• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.240-244
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• 1992

A new method for generation of binary random sequences, called random sampling method, has been proposed by the authors. However, the random sampling method has the defect that binary random sequence can not be rapidly generated. In this paper, two methods based on the random sampling method are proposed for fast generation of binary random sequences. The optimum conditions for obtaining ideal binary random sequences are derived.

• Journal of Applied Reliability
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• v.18 no.2
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• pp.122-129
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• 2018

Purpose: This article develops an optimal reliability acceptance sampling plan for the case where the degradation quantity of the performance characteristic follows Weibull distribution. Method: For developing reliability acceptance sampling plans, the sample size and the acceptance constant are determined based on the accelerated characteristic of the test condition and the product. Results: The sample size and the acceptance constant are provided such that the constraints of the producer and the consumer risks are satisfied. Conclusion: Reliability acceptance sampling plans based on the accelerated degradation test method can be used for the quality control within a resonable amount of cost and time. In this article. an optimal reliability sampling plans are newly developed for this purpose.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.86-89
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• 2003

To model a numerical problem space under the limitation of available data, we need to extract sparse but key points from the space and to efficiently approximate the space with them. This study proposes a sampling method based on the search process of genetic algorithm and a space modeling method based on least-squares approximation using the summation of Gaussian functions. We conducted simulations to evaluate them for several kinds of problem spaces: DeJong's, Schaffer's, and our original one. We then compared the performance between our sampling method and sampling at regular intervals and that between our modeling method and modeling using a polynomial. The results showed that the error between a problem space and its model was the smallest for the combination of our sampling and modeling methods for many problem spaces when the number of samples was considerably small.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.381-389
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• 2017

The kernel density was determined based on sampling points obtained in a Markov chain simulation and was assumed to be an important sampling function. A Kriging metamodel was constructed in more detail in the vicinity of a limit state. The failure probability was calculated based on importance sampling, which was performed for the Kriging metamodel. A pre-existing method was modified to obtain more sampling points for a kernel density in the vicinity of a limit state. A stable numerical method was proposed to find a parameter of the kernel density. To assess the completeness of the Kriging metamodel, the possibility of changes in the calculated failure probability due to the uncertainty of the Kriging metamodel was calculated.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.455-471
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• 2000

A new method is developed for estimating the mean of a population which has a linear trend. The proposed estimator is based on the balanced systematic sampling method and the concept of interpolation. The efficiency of the proposed method is compared with that of existing methods.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.91-101
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• 2000

We propose a new method for estimating the mean of a population which has a linear trend. The suggested estimator is based on the centered balanced systematic sampling method and the concept of interpolation and extrapolation. The efficiency of the proposed method is compared with that of existing methods.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.384-390
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• 2004

In this paper we compare the performance of 4 different algorithms for converting the sampling frequency of an audio from 44.1KHz to 48KHz. The algorithms considered here include the basic polyphase method. sine function based method. multi-stage method. and B-spline based method. For a fair comparison, the sampling rate converters using the 4 algorithms are redesigned under a high fidelity condition. Then, their H/W complexities are compared in terms of the computational complexity and the memory size. As a result, it is shown that the basic polyphase method and sine function based method outperform the other two in terms of the computational complexity, while the B-spline based method requires less memory than the others.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.465-469
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• 2017

In this paper we propose a human body tracking method for application of smartphones. The conventional human body tracking method is divided into a sensor-based method and a vision-based method. The sensor-based methods have a weakness in that tracking accuracy is low due to cumulative error of position information. The vision-based method has no cumulative error, but it requires reduction of the computational complexity for application of smartphone. In this paper we use the improved HOG algorithm as a human body tracking method for application of smartphone. The improved HOG algorithm is implemented through downsampling and frame sampling. Gaussian pyramid is applied for downsampling, and uniform sampling is applied for frame sampling. We measured the proposed algorithm on two devices, four resolutions, and four frame sampling intervals. We derive the best detection rate among downsampling and frame sampling parameters that can be applied in realtime.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.45-53
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• 2011

This paper presents a novel random sampling method for generation adequacy assessment including wind-power. Although a time sequential sampling has advantages than a random sampling in its assessment results, it takes long assessment time. Therefore, an effective random sampling method for generation adequacy assessment is highly recommended to get specific reliability indices quickly. The proposed method is based on the Monte-Carlo simulation with state sampling and it can be applied to generation adequacy assessment with other intermittent power sources.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.1-13
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• 2018

In many studies, a researcher attempts to describe a population where units are measured for multiple outcomes, or responses. In this paper, we present an efficient procedure based on ranked set sampling to estimate and perform hypothesis testing on a multivariate mean. The method is based on ranking on an auxiliary covariate, which is assumed to be correlated with the multivariate response, in order to improve the efficiency of the estimation. We showed that the proposed estimators developed under this sampling scheme are unbiased, have smaller variance in the multivariate sense, and are asymptotically Gaussian. We also demonstrated that the efficiency of multivariate regression estimator can be improved by using Ranked set sampling. A bootstrap routine is developed in the statistical software R to perform inference when the sample size is small. We use a simulation study to investigate the performance of the method under known conditions and apply the method to the biomarker data collected in China Health and Nutrition Survey (CHNS 2009) data.