• Communications for Statistical Applications and Methods
• /
• v.18 no.5
• /
• pp.645-655
• /
• 2011

Chandrasekar et al. (2004) introduced a generalized Type-II hybrid censoring. In this paper, we propose generalized doubly Type-II hybrid censoring. In addition, this paper presents the statistical inference on the scale parameter for the half logistic distribution when samples are generalized doubly Type-II hybrid censoring. The approximate maximum likelihood(AMLE) method is developed to estimate the unknown parameter. The scale parameter is estimated by the AMLE method using two di erent Taylor series expansion types. We compar the AMLEs in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n = 20; 30; 40 and various censored samples. The $AMLE_I$ is better than $AMLE_{II}$ in the sense of the MSE.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.14 no.3
• /
• pp.268-275
• /
• 2003

In this paper, the capacity of VSG-CDMA system supporting multi-media service in the reverse link is analyzed by considering the two models according to the power control and user distribution. In analysis model I, assuming perfect power control and uniform distribution of users, the equation of blocking probability is calculated and the maximum number of voice and data user is derived in accordance with 1 % blocking probability. In analysis model II, it is analyzed by assuming power control error and non-uniform distribution of users. The result of analysis model I means the upper bound of system capacity in the 5 MHz wideband VSG-CDMA system, and the result of analysis model II shows the lower bound of system capacity. Also, the improved plan of performance for VSG-CDMA system is suggested by the analyzed result according to data activity and the value of $E_b/N_o$ in model II.

• The Bulletin of The Korean Astronomical Society
• /
• v.40 no.1
• /
• pp.46.4-47
• /
• 2015

Carnegie Hubble Program II (hereafter CHP II) is a large Hubble Space Telescope (HST) observing campaign in the cycle 22 composed of a total of 184 orbits (132 primes + 52 parallels), which aims to measure H0 directly with an unprecedented accuracy. Unlike our previous efforts in CHP I which used Cepheids as a yardstick, CHP II takes the Population II (Pop II) distance indicators such as RR Lyraes and tip of the red giant branch stars (TRGBs) to set up a new calibration to Type Ia supernovae (SN Ia) distance. The Pop II distance scales have two immediate advantages over the classical Cepheid method: 1) The period-luminosity relation of the RR Lyrae has a scatter that is a factor of 2 smaller; 2) The RR Lyrae/TRGB distance scale can be applied to both elliptical and spiral galaxies. This will provide a great systematic benefit by ultimately allowing us to double the number of SN Ia distances based on geometry. By taking advantage of this Pop II route, we expect to measure H0 value to 3 % of error which will be the highest accuracy H0 measurement to date using the "Distance Ladder" method. In this talk I will present a brief background/overview on the CHP II, observations/data acquisition status, and ongoing research progress/preliminary results.

• Communications for Statistical Applications and Methods
• /
• v.19 no.5
• /
• pp.697-704
• /
• 2012

The maximum likelihood(ML) estimation of the scale parameters of an exponential distribution based on progressive Type II censored samples is given. The sample is multiply censored (some middle observations being censored); however, the ML method does not admit explicit solutions. In this paper, we propose multiply progressive Type II censoring. This paper presents the statistical inference on the scale parameter for the exponential distribution when samples are multiply progressive Type II censoring. The scale parameter is estimated by approximate ML methods that use two different Taylor series expansion types ($AMLE_I$, $AMLE_{II}$). We also obtain the maximum likelihood estimator(MLE) of the scale parameter under the proposed multiply progressive Type II censored samples. We compare the estimators in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n = 20 and 40 and various censored schemes. The $AMLE_{II}$ is better than MLE and $AMLE_I$ in the sense of the MSE.

• Communications for Statistical Applications and Methods
• /
• v.27 no.4
• /
• pp.413-430
• /
• 2020

The multiply Type-II hybrid censoring scheme is disadvantaged by an experiment time that is too long. To overcome this limitation, we propose a generalized multiply Type-II hybrid censoring scheme. Some estimators of the scale parameter of the exponential distribution are derived under a generalized multiply Type-II hybrid censoring scheme. First, the maximum likelihood estimator of the scale parameter of the exponential distribution is obtained under the proposed censoring scheme. Second, we obtain the Bayes estimators under different loss functions with a noninformative prior and an informative prior. We approximate the Bayes estimators by Lindleys approximation and the Tierney-Kadane method since the posterior distributions obtained by the two priors are complicated. In addition, the Bayes estimators are obtained by using the Markov Chain Monte Carlo samples. Finally, all proposed estimators are compared in the sense of the mean squared error through the Monte Carlo simulation and applied to real data.

• Journal of the Korea Safety Management & Science
• /
• v.10 no.2
• /
• pp.205-210
• /
• 2008

The research interprets the principles of sampling error design for quality statistics models such as hypothesis test, interval estimation, control charts and acceptance sampling. Introducing the proper discussions of the design of significance level according to the use of hypothesis test, then it presents two methods to interpret significance by Neyman-Pearson and Fisher. Second point of the study proposes the design of confidence level for interval estimation by Bayesian confidence set, frequentist confidential set and fiducial interval. Third, the content also indicates the design of type I error and type II error considering both productivity and customer claim for control chart. Finally, the study reflects the design of producer's risk with operating charistictics curve, screening and switch rules for the purpose of purchasing and subcontraction.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2002.10a
• /
• pp.417-422
• /
• 2002

One of the major limitations of productivity and quality in metal cutting is the machining accuracy of machine tools. The machining accuracy is affected by geometric errors, thermally-induced errors, and the deterioration of the machine tools. Geometric and thermal errors of machine tools should be measured and compensated to manufacture high quality products. In metal cutting, the machining accuracy is more affected by thermal errors than by geometric errors. In this study, the compensation device and temperature-based algorithm have been implemented on the machining center in order to compensate thermal error of machine tools under the real-time. The thermal errors are predicted using the neural network and multi-regression modeling methods. In order to compensate thermal characteristics under several operating conditions, experiments performed with five gap sensors and manufactured compensation device on the horizontal machining center.

• Journal of Korean Institute of Industrial Engineers
• /
• v.25 no.1
• /
• pp.8-20
• /
• 1999

In this paper, a design and estimation procedure for the stochastically dependent nonstationary multiple acceptance sampling plans is developed. At first, the rough-cut acceptance and rejection numbers are given as an initial solution from the corresponding sequential sampling plan. A Monte-Carlo algorithm is used to find the acceptance and rejection probabilities of a lot. The conditional probability formula for a sample path is found. The acceptance and rejection probabilities are found when a decision boundary is given. Several decision criteria and the design procedure to select optimal plans are suggested. The formula for measuring performance of these sampling plans is developed. Type I and II error probabilities are also estimated. As a special case, by setting the stage size as 1 in a dependent sampling plan, a sequential sampling plan satisfying type I and II error probabilities is more accurate and a smaller average sample number can be found. In a numerical example, a Polya dependent process is examined. The sampling performances are shown to compare the selection scheme and the effect of the change of the dependency factor.

• Journal of Ocean Engineering and Technology
• /
• v.13 no.3B
• /
• pp.99-105
• /
• 1999

In order to reject the steady-state tracking error, it is common to introduce integral compensators in servosystems for constant reference signals. However, if the mathematical model of the plant is exact and no disturbance input exists, the integral compensation is not necessary. From this point of view, a two-degree-of-freedom(2DOF) servosystem has been proposed, in which the integral compensation is effective only when there is a modeling error or a disturbance input. The present paper considers a robust stability of this 2DOF servosystem with nonlinear type uncertainty in the system, and a robust stability condition for the servosystem is introduced. This result guarantees that if the plant uncertainty is in the permissible set defined by the condition, gain tuning can be carried out to suppress the influence of the plant uncertainties and disturbance inputs.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.4
• /
• pp.105-110
• /
• 1998

In this study, statistical and neural network methods were used to recognize the cutting tool states. This system employed the tool dynamometer and cutting force signals which are processed from the tool dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to the error back propagation method during off-line training. The cutting conditions, cutting force ratios and statistical values(standard deviation, coefficient of variation) attained from the cutting force signals were used as the inputs to the neural network. Through the suggested neural network a cutting tool states may be successfully diagnosed.