test and Cochran test to detect outliers in the data and we apply this method for clinical data.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.167-178
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• 2016

Spectrum sensing in cognitive radio (CR) has a great role in order to utilize idle spectrum opportunistically, since it is responsible for making available dynamic spectrum access efficiently. In this research area, collaboration among multiple cognitive radio users has been proposed for the betterment of detection reliability. Even though cooperation among them improves the spectrum sensing performance, some falsely reporting malicious users may degrade the performance rigorously. In this article, we have studied the detection and nullifying the harmful effects of such malicious users by applying some well known outlier detection methods based on Grubb's test, Boxplot method and Dixon's test in cooperative spectrum sensing. Initially, the performance of each technique is compared and found that Boxplot method outperforms both Grubb's and Dixon's test for the case where multiple malicious users are present. Secondly, a new algorithm based on reputation and weight is developed to identify malicious users and cancel out their negative impact in final decision making. Simulation results demonstrate that the proposed scheme effectively identifies the malicious users and suppress their harmful effects at the fusion center to decide whether the spectrum is idle.

• The Journal of the Korean dental association
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• v.55 no.9
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• pp.604-616
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• 2017

In clinical dental research, errors occur in spite of careful study design and conduct. Data cleaning procedures intend to identify and correct these errors or at least to minimize their influence on study. Outlier is the one of these errors. Outlier detection is the first step in data analysis process which has a serious effect in the field of dental research. Hence, this paper aims to introduce the methods to detect the outliers and to examine their influences in statistical data analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.626-629
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• 2004

Recently, the self-healing technique is being investigated to repair the damaged polymeric composites by the use of microcapsules with the healing agent. This technique can obtains both the damage detection and the damage repair simultaneously over the converntional repairing techniques. In this study, the effects of the catalyst ratio to the healing agent and thermal characteristics to the mixtures of healing agent are investigated through single lap shear tests and DSC. The Healing agents such as DCPD, ENB, and their mixtures are considered and Grubb's catalyst is used as a catalst.