• Journal of the Korean Association of Geographic Information Studies
• /
• v.23 no.4
• /
• pp.16-41
• /
• 2020

Geospatial input setting to represent the reality of spatial distribution or quantitative property within model has become a major interest in earth system simulation. Many studies showed the variation of grid resolution could lead to drastic changes of spatial model results because of insufficient surface property estimations. Hence, in this paper, the authors proposed Monte Carlo Integration (MCI) to apply spatial probability (SP) in a spatial-sampling framework using a three-dimensional point cloud (3DPC) to keep the optimized spatial distribution and area/volume property of buildings in urban area. Three different decision rule based building identification results were compared : SP threshold, cell size, and 3DPC density. Results shows the identified building area property tend to increase according to the spatial sampling grid area enlargement. Hence, areal building property manipulation in the sampling frameworks by using decision rules is strongly recommended to increase reliability of geospatial modeling and analysis results. Proposed method will support the modeling needs to keep quantitative building properties in both finer and coarser grids.

• Journal of Korean Society for Quality Management
• /
• v.28 no.3
• /
• pp.18-30
• /
• 2000

Recent studies have shown that the $\bar{X}$ chart with variable sampling intervals(VSI) and the $\bar{X}$ chart with variable sample size(VSS) are much quicker than Shewhart $\bar{X}$ chart in detecting shiks in the process. Shewhart $\bar{X}$ chart has been beneficial to detect large shifts but it is hard to apply Shewhart $\bar{X}$ chart in detecting moderate shifts in the process mean. In this article the $\bar{X}$ chart using variable sample size(VSS) and variable sampling Intervals(VSI) has been proposed to supplement the weak point mentioned above. So the purpose of this paper is to consider finding the design parameters which minimize expected loss costs for unit process time and measure the performance of VSSI(variable sample size and sampling interval) $\bar{X}$ chart. It is important that assignable causes be detected to maintain the process controlled. This paper has been studied under the assumption that one cycle is from starting of the process to eliminating the assignable causes in the process. The other purpose of this article is to represent the expected loss costs in one cycle with three process parameters(sample size, sampling interval and control limits) function and find the three parameters.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.6
• /
• pp.2106-2117
• /
• 2017

An effective statistical feature extraction approach of data sampling of fault in the combined transmission system is presented in this paper. The proposed algorithm leads to high accuracy at minimum cost to predict fault location and fault type classification. This algorithm requires impedance measurement data from one end of the transmission line. Modal decomposition is used to extract positive sequence impedance. Then, the fault signal is decomposed by using discrete wavelet transform. Statistical sampling is used to extract appropriate fault features as benchmark of decomposed signal to train classifier. Support Vector Machine (SVM) is used to illustrate the performance of statistical sampling performance. The overall time of sampling is not exceeding 1 1/4 cycles, taking into account the interval time. The proposed method takes two steps of sampling. The first step takes 3/4 cycle of during-fault and the second step takes 1/4 cycle of post fault impedance. The interval time between the two steps is assumed to be 1/4 cycle. Extensive studies using MATLAB software show accurate fault location estimation and fault type classification of the proposed method. The classifier result is presented and compared with well-established travelling wave methods and the performance of the algorithms are analyzed and discussed.

• Journal of Korean Society for Atmospheric Environment
• /
• v.18 no.5
• /
• pp.355-362
• /
• 2002

There has been relatively a few studies that focused on evaluation of uncertainty for standard methods by which criteria pollutants are analyzed in ambient air. Especially, uncertainty evaluation has not been made yet for sampling and analysis of airborne NO$_2$. Ambient NO$_2$ has been thought to be a major criteria pollutant worldwide because of the potential of ozone formation as well as of its own toxicity. In this study, we tried to assess uncertainties associated with the every step of sampling and of analytical procedure of Griess-Saltzman method. Quality assurance (QA) and quality control (QC) were also emphasized with the uncertainty characterization. The use of Griess-Saltzman method for ambient NO$_2$ analysis showed very uniform daily concentration distribution with the mean of 10.8 ppb and the standard deviation of 1.08ppb during the sampling period. However, seven daily samples collected at the same sampling time and place exhibited highly different concentration distribution. Therefore, we evaluated uncertainties associated with sampling and analysis through the precise application of ISO Guide. Estimates of expanded uncertainties for a total of 62 samples fell in a relatively broad range of 5.17% to 11.85%. On the other hand. the expanded uncertainties were smaller for the high concentration range of greater than 15ppb.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.03a
• /
• pp.134-141
• /
• 2008

To evaluate the engineering properties of soil, the laboratory test always is carried out using samples obtained from the field. There are many studies to estimate the effect of sampling disturbance. The objective of this study appraises the disturbance using the shear wave velocity. The new shelby tube which three transducers are installed every 20cm interval is used. To laboratory tests, the large-scale consolidometer (calibration chamber) is used. During 1cm penetration, the shear wave velocity is measured by every transducer. The initial sampling disturbance is assessed through the velocity difference from bottom to right upside transducer. After finishing the sampling, the velocity is still measured every time to assess the soil disturbance in shelby tube itself. Through the measured velocity, the effect of disturbance is appraised. This study suggests that the sampling disturbance of shelby tube is effectively evaluated using shear wave velocity.

• Proceedings of the Korean Statistical Society Conference
• /
• 2002.11a
• /
• pp.327-332
• /
• 2002

Variance estimation for the regression estimator for a two-phase sample is investigated. A replication variance estimator with number of replicates equal to or slightly larger than the size of the second-phase sample is developed. In these cases, the proposed method is asymptotically equivalent to the full jackknife, but uses smaller number of replications.

• Communications for Statistical Applications and Methods
• /
• v.6 no.1
• /
• pp.169-180
• /
• 1999

A Markov Chain Monte Carlo method with development of computation is used to be the software system reliability probability model. For Bayesian estimator considering computational problem and theoretical justification we studies relation Markov Chain with Gibbs sampling. Special case of GOS with Superposition for Goel-Okumoto and Weibull models using Gibbs sampling and Metropolis algorithm considered. In this paper discuss Bayesian computation and model selection using posterior predictive likelihood criterion. We consider in this paper data using method by Cox-Lewis. A numerical example with a simulated data set is given.

• Journal of Korean Institute of Industrial Engineers
• /
• v.13 no.2
• /
• pp.97-103
• /
• 1987

Skip-Lot sampling plan is formulated in terms of renewal process. This approach facilitates studying the average outgoing quality in a short production run of length t, AOQ (t). By numerical studies it is found that the long-run average outgoing quality (AOQ) greatly overestimates AOQ (t) for short runs.

• Communications for Statistical Applications and Methods
• /
• v.17 no.3
• /
• pp.391-411
• /
• 2010

For estimating the mean of a finite population, three classes of estimators using multi-auxiliary information with unknown means using two phase sampling in presence of non-response have been proposed with their properties. Asymptotically optimum estimator(AOE) in each class has been identified along with their mean squared error formulae. An empirical study is also given.

• Journal of Animal Science and Technology
• /
• v.46 no.5
• /
• pp.719-728
• /
• 2004

The aims of this study were to estimate genetic parameters for carcass traits on Hanwoo(Korean Native Cattle) and to compare two different statistical algorithms for estimating genetic parameters. Data obtained from 1526 steers at Hanwoo Improvement Center and Hanwoo Improvement Complex Area from 1996 to 2001 were used for the analyses. The carcass traits considered in these studies were carcass weight, dressing percent, eye muscle area, backfat thickness, and marbling score. Estimated genetic parameters using EM-REML algorithm were compared to those by Bayesian inference via Gibbs Sampling to find out statistical properties. The estimated heritabilities of carcass traits by REML method were 0.28, 0.25, 0.35, 0.39 and 0.51, respectively and those by Gibbs Sampling method were 0.29, 0.25, 0.40, 0.42 and 0.54, respectively. This estimates were not significantly different, even though the estimated heritabilities by Gibbs Sampling method were higher than ones by REML method. Since the estimated statistics by REML method and Gibbs Sampling method were not significantly different in this study, it is inferred that both mothods could be efficiently applied for the analysis of carcass traits of cattle. However, further studies are demanded to define an optimal statistical method for handling large scale performance data.