• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.50-52
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• 2010

통신 시스템의 성능을 향상시키는 핵심 문제 중에 하나인 채널을 추정하는 문제는 다양한 분야에서 연구되고 있다. 채널의 sparse한 특징으로 인해 기존의 linear square나 minimum mean square error보다 발전된 $l_1$-norm minimization 방법 등이 많이 연구되고 있다. 이에 본 논문은 sparse한 채널의 특징과 천천히 변화하는 채널환경 특징을 이용하여 기존의 방법에 비해 더 높은 성능의 채널 추정 기법을 연구한다. 천천히 변화하는 채널환경의 특징으로 인해 이전 채널 정보를 현재 채널 추정에 사용할 수 있고 sparse한 채널의 특징으로 $l_1$-norm minimization을 사용할 수 있다. 이러한 두 가지의 정보를 이용하여 weighted $l_1$-norm minimization 이용한 support detection후 MMSE를 이용한 채널 추정기법을 연구한다.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.17-19
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• 2014

In this paper, we consider an interesting variant of the inverse minimum traveling salesman problem. Given an instance (G, w) of the minimum traveling salesman problem defined on a metric space, we fix a specified Hamiltonian cycle $HC_0$. The task is then to adjust the edge cost vector w to w' so that the new cost vector w' satisfies the triangle inequality condition and $HC_0$ can be returned by the minimum spanning tree algorithm in the TSP-instance defined with w'. The objective is to minimize the total deviation between the original and the new cost vectors with respect to the $L_1$-norm. We call this problem the inverse metric traveling salesman problem against the minimum spanning tree algorithm and show that it is closely related to the inverse metric spanning tree problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.55-61
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• 2014

This paper related with the ECMA (Enchanced CMA) algorithm performance which is possible to simultaneously compensation of the amplitude and phase by appling the minimum disturbance techniques in the CMA adatpve equalizer. The ECMA can improving the gradient noise amplification problem, stability and roburstness performance by the minimum disturbance technique that is the minimization of the equalizer tap weight variation in the point of squared euclidiean norm and the decision directed mode, and then the now cost function were proposed in order to simultaneouly compensation of amplitude and phase of the received signal with the minimum increment of computational operations. The performance of ECMA algorithm was compared to present MCMA by the computer simulation. For proving the performance, the recovered signal constellation that is the output of equalizer output signal and the residual isi and Maximum Distortion charateristic and MSE learning curve that are presents the convergence performance in the equalizer and the overall frequency transfer function of channel and equalizer were used. As a result of computer simulation, the ECMA has more better compensation capability of amplitude and phase in the recovered constellation, and the convergence time of adaptive equalization has improved compared to the MCMA.

• 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.

• East Asian mathematical journal
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• v.5 no.1
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• pp.95-110
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• 1989

In a general variance component model, nonnegative quadratic estimators of the components of variance are considered which are invariant with respect to mean value translaion and have minimum bias (analogously to estimation theory of mean value parameters). Here the minimum is taken over an appropriate cone of positive semidefinite matrices, after having made a reduction by invariance. Among these estimators, which always exist the one of minimum norm is characterized. This characterization is achieved by systems of necessary and sufficient condition, and by a cone restricted pseudoinverse. In models where the decomposing covariance matrices span a commutative quadratic subspace, a representation of the considered estimator is derived that requires merely to solve an ordinary convex quadratic optimization problem. As an example, we present the two way nested classification random model. An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator, for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced two way nested classification model with ramdom effects if considered.

• Journal of the Korean Statistical Society
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• v.10
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• pp.91-96
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• 1981

The development of Minimum Norm Quadratic Unbiased Estimation (MINQUE) has introduced a unified approach for the estimation of variance components in general linear models. The computational problem has been studied by Liu and Senturia (1977) and Goodnight (1978, setting a-priori values to 0). This paper further simplifies the computation and gives efficient and compact computational algorithm for the MINQUE and dispersion matrix in general linear random model.

• Journal of the Korean Statistical Society
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• v.18 no.1
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• pp.4-12
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• 1989

An efficient method for computing minimum norm quadratic unbiased estimates (MINQUE) of variance components in the mixed model is developed. This computing algorithm which used W-matrix saves both storage usage and computing time.

• Journal of the Chungcheong Mathematical Society
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• v.2 no.1
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• pp.65-73
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• 1989

To smooth a given data in two-way tables with the order restriction, we propose the dual problem and construct an algorithm utilizing the network flows which ends up with the minimum L1-norm after a finite number of iterations.

• 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.