• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.189-199
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• 2000

A major deficiency of laboratory testing of rock structure is that the structures are limited in size and therefore present a very small and highly selective sample of the rock mass from which were removed. In a typical engineering project, the samples tested in the laboratory represent only a very small fraction of one percent of the volume of the rock mass. In this paper, we calculate the representative orientation of the resultant vector, the measure of the degree of clustering, the volume of rock mass, the trace length of discontinuity spacing under underlying distributions. And we generate the random fracture networks using real data. We propose the calculating the trace length.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.300-300
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• 2016

지점빈도해석은 해당 지점에서 기록된 수문자료를 바탕으로 확률론적 방법을 이용하여 해당 지역의 수문학적 현상을 해석하는 방법이다. 최근 이상 기후현상을 통해 극치 사상이 발생하고 있다. 이러한 극치 사상은 지점빈도해석을 이용하여 확률수문량을 추정하는데 많은 영향을 미친다. 특히 해당 지점의 표본 크기가 작을수록 이러한 영향은 좀 더 크게 반영 될 수 있다. 반면 지역빈도해석은 지점의 표본 수가 적거나 수문자료의 수집이 불가능한 미계측지점인 경우, 해당 지점과 수문학적으로 동질하다고 여겨지는 주변 지점들의 자료를 확보하여 확률수문량을 추정함으로써 상대적으로 지점빈도해석 보다 roubst한 추정값을 얻을 수 있다. 따라서 최근 확률수문량 산정 기법으로 지역빈도해석 방법에 관한 관심이 높아지고 있는 실정이다. 지역구분은 지역빈도해석이 지점빈도해석과 구분 될 수 있는 큰 특징이고 지역구분 결과 따라 지역의 표본 크기가 결정되기 때문에 수문학적으로 동질한 지역을 나누는 방법은 매우 중요하다고 볼 수 있다. 본 연구에서는 한강유역을 대상으로 인공신경망을 이용한 군집분석을 수행하고 구분된 지역을 이용하여 지역빈도 해석을 수행하였다.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1271-1284
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• 2011

The entropy-based test of fit for the inverse Gaussian distribution presented by Mudholkar and Tian(2002) can only be applied to the composite hypothesis that a sample is drawn from an inverse Gaussian distribution with both the location and scale parameters unknown. In application, however, a researcher may want a test of fit either for an inverse Gaussian distribution with one parameter known or for an inverse Gaussian distribution with both the two partameters known. In this paper, we introduce tests of fit for the inverse Gaussian distribution based on the Kullback-Leibler information as an extension of the entropy-based test. A window size should be chosen to implement the proposed tests. By means of Monte Carlo simulations, window sizes are determined for a wide range of sample sizes and the corresponding critical values of the test statistics are estimated. The results of power analysis for various alternatives report that the Kullback-Leibler information-based goodness-of-fit tests have good power.

• Korean Journal of Soil Science and Fertilizer
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• v.17 no.3
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• pp.212-217
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• 1984

Spatial variabilities of particle size distribution of 96 samples from Hwadong SiCL and Jungdong Sl were studied by using geostatistical concepts. The measurement was made at the nodes of the regular grid consisting of 12 rows and 8 columns. Sample spacing within rows and columns was 3 and 2 meters, respectively. The results are summarized as follows. 1. Variograms of Hwadong SiCL were fitted for the linear model and those of Jungdong SL for the spherical model. 2. Variograms of properties for Hwadong and clay for Jungdong showed the pure nugget effect. Those of silt and clay for Jungdong, however, appeared the nugget effect. 3. The minimum number of samples necessary to reproduce results similar to the true mean of the 96 measured values was approximately estimated. The minimum sample sizes of silt, clay, and sand in Hwadong SiCL were 27, 13, and 6, respectively. And the minimum sample size of clay in Jungdong SL was 17. 4. The approximate number of samples required to detect the difference of 5% of the true mean with 0.95 confidence level was estimated. The resulting number of samples for silt and sand in Jungdong was 14, and 26, respectively.

• Spatial Information Research
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• v.8 no.1
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• pp.171-182
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• 2000

Random sampling or systematic sampling method is commonly used to assess the accuracy of classification results. In remote sensing, with these sampling method, much time and tedious works are required to acquire sufficient ground truth data. So , a more effective sampling method that can retain the characteristics of the population is required. In this study, fractal analysis is adopted as an index for reference sampling . The fractal dimensions of the whole study area and the sub-regions are calculated to choose sub-regions that have the most similar dimensionality to that of whole-area. Then the whole -area s classification accuracy is compared to those of sub-regions, respectively, and it is verified that the accuracies of selected sub regions are similar to that of full-area . Using the above procedure, a new kind of reference sampling method is proposed. The result shows that it is possible to reduced sampling area and sample size keeping up the same results as existing methods in accuracy tests. Thus, the proposed method is proved cost-effective for reference data sampling.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.435-443
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• 2010

When testing systems that incorporate probabilistic behavior, it is necessary to apply test inputs a number of times in order to give a test verdict. Interval estimation can be used to assert the correctness of probabilities where the selection of confidence interval is one of the important issues for quality of testing. The Wald interval has been widely accepted for interval estimation. In this paper, we compare the Wald interval and the Agresti-Coull interval for various sizes of samples. The comparison is carried out based on the test pass probability of correct implementations and the test fail probability of incorrect implementations when these confidence intervals are used for probability testing. We consider two-sided confidence intervals to check if the probability is close to a given value. Also one-sided confidence intervals are considered in the comparison in order to check if the probability is not less than a given value. When testing probabilities using two-sided confidence intervals, we recommend the Agresti-Coull interval. For one-sided confidence intervals, the Agresti-Coull interval is recommended when the size of samples is large while either one of two confidence intervals can be used for small size samples.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.515-524
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• 2017

In this study, a measure of discrepancy based on MV (margin of victory) has been suggested that might be useful in determining the size of random forest for classification. Here MV is a scaled difference in the votes, at infinite random forest, of two most popular classes of current random forest. More specifically, max(-MV,0) is proposed as a reasonable measure of discrepancy by noting that negative MV values mean a discrepancy in two most popular classes between the current and infinite random forests. We propose an appropriate diagnostic statistic based on this measure that might be useful for the determination of random forest size, and then we derive its asymptotic distribution. Finally, a simulation study has been conducted to compare the performances, in finite samples, between this proposed statistic and other recently proposed diagnostic statistics.

• The Journal of Korean Academic Society of Nursing Education
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• v.15 no.1
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• pp.120-127
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• 2009

Purpose: In the literature power analysis in experimental studies is often executed and reported falsely. This descriptive study was done to promote the correct application of the Cohen(1988)'s power analysis method. Method: Articles of experimental studies from a nursing journal were selected and reviewed to examine the uses of and the reports on the power analysis process. Also, the appropriate power analysis process was discussed with an example of the most common experimental design, an independent two-group design with t test analysis plan. Result: Around half the experimental studies examined reported that they carried out power analysis. Cohen's method was the most frequently utilized but with accuracy in question. Conclusion: Power analysis to estimate sample size is the interplay between alpha, power, and effect size, and other factors in the case of t test analysis. Researchers should have a clear understanding of how to apply the Cohen's power analysis method so they do not produce poorly estimated sample sizes.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.173-181
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• 1993

Nonparametric empirical Bayes estimation of a distribution function with respect to dirichlet process prior is considered when sample sizes are varying from component to component. Zehnwirth's estimate of $\alpha$(R) is modified to be used in our empirical Bayes problem with non-identical components.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.9-15
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• 2006

In this paper we propose Wilcoxon test, Median test and Van der Waerden test such as linear rank tests in two-sample location problem for detecting edges effectively in noisy images. These methods are based on detecting image intensity changes between two pixel neighborhoods using an edge-height model to perform effectively on noisy images. The neighborhood size used here is small and its shape is varied adaptively according to edge orientations. We compare and analysis the performance of these statistical edge detectors on both natural images and synthetic images with and without noise.