• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.1
• /
• pp.116-122
• /
• 2016

Recently, the production cycle in manufacturing process has been getting shorter and different types of product have been produced in the same process line. In this case, the control chart using coefficient of variation would be applicable to the process. The theory that random variables are located in the three times distance of the deviation from mean value is applicable to the control chart that monitor the process in the manufacturing line, when the data of process are changed by the type of normal distribution. It is possible to apply to the control chart of coefficient of variation too. ${\bar{x}}$, s estimates that taken in the coefficient of variation have just used all of the data, but the upper control limit, center line and lower control limit have been settled by the effect of abnormal values, so this control chart could be in trouble of detection ability of the assignable value. The purpose of this study was to present the robust control chart than coefficient of variation control chart in the normal process. To perform this research, the location parameter, ${\bar{x_{\alpha}}}$, $s_{\alpha}$ were used. The robust control chart was named Tim-CV control chart. The result of simulation were summarized as follows; First, P values, the probability to get away from control limit, in Trim-CV control chart were larger than CV control chart in the normal process. Second, ARL values, average run length, in Trim-CV control chart were smaller than CV control chart in the normal process. Particularly, the difference of performance of two control charts was so sure when the change of the process was getting to bigger. Therefore, the Trim-CV control chart proposed in this paper would be more efficient tool than CV control chart in small quantity batch production.

• Transactions of the Korean hydrogen and new energy society
• /
• v.13 no.3
• /
• pp.233-241
• /
• 2002

To achieve hydrogen power system with high performance and stable operation, the COVimep of hydrogen fueled engine with direct injection was evaluated with the change of engine speed, injection timing, air-fuel equivalence ratio and spark timing. And the cause of cycle variation was analyzed by using coefficient of variation in combustion period defined in this study. the results showed that the cycle variation of hydrogen fueled engine is mainly dependent on the early combustion period.

• KIEE International Transactions on Electrophysics and Applications
• /
• v.4C no.4
• /
• pp.185-189
• /
• 2004

The temporal variation of a secondary electron emission coefficient (${\gamma}$ coefficient) of hydrogenated amorphous silicon (a-Si:H) was investigated in a dc silane plasma. Estimated ${\gamma}$ coefficients have a value of 2.73 ${\times}$ 10$^{-2}$ on the pure aluminum electrode and 1.5 ${\times}$ 10$^{-3}$ after 2 hours deposition of -Si:H thin films on a cathode. It showed an abrupt decrease for about 30 minutes before saturation. The variation of the ${\gamma}$ coefficient was estimated as a function of the thin film thickness, and the film thickness was about 80 nm after 30 minutes deposition time. These results are compared with the results of a computer simulation for ion penetration into a cathode.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2003.11a
• /
• pp.11-14
• /
• 2003

The purpose of this paper is to offer a base data of specification, so that the strength information of concrete in the structure for irrigation can be rationally determined the coefficient of variation of an existing irrigation structure and the best fit the ready-mixed concrete strength to specified strength $f_ck$. From analysis of concrete cylinders from about 30 numbers in southern Korea, it was concluded that the coefficient of variation of cylinder strength were approximately 5.9%. On the basis of the core strength test data, it was appeared that the average coefficient of variation for the existing irrigation structure can be taken as 17.8% for strength level 21MPa.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.24 no.5
• /
• pp.709-717
• /
• 2000

Cyclic variability has long been recognized as limiting the range of operating conditions of spark ignition engines, in particular, under lean and highly diluted operation conditions. The cyclic combustion variations can be characterized by the pressure parameters, combustion parameters, and flame front parameters. The coefficient of variation in indicated mean effective pressure ($COV_{IMEP}$) defines the cyclic variability in indicated work per cycle, and it has been found that vehicle driveability problems usually result when $COV_{IMEP}$ exceeds about 10%. For analysis of the cyclic variability in SI engines at idling, the results show that cyclic variability by the $COV_{IMEP}$ or the coefficient of variation in maximum pressure can be explained and may be consequently reduced by the help of the optimum spark timings.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 1998.06a
• /
• pp.343-346
• /
• 1998

Degradation characteristics of the Pr-based ZnO varistor with $Y_2O_3$content (0.0-4.0 mol!%j were investigated. It was appeared that the variation of the J-E characteristics in the reverse direction before and after the applied stress with increasing $Y_2O_3$ content was larger than that of the forward direction but the variation was extrernly small. For all varistor, the leakage current with the stress time during the applied stress somewhat increased initialy but afterthat was almost constant or slightly decreased. The overall varistor voltage and nonlinear coefficient were less than 5%. Especially, in the case of Pr-based ZnO varistor containing 2.0 mol% to 4.0 mol% $Y_2O_3$, the variation of breakdown voltage and nonlinear coefficient was less than 1% and 5%. respectively. As a result, they showed good stability.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.2
• /
• pp.297-308
• /
• 2010

This article deals with the problem of testing mean when the coefficient of variation in normal distribution is known. We propose Bayesian hypothesis testing procedures for the normal mean under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Specially, we develop intrinsic priors which give asymptotically same Bayes factor with the intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

• Communications for Statistical Applications and Methods
• /
• v.15 no.3
• /
• pp.429-440
• /
• 2008

In this paper, we develop the noninformative priors when the parameter of interest is the common coefficient of variation in two inverse Gaussian distributions. We want to develop the first and second order probability matching priors. But we prove that the second order probability matching prior does not exist. It turns out that the one-at-a-time and two group reference priors satisfy the first order matching criterion but Jeffreys' prior does not. The Bayesian credible intervals based on the one-at-a-time reference prior meet the frequentist target coverage probabilities much better than that of Jeffreys' prior. Some simulations are given.

• Imaging Science in Dentistry
• /
• v.31 no.2
• /
• pp.85-91
• /
• 2001

Purpose: To evaluate the effect of the slice thickness and the size of region of interest (ROI) on CT number using quantitative CT phantom Materials and Methods: The phantom containing 150 mg/cc, 75 mg/cc and 0 mg/cc calcium hydroxyapatite was scanned with 1, 3, 5 and 10 mm slice thicknesses by single energy quantitative computed tomography (QCT). CT numbers were measured on center position of the phantom. Shape of ROI was circular and sizes were 1, 3, 5, 11, 16, 21, 26 and 33 mm². ANOVA and Tukey's multiple comparison method were performed for statistical comparison of CT numbers according to different slice thicknesses. Coefficient of variation of CT number measured in each size of ROI was evaluated in same slice thickness. Results : CT numbers had statistically significant difference according to slice thicknesses (p<0.05). As the slice thickness increased, CT number also increased. As the density of phantom became lower and the size of ROI became smaller, the coefficient of variation of CT number increased. When the size of ROI was more than 11 mm² in 1 mm slice thickness, 5 mm² in 3 mm slice thickness and 3 mm² in 5 mm slice thickness, the coefficient of variation became consistent. In 10 mm slice thickness, the size of ROI had little effect on the coefficient of variation. Conclusion: CT number had variation according to the slice thickness and the size of ROI although the object was homogeneous. The slice thickness and the size of ROI are critical factors in precision of the CT number measurements.

• Journal of the Speleological Society of Korea
• /
• v.14 no.15
• /
• pp.42-90
• /
• 1987

Lee, Moon Won reported by 63 kinds lescribing the petrography and bulk chemical Composition in Petrology of Cheju volcanic island. The total Chemical Composition data was analyzed by the program of FORTRAN77. First, the Conversition equations and the scatter diagram were examined to the analysis, by the least square method. Next, a statistical data requested a mean Value, maximum value, minimum value, the range, the standard deviation, the variance, the Standord Error and the Coefficient of variation. In the standard deviation, a small Composition is MnO and P$_2$O$\sub$5/, a large Composition is SiO$_2$, Mgo and FeO. The Standard error and the variance were the tandency looked like the Standard deviation well. However, the Coefficient Variation differs from the Standard deviation. Where, a large Coefficient of variation are H$_2$O$\^$-/ and H$_2$O$\^$+/, a small Coefficient of variation are Al$_2$O$_3$ and SiO$_2$. The Correlation of Coefficient Can be Calculated numerically from the relation between SiO$_2$, Al$_2$O$_3$ and TiO$_2$ to other Compositions.