• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2617-2622
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• 2003

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach is suffered from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis (1996). The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks and etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the global approximator, the neural network which requires training and k-nearest neighbor method which requires querying instead of training are investigated. The global approximator requires optimal control strategy. If the optimal control strategy is not available, suboptimal control strategy can be used even though the laborious Bellman iterations are necessary. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.4
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• pp.27-37
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• 1993

An efficient method to solve the minimum weight design problem for frame structures subjected to stress and displacement constraints is presented. The different cross-sectional shapes are conside red in order to apply engineering design in which usually required custom fabrication. To increase the efficiency of the optimization process, the structural response quantities(nodal forces, displacements) are linearized with respect to cross-sectional properties or their reciprocal, based on first order Taylor series expansion, while cross-sectional dimensions are considered as design variables. Numerical examples are performed and compared with other methods to demonstrate the efficiency and reliability of approximation method for frame structural optimization with different cross-sectional shapes. It is shown that the number of finite element analysis is greatly reduced and it leads to a highly efficient method of optimization of frame structures.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.385-390
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• 2005

LMTT (Linear Motor-based Transfer Techn-ology) is a horizontal transfer system for the yard automation, which has been proposed to take the place of AGV (Automated Guided Vehicle) in the maritime container terminal. The system is based on PLMSL (Permanent Magnetic Linear Synchronous Motor) that consists of stator modules on the rail and shuttle car. It is desirable to reduce the weight of LMTT in order to control the electronic devices with minimum energy. In this research, the DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the structural responses. Then, the GRG(Generalized Reduced Gradient) method built in Excel is adopted to determine the optimum. The objective function is set up as weight. On the contrary, the design variables are considered as transverse, longitudinal and wheel beam's thicknesses, and the constraints are the maximum stresses generated by four loading conditions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.252-256
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• 1997

The success of a fuzzy neural network(FNN) control system solving any given problem critically depends on the architecture of the network. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. A messy genetic algorithm is used to obtain structurally optimized FNN models. Structural optimization is regarded important before neural networks based learning is switched into. We have applied the method to the problem of a numerical approximation

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• Journal of the Society of Naval Architects of Korea
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• v.37 no.4
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• pp.11-18
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• 2000

This paper is concerned with the generation of an optimal forward hull form by a nonlinear programming method. A Rankine source panel method based on the inviscid and potential flow approximation is employed to calculate the wave-making resistance and SQP method is also used for the optimization. The hull form is represented by a spline function. The forward hull form of a minimum wave resistance with the given design constraints is generated. In addition, the forward hull form of a minimum total resistance by considering the frictional resistance together with an empirical form factor is produced and compared with the former result.

• Journal of KIISE
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• v.44 no.7
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• pp.719-725
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• 2017

To reduce management costs and improve performance, the European Telecommunication Standards Institute (ETSI) introduced the concept of network function virtualization (NFV), which can implement network functions (NFs) on cloud/datacenters. Within the NFV architecture, NFs can share physical resources by hosting NFs on physical nodes (commodity servers). For network service providers who support NFV architectures, an efficient resource allocation method finds utility in being able to reduce operating expenses (OPEX) and capital expenses (CAPEX). Thus, in this paper, we analyzed the network service chain embedding problem via an optimization formulation and found a close-optimal solution based on the Markov approximation framework. Our simulation results show that our approach could increases on average CPU utilization by up to 73% and link utilization up to 53%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.23-32
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• 2007

The H.264/AVC video coding standard uses rate distortion optimization (RDO) method to improve the compression performance in the intra prediction. The complexity and computational load are increased more than previous standard by using this method, even though this standard selects the best coding mode for the current macroblock. This paper proposes a fast intra mode decision algorithm for H.264/AVC encoder based on dominant edge direction (DED). To apply the idea, this algorithm uses the approximation of discrete cosine transform (DCT) coefficient. By detecting the DED, 3 modes instead of 9 modes are chosen for RDO calculation to decide the best mode in the $4{\times}4$ luma block. As for the $16{\times}16$ luma and $8{\times}8$ chroma block, instead of 4 modes, only 2 modes are searched. Experimental results show that the computation time of the proposed algorithm is decreased to about 72% of the full search method with negligible quality loss.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.537-542
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• 2001

In recent engineering, the designer has become more and more dependent on computer simulation. But defining exact model using computer simulation is too expensive and time consuming in the complicate systems. Thus, designers often use approximation models, which express the relation between design variables and response variables. These models are called metamodel. In this paper, we introduce one of the metamodel, named Kriging. This model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. This class of interpolating model has flexibility to model response data with multiple local extreme. By reason of this multi modality, we can't use any gradient-based optimization algorithm to find global extreme value of this model. Thus we have to introduce global optimization algorithm. To do this, we introduce DE(Differential Evolution). DE algorithm is developed by Ken Price and Rainer Storn, and it has recently proven to be an efficient method for optimizing real-valued multi-modal objective functions. This algorithm is similar to GA(Genetic Algorithm) in populating points, crossing over, and mutating. But it introduces vector concept in populating process. So it is very simple and easy to use. Finally, we show how we determine Kriging metamodel and find global extreme value through two mathematical examples.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.589-594
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• 2004

In design of suspension system for KHST, it was applied the approximated optimization method using meta-models which called Response Surface Model and Neural Network Model for 29 design variables and 46 performance index. These models was coded using correlation between design variables and performance indices that is made by the 66 times iterative execution through the design of experimental table consisted orthogonal array L32 and D-Optimal design table. The results show that the optimization process is very efficient and simply applicable for complex mechanical system such as railway vehicle system. Also it was compared with the sensitivity of some design variables in order to know the characteristics of two models. This paper describes the general method for dynamic analysis and design process of railway vehicle system applied to KHST development, and proposed the efficient methods for vibration mode analysis process dealing with test data and the function based approximation method using meta-model applicable for a complex mechanical system. This method will be able to apply to the other railway vehicle system in oder to systematize and generalize the design process of railway vehicle dynamic system.