• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.23 no.4
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• pp.341-347
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• 2013

Objectives: The aim of this study is to investigate the differences in airborne TDI concentrations based on the filter collection method and liquid collection method and to compare airborne TDIs concentrations by sampling method change when using the filter collection method in the spray-painting process. Methods: For the sample measurement, the filter collection method(OSHA#42) and liquid collection method(NIOSH#5522) were used; for the sampling method, the full-period single sampling and full-period consecutive sampling methods were used. The samples were collected in spray-painting and drying process locations. Results: In all samples collected from the spray-painting and drying process locations through the filter collection and liquid collection methods, greater amounts of 2,6-TDI than 2,4-TDI were detected. When the TDI collection concentrations based on the sampling methods were compared, the concentrations of 2,4-TDI and 2,6-TDI collected by the consecutive sampling method were higher than the concentrations of 2,4-TDI and 2,6-TDI collected by the single sampling method for both the filter collection method and liquid collection method used in the spray-painting process. These differences were statistically significant. Conclusions: When TDI collection concentrations based on the sample measurement methods were compared, the concentration of 2,4-TDI and 2,6-TDI collected through the liquid collection method were higher than the concentrations of 2,4-TDI and 2,6-TDI collected by the filter collection method, and the differences were statistically significant. In the drying process, no difference was shown in the collection concentrations of 2,4-TDI and 2,6-TDI with the two measurement methods.

• Journal of Environmental Science International
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• v.6 no.1
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• pp.33-44
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• 1997

In investigating heavy metal contents at specific areas, the method of selecting sampling points is Important A general method is, according to the law , random sampling of zigzag-form in the selected field. In this work, we studied whether the measured values obtained from a certain method of selecting sampling points is a representative of heavy metal contents in the selected field or not. The selected field for this study is located on the lower Yangsan-river: Gasan-li, Mulgum-myon, Yangsan-gun, KyoungNam, 1 km away from the mi, h stream of Nakdong river. The heavy metals investigated were Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn. The inductively coupled plasma(ICPI atomic emission spectrometer was used to measure these metals quantitatively. The number of total sampling points were 24. We compared the total mean values with the mean obtained from various methods of selecting sampling points.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1295-1299
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• 1990

A new method called random sampling method has been proposed for generation of binary random sequences. In this paper, a new concept, called merit factor Fn, is proposed for evaluating the randomness of the binary random sequences generated by the random sampling method. Using this merit factor Fn, some desirable conditions are investigated for uniform random numbers used in the random sampling method.

• Survey Research
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• v.5 no.2
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• pp.3-30
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• 2004

In Korea, bellwether voting places were selected for exit poll based on the past voting results. Sometimes, voting place stratification were used to improve the exit poll performance. The sampled voting places are intended to mirror the general voters of the entire electoral district. But few studies have been done as to which sampling method works better. This study compared the four sampling methods-bellwether voting place sampling method, random sampling method, stratified bellwether sampling method and systematic sampling from ordered voting places method. When we applied the four methods to the 2004 general election data, the systematic sampling from ordered voting places method outperformed the other three sampling method. Also, we found that the additional sampling of voting places over nine contribute little to the accuracy of the estimation.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.385-398
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• 2018

Although the importance of developing reliable and systematic GHG inventory has increased, the GIS/RS-based national scale LULUCF (Land Use, Land-Use Change and Forestry) sector analysis is insufficient in the context of the Paris Agreement. In this study, the change in $CO_2$ storage of forest land due to land use change is estimated using two GIS/RS methodologies, Sampling and Wall-to-Wall methods, from 2000 to 2010. Particularly, various imagery with sampling data and land cover maps are used for Sampling and Wall-to-Wall methods, respectively. This land use matrix of these methodologies and the national cadastral statistics are classified by six land-use categories (Forest land, Cropland, Grassland, Wetlands, Settlements, and Other land). The difference of area between the result of Sampling methods and the cadastral statistics decreases as the sample plot distance decreases. However, the difference is not significant under a 2 km sample plot. In the 2000s, the Wall-to-Wall method showed similar results to sampling under a 2 km distance except for the Settlement category. With the Wall-to-Wall method, $CO_2$ storage is higher than that of the Sampling method. Accordingly, the Wall-to-Wall method would be more advantageous than the Sampling method in the presence of sufficient spatial data for GHG inventory assessment. These results can contribute to establish an annual report system of national greenhouse gas inventory in the LULUCF sector.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1110-1127
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• 2022

To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.

• Structural Engineering and Mechanics
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• v.82 no.3
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• pp.271-282
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• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

• 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.