• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.991-997
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• 2006

Recently, Liu (2005) proposed a special type of weighting function under a given preference index level with the minimal variability similar to the minimal variability OWA operator weights problem proposed by Fuller and Majlender (2003). He solved this problem using a result of classical optimal control theory. In this note, we give a direct elementary proof of this problem without using any known results.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.547-552
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• 2007

In this note, we extends some of the results of Liu [Fuzzy Sets and systems 157 (2006) 869-878]. This extension consists of a simple proof involving weighted functions and their preference index. We also give an elementary simple proof of the maximum entropy weighting function problem with a given preference index value without using any advanced theory like variational principles or without using Lagrangian multiplier methods.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.12
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• pp.49-54
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• 2018

Applying metric(distance) factor as weighting to spatial syntax is known to not contribute to the explanatory power for the human movement behavior as compared to the geometric(angle) factor according to the negative results of several related studies. However, Kim & Piao (2017) assumed that there is not a problem of the metric factor itself but a problem of the way of applying the metric factor as weighting, and presented a new possibility of the metric factor as weighting by proposing and verifying the methods of applying the metric weighting, which are different from the existing ones. The purpose of this study is to propose advanced methods of applying the metric weighting to space syntax, and to verify whether they contribute to the improvement of explanatory power of space syntax analysis. In this paper, we propose functions for combined depth of distance-step that combine the distance-weighted depth function with the step depth function and apply them to axial segment analysis to check the improvement of explanatory power of them.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2081-2086
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• 2005

This paper proposes the receding horizon $H_{\infty}$ predictive control (RHHPC) for systems with a state-delay. We first proposes a new cost function for a finite horizon dynamic game problem. The proposed cost function includes two terminal weighting terns, each of which is parameterized by a positive definite matrix, called a terminal weighting matrix. Secondly, we derive the RHHPC from the solution to the finite dynamic game problem. Thirdly, we propose an LMI condition under which the saddle point value satisfies the well-known nonincreasing monotonicity. Finally, we shows the asymptotic stability and $H_{\infty}$-norm boundedness of the closed-loop system controlled by the proposed RHHPC. Through a numerical example, we show that the proposed RHHC is stabilizing and satisfies the infinite horizon $H_{\infty}$-norm bound.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.445-454
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• 2020

Structural damage identification (SDI) is a crucial step in structural health monitoring. However, some of the existing SDI methods cannot provide enough identification accuracy and efficiency in practice. A novel whale optimization algorithm (WOA) based method is proposed for SDI by weighting modal data and flexibility assurance criterion in this study. At first, the SDI problem is mathematically converted into a constrained optimization problem. Unlike traditional objective function defined using frequencies and mode shapes, a new objective function on the SDI problem is formulated by weighting both modal data and flexibility assurance criterion. Then, the WOA method, due to its good performance of fast convergence and global searching ability, is adopted to provide an accurate solution to the SDI problem, different predator mechanisms are formulated and their probability thresholds are selected. Finally, the performance of the proposed method is assessed by numerical simulations on a simply-supported beam and a 31-bar truss structures. For the given multiple structural damage conditions under environmental noises, the WOA-based SDI method can effectively locate structural damages and accurately estimate severities of damages. Compared with other optimization methods, such as particle swarm optimization and dragonfly algorithm, the proposed WOA-based method outperforms in accuracy and efficiency, which can provide a more effective and potential tool for the SDI problem.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.884-889
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• 1989

In this paper, a new procedure for selecting weighting matrices in linear discrete time quadratic optimal control problem (LQ-problem) is proposed. In LQ-problems, the quadratic weighting matrices are usually decided on trial and error in order to get a good response. But using the proposed method, the quadratic weights are decided in such a way that all poles of the closed loop system are located in a desired region for good responses as well as for stability and values of the quadratic cost function are kept less then a specified value.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.193-197
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• 2004

In this paper, a combination problem of controllers which are the same type of $H_{\infty}$ controllers designed with different weighting functions. This approach can remove the difficulty in the selection of the weighting functions. As a sub-controller, the Youla type of $H_{\infty}$ controller is used. In the $H_{\infty}$ controller, Youla parameterization is used to minimize $H_{\infty}$ norm of mixed sensitivity function by using polynomial approach. Computer simulation results show the robustness improvement and the performance improvement.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.9
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• pp.805-812
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• 2007

This paper present a robust controller design method based on the estimation of velocity disturbance to construct a robust fine seek control system. A loop gain adjustment algorithm is introduced to accurately estimate the velocity disturbance in spite of the uncertainties of fine actuator. A weighting function is optimally selected from a minimum fine seek open-loop gain, calculated by estimating the velocity disturbance. A robust fine seek controller is designed by considering a robust $H_{\infty}$ control problem using the weighting function. The proposed controller design method is applied to the fine seek control system of a DVD rewritable drive and is evaluated through the experimental results.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.1
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• pp.65-71
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• 1997

In this paper, we propose a new .mu.-controller design method using an equivalent weighting function $W_{\mu}$(s). The proposed mehtod is not guaranteed to converge to the minimum as D-K and .mu.-K iteration method. However, the robust performance problem can be converted into an equivalent $H^{\infty}$ optimization problem of unstructured uncertainty by using an equivalent weightng function $W_{\mu}$(s). Also we can find a .mu.-optimal controller iteratively using an error index $d_{\epsilon}$ of differnce between maximum singular value and .mu.-norm. And under the condition of the same order of scaling functions, the proposed method provides the .mu.-optimal controller with the degree less than that obtained by D-K iteration..

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.6-15
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• 1993

A generalization of one machine maximum lateness minimization problem is considered. There are one achine with controllable speed and n weighting jobs $J_{j}$, j=1, 2, ..., n with ambiguous duedates. Introducing fuzzy formulation, a membership function of the duedate associated with each job $J_{j}$, which describes the satisfaction level with respect to completion time of $J_{j}$. Thus the duedates are not constants as in conventional scheduling problems but decision variables reflecting the fuzzy circumstance of the job completing. We develop the polynomial time algorithm to find an optimal schedule and jobwise machine speeds, and to minimize the total sum of costs associated with jobwise machine speeds and dissatisfaction with respect to completion times of weighting jobs. jobs.