• Journal of Communications and Networks
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• v.8 no.1
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• pp.123-131
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• 2006

Several distributed architectures, incorporating mobile agent technology, have been recently proposed to answer the scalability limitations of their centralized counterparts. However, these architectures fail to address scalability problems, when distributed tasks requiring the employment of itinerant agents is considered. This is because they lack mechanisms that guarantee optimization of agents' itineraries so as to minimize the total migration cost in terms of the round-trip latency and the incurred traffic. This is of particular importance when MAs itineraries span multiple subnets. The work presented herein aspires to address these issues. To that end, we have designed and implemented an algorithm that adapts methods usually applied for addressing network design problems in the specific area of mobile agent itinerary planning. The algorithm not only suggests the optimal number of mobile agents that minimize the overall cost but also constructs optimal itineraries for each of them. The algorithm implementation has been integrated into our mobile agent framework research prototype and tested in real network environments, demonstrating significant cost savings.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.2
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• pp.12-19
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• 2001

In this paper, we propose the new algorithm which can design on the optimal structure of modular system. This system is composed to the wavelet neural network in order to simplify the structure of modular system and use the time-frequency analysis. We will determine the number of module and node of each sub-system using the proposed algorithm. This algorithm provides the methodology, which we will design optimal structure of modular wavelet neural network through analyzing the character of system. We apply the proposed new structure and algorithm to approximation problem and evaluate the effectiveness of the proposed system and algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.9
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• pp.103-110
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• 2010

This paper presents the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method and approximation modeling method with Radial Basis Function (RBF) neural network structure. The complex tool holder is used for holding a (milling/drilling) tool of a machine tool. The engineering problem of complex tool holder results from the twisting of spindle of tool holder. For this purpose, we present the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method (specifically a module of $iSight^{(R)}$ FD-3.1) and approximation modeling method with Radial Basis Function (RBF) (another module of $iSight^{(R)}$ FD-3.1) neural network structure

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.436-437
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• 2018

This paper describes the structure and design of a modular DC/DC converter for connecting DC sources such as battery, solar cell, etc. to DC distribution network. The modular converter structure of IPOS type and the optimal design and implementation of the unit converter cell are discussed.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.317-330
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• 2004

In this paper, we explore the utility of recently discovered multiple-antenna techniques (namely MIMO techniques) for medium access control (MAC) design and routing in mobile ad hoc networks. Specifically, we focus on ad hoc networks where the spatial diversity technique is used to combat fading and achieve robustness in the presence of user mobility. We first examine the impact of spatial diversity on the MAC design, and devise a MIMO MAC protocol accordingly. We then develop analytical methods to characterize the corresponding saturation throughput for MIMO multi-hop networks. Building on the throughout analysis, we study the impact of MIMO MAC on routing. We characterize the optimal hop distance that minimizes the end-to-end delay in a large network. For completeness, we also study MAC design using directional antennas for the case where the channel has a strong line of sight (LOS) component. Our results show that the spatial diversity technique and the directional antenna technique can enhance the performance of mobile ad hoc networks significantly.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.325-331
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• 2004

The multiobjective optimization (MO) problem usually includes the conflicting objectives and the use of conventional optimization algorithms for MO problem does not so good approach to obtain an effective optimal solution. In this paper, genetic algorithm (GA) as an effective method is used to solve such MO problem of brushless DC motor (BLDCM). 3D equivalent magnetic circuit network (EMCN) method which enables us to reduce the computational burden is also used to consider the 3D structure of BLDCM. In order to effectively obtain a set of Pareto optimal solutions in MO problem, ranking method proposed by Fonseca is applied. The objective functions are decrease of cogging torque and increase of torque respectively. The airgap length, teeth width and magnetization angle of PM are selected for the design variables. The experimental results are also shown to confirm the validity of the optimization results.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1458-1459
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• 2005

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.339-339
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• 1994

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.221-225
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• 2004

This paper deals with a nonlinear system modelling using neural network and genetic algorithm Application q{ neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. in this paper, we optimize a neural network structure using genetic algorithm The genetic algorithm uses binary coding for neural network structure and searches for an optimal neural network structure of minimum error and fast response time. Through an extensive simulation, the optimal neural network structure is shown to be effective for identification of nonlinear system.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.3
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• pp.119-135
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• 2005

Design of location erea(LA) in a cellular network is to partition the network into clusters of cells so as to minimize the cost of location updating and paging. Most research works dealing with the LA design problem assume that the call. arrival rate and mobile flow rate are fixed parameters which can be estimated independently. In this aspect, most Problems addressed so far are deterministic LA design problems(DLADP), known to be NP hard. The mobile flow and call arrival rate are, however, varying with time and should be treated simultaneously because the call arrival rate in a cell during a day is influenced by the change of a population size of the cell. This Paper Presents a new model on IA design problems considering the time-dependent call arrival and mobile flow rate. The new model becomes a stochastic LA design problem(SLADP) because It takes into account the possibility of paging waiting and blocking caused by the changing call arrival rate and finite paging capacity. Un order to obtain the optimal solution of the LA design problem, the SIADP is transformed Into the DLADP by introducing the utilization factor of paging channels and the problem is solved iteratively until the required paging quality is satisfied. Finally, an illustrative example reflecting the metropolitan area, Seoul, is provided and the optimal partitions of a cell structure are presented.