• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.183-186
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• 2007

We introduction some properties for fuzzy power function of performance of a test. First we define fuzzy type I error and type II error for the probability of the two types of error. And we show that an fuzzy error probability of one kind can only be reduced at cost of increasing the other fuzzy error probability.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.17-23
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• 2004

At the conclusion from the hypothesis testing, there is a possibility of making Type I error and Type II error. The purpose of this article is to use this program in statistics teaching through developing the program for studying on the concept about these two errors, two kinds of the probability of errors by the variation of rejection region, two kinds of the probability of errors by the variation of sample size, the relations of the probability $\alpha$ and $\beta$ by these two errors, and power function, power curve.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.139-149
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• 1997

For split plot designs exact univariate F tests of the within-subjects main effect are based on the assumption of multisample sphericity. Type I error rates and power are reported for the F test and two tests designed for use when multisample sphericity is violated: the $\widetilde{\xi}$-adjusted test and the Corrected Improved General Approximation(CIGA) test.The results indicate that even though the F test and the $\widetilde{\xi}$-adjusted test have better power than the CIGA test in some conditions, the F test and the $\widetilde{\xi}$-adjusted test do not control Type I error rates when the design is unbalanced and the F test dose not have a good control of Type I error rates when sphericity assumption is severely violated.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.535-545
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• 2015

In a various range of applications including hydrology, the type-I extreme value distribution has been extensively used as a probabilistic model for analyzing extreme events. In this paper, we introduce methods for estimating the scale parameter of the type-I extreme value distribution. A simulation study is performed to compare the estimators in terms of mean-squared error and bias, and the obtained results are provided.

• The Journal of Korean Association of Computer Education
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• v.7 no.2
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• pp.35-46
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• 2004

In this paper, I analyze error cases a learner made during learning using ICT, set up error types, and search for effective treatment methods in order to enhance the effects of ICT education. I search for error case to use the methodology of the study which is observation, interviews, and survey. I set up the error types which is the error type of confusion with functions, that of confusion with concepts, that of barriers in interface interpretation, that caused by psychological anxiety, that according to learner personality patterns, and habitual error type. The biggest frequency of errors was found in the error type of confusion with function and that of confusion with concepts, whose treatment methods were searched for using the web-based Q&A learning. Also, I apply the error treatment methods on the classroom and prove the effect.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.12
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• pp.1391-1400
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• 1992

In this paper, we suggested a method for evaluating the type-I/II error which is proposed by the CCITT as a criterion for the accuracy of policing algorithms in ATM networks, By the analysis of the type-I/II error of the Leaky Bucket(LB) algorithm, we investigated the relationships between the traffic parameters and the LB parameters to police the mean and peak cell rate effectively in the ON/OFF traffic. We showed that the LB parameters, the leaky rate a and the threshold M of the LB counter, could be determined as a pair of (a, M) satisfying the type-I/II error and minimizing the response time. In the ON/OFF traffic, it has been observed that the a-M characteristic curve of the LB policing algorithm only depends on the burstiness. As the results of the performance analysis, we found that the LB algorithm exhibits a good performance in the peak rate policing, but has some problems in the mean rate policing due to the trade-off between the accuracy and the response time.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1271-1277
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• 2012

In this paper, we propose an estimation method of the parameter in an exponential distribution based on a progressive Type I interval censored sample with semi-missing observation. The maximum likelihood estimator (MLE) of the parameter in the exponential distribution cannot be obtained explicitly because the intervals are not equal in length under the progressive Type I interval censored sample with semi-missing data. To obtain the MLE of the parameter for the sampling scheme, we propose a method by which progressive Type I interval censored sample with semi-missing data is converted to the progressive Type II interval censored sample. Consequently, the estimation procedures in the progressive Type II interval censored sample can be applied and we obtain the MLE of the parameter and survival function. It will be shown that the obtained estimators have good performance in terms of the mean square error (MSE) and mean integrated square error (MISE).

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.3
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• pp.193-198
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• 2002

In this paper, we Propose a PD+I fuzzy controller using an error-accumulating applying factor. In fuzzy control, analytical study was done formerly, in which fuzzy control can be classified by PD type and PI type, and also the study for getting merits of both types was done, too. But the mixed type has a complex structure and many parameters. The proposed fuzzy controller is 2-input 2-out-put and PD type fuzzy control is used as a basic structure. And the proposed controller annihilates a steady-state error and improves transient responses because of using the error-accumulating applying factor which is determined in the real time along the current state of controlled process. Futhermore it is easy to tune the system because of decreasing the number of scaling factors and the I type controller with resetting resolves the integral wind-up problem. Finally we apply the proposed scheme to various plants and show the performance betterment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.117-132
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• 2018

There always exist nonzero inspection errors whether inspectors are humans or automatic inspection machines. Inspection errors can be categorized by two types, type I error and type II error, and they can be regarded as either a constant or a random variable. Under the assumption that two types of random inspection errors are distributed with the "uniform" distribution on a half-open interval starting from zero, it was proved that inspectors overestimate any given fraction defective with the probability more than 50%, if and only if the given fraction defective is smaller than a critical value, which depends upon only the ratio of a type II error over a type I error. In addition, it was also proved that the probability of overestimation approaches one hundred percent as a given fraction defective approaches zero. If these critical phenomena hold true for any error distribution, then it might have great economic impact on commercial inspection plans due to the unfair overestimation and the recent trend of decreasing fraction defectives in industry. In this paper, we deal with the same overestimation problem, but assume a "symmetrical triangular" distribution expecting better results since our triangular distribution is closer to a normal distribution than the uniform distribution. It turns out that the overestimation phenomenon still holds true even for the triangular error distribution.

• The Mathematical Education
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• v.42 no.3
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• pp.419-423
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• 2003

The main purpose of this paper is to investigate effects of skewness and kurtosis on the one-sample test. We have found that type I error brought about a little bit change which is ignorable in relation to kurtosis. Also the change of type I error was completely based on skewness under the same size of the sample. We conclude that using t-test is more similar to robust than using z-test. In introductory statistics classes where data analysis includes techniques for detecting skewness, we recommend the t-test when skewness is smaller than the value 1 to the one-sample test for a mean when the variances is unknown using the probability of a type I error as the criterion of interest.