• 디지털콘텐츠학회 논문지
• /
• 제14권4호
• /
• pp.429-437
• /
• 2013

무선 메쉬 네트워크는 확장성이 좋으며 넓은 지역에 서비스가 가능하므로 네트워크 음영지역 해소 및 우회, 분산 경로 구축을 위한 솔루션으로 각광받고 있다. 하지만 메쉬 네트워크는 인프라 기반의 네트워크보다 사용자에게 낮은 QoS를 제공한다. 본 논문에서는 모바일 WiMAX 기반의 메쉬 네트워크에서 라우팅 성능을 향상시키고 QoS를 보장하기위한 LSQR (Load Sensing QoS Routing) 기법을 제안한다. LSQR 기법은 각각의 노드가 네트워크의 혼잡 상황을 인지하여 우회 경로를 선택한다. 이는 대량의 인터넷 트래픽이 발생할 때 centralized link에서 distributed link로 라우팅 경로를 변경하여 효과적인 부하 분산을 기대할 수 있다. NS-2를 이용한 시뮬레이션 결과로부터, 제안한 LSQR 기법이 기존의 다른 대표적인 라우팅 기법에 비해 패킷 손실을 감소하고 동시에 데이터 전송 속도를 증가함을 검증하였다.

• 충청수학회지
• /
• 제32권1호
• /
• pp.149-156
• /
• 2019

This paper present the global generalized cross validation as the appropriate choice of the regularization parameter in the preconditioned Gl-LSQR method in solving image deblurring problems. The regularization parameter, chosen from the global generalized cross validation, with preconditioned Gl-LSQR method can give better reconstructions of the true image than other parameters considered in this study.

• Journal of applied mathematics & informatics
• /
• 제26권1_2호
• /
• pp.213-222
• /
• 2008

Iterative methods are often suitable for solving least squares problems min$||Ax-b||_2$, where A $\epsilon\;\mathbb{R}^{m{\times}n}$ is large and sparse. The well known LSQR algorithm is among the iterative methods for solving these problems. A good preconditioner is often needed to speedup the LSQR convergence. In this paper we present the numerical experiments of applying a well known preconditioner for the LSQR algorithm. The preconditioner is based on the $A^T$ A-orthogonalization process which furnishes an incomplete upper-lower factorization of the inverse of the normal matrix $A^T$ A. The main advantage of this preconditioner is that we apply only one of the factors as a right preconditioner for the LSQR algorithm applied to the least squares problem min$||Ax-b||_2$. The preconditioner needs only the sparse matrix-vector product operations and significantly reduces the solution time compared to the unpreconditioned iteration. Finally, some numerical experiments on test matrices from Harwell-Boeing collection are presented to show the robustness and efficiency of this preconditioner.

• 충청수학회지
• /
• 제22권2호
• /
• pp.177-186
• /
• 2009

The global least squares method (Gl-LSQR) is a generalization of LSQR method for solving linear system with multiple right hand sides. In this paper, we present how to apply this algorithm for solving the image restoration problem and illustrate the usefulness and effectiveness of this method from numerical experiments.

• 충청수학회지
• /
• 제24권4호
• /
• pp.895-903
• /
• 2011

A two dimensional version of LSQR iterative algorithm which takes advantages of working solely with the 2-dimensional arrays is developed and applied to the image deblurring problem. The efficiency of the method comparing to the Fourier-based LSQR method and the 2-D version CGLS algorithm methods proposed by Hanson ([4]) is analyzed.

• Journal of applied mathematics & informatics
• /
• 제34권1_2호
• /
• pp.95-106
• /
• 2016

In this paper, according to the classical LSQR algorithm forsolving least squares (LS) problem, an iterative method is proposed for finding the minimum-norm pure imaginary solution of the quaternionic least squares (QLS) problem. By means of real representation of quaternion matrix, the QLS's correspongding vector algorithm is rewrited back to the matrix-form algorthm without Kronecker product and long vectors. Finally, numerical examples are reported that show the favorable numerical properties of the method.

• Journal of applied mathematics & informatics
• /
• 제27권1_2호
• /
• pp.1-12
• /
• 2009

Iterative algorithms are proposed for the least-squares symmetric solution of AXB = E with a submatrix constraint. We characterize the linear mappings from their independent element space to the constrained solution sets, study their properties and use these properties to propose two matrix iterative algorithms that can find the minimum and quasi-minimum norm solution based on the classical LSQR algorithm for solving the unconstrained LS problem. Numerical results are provided that show the efficiency of the proposed methods.

• Journal of applied mathematics & informatics
• /
• 제31권3_4호
• /
• pp.353-363
• /
• 2013

A variant of the global conjugate gradient method for solving general linear systems with multiple right-hand sides is proposed. This method is called as the global conjugate gradient linear least squares (Gl-CGLS) method since it is based on the conjugate gradient least squares method(CGLS). We present how this method can be implemented for the image deblurring problems with Neumann boundary conditions. Numerical experiments are tested on some blurred images for the purpose of comparing the computational efficiencies of Gl-CGLS with CGLS and Gl-LSQR. The results show that Gl-CGLS method is numerically more efficient than others for the ill-posed problems.