• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.109-112
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• 2005

In this paper, we propose an improved delay-constrained unicast routing (I-DCUR) algorithm for real-time networks which is based on the delay-constrained unicast routing (DCUR) algorithm. Our I-DCUR algorithm is quite different from DCUR algorithm, because the node will choose the link between the active node and the previous node, and it will replace the original loop path when it detects a loop. Thus, firstly consider to choose the link between the active node and the previous node to replace the original loop path when a node detects a loop. So our algorithm can make the construction of path more efficiently, as compared to DCUR algorithm. We could see that the performance of I-DCUR algorithm is much better than DCUR algorithm in the experimental results. There were over 40% improvement in 100 nodes, 60% in 200 nodes, and 9% reduction of costs.

• The Journal of the Acoustical Society of Korea
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• v.17 no.3E
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• pp.43-46
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• 1998

Many of the active noise control system utilize a form of the least mean square(LMS) algorithm. This paper discusses the dependence of the convergence rate on the acoustic error path in the popular algorithm which is conventional "filtered-x LMS" and introduces new algorithm "constrained filtered-x LMS". The proposed method increase the convergence region regardless of the time-delay in the acoustic error path. In the algorithms, coefficients of the controller are adapted using the residuals of constrained structure which are defined in such a way that the control process become stationary. Advantages of constrained filtered-x LMS algorithm is illustrated by convergence analysis in the mean sense.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.19-30
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• 2013

Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochastic optimal control problems cannot be solved exactly except in very simple cases, approximations are required in most practical problems to obtain good suboptimal policies. In this paper, we present a procedure for finding a suboptimal solution to the constrained index tracking problem based on approximate dynamic programming. Illustrative simulation results show that this procedure works well when applied to a set of real financial market data.

• Journal of Mechanical Science and Technology
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• v.17 no.4
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• pp.538-544
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• 2003

Although many methodologies exist for determining the constrained equations of motion, most of these methods depend on numerical approaches such as the Lagrange multiplier's method expressed in differential/algebraic systems. In 1992, Udwadia and Kalaba proposed explicit equations of motion for constrained systems based on Gauss's principle and elementary linear algebra without any multipliers or complicated intermediate processes. The generalized inverse method was the first work to present explicit equations of motion for constrained systems. However, numerical integration results of the equation of motion gradually veer away from the constraint equations with time. Thus, an objective of this study is to provide a numerical integration scheme, which modifies the generalized inverse method to reduce the errors. The modified equations of motion for constrained systems include the position constraints of index 3 systems and their first derivatives with respect to time in addition to their second derivatives with respect to time. The effectiveness of the proposed method is illustrated by numerical examples.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.211-216
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• 2006

The Constrained Linear Spectral Unmixing(CLSU) is investigated for sub-pixel image processing, Its result is the abundance map which mean fractions of endmember existing in a mixed pixel. Compared to the Linear Spectral Unmixing using least square method, CLSU uses the NNLS (Non-Negative Least Square) algorithm to guarantee that the estimated fractions are constrained. But, CLSU gets Into difficulty in image processing due to select endmember at a user's disposition. In this study, endmember selection optimization method using entropy in the error-image analysis is proposed. In experiments which is used hyperion image, it is shown that our method can select endmember number than CLSU based on unsupervised endemeber selection.

• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.140-144
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• 2014

A general linearly constrained adaptive array is examined in the weight vector space to illustrate the array performance with respect to the gain factor. A narrowband linear adaptive array is implemented in a coherent signal environment. It is shown that the gain factor in the general linearly constrained adaptive array has an effect on the linear constraint gain of the conventional linearly constrained adaptive array. It is observed that a variation of the gain factor of the general linearly constrained adaptive array results in a variation of the distance between the constraint plane and the origin in the translated weight vector space. Simulation results are shown to demonstrate the effect of the gain factor on the nulling performance.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.137-142
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• 2017

A general linearly constrained narrowband adaptive array is examined in the eigenvector space. The optimum weight vector in the eigenvector space is shown to have the same performance as in the standard coordinate system, except that the input signal correlation matrix and look direction steering vector are replaced with the eigenvalue matrix and transformed steering vector. It is observed that the variation in gain factor results in the variation in the distance between the constraint plane and the origin in the translated weight vector space such that the increase in gain factor decreased the distance from the constraint plane to the origin, thus affecting the nulling performance. Simulation results showed that the general linearly constrained adaptive array performed better at an optimal gain factor compared with the conventional linearly constrained adaptive array in a coherent signal environment and the former showed similar performance as the latter in a noncoherent signal environment.

• Journal of Mechanical Science and Technology
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• v.18 no.11
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• pp.1891-1899
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• 2004

The characteristics of dynamic systems subjected to multiple linear constraints are determined by considering the constrained effects. Although there have been many researches to investigate the dynamic characteristics of constrained systems, most of them depend on numerical analysis like Lagrange multipliers method. In 1992, Udwadia and Kalaba presented an explicit form to describe the motion for constrained discrete systems. Starting from the method, this study determines the dynamic characteristics of the systems to have positive semidefinite mass matrix and the continuous systems. And this study presents a closed form to calculate frequency response matrix for constrained systems subjected to harmonic forces. The proposed methods that do not depend on any numerical schemes take more generalized forms than other research results.

• Korean Management Science Review
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• v.23 no.2
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• pp.93-107
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• 2006

This paper attempts to improve the typical DEA model that is often used as a performance evaluation method for service firms. The after-sales service system in a Korean electronic company is chosen as a sample case. The performance evaluation method currently practiced in the firm was reviewed and some critical issues of the current method were discussed. DEA model would be considered a solution for solving those Issues. However, typical DEA models has various drawbacks in determining the weights over evaluation criteria. Proposed is the priority-constrained DEA model that accommodates the strategic Importance of each evaluation criterion. The results of the proposed model are compared with those of typical DEA models. It is shown that the priority-constrained DEA model is superior to the others in terms of evaluation quality.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.159-163
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• 2001

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. for constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an offset error free controller. The resulting algorithm is formulated so that it can be solved using LP.