• Smart Structures and Systems
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• v.10 no.4_5
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• pp.427-442
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• 2012

An effective approach for updating finite element model is presented which can provide reliable estimates for structural updating parameters from identified operational modal data. On the basis of the dynamic perturbation method, an exact relationship between the perturbation of structural parameters such as stiffness change and the modal properties of the tested structure is developed. An iterative solution procedure is then provided to solve for the structural updating parameters that characterise the modifications of structural parameters at element level, giving optimised solutions in the least squares sense without requiring an optimisation method. A regularization algorithm based on the Tikhonov solution incorporating the generalised cross-validation method is employed to reduce the influence of measurement errors in vibration modal data and then to produce stable and reasonable solutions for the structural updating parameters. The Canton Tower benchmark problem established by the Hong Kong Polytechnic University is employed to demonstrate the effectiveness and applicability of the proposed model updating technique. The results from the benchmark problem studies show that the proposed technique can successfully adjust the reduced finite element model of the structure using only limited number of frequencies identified from the recorded ambient vibration measurements.

• International Journal of Aeronautical and Space Sciences
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• v.8 no.1
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• pp.140-147
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• 2007

Whenever the hinge axis of aircraft wing rotates, its stiffness varies. Also, there are nonlinearities in the connection of the actuator and the hinge axis, and it is necessary to inspect the coupled effects between the actuator dynamics and the hinge nonlinearity. Nonlinear aeroelastic characteristics are investigated by using the iterative V-g method. Time domain analyses are also performed by using Karpel's minimum state approximation technique. The doublet hybrid method(DHM) is used to calculate the unsteady aerodynamic forces in subsonic regions. Structural nonlinearity located in the load links of the actuator is assumed to be friction. The friction nonlinearity of an actuator is identified by using the describing function technique. The nonlinear flutter analyses have shown that the flutter characteristics significantly depends on the structural nonlinearity as well as the dynamic stiffness of an actuator. Therefore, the dynamic stiffness of an actuator as well as the nonlinear effect of hinge axis are important factors to determine the flutter stability.

• Journal of ILASS-Korea
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• v.5 no.1
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• pp.13-22
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• 2000

The present study has numerically analyzed the vaporization characteristics of fuel droplets in the high temperature convective flow field. The axisymmetric governing equations for mass, momentum, energy, and species are solved by an iterative and implicite unstructured finite-volume method. The moving boundary due to vaporization is handled by the deformable unstructured grid technique. The pressure-velocity coupling in the density-variable flows is treated by the SIMPLEC algorithm. In terms of the matrix solver, Bi-CGSTAB is employed for the numerically efficient and stable convergence. The n-decane is used as a liquid fuel and the initial droplet temperature is 300K. Computations are performed for the nonevaporating and evaporating droplets with the relative interphase velocity(25m/s). The unsteady vaporization process has been simulated up to the nondimensional time, 25. Numerical results indicate that the mathematical model developed in this study succesfully simulates the main features of the droplet vaporization process in the convective environment.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.81-87
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• 1999

In this research, we propose a field mismatch compensation method for interlaced scan image and a image restoration technique for removing motion blur. In order to compensate field mismatch, the edge classification-based linear interpolation technique and the method using the object-based motion compensation are described. We also propose an edge estimation method and an motion-based image segmentation algorithm. For removing motion blur, we adopt an adaptive iterative image restoration method using the motion-based segmentation result to improve the quality of restored image.

• Journal of Theoretical and Applied Mechanics
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• v.2 no.1
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• pp.31-50
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• 1996

In this paper, a finite element analysis based on the local approach concept to fracture in the continuum damage mechanics is performed to analyze ductile fracture in two dimensional quasi-static state. First an isotropic damage model based on the generalized concept of effective stress is proposed for structural materials in the context of large deformation. In this model, the stiffness degradation is taken as a measure of damage and so, the fracture phenomenon can be explained as the critical deterioration of stiffness at a material point. The modified Riks' continuation technique is used to solve incremental iterative equations. Crack propagation is achieved by removing critically damaged elements. The mesh size sensitivity analysis and the simulation of the well known shearing mode failure in plane strain state are carried out to verify the present formulation. As numerical examples, an edge cracked plate and the specimen with a circular hole under plane stress are taken. Load-displacement curves and successively fractured shapes are shown. From the results, it can be concluded that the proposed model based on the local approach concept in the continuum damage mechanics may be stated as a reasonable tool to explain ductile fracture initiation and crack propagation.

• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.33-39
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• 2015

Purpose The head of infants under 24 months old who has Craniosynostosis grows extraordinarily that makes head shape unusual. To diagnose the Craniosynostosis, surgeon has to inspect computed tomography(CT) images of the patient in person. It's very time consuming process. Moreover, without a surgeon, it's difficult to diagnose the Craniosynostosis. Therefore, we developed technique which detects Craniosynostosis automatically from the CT volume. Materials and Methods At first, rotation correction is performed to the 3D CT volume for detection of the Craniosynostosis. Then, cranial area is extracted using the iterative thresholding method we proposed. Lastly, we diagnose Craniosynostosis by analyzing centroid relationships of clusters of cranial bone which was divided by cranial suture. Results Using this automatical cranial detection technique, we can diagnose Craniosynostosis correctly. The proposed method resulted in 100% sensitivity and 90% specificity. The method perfectly diagnosed abnormal patients. Conclusion By plugging-in the software on CT machine, it will be able to warn the possibility of Craniosynostosis. It is expected that early treatment of Craniosynostosis would be possible with our proposed algorithm.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.3
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• pp.142-152
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• 2014

Vehicular Ad-hoc Networks (VANETs) are comprised of wireless mobile nodes characterized by a randomly changing topology, high mobility, availability of geographic position, and fewer power constraints. Orthogonal Frequency Division Multiplexing (OFDM) is a promising candidate for the physical layer of VANET because of the inherent characteristics of the spectral efficiency and robustness to channel impairments. The susceptibility of OFDM to Inter-Carrier Interference (ICI) is a challenging issue. The high mobility of nodes in VANET causes higher Doppler shifts, which results in ICI in the OFDM system. In this paper, a frequency domain com-btype channel estimation was used to cancel out ICI. The channel frequency response at the pilot tones was estimated using a Least Square (LS) estimator. An efficient interpolation technique is required to estimate the channel at the data tones with low interpolation error. This paper proposes a robust interpolation technique to estimate the channel frequency response at the data subcarriers. The channel induced noise tended to degrade the Bit Error Rate (BER) performance of the system. Parallel concatenated Convolutional codes were used for error correction. At the decoding end, different decoding algorithms were considered for the component decoders of the iterative Turbo decoder. A performance and complexity comparison among the various decoding algorithms was also carried out.

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.286-295
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• 2000

We present a novel statistically adaptive method using the Minimum Description Length(MDL) principle for unsupervised segmentation of magnetic resonance(MR) images. In the method, Markov random filed(MRF) modeling of tissue region accounts for random noise. Intensity measurements on the local region defined by a window are modeled by a finite Gaussian mixture, which accounts for image inhomogeneities. The segmentation algorithm is based on an iterative conditional modes(ICM) algorithm, approximately finds maximum ${\alpha}$ posteriori(MAP) estimation, and estimates model parameters on the local region. The size of the window for parameter estimation and segmentation is estimated from the image using the MDL principle. In the experiments, the technique well reflected image characteristic of the local region and showed better results than conventional methods in segmentation of MR images with inhomogeneities, especially.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.5 no.2
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• pp.63-69
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• 2001

In this study, we shall be concerned with computing in parallel a few of the smallest eigenvalues and their corresponding eigenvectors of the eigenvalue problem, $Ax={\lambda}Bx$, where A is symmetric, and B is symmetric positive definite. Both A and B are large and sparse. Recently iterative algorithms based on the optimization of the Rayleigh quotient have been developed, and CG scheme for the optimization of the Rayleigh quotient has been proven a very attractive and promising technique for large sparse eigenproblems for small extreme eigenvalues. As in the case of a system of linear equations, successful application of the CG scheme to eigenproblems depends also upon the preconditioning techniques. A proper choice of the preconditioner significantly improves the convergence of the CG scheme. The idea underlying the present work is a parallel computation of the Multi-Color Block SSOR preconditioning for the CG optimization of the Rayleigh quotient together with deflation techniques. Multi-Coloring is a simple technique to obatin the parallelism of order n, where n is the dimension of the matrix. Block SSOR is a symmetric preconditioner which is expected to minimize the interprocessor communication due to the blocking. We implemented the results on the CRAY-T3E with 128 nodes. The MPI(Message Passing Interface) library was adopted for the interprocessor communications. The test problems were drawn from the discretizations of partial differential equations by finite difference methods.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.