• Journal of Mechanical Science and Technology
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• v.16 no.8
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• pp.1072-1078
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• 2002

The constrained motion requires the determination of constraint force acting on unconstrained systems for satisfying given constraints. Most of the methods to decide the force depend on numerical approaches such that the Lagrange multiplier method, and the other methods need vector analysis or complicated intermediate process. In 1992, Udwadia and Kalaba presented the generalized inverse method to describe the constrained motion as well as to calculate the constraint force. The generalized inverse method has the advantages which do not require any linearization process for the control of nonlinear systems and can explicitly describe the motion of holonomically and/or nongolonomically constrained systems. In this paper, an explicit equation to describe the constrained motion is derived by minimizing the performance index, which is a function of constraint force vector, with respect to the constraint force. At this time, it is shown that the positive-definite weighting matrix in the performance index must be the inverse of mass matrix on the basis of the Gauss's principle and the derived differential equation coincides with the generalized inverse method. The effectiveness of this method is illustrated by means of two numerical applications.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.203-206
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• 2003

This paper studies constrained optimization of an M/G/1 queue with a server that can be switched on and off. One criterion is an average number of customers in the system and another criterion is an average operating cost per unit time, where operating costs consist of switching and running costs. With the help of queueing theory, we solve the problems of optimization of one of these criteria under a constraint for another one.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.69-72
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• 2001

In this paper, we propose a new design method for bidirectional associative memories model with high error correction ratio. We extend the conventional GBAM model using bias terms and formulate a design procedure in the form of a constrained optimization problem. The constrained optimization problem is then transformed into a GEVP(generalized eigenvalue problem), which can be efficiently solved by recently developed interior point methods. The effectiveness of the proposed approach is illustrated by a example.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.20 no.3
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• pp.225-242
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• 2016

The phase-type representation is strongly connected with the positive realization in positive system. We attempt to transform phase-type representation into sparse mono-cyclic positive representation with as low order as possible. Because equivalent positive representations of a given phase-type distribution are non-unique, it is important to find a simple sparse positive representation with lower order that leads to more effective use in applications. A Hypo-Feedback-Coxian Block (HFCB) representation is a good candidate for a simple sparse representation. Our objective is to find an HFCB representation with possibly lower order, including all the eigenvalues of the original generator. We introduce an efficient nonlinear optimization method for computing an HFCB representation from a given phase-type representation. We discuss numerical problems encountered when finding efficiently a stable solution of the nonlinear constrained optimization problem. Numerical simulations are performed to show the effectiveness of the proposed algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.519-524
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• 1996

This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are applicaiton of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an modified GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.269-279
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• 2007

This paper describes a direct search method for a class of linearly constrained optimization problem. Through research we find it can be treated as an unconstrained optimization problem. And with the decrease of dimension of the variables need to be computed in the algorithms, the implementation of convergence to KKT points will be simplified to some extent. Convergence is shown under mild conditions which allow successive frames to be rotated, translated, and scaled relative to one another.

• Structural Engineering and Mechanics
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• v.70 no.1
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• pp.81-96
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• 2019

In this paper, a hybrid uncertain model is applied to system reliability based design optimization (RBDO) of trusses. All random variables are described by random distributions but some key distribution parameters of them which lack information are defined by variation intervals. For system RBDO of trusses, the first order reliability method, as well as monotonicity analysis and the branch and bound method, are utilized to determine the system failure probability; and Improved (${\mu}+{\lambda}$) constrained differential evolution (ICDE) is employed for the optimization process. System reliability assessment of several numerical examples and system RBDO of different truss structures are proposed to verify our results. Moreover, the effect of different classes of interval distribution parameters on the optimum weight of the structure and the reliability index are also investigated. The results indicate that the weight of the structure is increased by increasing the uncertainty level. Moreover, it is shown that for a certain random variable, the optimum weight is more increased by the translation interval parameters than the rotation ones.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1048-1052
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• 2007

This paper presents a rank-constrained linear matrix inequality(LMI) approach to simultaneous linear-quadratic(LQ) optimal control by static output feedback. Simultaneous LQ optimal control is formulated as an LMI optimization problem with a nonconvex rank condition. An iterative penalty method recently developed is applied to solve this rank-constrained LMI optimization problem. Numerical experiments are performed to illustrate the proposed method, and the results are compared with those of previous work.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.1023-1026
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• 2007

Optimal damping layout of the constrained viscoelastic damping layer on beam is identified with temperatures by using a gradient-based numerical search algorithm. An optimal design problem is defined in order to determine the constrained damping layer configuration. A finite element formulation is introduced to model the constrained damping layer beam. The four-parameter fractional derivative model and the Arrhenius shift factor are used to describe dynamic characteristics of viscoelastic material with respect to frequency and temperature. Frequency-dependent complex-valued eigenvalue problems are solved by using a simple resubstitution algorithm in order to obtain the loss factor of each mode and responses of the structure. The results of the numerical example show that the proposed method can reduce frequency responses of beam at peaks only by reconfiguring the layout of constrained damping layer within a limited weight constraint.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.305-308
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• 2007

In this paper we suggest an efficient evolutionary approach based on topological sort techniques for precedence constrained TSPs. The determination of optimal sequence has much to offer to downstream project management and opens up new opportunities for supply chains and logistics. Experimental results show that the suggested approach is a good alternative to locate optimal solution for complicated precedence constrained sequencing as in optimization method for instance.