• Title, Summary, Keyword: Sampling

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A Complex Bandpass Sampling Method for Downconversion of Multiple Bandpass Signals (다중 대역통과 신호의 하향변환을 위한 Complex Bandpass Sampling 기법)

  • Bae, Jung-Hwa;Ha, Won;Park, Jin-Woo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.9C
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    • pp.913-921
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    • 2005
  • A complex bandpass sampling technique can provide a more flexible architecture for designing a software- defined radio(SDR) system, because it has several advantageous features of larger sampling range and lower minimum sampling frequency than a real bandpass sampling method. In spite of the potential advantages of the complex bandpass sampling, solid investigation for the direct downconversion of multiple signals by the complex sampling theory has not been reported yet. Thus, we propose in this paper a novel scheme for the downconversion of multiple signals using the complex bandpass sampling, and develop the formulae related to the complex bandpass sampling for practical usage, such as the valid sampling range, the intermediate frequency (If), and the minimum sampling frequency of the downconversion of multiple RE signals. Such derived formulae are verified from simulations.

A Note on Complex Two-Phase Sampling with Different Sampling Units of Each Phase

  • Lee, Sang Eun;Jin, Young;Shin, Key-Il
    • Communications for Statistical Applications and Methods
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    • v.22 no.5
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    • pp.435-443
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    • 2015
  • Two phase sampling design is useful to increase estimation efficiency using deep stratification, improved non-response adjustment and reduced coverage bias. The same sampling units are commonly used for the first and the second phases in complex two-phase sampling design. In this paper we consider a sampling scheme where the first phase sampling units are clusters and the second phase sampling units are list samples. Using selected clusters in first phase requires that we list up elements in the selected clusters from the first phase and then use the list as a secondary sampling frame for the second phase sampling design. Then we select second phase samples from the listed sampling frame. We suggest an estimator based on the complex two-phase sampling design with different sampling units of each phase. Also the estimated variances of the estimator obtained by using classic and replication variance methods are considered and compared using simulation studies. For real data analysis, 2010 Korea Farm Household Economy Survey (KFHES) and 2011 Korea Agriculture Survey (KAS) are used.

Comparison of Simple Random Sampling and Two-stage P.P.S. Sampling Methods for Timber Volume Estimation (임목재적(林木材積) 산정(算定)을 위(爲)한 Simple Random Sampling과 Two-stage P.P.S. Sampling 방법(方法)의 비교(比較))

  • Kim, Je Su;Horning, Ned
    • Journal of Korean Society of Forest Science
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    • v.65 no.1
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    • pp.68-73
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    • 1984
  • The purpose of this paper was to figure out the efficiencies of two sampling techniques, a simple random sampling and a two-stage P.P.S. (probability proportional to size) sampling, in estimating the volume of the mature coniferous stands near Salzburg, Austria. With black-and-white infrared photographs at a scale 1:10,000, the following four classes were considered; non-forest, young stands less than 40 years, mature beech and mature coniferous stands. After the classification, a field survey was carried out using a relascope with a BAF (basal area factor) 4. For the simple random sampling, 99 points were sampled, while for the P.P.S. sampling, 75 points were sampled in the mature coniferous stands. The following results were obtained. 1) The mean standing coniferous volume estimate was $422.0m^3/ha$ for the simple random sampling and $433.5m^3/ha$ for the P.P.S. sampling method. However, the difference was not statistically significant. 2) The required number of sampling points for a 5% sampling error were 170 for the two stage P.P.S. sampling, but 237 for the simple random sampling. 3) The two stage P.P.S. method reduced field survey time by 17% as compared to the simple random sampling.

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A Study of Using the Terminology of Sampling Error and Sampling Distribution (표집오차(sampling error)와 표집분포(sampling distribution)의 용어 사용에 관한 연구)

  • Kim, Yung-Hwan
    • Journal of the Korean School Mathematics Society
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    • v.9 no.3
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    • pp.309-316
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    • 2006
  • This study examined the ambiguous using the terminology of statistics at mathematics textbook of highschool in Korea and proposed the correct using of sampling error and sampling distribution of sample mean with consistency. And this paper proposed that the concept of error have to teach in context of sampling action in school mathematics.

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A Comparison of Systematic Sampling Designs for Forest Inventory

  • Yim, Jong Su;Kleinn, Christoph;Kim, Sung Ho;Jeong, Jin-Hyun;Shin, Man Yong
    • 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.

A Sampling Inspection Plan with Human Error: Considering the Relationship between Visual Inspection Time and Human Error Rate

  • Lee, Yong-Hwa;Hong, Seung-Kweon
    • Journal of the Ergonomics Society of Korea
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    • v.30 no.5
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    • pp.645-650
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    • 2011
  • Objective: The aim of this study is to design a sampling inspection plan with human error which is changing according to inspection time. Background: Typical sampling inspection plans have been established typically based on an assumption of the perfect inspection without human error. However, most of all inspection tasks include human errors in the process of inspection. Therefore, a sampling inspection plan should be designed with consideration of imperfect inspection. Method: A model for single sampling inspection plans were proposed for the cases that visual inspection error rate is changing according to inspection time. Additionally, a sampling inspection plan for an optimal inspection time was proposed. In order to show an applied example of the proposed model, an experiment for visual inspection task was performed and the inspection error rates were measured according to the inspection time. Results: Inspection error rates changed according to inspection time. The inspection error rate could be reflected on the single sampling inspection plans for attribute. In particular, inspection error rate in an optimal inspection time may be used for a reasonable single sampling plan in a practical view. Conclusion: Human error rate in inspection tasks should be reflected on typical single sampling inspection plans. A sampling inspection plan with consideration of human error requires more sampling number than a typical sampling plan with perfect inspection. Application: The result of this research may help to determine more practical sampling inspection plan rather than typical one.

Sensitivity Approach of Sequential Sampling for Kriging Model (민감도법을 이용한 크리깅모델의 순차적 실험계획)

  • Lee, Tae-Hee;Jung, Jae-Jun;Hwang, In-Kyo;Lee, Chang-Seob
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.28 no.11
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    • pp.1760-1767
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    • 2004
  • Sequential sampling approaches of a metamodel that sampling points are updated sequentially become a significant consideration in metamodeling technique. Sequential sampling design is more effective than classical space filling design of all-at-once sampling because sequential sampling design is to add new sampling points by means of distance between sampling points or precdiction error obtained from metamodel. However, though the extremum points can strongly reflect the behaviors of responses, the existing sequential sampling designs are inefficient to approximate extremum points of original model. In this research, new sequential sampling approach using the sensitivity of Kriging model is proposed, so that new approach reflects the behaviors of response sequentially. Various sequential sampling designs are reviewed and the performances of the proposed approach are compared with those of existing sequential sampling approaches by using mean squared error. The accuracy of the proposed approach is investigated against optimization results of test problems so that superiority of the sensitivity approach is verified.

A Study of Dependent Nonstationary Multiple Sampling Plans (종속적 비평형 다중표본 계획법의 연구)

  • 김원경
    • Journal of the Korea Society for Simulation
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    • v.9 no.2
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    • pp.75-87
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    • 2000
  • In this paper, nonstationary multiple sampling plans are discussed which are difficult to solve by analytical method when there exists dependency between the sample data. The initial solution is found by the sequential sampling plan using the sequential probability ration test. The number of acceptance and rejection in each step of the multiple sampling plan are found by grouping the sequential sampling plan's solution initially. The optimal multiple sampling plans are found by simulation. Four search methods are developed U and the optimum sampling plans satisfying the Type I and Type ll error probabilities. The performance of the sampling plans is measured and their algorithms are also shown. To consider the nonstationary property of the dependent sampling plan, simulation method is used for finding the lot rejection and acceptance probability function. As a numerical example Markov chain model is inspected. Effects of the dependency factor and search methods are compared to analyze the sampling results by changing their parameters.

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Two-phase Adaptive Cluster Sampling with Unequal Probabilities Selection

  • Lee, Keejae
    • Journal of the Korean Statistical Society
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    • v.27 no.3
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    • pp.265-278
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    • 1998
  • In this paper, we suggest two-phase adaptive cluster sampling schemes. The main feature of the two-phase sampling is that the information collected in the first phase sample is utilized in the selection of the second phase sample. The conventional two-phase sampling is, however, not sufficient to increase efficiency when the population of interest is rare and clustered. In the proposed sampling scheme, the first phase sample is selected with adaptive cluster sampling procedure and the second phase sample is selected by PPSWR and $\pi$PS sampling. We investigate unbiased estimators of population total and their variance for the proposed sampling schemes respectively. Finally we compare these suggested sampling schemes using numerical examples .

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Sequential Feasible Domain Sampling of Kriging Metamodel by Using Penalty Function (벌칙함수 기반 크리깅메타모델의 순차적 유용영역 실험계획)

  • Lee Tae-Hee;Seong Jun-Yeob;Jung Jae-Jun
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.6
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    • pp.691-697
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    • 2006
  • Metamodel, model of model, has been widely used to improve an efficiency of optimization process in engineering fields. However, global metamodels of constraints in a constrained optimization problem are required good accuracy around neighborhood of optimum point. To satisfy this requirement, more sampling points must be located around the boundary and inside of feasible region. Therefore, a new sampling strategy that is capable of identifying feasible domain should be applied to select sampling points for metamodels of constraints. In this research, we suggeste sequential feasible domain sampling that can locate sampling points likely within feasible domain by using penalty function method. To validate the excellence of feasible domain sampling, we compare the optimum results from the proposed method with those form conventional global space-filling sampling for a variety of optimization problems. The advantages of the feasible domain sampling are discussed further.