• Journal of Wetlands Research
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• v.4 no.2
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• pp.43-56
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• 2002

About 12 rain gauge stations of Korea, annual maximum rainfall series of before and after 1980 whose durations are 1, 2, 3, 6, 12, 24, 48, 72 hours respectively were composed and statistical characteristics of those time series were calculated and probability rainfall were estimated by L-moment frequency analysis method and compared each other in order to investigate the recent quantitative rainfall variations. And also, distribution curves of each statistical variations for each duration were constructed by using Kigging method to look into spacial rainfall variation aspects. As a result, We could confirm recent rainfall increase in the South Korea. And spatial increase pattern of standard deviation and frequency rainfall appeared analogously each other. 1n the cases of comparatively short rainfall duration, we could see relatively low increase or decrease tendency in Chungchong Province, Cholla-bukdo, Cholla-namdo eastern part, Kyongsang-namdo western part area. While, variations happened great1y in seaside district of east coast, southwest seashore, Inchon area etc. In the cases of longer durations relatively low increase was showed in southern seashore such as Yeosoo area and as distance recedes from this area, showed gradually augmented tendency. The aspect of mean looks similar tendency of above except that the variation rate of almost seaside district are big in the case of shorter durations. In addition, rainfall increases of short durations which became the center of hydrologist and meteorologist are unconfirmed in this study.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.4
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• pp.346-352
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• 2009

In the present study, the Weibull statistical analysis using the Monte-Carlo simulation has been performed to investigate the micro-Vickers hardness measurement reliability considering the variability. Experimental indentation test were performed with a micro-Vickers hardness tester for as-received and quenching and tempering specimens in SCM440 steels. The distribution of micro-Vickers hardness is found to be 2-parameter Weibull distribution function. The mean values and coefficients of variation (COV) for both data set are compared with results based on Weibull statistical analysis. Finally, Monte-Carlo simulation was performed in order to evaluate the effect of sample size on the micro-Vickers hardness measurement reliability. For the parent distribution with shape parameter 30.0 and scale parameter 200.0 (COV=0.040), the number of sample data required to obtain the true Weibull parameters was founded by 20. For the parent distribution with shape parameter 10.0 and scale parameter 200.0 (COV=0.1240), the number of sample data required to obtain the true Weibull parameters was founded by 30.

• Journal of the Korean Society of Tobacco Science
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• v.20 no.1
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• pp.108-114
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• 1998

This study was conducted to evaluate the techniques or analyzing tobacco smoke by statistical treatment method for the analytical data through Asia Collaborative Study V. In addition to five smoke components analysis, consisting of TPM, water, nicotine, NFDPM, and puff count of four cigarettes samples, statistical parameters such as mean, standard deviation, box-and-whisker plots, h plots, k plots, regression coefficients, reproducibility (R), and repeatability (r) were also calculated. Analysis of water content of cigarette smoke was the most difficult task, whereas puff count analysis was the easiest as well recognized by all laboratories. Analysis of nicotine and puff count accounted for both the lowest and the highest variation among four parameters. The water coefficients indicated more randomness or variation in the slops. The NFDPM data exhibited both types of deviations from linearity. Water content of sample D indicated the highest difference between two single results and between two interlaboratory test results. As a whole, KGTRI ranked higher in the analytical techniques for statistical evaluation of results when compared with the practices of 28 other laboratories.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.2
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• pp.184-193
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• 2015

This paper proposes a cost-efficient and automatic method for large data acquisition from a test chip without expensive equipment to characterize random process variation in an integrated circuit. Our method requires only a test chip, a personal computer, a cheap digital-to-analog converter, a controller and multimeters, and thus large volume measurement can be performed on an office desk at low cost. To demonstrate the proposed method, we designed a test chip with a current model logic driver and an array of 128 current mirrors that mimic the random process variation of the driver's tail current mirror. Using our method, we characterized the random process variation of the driver's voltage due to the random process variation on the driver's tail current mirror from large volume measurement data. The statistical characteristics of the driver's output voltage calculated from the measured data are compared with Monte Carlo simulation. The difference between the measured and the simulated averages and standard deviations are less than 20% showing that we can easily characterize the random process variation at low cost by using our cost-efficient automatic large data acquisition method.

• Ocean and Polar Research
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• v.38 no.2
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• pp.149-159
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• 2016

The statistical measures of the coastal environmental data are used in a variety of statistical inferences, hypothesis tests, and data-driven modeling. If the measures are biased, then the statistical estimations and models may also be biased and this potential for bias is great when data contain some outliers defined as extraordinary large or small data values. This study aims to suggest more robust statistical measures as alternatives to more commonly used measures and to assess the performance these robust measures through a quantitative evaluation of more typical measures, such as in terms of locations, spreads, and shapes, with regard to environmental monitoring data in the Busan coastal sea. The detection of outliers within the data was carried out on the basis of Rosner's test. About 5-10% of the nutrient data were found to contain outliers based on Rosner's test. After removal (zero-weighting) of the outliers in the data sets, the relative change ratios of the mean and standard deviation between before and after outlier-removal conditions revealed the figures 13 and 33%, respectively. The variation magnitudes of skewness and kurtosis are 1.36 and 8.11 in a decreasing trend, respectively. On the other hand, the change ratios for more robust measures regarding the mean and standard deviation are 3.7-10.5%, and the variation magnitudes of robust skewness and kurtosis are about only 2-4% of the magnitude of the non-robust measures. The robust measures can be regarded as outlier-resistant statistical measures based on the relatively small changes in the scenarios before and after outlier removal conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.411-414
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• 1995

Statistical Process Control (SPC) is a set of methodologies for signaling the presence of undesired sources of variation in manufacturing processes. Expert System in SPC can serve as a valuable tool to automate the analysis and interpretation of control charts. In this paper we put forward a method of successful application of Expert System to SPC in manufacturing process.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.11
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• pp.1511-1514
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• 2012

Epistasis that may explain a large portion of the phenotypic variation for complex economic traits of animals has been ignored in many genetic association studies. A Baysian method was introduced to draw inferences about multilocus genotypic effects based on their marginal posterior distributions by a Gibbs sampler. A simulation study was conducted to provide statistical powers under various unbalanced designs by using this method. Data were simulated by combined designs of number of loci, within genotype variance, and sample size in unbalanced designs with or without null combined genotype cells. Mean empirical statistical power was estimated for testing posterior mean estimate of combined genotype effect. A practical example for obtaining empirical statistical power estimates with a given sample size was provided under unbalanced designs. The empirical statistical powers would be useful for determining an optimal design when interactive associations of multiple loci with complex phenotypes were examined.

• Journal of the Korea Concrete Institute
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• v.23 no.4
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• pp.421-430
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• 2011

As a fundamental study to introduce the reliability-based design code, a statistical study is conducted for the material strength data collected from domestic construction sites. In order to develop a rational design code based on statistics and reliability theory, it is essential to obtain the statistical properties of material strength. Material strength data for concrete, reinforcing bars, and prestressing strands which are used in domestic construction sites are collected and statistically analyzed. Then, the statistical properties are compared with those used in the process of the reliability-based calibration of internationally leading design codes. The statistical properties of the domestic data are such that the bias factor is relatively uniform between 1.13 and 1.20 and the coefficient of variation is below 0.10. Reinforcing bar data show difference among different manufacturers but there is not much difference among re-bar diameters. In the case of tendons, which are high strength materials, both of the domestic and foreign data show smaller values of the bias factor and the coefficient of variation than those of concrete and re-bar. Statistical distribution of all the material strength can be properly assumed as normal, log-normal, or Gumbel distribution after analyzing the classified data by individual construction site and manufacturer rather than the mixed data obtained from different sources in order to express the individual distribution of each structure.

• Journal of Korean Society for Quality Management
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• v.25 no.2
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• pp.140-153
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• 1997

As product quality is maily determined in the product design and process design step, systematic design should be performed through parameter design and tolerance design. Therefore, we introduced analysis of variance and regression analysis as a statistical method which determine optimal levels of affective design factors to product characteristics, then we compared that process and result. In analysis of variance, variation of quality characteristics arises from noise factors, so the optimal levels of design factors are selected to minimize the effect of noise factors. In regression analysis, variation of quality characteristics aries from variation of each own design factors. As a method to reduce variation of these quality characteristics, sensitivity analysis was performed about each design factors. Through this sensitivity analysis, we represented process to calculate the interaction term of the factors.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.435-446
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• 2000

Statistical process control(SPC) and engineering process control(EPC) are two strategies for quality improvement that have been developed independently. EPC seeks to minimize variability by adjusting compensatory variables in order to make the process level close to the target, while SPC seeks to reduce variability by monitoring and eliminating causes of variation. One purpose of this paper is to propose the IMA(0,1,1) model as the in-control process model. For the out-of-control process model we consider two cases; one is the case with a step shift in the level, and the other is the case with a change in the nonstationarity. Another purpose is to suggest the use of an integrated process control procedure with adjustment and monitoring, which can consider the proposed process model effectively. An integrated control procedure will improve the process control activity significantly for cases of the proposed model, when compared to the procedure of using either EPC or SPC, since EPC will keep the process close to the target and SPC will eliminate special causes.