• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.615-622
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• 2002

In this paper, the modification of double projection method for the adaptive analysis of Element-free Galerkin(EFG) method were proposed. As results of the double projection method, the smoothed error profile that is adequate for adaptive analysis was obtained by re-projection of error that means the differences of EFG stress and projected stress. However, it was found that the efficiency of double projection method is degraded as increase of the numerical integration order. Since, the iterative refinement to single step error estimation made the same effect as increasing of integration order, the application of the iterative refinement base on double projection method could be produced the inadequately refined analysis model. To overcome this defect, a modified scheme of double projection were proposed. In the numerical example, the results did not show degradation of double projection effect in iterative refinement and the efficiency of proposed scheme were proved.

• ETRI Journal
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• v.37 no.6
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• pp.1251-1258
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• 2015

We investigate an image recovery method for sparse-view computed tomography (CT) using an iterative shrinkage algorithm based on a second-order approach. The two-step iterative shrinkage-thresholding (TwIST) algorithm including a total variation regularization technique is elucidated to be more robust than other first-order methods; it enables a perfect restoration of an original image even if given only a few projection views of a parallel-beam geometry. We find that the incoherency of a projection system matrix in CT geometry sufficiently satisfies the exact reconstruction principle even when the matrix itself has a large condition number. Image reconstruction from fan-beam CT can be well carried out, but the retrieval performance is very low when compared to a parallel-beam geometry. This is considered to be due to the matrix complexity of the projection geometry. We also evaluate the image retrieval performance of the TwIST algorithm -sing measured projection data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1C
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• pp.82-91
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• 2005

This paper introduces an efficient affine projection algorithm(APA) using iterative hyperplane projection. Among various fast converging adaptation algorithms, APA has been preferred to be employed for various applications due to its inherent effectiveness against the rank deficient problem. However, the amount of complexity of the conventional APA could not be negligible because of the accomplishment of sample matrix inversion(SMI). Moreover, the 'shifting invariance property' usually exploited in single channel case does not hold for the application of space-time decision-directed equalizer(STDE) deployed in single-input-multi-output(SIMO) systems. Thus, it is impossible to utilize the fast adaptation schemes such as fast transversal filter(FlF) having low-complexity. To accomplish such tasks, this paper introduces the low-complexity APA by employing hyperplane projection algorithm, which shows the excellent tracking capability as well as the fast convergence. In order to confirm th validity of the proposed method, its performance is evaluated under wireless SIMO channel in respect to bit error rate(BER) behavior and computational complexity.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.205-215
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• 2021

Sufficient dimension reduction is useful dimension reduction tool in regression, and sliced inverse regression (Li, 1991) is one of the most popular sufficient dimension reduction methodologies. In spite of its popularity, it is known to be sensitive to the number of slices. To overcome this shortcoming, the so-called fused sliced inverse regression is proposed by Cook and Zhang (2014). Unfortunately, the two existing methods do not have the direction application to large p-small n regression, in which the dimension reduction is desperately needed. In this paper, we newly propose seeded sliced inverse regression and seeded fused sliced inverse regression to overcome this deficit by adopting iterative projection approach (Cook et al., 2007). Numerical studies are presented to study their asymptotic estimation behaviors, and real data analysis confirms their practical usefulness in high-dimensional data analysis.

• Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.116-127
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• 2014

This paper aims to review methods for computing orthogonal projection of points onto curves and surfaces, which are given in implicit or parametric form or as point clouds. Special emphasis is place on orthogonal projection onto conics along with reviews on orthogonal projection of points onto curves and surfaces in implicit and parametric form. Except for conics, computation methods are classified into two groups based on the core approaches: iterative and subdivision based. An extension of orthogonal projection of points to orthogonal projection of curves onto surfaces is briefly explored. Next, the discussion continues toward orthogonal projection of points onto point clouds, which spawns a different branch of algorithms in the context of orthogonal projection. The paper concludes with comments on guidance for an appropriate choice of methods for various applications.

• Communications of the Korean Mathematical Society
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• v.19 no.2
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• pp.375-388
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• 2004

For solving symmetric systems of linear equations, it is shown that a new Krylov subspace method can be obtained. The new approach is one of the projection methods, and we call it the projection method for convenience in this paper. The projection method maintains the residual vector like simpler GMRES, symmetric QMR, SYMMLQ, and MINRES. By studying the quasiminimal residual method, we show that an extended projection method and the scaled symmetric QMR method are equivalent.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.275-280
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• 1994

In the case of the image reconstruction from unknown projection data such as imaging the object with opaque obstructions, conventional reconstruction algorithms may reconstruct a degraded image. In this paper, a new method for the estimation of the unknown projection data based on known projection data and the bandwidth of projection data is proposed. The proposed method successfully estimates the unknown projection data through iterative transformation between projection space and frequency space using the known projection data and the bandwidth of the projection data. Computer simulation shows that the proposed method significantly improves image quality and convergence behavior over conventional algorithms. In addition, the proposed method is successfully applied to ultrasound attenuation CT using a sponge phantom.

• Journal of the Korean Mathematical Society
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• v.52 no.2
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• pp.349-372
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• 2015

This paper concerns with exploiting an oblique projection technique to solve a general class of large and sparse least squares problem over symmetric arrowhead matrices. As a matter of fact, we develop the conjugate gradient least squares (CGLS) algorithm to obtain the minimum norm symmetric arrowhead least squares solution of the general coupled matrix equations. Furthermore, an approach is offered for computing the optimal approximate symmetric arrowhead solution of the mentioned least squares problem corresponding to a given arbitrary matrix group. In addition, the minimization property of the proposed algorithm is established by utilizing the feature of approximate solutions derived by the projection method. Finally, some numerical experiments are examined which reveal the applicability and feasibility of the handled algorithm.

• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.151-160
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• 1987

An algorithm is suggested that truncted projection among the incomplete projections can be recovered from non-iterative extrapolation matrix by band-limited function. After the image being reconstructed from the recovered signals by non-iterative extrapolation, a known controur information and reprojection algorithm are used. It is shown that the reconstructed image using these algorithms is close to the original image. The effectiveness for these algorithms is proved by computer simulation.

• Korean Journal of Mathematics
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• v.18 no.2
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• pp.105-118
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• 2010

The purpose of this paper is to consider an iterative method for an equilibrium problem and a family relatively nonexpansive mappings. Weak convergence theorems are established in uniformly smooth and uniformly convex Banach spaces.