• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.825-835
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• 2017

Significant improvements to methodologies on structural damage detection (SDD) have emerged in recent years. However, many methods are related to inversion computation which is prone to be ill-posed or ill-conditioning, leading to low-computing efficiency or inaccurate results. To explore a more accurate solution with satisfactory efficiency, a PSO-INM algorithm, combining particle swarm optimization (PSO) algorithm and an improved Nelder-Mead method (INM), is proposed to solve multi-sample objective function defined based on Bayesian inference in this study. The PSO-based algorithm, as a heuristic algorithm, is reliable to explore solution to SDD problem converted into a constrained optimization problem in mathematics. And the multi-sample objective function provides a stable pattern under different level of noise. Advantages of multi-sample objective function and its superior over traditional objective function are studied. Numerical simulation results of a two-storey frame structure show that the proposed method is sensitive to multi-damage cases. For further confirming accuracy of the proposed method, the ASCE 4-storey benchmark frame structure subjected to single and multiple damage cases is employed. Different kinds of modal identification methods are utilized to extract structural modal data from noise-contaminating acceleration responses. The illustrated results show that the proposed method is efficient to exact locations and extents of induced damages in structures.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.1
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• pp.59-63
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• 2017

We present a multi-sample array device based on a pneumatic system. Solenoid valves were used to control a micro valve in a pneumatic system. The use of a compressor together with a vacuum pump ensured that one outlet could supply both compression and vacuum pressure. The multi-sample array device was fabricated using conventional photolithography and PDMS casting. The device was composed of a multiplexer, sample array, and rinsing. The multiplexer could control four sample solutions injecting into the sample array chamber. Sample solution not arrayed was removed by DI-water from the rinsing inlet. To prevent trapping of microbubbles in the channel during injection of sample solution into the device, surfactant was added in PDMS solution to serve as a hydrophilic surface treatment. As a result, the device could be used as a sample array for 64 cases, using four samples and three columns of three chambers.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.237-250
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• 2009

We consider a nonparametric test procedure for the multi-sample problem with grouped data. We construct the test statistics based on the scores obtained from the likelihood ratio principle and derive the limiting distribution under the null hypothesis. Also we illustrate our procedure with an example and obtain the asymptotic properties under the Pitman translation alternatives. Also we discuss some concluding remarks. Finally we derive the covariance between components in the Appendix.

• Journal of the Korean Statistical Society
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• v.32 no.3
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• pp.245-259
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• 2003

The 2-way balanced one-level rotation design has been discussed (Park et al., 2001), where the 2-way balancing is done on interview time in monthly sample and rotation group. We extend it to 3-way balanced multi-level design to obtain more information of the same sample unit for one or more previous months. The 3-way balancing is accomplished not only on interview time in monthly sample and rotation group but also on recall time as well. The 3-way balancing eliminates or reduces any bias arising from unbalanced interview time, rotation group and recall time, and all rotation groups are equally represented in the monthly sample. We present the rule and rotation algorithm which guarantee the 3-way balancing. In particular, we specify the necessary and sufficient condition for the 3-way balanced multi-level rotation design.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.252-264
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• 2004

Variable sampling Interval (VSI) control charts vary the sampling interval according to value of the control statistic while the sample size is fixed. It is known that control charts with 2-state VSI scheme, which uses only two sampling intervals, give good statistical properties. Variable sample size (VSS) control charts vary the sample size according to value of the control statistic while the sampling interval is fixed. In the VSS scheme no optimal results are known for the number of sample sizes. It is also known that the variable sampling rate (VSR) $\bar{X}$ control chart with 2-state VSS and 2-state VSI scheme leads to large improvements In performance over the fixed sampling rate (FSR) $\bar{X}$ chart, but the optimal number of states for sample size Is not known. In this paper, the VSR Χ charts with multi-state VSS and 2-state VSI scheme are designed and compared to 2-state VSS and 2-state VSI scheme. The multi-state VSS scheme is considered to, achieve an additional improvement by switching from the 2-state VSS scheme. On the other hand, the multi-state VSI scheme is not considered because the 2-state scheme is known to be optimal. The 3-state VSS scheme improves substantially the sensitivity of the $\bar{X}$ chart especially for small and moderate mean shifts.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.81-90
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• 2001

One of the Repeated survey which estimates variability of population, we can be consider rotation sample survey. There are two kinds of rotation sample survey - onelevel rotation sample survey and multi-level rotation sample survey. In rotation sample survey, Composite estimator is used to measure level or level change of the population. This study suggests Generalized Composite estimator as considering intraclass correlation in multi-level rotation sample survey, and optimal weight minimizing variance of estimator. Numerical example shows efficiency of Generalized Composite estimator as considering intraclass correlation according to the sample unit and change degree of intraclass correlation in the rotation group.

• Progress in Superconductivity and Cryogenics
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• v.24 no.3
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• pp.52-56
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• 2022

Single-grain (c-normal or c-parallel) and multi-grain YBCO superconductors were prepared by a melt growth process with/without seeding. The magnetic levitation force and trapped magnetic field at liquid N₂ temperature (77 K) of the YBCO superconductors were investigated. Samples for the levitation force measurement were zero-field cooled (ZFC) to 77 K, and samples for trapped field measurement were field-cooled (FC) using Nd magnets. As for the magnetic levitation force, the c-normal, single grain sample showed the largest value, whereas the multi-grain sample showed the lowest value. The trapped magnetic field of the c-normal and c-parallel single-grain samples was 4-5 times that of the multi-grain sample. In addition, as the external magnetic field (the number of magnets) increased, the both properties increased proportionally. These results were explained in terms of the orientation dependence of the levitation forces and the magnetic field trapping capability of the YBCO superconductor.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.321-328
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• 2018

In clinical trials with repeated measurements, the time-averaged difference (TAD) may provide a more powerful evaluation of treatment efficacy than the rate of changes over time when the treatment effect has rapid onset and repeated measurements continue across an extended period after a maximum effect is achieved (Overall and Doyle, Controlled Clinical Trials, 15, 100-123, 1994). The sample size formula has been investigated by many researchers for the evaluation of TAD in two treatment groups. For the evaluation of TAD in multi-arm trials, Zhang and Ahn (Computational Statistics & Data Analysis, 58, 283-291, 2013) and Lou et al. (Communications in Statistics-Theory and Methods, 46, 11204-11213, 2017b) developed the sample size formulas for continuous outcomes and count outcomes, respectively. In this paper, we derive a sample size formula to evaluate the TAD of the repeated binary outcomes in multi-arm trials using the generalized estimating equation approach. This proposed sample size formula accounts for various correlation structures and missing patterns (including a mixture of independent missing and monotone missing patterns) that are frequently encountered by practitioners in clinical trials. We conduct simulation studies to assess the performance of the proposed sample size formula under a wide range of design parameters. The results show that the empirical powers and the empirical Type I errors are close to nominal levels. We illustrate our proposed method using a clinical trial example.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.19-24
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• 2002

The two-way balanced one-level rotation design has been discussed (Park, Kim and Choi, 2001), where the two-way balancing is done on interview time in monthly sample and rotation group. We extend it to three-way balanced multi-level design under the most general rotation system. The three-way balancing is accomplished on interview time not only in monthly sample and rotation group but also in recall time. We present the necessary condition and rotation algorithm which guarantee the three-way balancing. We propose multi-level composite estimators (MCE) from this design and derive their variances and mean squared errors (MSE), assuming the correlation from the measurements of the same sample unit and three types of biases in monthly sample.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.327-341
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• 2006

Winkler (1990, 2001), Sitter and Skinner (1994), Wilson and Sitter (2002) present a method which applies linear programing to designing surveys with multi-way stratification, primarily in situation where the desired sample size is less than or only slightly larger than the total number of stratification cells. A comparison is made with existing methods both by illustrating the sampling schemes generated for specific examples, by evaluating sample mean, variance estimation, and mean squared errors, and by simulating sample mean for all methods. The computations required can, however, increase rapidly as the number of cells in the multi-way classification increase. In this article their approach is applied to multi-way stratification using real data.