• Journal of the Korean Society of Visualization
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• v.2 no.1
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• pp.8-11
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• 2004

Conditional sampling measurement is required for conditional averages as well as unconditional Favre averages to resolve different flame structures of turbulent combustion. A Favre average can be obtained as an integral of conditional average and Favre PDF in terms of the mixture fraction, which is a preferred choice as a sampling variable in diffusion controlled turbulent combustion. MILD combustion data are presented as an example for a conditionally averaged data set and comparison with CMC calculation results.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.55-69
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• 2009

A quasi ideal importance sampling simulation method combined in the conditional expectation is proposed for the structural reliability estimation. The quasi ideal importance sampling joint probability density function (p.d.f.) is so composed on the basis of the ideal importance sampling concept as to be proportional to the conditional failure probability multiplied by the p.d.f. of the sampling variables. The respective marginal p.d.f.s of the ideal importance sampling joint p.d.f. are determined numerically by the simulations and partly by the piecewise integrations. The quasi ideal importance sampling simulations combined in the conditional expectation are executed to estimate the failure probabilities of structures with multiple failure surfaces and it is shown that the proposed method gives accurate estimations efficiently.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.3
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• pp.31-39
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• 2001

In this paper, we proposed a new pixel circuit of the CMOS image sensor for high dynamic range operation, which is based on a multiple sampling scheme and a conditional reset circuit. To expand the pixel dynamic range, the output is multiple-sampled in the integration time. In each sampling, the pixel output is compared with a reference voltage, and the result of comparison may activate the conditional reset circuit. The times of conditional reset, N, during the integration will contribute to the increase of the dynamic range by the times of N. The test chip was fabricated with 0.65-${\mu}m$ CMOS technology (2-P, 2-M).

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1155-1168
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• 2016

This paper considers a probability density estimation problem of climate values. In particular, we focus on estimating probability densities of summer extreme temperature over South Korea. It is known that the probability density of climate values at one location is similar to those at near by locations and one doesn't follow well known parametric distributions. To accommodate these properties, we use a mixture of conditional autoregressive species sampling model, which is a nonparametric Bayesian model with a spatial dependency. We apply the model to a dataset consisting of summer maximum temperature and minimum temperature over South Korea. The dataset is obtained from University of East Anglia.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.107-120
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• 1998

This research aims here to develop a certain extended double sampling plan, EDS, which is an extension of ordinary double sampling plan in the sense that the second-stage sampling effort and second-stage critical value are allowed to depend on the point at which the first-stage continuation region is traversed. For purpose of comparison, single sampling plan, optimal ordinary double sampling plan(ODS) and sequential probability ratio test are considered with the same overall error rates, respectively. It is observed that the EDS idea allows less sampling effort than the optimal ODS.

• Wind and Structures
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• v.18 no.6
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• pp.651-668
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• 2014

Wind tunnel tests are conducted to investigate the wind loads on vertical fixed-roof cylindrical tanks with a very low aspect ratio of 0.275, which is a typical ratio for practical tanks with a volume of $100,000m^3$. Both the flat-roof tank and the dome-roof tank are investigated in present study. The first four moments of the measured wind pressure, including the mean and normalized deviation pressure, kurtosis and skewness of the pressure signal, are obtained to study the feature of the wind loads. It is shown that the wind loads are closely related to the behavior of flow around the structure. For either tank, the mean wind pressures on the cylinder are positive on the windward area and negative on the sides and the wake area, and the mean wind pressures on the whole roof are negative. The roof configurations have no considerable influence on the mean pressure distributions of cylindrical wall in general. Highly non-Gaussian feature is found in either tank. Conditional sampling technique, envelope method, and the proper orthogonal decomposition (POD) analysis are employed to investigate the characteristics of wind loads on the cylinder in more detail. It is shown that the patterns of wind pressure obtained from conditional sampling are similar to the mean pressure patterns.An instantaneous pressure coefficient can present a wide range from the maximum value to the minimum value. The quasi-steady assumption is not valid for structures considered in this paper according to the POD analysis.

• ETRI Journal
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• v.14 no.4
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• pp.19-27
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• 1992

Since. as is well known, direct computation of the reliability for a large-scaled and complex net work generally requires exponential time, a variety of alternative methods to estimate the network reliability using simulation have been proposed. Monte Carlo sampling is the major approach to estimate the network reliability using simulation. In the paper, a dynamic Monte Carlo sampling method, called conditional minimal cut set (CMCS) algorithm, is suggested. The CMCS algorithm simulates a minimal cut set composed of arcs originated from the (conditional) source node until s-t connectedness is confirmed, then reduces the network on the basis of the states of simulated arcs. We develop the importance sampling estimator and the total hazard estimator and compare the performance of these simulation estimators. It is found that the CMCS algorithm is useful in reducing variance of network reliability estimator.

• Journal of the Korean Geotechnical Society
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• v.23 no.3
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• pp.51-59
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• 2007

A reliability-based approach that can systematically model various sources of uncertainty is presented in the context of slope stability. Expressions for characterization of soil properties are developed in order to incorporate sampling errors, spatial variability and its effect of spatial averaging. Reliability analyses of slope stability with different statistical representations of soil properties show that the incorporation of sampling error, spatial correlation, and conditional simulation leads to significantly lower probability of failure than that obtained by using simple random variable approach. The results strongly suggest that the spatial variability and sampling error have to be properly incorporated in slope stability analysis.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.4
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• pp.179-189
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• 2021

Conditional spectra (CS) are applied to the seismic fragility assessment of a nuclear power plant (NPP) containment building for comparison with a relevant conventional uniform hazard response spectrum (UHRS). Three different control frequencies are considered in developing conditional spectra. The contribution of diverse magnitudes and epicentral distances is identified from deaggregation for the UHRS at a control frequency and incorporated into the conditional spectra. A total of 30 ground motion records are selected and scaled to simulate the probability distribution of each conditional spectra, respectively. A set of lumped mass stick models for the containment building are built considering nonlinear bending and shear deformation and uncertainty in modeling parameters using the Latin hypercube sampling technique. Incremental dynamic analysis is conducted for different seismic input models in order to estimate seismic fragility functions. The seismic fragility functions and high confidence of low probability of failure (HCLPF) are calculated for different seismic input models and analyzed comparatively.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.557-573
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• 2015

In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.