• International Journal of Reliability and Applications
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• v.4 no.3
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• pp.97-111
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• 2003

By applying Theorem 2.6.4 (Fang and Zhang, 1990, p.66) the dispersion matrix of a multivariate power exponential (MPE) distribution is derived. It is shown that the MPE and the gamma distributions are related and thus the MPE and chi-square distributions are related. By extending Fang and Xu's Theorem (1987) from the normal distribution to the Univariate Power Exponential (UPE) distribution an explicit expression is derived for calculating the probability of an UPE random variable over an interval. A representation of the characteristic function (c.f.) for an UPE distribution is given. Based on the MPE distribution the probability density functions of the generalized non-central chi-square, the generalized non-central t, and the generalized non-central F distributions are derived.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.10-15
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• 2004

We present a state-space approach to make non-square linear systems strictly passive by using an input-dimensional parallel feedforward compensator. A necessary and sufficient condition for the existence of the parallel feedforward compensator is given by the static output feedback formulation, which enables to utilize linear matrix inequality. By modifying the structure of the compensator the additional technical assumption in the previous result [1] is removed. The effectiveness of the proposed method is illustrated by some numerical examples which can be stabilized by the proportional-derivative (PD) and proportional-derivative-integral (PID) control laws. The proposed control scheme can successfully replace the measurements of derivative terms in the control laws.

• Earthquakes and Structures
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• v.15 no.3
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• pp.263-273
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• 2018

Operational modal analysis is being widely used in aerospace, mechanical and civil engineering. Common research fields include optimal design and rehabilitation under dynamic loads, structural health monitoring, modification and control of dynamic response and analytical model updating. In many practical cases, influence of noise contamination in the recorded data makes it difficult to identify the modal parameters accurately. In this paper, an improved frequency domain method called Enhanced Least Square Complex Frequency (eLSCF) is developed to extract modal parameters from noisy recorded data. The proposed method makes the use of pre-defined approximate mode shape vectors to refine the cross-power spectral density matrix and extract fundamental frequency for the mode of interest. The efficiency of the proposed method is illustrated using an example five story shear frame loaded by random excitation and different noise signals.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.683-687
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• 2007

In last decades, several linearization methods for the AOA measurements have been proposed, for example, Gauss-Newton method and Closed-Form solution. Gauss-Newton method can achieve high accuracy, but the convergence of the iterative process is not always ensured if the initial guess is not accurate enough. Closed-Form solution provides a non-iterative solution and it is less computational. It does not suffer from convergence problem, but estimation error is somewhat larger. This paper proposes a Self-Tuning Weighted Least Square AOA algorithm that is a modified version of the conventional Closed-Form solution. In order to estimate the error covariance matrix as a weight, a two-step estimation technique is used. Simulation results show that the proposed method has smaller positioning error compared to the existing methods.

• Electronics and Telecommunications Trends
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• v.13 no.4 s.52
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• pp.59-69
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• 1998

The terminology of data prefetching is introduced, which includes stride, repeat distance, stall, pending stall, prefetch degree, prefetch distance, and prefetch offset. The effectiveness of hardware data prefetching in reducing cache misses is shown by presenting a square matrix multiplication example. Thereafter the pitfalls of prefetching and possible solutions are discussed.

• Communications of the Korean Mathematical Society
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• v.11 no.1
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• pp.191-199
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• 1996

The strong shift equivalence of nonnegative integral square matrices is a necessary and sufficient condition for the topological conjugacy of topological Markov chains. In this paper we study the relation between strong shift equivalence and matrix conjugation.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2002.11a
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• pp.380-383
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• 2002

본 논문에서는 20㎓~50㎓ 대역에서 2 차원 PBG(Photonic-Bandgap) 구조의 투과 특성을 RCWA(Rigorous coupled-wave analysis)방법에 의한 Transfer matrix로 해석하였다. Square lattice의 PBG 구조에 대하여 TE 편파에서 유전율의 변화와 결함(defect)의 유무에 대한 투과 특성을 고찰하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.8 no.4
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• pp.33-39
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• 1988

The objective of this paper is to adjust precise traverse nets by matrix analysis. As the result of this paper, In positioning by Traverse nets adjustment, the application of matrix analysis improved the accuracy. And also, the difference between adjustment values of rigorous-method and those of approximate-method appears to be within the mean square errors(0.4 mm~0.9 mm), therefore, the efficiency of approximate-method was proved.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.129-134
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• 1995

The ordinary least square estimator of the disturbance variance in the pooled cross-sectional and time series regression model is shown to be asymptotically unbiased without any restrictions on the regressor matrix when the disturbances follow an equicorrelated error component models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.125-130
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• 2016

The proposal of this study is a fast version of the conventional extreme learning machine (ELM), called pseudoinverse matrix decomposition based incremental ELM (PDI-ELM). One of the main problems in ELM is to determine the number of hidden nodes. In this study, the number of hidden nodes is automatically determined. The proposed model is an incremental version of ELM which adds neurons with the goal of minimization the error of the ELM network. To speed up the model the information of pseudoinverse from previous step is taken into account in the current iteration. To show the ability of the PDI-ELM, it is applied to few benchmark classification datasets in the University of California Irvine (UCI) repository. Compared to ELM learner and two other versions of incremental ELM, the proposed PDI-ELM is faster.