• Structural Monitoring and Maintenance
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• v.4 no.1
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• pp.33-52
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• 2017

A cantilever beam with a breathing crack is studied to improve the breathing crack identification sensitivity by the parametric optimization of sample entropy and wavelet transformation. Crack breathing is a special bi-linear phenomenon experienced by fatigue cracks which are under dynamic loadings. Entropy is a measure, which can quantify the complexity or irregularity in system dynamics, and hence employed to quantify the bi-linearity/irregularity of the vibration response, which is induced by the breathing phenomenon of a fatigue crack. To improve the sensitivity of entropy measurement for crack identification, wavelet transformation is merged with entropy. The crack identification is studied under different sinusoidal excitation frequencies of the cantilever beam. It is found that, for the excitation frequencies close to the first modal frequency of the beam structure, the method is capable of detecting only 22% of the crack depth percentage ratio with respect to the thickness of the beam. Using parametric optimization of sample entropy and wavelet transformation, this crack identification sensitivity is improved up to 8%. The experimental studies are carried out, and experimental results successfully validate the numerical parametric optimization process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.10
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• pp.1561-1570
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• 1997

This study explores the treatment of the max-value cost function over a parameter interval in parametric optimization. To avoid the computational burden of the transformation treatment using an artificial variable, a direct treatment of the original max-value cost function is proposed. It is theoretically shown that the transformation treatment results in demanding an additional equality constraint of dual variables as a part of the Kuhn-Tucker necessary conditions. Also, it is demonstrated that the usability and feasibility conditions on the search direction of the transformation treatment retard convergence rate. To investigate numerical performances of both treatments, typical optimization algorithms in ADS are employed to solve a min-max steady-state response optimization. All the algorithm tested reveal that the suggested direct treatment is more efficient and stable than the transformation treatment. Also, the better performing of the direct treatment over the transformation treatment is clearly shown by constrasting the convergence paths in the design space of the sample problem. Six min-max transient response optimization problems are also solved by using both treatments, and the comparisons of the results confirm that the performances of the direct treatment is better than those of the tranformation treatment.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1745-1753
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• 2004

A method of estimating the pose of a three-dimensional object from a set of two-dimensioal images based on parametric eigenspace method is proposed. A Gaussian blurred edge image is used as an input image instead of the original image itself as has been used previously. The set of input images is compressed using K-L transformation. By comparing the estimation errors for the original, blurred original, edge, and blurred edge images, we show that blurring with the Gaussian function and the use of edge images enhance the data compression ratio and decrease the resulting from smoothing the trajectory in the parametric eigenspace, thereby allowing better pose estimation to be achieved than that obtainable using the original images as it is. The proposed method is shown to have improved efficiency, especially in cases with occlusion, position shift, and illumination variation. The results of the pose angle estimation show that the blurred edge image has the mean absolute errors of the pose angle in the measure of 4.09 degrees less for occlusion and 3.827 degrees less for position shift than that of the original image.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1906-1908
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• 2004

We propose a result on the stabilization of nonlinear time-delay systems via the feedback linearization method. Using the predictor based control and the parametric coordinate transformation, we introduce a stabilizing controller to compensate time delay. Specifically, we present the delay-dependent stability analysis to makes the considered system stable. Also, an illustrative example is provided

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.971-979
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• 2009

In this paper, we introduce the method that reduces the bias when the transformation and back-transformation approach is applied in GARCH models. A parametric bootstrap is employed to compute the conditional expectation which is the prediction value to minimize mean square errors in the original scale. Through the analyese of returns of KOSPI and KOSDAQ, we verified that the proposed method provides a bias-reduced estimation for the prediction value.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.111-120
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• 2006

In a two-stage two-level balanced nested design, type I error rates for the parametric tests and the rank transformed tests for the main effects and the nested effects are in overall similar to each other. Furthermore, powers for the rank transformed statistic for the main effects and the nested effects in a two-stage two-level balanced nested design are generally superior to powers for the parametric statistic When the effect size and the sample size are increased, we can find that powers increase for the parametric statistic and the rank transformed statistic are dramatically improved. Especially for the case of the fixed effects in the asymmetric distributions such as an exponential distribution, powers for the rank transformed tests are quite high rather than powers for the parametric tests.

• International Journal of CAD/CAM
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• v.6 no.1
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• pp.59-64
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• 2006

A new method to obtain explicit re-parameterization that preserves the curve degree and parametric domain is presented in this paper. The re-parameterization brings a curve very close to the arc length parameterization under $L_2$ norm but with less segmentation. The re-parameterization functions we used are $C^1$ continuous piecewise rational linear functions, which provide more flexibility and can be easily identified by solving a quadratic equation. Based on the outstanding performance of Mobius transformation on modifying pieces with monotonic parametric speed, we first create a partition of the original curve, in which the parametric speed of each segment is of monotonic variation. The values of new parameters corresponding to the subdivision points are specified a priori as the ratio of its cumulative arc length and its total arc length. $C^1$ continuity conditions are imposed to each segment, thus, with respect to the new parameters, the objective function is linear and admits a closed-form optimization. Illustrative examples are also given to assess the performance of our new method.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.71-77
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• 2011

In this paper, asymptotic results are investigated when a parametric transformation is applied to ARMA models. The conditions are determined to ensure the strong consistency and the asymptotic normality of maximum likelihood estimators and the correct coverage probability of the forecast interval obtained by the transformation and backtransformation approach.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.519-526
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• 2019

Response dimension reduction in a sufficient dimension reduction (SDR) context has been widely ignored until Yoo and Cook (Computational Statistics and Data Analysis, 53, 334-343, 2008) founded theories for it and developed an estimation approach. Recent research in SDR shows that a semi-parametric approach can outperform conventional non-parametric SDR methods. Yoo (Statistics: A Journal of Theoretical and Applied Statistics, 52, 409-425, 2018) developed a semi-parametric approach for response reduction in Yoo and Cook (2008) context, and Yoo (Journal of the Korean Statistical Society, 2019) completes the semi-parametric approach by proposing an unstructured method. This paper theoretically discusses and provides insightful remarks on three versions of semi-parametric approaches that can be useful for statistical practitioners. It is also possible to avoid numerical instability by presenting the results for an orthogonal transformation of the response variables.

• The Pure and Applied Mathematics
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• v.30 no.4
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• pp.347-355
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• 2023

For numerical evaluation of Cauchy principal value integrals, we present a simple rational function with a parameter satisfying some reasonable conditions. The proposed rational function is employed in coordinate transformation for accelerating the accuracy of the Gauss quadrature rule. The efficiency of the proposed rational transformation method is demonstrated by the numerical result of a selected test example.