• 제목/요약/키워드: sampling Method

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수치표고모형에 있어서 표고추출법의 연구 (Study on Sampling Techniques for Digital Elevation Model)

  • 강인준;정재형;곽재하
    • 한국측량학회지
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    • 제10권2호
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    • pp.49-55
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    • 1992
  • 수피표고모형에 있어서 표고자료의 추출은 매우 중요하다. 표고의 추출은 스캐닝방법과 디지타이징 방법이 있으나 본 연구에서는 디지타이징에 대한 것이다. 연속적 추출법은 하나의 등고선을 동일한 개체로 묶는 것이며, 정규적자법은 각 격자를 대표할 수 있는 표고데이타를 직접 읽는 방법이다. 그리고 지성선을 구성하는 등고선내의 추출점을 동일의 개체로 묶는 방법이 불규칙 삼각망법이다. 연구결과에서 연속적 추출법은 입력시간이 적게 걸렸으며, 불규칙 삼각망법은 추출점의 수가 가장 적게 나타났다.

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가변적인 샘플링을 이용한 차원 감소법에 의한 신뢰도 해석 기법 (Reliability Analysis Using Dimension Reduction Method with Variable Sampling Points)

  • 육순민;민준홍;김동호;최동훈
    • 대한기계학회논문집A
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    • 제33권9호
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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.

가변적인 샘플링을 이용한 신뢰도 해석 기법 (Reliability Analysis Method with Variable Sampling Points)

  • 육순민;최동훈
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2008년도 추계학술대회A
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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.

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Sequential sampling method for monitoring potato tuber moths (Phthorimaea operculella) in potato fields

  • Jung, Jae-Min;Byeon, Dae-hyeon;Kim, Eunji;Byun, Hye-Min;Park, Jaekook;Kim, Jihoon;Bae, Jongmin;Kim, Kyutae;Roca-Cusachs, Marcos;Kang, Minjoon;Choi, Subin;Oh, Sumin;Jung, Sunghoon;Lee, Wang-Hee
    • 농업과학연구
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    • 제47권3호
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    • pp.615-624
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    • 2020
  • An effective sampling method is necessary to monitor potato tuber moths (Phthorimaea operculella) because they are the biggest concern in potato-cultivating areas. In this study, a sequential sampling method was developed based on the results of field surveys of potato tuber moths in South Korea. Potato tuber moths were collected in fields cultivating potatoes at six sites, and their spatial distribution was investigated using the Taylor power law. The optimal sampling size and cumulative number of potato tuber moths in traps to stop sampling were determined based on the spatial distribution pattern and mean density of the collected potato tuber moths. Finally, the developed sampling method was applied to propose a control action, and its sampling efficiency was compared with that of the traditional sampling method using a binomial distribution. The potato tuber moths tended to aggregate; the optimal number was approximately 5 - 16 traps for sampling, and the number varied with the mean density of potato tuber moths according to the sampling sites. In addition, one, two, and three sites might require the following actions: Continued sampling, control, and no control, respectively. Sampling with the binomial distribution showed the minimum sample size was 12 when considering the economic threshold level. Here, we propose an effective sampling method that can be applied for future monitoring and field surveys of potato tuber moths in South Korea.

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

  • 배정화;하원;박진우
    • 한국통신학회논문지
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    • 제30권9C호
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    • pp.913-921
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    • 2005
  • 일반적인 bandpass sampling 방법인 real bandpass sampling 기법은 하향변환(downconversion)을 시행할 때 음의 주파수 대역의 RF 신호와의 에일리어싱(aliasing) 현상을 피해야 하므로 신중한 sampling 주파수 선택이 요구된다. 더욱이 다중신호(multiple signals)가 하향변환 될 경우에 이 sampling 방법은 더욱 많은 제약이 따르게 된다. 그러나 Hilbert 변환을 사용하는 complex bandpass sampling 방법은 음의 주파수 영역의 신호를 제거함으로써, real bandpass sampling 기법보다 유연하고 넓은 sampling 주파수 범위를 제공하며, 또한 더욱 낮은 sampling 주파수를 얻을 수 있는 장점이 있다. 본 논문에서는 이러한 complex bandpass sampling의 특징을 사용하여, 다중 신호를 하나의 통신 기기에서 동시에 하향 변환하는 수신기의 구조를 제시한다. 그리고 하나 또는 2개 신호의 하향변환에 관한 내용으로 제한하지 않고 N개의 신호로 확장하여 유효 sampling 주파수 영역 및 보호대역(guard-band)이 고려된 sampling 가능 최소 주파수에 관한 수식들을 일반화한다. 또한 모의실험을 통해 유도된 수식들을 증명한다.

순차적 샘플링과 크리깅 메타모델을 이용한 신뢰도 기반 최적설계 (Reliability-Based Design Optimization Using Kriging Metamodel with Sequential Sampling Technique)

  • 최규선;이갑성;최동훈
    • 대한기계학회논문집A
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    • 제33권12호
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    • pp.1464-1470
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    • 2009
  • RBDO approach based on a sampling method with the Kriging metamodel and Constraint Boundary Sampling (CBS), which is sequential sampling method to generate metamodels is proposed. The major advantage of the proposed RBDO approach is that it does not require Most Probable failure Point (MPP) which is essential for First-Order Reliability Method (FORM)-based RBDO approach. The Monte Carlo Sampling (MCS), most well-known method of the sampling methods for the reliability analysis is used to assess the reliability of constraints. In addition, a Cumulative Distribution Function (CDF) of the constraints is approximated using Moving Least Square (MLS) method from empirical distribution function. It is possible to acquire a probability of failure and its analytic sensitivities by using an approximate function of the CDF for the constraints. Moreover, a concept of inactive design is adapted to improve a numerical efficiency of the proposed approach. Computational accuracy and efficiency of the proposed RBDO approach are demonstrated by numerical and engineering problems.

Reliability Analysis for Structure Design of Automatic Ocean Salt Collector Using Sampling Method of Monte Carlo Simulation

  • Song, Chang Yong
    • 한국해양공학회지
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    • 제34권5호
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    • pp.316-324
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    • 2020
  • This paper presents comparative studies of reliability analysis and meta-modeling using the sampling method of Monte Carlo simulation for the structure design of an automatic ocean salt collector (AOSC). The thickness sizing variables of structure members are considered as random variables. Probabilistic performance functions are selected from strength performances evaluated via the finite element analysis of an AOSC. The sampling methods used in the comparative studies are simple random sampling and Sobol sequences with varied numbers of sampling. Approximation methods such as the Kriging model is applied to the meta-model generation. Reliability performances such as the probability failure and distribution are compared based on the variation of the sampling method of Monte Carlo simulation. The meta-modeling accuracy is evaluated for the Kriging model generated from the Monte Carlo simulation and Sobol sequence results. It is discovered that the Sobol sequence method is applicable to not only to the reliability analysis for the structural design of marine equipment such as the AOSC, but also to Kriging meta-modeling owing to its high numerical efficiency.

샘플링 기법에 의한 작업순서의 결정 (II) (A Study on Determining Job Sequence by Sampling Method (II))

  • 강성수;노인규
    • 산업경영시스템학회지
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    • 제12권19호
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    • pp.25-30
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    • 1989
  • This study is concerned with a job sequencing method using the concept of sampling technique. This sampling technique has never been applied to develop the scheduling algorithms. The most job sequencing algorithms have been developed to determine the best or good solution under the special conditions. Thus, it is not only very difficult, but also taken too much time to develop the appropriate job schedules that satisfy the complex work conditions. The application areas of these algorithms are also very narrow. Under these circumstances it is very desirable to develop a simple job sequencing method which can produce the good solution with the short tine period under any complex work conditions. It is called a sampling job sequencing method in this study. This study is to examine the selection of the good job sequence of 1%-5% upper group by the sampling method. The result shows that there is the set of 0.5%-5% job sequence group which has to same amount of performance measure with the optimal job sequence in the case of experiment of 2/n/F/F max. This indicates that the sampling job sequencing method is a useful job sequencing method to find the optimal or good job sequence with a little effort and time consuming. The results of ANOVA show that the two factors, number of jobs and the range of processing time are the significant factors for determining the job sequence at $\alpha$=0.01. This study is extended to 3 machines to machines job shop problems further.

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Asymptotic Comparison of Latin Hypercube Sampling and Its Stratified Version

  • Lee, Jooho
    • Journal of the Korean Statistical Society
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    • 제28권2호
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    • pp.135-150
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    • 1999
  • Latin hypercube sampling(LHS) introduced by McKay et al. (1979) is a widely used method for Monte Carlo integration. Stratified Latin hypercube sampling(SLHS) proposed by Choi and Lee(1993) improves LHS by combining it with stratified sampling. In this article it is shown that SLHS yields an asymptotically more accurate than both stratified sampling and LHS.

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