Earticle

탈착형 계류 장치인 페어리드 체인 스토퍼(Fairlead Chain Stopper, FCS)는 10MW급 부유식 해상풍력 발전기에 적용하기 위해 새롭게 개발된 시스템이다. FCS는 거친 해양 환경에서 부유식 해상풍력 발전기의 안정적인 계류를 유지하 기 위해 선급 규정 설계하중조건을 고려한 구조설계가 필수적이며, 고강도의 두꺼운 강재가 사용되기 때문에 강재 생산시 발생하는 공차와 같은 불확실성을 반영한 구조 신뢰성 해석이 요구된다. 본 연구에서는 FCS의 구조 신뢰성 평가에 활용 가능한 랜덤 샘플링 기법의 적용 가능성을 검토하였다. 구조 신뢰성 해석을 위한 랜덤 샘플링 방법으로 소볼 수열법을 적용하여 표본 데이터를 구성하고, 표본 수의 변화에 따른 신뢰도 확률을 평가하였다. FCS의 구조 신뢰성 해석에 적합한 랜덤 샘플링의 표본 수는 몬테카를로 시뮬레이션의 결과와 비교하여 검증하였다. 본 연구를 통해 FCS의 구조 신뢰성 해석 에 적합한 랜덤 샘플링 절차를 규명하였다.

The Fairlead Chain Stopper (FCS), a newly developed detachable mooring device, was designed for application to a 10 MW-class floating offshore wind turbine. To ensure stable mooring under harsh marine conditions, the structural design of the FCS must consider the design load conditions prescribed by relevant classification society rules. Since the FCS utilizes high-strength, thick steel plates, it is essential to account for structural uncertainties such as manufacturing tolerances in material properties during production. Accordingly, a structural reliability analysis that incorporates such uncertainties is required. This study investigates the applicability of random sampling method for structural reliability assessment of the FCS. In particular, Sobol sequence sampling was employed to generate sample datasets for the reliability analysis, and the probability of reliability was evaluated with respect to varying sample sizes. The suitability of the selected sample size for reliability evaluation was validated by comparing with the results obtained from a conventional Monte Carlo simulation.The findings of this study establish an effective random sampling procedure for the structural reliability analysis of the FCS.

This paper introduces a system called Simulation-based Stochastic Bidding Cost Estimation System (S2BC) that use simulation modeling and analysis technique. It better represents the real world system involved in construction bidding. The prediction accuracy was compromised because of the assumptions which existing models have adopted. However, The S2BC complements the lack of accuracy. Historical bidding data obtained from the Korea National Housing Corporation was used to validate the method. Bidding projects in the Historical DB were selected by random sampling. The ration of bidding let was calculated using the occurrence of participants who entered into a specific bidding. On the assumption that bidding pattern retained on the historical DB has reproducibility, the probability of winning was computed using the cumulative probability distribution that is obtained from the user defined numbe r of random sampling of bidding projects out of the Historical DB. The existing computation, which is handled by means of deterministic procedure, was converted into stochastic model using simulation modeling and analysis technique. The best fitted probability distribution function is estimated using the historical data. The reliability of estimating probability of winning on specified bidding let is improved using the S2BC.

Random equivalent sampling (RES) can composite a waveform with high equivalent sampling rate from multiple low speed sampling sequences. In practical application, the performance of RES signal reconstruction would be degraded by the non-uniform distribution of sampling time. Compressed sensing (CS) theory is adopted to reconstruct RES samples, which could mitigate the inherent coherence of sampling time. However, the CS reconstruction algorithm is sensitive to the signal sparsity level that is unknown in the reconstruction stage. In this paper, we propose a redundancy reduction algorithm for CS base RES signal reconstruction that can ensure reconstruction accuracy while reducing the number of random samples. The experimental results are reported to evaluate the performance of the proposed algorithm.

Random demodulation based compressive sampling technique is a novel approach that it can break through the Shannon sampling theorem for the sparse signal capturing. A major challenge in the random demodulation based sampling system is the random sequence generation. In this paper, we introduce an approach to generate the high-speed random sequence that meets the incoherence of compressive sampling. The proposed technique employs a field programmable gate array (FPGA). First, the random sequence is parallel stored in the memory of FPGA, and it is read out byte by byte using a low speed clock. Second, the low-speed byte sequence is converted to a high-speed bit sequence by a circuitry. This proposed approach can program the random sequence dynamically without making any change to the circuitry system. Experimental results indicate that, the random sequence generated by the proposed approach is feasible to sensing the signal, and the constructed system can compressively sample and reconstruct the sparse signal.

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

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.

This study covers the sampling methods based on ranked set sampling (RSS) to estimate the population mean. The purpose of this study is to compare the relative efficiencies (RE) of several sampling methods which we consider in this paper and to find the sampling method with the best efficiency. Including the RSS, the extreme ranked set sampling (ERSS) proposed by Samawi et al. (1996), median ranked set sampling (MRSS) by Muttlak (1998), L ranked set sampling (LRSS) proposed by Al-Nasser (2007), adjusted ranked ordering set sampling (AROSS) proposed by Kim et al. (2007) and folded ranked set sampling (FRSS) proposed by Bani-Mustafa et al. (2011) are introduced and compared with each other in terms of the RE. First of all, we derive the RE for each sampling method analytically. Assuming that the distributions are symmetric about zero, Var(bar{X}_{MRSS}) is the smallest and Var(bar{X}_{LRSS}) is the second smallest, which means that the MRSS has the highest RE except for the AROSS. As a result, comparing the values of RE's for underlying distributions when the sample size is 3 to 6, we show that the AROSS has the best efficiency followed by the MRSS, LRSS, RSS, ERSS and FRSS for most distributions.

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.

In this paper, we mainly study the random sampling and reconstruction of signals living in the subspace V^p(𝚽, 𝚲) of L^p(ℝ^d), which is generated by a family of molecules 𝚽 located on a relatively separated subset 𝚲 ⊂ ℝ^d. The space V^p(𝚽, 𝚲) is used to model signals with finite rate of innovation, such as stream of pulses in GPS applications, cellular radio and ultra wide-band communication. The sampling set is independently and randomly drawn from a general probability distribution over ℝ^d. Under some proper conditions for the generators 𝚽 = {𝜙_λ : λ ∈ 𝚲} and the probability density function 𝜌, we first approximate V^p(𝚽, 𝚲) by a finite dimensional subspace V^p_N (𝚽, 𝚲) on any bounded domains. Then, we prove that the random sampling stability holds with high probability for all signals in V^p(𝚽, 𝚲) whose energy concentrate on a cube when the sampling size is large enough. Finally, a reconstruction algorithm based on random samples is given for signals in V^p_N (𝚽, 𝚲).

A new method to estimate the mean areal precipitation using kriging is developed. Urlike the conventional approach, points for double and quadruple numerical integrations in the kriging equation are selected randomly, given the boundary of area of interest. This feature eliminates the conventional approach's necessity of dividing the area into subareas and calculating the center of each subarea, which in turn makes the developed method more powerful in the case of complex boundaries. The algorithm to select random points within an arbitrary boundary, based on the theory of complex variables, is described. The results of Monte Carlo simulation showed that the error associated with estimation using randomly selected points is inversely proportional to the square root of the number of sampling points.

This study described the application of constructivism and information technology for teaching simple random sampling. We considered more student's participation, more interaction, and more flow in their introductory statistics class. In addition, we presented a web-based teaching and learning system for simple random sampling to demonstrate.

This paper proposes an efficient matching algorithm based on ASIFT (Affine Scale-Invariant Feature Transform) which is fully invariant to affine transformation. In our approach, we proposed a method of reducing similar measure matching cost and the number of outliers. First, we combined the Manhattan and Chessboard metrics replacing the Euclidean metric by a linear combination for measuring the similarity of keypoints. These two metrics are simple but really efficient. Using our method the computation time for matching step was saved and also the number of correct matches was increased. By applying an Optimized Random Sampling Algorithm (ORSA), we can remove most of the outlier matches to make the result meaningful. This method was experimented on various combinations of affine transform. The experimental result shows that our method is superior to SIFT and ASIFT.

This paper suggests a ratio-cum product estimator of a finite population mean using information on the coefficient of variation and the fcoefficient of kurtosis of auxiliary variate in stratified random sampling. Bias and MSE expressions of the suggested estimator are derived up to the first degree of approximation. The suggested estimator has been compared with the combined ratio estimator and several other estimators considered by Kadilar and Cingi (2003). In addition, an empirical study is also provided in support of theoretical findings.

In this paper, a family of estimators for the finite population variance investigated by Srivastava and Jhajj (1980) is studied under two different situations of random non-response considered by Tracy and Osahan (1994). Asymptotic expressions for the biases and mean squared errors of members of the proposed family are obtained; in addition, an asymptotic optimum estimator(AOE) is also identified. Estimators suggested by Singh and Joarder (1998) are shown to be members of the proposed family. A correction to the Singh and Joarder (1998) results is also presented.

The stress-strength models have been intensively investigated in the literature in regards of estimating the reliability ${\theta}$ = P(X > Y) using parametric and nonparametric approaches under different sampling schemes when X and Y are independent random variables. In this paper, we consider the problem of estimating ${\theta}$ when (X, Y) are dependent random variables with a bivariate underlying distribution. The empirical and kernel estimates of ${\theta}$ = P(X > Y), based on bivariate ranked set sampling (BVRSS) are considered, when (X, Y) are paired dependent continuous random variables. The estimators obtained are compared to their counterpart, bivariate simple random sampling (BVSRS), via the bias and mean square error (MSE). We demonstrate that the suggested estimators based on BVRSS are more efficient than those based on BVSRS. A simulation study is conducted to gain insight into the performance of the proposed estimators. A real data example is provided to illustrate the process.

As semiconductor devices are being scaled down, random variation becomes a critical issue, especially in the case of static random access memory (SRAM). Thus, there is an urgent need for statistical methodologies to analyze the impact of random variations on the SRAM. In this paper, we propose a novel sampling method based on the concept of a confidence ellipse. Results show that the proposed method estimates the SRAM margin metrics in high-sigma regimes more efficiently than the standard Monte Carlo (MC) method.

공간 샘플링은 공간모델링 연구에 활용되어 샘플링 비용을 줄이면서 모델링의 효율성을 높이는 역할을 한다. 농업분야에서는 기후변화 영향을 예측하고 평가하기 위한 고해상도 공간자료 기반 모델링에 대한 연구 수요가 빠르게 증가하고 있으며, 이에 따라 공간 샘플링의 필요성과 중요성이 증가하고 있다. 본 연구는 국내 농지 공간샘플링 연구를 통해 농업분야 기후변화연구의 공간자료 활용의 효율성을 제고하고자 하였다. 본 연구는 층화랜덤샘플링을 기반으로 하였으며, 1 km 해상도의 농지 공간격자자료 모집단(11,386개 격자)에 대해서 RCP 시나리오별(RCP 4.5/8.5) 연대별(2030/2050/2080년대) 공간샘플링을 설계하였다. 국내 농지는 기상 및 토양 특성에 따라 계층화 되었으며, 샘플링 효율 극대화를 위해 최적 층화 및 샘플 배정 최적화를 수행하였다. 최적화는 작물수량, 온실가스 배출량, 해충 분포 확률을 포함하는 16개 목표 변수에 대해 주어진 정밀도 제한 내에서 샘플 수를 최소화하는 방향으로 진행되었다. 샘플링의 정밀도와 정확도 평가는 각각 변동계수(CV)와 상대적 편향을 기반으로 하였다. 국내 농지 공간격자 모집단 계층화 및 샘플 배정 및 샘플 수 최적화 결과, 전체 농지는 5~21개 계층, 46~69개 샘플 수 수준에서 최적화되었다. 본 연구결과물들은 국내 농업시스템 대표 공간격자로써 널리 활용될 수 있을 것으로 기대된다. 또한, 기후변화 영향예측 공간모델링 연구들에 활용되어 샘플링 비용 및 계산 시간을 줄이면서도 모델의 효율성을 높이는 데에 기여할 수 있다.

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.

본 연구는 전화조사 상황에서 무선표집절차를 적용하여 조사를 실시한 후 그 결과를 분석한 것이다. 2009년 10월 26일부터 11월 18일까지 무선표집방법을 이용하여 전화조사를 실시한 결과, 전화번호 수 2,454개 가운데 조사성공 637건, 조사실패 1,817건으로 나타났다. 조사결과를 조사성공률, 성공 및 실패사례의 응답자의 인구학적 특성, 응답단계별 이슈에 대한 응답특성 등을 중심으로 분석했다. 분석결과 부재중을 제외한 조사 성공비율은 45.7%로 나타났다. 조사실패의 가장 큰 원인은 부재중이었으며, 조사단계가 올라가도 젊은 층의 응답률 저조는 개선되지 않았다. 응답단계에 따라 응답자의 이슈에 대한 인지도나 능동성이 달라지지는 않았으나 전반적으로 단계를 높일수록 다양한 시각의 응답이 포함되는 경향이 나타났다. 무선표집은 비접촉자의 문제를 완화할 수 있고 조사정확도를 높이기 위한 해결책을 찾아 나갈 수 있다는 점에서 후속연구가 이어질 필요가 있다.

This study aims to analyze the usefulness of the survey procedures developed for telephone surveys using a random sampling method. We conducted a telephone survey of residents in a local area from October 26 to November18,2009. We applied random sampling procedures with five call-backs. Particular attention was given to the conversion of refusals. Out of 2,454 phone numbers, we succeeded in getting responses from 637 numbers. The absence of residents was the primary cause of non-response. We received responses from 45.7% of those with whom we successfully made contact. One finding of this study is that, contrary to our initial expectations, call-backs do not significantly increase the percentage of responses from young people. Also we cannot find any significant differences in responses to various questions between the responses received from the first round of calls and later responses. However, these results are not conclusive; another survey may produce different results. Therefore, further research is needed.