• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.303-310
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• 2004

We suggest a method to procure information from the sensitive population which combine a direct survey method, BB and an indirect survey one, RRT, and a combined estimator that uses the stratified double sampling to estimate the sensitive parameter. We compare the efficiency of our estimator with that of Mangat and Singh model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.351-358
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• 1999

The structural responses of underground structures are examined in probability by using the elasto-plastic stochastic finite element method in which the spatial distributions of material properties are assumed to be stochastic fields. In addition, the adaptive importance sampling method using the response surface technique is used to improve simulation efficiency. The method is found to provide appropriate information although the nonlinear Limit State involves a large number of basic random variables and the failure probability is small. The probability of plastic local failures around an excavated area is effectively evaluated and the reliability for the limit displacement of the ground is investigated. It is demonstrated that the adaptive importance sampling method can be very efficiently used to evaluate the reliability of a large scale stochastic finite element model, such as the underground structures located in the multi-layered ground.

• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.37-43
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• 2015

In this paper, a new background subtraction algorithm for video based fence monitoring surveillance systems is proposed. We adopt the sampling based background subtraction technique and focus on the two main issues: handling highly dynamic environment and handling the flickering nature of pulse based IR (infrared) lamp. Natural scenes from fence monitoring system are usually composed of several dynamic entities such as swaying trees, moving water, waves and rain. To deal with such dynamic backgrounds, we utilize the confidence factor for each background value of the input image. For the flickering IR lamp, the original sampling based technique is extended to handle double background models. Experimental results revealed that our method works well in real fence monitoring surveillance systems.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.244-249
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• 1998

In this paper, the hybrid state space self-tuning control technique Is studied within the framework of fuzzy systems and dual-rate sampling control theory. We show that fuzzy modeling techniques can be used to formulate chaotic dynamical systems. Then, we develop the hybrid state space self-tuning fuzzy control techniques with dual-rate sampling for digital control of chaotic systems. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the continuous-time optimal state feedback gains, the constructed discrete-time fuzzy system is converted into a continuous-time system. The developed optimal continuous-time control law is then convened into an equivalent slow-rate digital control law using the proposed digital redesign method. The proposed technique enables us to systematically and effective]y carry out framework for modeling and control of chaotic systems. The proposed method has been successfully applied for controlling the chaotic trajectories of Chua's circuit.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.761-770
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• 1999

A combination procedure of successive occasions sampling and randomized response method is investigated. Randomized response technique is very simple for use in a telephone survey of a sensitive subject. In the suggested randomized response method. the interviewee replies "yes" or "no" to a randomly selected question and the investigator can estimate the proportion of "yes" or "no" answer. When this procedure is used on successive occasions, not only the proportion supporting a candidate and the time change in this supporting proportion can be derived but also the voters' swing in the trend of voters' support can be estimated. A numerical example is given to show how the suggested sampling strategy can be applied to a practical telephone survey.

• Journal of Web Engineering
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• v.21 no.4
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• pp.1225-1238
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• 2022

Holographic stereogram (HS) printing is a promising holographic technique for three-dimensional (3D) visualization of an object with accurate depth cues. In this paper, unlike the conventional rectangular hogel based HS, efficient hexagonal hogels sampling for HS printing that enhances the volumetric visualization of reconstruction while providing rapidly generated data using inverse-directed propagation (IDP) is proposed. Specifically, an array of hexagonal hogels is sampled by a computer-generated integral imaging technique using an IDP, which acquires the full information of the 3D object prior to higher volumetric 3D reconstruction. To demonstrate the proposed approach, IDP-based hexagonal hogel sampling for HS printing is implemented, and the enhanced image quality of printed holograms is verified both by numerical simulation and in an optical experiment.

• Journal of the Korean Statistical Society
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• v.34 no.2
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• pp.125-140
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• 2005

In this paper, we consider the estimation of the correlation coefficient in the bivariate normal distribution, based on a sample obtained using a modification of the moving extreme ranked set sampling technique (MERSS) that was introduced by Al-Saleh and Al-Hadhrami (2003a). The modification involves using a concomitant random variable. Nonparametric-type methods as well as the maximum likelihood estimation are considered under different settings. The obtained estimators are compared to their counterparts that are obtained based simple random sampling (SRS). It appears that the suggested estimators are more efficient

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.9
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• pp.870-877
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• 2009

This study provides how the Dimension Reduction (DR) method as an efficient technique for reliability analysis can acquire its increased efficiency when it is applied to highly nonlinear problems. In the highly nonlinear engineering systems, 4N+1 (N: number of random variables) sampling is generally recognized to be appropriate. However, there exists uncertainty concerning the standard for judgment of non-linearity of the system as well as possibility of diverse degrees of non-linearity according to each of the random variables. In this regard, this study judged the linearity individually on each random variable after 2N+1 sampling. If high non-linearity appeared, 2 additional sampling was administered on each random variable to apply the DR method. The applications of the proposed sampling to the examples produced the constant results with increased efficiency.

• Structural Engineering and Mechanics
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• v.38 no.1
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• pp.125-140
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• 2011

This study presents an innovative method to estimate the reliability sensitivity based on the low-discrepancy sampling which is a new technique for structural reliability analysis. Two advantages are contributed to the method: one is that, by developing a general importance sampling procedure for reliability sensitivity analysis, the partial derivative of the failure probability with respect to the distribution parameter can be directly obtained with typically insignificant additional computations on the basis of structural reliability analysis; and the other is that, by combining various low-discrepancy sequences with the above importance sampling procedure, the proposed method is far more efficient than that based on the classical Monte Carlo method in estimating reliability sensitivity, especially for problems of small failure probability or problems that require a large number of costly finite element analyses. Examples involving both numerical and structural problems illustrate the application and effectiveness of the method developed, which indicate that the proposed method can provide accurate and computationally efficient estimates of reliability sensitivity.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1162-1168
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• 2008

This study provides how the Dimension Reduction (DR) method as an efficient technique for reliability analysis can acquire its increased efficiency when it is applied to highly nonlinear problems. In the highly nonlinear engineering systems, 4N+1 (N: number of random variables) sampling is generally recognized to be appropriate. However, there exists uncertainty concerning the standard for judgment of non-linearity of the system as well as possibility of diverse degrees of non-linearity according to each of the random variables. In this regard, this study judged the linearity individually on each random variable after 2N+1 sampling. If high non-linearity appeared, 2 additional sampling was administered on each random variable to apply the DR method. The applications of the proposed sampling to the examples produced the constant results with increased efficiency.