• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.201-205
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• 2003

We show how some probability nonreplacement sampling designs can be implemented using nonlinear programming, The efficiency of the proposed approach is compared with selected probability sampling schemes in the literature. The approach is simple to use and appears to have reasonable variance.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.26 no.4
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• pp.369-379
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• 1993

Using a successive removal approach the mechanism of sampling capture efficiency of blue crabs by dredges was studied in Chesapeake Bay during winter 1992. For the twenty-six field experiments no significant statistical differences were detected in dredge efficiency using general linear model analysis by factors including bottom sediments, water depths, and sampling vessels. Dredge efficiency (i.e., catchability) was estimated by two methods, Leslie (Leslie and Davis, 1939) and a simple revised method. Mean catchability was estimated at 0.26 (SE=0.03), indicating that only $26\%$($95\%\;C. I.=20{\sim}32\%$) of crabs present in the path of the dredge of a given sampling area are caught with a single dredge tow. Dredge efficiency declined exponentially as crab density increased.

• 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.

• 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.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.103-116
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• 2010

The key objective of this study was to analyze sampling efficiency of various fish sampling gears for a lentic ecosystem health assessment. This survey was conducted at twelve sampling locations of Jinyang Reservoir in November, 2008 and June, 2009. Sampling gears used in the field were fyke net (FN), gill net (GN), trammel net (TN), casting net (CN), minnow trap (MT), and kick net (KN). Fishes sampled in Jinyang Reservoir were 29 species, in which tolerant species, as a proportions of the total numbers, dominated the fish community compared to the sensitive species. Overall sampling indicated that 28 species (3,567 individuals) were sampled by the CN and 15 species (3,108 individuals) were sampled by the FN along with 14 species (577 individuals) by the TN, 7 species (107 individuals) by the GN, 6 species (59 individuals) by MT, and 5 species (95 individuals) by KN, respectively. Statistical analysis (MANOVA), based on CPUE of the fishing gears showed that fish species and numbers of the CN were significantly (p < 0.05) greater than those of any other sampling gears. In contrast, sampling gear of MT and KN showed the least efficiency in our survey. This survey is a preliminary results for the tests of sampling gear's efficiency in lentic ecosystems, thus further extensive studies are required for the verification.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.237-244
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• 2023

A signal generation simulator is an economical and useful solution in Global Navigation Satellite System (GNSS) receiver design and testing. A software-defined radio approach is widely used both in receivers and simulators, and its flexible structure to adopt to new signals is ideally suited to the testing of a receiver and signal processing algorithm in the signal design phase of a new satellite-based navigation system before the deployment of satellites in space. The generation of highly accurate delayed sampled codes is essential for generating signals in the simulator, where its sampling rate should be chosen to satisfy constraints such as Nyquist criteria and integer and non-commensurate properties in order not to cause any distortion of original signals. A high sampling rate increases the accuracy of code delay, but decreases the computational efficiency as well, and vice versa. Therefore, the selected sampling rate should be as low as possible while maintaining a certain level of code delay accuracy. This paper presents the lower limits of the sampling rate for GNSS signal generation simulators. In the simulation, two distinct code generation methods depending on the sampling position are evaluated in terms of accuracy versus computational efficiency to show the lower limit of the sampling rate for several GNSS signals.

• 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.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.57-69
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• 2007

Adaptive cluster sampling design is known as a sampling method for rare clustered population. Three modified adaptive cluster sampling designs are proposed. The adjusted Hansen-Hurwitz estimator and the Horvitz-Thompson estimator are considered. Efficiency issue of the proposed sampling designs is discussed in a Monte-Carlo simulation study.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.133-141
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• 2009

This study was conducted to support for determining an efficient sampling design for forest resources assessments in South Korea with respect to statistical efficiency. For this objective, different systematic sampling designs were simulated and compared based on an artificial forest population that had been built from field sample data and satellite data in Yang-Pyeong County, Korea. Using the k-NN technique, two thematic maps (growing stock and forest cover type per pixel unit) across the test area were generated; field data (n=191) and Landsat ETM+ were used as source data. Four sampling designs (systematic sampling, systematic sampling for post-stratification, systematic cluster sampling, and stratified systematic sampling) were employed as optimum sampling design candidates. In order to compute error variance, the Monte Carlo simulation was used (k=1,000). Then, sampling error and relative efficiency were compared. When the objective of an inventory was to obtain estimations for the entire population, systematic cluster sampling was superior to the other sampling designs. If its objective is to obtain estimations for each sub-population, post-stratification gave a better estimation. In order to successfully perform this procedure, it requires clear definitions of strata of interest per field observation unit for efficient stratification.