• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1609-1612
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• 1997

Kinematically redundantant manipulators have a nimber of potential advantages over nonredundant ones. Questions associated with manipulability measures for (non)redundant manipulators derived by minimum 2-norm solution and minimum infinity-norm solution in unit joint velocity are examined in detail.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.130-139
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• 2002

In this paper, at first, we investigate existing algorithms for finding the minimum infinity-norm solution of consistent linear equations and then propose a new algorithm. The proposed algorithm is intended to includes the advantages of computational efficiency as well as geometric explicitness. As a practical application example, optimum trajectory planning for redundant robot manipulators is considered. Also, an efficient approach avoiding discontinuity in trajectory is proposed by resolving the non-uniqueness problem of minimum infinity-norm solution. To be specific, the proposed method for checking possible discontinuity does not need any other algorithms in checking the possibility of discontinuity while previous work needs specially designed checking courses. To show the usefulness of the proposed techniques, an example calculating minimum infinity-norm solution for comparing the computational efficiency as well as the trajectory planning for a redundant robot manipulator are included.

• Communications of the Korean Mathematical Society
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• v.10 no.1
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• pp.235-249
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• 1995

Let K be a bounded linear operator from Hilbert space $H_1$ into Hilbert space $H_2$. When numerically solving the first kind equation Kf = g, one usually picks n orthonormal functions $\phi_1, \phi_2,...,\phi_n$ in $H_1$ which depend on the numerical method and on the problem, see Varah [12] for more details. Then one findes the unique minimum norm element $f_M \in M$ that satisfies $\Vert K f_M - g \Vert = inf {\Vert K f - g \Vert : f \in M}$, where M is the linear span of $\phi_1, \phi_2,...,\phi_n$. Such a solution $f_M \in M$ is called the M-solution of K f = g. Some methods for finding the M-solution of K f = g were proposed by Banks [2] and Marti [9,10]. See [5,6,8] for convergence results comparing the M-solution of K f = g with $f_0$, the least squares solution of minimum norm (LSSMN) of K f = g.

• Journal of applied mathematics & informatics
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• v.27 no.1_2
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• pp.1-12
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• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.811-814
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• 1997

In this paper, the torque optimization of a kinematically redundant manipulator for minimizing the torque demands is discussed. The minimum torque solution based on a local optimization has been known to encounter the instability problem and then the global torque optimization was suggested as one of the alternatives. Herein, by adopting the infinity-norm rather than the 2-norm for the magnitude of torques, we are to propose a new cost function more advantageous to the avoidance of torque limits. By the way, a solution to the global torque optimization formulated with the new cost function can not be obtained by the previous methods due to their difficulties such as inability to treat discontinuous cost functions and various constraints on the joint variables. Thus, to overcome those deficiencies, we are developing a new approach using the dynamic programming. The effectiveness of the proposed method is shown through simulation examples for a 3-link planar redundant manipulator.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.373-385
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• 2006

To solve a class of nonlinear parameter estimation problems, a method combining the regularized structured nonlinear total least norm (RSNTLN) method and parameter separation scheme is suggested. The method guarantees the convergence of parameters and has an advantages in reducing the residual norm over the use of RSNTLN only. Numerical experiments for two models appeared in signal processing show that the suggested method is more effective in obtaining solution and parameter with minimum residual norm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3194-3216
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• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7A
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• pp.705-710
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• 2002

In this paper, we present a method to find motion vectors in frequency domain for video data compression. The proposed algorithm is based on the Format Number Transform (FNT), and it declares the most correlated-block as the best matching block, as opposed to declaring the block with least sum of differences between blocks. We show that the proposed method is equivalent to declaring the block with the minimum L2-norm as the best matching block. Unlike other previous fast algorithms, the time requirement for the proposed algorithm does not defend on the image type for finding the optimum solution.

• Journal of applied mathematics & informatics
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• v.34 no.1_2
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• pp.95-106
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• 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 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.