• Structural Engineering and Mechanics
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• v.38 no.5
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• pp.651-674
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• 2011

This paper presents a novel method for predicting the failure probability of structural or mechanical systems subjected to random loads and material properties involving multiple design points. The method involves Multicut High Dimensional Model Representation (Multicut-HDMR) technique in conjunction with moving least squares to approximate the original implicit limit state/performance function with an explicit function. Depending on the order chosen sometimes truncated Cut-HDMR expansion is unable to approximate the original implicit limit state/performance function when multiple design points exist on the limit state/performance function or when the problem domain is large. Multicut-HDMR addresses this problem by using multiple reference points to improve accuracy of the approximate limit state/performance function. Numerical examples show the accuracy and efficiency of the proposed approach in estimating the failure probability.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.905-912
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• 2006

Support vector classification(SVC) provides more complete description of the linear and nonlinear relationships between input vectors and classifiers. In this paper we propose to solve the optimization problem of SVC with a modified hinge loss function, which enables to use an iterative reweighted least squares(IRWLS) procedure. We also introduce the approximate cross validation function to select the hyperparameters which affect the performance of SVC. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4567-4583
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• 2021

This study proposes an analytical approximation algorithm based on extreme value theory (EVT) for the inverse of the power of the incomplete Gamma function. First, the Gumbel function is used to approximate the power of the incomplete Gamma function, and the corresponding inverse problem is transformed into the inversion of an exponential function. Then, using the tail equivalence theorem, the normalized coefficient of the general Weibull distribution function is employed to replace the normalized coefficient of the random variable following a Gamma distribution, and the approximate closed form solution is obtained. The effects of equation parameters on the algorithm performance are evaluated through simulation analysis under various conditions, and the performance of this algorithm is compared to those of the Newton iterative algorithm and other existing approximate analytical algorithms. The proposed algorithm exhibits good approximation performance under appropriate parameter settings. Finally, the performance of this method is evaluated by calculating the thresholds of space-time block coding and space-frequency block coding pattern recognition in multiple-input and multiple-output orthogonal frequency division multiplexing. The analytical approximation method can be applied to other related situations involving the maximum statistics of independent and identically distributed random variables following Gamma distributions.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1087-1094
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• 2005

e-insensitive support vector regression(e-SVR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the quadratic problem of e-SVR with a modified loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of e-SVR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for e-SVR.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.309-316
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• 2010

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among response and input variables. In this paper we propose a weighted SVQR for the longitudinal data. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of SVQR. Experimental results are the presented, which illustrate the performance of the proposed SVQR.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.979-983
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• 2001

We develop a new method of simultaneous optimization of trajectory and shape of redundant flexible manipulators for collision-free utilizing the B-spline function and a mathematical programming method We adopt an approximate flexible manipulator model which consists of rigid bar elements and spring elements. We use B-spline function for determining the approximate trajectory and the expressions of the outline of obstacles. The used total performance index consists of 2 performance indices. The first is the driving energy, and the second is the trajectory deviation which is caused by the approximate modeling for the flexible manipulator. We design optimal collision-free trajectory of flexible manipulators by searching optimum positions of the control points for B-spline approximation which minimize the performance index subject to constraint condition for collision-free. Some examinations through numerical examples show the effectiveness of the method

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1045-1052
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• 2005

This paper deals with the estimations of kernel ridge regression when the responses are subject to randomly right censoring. The weighted data are formed by redistributing the weights of the censored data to the uncensored data. Then kernel ridge regression can be taken up with the weighted data. The hyperparameters of model which affect the performance of the proposed procedure are selected by a generalized approximate cross validation(GACV) function. Experimental results are then presented which indicate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.383-388
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• 2009

In this paper we propose a variance function estimation method based on kernel trick for replicated data or data consisted of sample variances. Newton-Raphson method is used to obtain associated parameter vector. Furthermore, the generalized approximate cross validation function is introduced to select the hyper-parameters which affect the performance of the proposed variance function estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.183-191
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• 2010

Support vector quantile regression(SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the problem of SVQR with a weighted quadratic loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of SVQR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for SVQR.

• Journal of Magnetics
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• v.19 no.4
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• pp.378-384
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• 2014

Optimization design and robust design are significant measures for improving the performance and reliability of electromagnetic devices (EMDs, specifically refer to relays, contactors in this paper). However, the implementation of the above-mentioned design requires substantial calculation; consequently, on the premise of guaranteeing precision, how to improve the calculation speed is a problem that needs to be solved. This paper proposes a new method for establishing an approximate model for the EMD. It builds a relationship between the input and output of the EMD with different coil voltages and air gaps, by using a user-defined interpolating function. The coefficient of the fitting function is determined based on a quantum particle swarm optimization (QPSO) method. The effectiveness of the method proposed in this paper is verified by the electromagnetic force calculation results of an electromagnetic relay with permanent magnet.