• Journal of Environmental Health Sciences
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• v.26 no.3
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• pp.50-54
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• 2000

Ventilation rates can be measured directly by a tracer decay method, although little is known of the effects of different sampling intervals on decay rte calculations. This study determined variations in decay rates calculated by three techniques using residential ozone decay data. The calculation techniques were a regression technique, decay techniques using half-life and average-life, and finite difference techniques using two different time intervals. Variation associated with regression technique calculations for residential ozone decay rates based on data from both sample intervals were within 10% (2.81$\pm$1.88 hr-1). However, both half-life and finite difference technique calculations using a shorter-time interval were significantly different from those obtained with the regression technique(p<0.05). Therefore, the use of short sampling intervals in tracer decay may cause significant error in decay rate calculations.

• Journal of the Korea Society for Simulation
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• v.8 no.3
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• pp.77-89
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• 1999

Simulating rare events, such as probability of cell loss in ATM networks, machine failure in highly reliable systems, requires huge simulation efforts due to the low chance of occurrence. Importance Sampling (IS) has been applied to accelerate the occurrence of rare events. However, it has a drawback of effective biasing scheme to make the estimator of IS unbiased. Adaptive Importance Sampling (AIS) employs an estimated sampling distribution of IS to the system of interest during the course of simulation. We propose Nonparametric Adaptive Importance Sampling (NAIS) technique which is nonparametrical version of AIS. We test NAIS to estimate a probability of rare event in M/M/1 queueing model. Comparing with classical Monte Carlo simulation, the computational efficiency and variance reductions gained via NAIS are substantial. A possible extension of NAIS regarding with random number generation is also discussed.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.69-80
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• 2006

In this study, we make an effort to find a method to acquire sensitive information when sensitive populations are consisted of several clusters that vary in size. We suggest and systemize the theoretical validity for applying RRT(Randomized Response Technique) to PPS(Probability Proportional to Size) sampling method and derive the estimate and it's variance of the proportion of sensitive characteristic of population by using the suggested method. We compare the efficiency of the suggested technique by two-stage equal probability sampling. We examine practical aspects of the suggested method of RRT by PPS sampling through field survey.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.359-364
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• 2002

In case of pn control chart often used in mass production system of plant industry and so on, we could evaluate it's performance by the approximation to normal distribution. It has many differences according to sample sizes and defective fraction, and have disadvantage that needs much samples to use the normal distribution approximation. Existent control charts can not detect the cause of process something wrong because it is taking the sampling intervals of fixed length about all times from the process. Therefore, to overcome this shortcoming we use VSI(variable sampling intervals) techniques in this paper. This technique takes a long sampling interval to have the next sampling point if the sample point is in stable state, and if the sample point is near control lines, it takes short sampling interval because the probability to escape control limit is high. To analyze performance of pn control charts that have existent fixed sampling intervals(FSI) and that use VSI technique, we compare ATS of two charts, and analyze the performance of each control chart by the sample sizes, process fraction defective and control limits that Ryan and Schwertman had proposed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.5
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• pp.541-547
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• 2010

The rigorous validation of the accuracy of metamodels is an important topic in research on metamodel techniques. Although a leave-k-out cross-validation technique involves a considerably high computational cost, it cannot be used to measure the fidelity of metamodels. Recently, the mean$_0$ validation technique has been proposed to quantitatively determine the accuracy of metamodels. However, the use of mean$_0$ validation criterion may lead to premature termination of a sampling process even if the kriging model is inaccurate. In this study, we propose a new validation technique based on the mean and variance of the response evaluated when sequential sampling method, such as maximum entropy sampling, is used. The proposed validation technique is more efficient and accurate than the leave-k-out cross-validation technique, because instead of performing numerical integration, the kriging model is explicitly integrated to accurately evaluate the mean and variance of the response evaluated. The error in the proposed validation technique resembles a root mean squared error, thus it can be used to determine a stop criterion for sequential sampling of metamodels.

• Journal of Animal Science and Technology
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• v.48 no.4
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• pp.541-554
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• 2006

Present review is to introduce an omasal sampling technique in rumen proteolysis and to consider some information on the omasal sampling technique with particular emphasis on methodological aspects. Use of the omasal sampling technique provides a new opportunity for accurate estimation of rumen metabolism with overcoming limitations of previous in vivo, in vitro and/or in situ methods. The potential advantages of the present technique compared with post-ruminal sampling techniques include following points; 1) only rumen cannulated animals are required, 2) less endogenous nitrogen (N) is contaminated in omasal digesta and 3) omasal digesta are devoid of exposure to acid peptide hydrolysis occurring in the abomasum. Estimates of soluble non-ammonia N (SNAN) in omasal digesta indicate that the assumptions underlying the in situ method that rapidly degradable N fraction can be degraded at an infinite rate and only insoluble dietary N escapes the rumen may be not valid. Quatitatively higher peptide concentration rather than free amino acid and soluble protein in escapable SNAN suggests that hydrolysis of peptide to amino acid may be the rate-limiting step in rumen proteolysis.

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.73-83
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• 2005

Thompson's (1990) adaptive cluster sampling is a promising sampling technique to ensure effective representation of rare or localized population units in the sample. We consider the problem of simultaneous estimation of the numbers of earners through a number of rural unorganized industries of which some are concentrated in specific geographic locations and demonstrate how the performance of a conventional Rao-Hartley-Cochran (RHC, 1962) estimator can be improved upon by using auxiliary information in the form of generalized regression (greg) estimators and then how further improvements are also possible to achieve by adopting adaptive cluster sampling.

• Journal of the Chungcheong Mathematical Society
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• v.21 no.4
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• pp.573-583
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• 2008

In this paper, we introduce the techniques of estimating the probability of failure in reliability analysis. The basic idea of each technique is explained and drawbacks of these techniques are examined.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.91-94
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• 2005

This paper presents a new linear- matrix- inequality- basedintelligent digital redesign (LMI-based IDR) technique to match he states of the analog and the digital control systems at the intersampling instants as well as the sampling ones. The main features of the proposed technique are: 1) the multirate control is employed, and the control input is changed N times during one sampling period; 2) The proposed IDR technique is based on the compensated bilinear transformation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.19
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• pp.25-30
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• 1989

This study is concerned with a job sequencing method using the concept of sampling technique. This sampling technique has never been applied to develop the scheduling algorithms. The most job sequencing algorithms have been developed to determine the best or good solution under the special conditions. Thus, it is not only very difficult, but also taken too much time to develop the appropriate job schedules that satisfy the complex work conditions. The application areas of these algorithms are also very narrow. Under these circumstances it is very desirable to develop a simple job sequencing method which can produce the good solution with the short tine period under any complex work conditions. It is called a sampling job sequencing method in this study. This study is to examine the selection of the good job sequence of 1%-5% upper group by the sampling method. The result shows that there is the set of 0.5%-5% job sequence group which has to same amount of performance measure with the optimal job sequence in the case of experiment of 2/n/F/F max. This indicates that the sampling job sequencing method is a useful job sequencing method to find the optimal or good job sequence with a little effort and time consuming. The results of ANOVA show that the two factors, number of jobs and the range of processing time are the significant factors for determining the job sequence at $\alpha$=0.01. This study is extended to 3 machines to machines job shop problems further.