• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.96-102
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• 2004

This study presents an effective stiffness-based optimal technique to control quantitatively lateral drift for 3-D steel frameworks subject to lateral loads. To this end, the displacement sensitivity depending on behavior characteristics of 3-D steel frameworks is established. Also, approximation concept that can preserve the generality of the mathematical programming and can efficiently solve large scale problems is introduced. Resizing sections in the stiffness-based optimal design are assumed to be uniformly varying in size. Two types of 30-story frames are presented to illustrate the features of the Quantitative lateral drift control technique proposed in this study.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.416-419
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• 1995

A finite-dimensional approximation technique is developed for a class of spectral systems with input and output operators which are unbounded. A corresponding bounding technique on the frequency-response error is also established for control system design. Our goal is to construct an uncertainty model including a nominal plant and its error bounds so that the results from robust linear control theory can be applied to guarantee a closed loop control performance. We demonstrate by numerical example that these techniques are applicable, with a modest computational burden, to a wide class of distributed parameter system plants.

• Kyungpook Mathematical Journal
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• v.58 no.2
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• pp.333-346
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• 2018

An approximation scheme utilizing Bezier curves is considered for solving time-delayed optimal control problems with terminal inequality constraints. First, the problem is transformed, using a $P{\acute{a}}de$ approximation, to one without a time-delayed argument. Terminal inequality constraints, if they exist, are converted to equality constraints. A computational method based on Bezier curves in the time domain is then proposed for solving the obtained non-delay optimal control problem. Numerical examples are introduced to verify the efficiency and accuracy of the proposed technique. The findings demonstrate that the proposed method is accurate and easy to implement.

• Korean Management Science Review
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• v.26 no.3
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• pp.117-131
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• 2009

Over the last three decades, there are many researches focusing on the practice and theory of RM in airlines. Most of them have dealt with a seat assignment problem for maximizing the total revenue. In this study, we focus on a seat assignment problem in airlines. The seat assignment problem can be modeled as a stochastic programming model which is difficulty to solve optimally. However, with some assumptions on the demand distribution functions and a linear approximation technique, we can transform the complex stochastic programming model to a Linear Programming model. Some computational experiments are performed to evaluate out model with randomly generated data. They show that our model has a good performance comparing to existing models, and can be considered as a basis for further studies on improving existing seat assignment models.

• Proceeding of KASS Symposium
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• 2005.05a
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• pp.191-198
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• 2005

The effective stiffness-based optimal technique to control quantitatively lateral drift for shear wall-Frame structure system using composit member subject to lateral loads is presented. Also, displacement sensitivity depending on behavior characteristics of structure system is established and approximation concept that preserves the generality of the mathematical programming is introduced. Finally, the resizing technique of shear wall, frame and composite member is developed and the example of 20 story framework is presented to illustrate the features of the quantitative lateral drift control technique.

• IE interfaces
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• v.11 no.3
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• pp.55-63
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• 1998

In this study we consider a CONWIP system in which the processing times at each station follow an exponential distribution and the demands for the finished products arrive according to a compound Poisson process. The demands that are not satisfied instantaneously are assumed to be lost. We assume that the lot size at each station is greater than one. For this system we develop an approximation method to obtain the performance measures such as steady state probabilities of the number of parts at each station, average number of parts at each station and the proportion of lost demands. For the analysis of the proposed CONWIP system, we model the CONWIP system as a closed queueing network with a synchronization station and analyze the closed queueing network using a product form approximation method. A recursive technique is used to solve the subnetwork in the application of the product-form approximation method. To test the accuracy of the approximation method, the results obtained from the approximation method were compared with those obtained by simulation. Comparisons with simulation have shown that the accuracy of the approximate method is acceptable.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.497-504
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• 2002

In meshless methods, the moving least squares approximation technique is widely used to approximate a solution space because of its useful numerical characters such as non-element approximation, easily controllable smoothness, and others. In this work, a generalized version of the moving least squares method Is introduced to enhance the approximation performance through the Information converning to the derivative of the field variable. The results of numerical tests for approximation verify the improved accuracy of the generalized meshless approximation procedure compared to the conventional moving least squares method. By using this generalized moving least squares method, meshless analysis of thin beam is carried out, and its performance is investigated.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.174-180
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• 2012

This paper proposes a decentralized adaptive output-feedback controller design for nonlinear interconnected systems with unknown time delays. The interaction terms with unknown delays are related to all states of subsystems. The time-delayed functions are compensated by using appropriate Lyapunov-Krasovskii functionals and function approximation technique. The observer dynamic surface design technique is employed to design the proposed memoryless local controller for each subsystem. In addition, we prove that all signals in the closed-loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin.

• ETRI Journal
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• v.37 no.4
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• pp.772-779
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• 2015

This paper presents a power-efficient hardware realization for a motion estimation technique that is based on the full-search block matching algorithm (FSBMA). The considered input is the quarter common intermediate format of digital video. The mean of absolute difference (MAD) is the distortion criteria employed for the block matching process. The conventional architecture considered for the hardware realization of FSBMA is that of the shift register-based 2-D systolic array. For this architecture, a conservative approximation technique is adapted to eliminate unnecessary MAD computations involved in the block matching process. Upon introducing the technique to the conventional architecture, the power and complexity of its implantation is reduced, while the accuracy of the motion vector extracted from the block matching process is preserved. The proposed architecture is verified for its functional specifications. A performance evaluation of the proposed architecture is carried out using parameters such as power, area, operating frequency, and efficiency.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.202-208
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• 2015

In this study, we propose a new inference algorithm for a multiclass Gaussian process classification model using a variational EM framework and the Laplace approximation (LA) technique. This is performed in two steps, called expectation and maximization. First, in the expectation step (E-step), using Bayes' theorem and the LA technique, we derive the approximate posterior distribution of the latent function, indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. In the maximization step, we compute the maximum likelihood estimators for hyper-parameters of a covariance matrix necessary to define the prior distribution of the latent function by using the posterior distribution derived in the E-step. These steps iteratively repeat until a convergence condition is satisfied. Moreover, we conducted the experiments by using synthetic data and Iris data in order to verify the performance of the proposed algorithm. Experimental results reveal that the proposed algorithm shows good performance on these datasets.