• Journal of Applied Reliability
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• v.18 no.2
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• pp.122-129
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• 2018

Purpose: This article develops an optimal reliability acceptance sampling plan for the case where the degradation quantity of the performance characteristic follows Weibull distribution. Method: For developing reliability acceptance sampling plans, the sample size and the acceptance constant are determined based on the accelerated characteristic of the test condition and the product. Results: The sample size and the acceptance constant are provided such that the constraints of the producer and the consumer risks are satisfied. Conclusion: Reliability acceptance sampling plans based on the accelerated degradation test method can be used for the quality control within a resonable amount of cost and time. In this article. an optimal reliability sampling plans are newly developed for this purpose.

• Journal of Korean Society of Forest Science
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• v.5 no.1
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• pp.10-15
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• 1966

1) The purpose of this study was to compare the forest survey by ground method with that by aerial photo method. 2) In this study, the forest type map was made by use of the radial line plotter and radial line triangulation method. 3) The difference between the area found by the forest type map above mentioned and that by compass surveying on the ground was none-significant. 4) On aerial photo the stratification was carried out very easily. 5) The following sampling methods were applied : line plot method, representative sampling method and stratified random sampling on the aerial photo. 6) In confirming sampling point the line plot method and the representative sampling method were easier than another. 7) As to stands volume the maximum value was given by stratification, and the minimum by line plot method.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.171-177
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• 2011

Accuracy and computing time are considerable issues in machine learning. In general, the computing time for data analysis is increased in proportion to the size of given data. So, we need a sampling approach to reduce the size of training data. But, the accuracy of constructed model is decreased by going down the data size simultaneously. To solve this problem, we propose a new statistical sampling method having similar performance to the total data. We suggest a rule to select optimal sampling techniques according to given data structure. This paper shows a sampling method for reducing computing time with keeping the most of accuracy using cluster sampling, stratified sampling, and systematic sampling. We verify improved performance of proposed method by accuracy and computing time between sample data and total data using objective machine learning data sets.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.95-126
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• 2009

The present contribution addresses the parallelization of advanced simulation methods for structural reliability analysis, which have recently been developed for large-scale structures with a high number of uncertain parameters. In particular, the Line Sampling method and the Subset Simulation method are considered. The proposed parallel algorithms exploit the parallelism associated with the possibility to simultaneously perform independent FE analyses. For the Line Sampling method a parallelization scheme is proposed both for the actual sampling process, and for the statistical gradient estimation method used to identify the so-called important direction of the Line Sampling scheme. Two parallelization strategies are investigated for the Subset Simulation method: the first one consists in the embarrassingly parallel advancement of distinct Markov chains; in this case the speedup is bounded by the number of chains advanced simultaneously. The second parallel Subset Simulation algorithm utilizes the concept of speculative computing. Speedup measurements in context with the FE model of a multistory building (24,000 DOFs) show the reduction of the wall-clock time to a very viable amount (<10 minutes for Line Sampling and ${\approx}$ 1 hour for Subset Simulation). The measurements, conducted on clusters of multi-core nodes, also indicate a strong sensitivity of the parallel performance to the load level of the nodes, in terms of the number of simultaneously used cores. This performance degradation is related to memory bottlenecks during the modal analysis required during each FE analysis.

• The Journal of the Acoustical Society of Korea
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• v.16 no.6
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• pp.110-114
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• 1997

When applying the nonuniform sampling to noisy speech signal, the required data rate increases to be comparable to or more than that by uniform sampling such as PCM. To solve this problem, we have proposed the waveform coding method, multiband nonuniform waveform coding(MNWC), applying the nonuniform sampling to band-separated speech signal[7]. However, the speech quality is deteriorated when it is compared to the uniform sampling method, since the high band is simply modeled as a Gaussian noise with average level. In this paper, as a good method to overcome this drawback, the high band is modeled as one of 16 codewords having different center frequencies. By doing this, with maintaining high speech quality as MOS score of average 3.16, the proposed method achieves 1.5 times higher compression ratio than that of the conventional nonuniform sampling method(CNSM).

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.171-181
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• 2012

This paper introduces the double-density discrete wavelet transform(DWT) using quincunx sampling, which is a DWT that combines the double-density DWT and quincunx sampling method, each of which has its own characteristics and advantages. The double-density DWT is an improvement upon the critically sampled DWT with important additional properties: Firstly, It employs one scaling function and two distinct wavelets, which are designed to be offset from one another by one half. Secondly, the double-density DWT is overcomplete by a factor of two, and Finally, it is nearly shift-invariant. In two dimensions, this transform outperforms the standard DWT in terms of denoising; however, there is room for improvement because not all of the wavelets are directional. That is, although the double-density DWT utilizes more wavelets, some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. A solution to this problem is a quincunx sampling method. The quincunx lattice is a sampling method in image processing. It treats the different directions more homogeneously than the separable two dimensional schemes. Proposed wavelet transformation can generate sub-images of multiple degrees rotated versions. Therefore, This method services good performance in image processing fields.

• Structural Engineering and Mechanics
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• v.68 no.5
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• pp.549-561
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• 2018

Sobol method is applied as a powerful variance decomposition technique in the field of global sensitivity analysis (GSA). The paper is devoted to increase convergence speed of the extracted Sobol indices using a new proposed sampling technique called genetic based Latine hypercube sampling (GBLHS). This technique is indeed an improved version of restricted Latine hypercube sampling (LHS) and the optimization algorithm is inspired from genetic algorithm in a new approach. The new approach is based on the optimization of minimax value of LHS arrays using manipulation of array indices as chromosomes in genetic algorithm. The improved Sobol method is implemented to perform factor prioritization and fixing of an uncertain comprehensive high speed rotor-bearing system. The finite element method is employed for rotor-bearing modeling by considering Eshleman-Eubanks assumption and interaction of axial force on the rotor whirling behavior. The performance of the GBLHS technique are compared with the Monte Carlo Simulation (MCS), LHS and Optimized LHS (Minimax. criteria). Comparison of the GBLHS with other techniques demonstrates its capability for increasing convergence speed of the sensitivity indices and improving computational time of the GSA.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.2
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• pp.77-84
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• 2021

A sampling-based approach was devised as a nuclear seismic probabilistic risk assessment (SPRA) method to account for the partially correlated relationships between components. However, since this method is based on sampling, there is a limitation that a large number of samples must be extracted to estimate the results accurately. Thus, in this study, we suggest an effective approach to improve the existing sampling method. The main features of this approach are as follows. In place of the existing Monte Carlo sampling (MCS) approach, the Latin hypercube sampling (LHS) method that enables effective sampling in multiple dimensions is introduced to the SPRA method. In addition, the degree of segmentation of the seismic intensity is determined with respect to the final seismic risk result. By applying the suggested approach to an actual nuclear power plant as an example, the accuracy of the results were observed to be almost similar to those of the existing method, but the efficiency was increased by a factor of two in terms of the total number of samples extracted. In addition, it was confirmed that the LHS-based method improves the accuracy of the solution in a small sampling region.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.91-101
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• 2000

We propose a new method for estimating the mean of a population which has a linear trend. The suggested estimator is based on the centered balanced systematic sampling method and the concept of interpolation and extrapolation. The efficiency of the proposed method is compared with that of existing methods.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.147.3-147
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• 2001

We have proposed a digital control method, where the controlled variable is a joint angular velocity, of space robot manipulators using the transpose of Generalized Jacobian Matrix. The explicit relationship between the control law and the sampling period, however, is unknown because the controller gains include the sampling period implicitly. This paper presents a novel digital control method which explicitly describes the relation between the sampling period and the controller gains. Computer simulation of a 3-DOF planar space robot manipulator is peformed. Simulation result demonstrates the effctiveness of the proposed method.