• Korean Journal of Fisheries and Aquatic Sciences
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• v.29 no.3
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• pp.386-392
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• 1996

A numerical method based on genetic algorithms was introduced to characterize the grain-size distribution more effectively. This technique was proved significant particularly for multimodal size distributions, as was verified for samples from Korea Strait continental shelf. Sediment samples collected from the Korea Strait continental shelf revealed that $96\%$ of the grain-size distributions were multimodal. Therefore, the use of grain-size parameters was not the ideal method. As an alternative method, the decomposition of sue curves into elementary normal component curves was used. Means and standard deviations of 593 decomposed normal components were calculated by a numerical method from 268 size curves of Korea Strait sediments. The mean values of decomposed normal components showed peaks at $1\~3\phi\;and\;7\~9\phi$ size classes. The plot of mean and standard deviation values of the coarse fraction normal components on the map showed a characteristic areal distribution. The characteristic distribution was found to derive from underlying Pleistocene sediment on the basis of sea bottom geologic distribution of the area. The method of decomposition into normal components was found to be more effective than the analysis using traditional grain-size parameters in investigation of multimodal size distribution of Korea Strait shelf sediment.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2002.05a
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• pp.104-109
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• 2002

In this paper, a statistical analysis of fatigue crack growth behavior under constant amplitude loads has been carried out. Fatigue crack growth tests were conducted on sixteen pre-cracked compact tension (CT) specimens of the pressure vessel (SPV50) steel in controlled identical load and environmental conditions. The assessment of the statistical distribution of fatigue crack growth experimental data obtained from SPV50 steel was studied and also the correlation of the parameter C and m in the Paris-Erdogan law was discussed. The probability distribution function of fatigue crack growth life seems to follow the 3-parameter Weibull. The fatigue crack growth rate seems to follow the 3-parameter Weibull and the log-normal distribution. The coefficient of variation (COV) of fatigue crack growth life was observed to decrease as the crack grows. A strong negative linear correlation exists between the coefficient C and the exponent m in Paris model. Fatigue crack growth rate data shows a normal distribution for both m and logC.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.6
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• pp.923-929
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• 2003

In this paper, an analysis of fatigue crack growth behavior from a statistical point of view has been carried out. Fatigue crack growth tests were conducted on sixteen pre-cracked compact tension (CT) specimens of the pressure vessel (SPV50) steel in controlled identical load and environmental conditions. The assessment of the statistical distribution of fatigue crack growth experimental data obtained from SPV50 steel was studied and also the correlation of the parameter C and m in the Paris-Erdogan law was discussed. The probability distribution function of fatigue crack growth life seems to follow the 3-parameter Weibull. The fatigue crack growth rate seems to follow the 3-parameter Weibull and the log-normal distribution. The coefficient of variation (COV) of fatigue crack growth life was observed to decrease as the crack grows. Fatigue crack growth rate data shows a normal distribution for both m and logC. A strong negative linear correlation exists between the coefficient C and the exponent m.

• Proceedings of the Safety Management and Science Conference
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• 2000.05a
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• pp.249-263
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• 2000

This paper considers an algorithm using Johnson transformation to calculate process capability index for non-normal distribution. Johnson transformation is well known as one of methods transforming the data with non-normal distribution to normal data.

• 한국해양학회지
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• v.28 no.2
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• pp.114-120
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• 1993

Numerical method of nonlinear regression was introduced to characterize grain-size distribution more effectively than using the traditional textural parameters. This technique proved critical particularly to multimodal size distributions, as exemplified by samples from Cheju strait continental shelf. Grain-size analysis of samples collected from the Cheju Strait continental shelf reveals that 86% of the grain-size distributions are multimodal. As multimodal grain-size distribution deviates from the statistical (log) normal distribution, the grain-size parameters traditionally used in sediment studies do not describe the distribution efficiently. Therefore, the use of grain-size curves into elementary normal component curves was used. Means and standard deviations of 387 decomposed normal components were decided by a decomposition method (nonlinear least square regression) from 167 size curves of the Cheju Strait sediments. The mean values of decomposed normal components show peaks at 1-3 phi and 8-9 phi size classes. The plot of mean values of the coarse fraction normal components on the map shows a characteristic and complex areal distribution. On the basis of the areal distribution of the mean values of the components and that of isopach of total Plenipotence sediment, the areal distribution of layers composing a transgressive sand of Late Plenipotence age were revealed.

• KSTLE International Journal
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• v.2 no.2
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• pp.114-119
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• 2001

The distribution of magnetic flux density of electro-magnetic chucks may clarify the clamping characteristics, which is strongly related to the machining efficiency and machining accuracy in surface grinding machine. Therefore the distribution of the normal and the tangential components of magnetic flux density have been analyzed theoretically. It appears that the normal component of magnetic flux density increases and the tangential component of magnetic flux density increases as the ratio of the separator width to the pitch, e/p decreases. The results seem to increase the stability and uniformity of normal component of magnetic flux density for the decreased e/p.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.235-247
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• 1993

The predictive density function of a potential future observation and its first four moments are obtained in this paper to study the effects of a non-normal prior of the unknown mean of a normal population. The derived predictive density function is modified to study changes in utility curves, used to choose the optimum treatment from a given set of treatments, at a given level of stimulus due to slight deviations from normality of the prior distribution. Numerical illustrations are provided to exhibit some effectsl.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.373-376
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• 2002

In this paper, we give the definition of the two dimensional q-Gabor wavelet. It consists of the q-normal distribution, which is also given in this paper. If the q-normal distribution is used as a kernel of the Gabor wavelet instead of the normal distribution, the q-Gabor wavelet is obtained. Furthermore, the q-Gabor wavelet is compared with the Gabor and the Haar wavelets to show how good The q-Gabor wavelet is.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.203-209
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• 1998

By assuming a progressively censored sample, we propose the approximate maximum likelihood estimator (AMLE) of the location nd the scale parameters of the two-parameter normal distribution and obtain the asymptotic variances and covariance of the AMLEs. An example is given to illustrate the methods of estimation discussed in this paper.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.595-600
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• 1999

We present a simple geometric approach which uses polar transformation and elementary trigonometry to evaluating an orthant probability in a bivariate normal distribution. Figures are provided to illustrate the situation for varying correlation coefficient. We derive the distribution of the sample correlation coefficient from a bivariate normal distribution when the sample size is 2 as an application.