• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.403-420
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• 1994

An economic design of the $\bar{X}-R$ chart using variable sample size (VSS) scheme is proposed in this paper. In this design the sample size at each sampling time changes according to the values of the previous two sample statistics, sample mean and range. The VSS scheme uses large sample if the sample statistics appear near inside the control limits and smaller sample otherwise. The set of process parameters, such as the sampling interval, control limits and the sample sizes, are chosen to minimize the expected cost per hour. The efficiency of the VSS scheme is compared to the fixed sample size one for cases where there is multiple of assignable causes. Percent reductions of the expected cost in the VSS design are calculated for some given sets of cost parameters. It is shown that the VSS scheme improves the confidence of the procedure and performs statistically better in terms of the number of false alarms and the average time to signal, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2139-2151
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• 2014

Embedded system testing, especially long-term reliability testing, of flash memory solutions such as embedded multi-media card, secure digital card and solid-state drive involves strategic decision making related to test sample size to achieve high test coverage. The test sample size is the number of flash memory devices used in a test. Earlier, there were physical limitations on the testing period and the number of test devices that could be used. Hence, decisions regarding the sample size depended on the experience of human testers owing to the absence of well-defined standards. Moreover, a lack of understanding of the importance of the sample size resulted in field defects due to unexpected user scenarios. In worst cases, users finally detected these defects after several years. In this paper, we propose that a large number of potential field defects can be detected if an adequately large test sample size is used to target weak features during long-term reliability testing of flash memory solutions. In general, a larger test sample size yields better results. However, owing to the limited availability of physical resources, there is a limit on the test sample size that can be used. In this paper, we address this problem by proposing a self-adaptive reliability testing scheme to decide the sample size for effective long-term reliability testing.

• Journal of Veterinary Clinics
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• v.28 no.4
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• pp.422-426
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• 2011

For a national survey in which wide geographic region or an entire country is targeted, multi-stage sampling approach is widely used to overcome the problem of simple random sampling, to consider both herd- and animallevel factors associated with disease occurrence, and to adjust clustering effect of disease in the population in the calculation of sample size. The aim of this study was to establish sample size for estimating bovine tuberculosis (TB) in Korea using stratified two-stage sampling design. The sample size was determined by taking into account the possible clustering of TB-infected animals on individual herds to increase the reliability of survey results. In this study, the country was stratified into nine provinces (administrative unit) and herd, the primary sampling unit, was considered as a cluster. For all analyses, design effect of 2, between-cluster prevalence of 50% to yield maximum sample size, and mean herd size of 65 were assumed due to lack of information available. Using a two-stage sampling scheme, the number of cattle sampled per herd was 65 cattle, regardless of confidence level, prevalence, and mean herd size examined. Number of clusters to be sampled at a 95% level of confidence was estimated to be 296, 74, 33, 19, 12, and 9 for desired precision of 0.01, 0.02, 0.03, 0.04, 0.05, and 0.06, respectively. Therefore, the total sample size with a 95% confidence level was 172,872, 43,218, 19,224, 10,818, 6,930, and 4,806 for desired precision ranging from 0.01 to 0.06. The sample size was increased with desired precision and design effect. In a situation where the number of cattle sampled per herd is fixed ranging from 5 to 40 with a 5-head interval, total sample size with a 95% confidence level was estimated to be 6,480, 10,080, 13,770, 17,280, 20.925, 24,570, 28,350, and 31,680, respectively. The percent increase in total sample size resulting from the use of intra-cluster correlation coefficient of 0.3 was 22.2, 32.1, 36.3, 39.6, 41.9, 42.9, 42,2, and 44.3%, respectively in comparison to the use of coefficient of 0.2.

• Journal of Veterinary Clinics
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• v.29 no.1
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• pp.68-77
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• 2012

Whenever planning a study design or preparing a research proposal it is highly recommended that investigators decide the optimum sample size that is required to yield an outcome of interest with a predetermined level of precision. This is because that, all else being equal, if a study with less than the optimum sample size would not detect the significance of differences in reality, and similarly, if a study with more than the optimum sample size will be costly. For these reasons, the majority of peer reviewed biomedical journals assess the adequacy of sample size requirements. The calculated sample size is used as a target number of samples to be collected to provide an estimate of the parameter with the desired and predetermined level of accuracy, and the sample size is a major determinant of the probability of detecting diseased animals from the population. There is no single method of calculating sample size for any given study design. In this context, the purpose of this article is to provide a collection of formulas and examples for some typical situations likely to be encountered in veterinary clinical practice and to highlight the importance of performing prospective sample size calculations when planning a research. Specifically, this paper is concerned with the basic principle of sample size calculation, and considerations for methodological applications were illustrated for a given data set. Also included in this paper is factors influencing sample size calculations using a statistically valid techniques. Appropriate methods to consider these factors are presented.

• Korean Journal of Environmental Biology
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• v.32 no.3
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• pp.211-215
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• 2014

In order to accurately estimate the litter mass, we evaluated the required sample sizes across 13 chronosequence stands for five years (1994~1996, 2003~2004) in northern hardwood forests in New Hampshire, USA. It was found that the number of required litter traps in our stands (0.25~0.5 ha) within ${\pm}10%$ of the sample mean was appeared to be similar or higher than the 15 litter traps installed in this study. Notably, in 1994 and 1995, the number of required litter trap was twice higher than the 15 litter traps. Further, within ${\pm}20%$ of the sample mean, the number of required litter traps was less than 10 across all 13 stands for five years, which indicates that we can reduce the sample size. Precisely, the number of sample size had increased in stands with steep and high elevation, but no relations with stand age across 13 stands were observed. Based on these results, we suggest that it is important to sample litter mass for several years, in order to determine the number of appropriate sample size, and stands with steep and high elevation may need more litter traps.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.29-37
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• 2009

This paper proposes a curtailed-sequential probability ratio test (SPRT) control chart. For using the conventional SPRT control chart, the number of items inspected in a sampling point should have no restriction since items in a sampling point are inspected one by one until an SPRT Is terminated. The number of observations taken in a sampling point, however, has an upper bound since sampling and testing of an item is time-consuming or expensive. When the sample size reaches the upper bound without evidence of an in-control or out-of-control state of a process, the proposed chart makes a decision using the sample mean of all observations taken in a sampling point. The properties of the Proposed chart are obtained by a Markov chain approach and the performance of the chart is compared with fixed sample size (FSS) and variable sample size (VSS) control charts. A comparative study shows that the proposed chart performs better than VSS control charts as well as conventional FSS control charts.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.78-88
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• 2009

In many face recognition problems, the number of available images is limited compared to the dimension of the input space which is usually equal to the number of pixels. This problem is called as the 'small sample size' problem and regularization methods are typically used to solve this problem in feature extraction methods such as LDA. By using regularization methods, the modified within class matrix becomes nonsingu1ar and LDA can be performed in its original form. However, in the process of adding a scaled version of the identity matrix to the original within scatter matrix, the scale factor should be set heuristically and the performance of the recognition system depends on highly the value of the scalar factor. By using the proposed resampling method, we can generate a set of images similar to but slightly different from the original image. With the increased number of images, the small sample size problem is alleviated and the classification performance increases. Unlike regularization method, the resampling method does not suffer from the heuristic setting of the parameter producing better performance.

• The Journal of the Korean dental association
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• v.52 no.9
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• pp.558-569
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• 2014

Sample size determination is critical, but not easy to do. Sample size defined as the number of observations in a sample to be studied should be big enough to have a high likelihood of detecting a true difference between groups. Practical procedure for determining sample size, using $G^*$power and previous dental articles, was shown in this study. Examples involving independent t-test, paired t-test, one-way analysis of variance(ANOVA), and one-way repeated-measures(RM) ANOVA were used. The purpose of this study is to enable researchers with non-statistical backgrounds to use in practice freely available statistical software G*power to determine sample size and power.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.252-264
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• 2004

Variable sampling Interval (VSI) control charts vary the sampling interval according to value of the control statistic while the sample size is fixed. It is known that control charts with 2-state VSI scheme, which uses only two sampling intervals, give good statistical properties. Variable sample size (VSS) control charts vary the sample size according to value of the control statistic while the sampling interval is fixed. In the VSS scheme no optimal results are known for the number of sample sizes. It is also known that the variable sampling rate (VSR) $\bar{X}$ control chart with 2-state VSS and 2-state VSI scheme leads to large improvements In performance over the fixed sampling rate (FSR) $\bar{X}$ chart, but the optimal number of states for sample size Is not known. In this paper, the VSR Χ charts with multi-state VSS and 2-state VSI scheme are designed and compared to 2-state VSS and 2-state VSI scheme. The multi-state VSS scheme is considered to, achieve an additional improvement by switching from the 2-state VSS scheme. On the other hand, the multi-state VSI scheme is not considered because the 2-state scheme is known to be optimal. The 3-state VSS scheme improves substantially the sensitivity of the $\bar{X}$ chart especially for small and moderate mean shifts.

• Particle and aerosol research
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• v.9 no.1
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• pp.1-6
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• 2013

New evaluation method for the particle size and morphology via change of ground particle during a grinding process was investigated. The grinding experiments were carried by a planetary ball mill. The relationship between the particle outline of the scanning electron microscopy photograph and measurement line, the measurement contact number was evaluated. The value of contact number decreased with the increase in the particle size of the ground sample, and varied with the experimental conditions. The value of contact number, which is related to the particle size of the raw sample, changed at the various experimental conditions.