• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.2
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• pp.135-144
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• 2008

A bootstrap circuit is widely used for the floating gate power supply of Intelligent power module (IPM). A bootstrap circuit is simple and inexpensive. However, the duty cycle and on-time are limited by the requirement to refresh the charge in the bootstrap capacitor. And the value of the bootstrap capacitor should be increased as the switching frequency decreases. A charge pump circuit can be used to overcome the problems. This paper deals with an analysis and design of a charge pump circuit for the floating gate power supply of an IPM. The simulation and experiment are carried out for an induction motor drive system. The results well verifies the validity of the proposed circuit and design method.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.341-353
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• 2008

The bootstrap method for the score test statistic is proposed in a bivariate negative binomial distribution. The Monte Carlo study shows that the score test for testing overdispersion underestimates the nominal significance level, while the score test for "intrinsic correlation" overestimates the nominal one. To overcome this problem, we propose a bootstrap method for the score test. We find that bootstrap methods keep the significance level close to the nominal significance level for testing the hypothesis. An empirical example is provided to illustrate the results.

• Journal of the Korean Data and Information Science Society
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• v.7 no.2
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• pp.235-245
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• 1996

In this paper, we studied about several methods of testing hypothesis of correlation, specially Approximate method, Empirical method and Bootstrap method. The Approximate method is based on the Fisher's Z-transformation and the Empirical and Bootstrap methods approximate the distribution of the sample correlation coefficient by Monte Carlo simulation and Bootstrap technique, respectively. In order to compare how good these tests are, we computed powers under various alternatives. Consequently, we see that the Approximate test performs very well even if in small sample and all tests have almost the same power in large sample.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.185-188
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• 2008

This paper introduces the Bootstrap method for statistical analysis of noise and vibration spectrum in aeronautic and space fields. Generally, all components of a launch vehicle and its payloads are subjected to high intensive noise and vibration environment during the lift-off phase and the ascent phase through Mach =1 and Max Q. In order to verify their survivabilities against these severe vibroacoustic environments during qualification tests and acceptance tests, it is most important to estimate the proper upper limits of the environmental condition. Although NASA has typically utilized the Normal Tolerance Limit method in deriving these levels, the reference[1] says that the Bootstrap can be also an alternative method to estimate the maximum expected environments. In this paper, a general procedure of the Bootstrap method is summarized, and it is applied to analyze acceleration power spectral density functions, which were measured during acoustic test on the upper stage of KSLV-I.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.287-297
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• 2009

In terms of generalized autoregressive conditional heteroscedastic(GARCH) model, estimation of prediction interval based on likelihood is quite sensitive to distribution of error. Moveover, it is not an easy job to construct prediction interval for conditional variance. Recent studies show that the bootstrap method can be one of the alternatives for solving the problems. In this paper, we introduced the bootstrap approach proposed by Pascual et al. (2006). We employed it to Korean stock price data set.

• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.467-482
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• 2000

Testing equality of two and k distributions has long been an interesting issue in statistical inference. To overcome the sparseness of data points in high-dimensional space and deal with the general cases, we suggest several projection pursuit type statistics. Some results on the limiting distributions of the statistics are obtained, some properties of Bootstrap approximation are investigated. Furthermore, for computational reasons an approximation for the statistics the based on Number theoretic method is applied. Several simulation experiments are performed.

• Journal of the Korean Statistical Society
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• v.20 no.2
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• pp.99-107
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• 1991

Behrens-Fisher problem is viewed from a model selection approach. Normal distribution is regarded as an approximating model, A criterion, called TIC, is derived and is compared with selection criteria such as AIC and a bootstrap estimator. Stochastic approximation is used since no closed form expression is available for the bootstrap estimator.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.365-370
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• 2002

In this study, according to MARMA(1,0) model which was suggested by Seppala, in case of existing autocorrelation in X control chart and EWMA control chart, the standard method and the non-parametric bootstrap method were compared and analysed using the bootstrap method which use the resampling prediction residual.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.757-764
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• 2009

In discriminant analysis, we consider a special pattern which contains a block of missing observations. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider the bootstrap confidence intervals of the error rate in the circular models when the covariance matrices are equal and not equal.

• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.64-73
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• 1995

In this paper, the stress strength model is assumed for the populations of X and Y, where distributions of X and Y are independent normal with unknown parameters. We construct bootstrap confidence intervals for reliability, R=P(X>Y) and compare the accuracy of the proposed bootstrap confidence intervals and classical confidence interval through Monte Carlo simulation.