• Journal of Advanced Navigation Technology
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• v.4 no.1
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• pp.45-49
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• 2000

A GPS measurement solution, in general, is obtained as a least squares solution since the measurement includes errors such as clock errors, ionospheric and tropospheric delays, multipath effect etc. Because of the nonlinearity of the measurement equation, we utilize the nonlinear Newton algorithm to obtain a least squares solution, or mostly, use its linearized algorithm which is more convenient and effective. In this study we developed a fixed point algorithm and proved its availability to replace the nonlinear Newton algorithm and the linearized algorithm. A nonlinear Newton algorithm and a linearized algorithm have the advantage of fast convergence, while their initial values have to be near the unknown solution. On the contrary, the fixed point algorithm provides more reliable but slower convergence even if the initial values are quite far from the solution. Therefore, two types of algorithms may be combined to achieve better performance.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.2 no.2
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• pp.41-47
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• 1998

We consider some numerical solution methods for equality-constrained quadratic problems in the context of structural analysis. Sparse orthogonal schemes for linear least squares problem are adapted to handle the solution step of the force method. We also examine these schemes with substructuring concepts.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2E
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• pp.43-48
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• 2006

Abstract-Using the recursive generalized eigendecomposition method, we develop a recursive form solution to the data least squares (DLS) problem, in which the error is assumed to lie in the data matrix only. Simulations demonstrate that DLS outperforms ordinary least square for certain types of deconvolution problems.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.213-222
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• 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.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.141-151
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• 2010

Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1059-1066
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• 2011

This paper makes a close inquiry into ill-conditioning that may be occurred in wireless localization of the sensor nodes based on network signals in the wireless sensor network and provides the clue for solving the problem. In order to estimate the location of a node based on the range information calculated using the signal propagation time, LS (Least Squares) method is usually used. The LS method estimates the solution that makes the squared estimation error minimal. When a nonlinear function is used for the wireless localization, ILS (Iterative Least Squares) method is used. The ILS method process the LS method iteratively after linearizing the nonlinear function at the initial nominal point. This method, however, has a problem that the final solution may converge into a LM (Local Minimum) instead of a GM (Global Minimum) according to the deployment of the fixed nodes and the initial nominal point. The conditions that cause the problem are explained and an adaptive method is presented to solve it, in this paper. It can be expected that the stable location solution can be provided in implementation of the wireless localization methods based on the results of this paper.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.5
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• pp.577-588
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• 2011

This paper provides a novel extended Moving Least Squares(MLS) difference method for the potential problem with weak and strong discontinuities. The conventional MLS difference method is enhanced with jump functions such as step function, wedge function and scissors function to model discontinuities in the solution and the derivative fields. When discretizing the governing equations, additional unknowns are not yielded because the jump functions are decided from the known interface condition. The Poisson type PDE's are discretized by the difference equations constructed on nodes. The system of equations built up by assembling the difference equations are directly solved, which is very efficient. Numerical examples show the excellence of the proposed numerical method. The method is expected to be applied to various discontinuity related problems such as crack problem, moving boundary problem and interaction problems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.4
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• pp.501-507
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• 2007

In the first part of this study, the moving least squares finite difference method for solving solid mechanics problems was formulated. This second part verified the accuracy, robustness and effectiveness of the developed method through several numerical examples. It was shown that the method gives excellent convergence rate for elasticity problem. The solution process of elastic crack problems showed the easiness in discontinuity modeling and demonstrates the accuracy and efficiency in finding singular stress solution based on adaptive node distribution. The applicability to the engineering problem with abrupt change in displacement and stresses gradient fields is verified through a localization band problem. The developed method is expected to be extended to the various special engineering problems.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.166-172
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• 2005

With the development of RAIM techniques for single failure, increasing interest has been shown in the multiple failure problem. As a result, numerous approaches have been used in attempts to tackle this problem. This paper considers the two failure problem with total least squares (TLS) technique, a solution that has rarely been addressed because TLS requires an immense number of computations. In this paper, the special form of the observation matrix H, (that is, one column is exactly known) is exploited so as to develop an algorithm in a sequential form, thereby reducing computational load. The algorithm permits the advantages of TLS without the excessive computational burden. The proposed algorithm is verified through a numerical simulation.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.2-7
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• 2008

A highly efficient moving least squares finite difference method (MLS FDM) for heat transfer analysis of composite material with interface. In the MLS FDM, governing differential equations are directly discretized at each node. No grid structure is required in the solution procedure. The discretization of governing equations are done by Taylor expansion based on moving least squares method. A wedge function is designed for the modeling of the derivative jump across the interface. Numerical examples showed that the numerical scheme shows very good computational efficiency together with high aocuracy so that the scheme for heat transfer problem with different heat conductivities was successfully verified.