• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.6
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• pp.844-852
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• 2017

Since guided weapon is high-cost and one-shot device which is non-reusable, it requires a lot of resources to prove required accuracy as a part of reliability demonstration. Once a test for proving accuracy rate of guided missile fails, it causes an additional cost and delay of schedule. This study introduces an equation for proper sample size and plan for guided-missile accuracy rate test in order to minimize the risk of test failure. Proper sample size for the test is derived by considering the reliability growth. Furthermore, each task for accuracy rate test is defined according to the development step. Therefore, this study can contribute to reduce sample size for accuracy rate test in order to meet the reliability requirement and assure transparency in the test process.

• Journal of Sericultural and Entomological Science
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• v.13 no.1
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• pp.69-72
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• 1971

1. To study proper sample size in boiling-of test, we extracted samples in size of 200, 100, 50, 20, and 10 grams and tested them. In the result of this test we couldn't find a statistical significance among them, but it is seemed that the proper sample size is 100 gram to think about the errors of measurement and treatment when the sample size is either too little or too much in ordinary test. 2. We tested boil-of result by twisted sample skein (sizing skein) and untwisted sample. In this test to use sodium carbonate (Na$_2$CO$_3$) having powerful sericin dissolving power as a scouring agent, boil-off percents made no statistical significance between two kinds of sample skein. And to use marseilles soap having less powerful sericin dissolving power as a scouring agent, boil-off percents made a difference between twisted and untwisted sample skein. 3. We compared five kinds of scouring agent, for example sodium carbonate, monogen, synthetic detergent (HiTi), marseilles soap A (made in Japan) and marseilles soap B (made in Korea). Among them synthetic detergent and marseilles soap B showed no statistical significance compared with marseilles soap A which we used in ordinary boil-off test. So synthetic detergent and marseilles soap B can be substituted for marseilles soap A. 4. Generally the higher pH value of boil of solution, tile more sericin dissolution, showed and vice versa.

• Korean Journal of Agricultural Science
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• v.37 no.3
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• pp.457-464
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• 2010

The low melting alloy impregnated wood composites with natural grain of thinned Juglans mandshurica was made and evaluated in this study. And the proper manufacturing conditions was also investigated in this study. The low melting alloy with bismuth(Bi) and tin(Sn) which are harmless to humans, was applied for this novel composites, which showed not only no defects of discoloration, delamination, swelling, and cracking, because of high dimensional stability and low thickness swelling, but also much improved performance such as high bending strength, high hardness, low abrasion, high thermal conductivity as floor materials. This study also suggested the proper impregnating condition, such as 10 minutes of the preliminary vacuum time, $187^{\circ}C$ of the heating temperature and 10 minutes of the maintaining pressure time at the pressure of 30kgf/$cm^2$. The produced composites showed 9 times higher density for small specimen, 6.6 times for actual size sample and great increase in bending strength from 102.05N/$mm^2$ to 189.47N/$mm^2$ for small size sample and to 205.4N/$mm^2$ for actual size sample, also great increase in hardness from 15.1N/$mm^2$ to 73.38N/$mm^2$ for small size sample and 64.87N/$mm^2$ for actual size sample. And the composites demonstrated great decrease in abrasion depth and in water absorption.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.205-216
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• 2019

Diagnostic tests in medical fields detect or diagnose a disease with results measured by continuous or discrete ordinal data. The performance of a diagnostic test is summarized using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The diagnostic test is considered clinically useful if the outcomes in actually-positive cases are higher than actually-negative cases and the ROC curve is concave. In this study, we apply the stochastic ordering method in a Bayesian hierarchical model to estimate the proper ROC curve and AUC when the diagnostic test results are measured in discrete ordinal data. We compare the conventional binormal model and binormal model under stochastic ordering. The simulation results and real data analysis for breast cancer indicate that the binormal model under stochastic ordering can be used to estimate the proper ROC curve with a small bias even though the sample sizes were small or the sample size of actually-negative cases varied from actually-positive cases. Therefore, it is appropriate to consider the binormal model under stochastic ordering in the presence of large differences for a sample size between actually-negative and actually-positive groups.

• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.651-658
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• 2018

Purpose: The algorithm described in Lim(1998) is available to determine the sample size directly given specified significance level, power and signal-to-noise ratio. We research on the efficient determination of the sample size by visual methods. Methods: We propose three graphs for investigating the mutual relationship between the sample size r, power $1-{\beta}$ and the detectable signal-to-noise ratio ${\Delta}$. First graph shows the relationship between ${\Delta}$ and $1-{\beta}$ for the given r and it can be checked whether the power is sufficient enough. Second graph shows the relationship between r and ${\Delta}$ for the given power $1-{\beta}$. Third graph shows the relationship between r and $1-{\beta}$ for the given ${\Delta}$. It can be checked that which effects are sensitive to the efficient sample size by investigating those graphs. Results: In factorial design, randomized block design and the split plot design how to determine the sample size directly given specified significance level, power and signal-to-noise ratio is programmed by using R. A experiment to study the split plot design in Hicks(1982) is used as an example. We compare the sample sizes calculated by randomized block design with those by split plot design. By using graphs, we can check the possibility of reducing the sample size efficiently. Conclusion: The proposed visual methods can help an engineer to make a proper plan to reduce the sample size.

• Fashion & Textile Research Journal
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• v.3 no.2
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• pp.140-147
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• 2001

The purpose of this study is to suggest the standard size system of Saenghwal Hanbok for design development and popularization. As a sample, 20's and 40's female data among the 1997's National Somatometry Survey data were used because two groups have different somatic characteristics and design tastes with each other. The results are as follows; From t-test of 35 somatometric items in 20's and 40's female data, we knew that the differentiated size system proper to each age is needed. We also knew that the 38 Saenghwal Hanbok brands don't have consistent size system from investigation of size list and size application. From consideration on the number of size for production and coverage rate, we suggested regression formula by bust circumference and stature, and suggested advanced standard size system which are proper to each 20' and 40' female group.

• Korean Journal of Veterinary Research
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• v.48 no.3
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• pp.357-361
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• 2008

Although power analysis is of important tool of research, investigators in veterinary medicine are unaware of the concepts of the statistical power. Two types of error occur in classical hypothesis testing and, those errors should be avoided, if possible. Since power is highly dependent on the sample size, whenever declaring non-statistically significant result they should consider the potential for committing a Type II error in their studies, which refers to the probability of falsely stating that two treatments are equivalent despite true difference between them. Also, sample size determination is one of the most important tasks facing the researcher when planning a diagnostic study, and provides valuable information on the characteristics of a test performance. This type of analysis forms the basis for proper interpretation of test results. The aim of this article was to re-evaluate some selected studies on diagnostic test reported in the domestic veterinary publications to determine the power and necessary sample size for inequality testing to ensure the desired power. Power calculations were illustrated using real-life examples of comparison of a new test and a reference test for detecting antibodies of various animal diseases. Factors affecting to the power were also discussed.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1115-1122
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• 2011

Reinforced-Roadbed materials are usually composed of crushed stones. Repeated load application can induce deformation in the reinforced-roadbed layer so that it causes irregularity of track. Thus it is important to develop a prediction model of elastic modulus based on stress-strain relation under repeatitive load in order to investigate behavior of reinforced roadbed. The prediction model of elastic modulus of the material can be obtained from repeated triaxial test. However, a proper size of the sample for the test must be used. In this study, a large repeatitive triaxial test apparatus with the sample size of diameter of 30 cm and height of 60cm was adapted for performing test of the crushed stone reinforced-roadbed considering large particle size to get resilient modulus Mr. The obtained resilient modulus was compared to shear modulus obtained from mid size resonant column test. The sample size effect is somewhat large enough so that it is required to design a scale factor based on similarity law in order to use smaller samples for getting elastic modulus of the crushed stone reinforced-roadbed material. A scale factor could be obtained from this study.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.10a
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• pp.429-436
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• 2005

In this study, we have compared with the differences between the extent of sampling disturbance and consolidation characteristics by experiments, which are consolidation test and have been performed with Kwang-Yang clay samples. The effects on sampling disturbance to consolidation characteristics of soft clay have been inverstigated by using soil samples obtained from large block sampling and piston sampling methods. Through a few experiments, we've got important results which are that the consoilidation parameter of large block sample(Pc, Cc, Cv) is much larger than the value of parameter of piston sample. We've also found the fact that the large block sample using the large size sampler is much better than piston sample in the quality of goods to lessen the effects on disturbance of sampling. When compared to the parameter of consolidation along with the methods of experiment, we found that the result performed by large size consolidation test is the greatest one and CRS is much better than standard consolidation test to seek for proper parameter.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.283-292
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• 2013

This paper aims to propose a new steps of hypothesis testing using analysis process and improvement process in the six sigma DMAIC. The six sigma implementation models proposed in this paper consist of six steps. The first step is to establish a research hypothesis by specification directionality and FBP(Falsibility By Popper). The second step is to translate the research hypothesis such as RHAT(Research Hypothesis Absent Type) and RHPT(Research Hypothesis Present Type) into statistical hypothesis such as $H_0$(Null Hypothesis) and $H_1$(Alternative Hypothesis). The third step is to implement statistical hypothesis testing by PBC(Proof By Contradiction) and proper sample size. The fourth step is to interpret the result of statistical hypothesis test. The fifth step is to establish the best conditions of product and process conditions by experimental optimization and interval estimation. The sixth step is to draw a conclusion by considering practical significance and statistical significance. Important for both quality practitioners and academicians, case analysis on six sigma projects with implementation guidelines are provided.